# Patent

## Title
Genetic polymorphisms associated with coronary events and drug response, methods of detection and uses thereof

## Abstract

The present invention provides compositions and methods based on genetic polymorphisms that are associated with coronary heart disease (particularly myocardial infarction), aneurysm/dissection, and/or response to drug treatment, particularly statin treatment. For example, the present invention relates to nucleic acid molecules containing the polymorphisms, variant proteins encoded by these nucleic acid molecules, reagents for detecting the polymorphic nucleic acid molecules and variant proteins, and methods of using the nucleic acid molecules and proteins as well as methods of using reagents for their detection.

## Background
CROSS-REFERENCE TO RELATED APPLICATIONS
This application claims the benefit of U.S. non-provisional application Ser. No. 12/077,935, filed on Mar. 21, 2007, and U.S. provisional application Ser. No. 60/919,885, filed on Mar. 22, 2007, the contents of which is hereby incorporated by reference in its entirety into this application.


FIELD OF THE INVENTION
The present invention is in the field of coronary heart disease (CHD), particularly myocardial infarction (MI), as well as aneurysm/dissection and drug response, particularly response to statin treatment. In particular, the present invention relates to specific single nucleotide polymorphisms (SNPs) in the human genome, and their association with CHD, aneurysm/dissection, and/or variability in responsiveness to statin treatment (including preventive treatment) between different individuals. The SNPs disclosed herein can be used as targets for the design of diagnostic reagents and the development of therapeutic agents, as well as for disease association and linkage analysis. In particular, the SNPs of the present invention are useful for identifying an individual who is at an increased or decreased risk of developing CHD (particularly MI) and aneurysm/dissection, for early detection of the disease, for providing clinically important information for the prevention and/or treatment of CHD and aneurysm/dissection, for predicting the seriousness or consequences of CHD and aneurysm/dissection in an individual, for determining the prognosis of an individual's recovery from CHD and aneurysm/dissection, for screening and selecting therapeutic agents, and for predicting a patient's response to therapeutic agents such as evaluating the likelihood of an individual responding positively to statins, particularly for the treatment or prevention of CHD (such as MI) and aneurysm/dissection. The SNPs disclosed herein are also useful for human identification applications. Methods, assays, kits, and reagents for detecting the presence of these polymorphisms and their encoded products are provided.
BACKGROUND OF THE INVENTION
Coronary Heart Disease (CHD), Aneurysm/Dissection, and Response to Statin Treatment
The present invention relates to SNPs that are associated with the occurrence of coronary heart disease (CHD), particularly myocardial infarction (MI), as well as aortic aneurysm and dissection. The present invention also relates to SNPs that are associated with variability between different individuals in their response to treatment (including preventive treatments) with statins (e.g., pravastatin, atorvastatin, etc.), particularly for treatment or prevention of CHD and aneurysm/dissection.
CHD is defined in the Framingham Heart Study as encompassing MI, angina pectoris, coronary insufficiency (which is manifested as ischemia, that is, impaired oxygen flow to the heart muscle), and coronary heart disease death. Wilson et al., Circulation 97:1837-1847 (1998). CHD is sometimes recorded through clinical records that indicate the following interventions: coronary artery bypass graft, angioplasty and stent placement, in addition to clinical records of MI, angina, or coronary death.
As used herein, CHD is defined in accordance with how this term is defined in the Framingham Heart Study (i.e., as encompassing MI, angina pectoris, coronary insufficiency, and coronary heart disease death), and may also include revascularization, percutaneous transluminal coronary angioplasty (PTCA), and coronary artery bypass graft (CABG). Angina pectoris includes unstable angina in particular.
The SNPs described herein may further be useful for such cardiovascular events as vulnerable plaque and stroke.
Myocardial Infarction (MI)
Myocardial infarction (MI), also referred to as a “heart attack”, is the most common cause of mortality in developed countries. The incidence of MI is still high despite currently available preventive measures and therapeutic intervention. More than 1,500,000 people in the U.S. suffer acute MI each year, many without seeking help due to unrecognized MI, and one third of these people die. The lifetime risk of coronary artery disease events at age 40 is 42.4% for men, nearly one in two, and 24.9% for women, or one in four. D. M. Lloyd-Jones, Lancet 353:89-92 (1999).
MI is a multifactorial disease that involves atherogenesis, thrombus formation and propagation. Thrombosis can result in complete or partial occlusion of coronary arteries. The luminal narrowing or blockage of coronary arteries reduces oxygen and nutrient supply to the cardiac muscle (cardiac ischemia), leading to myocardial necrosis and/or stunning. MI, unstable angina, and sudden ischemic death are clinical manifestations of cardiac muscle damage. All three endpoints are part of acute coronary syndrome since the underlying mechanisms of acute complications of atherosclerosis are considered to be the same.
Atherogenesis, the first step of pathogenesis of MI, is a complex interaction between blood elements, mechanical forces, disturbed blood flow, and vessel wall abnormality that results in plaque accumulation. An unstable (vulnerable) plaque was recognized as an underlying cause of arterial thrombotic events and MI. A vulnerable plaque is a plaque, often not stenotic, that has a high likelihood of becoming disrupted or eroded, thus forming a thrombogenic focus. MI due to a vulnerable plaque is a complex phenomenon that includes: plaque vulnerability, blood vulnerability (hypercoagulation, hypothrombolysis), and heart vulnerability (sensitivity of the heart to ischemia or propensity for arrhythmia). Recurrent myocardial infarction (RMI) can generally be viewed as a severe form of MI progression caused by multiple vulnerable plaques that are able to undergo pre-rupture or a pre-erosive state, coupled with extreme blood coagulability.
The current diagnosis of MI is based on the levels of troponin I or T that indicate the cardiac muscle progressive necrosis, impaired electrocardiogram (ECG), and detection of abnormal ventricular wall motion or angiographic data (the presence of acute thrombi). However, due to the asymptomatic nature of 25% of acute MIs (absence of atypical chest pain, low ECG sensitivity), a significant portion of MIs are not diagnosed and therefore not treated appropriately (e.g., prevention of recurrent MIs).
MI risk assessment and prognosis is currently done using classic risk factors or the recently introduced Framingham Risk Index. Both of these assessments put a significant weight on LDL levels to justify preventive treatment. However, it is well established that half of all MIs occur in individuals without overt hyperlipidemia.
Other emerging risk factors of MI are inflammatory biomarkers such as C-reactive protein (CRP), ICAM-1, SAA, TNF α, homocysteine, impaired fasting glucose, new lipid markers (ox LDL, Lp-a, MAD-LDL, etc.) and pro-thrombotic factors (fibrinogen, PAI-1). These markers have significant limitations such as low specificity and low positive predictive value, and the need for multiple reference intervals to be used for different groups of people (e.g., males-females, smokers-non smokers, hormone replacement therapy users, different age groups). These limitations diminish the utility of such markers as independent prognostic markers for MI screening.
Genetics plays an important role in MI risk. Families with a positive family history of MI account for 14% of the general population, 72% of premature MIs, and 48% of all MIs. R. R. Williams, Am J Cardiology 87:129 (2001). In addition, replicated linkage studies have revealed evidence of multiple regions of the genome that are associated with MI and relevant to MI genetic traits, including regions on chromosomes 14, 2, 3 and 7, implying that genetic risk factors influence the onset, manifestation, and progression of MI. U. Broeckel, Nature Genetics 30:210 (2002); S. Harrap, Arterioscler Thromb Vasc Biol 22:874-878 (2002); A. Shearman, Human Molecular Genetics 9:1315-1320 (2000). Recent association studies have identified allelic variants that are associated with acute complications of CHD, including allelic variants of the ApoE, ApoA5, Lpa, APOCIII, and Klotho genes.
Genetic markers such as single nucleotide polymorphisms (SNPs) are preferable to other types of biomarkers. Genetic markers that are prognostic for MI can be genotyped early in life and could predict individual response to various risk factors. The combination of serum protein levels and genetic predisposition revealed by genetic analysis of susceptibility genes can provide an integrated assessment of the interaction between genotypes and environmental factors, resulting in synergistically increased prognostic value of diagnostic tests.
Thus, there is an urgent need for novel genetic markers that are predictive of predisposition to CHD such as MI, particularly for individuals who are unrecognized as having a predisposition to MI. Such genetic markers may enable prognosis of MI in much larger populations compared with the populations that can currently be evaluated by using existing risk factors and biomarkers. The availability of a genetic test may allow, for example, appropriate preventive treatments for acute coronary events to be provided for susceptible individuals (such preventive treatments may include, for example, statin treatments and statin dose escalation, as well as changes to modifiable risk factors), lowering of the thresholds for ECG and angiography testing, and allow adequate monitoring of informative biomarkers. Moreover, the discovery of genetic markers associated with MI will provide novel targets for therapeutic intervention or preventive treatments of MI, and enable the development of new therapeutic agents for treating or preventing MI and other cardiovascular disorders.
Furthermore, novel genetic markers that are predictive of predisposition to MI can be particularly useful for identifying individuals who are at risk for early-onset MI. “Early-onset MI” may be defined as MI in men who are less than 55 years of age and women who are less than 65 years of age. K. O. Akosah et al., “Preventing myocardial infarction in the young adult in the first place: How do the National Cholesterol Education Panel III guidelines perform?” JACC 41(9):1475-1479(2003). Individuals who experience early-onset MI may not be effectively identified by current cholesterol treatment guidelines, such as those suggested by the National Cholesterol Education Program. In one study, for example, a significant number of individuals who suffered MI at an earlier age (≦50 years) were shown to have LDL cholesterol below 100 mg/dl. K. O. Akosah et al., “Myocardial infarction in young adults with low-density lipoprotein cholesterol levels less than or equal to 100 mg/dl. Clinical profile and 1-year outcomes.” Chest 120:1953-1958 (2001). Because risk for MI can be reduced by lifestyle changes and by treatment of modifiable risk factors, better methods to identify individuals at risk for early-onset MI could be useful for making preventive treatment decisions, especially considering that these patients may not be identified for medical management by conventional treatment guidelines. Genetic markers for risk of early-onset MI could potentially be incorporated into individual risk assessment protocols, as they have the advantage of being easily detected at any age.
Aortic Aneurysm and Dissection
Aortic aneurysm is the 13th most common cause of death in the United States (Majumber et al., Am J Hum Genet 48(1):164-170,1991). Aortic aneurysm is a silent killer since, in the majority of cases, the catastrophic sequelae is the first manifestation of the disease. Only 10-15% of individuals survive an aneurysm rupture. Of those, only about half survive emergency surgical repair (Powel et al., N Engl J Med 2003;348:1895-901).
Aortic aneurysm is a pathological dilation of the aorta, the largest vessel in the arterial system, which delivers oxygenated blood from the left ventricle of the heart to the organs. When abnormal dilation undergoes progressive expansion, the aorta's media layers become increasingly weakened due to micro apoplexy of the vessel wall, which leads to higher wall stress, thereby inducing further progression of dilation and aneurysm formation eventually resulting in aortic dissection, rupture and often death.
The clinical phenotype of aortic aneurysm may be divided into abdominal aortic aneurysms (AAA) and thoracic aortic aneurysms (TAA). Both TAA and AAA may be further subdivided into chronic aortic aneurysm (CAA) and aortic dissection (AD). Both AAA and TAA share risk factors such as chronic hypertension, smoking, older age, vascular inflammatory diseases, deceleration trauma for aortic dissections, dyslipidemia, and the main features of pathogenesis. Chronic hypertension, in particular, causes arterial wall intimal thickening, fibrosis, and calcification coupled with extracellular matrix degradation and elastolysis. This results in intimal disruption at the edges of plaques (a process similar to coronary plaque rupture) and impairment of the oxygen supply to the arterial wall, followed by necrosis of smooth muscle cells and fibrosis. This process compromises elasticity of vessel walls and its resistance to pulsatile forces—a morphologic basis for development of aneurysms and dissections (Nienaber et al., Circulation 2003 108:628-635).
Chronic aortic aneurysm (which may also be referred to as arteriosclerotic aneurysm) results from fusiform dilation. The abdominal aorta distal to the renal arteries is most commonly affected. When the thoracic aorta is involved, the aneurysm is usually in the descending aorta. Organized thrombi are typically present inside the fusiform dilation. Although the aorta is markedly abnormal at the location of the aneurysm, the aorta is typically stretched concentrically, not dissected longitudinally, and the layers of the aortic wall are normally adherent. These aneurysms can rupture but they do not commonly dissect. Surgery is carried out electively (if an aneurysm is detected) when these aneurysms reach a size at which rupture becomes a significant danger (usually 6-8 cm, or when the aneurysm begins to change rapidly).
Aortic dissection refers to a condition in which the layers of the aorta are split from each other, with blood under pressure entering between two layers propagating the split and forming an additional false lumen. This process typically begins with a tear through the intima and inner media at one specific site in the ascending or descending aorta. Dissections can harm an individual in several ways, including rupture of the aorta into the perecardium, chest or abdomen, cardiac tamponade, and aortic branch occlusion that can be followed by paraplegia, stroke, or renal failure (Elefteriades et al, House Officer Guide to ICU Care, Reven Press, NY).
Aortic aneurysm has a strong genetic component. 18% of patients with AAA and 19% of patients with TAA were confirmed to have a first degree relative affected with aortic aneurysm (Coady et al., Cardiol Clin. 1999 November; 17(4):615-35; vii). For patients with a family history of aortic aneurysm, the major risk factor is greater than 4 (Baird et al., Lancet. 1995 Sep. 2; 346(8975):601-4) and it is twice as much for people with coronary artery disease. In addition, familial studies have identified QTL of 5q13-14 in chromosome 5 (Guo et al., Circulation. 2001 May 22; 103(20):2461-8) and QTL of 11q23.2-q24 in chromosome 11(Vaughan et al., Circulation. 2001 May 22; 103(20):2469-75) associated with aneurysm formation.
The current diagnosis of aortic aneurysms is based on imaging technologies. However, imaging technologies have significant limitations. For example, ultrasound is the procedure of choice for diagnosing AAA. However, periodic screening for AAA by ultrasound is not a part of current guidelines for AAA diagnosis and treatment because it is not time and cost effective to screen individuals who do not have clinical manifestation of disease. Consequently, due to the asymptomatic nature of the disease, a significant portion of aortic aneurysms are not diagnosed until rupture or dissection occurs or, in rare instances, aneurysm is found accidentally during an unrelated ultrasound, CT, or MRI. The situation is even worse for TAA because diagnosis of TAA using relatively inexpensive ultrasound is not useful due to ultrasound's lack of sensitivity, and CT and MRI, although sensitive enough, are too expensive to justify for screening asymptomatic individuals.
The lack of reliable, inexpensive prognostic and diagnostic tests prevents health practitioners from appropriate monitoring of aneurysms and using treatments of choice, such as beta blockers to slow down progression of the disease or elective surgical intervention. Mortality among aortic aneurysm patients with non-ruptured aortic aneurysms treated by elective surgery is 3-5% compared with 50-60% mortality during emergency surgery of ruptured aneurysms or dissections.
Thus, there is an urgent need for genetic markers that are predictive of predisposition to aortic aneurysm or dissection. Such genetic markers could, for example, enable prognosis, diagnosis, and monitoring of AAA and TAA in much larger populations compared with the populations that can currently be evaluated using existing risk factors. Furthermore, the availability of a genetic test could also enable preventive measures to be initiated in susceptible individuals prior to the onset of catastrophic events. Such preventative measures may include, for example, aggressive treatment of hypertension, use of beta-blockers and/or statin therapy, elective surgical intervention, and adequate monitoring via imaging technologies and/or informative biomarkers.
Statin Treatment
Reduction of coronary and cerebrovascular events and total mortality by treatment with HMG-CoA reductase inhibitors (statins) has been demonstrated in a number of randomized, double-blinded, placebo-controlled prospective trials. D. D. Waters, Clin Cardiol 24(8 Suppl):III3-7 (2001); B. K. Singh and J. L. Mehta, Curr Opin Cardiol 17(5):503-11 (2002). These drugs have their primary effect through the inhibition of hepatic cholesterol synthesis, thereby upregulating LDL receptors in the liver. The resultant increase in LDL catabolism results in decreased circulating LDL, a major risk factor for cardiovascular disease.
Statins can be divided into two types according to their physicochemical and pharmacokinetic properties. Statins such as lovastatin, simvastatin, atorvastatin, and cerevastatin are lipophilic in nature and, as such, diffuse across membranes and thus are highly cell permeable. Hydrophilic statins such as pravastatin are more polar, such that they require specific cell surface transporters for cellular uptake. K. Ziegler and W. Stunkel, Biochim Biophys Acta 1139(3):203-9 (1992); M. Yamazaki et al., Am J Physiol 264(1 Pt 1):G36-44 (1993); T. Komai et al., Biochem Pharmacol 43(4):667-70 (1992). The latter statin utilizes a transporter, OATP2, whose tissue distribution is confined to the liver and, therefore, they are relatively hepato-specific inhibitors. B. Hsiang et al., J Biol Chem 274(52):37161-37168 (1999). The former statins, not requiring specific transport mechanisms, are available to all cells and they can directly impact a much broader spectrum of cells and tissues. These differences in properties may influence the spectrum of activities that each statin possesses. Pravastatin, for instance, has a low myopathic potential in animal models and myocyte cultures compared to lipophilic statins. B. A. Masters et al., Toxicol Appl Pharmacol 131(1):163-174 (1995); K. Nakahara et al., Toxicol Appl Pharmacol 152(1):99-106 (1998); J. C. Reijneveld et al., Pediatr Res 39(6):1028-1035 (1996).
Evidence from gene association studies is accumulating to indicate that responses to drugs are, indeed, at least partly under genetic control. As such, pharmacogenetics—the study of variability in drug responses attributed to hereditary factors in different populations—may significantly assist in providing answers toward meeting this challenge. A. D. Roses, Nature 405(6788):857-865 (2000); V. Mooser et al., J Thromb Haemost 1(7):1398-1402 (2003); L. M. Humma and S. G. Terra, Am J Health Syst Pharm 59(13):1241-1252 (2002). Numerous associations have been reported between selected genotypes, as defined by SNPs and other genetic sequence variations, and specific responses to cardiovascular drugs. Polymorphisms in several genes have been suggested to influence responses to statins including CETP (J. A. Kuivenhoven et al., N Engl J Med 338(2):86-93 (1998)), beta-fibrinogen (M. P. de Maat et al., Arterioscler Thromb Vasc Biol 18(2):265-71 (1998)), hepatic lipase (A. Zambon et al., Circulation 103(6):792-798 (2001)), lipoprotein lipase (J. W. Jukema et al., Circulation 94(8):1913-1918 (1996)), glycoprotein IIIa (P. F. Bray et al., Am J Cardiol 88(4):347-352 (2001)), stromelysin-1 (M. P. de Maat et al., Am J Cardiol 83(6):852-856 (1999)), and apolipoprotein E (L. U. Gerdes et al., Circulation 101(12):1366-1371 (2000); J. Pedro-Botet et al., Atherosclerosis 158(1):183-193 (2001)). Some of these variants were shown to effect clinical events while others were associated with changes in surrogate endpoints.
Thus, there is a need for genetic markers that can be used to predict an individual's responsiveness to statins. For example, there is a growing need to better identify people who have highest chance of benefiting from statins, and those who have the lowest risk of developing side-effects. For example, severe myopathies represent a significant risk for a low percentage of the patient population, and this may be a particular concern for patients who are treated more aggressively with statins.
Single Nucleotide Polymorphisms (SNPs)
The genomes of all organisms undergo spontaneous mutation in the course of their continuing evolution, generating variant forms of progenitor genetic sequences. Gusella, Ann Rev Biochem 55:831-854 (1986). A variant form may confer an evolutionary advantage or disadvantage relative to a progenitor form or may be neutral. In some instances, a variant form confers an evolutionary advantage to the species and is eventually incorporated into the DNA of many or most members of the species and effectively becomes the progenitor form. Additionally, the effects of a variant form may be both beneficial and detrimental, depending on the circumstances. For example, a heterozygous sickle cell mutation confers resistance to malaria, but a homozygous sickle cell mutation is usually lethal. In many cases, both progenitor and variant forms survive and co-exist in a species population. The coexistence of multiple forms of a genetic sequence gives rise to genetic polymorphisms, including SNPs.
Approximately 90% of all genetic polymorphisms in the human genome are SNPs. SNPs are single base positions in DNA at which different alleles, or alternative nucleotides, exist in a population. The SNP position (interchangeably referred to herein as SNP, SNP site, SNP locus, SNP marker, or marker) is usually preceded by and followed by highly conserved sequences of the allele (e.g., sequences that vary in less than 1/100 or 1/1000 members of the populations). An individual may be homozygous or heterozygous for an allele at each SNP position. A SNP can, in some instances, be referred to as a “cSNP” to denote that the nucleotide sequence containing the SNP is an amino acid coding sequence.
A SNP may arise from a substitution of one nucleotide for another at the polymorphic site. Substitutions can be transitions or transversions. A transition is the replacement of one purine nucleotide by another purine nucleotide, or one pyrimidine by another pyrimidine. A transversion is the replacement of a purine by a pyrimidine, or vice versa. A SNP may also be a single base insertion or deletion variant referred to as an “indel.” Weber et al., “Human diallelic insertion/deletion polymorphisms,” Am J Hum Genet 71(4):854-62 (October 2002).
A synonymous codon change, or silent mutation/SNP (terms such as “SNP,” “polymorphism,” “mutation,” “mutant,” “variation,” and “variant” are used herein interchangeably), is one that does not result in a change of amino acid due to the degeneracy of the genetic code. A substitution that changes a codon coding for one amino acid to a codon coding for a different amino acid (i.e., a non-synonymous codon change) is referred to as a missense mutation. A nonsense mutation results in a type of non-synonymous codon change in which a stop codon is formed, thereby leading to premature termination of a polypeptide chain and a truncated protein. A read-through mutation is another type of non-synonymous codon change that causes the destruction of a stop codon, thereby resulting in an extended polypeptide product. While SNPs can be bi-, tri-, or tetra-allelic, the vast majority of the SNPs are bi-allelic, and are thus often referred to as “bi-allelic markers,” or “di-allelic markers.”
As used herein, references to SNPs and SNP genotypes include individual SNPs and/or haplotypes, which are groups of SNPs that are generally inherited together. Haplotypes can have stronger correlations with diseases or other phenotypic effects compared with individual SNPs, and therefore may provide increased diagnostic accuracy in some cases. Stephens et al., Science 293:489-493 (July 2001).
Causative SNPs are those SNPs that produce alterations in gene expression or in the expression, structure, and/or function of a gene product, and therefore are most predictive of a possible clinical phenotype. One such class includes SNPs falling within regions of genes encoding a polypeptide product, i.e. cSNPs. These SNPs may result in an alteration of the amino acid sequence of the polypeptide product (i.e., non-synonymous codon changes) and give rise to the expression of a defective or other variant protein. Furthermore, in the case of nonsense mutations, a SNP may lead to premature termination of a polypeptide product. Such variant products can result in a pathological condition, e.g., genetic disease. Examples of genes in which a SNP within a coding sequence causes a genetic disease include sickle cell anemia and cystic fibrosis.
Causative SNPs do not necessarily have to occur in coding regions; causative SNPs can occur in, for example, any genetic region that can ultimately affect the expression, structure, and/or activity of the protein encoded by a nucleic acid. Such genetic regions include, for example, those involved in transcription, such as SNPs in transcription factor binding domains, SNPs in promoter regions, in areas involved in transcript processing, such as SNPs at intron-exon boundaries that may cause defective splicing, or SNPs in mRNA processing signal sequences such as polyadenylation signal regions. Some SNPs that are not causative SNPs nevertheless are in close association with, and therefore segregate with, a disease-causing sequence. In this situation, the presence of a SNP correlates with the presence of, or predisposition to, or an increased risk in developing the disease. These SNPs, although not causative, are nonetheless also useful for diagnostics, disease predisposition screening, and other uses.
An association study of a SNP and a specific disorder involves determining the presence or frequency of the SNP allele in biological samples from individuals with the disorder of interest, such as CHD, and comparing the information to that of controls (i.e., individuals who do not have the disorder; controls may be also referred to as “healthy” or “normal” individuals) who are preferably of similar age and race. The appropriate selection of patients and controls is important to the success of SNP association studies. Therefore, a pool of individuals with well-characterized phenotypes is extremely desirable.
A SNP may be screened in diseased tissue samples or any biological sample obtained from a diseased individual, and compared to control samples, and selected for its increased (or decreased) occurrence in a specific pathological condition, such as pathologies related to CHD and in particular, MI. Once a statistically significant association is established between one or more SNP(s) and a pathological condition (or other phenotype) of interest, then the region around the SNP can optionally be thoroughly screened to identify the causative genetic locus/sequence(s) (e.g., causative SNP/mutation, gene, regulatory region, etc.) that influences the pathological condition or phenotype. Association studies may be conducted within the general population and are not limited to studies performed on related individuals in affected families (linkage studies).
Clinical trials have shown that patient response to treatment with pharmaceuticals is often heterogeneous. There is a continuing need to improve pharmaceutical agent design and therapy. In that regard, SNPs can be used to identify patients most suited to therapy with particular pharmaceutical agents (this is often termed “pharmacogenomics”). Similarly, SNPs can be used to exclude patients from certain treatment due to the patient's increased likelihood of developing toxic side effects or their likelihood of not responding to the treatment. Pharmacogenomics can also be used in pharmaceutical research to assist the drug development and selection process. Linder et al., Clinical Chemistry 43:254 (1997); Marshall, Nature Biotechnology 15:1249 (1997); International Patent Application WO 97/40462, Spectra Biomedical; and Schafer et al., Nature Biotechnology 16:3 (1998).
SUMMARY OF THE INVENTION
The present invention relates to the identification of SNPs, as well as unique combinations of such SNPs and haplotypes of SNPs, that are associated with CHD (particularly MI), aneurysm/dissection, and/or drug response, particularly response to statin treatment (including preventive treatment), such as for the treatment or prevention of CHD or aneurysm/dissection. The polymorphisms disclosed herein are directly useful as targets for the design of diagnostic and prognostic reagents and the development of therapeutic and preventive agents for use in the diagnosis, prognosis, treatment, and/or prevention of CHD (particularly MI) and aneurysm/dissection, as well as for predicting a patient's response to therapeutic agents such as statins, particularly for the treatment or prevention of CHD or aneurysm/dissection. Furthermore, the polymorphisms disclosed herein are also useful for predicting an individual's responsiveness to statins for the treatment or prevention of disorders other than CHD and aneurysm/dissection, such as cancer, and are also useful for predicting an individual's responsiveness to drugs other than statins that are used to treat or prevent CHD or aneurysm/dissection.
Certain exemplary embodiments of the invention relate in particular to SNP rs20455 and the association of this SNP with CHD (particularly MI), aneurysm/dissection, and drug response (particularly statin response), as well as SNPs that are in linkage disequilibrium with this SNP and/or are located in the genomic region surrounding this SNP. SNP rs20455 (the public rs identification number for this SNP), which is located in the kinesin-like protein 6 (KIF6) gene, is interchangeably referred to herein as “hCV3054799” (the internal identification number for this SNP), the “KIF6 SNP”, the “Trp719Arg SNP”, the “Trp719Arg polymorphism”, “KIF6 Trp719Arg”, or just “Trp719Arg”. The risk allele for this SNP may be interchangeably referred to herein as the “719Arg allele”, the “KIF6 719Arg allele”, or just “719Arg” (further, an “allele” may be interchangeably referred to herein as a “variant”). This is the allele that is associated with increased risk for CHD and aneurysm/dissection, and which is also associated with greater benefit from statin treatment, as described herein and shown in the tables. As used herein, the term “benefit” (with respect to a drug) is defined as achieving a reduced risk for a disease that the drug is intended to treat or prevent (e.g., a CHD event such as MI) by administrating the drug treatment, compared with the risk for the disease in the absence of receiving the drug treatment (or receiving a placebo in lieu of the drug treatment) for the same genotype.
SNP rs20455, and its association with CHD (particularly MI) and statin response, is also described in the following references, each of which is incorporated herein by reference in their entirety: Iakoubova et al., “Association of the Trp719Arg polymorphism in kinesin-like protein 6 with myocardial infarction and coronary heart disease in 2 prospective trials: the CARE and WOSCOPS trials”, J. Am Coll Cardiol. 2008 Jan. 29; 51(4):435-43, including Online Appendix (which relates in particular to the association of SNP rs20455 with risk for CHD, including MI, and benefit from statin treatment; the corresponding Online Appendix relates to SNPs in linkage disequilibrium with rs20455); Shiffman et al., “A kinesin family member 6 variant is associated with coronary heart disease in the Women's Health Study”, J Am Coll Cardiol. 2008 Jan. 29; 51(4):444-8 (which relates in particular to the association of SNP rs20455 with risk for CHD, including MI, in women); Iakoubova et al., “Polymorphism in KIF6 gene and benefit from statins after acute coronary syndromes: results from the PROVE IT-TIMI 22 study”, J Am Coll Cardiol. 2008 Jan. 29; 51(4):449-55 (which relates in particular to the association of SNP rs20455 with benefit from statin treatment, including both pravastatin and atorvastatin); and Shiffman et al., “Association of gene variants with incident myocardial infarction in the Cardiovascular Health Study”, Arterioscler Thromb Vasc Biol. 2008 January; 28(1):173-9 (which relates in particular to the association of SNP rs20455 with risk for CHD, including MI, in elderly individuals ages 65 and older).
As shown and described herein, the KIF6 SNP (hCV3054799/rs20455) is associated with risk for aortic aneurysm and aortic dissection, as well as risk for other coronary events such as CHD (including MI). Therefore, this SNP has been shown to be associated with multiple different coronary events, and is therefore expected to have similar utilities in other coronary events. Consequently, the KIF6 SNP (hCV3054799/rs20455), as well as the other SNPs disclosed herein, is broadly useful with respect to the full spectrum of coronary events. Furthermore, the KIF6 SNP (hCV3054799/rs20455) has also been associated with other cardiovascular events such as stroke (see, e.g., U.S. provisional patent application 61/066,584, Luke et al., filed Feb. 20, 2008). Therefore, this SNP has been shown to be associated with multiple different cardiovascular events, and is therefore expected to have similar utilities in other cardiovascular events. Consequently, the KIF6 SNP (hCV3054799/rs20455), as well as the other SNPs disclosed herein, is broadly useful with respect to the full spectrum of cardiovascular events. Moreover, this SNP has been specifically shown to be associated with benefit from statin treatment for reduction of MI, unstable angina, revascularization, and stroke events (see, e.g., Example 2). The KIF6 SNP (hCV3054799/rs20455), as well as the other SNPs disclosed herein, is also particularly useful for determining an individual's risk for vulnerable plaque.
Moreover, the exemplary utilities described herein for the KIF6 SNP (hCV3054799/rs20455), as well as the other SNPs disclosed herein, apply to both primary (i.e., first) and recurrent cardiovascular and coronary events. For example, the KIF6 SNP (hCV3054799/rs20455) can be used for determining the risk for a primary MI in an individual who has never had an MI, and can also be used for determining the risk for a recurrent MI in an individual who has already had an MI.
Furthermore, the exemplary utilities described herein for the KIF6 SNP (hCV3054799/rs20455), as well as the other SNPs disclosed herein, apply to all individuals, including, but not limited to, both men and women (see Shiffman et al., “A kinesin family member 6 variant is associated with coronary heart disease in the Women's Health Study”, J Am Coll Cardiol. 2008 Jan. 29; 51(4):444-8 which relates to women in particular), and all ages of individuals including elderly individuals (see Shiffman et al., “Association of gene variants with incident myocardial infarction in the Cardiovascular Health Study”, Arterioscler Thromb Vasc Biol. 2008 Jan;28(1):173-9, which relates in particular to elderly individuals ages 65 and older, and the PROSPER study described in Example 3 below, which relates in particular to elderly individuals ages 70-82) as well as younger individuals, etc.
Based on the identification of SNPs associated with CHD (particularly MI), aneurysm/dissection, and/or response to statin treatment, the present invention also provides methods of detecting these variants as well as the design and preparation of detection reagents needed to accomplish this task. The invention specifically provides, for example, SNPs associated with CHD, aneurysm/dissection, and/or responsiveness to statin treatment, isolated nucleic acid molecules (including DNA and RNA molecules) containing these SNPs, variant proteins encoded by nucleic acid molecules containing such SNPs, antibodies to the encoded variant proteins, computer-based and data storage systems containing the novel SNP information, methods of detecting these SNPs in a test sample, methods of identifying individuals who have an altered (i.e., increased or decreased) risk of developing CHD (particularly MI) and aneurysm/dissection, methods for determining the risk of an individual for recurring CHD (e.g., recurrent MI) or recurring aneurysm/dissection, methods for prognosing the severity or consequences of CHD or aneurysm/dissection, methods of treating an individual who has an increased risk for CHD or aneurysm/dissection, and methods for identifying individuals (e.g., determining a particular individual's likelihood) who have an altered (i.e., increased or decreased) likelihood of responding to statin treatment, particularly statin treatment of CHD (e.g., treatment or prevention of MI using statins) or aneurysm/dissection, based on the presence or absence of one or more particular nucleotides (alleles) at one or more SNP sites disclosed herein or the detection of one or more encoded variant products (e.g., variant mRNA transcripts or variant proteins), methods of identifying individuals who are more or less likely to respond to a treatment (or more or less likely to experience undesirable side effects from a treatment), methods of screening for compounds useful in the treatment or prevention of a disorder associated with a variant gene/protein, compounds identified by these methods, methods of treating or preventing disorders mediated by a variant gene/protein, methods of using the novel SNPs of the present invention for human identification, etc. The present invention also provides methods for identifying individuals who possess SNPs that are associated with an increased risk of developing CHD (such as MI) or aneurysm/dissection, and yet can benefit from being treated with statin because statin treatment can lower their risk of developing CHD (such as MI) and statins may also be used in the prevention or treatment of aneurysm/dissection.
The present invention further provides methods for selecting or formulating a treatment regimen (e.g., methods for determining whether or not to administer statin treatment to an individual having CHD or aneurysm/dissection, or who is at risk for developing CHD or aneurysm/dissection in the future, or who has previously had CHD or aneurysm/dissection, methods for selecting a particular statin-based treatment regimen such as dosage and frequency of administration of statin, or a particular form/type of statin such as a particular pharmaceutical formulation or statin compound, methods for administering an alternative, non-statin-based treatment to individuals who are predicted to be unlikely to respond positively to statin treatment, etc.), and methods for determining the likelihood of experiencing toxicity or other undesirable side effects from statin treatment, etc. The present invention also provides methods for selecting individuals to whom a statin or other therapeutic will be administered based on the individual's genotype, and methods for selecting individuals for a clinical trial of a statin or other therapeutic agent based on the genotypes of the individuals (e.g., selecting individuals to participate in the trial who are most likely to respond positively from the statin treatment and/or excluding individuals from the trial who are unlikely to respond positively from the statin treatment). The present invention further provides methods for reducing an individual's risk of developing CHD (such as MI) or aneurysm/dissection using statin treatment, including preventing recurring CHD (e.g., recurrent MI) or aneurysm/dissection using statin treatment, when said individual carries one or more SNPs identified herein as being associated with CHD.
In Tables 1 and 2, the present invention provides gene information, references to the identification of transcript sequences (SEQ ID NO: 1), encoded amino acid sequences (SEQ ID NO: 2), genomic sequences (SEQ ID NOS: 4-9), transcript-based context sequences (SEQ ID NO: 3) and genomic-based context sequences (SEQ ID NOS: 10-132) that contain the SNPs of the present invention, and extensive SNP information that includes observed alleles, allele frequencies, populations/ethnic groups in which alleles have been observed, information about the type of SNP and corresponding functional effect, and, for cSNPs, information about the encoded polypeptide product. The actual transcript sequences (SEQ ID NO: 1), amino acid sequences (SEQ ID NO: 2), genomic sequences (SEQ ID NOS: 4-9), transcript-based SNP context sequences (SEQ ID NO: 3), and genomic-based SNP context sequences (SEQ ID NOS: 10-132), together with primer sequences (SEQ ID NOS: 133-189) are provided in the Sequence Listing.
In certain exemplary embodiments, the invention provides a method for identifying an individual who has an altered risk for developing a first or recurrent CHD (e.g., MI) or aneurysm/dissection, in which the method comprises detecting a single nucleotide polymorphism (SNP) in any one of the nucleotide sequences of SEQ ID NO:1, SEQ ID NO:3, SEQ ID NOS:4-9, and SEQ ID NOS:10-132 in said individual's nucleic acids, wherein the SNP is specified in Table 1 and/or Table 2, and the presence of the SNP is indicative of an altered risk for CHD or aneurysm/dissection in said individual. In certain embodiments, the CHD is MI. In certain exemplary embodiments of the invention, SNPs that occur naturally in the human genome are provided as isolated nucleic acid molecules. These SNPs are associated with CHD (particular MI), aneurysm/dissection, and/or drug response, particularly response to statin treatment, such that they can have a variety of uses in the diagnosis, prognosis, treatment, and/or prevention of CHD, aneurysm/dissection, and related pathologies, and particularly in the treatment or prevention of CHD or aneurysm/dissection using statins. In an alternative embodiment, a nucleic acid of the invention is an amplified polynucleotide, which is produced by amplification of a SNP-containing nucleic acid template. In another embodiment, the invention provides for a variant protein that is encoded by a nucleic acid molecule containing a SNP disclosed herein.
In yet another embodiment of the invention, a reagent for detecting a SNP in the context of its naturally-occurring flanking nucleotide sequences (which can be, e.g., either DNA or mRNA) is provided. In particular, such a reagent may be in the form of, for example, a hybridization probe or an amplification primer that is useful in the specific detection of a SNP of interest. In an alternative embodiment, a protein detection reagent is used to detect a variant protein that is encoded by a nucleic acid molecule containing a SNP disclosed herein. A preferred embodiment of a protein detection reagent is an antibody or an antigen-reactive antibody fragment.
Various embodiments of the invention also provide kits comprising SNP detection reagents, and methods for detecting the SNPs disclosed herein by employing detection reagents. In a specific embodiment, the present invention provides for a method of identifying an individual having an increased or decreased risk of developing CHD (e.g., having an MI) or aneurysm/dissection by detecting the presence or absence of one or more SNP alleles disclosed herein. In another embodiment, a method for diagnosis of CHD or aneurysm/dissection by detecting the presence or absence of one or more SNP alleles disclosed herein is provided. The present invention also provides methods for evaluating whether an individual is likely (or unlikely) to respond to statin treatment, particularly statin treatment of CHD or aneurysm/dissection, by detecting the presence or absence of one or more SNP alleles disclosed herein.
The nucleic acid molecules of the invention can be inserted in an expression vector, such as to produce a variant protein in a host cell. Thus, the present invention also provides for a vector comprising a SNP-containing nucleic acid molecule, genetically-engineered host cells containing the vector, and methods for expressing a recombinant variant protein using such host cells. In another specific embodiment, the host cells, SNP-containing nucleic acid molecules, and/or variant proteins can be used as targets in a method for screening and identifying therapeutic agents or pharmaceutical compounds useful in the treatment or prevention of CHD (particularly MI) or aneurysm/dissection.
An aspect of this invention is a method for treating or preventing a first or recurrent CHD (e.g., MI) or aneurysm/dissection, in a human subject wherein said human subject harbors a SNP, gene, transcript, and/or encoded protein identified in Tables 1 and 2, which method comprises administering to said human subject a therapeutically or prophylactically effective amount of one or more agents (e.g., statins, including but not limited to, storvastatin, pravastatin, atorvastatin, etc.) counteracting the effects of the disease, such as by inhibiting (or stimulating) the activity of a gene, transcript, and/or encoded protein identified in Tables 1 and 2.
Another aspect of this invention is a method for identifying an agent useful in therapeutically or prophylactically treating CHD (particularly MI) or aneurysm/dissection, in a human subject wherein said human subject harbors a SNP, gene, transcript, and/or encoded protein identified in Tables 1 and 2, which method comprises contacting the gene, transcript, or encoded protein with a candidate agent (e.g., a statin, including but not limited to, storvastatin, pravastatin, atorvastatin, etc.) under conditions suitable to allow formation of a binding complex between the gene, transcript, or encoded protein and the candidate agent and detecting the formation of the binding complex, wherein the presence of the complex identifies said agent.
Another aspect of this invention is a method for treating or preventing CHD (such as MI) or aneurysm/dissection, in a human subject, in which the method comprises:
(i) determining that said human subject harbors a SNP, gene, transcript, and/or encoded protein identified in Tables 1 and 2, and
(ii) administering to said subject a therapeutically or prophylactically effective amount of one or more agents (such as a statin, including but not limited to, storvastatin, pravastatin, atorvastatin, etc.) counteracting the effects of the disease such as statins.
Another aspect of the invention is a method for identifying a human who is likely to benefit from statin treatment, in which the method comprises detecting the presence of the risk allele of SNP rs20455 in said human's nucleic acids, wherein the presence of the risk allele indicates that said human is likely to benefit from statin treatment.
Another aspect of the invention is a method for identifying a human who is likely to benefit from statin treatment, in which the method comprises detecting the presence of the risk allele of a SNP that is in LD with SNP rs20455 in said human's nucleic acids, wherein the presence of the risk allele of the LD SNP indicates that said human is likely to benefit from statin treatment.
Exemplary embodiments of the invention include methods of using SNP rs20455 related to any statin. It has been specifically shown herein that carriers of the KIF6 719Arg allele benefit from not only pravastatin (Pravachol®), which is a hydrophilic statin, but also atorvastatin (Lipitor®), which is a lipophilic statin (as described in Example 2 below, in particular). This SNP is therefore expected to have similar utilities across the entire class of statins, particularly since it has specifically been shown to be useful for both hydrophilic and lipophilic statins. Thus, this SNP, as well as the other statin response-associated SNPs disclosed herein, is broadly useful in predicting the therapeutic effect for the entire class of statins, such as for determining whether an individual will benefit from any of the statins, including, but not limited to, fluvastatin (Lescol®), lovastatin (Mevacor®), rosuvastatin (Crestor®), and simvastatin (Zocor®), as well as combination therapies that include a statin such as simvastatin +ezetimibe (Vytorin®), lovastatin +niacin extended-release (Advicor®), and atorvastatin +amlodipine besylate (Caduet®).
In certain exemplary embodiments of the invention, the diagnostic methods are directed to the determination of which patients would have greater protection against CHD (e.g., MI, recurrent MI, etc.) or aneurysm/dissection when they are given an intensive statin treatment as compared to a standard statin treatment. In certain embodiments, the statin can comprise a statin selected from the group consisting of atorvastatin, pravastatin, and storvastatin. In certain embodiments, intensive statin treatment comprises administering higher doses of a statin and/or increasing the frequency of statin administration as compared with standard statin treatment. In certain further embodiments, intensive statin treatment can utilize a different type of statin than standard statin treatment; for example, atorvastatin can be used for intensive statin treatment and pravastatin can be used for standard statin treatment.
Many other uses and advantages of the present invention will be apparent to those skilled in the art upon review of the detailed description of the preferred embodiments herein. Solely for clarity of discussion, the invention is described in the sections below by way of non-limiting examples.
Description of the Files Contained on the CD-R Named CDR Duplicate Copy 1 and CDR Duplicate Copy 2
Each of the CD-Rs contains the following text file:
File CD000014ORD_SEQLIST.txt provides the Sequence Listing. The Sequence Listing provides the transcript sequences (SEQ ID NO: 1) and protein sequences (SEQ ID NO: 2) as referred to in Table 1, and genomic sequences (SEQ ID NOS: 4-9) as referred to in Table 2, for each CHD, aneurysm/dissection, and/or drug response-associated gene (or genomic region for intergenic SNPs) that contains one or more SNPs of the present invention. Also provided in the Sequence Listing are context sequences flanking each SNP, including both transcript-based context sequences as referred to in Table 1 (SEQ ID NO: 3) and genomic-based context sequences as referred to in Table 2 (SEQ ID NOS: 10-132). In addition, the Sequence Listing provides the primer sequences from Table 3 (SEQ ID NOS: 133-189), which are oligonucleotides that have been synthesized and used in the laboratory to assay certain SNPs disclosed herein by allele-specific PCR during the course of association studies to verify the association of these SNPs with CHD, aneurysm/dissection, and/or drug response. The context sequences generally provide 100 bp upstream (5′) and 100 bp downstream (3′) of each SNP, with the SNP in the middle of the context sequence, for a total of 200 bp of context sequence surrounding each SNP.
File CD000014ORD_SEQLIST.txt is 614 KB in size, and was created on Mar. 20, 2008. A computer readable format of the sequence listing is also submitted herein on a separate CDR labeled CRF. In accordance with 37 C.F.R. §1.821(f), the information recorded in the CRF CDR is identical to the sequence listing as provided on the CDR Duplicate Copy 1 and Copy 2.
The material contained on the CD-R is hereby incorporated by reference pursuant to 37 CFR 1.77(b)(4).
Description of Table 1 and Table 2
Table 1 and Table 2 (both provided on the CD-R) disclose the SNP and associated gene/transcript/protein information of the present invention. For each gene, Table 1 provides a header containing gene, transcript and protein information, followed by a transcript and protein sequence identifier (SEQ ID NO), and then SNP information regarding each SNP found in that gene/transcript including the transcript context sequence. For each gene in Table 2, a header is provided that contains gene and genomic information, followed by a genomic sequence identifier (SEQ ID NO) and then SNP information regarding each SNP found in that gene, including the genomic context sequence.
Note that SNP markers may be included in both Table 1 and Table 2; Table 1 presents the SNPs relative to their transcript sequences and encoded protein sequences, whereas Table 2 presents the SNPs relative to their genomic sequences. In some instances Table 2 may also include, after the last gene sequence, genomic sequences of one or more intergenic regions, as well as SNP context sequences and other SNP information for any SNPs that lie within these intergenic regions. Additionally, in either Table 1 or 2 a “Related Interrogated SNP” may be listed following a SNP which is determined to be in LD with that interrogated SNP according to the given Power value. SNPs can be readily cross-referenced between all Tables based on their Celera hCV (or, in some instances, hDV) identification numbers and/or public rs identification numbers, and to the Sequence Listing based on their corresponding SEQ ID NOs.
The gene/transcript/protein information includes:

    
    
        a gene number (1 through n, where n =the total number of genes in the Table),
        a Celera hCG and UID internal identification numbers for the gene,
        a Celera hCT and UID internal identification numbers for the transcript (Table 1 only),
        a public Genbank accession number (e.g., RefSeq NM number) for the transcript (Table 1 only),
        a Celera hCP and UID internal identification numbers for the protein encoded by the hCT transcript (Table 1 only),
        a public Genbank accession number (e.g., RefSeq NP number) for the protein (Table 1 only),
        an art-known gene symbol,
        an art-known gene/protein name,
        Celera genomic axis position (indicating start nucleotide position-stop nucleotide position),
        the chromosome number of the chromosome on which the gene is located,
        an OMIM (Online Mendelian Inheritance in Man; Johns Hopkins University/NCBI) public reference number for obtaining further information regarding the medical significance of each gene, and
        alternative gene/protein name(s) and/or symbol(s) in the OMIM entry.
    
    


Note that, due to the presence of alternative splice forms, multiple transcript/protein entries may be provided for a single gene entry in Table 1; i.e., for a single Gene Number, multiple entries may be provided in series that differ in their transcript/protein information and sequences.
Following the gene/transcript/protein information is a transcript context sequence (Table 1), or a genomic context sequence (Table 2), for each SNP within that gene.
After the last gene sequence, Table 2 may include additional genomic sequences of intergenic regions (in such instances, these sequences are identified as “Intergenic region:” followed by a numerical identification number), as well as SNP context sequences and other SNP information for any SNPs that lie within each intergenic region (such SNPs are identified as “INTERGENIC” for SNP type).
Note that the transcript, protein, and transcript-based SNP context sequences are all provided in the Sequence Listing. The transcript-based SNP context sequences are provided in both Table 1 and also in the Sequence Listing. The genomic and genomic-based SNP context sequences are provided in the Sequence Listing. The genomic-based SNP context sequences are provided in both Table 2 and in the Sequence Listing. SEQ ID NOs are indicated in Table 1 for the transcript-based context sequences (SEQ ID NO: 3); SEQ ID NOs are indicated in Table 2 for the genomic-based context sequences (SEQ ID NOS: 10-132).
The SNP information includes:

    
    
        Context sequence (taken from the transcript sequence in Table 1, the genomic sequence in Table 2) with the SNP represented by its IUB code, including 100 bp upstream (5′) of the SNP position plus 100 bp downstream (3′) of the SNP position (the transcript-based SNP context sequences in Table 1 are provided in the Sequence Listing as SEQ ID NO: 3; the genomic-based SNP context sequences in Table 2 are provided in the Sequence Listing as SEQ ID NOS: 10-132).
        Celera hCV internal identification number for the SNP (in some instances, an “hDV” number is given instead of an “hCV” number).
        The corresponding public identification number for the SNP, the rs number.
        SNP position (position of the SNP within the given transcript sequence (Table 1) or within the given genomic sequence (Table 2)).
        “Related Interrogated SNP” as the interrogated SNP with which the listed SNP is in LD at the given value of Power.
        SNP source (may include any combination of one or more of the following five codes, depending on which internal sequencing projects and/or public databases the SNP has been observed in: “Applera”=SNP observed during the re-sequencing of genes and regulatory regions of 39 individuals, “Celera”=SNP observed during shotgun sequencing and assembly of the Celera human genome sequence, “Celera Diagnostics”=SNP observed during re-sequencing of nucleic acid samples from individuals who have a disease, “dbSNP”=SNP observed in the dbSNP public database, “HGBASE”=SNP observed in the HGBASE public database, “HGMD”=SNP observed in the Human Gene Mutation Database (HGMD) public database, “HapMap”=SNP observed in the International HapMap Project public database, “CSNP”=SNP observed in an internal Applied Biosystems (Foster City, Calif.) database of coding SNPS (cSNPs).
    
    


Note that multiple “Applera” source entries for a single SNP indicate that the same SNP was covered by multiple overlapping amplification products and the re-sequencing results (e.g., observed allele counts) from each of these amplification products is being provided.

    
    
        Population/allele/allele count information in the format of [population1(first_allele,count|second_allele,count)population2(first_allele,count|second_allele,count) total (first_allele,total count|second_allele,total count)]. The information in this field includes populations/ethnic groups in which particular SNP alleles have been observed (“cau”=Caucasian, “his”=Hispanic, “chn”=Chinese, and “afr”=African-American, “jpn”=Japanese, “ind”=Indian, “mex”=Mexican, “ain”=“American Indian, “cra”=Celera donor, “no_pop”=no population information available), identified SNP alleles, and observed allele counts (within each population group and total allele counts), where available [“-” in the allele field represents a deletion allele of an insertion/deletion (“indel”) polymorphism (in which case the corresponding insertion allele, which may be comprised of one or more nucleotides, is indicated in the allele field on the opposite side of the “|”); “-”in the count field indicates that allele count information is not available]. For certain SNPs from the public dbSNP database, population/ethnic information is indicated as follows (this population information is publicly available in dbSNP): “HISP1”=human individual DNA (anonymized samples) from 23 individuals of self-described HISPANIC heritage; “PAC1”=human individual DNA (anonymized samples) from 24 individuals of self-described PACIFIC RIM heritage; “CAUC1”=human individual DNA (anonymized samples) from 31 individuals of self-described CAUCASIAN heritage; “AFR1”=human individual DNA (anonymized samples) from 24 individuals of self-described AFRICAN/AFRICAN AMERICAN heritage; “P1”=human individual DNA (anonymized samples) from 102 individuals of self-described heritage; “PA130299515”; “SC—12_A”=SANGER 12 DNAs of Asian origin from Corielle cell repositories, 6 of which are male and 6 female; “SC—12_C”=SANGER 12 DNAs of Caucasian origin from Corielle cell repositories from the CEPH/UTAH library, six male and six female; “SC—12_AA”=SANGER 12 DNAs of African-American origin from Corielle cell repositories 6 of which are male and 6 female; “SC—95_C”=SANGER 95 DNAs of Caucasian origin from Corielle cell repositories from the CEPH/UTAH library; and “SC—12_CA”=Caucasians—12 DNAs from Corielle cell repositories that are from the CEPH/UTAH library, six male and six female.
    
    


Note that for SNPs of “Applera” SNP source, genes/regulatory regions of 39 individuals (20 Caucasians and 19 African Americans) were re-sequenced and, since each SNP position is represented by two chromosomes in each individual (with the exception of SNPs on X and Y chromosomes in males, for which each SNP position is represented by a single chromosome), up to 78 chromosomes were genotyped for each SNP position. Thus, the sum of the African-American (“afr”) allele counts is up to 38, the sum of the Caucasian allele counts (“cau”) is up to 40, and the total sum of all allele counts is up to 78.
Note that semicolons separate population/allele/count information corresponding to each indicated SNP source; i.e., if four SNP sources are indicated, such as “Celera,” “dbSNP,” “HGBASE,” and “HGMD,” then population/allele/count information is provided in four groups which are separated by semicolons and listed in the same order as the listing of SNP sources, with each population/allele/count information group corresponding to the respective SNP source based on order; thus, in this example, the first population/allele/count information group would correspond to the first listed SNP source (Celera) and the third population/allele/count information group separated by semicolons would correspond to the third listed SNP source (HGBASE); if population/allele/count information is not available for any particular SNP source, then a pair of semicolons is still inserted as a place-holder in order to maintain correspondence between the list of SNP sources and the corresponding listing of population/allele/count information.

    
    
        SNP type (e.g., location within gene/transcript and/or predicted functional effect) [“MIS-SENSE MUTATION”=SNP causes a change in the encoded amino acid (i.e., a non-synonymous coding SNP); “SILENT MUTATION”=SNP does not cause a change in the encoded amino acid (i.e., a synonymous coding SNP); “STOP CODON MUTATION”=SNP is located in a stop codon; “NONSENSE MUTATION”=SNP creates or destroys a stop codon; “UTR 5”=SNP is located in a 5′ UTR of a transcript; “UTR 3”=SNP is located in a 3′ UTR of a transcript; “PUTATIVE UTR 5”=SNP is located in a putative 5′ UTR; “PUTATIVE UTR 3”=SNP is located in a putative 3′ UTR; “DONOR SPLICE SITE”=SNP is located in a donor splice site (5′ intron boundary); “ACCEPTOR SPLICE SITE”=SNP is located in an acceptor splice site (3′ intron boundary); “CODING REGION”=SNP is located in a protein-coding region of the transcript; “EXON”=SNP is located in an exon; “INTRON”=SNP is located in an intron; “hmCS”=SNP is located in a human-mouse conserved segment; “TFBS”=SNP is located in a transcription factor binding site; “UNKNOWN”=SNP type is not defined; “INTERGENIC”=SNP is intergenic, i.e., outside of any gene boundary].
        Protein coding information (Table 1 only), where relevant, in the format of [protein SEQ ID NO, amino acid position, (amino acid-1, codon1) (amino acid-2, codon2)]. The information in this field includes SEQ ID NO of the encoded protein sequence, position of the amino acid residue within the protein identified by the SEQ ID NO that is encoded by the codon containing the SNP, amino acids (represented by one-letter amino acid codes) that are encoded by the alternative SNP alleles (in the case of stop codons, “X” is used for the one-letter amino acid code), and alternative codons containing the alternative SNP nucleotides which encode the amino acid residues (thus, for example, for missense mutation-type SNPs, at least two different amino acids and at least two different codons are generally indicated; for silent mutation-type SNPs, one amino acid and at least two different codons are generally indicated, etc.). In instances where the SNP is located outside of a protein-coding region (e.g., in a UTR region), “None” is indicated following the protein SEQ ID NO.
    
    


Description of Table 3
Table 3 provides sequences (SEQ ID NOS:133-189) of primers that have been synthesized and used in the laboratory to assay certain SNPs disclosed herein by allele-specific PCR during the course of association studies to verify the association of these SNPs with CHD, aneurysm/dissection, and/or statin response (see Examples section).
Table 3 provides the following:

    
    
        the column labeled “Marker” provides an hCV identification number for each SNP that can be detected using the corresponding primers.
        the column labeled “Alleles” designates the two alternative alleles (i.e., nucleotides) at the
    
    


SNP site. These alleles are targeted by the allele-specific primers (the allele-specific primers are shown as Primer 1 and Primer 2). Note that alleles may be presented in Table 3 based on a different orientation (i.e., the reverse complement) relative to how the same alleles are presented in Tables 1-2.

    
    
        the column labeled “Primer 1 (Allele-Specific Primer)” provides an allele-specific primer that is specific for an allele designated in the “Alleles” column.
        the column labeled “Primer 2 (Allele-Specific Primer)” provides an allele-specific primer that is specific for the other allele designated in the “Alleles” column.
        the column labeled “Common Primer” provides a common primer that is used in conjunction with each of the allele-specific primers (i.e., Primer 1 and Primer 2) and which hybridizes at a site away from the SNP position.
    
    


All primer sequences are given in the 5′ to 3′ direction.
Each of the nucleotides designated in the “Alleles” column matches or is the reverse complement of (depending on the orientation of the primer relative to the designated allele) the 3′ nucleotide of the allele-specific primer (i.e., either Primer 1 or Primer 2) that is specific for that allele.
Description of Table 4
Table 4 provides a list of LD SNPs that are related to and derived from certain interrogated SNPs. The interrogated SNPs, which are shown in column 1 (which indicates the hCV identification numbers of each interrogated SNP) and column 2 (which indicates the public rs identification numbers of each interrogated SNP) of Table 4, are statistically significantly associated with CHD, aneurysm/dissection, and/or statin response, as described and shown herein, particularly in Tables 5-22 and in the Examples section below. The LD SNPs are provided as an example of SNPs which can also serve as markers for disease association based on their being in LD with an interrogated SNP. The criteria and process of selecting such LD SNPs, including the calculation of the r 2 value and the r2 threshold value, are described in Example 6, below.
In Table 4, the column labeled “Interrogated SNP” presents each marker as identified by its unique hCV identification number. The column labeled “Interrogated rs” presents the publicly known rs identification number for the corresponding hCV number. The column labeled “LD SNP” presents the hCV numbers of the LD SNPs that are derived from their corresponding interrogated SNPs. The column labeled “LD SNP rs” presents the publicly known rs identification number for the corresponding hCV number. The column labeled “Power” presents the level of power where the r2 threshold is set. For example, when power is set at 0.51, the threshold r2 value calculated therefrom is the minimum r2 that an LD SNP must have in reference to an interrogated SNP, in order for the LD SNP to be classified as a marker capable of being associated with a disease phenotype at greater than 51% probability. The column labeled “Threshold r2” presents the minimum value of r2 that an LD SNP must meet in reference to an interrogated SNP in order to qualify as an LD SNP. The column labeled “r2” presents the actual r2 value of the LD SNP in reference to the interrogated SNP to which it is related.
Description of Tables 5-22
Tables 5-22 provide the results of statistical analyses for SNPs disclosed in Tables 1 and 2 (SNPs can be cross-referenced between all the tables herein based on their hCV and/or rs identification numbers). The results shown in Tables 5-22 provide support for the association of these SNPs with CHD, particularly MI and recurrent MI (RMI), aneurysm/dissection, and/or the association of these SNPs with drug response, such as statins administered as a preventive treatment for MI/RMI. As an example, the statistical results provided in Tables 5-7 show that the association of SNP hCV3054799 in the kinesin-like protein 6 (KIF6) gene (this SNP is also interchangeably referred to herein as KIF6 Trp719Arg or rs20455, its public rs identification number) with risk of MI/RMI, as well as with response to statin treatment in the prevention of MI/RMI, is supported by p-values<0.05 in genotypic association tests.
Tables 5-7 show the association of SNP hCV3054799 with CHD risk and drug response (p<0.05 in either a genotypic or dominant test) in CARE and WOSCOPS samples. Tables 5-7 specifically provide analysis showing the association of hCV3054799 with MI/CHD risks and the association of this SNP with prevention of MI/CHD by statin treatment. Table 5 shows the association of KIF6 Trp719Arg (hCV3054799) with MI and CHD in the placebo arms of the CARE and WOSCOPS trials. Table 6 shows the effect of pravastatin on MI and CHD in KIF6 Trp719Arg (hCV3054799) subgroups from the CARE and WOSCOPS trials. Table 7 shows the effect on CHD of atorvastatin compared with pravastatin according to KIF6 Trp719Arg genotype. See Example 1 for further information relating to Tables 5-6, and see Example 2 for further information relating to Table 7.
Tables 8-13 show the association of SNP hCV3054799 with CHD risk and drug response (p<0.05 in either a genotypic or dominant test) in PROSPER samples. Tables 8 and 9 show the association of the KIF6 SNP (rs20455) with risk of CHD in the placebo arm of the PROSPER trial (an elderly population, ages 70-82). Table 8 shows the number of patients of the three genotypes and carriers (minor homozygote+heterozygote) in the placebo arm of the PROSPER trial. Table 9 shows that, for example, among those in the placebo group with prior vascular disease, 719Arg carriers (59.0%) were at nominally greater risk for coronary events compared with noncarriers: hazard ratio=1.25 (95% CI 0.95 to 1.64). Tables 10-13 show the effect of pravastatin compared with placebo on risk of CHD in population subgroups of the PROSPER trial defined by KIF6 SNP (rs20455) genotypes. Table 10 shows the number of patients of the three genotypes and carriers (minor homozygote+heterozygote) in the placebo arm of the PROSPER trial. Table 11 shows the number of patients of the three genotypes and carriers (minor homozygote+heterozygote) in the pravastatin arm of the PROSPER trial. To assess the effect of pravastatin, risk estimates were used to compare the pravastatin arm with the placebo arm in subgroups defined by genotypes. Table 12 shows that, for example, among 719Arg carriers with prior vascular disease, a substantial and significant benefit from pravastatin therapy was observed (hazard ratio 0.67, 95% CI 0.52 to 0.87), whereas no significant benefit was observed in noncarriers (hazard ratio 0.92, 95% CI 0.68 to 1.25). Table 13 shows the interaction between 719Arg carrier status and pravastatin treatment (p =0.12). See Example 3 for further information relating to Tables 8-13.
Table 14 shows SNPs from the genomic region around SNP hCV3054799 that are associated with CHD risk (p<0.15 in either a genotypic, additive, dominant, or recessive test) in CARE and WOSCOPS samples. The meta-analysis results presented in Table 14 specifically show that six SNPs (the KIF6 Trp719Arg SNP (rs20455), as well as rs9471077, rs9462535, rs9394584, rs11755763, and rs9471080) were associated with recurrent MI in the placebo arm of CARE and with CHD in the placebo arm of WOSCOPS (p<0.05 in meta-analysis), with KIF6 Trp719Arg and rs9471077 being particularly strongly associated. rs9471077 is in strong linkage disequilibrium with the Trp719Arg SNP (r2=0.79 in placebo—treated patients), and the risk ratios for Trp719Arg and rs9471077 were similar in the placebo arms of CARE and WOSCOPS. After adjusting for conventional risk factors, the hazard ratios were 1.57 and 1.54 (p =0.01 and 0.02) in CARE for Trp719Arg and rs9471077, respectively, and the adjusted odds ratios were 1.59 and 1.46 (p=0.003 and 0.01) in WOSCOPS for Trp719Arg and rs9471077, respectively. See Example 4 for further information relating to Table 14.
Table 15 shows SNPs from a fine-mapping analysis of the genomic region around SNP hCV3054799 that are associated with CHD risk (p<0.1 in either a genotypic, additive, dominant, or recessive test) in CARE samples. Table 15, which provides the association results for recurrent MI and the counts for different genotypes in the placebo arm of CARE, shows that certain genotypes in six SNPs were associated with recurrent MI. See “Example 4—Supplemental Analysis” section for further information relating to Table 15.
Table 16 shows SNPs from the genomic region around SNP hCV3054799 that are associated with drug response (p<0.05 in either genotypes) in CARE samples. Table 16 specifically shows SNPs having a significant (p-value of less than 0.05) association with an effect of pravastatin treatment (compared with placebo) on risk of recurrent MI (RMI) in subgroups of CARE defined by genotypes. See “Example 4—Supplemental Analysis” section for further information relating to Table 16.
Tables 17-20 show that the KIF6 SNP (hCV3054799) is associated with risk for aortic aneurysm and dissection (p<0.05 in either a genotypic, allelic, dominant, or recessive test). To determine whether the KIF6 SNP (hCV3054799) is associated with aortic aneurysm or dissection, this SNP was analyzed using the following two endpoints: (1) using aortic aneurysm or aortic dissection as an endpoint, which compared patients with aneurysm or dissection against patients without aneurysm or dissection (shown in Table 17), and (2) using aortic dissection as an endpoint, which compared patients with dissection against patients without dissection (shown in Table 18). Based on this analysis, the KIF6 SNP (hCV3054799) was found to be associated (p-value≦0.05) with aneurysm or dissection and with dissection only, and the risk allele in both instances was the same allele that was previously associated with MI for this SNP (genotype counts for the aneurysm or dissection endpoint are shown in Table 19, and genotype counts for the dissection endpoint are shown in Table 20). See Example 5 for further information relating to Tables 17-20.
Table 21 shows that the KIF6 SNP (hCV3054799) is associated with aneurysm/dissection among patients without CHD and, therefore, the association of the KIF6 SNP with aneurysm/dissection is independent of CHD. See the “Example 5—Supplemental Analysis” section for further information relating to Table 21.
Table 22 shows the results of an analysis of SNPs from Table 14 (other than the KIF6 Trp719Arg SNP, which is shown elsewhere herein to be associated with drug response, particularly benefit from statin treatment) to determine whether individuals with increased risk of CHD will benefit from pravastatin treatment. Table 22 shows that, besides the KIF6 Trp719Arg SNP, four other SNPs in Table 14 (rs9471080/hCV29992177, rs9394584/hCV30225864, rs9471077/hCV3054813, and rs9462535/hCV3054808) are associated with benefit from pravastatin treatment in CARE and WOSCOPS samples, as well as being associated with CHD risk (as shown in Table 14).
Throughout Tables 5-22, “OR” refers to the odds ratio, “HR” or “HRR” refers to the hazard ratio, and “90% CI” or “95% CI” refers to the 90% or 95% confidence interval (respectively) for the odds ratio or hazard ratio (the confidence intervals, as presented in the tables, may specify the lower and upper bounds as a range, or may individually specify the lower bound and the upper bound). Odds ratios (OR) or hazard ratios (HR or HRR) that are greater than one indicate that a given allele (or combination of alleles such as a haplotype, diplotype, or two-locus diplotype) is a risk allele (which may also be referred to as a susceptibility allele), whereas odds ratios or hazard ratios that are less than one indicate that a given allele is a non-risk allele (which may also be referred to as a protective allele). For a given risk allele, the other alternative allele at the SNP position (which can be derived from the information provided in Tables 1-2, for example) may be considered a non-risk allele. For a given non-risk allele, the other alternative allele at the SNP position may be considered a risk allele.
Thus, with respect to disease risk (e.g., CHD such as MI, or aneurysm/dissection), if the risk estimate (odds ratio or hazard ratio) for a particular genotype is greater than one, this indicates that an individual with this particular genotype has a higher risk for the disease than an individual who has the reference genotype. In contrast, if the risk estimate (odds ratio or hazard ratio) for a particular genotype is less than one, this indicates that an individual with this particular genotype has a reduced risk for the disease compared with an individual who has the reference genotype.
With respect to drug response (e.g., response to a statin such as pravastatin), if the risk estimate (odds ratio or hazard ratio) of a particular genotype is less than one, this indicates that an individual with this particular genotype would benefit from the drug (an odds ratio or hazard ratio equal to one would indicate that the drug has no effect). As used herein, the term “benefit” (with respect to a drug) is defined as achieving a reduced risk for a disease that the drug is intended to treat or prevent (e.g., a CHD event such as MI) by administrating the drug treatment, compared with the risk for the disease in the absence of receiving the drug treatment (or receiving a placebo in lieu of the drug treatment) for the same genotype.
For risk and drug response associations based on samples from the CARE, WOSCOPS, and PROSPER trials described herein, risk is assessed by comparing the risk of CHD (e.g., MI) for a given genotype with the risk of CHD for a reference genotype in the placebo arm of the trial, and drug response is assessed by comparing the risk of CHD (e.g., MI) in the pravastatin arm of the trial with the risk of CHD in the placebo arm of the trial for the same genotype.
In terms of risk, the KIF6 719Arg allele was associated with increased risk of CHD (e.g., MI) in samples from the CARE, WOSCOPS, and PROSPER trials, as shown in the tables and described herein (particularly in the Examples). In terms of drug response, carriers of the KIF6 719Arg allele benefited from pravastatin treatment in samples from the CARE, WOSCOPS, and PROSPER trials (whereas noncarriers did not benefit), as shown in the tables and described herein (particularly in the Examples).
Following are descriptions of certain column headings that may be used in any of Tables 5-22 (various derivatives of these headings may be used as well).











Column Heading
Definition







Study
CARE = “Cholesterol and Recurrent Events” study.



WOSCOPS = “West of Scotland Coronary Prevention



Study”.



PROSPER = “Prospective Study of Pravastatin in the



Elderly at Risk” study.



PROVE-IT = “Pravastatin or Atorvastatin Evaluation and



Infection Therapy” study.



PROVE-IT-TIMI = “Pravastatin or Atorvastatin



Evaluation and Infection Therapy-Thrombolysis in



Myocardial Infraction” study.


Strata
Subpopulation used for analysis (e.g., placebo- or



pravastatin-treated, or all patients unstratified by



treatment).


All Count, Case Count, Cont
Number of individuals analyzed in each study, separated


Count
into Case and Control groups according to individuals



having the disorder (cases) or not having the disorder



(controls), or ALL samples combined. Depending on the



particular study (as explained in the Examples section



and/or in the Description of Tables section), the disorder



may be CHD (such as MI or recurrent MI) or



aneurysm/dissection.


Allele
Particular SNP variant (nucleotide) investigated.


Case Freq, Cont Freq
Total number of chromosomes with each allele in each



study, divided by twice the total number of individuals



tested (i.e., two alleles per individual) for Cases or



Controls.


Major Allele
Major = high frequency allele.


Minor Allele
Minor = rare (low frequency) allele.


Genot
Diploid genotypes present for this SNP.


Major homozygotes (“Maj Hom”)
Individuals with 0 rare alleles.


Minor homozygotes (“Min Hom”)
Carriers of 2 rare alleles.


Heterozygotes (“Het”)
Carriers of 1 rare allele.


Maj hom + Het
Individuals with 0 or 1 rare allele.


Het + Min hom
All carriers of 1 or 2 rare alleles.


Dom
Dominant model (minor homozygote plus heterozygote



vs major homozygote).


Rec
Recessive model (minor homozygote vs heterozygote



plus major homozygote).


OR (min hom vs maj hom)
Minor homozygotes OR: odds ratio for a carrier with 2



rare alleles, using individuals with 0 rare alleles as a



reference.


OR (het vs maj hom)
Heterozygotes OR: odds ratio for a carrier with 1 rare



allele, using individuals with 0 rare alleles as a reference.


OR (Dom)
Dominant OR: odds ratio for a carrier with 1 or 2 rare



alleles, using individuals with 0 rare alleles as a reference.


OR (Rec)
Recessive OR: odds ratio for a carrier with 2 rare alleles,



using individuals with 0 or 1 rare alleles as a reference.


Allelic OR
Odds ratio in those individuals with genotypes possessing



the rare allele vs. individuals with no rare allele.


HW(ALL)pExact or
Hardy-Weinberg expectations using an exact test (e.g.,


HW(Control)pExact
for all individuals in the study, or just for controls).


allelicAsc pExact
Allelic association results using an exact test.


BDpvalue
Breslow-Day p-value. Breslow-Day statistic tests the null



hypothesis of homogeneous odds ratio.











DETAILED DESCRIPTION OF THE INVENTION
The present invention provides SNPs associated with coronary heart disease (CHD), particularly myocardial infarction (MI), as well as aneurysm/dissection, and SNPs that are associated with an individual's responsiveness to therapeutic agents, particularly statins, which may be used for the treatment (including preventive treatment) of CHD and aneurysm/dissection. The present invention further provides nucleic acid molecules containing SNPs, methods and reagents for the detection of the SNPs disclosed herein, uses of these SNPs for the development of detection reagents, and assays or kits that utilize such reagents. The SNPs disclosed herein are useful for diagnosing, prognosing, screening for, and evaluating predisposition to CHD, aneurysm/dissection, and related pathologies in humans. The drug response-associated SNPs disclosed herein are particularly useful for predicting, screening for, and evaluating response to statin treatment, particularly treatment or prevention of CHD and aneurysm/dissection using statins, in humans. Furthermore, such SNPs and their encoded products are useful targets for the development of therapeutic and preventive agents.
Furthermore, the drug response-associated SNPs disclosed herein are also useful for predicting an individual's responsiveness to drugs other than statins that are used to treat or prevent CHD or aneurysm/dissection, and these SNPs are also useful for predicting an individual's responsiveness to statins for the treatment or prevention of disorders other than CHD or aneurysm/dissection, particularly cancer. For example, the use of statins in the treatment of cancer is reviewed in: Hindler et al., “The role of statins in cancer therapy”, Oncologist. 2006 March; 11(3):306-15; Demierre et al., “Statins and cancer prevention”, Nat Rev Cancer. 2005 December; 5(12):930-42; Stamm et al., “The role of statins in cancer prevention and treatment”, Oncology. 2005 May; 19(6):739-50; and Sleijfer et al., “The potential of statins as part of anti-cancer treatment”, Eur J Cancer. 2005 March; 41(4):516-22, each of which is incorporated herein by reference in their entirety.
A large number of SNPs have been identified from re-sequencing DNA from 39 individuals, and they are indicated as “Applera” SNP source in Tables 1-2. Their allele frequencies observed in each of the Caucasian and African-American ethnic groups are provided. Additional SNPs included herein were previously identified during “shotgun” sequencing and assembly of the human genome, and they are indicated as “Celera” SNP source in Tables 1 and 2. Furthermore, the information provided in Tables 1 and 2, particularly the allele frequency information obtained from 39 individuals and the identification of the precise position of each SNP within each gene/transcript, allows haplotypes (i.e., groups of SNPs that are co-inherited) to be readily inferred. The present invention encompasses SNP haplotypes, as well as individual SNPs.
Thus, the present invention provides individual SNPs associated with CHD (particularly MI), aneurysm/dissection, and/or drug response (particularly statin response), as well as combinations of SNPs and haplotypes, polymorphic/variant transcript sequences (SEQ ID NO: 1) and genomic sequences (SEQ ID NOS: 4-9) containing SNPs, encoded amino acid sequences (SEQ ID NO: 2), and both transcript-based SNP context sequences (SEQ ID NO: 3) and genomic-based SNP context sequences (SEQ ID NOS: 10-132) (transcript sequences, protein sequences, and transcript-based SNP context sequences are provided in Table 1 and the Sequence Listing; genomic sequences and genomic-based SNP context sequences are provided in Table 2 and the Sequence Listing), methods of detecting these polymorphisms in a test sample, methods of determining the risk of an individual of having or developing CHD or aneurysm/dissection, methods of determining if an individual is likely to respond to a particular treatment such as statins (particularly for treating or preventing CHD or aneurysm/dissection), methods of screening for compounds useful for treating disorders associated with a variant gene/protein such as CHD or aneurysm/dissection, compounds identified by these screening methods, methods of using the disclosed SNPs to select a treatment/preventive strategy or therapeutic agent, methods of treating or preventing a disorder associated with a variant gene/protein, and methods of using the SNPs of the present invention for human identification.
The present invention further provides methods for selecting or formulating a treatment regimen (e.g., methods for determining whether or not to administer statin treatment to an individual having CHD or aneurysm/dissection, or who is at risk for developing CHD or aneurysm/dissection in the future, or who has previously had CHD or aneurysm/dissection, methods for selecting a particular statin-based treatment regimen such as dosage and frequency of administration of statin, or a particular form/type of statin such as a particular pharmaceutical formulation or statin compound, methods for administering an alternative, non-statin-based treatment to individuals who are predicted to be unlikely to respond positively to statin treatment, etc.), and methods for determining the likelihood of experiencing toxicity or other undesirable side effects from statin treatment, etc. The present invention also provides methods for selecting individuals to whom a statin or other therapeutic will be administered based on the individual's genotype, and methods for selecting individuals for a clinical trial of a statin or other therapeutic agent based on the genotypes of the individuals (e.g., selecting individuals to participate in the trial who are most likely to respond positively from the statin treatment and/or excluding individuals from the trial who are unlikely to respond positively from the statin treatment).
The present invention provides novel SNPs associated with CHD, aneurysm/dissection, and/or response to statin treatment, as well as SNPs that were previously known in the art, but were not previously known to be associated with CHD, aneurysm/dissection, or response to statin treatment. Accordingly, the present invention provides novel compositions and methods based on the novel SNPs disclosed herein, and also provides novel methods of using the known, but previously unassociated, SNPs in methods relating to evaluating an individual's likelihood of having or developing CHD (particularly MI) or aneurysm/dissection, predicting the likelihood of an individual experiencing a reoccurrence of CHD (e.g., experiencing a recurrent MI) or aneurysm/dissection, prognosing the severity of CHD or aneurysm/dissection in an individual, or prognosing an individual's recovery from CHD or aneurysm/dissection, and methods relating to evaluating an individual's likelihood of responding to statin treatment (particularly statin treatment, including preventive treatment, of CHD or aneurysm/dissection). In Tables 1 and 2, known SNPs are identified based on the public database in which they have been observed, which is indicated as one or more of the following SNP types: “dbSNP”=SNP observed in dbSNP, “HGBASE”=SNP observed in HGBASE, and “HGMD”=SNP observed in the Human Gene Mutation Database (HGMD).
Particular SNP alleles of the present invention can be associated with either an increased risk of having or developing CHD (e.g., MI) or aneurysm/dissection or increased likelihood of responding to statin treatment (particularly statin treatment, including preventive treatment, of CHD or aneurysm/dissection), or a decreased risk of having or developing CHD or aneurysm/dissection or decreased likelihood of responding to statin treatment. Thus, whereas certain SNPs (or their encoded products) can be assayed to determine whether an individual possesses a SNP allele that is indicative of an increased risk of having or developing CHD (e.g., MI) or aneurysm/dissection or increased likelihood of responding to statin treatment, other SNPs (or their encoded products) can be assayed to determine whether an individual possesses a SNP allele that is indicative of a decreased risk of having or developing CHD or aneurysm/dissection or decreased likelihood of responding to statin treatment. Similarly, particular SNP alleles of the present invention can be associated with either an increased or decreased likelihood of having a reoccurrence of CHD (e.g., recurrent MI) or aneurysm/dissection, of fully recovering from CHD or aneurysm/dissection, of experiencing toxic effects from a particular treatment or therapeutic compound, etc. The term “altered” may be used herein to encompass either of these two possibilities (e.g., an increased or a decreased risk/likelihood). SNP alleles that are associated with a decreased risk of having or developing CHD (such as MI) or aneurysm/dissection may be referred to as “protective” alleles, and SNP alleles that are associated with an increased risk of having or developing CHD or aneurysm/dissection may be referred to as “susceptibility” alleles, “risk” alleles, or “risk factors”.
Those skilled in the art will readily recognize that nucleic acid molecules may be double-stranded molecules and that reference to a particular site on one strand refers, as well, to the corresponding site on a complementary strand. In defining a SNP position, SNP allele, or nucleotide sequence, reference to an adenine, a thymine (uridine), a cytosine, or a guanine at a particular site on one strand of a nucleic acid molecule also defines the thymine (uridine), adenine, guanine, or cytosine (respectively) at the corresponding site on a complementary strand of the nucleic acid molecule. Thus, reference may be made to either strand in order to refer to a particular SNP position, SNP allele, or nucleotide sequence. Probes and primers, may be designed to hybridize to either strand and SNP genotyping methods disclosed herein may generally target either strand. Throughout the specification, in identifying a SNP position, reference is generally made to the protein-encoding strand, only for the purpose of convenience.
References to variant peptides, polypeptides, or proteins of the present invention include peptides, polypeptides, proteins, or fragments thereof, that contain at least one amino acid residue that differs from the corresponding amino acid sequence of the art-known peptide/polypeptide/protein (the art-known protein may be interchangeably referred to as the “wild-type,” “reference,” or “normal” protein). Such variant peptides/polypeptides/proteins can result from a codon change caused by a nonsynonymous nucleotide substitution at a protein-coding SNP position (i.e., a missense mutation) disclosed by the present invention. Variant peptides/polypeptides/proteins of the present invention can also result from a nonsense mutation (i.e., a SNP that creates a premature stop codon, a SNP that generates a read-through mutation by abolishing a stop codon), or due to any SNP disclosed by the present invention that otherwise alters the structure, function, activity, or expression of a protein, such as a SNP in a regulatory region (e.g. a promoter or enhancer) or a SNP that leads to alternative or defective splicing, such as a SNP in an intron or a SNP at an exon/intron boundary. As used herein, the terms “polypeptide,” “peptide,” and “protein” are used interchangeably.
As used herein, an “allele” may refer to a nucleotide at a SNP position (wherein at least two alternative nucleotides are present in the population at the SNP position, in accordance with the inherent definition of a SNP) or may refer to an amino acid residue that is encoded by the codon which contains the SNP position (where the alternative nucleotides that are present in the population at the SNP position form alternative codons that encode different amino acid residues). Using the KIF6 SNP (hCV3054799/rs20455) as an example, the term “allele” can refer to, for example, an ‘A’ or ‘G’ nucleotide (or the reverse complements thereof) at the SNP position, or a tryptophan (Trp) or arginine (Arg) residue at amino acid position 719 of the KIF6 protein. An “allele” may also be referred to herein as a “variant”. Also, an amino acid residue that is encoded by a codon containing a particular SNP may simply be referred to as being encoded by the SNP.
The results of a test (e.g., an individual's risk for CHD or aneurysm/dissection, or an individual's predicted drug responsiveness, based on assaying one or more SNPs disclosed herein, and/or an individual's genotype for one or more SNPs disclosed herein, etc.), and/or any other information pertaining to a test, may be referred to herein as a “report”. A tangible report can optionally be generated as part of a testing process (which may be interchangeably referred to herein as “reporting”, or as “providing” a report, “producing” a report, or “generating” a report). Examples of tangible reports may include, but are not limited to, reports in paper (such as computer-generated printouts of test results) or equivalent formats and reports stored on computer readable medium (such as a CD, computer hard drive, or computer network server, etc.). Reports, particularly those stored on computer readable medium, can be part of a database (such as a database of patient records, which may be a “secure database” that has security features that limit access to the report, such as to allow only the patient and the patient's medical practioners to view the report, for example). In addition to, or as an alternative to, generating a tangible report, reports can also be displayed on a computer screen (or the display of another electronic device or instrument).
A report can further be “transmitted” or “communicated” (these terms may be used herein interchangeably), such as to the individual who was tested, a medical practitioner (e.g., a doctor, nurse, clinical laboratory practitioner, genetic counselor, etc.), a healthcare organization, a clinical laboratory, and/or any other party intended to view or possess the report. The act of “transmitting” or “communicating” a report can be by any means known in the art, based on the form of the report. Furthermore, “transmitting” or “communicating” a report can include delivering a report (“pushing”) and/or retrieving (“pulling”) a report. For example, reports can be transmitted/communicated by such means as being physically transferred between parties (such as for reports in paper format), such as by being physically delivered from one party to another, or by being transmitted electronically or in signal form (e.g., via e-mail or over the internet, by facsimile, and/or by any wired or wireless communication methods known in the art), such as by being retrieved from a database stored on a computer network server, etc.
Isolated Nucleic Acid Molecules and SNP Detection Reagents & Kits
Tables 1 and 2 provide a variety of information about each SNP of the present invention that is associated with CHD (particularly MI), aneurysm/dissection, and/or drug response (particularly response to statin treatment), including the transcript sequences (SEQ ID NO: 1), genomic sequences (SEQ ID NOS: 4-9), and protein sequences (SEQ ID NO: 2) of the encoded gene products (with the SNPs indicated by IUB codes in the nucleic acid sequences). In addition, Tables 1 and 2 include SNP context sequences, which generally include 100 nucleotide upstream (5′) plus 100 nucleotides downstream (3′) of each SNP position (SEQ ID NO: 3 correspond to transcript-based SNP context sequences disclosed in Table 1, and SEQ ID NOS: 10-132 correspond to genomic-based context sequences disclosed in Table 2), the alternative nucleotides (alleles) at each SNP position, and additional information about the variant where relevant, such as SNP type (coding, missense, splice site, UTR, etc.), human populations in which the SNP was observed, observed allele frequencies, information about the encoded protein, etc.
Isolated Nucleic Acid Molecules
The present invention provides isolated nucleic acid molecules that contain one or more SNPs disclosed Table 1 and/or Table 2. Isolated nucleic acid molecules containing one or more SNPs disclosed in at least one of Tables 1 and 2 may be interchangeably referred to throughout the present text as “SNP-containing nucleic acid molecules.” Isolated nucleic acid molecules may optionally encode a full-length variant protein or fragment thereof. The isolated nucleic acid molecules of the present invention also include probes and primers (which are described in greater detail below in the section entitled “SNP Detection Reagents”), which may be used for assaying the disclosed SNPs, and isolated full-length genes, transcripts, cDNA molecules, and fragments thereof, which may be used for such purposes as expressing an encoded protein.
As used herein, an “isolated nucleic acid molecule” generally is one that contains a SNP of the present invention or one that hybridizes to such molecule such as a nucleic acid with a complementary sequence, and is separated from most other nucleic acids present in the natural source of the nucleic acid molecule. Moreover, an “isolated” nucleic acid molecule, such as a cDNA molecule containing a SNP of the present invention, can be substantially free of other cellular material, or culture medium when produced by recombinant techniques, or chemical precursors or other chemicals when chemically synthesized. A nucleic acid molecule can be fused to other coding or regulatory sequences and still be considered “isolated.” Nucleic acid molecules present in non-human transgenic animals, which do not naturally occur in the animal, are also considered “isolated.” For example, recombinant DNA molecules contained in a vector are considered “isolated.” Further examples of “isolated” DNA molecules include recombinant DNA molecules maintained in heterologous host cells, and purified (partially or substantially) DNA molecules in solution. Isolated RNA molecules include in vivo or in vitro RNA transcripts of the isolated SNP-containing DNA molecules of the present invention. Isolated nucleic acid molecules according to the present invention further include such molecules produced synthetically.
Generally, an isolated SNP-containing nucleic acid molecule comprises one or more SNP positions disclosed by the present invention with flanking nucleotide sequences on either side of the SNP positions. A flanking sequence can include nucleotide residues that are naturally associated with the SNP site and/or heterologous nucleotide sequences. Preferably, the flanking sequence is up to about 500, 300, 100, 60, 50, 30, 25, 20, 15, 10, 8, or 4 nucleotides (or any other length in-between) on either side of a SNP position, or as long as the full-length gene or entire protein-coding sequence (or any portion thereof such as an exon), especially if the SNP-containing nucleic acid molecule is to be used to produce a protein or protein fragment.
For full-length genes and entire protein-coding sequences, a SNP flanking sequence can be, for example, up to about 5 KB, 4 KB, 3 KB, 2 KB, 1 KB on either side of the SNP. Furthermore, in such instances the isolated nucleic acid molecule comprises exonic sequences (including protein-coding and/or non-coding exonic sequences), but may also include intronic sequences. Thus, any protein coding sequence may be either contiguous or separated by introns. The important point is that the nucleic acid is isolated from remote and unimportant flanking sequences and is of appropriate length such that it can be subjected to the specific manipulations or uses described herein such as recombinant protein expression, preparation of probes and primers for assaying the SNP position, and other uses specific to the SNP-containing nucleic acid sequences.
An isolated SNP-containing nucleic acid molecule can comprise, for example, a full-length gene or transcript, such as a gene isolated from genomic DNA (e.g., by cloning or PCR amplification), a cDNA molecule, or an mRNA transcript molecule. Polymorphic transcript sequences are referred to in Table 1 and provided in the Sequence Listing (SEQ ID NO: 1), and polymorphic genomic sequences are referred to in Table 2 and provided in the Sequence Listing (SEQ ID NOS: 4-9). Furthermore, fragments of such full-length genes and transcripts that contain one or more SNPs disclosed herein are also encompassed by the present invention, and such fragments may be used, for example, to express any part of a protein, such as a particular functional domain or an antigenic epitope.
Thus, the present invention also encompasses fragments of the nucleic acid sequences as disclosed in Tables 1 and 2 (transcript sequences are referred to in Table 1 as SEQ ID NO: 1, genomic sequences are referred to in Table 2 as SEQ ID NOS: 4-9, transcript-based SNP context sequences are referred to in Table 1 as SEQ ID NO: 3, and genomic-based SNP context sequences are referred to in Table 2 as SEQ ID NOS: 10-132) and their complements. The actual sequences referred to in the tables are provided in the Sequence Listing. A fragment typically comprises a contiguous nucleotide sequence at least about 8 or more nucleotides, more preferably at least about 12 or more nucleotides, and even more preferably at least about 16 or more nucleotides. Furthermore, a fragment could comprise at least about 18, 20, 22, 25, 30, 40, 50, 60, 80, 100, 150, 200, 250 or 500 nucleotides in length (or any other number in between). The length of the fragment will be based on its intended use. For example, the fragment can encode epitope-bearing regions of a variant peptide or regions of a variant peptide that differ from the normal/wild-type protein, or can be useful as a polynucleotide probe or primer. Such fragments can be isolated using the nucleotide sequences provided in Table 1 and/or Table 2 for the synthesis of a polynucleotide probe. A labeled probe can then be used, for example, to screen a cDNA library, genomic DNA library, or mRNA to isolate nucleic acid corresponding to the coding region. Further, primers can be used in amplification reactions, such as for purposes of assaying one or more SNPs sites or for cloning specific regions of a gene.
An isolated nucleic acid molecule of the present invention further encompasses a SNP-containing polynucleotide that is the product of any one of a variety of nucleic acid amplification methods, which are used to increase the copy numbers of a polynucleotide of interest in a nucleic acid sample. Such amplification methods are well known in the art, and they include but are not limited to, polymerase chain reaction (PCR) (U.S. Pat. Nos. 4,683,195 and 4,683,202; PCR Technology: Principles and Applications for DNA Amplification, ed. H.A. Erlich, Freeman Press, NY, N.Y. (1992)), ligase chain reaction (LCR) (Wu and Wallace, Genomics 4:560 (1989); Landegren et al., Science 241:1077 (1988)), strand displacement amplification (SDA) (U.S. Pat. Nos. 5,270,184 and 5,422,252), transcription-mediated amplification (TMA) (U.S. Pat. No. 5,399,491), linked linear amplification (LLA) (U.S. Pat. No. 6,027,923) and the like, and isothermal amplification methods such as nucleic acid sequence based amplification (NASBA) and self-sustained sequence replication (Guatelli et al., Proc Natl Acad Sci USA 87:1874 (1990)). Based on such methodologies, a person skilled in the art can readily design primers in any suitable regions 5′ and 3′ to a SNP disclosed herein. Such primers may be used to amplify DNA of any length so long that it contains the SNP of interest in its sequence.
As used herein, an “amplified polynucleotide” of the invention is a SNP-containing nucleic acid molecule whose amount has been increased at least two fold by any nucleic acid amplification method performed in vitro as compared to its starting amount in a test sample. In other preferred embodiments, an amplified polynucleotide is the result of at least ten fold, fifty fold, one hundred fold, one thousand fold, or even ten thousand fold increase as compared to its starting amount in a test sample. In a typical PCR amplification, a polynucleotide of interest is often amplified at least fifty thousand fold in amount over the unamplified genomic DNA, but the precise amount of amplification needed for an assay depends on the sensitivity of the subsequent detection method used.
Generally, an amplified polynucleotide is at least about 16 nucleotides in length. More typically, an amplified polynucleotide is at least about 20 nucleotides in length. In a preferred embodiment of the invention, an amplified polynucleotide is at least about 30 nucleotides in length. In a more preferred embodiment of the invention, an amplified polynucleotide is at least about 32, 40, 45, 50, or 60 nucleotides in length. In yet another preferred embodiment of the invention, an amplified polynucleotide is at least about 100, 200, 300, 400, or 500 nucleotides in length. While the total length of an amplified polynucleotide of the invention can be as long as an exon, an intron or the entire gene where the SNP of interest resides, an amplified product is typically up to about 1,000 nucleotides in length (although certain amplification methods may generate amplified products greater than 1000 nucleotides in length). More preferably, an amplified polynucleotide is not greater than about 600-700 nucleotides in length. It is understood that irrespective of the length of an amplified polynucleotide, a SNP of interest may be located anywhere along its sequence.
In a specific embodiment of the invention, the amplified product is at least about 201 nucleotides in length, comprises one of the transcript-based context sequences or the genomic-based context sequences shown in Tables 1 and 2. Such a product may have additional sequences on its 5′ end or 3′ end or both. In another embodiment, the amplified product is about 101 nucleotides in length, and it contains a SNP disclosed herein. Preferably, the SNP is located at the middle of the amplified product (e.g., at position 101 in an amplified product that is 201 nucleotides in length, or at position 51 in an amplified product that is 101 nucleotides in length), or within 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 15, or 20 nucleotides from the middle of the amplified product. However, as indicated above, the SNP of interest may be located anywhere along the length of the amplified product.
The present invention provides isolated nucleic acid molecules that comprise, consist of, or consist essentially of one or more polynucleotide sequences that contain one or more SNPs disclosed herein, complements thereof, and SNP-containing fragments thereof.
Accordingly, the present invention provides nucleic acid molecules that consist of any of the nucleotide sequences shown in Table 1 and/or Table 2 (transcript sequences are referred to in Table 1 as SEQ ID NO: 1, genomic sequences are referred to in Table 2 as SEQ ID NOS: 4-9, transcript-based SNP context sequences are referred to in Table 1 as SEQ ID NO: 3, and genomic-based SNP context sequences are referred to in Table 2 as SEQ ID NOS: 10-132), or any nucleic acid molecule that encodes any of the variant proteins referred to in Table 1 (SEQ ID NO: 2). The actual sequences referred to in the tables are provided in the Sequence Listing. A nucleic acid molecule consists of a nucleotide sequence when the nucleotide sequence is the complete nucleotide sequence of the nucleic acid molecule.
The present invention further provides nucleic acid molecules that consist essentially of any of the nucleotide sequences referred to in Table 1 and/or Table 2 (transcript sequences are referred to in Table 1 as SEQ ID NO: 1, genomic sequences are referred to in Table 2 as SEQ ID NOS: 4-9, transcript-based SNP context sequences are referred to in Table 1 as SEQ ID NO: 3, and genomic-based SNP context sequences are referred to in Table 2 as SEQ ID NOS: 10-132), or any nucleic acid molecule that encodes any of the variant proteins referred to in Table 1 (SEQ ID NO: 2). The actual sequences referred to in the tables are provided in the Sequence Listing. A nucleic acid molecule consists essentially of a nucleotide sequence when such a nucleotide sequence is present with only a few additional nucleotide residues in the final nucleic acid molecule.
The present invention further provides nucleic acid molecules that comprise any of the nucleotide sequences shown in Table 1 and/or Table 2 or a SNP-containing fragment thereof (transcript sequences are referred to in Table 1 as SEQ ID NO: 1, genomic sequences are referred to in Table 2 as SEQ ID NOS: 4-9, transcript-based SNP context sequences are referred to in Table 1 as SEQ ID NO: 3, and genomic-based SNP context sequences are referred to in Table 2 as SEQ ID NOS: 10-132), or any nucleic acid molecule that encodes any of the variant proteins provided in Table 1 (SEQ ID NO: 2). The actual sequences referred to in the tables are provided in the Sequence Listing. A nucleic acid molecule comprises a nucleotide sequence when the nucleotide sequence is at least part of the final nucleotide sequence of the nucleic acid molecule. In such a fashion, the nucleic acid molecule can be only the nucleotide sequence or have additional nucleotide residues, such as residues that are naturally associated with it or heterologous nucleotide sequences. Such a nucleic acid molecule can have one to a few additional nucleotides or can comprise many more additional nucleotides. A brief description of how various types of these nucleic acid molecules can be readily made and isolated is provided below, and such techniques are well known to those of ordinary skill in the art. Sambrook and Russell, Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Press, N.Y. (2000).
The isolated nucleic acid molecules can encode mature proteins plus additional amino or carboxyl-terminal amino acids or both, or amino acids interior to the mature peptide (when the mature form has more than one peptide chain, for instance). Such sequences may play a role in processing of a protein from precursor to a mature form, facilitate protein trafficking, prolong or shorten protein half-life, or facilitate manipulation of a protein for assay or production. As generally is the case in situ, the additional amino acids may be processed away from the mature protein by cellular enzymes.
Thus, the isolated nucleic acid molecules include, but are not limited to, nucleic acid molecules having a sequence encoding a peptide alone, a sequence encoding a mature peptide and additional coding sequences such as a leader or secretory sequence (e.g., a pre-pro or pro-protein sequence), a sequence encoding a mature peptide with or without additional coding sequences, plus additional non-coding sequences, for example introns and non-coding 5′ and 3′ sequences such as transcribed but untranslated sequences that play a role in, for example, transcription, mRNA processing (including splicing and polyadenylation signals), ribosome binding, and/or stability of mRNA. In addition, the nucleic acid molecules may be fused to heterologous marker sequences encoding, for example, a peptide that facilitates purification.
Isolated nucleic acid molecules can be in the form of RNA, such as mRNA, or in the form DNA, including cDNA and genomic DNA, which may be obtained, for example, by molecular cloning or produced by chemical synthetic techniques or by a combination thereof. Sambrook and Russell, Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Press, N.Y. (2000). Furthermore, isolated nucleic acid molecules, particularly SNP detection reagents such as probes and primers, can also be partially or completely in the form of one or more types of nucleic acid analogs, such as peptide nucleic acid (PNA). U.S. Pat. Nos. 5,539,082; 5,527,675; 5,623,049; and 5,714,331. The nucleic acid, especially DNA, can be double-stranded or single-stranded. Single-stranded nucleic acid can be the coding strand (sense strand) or the complementary non-coding strand (anti-sense strand). DNA, RNA, or PNA segments can be assembled, for example, from fragments of the human genome (in the case of DNA or RNA) or single nucleotides, short oligonucleotide linkers, or from a series of oligonucleotides, to provide a synthetic nucleic acid molecule. Nucleic acid molecules can be readily synthesized using the sequences provided herein as a reference; oligonucleotide and PNA oligomer synthesis techniques are well known in the art. See, e.g., Corey, “Peptide nucleic acids: expanding the scope of nucleic acid recognition,” Trends Biotechnol 15(6):224-9 (June 1997), and Hyrup et al., “Peptide nucleic acids (PNA): synthesis, properties and potential applications,” Bioorg Med Chem 4(1):5-23) (January 1996). Furthermore, large-scale automated oligonucleotide/PNA synthesis (including synthesis on an array or bead surface or other solid support) can readily be accomplished using commercially available nucleic acid synthesizers, such as the Applied Biosystems (Foster City, Calif.) 3900 High-Throughput DNA Synthesizer or Expedite 8909 Nucleic Acid Synthesis System, and the sequence information provided herein.
The present invention encompasses nucleic acid analogs that contain modified, synthetic, or non-naturally occurring nucleotides or structural elements or other alternative/modified nucleic acid chemistries known in the art. Such nucleic acid analogs are useful, for example, as detection reagents (e.g., primers/probes) for detecting one or more SNPs identified in Table 1 and/or Table 2. Furthermore, kits/systems (such as beads, arrays, etc.) that include these analogs are also encompassed by the present invention. For example, PNA oligomers that are based on the polymorphic sequences of the present invention are specifically contemplated. PNA oligomers are analogs of DNA in which the phosphate backbone is replaced with a peptide-like backbone. Lagriffoul et al., Bioorganic & Medicinal Chemistry Letters 4:1081-1082 (1994); Petersen et al., Bioorganic & Medicinal Chemistry Letters 6:793-796 (1996); Kumar et al., Organic Letters 3(9):1269-1272 (2001); WO 96/04000. PNA hybridizes to complementary RNA or DNA with higher affinity and specificity than conventional oligonucleotides and oligonucleotide analogs. The properties of PNA enable novel molecular biology and biochemistry applications unachievable with traditional oligonucleotides and peptides.
Additional examples of nucleic acid modifications that improve the binding properties and/or stability of a nucleic acid include the use of base analogs such as inosine, intercalators (U.S. Pat. No. 4,835,263) and the minor groove binders (U.S. Patent No. 5,801,115). Thus, references herein to nucleic acid molecules, SNP-containing nucleic acid molecules, SNP detection reagents (e.g., probes and primers), oligonucleotides/polynucleotides include PNA oligomers and other nucleic acid analogs. Other examples of nucleic acid analogs and alternative/modified nucleic acid chemistries known in the art are described in Current Protocols in Nucleic Acid Chemistry, John Wiley & Sons, N.Y. (2002).
The present invention further provides nucleic acid molecules that encode fragments of the variant polypeptides disclosed herein as well as nucleic acid molecules that encode obvious variants of such variant polypeptides. Such nucleic acid molecules may be naturally occurring, such as paralogs (different locus) and orthologs (different organism), or may be constructed by recombinant DNA methods or by chemical synthesis. Non-naturally occurring variants may be made by mutagenesis techniques, including those applied to nucleic acid molecules, cells, or organisms. Accordingly, the variants can contain nucleotide substitutions, deletions, inversions and insertions (in addition to the SNPs disclosed in Tables 1 and 2). Variation can occur in either or both the coding and non-coding regions. The variations can produce conservative and/or non-conservative amino acid substitutions.
Further variants of the nucleic acid molecules disclosed in Tables 1 and 2, such as naturally occurring allelic variants (as well as orthologs and paralogs) and synthetic variants produced by mutagenesis techniques, can be identified and/or produced using methods well known in the art. Such further variants can comprise a nucleotide sequence that shares at least 70-80%, 80-85%, 85-90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% sequence identity with a nucleic acid sequence disclosed in Table 1 and/or Table 2 (or a fragment thereof) and that includes a novel SNP allele disclosed in Table 1 and/or Table 2. Further, variants can comprise a nucleotide sequence that encodes a polypeptide that shares at least 70-80%, 80-85%, 85-90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% sequence identity with a polypeptide sequence disclosed in Table 1 (or a fragment thereof) and that includes a novel SNP allele disclosed in Table 1 and/or Table 2. Thus, an aspect of the present invention that is specifically contemplated are isolated nucleic acid molecules that have a certain degree of sequence variation compared with the sequences shown in Tables 1-2, but that contain a novel SNP allele disclosed herein. In other words, as long as an isolated nucleic acid molecule contains a novel SNP allele disclosed herein, other portions of the nucleic acid molecule that flank the novel SNP allele can vary to some degree from the specific transcript, genomic, and context sequences referred to and shown in Tables 1 and 2, and can encode a polypeptide that varies to some degree from the specific polypeptide sequences referred to in Table 1.
To determine the percent identity of two amino acid sequences or two nucleotide sequences of two molecules that share sequence homology, the sequences are aligned for optimal comparison purposes (e.g., gaps can be introduced in one or both of a first and a second amino acid or nucleic acid sequence for optimal alignment and non-homologous sequences can be disregarded for comparison purposes). In a preferred embodiment, at least 30%, 40%, 50%, 60%, 70%, 80%, or 90% or more of the length of a reference sequence is aligned for comparison purposes. The amino acid residues or nucleotides at corresponding amino acid positions or nucleotide positions are then compared. When a position in the first sequence is occupied by the same amino acid residue or nucleotide as the corresponding position in the second sequence, then the molecules are identical at that position (as used herein, amino acid or nucleic acid “identity” is equivalent to amino acid or nucleic acid “homology”). The percent identity between the two sequences is a function of the number of identical positions shared by the sequences, taking into account the number of gaps, and the length of each gap, which need to be introduced for optimal alignment of the two sequences.
The comparison of sequences and determination of percent identity between two sequences can be accomplished using a mathematical algorithm. Computational Molecular Biology, A. M. Lesk, ed., Oxford University Press, N.Y (1988); Biocomputing: Informatics and Genome Projects, D. W. Smith, ed., Academic Press, N.Y. (1993); Computer Analysis of Sequence Data, Part 1, A. M. Griffin and H. G. Griffin, eds., Humana Press, N.J. (1994); Sequence Analysis in Molecular Biology, G. von Heinje, ed., Academic Press, N.Y. (1987); and Sequence Analysis Primer, M. Gribskov and J. Devereux, eds., M. Stockton Press, N.Y. (1991). In a preferred embodiment, the percent identity between two amino acid sequences is determined using the Needleman and Wunsch algorithm (J Mol Biol (48):444-453 (1970)) which has been incorporated into the GAP program in the GCG software package, using either a Blossom 62 matrix or a PAM250 matrix, and a gap weight of 16, 14, 12, 10, 8, 6, or 4 and a length weight of 1, 2, 3, 4, 5, or 6.
In yet another preferred embodiment, the percent identity between two nucleotide sequences is determined using the GAP program in the GCG software package using a NWSgapdna.CMP matrix and a gap weight of 40, 50, 60, 70, or 80 and a length weight of 1, 2, 3, 4, 5, or 6. J. Devereux et al., Nucleic Acids Res. 12(1):387 (1984). In another embodiment, the percent identity between two amino acid or nucleotide sequences is determined using the algorithm of E. Myers and W. Miller (CABIOS 4:11-17 (1989)) which has been incorporated into the ALIGN program (version 2.0), using a PAM120 weight residue table, a gap length penalty of 12, and a gap penalty of 4.
The nucleotide and amino acid sequences of the present invention can further be used as a “query sequence” to perform a search against sequence databases; for example, to identify other family members or related sequences. Such searches can be performed using the NBLAST and XBLAST programs (version 2.0). Altschul et al., J Mol Biol 215:403-10 (1990). BLAST nucleotide searches can be performed with the NBLAST program, score=100, wordlength=12 to obtain nucleotide sequences homologous to the nucleic acid molecules of the invention. BLAST protein searches can be performed with the XBLAST program, score=50, wordlength=3 to obtain amino acid sequences homologous to the proteins of the invention. To obtain gapped alignments for comparison purposes, Gapped BLAST can be utilized. Altschul et al., Nucleic Acids Res 25(17):3389-3402 (1997). When utilizing BLAST and gapped BLAST programs, the default parameters of the respective programs (e.g., XBLAST and NBLAST) can be used. In addition to BLAST, examples of other search and sequence comparison programs used in the art include, but are not limited to, FASTA (Pearson, Methods Mol Biol 25, 365-389 (1994)) and KERR (Dufresne et al., Nat Biotechnol 20(12):1269-71 (December 2002)). For further information regarding bioinformatics techniques, see Current Protocols in Bioinformatics, John Wiley & Sons, Inc., N.Y.
The present invention further provides non-coding fragments of the nucleic acid molecules disclosed in Table 1 and/or Table 2. Preferred non-coding fragments include, but are not limited to, promoter sequences, enhancer sequences, intronic sequences, 5′ untranslated regions (UTRs), 3′ untranslated regions, gene modulating sequences and gene termination sequences. Such fragments are useful, for example, in controlling heterologous gene expression and in developing screens to identify gene-modulating agents.
SNP Detection Reagents
In a specific aspect of the present invention, the SNPs disclosed in Table 1 and/or Table 2, and their associated transcript sequences (referred to in Table 1 as SEQ ID NO: 1), genomic sequences (referred to in Table 2 as SEQ ID NOS: 4-9), and context sequences (transcript-based context sequences are referred to in Table 1 as SEQ ID NO: 3; genomic-based context sequences are provided in Table 2 as SEQ ID NOS: 10-132), can be used for the design of SNP detection reagents. The actual sequences referred to in the tables are provided in the Sequence Listing. As used herein, a “SNP detection reagent” is a reagent that specifically detects a specific target SNP position disclosed herein, and that is preferably specific for a particular nucleotide (allele) of the target SNP position (i.e., the detection reagent preferably can differentiate between different alternative nucleotides at a target SNP position, thereby allowing the identity of the nucleotide present at the target SNP position to be determined). Typically, such detection reagent hybridizes to a target SNP-containing nucleic acid molecule by complementary base-pairing in a sequence specific manner, and discriminates the target variant sequence from other nucleic acid sequences such as an art-known form in a test sample. An example of a detection reagent is a probe that hybridizes to a target nucleic acid containing one or more of the SNPs referred to in Table 1 and/or Table 2. In a preferred embodiment, such a probe can differentiate between nucleic acids having a particular nucleotide (allele) at a target SNP position from other nucleic acids that have a different nucleotide at the same target SNP position. In addition, a detection reagent may hybridize to a specific region 5′ and/or 3′ to a SNP position, particularly a region corresponding to the context sequences referred to in Table 1 and/or Table 2 (transcript-based context sequences are referred to in Table 1 as SEQ ID NO: 3; genomic-based context sequences are referred to in Table 2 as SEQ ID NOS: 10-132). Another example of a detection reagent is a primer that acts as an initiation point of nucleotide extension along a complementary strand of a target polynucleotide. The SNP sequence information provided herein is also useful for designing primers, e.g. allele-specific primers, to amplify (e.g., using PCR) any SNP of the present invention.
In one preferred embodiment of the invention, a SNP detection reagent is an isolated or synthetic DNA or RNA polynucleotide probe or primer or PNA oligomer, or a combination of DNA, RNA and/or PNA, that hybridizes to a segment of a target nucleic acid molecule containing a SNP identified in Table 1 and/or Table 2. A detection reagent in the form of a polynucleotide may optionally contain modified base analogs, intercalators or minor groove binders. Multiple detection reagents such as probes may be, for example, affixed to a solid support (e.g., arrays or beads) or supplied in solution (e.g. probe/primer sets for enzymatic reactions such as PCR, RT-PCR, TaqMan assays, or primer-extension reactions) to form a SNP detection kit.
A probe or primer typically is a substantially purified oligonucleotide or PNA oligomer. Such oligonucleotide typically comprises a region of complementary nucleotide sequence that hybridizes under stringent conditions to at least about 8, 10, 12, 16, 18, 20, 22, 25, 30, 40, 50, 55, 60, 65, 70, 80, 90, 100, 120 (or any other number in-between) or more consecutive nucleotides in a target nucleic acid molecule. Depending on the particular assay, the consecutive nucleotides can either include the target SNP position, or be a specific region in close enough proximity 5′ and/or 3′ to the SNP position to carry out the desired assay.
Other preferred primer and probe sequences can readily be determined using the transcript sequences (SEQ ID NO: 1), genomic sequences (SEQ ID NOS: 4-9), and SNP context sequences (transcript-based context sequences are referred to in Table 1 as SEQ ID NO:3; genomic-based context sequences are referred to in Table 2 as SEQ ID NOS: 10-132) disclosed in the Sequence Listing and in Tables 1 and 2. The actual sequences referred to in the tables are provided in the Sequence Listing. It will be apparent to one of skill in the art that such primers and probes are directly useful as reagents for genotyping the SNPs of the present invention, and can be incorporated into any kit/system format.
In order to produce a probe or primer specific for a target SNP-containing sequence, the gene/transcript and/or context sequence surrounding the SNP of interest is typically examined using a computer algorithm that starts at the 5′ or at the 3′ end of the nucleotide sequence. Typical algorithms will then identify oligomers of defined length that are unique to the gene/SNP context sequence, have a GC content within a range suitable for hybridization, lack predicted secondary structure that may interfere with hybridization, and/or possess other desired characteristics or that lack other undesired characteristics.
A primer or probe of the present invention is typically at least about 8 nucleotides in length. In one embodiment of the invention, a primer or a probe is at least about 10 nucleotides in length. In a preferred embodiment, a primer or a probe is at least about 12 nucleotides in length. In a more preferred embodiment, a primer or probe is at least about 16, 17, 18, 19, 20, 21, 22, 23, 24 or 25 nucleotides in length. While the maximal length of a probe can be as long as the target sequence to be detected, depending on the type of assay in which it is employed, it is typically less than about 50, 60, 65, or 70 nucleotides in length. In the case of a primer, it is typically less than about 30 nucleotides in length. In a specific preferred embodiment of the invention, a primer or a probe is within the length of about 18 and about 28 nucleotides. However, in other embodiments, such as nucleic acid arrays and other embodiments in which probes are affixed to a substrate, the probes can be longer, such as on the order of 30-70, 75, 80, 90, 100, or more nucleotides in length (see the section below entitled “SNP Detection Kits and Systems”).
For analyzing SNPs, it may be appropriate to use oligonucleotides specific for alternative SNP alleles. Such oligonucleotides that detect single nucleotide variations in target sequences may be referred to by such terms as “allele-specific oligonucleotides,” “allele-specific probes,” or “allele-specific primers.” The design and use of allele-specific probes for analyzing polymorphisms is described in, e.g., Mutation Detection: A Practical Approach, Cotton et al., eds., Oxford University Press (1998); Saiki et al., Nature 324:163-166 (1986); Dattagupta, EP235,726; and Saiki, WO 89/11548.
While the design of each allele-specific primer or probe depends on variables such as the precise composition of the nucleotide sequences flanking a SNP position in a target nucleic acid molecule, and the length of the primer or probe, another factor in the use of primers and probes is the stringency of the condition under which the hybridization between the probe or primer and the target sequence is performed. Higher stringency conditions utilize buffers with lower ionic strength and/or a higher reaction temperature, and tend to require a more perfect match between probe/primer and a target sequence in order to form a stable duplex. If the stringency is too high, however, hybridization may not occur at all. In contrast, lower stringency conditions utilize buffers with higher ionic strength and/or a lower reaction temperature, and permit the formation of stable duplexes with more mismatched bases between a probe/primer and a target sequence. By way of example and not limitation, exemplary conditions for high stringency hybridization conditions using an allele-specific probe are as follows: prehybridization with a solution containing 5× standard saline phosphate EDTA (SSPE), 0.5% NaDodSO4 (SDS) at 55° C., and incubating probe with target nucleic acid molecules in the same solution at the same temperature, followed by washing with a solution containing 2×SSPE, and 0.1% SDS at 55° C. or room temperature.
Moderate stringency hybridization conditions may be used for allele-specific primer extension reactions with a solution containing, e.g., about 50 mM KCl at about 46° C. Alternatively, the reaction may be carried out at an elevated temperature such as 60° C. In another embodiment, a moderately stringent hybridization condition suitable for oligonucleotide ligation assay (OLA) reactions wherein two probes are ligated if they are completely complementary to the target sequence may utilize a solution of about 100 mM KCl at a temperature of 46° C.
In a hybridization-based assay, allele-specific probes can be designed that hybridize to a segment of target DNA from one individual but do not hybridize to the corresponding segment from another individual due to the presence of different polymorphic forms (e.g., alternative SNP alleles/nucleotides) in the respective DNA segments from the two individuals. Hybridization conditions should be sufficiently stringent that there is a significant detectable difference in hybridization intensity between alleles, and preferably an essentially binary response, whereby a probe hybridizes to only one of the alleles or significantly more strongly to one allele. While a probe may be designed to hybridize to a target sequence that contains a SNP site such that the SNP site aligns anywhere along the sequence of the probe, the probe is preferably designed to hybridize to a segment of the target sequence such that the SNP site aligns with a central position of the probe (e.g., a position within the probe that is at least three nucleotides from either end of the probe). This design of probe generally achieves good discrimination in hybridization between different allelic forms.
In another embodiment, a probe or primer may be designed to hybridize to a segment of target DNA such that the SNP aligns with either the 5′ most end or the 3′ most end of the probe or primer. In a specific preferred embodiment that is particularly suitable for use in a oligonucleotide ligation assay (U.S. Pat. No. 4,988,617), the 3′most nucleotide of the probe aligns with the SNP position in the target sequence.
Oligonucleotide probes and primers may be prepared by methods well known in the art. Chemical synthetic methods include, but are not limited to, the phosphotriester method described by Narang et al., Methods in Enzymology 68:90 (1979); the phosphodiester method described by Brown et al., Methods in Enzymology 68:109 (1979); the diethylphosphoamidate method described by Beaucage et al., Tetrahedron Letters 22:1859 (1981); and the solid support method described in U.S. Pat. No. 4,458,066.
Allele-specific probes are often used in pairs (or, less commonly, in sets of 3 or 4, such as if a SNP position is known to have 3 or 4 alleles, respectively, or to assay both strands of a nucleic acid molecule for a target SNP allele), and such pairs may be identical except for a one nucleotide mismatch that represents the allelic variants at the SNP position. Commonly, one member of a pair perfectly matches a reference form of a target sequence that has a more common SNP allele (i.e., the allele that is more frequent in the target population) and the other member of the pair perfectly matches a form of the target sequence that has a less common SNP allele (i.e., the allele that is rarer in the target population). In the case of an array, multiple pairs of probes can be immobilized on the same support for simultaneous analysis of multiple different polymorphisms.
In one type of PCR-based assay, an allele-specific primer hybridizes to a region on a target nucleic acid molecule that overlaps a SNP position and only primes amplification of an allelic form to which the primer exhibits perfect complementarity. Gibbs, Nucleic Acid Res 17:2427-2448 (1989). Typically, the primer's 3′-most nucleotide is aligned with and complementary to the SNP position of the target nucleic acid molecule. This primer is used in conjunction with a second primer that hybridizes at a distal site. Amplification proceeds from the two primers, producing a detectable product that indicates which allelic form is present in the test sample. A control is usually performed with a second pair of primers, one of which shows a single base mismatch at the polymorphic site and the other of which exhibits perfect complementarity to a distal site. The single-base mismatch prevents amplification or substantially reduces amplification efficiency, so that either no detectable product is formed or it is formed in lower amounts or at a slower pace. The method generally works most effectively when the mismatch is at the 3′-most position of the oligonucleotide (i.e., the 3′-most position of the oligonucleotide aligns with the target SNP position) because this position is most destabilizing to elongation from the primer (see, e.g., WO 93/22456). This PCR-based assay can be utilized as part of the TaqMan assay, described below.
In a specific embodiment of the invention, a primer of the invention contains a sequence substantially complementary to a segment of a target SNP-containing nucleic acid molecule except that the primer has a mismatched nucleotide in one of the three nucleotide positions at the 3′-most end of the primer, such that the mismatched nucleotide does not base pair with a particular allele at the SNP site. In a preferred embodiment, the mismatched nucleotide in the primer is the second from the last nucleotide at the 3′-most position of the primer. In a more preferred embodiment, the mismatched nucleotide in the primer is the last nucleotide at the 3′-most position of the primer.
In another embodiment of the invention, a SNP detection reagent of the invention is labeled with a fluorogenic reporter dye that emits a detectable signal. While the preferred reporter dye is a fluorescent dye, any reporter dye that can be attached to a detection reagent such as an oligonucleotide probe or primer is suitable for use in the invention. Such dyes include, but are not limited to, Acridine, AMCA, BODIPY, Cascade Blue, Cy2, Cy3, Cy5, Cy7, Dabcyl, Edans, Eosin, Erythrosin, Fluorescein, 6-Fam, Tet, Joe, Hex, Oregon Green, Rhodamine, Rhodol Green, Tamra, Rox, and Texas Red.
In yet another embodiment of the invention, the detection reagent may be further labeled with a quencher dye such as Tamra, especially when the reagent is used as a self-quenching probe such as a TaqMan (U.S. Pat. Nos. 5,210,015 and 5,538,848) or Molecular Beacon probe (U.S. Pat. Nos. 5,118,801 and 5,312,728), or other stemless or linear beacon probe (Livak et al., PCR Method Appl 4:357-362 (1995); Tyagi et al., Nature Biotechnology 14:303-308 (1996); Nazarenko et al., Nucl Acids Res 25:2516-2521 (1997); U.S. Pat. Nos. 5,866,336 and 6,117,635.
The detection reagents of the invention may also contain other labels, including but not limited to, biotin for streptavidin binding, hapten for antibody binding, and oligonucleotide for binding to another complementary oligonucleotide such as pairs of zipcodes.
The present invention also contemplates reagents that do not contain (or that are complementary to) a SNP nucleotide identified herein but that are used to assay one or more SNPs disclosed herein. For example, primers that flank, but do not hybridize directly to a target SNP position provided herein are useful in primer extension reactions in which the primers hybridize to a region adjacent to the target SNP position (i.e., within one or more nucleotides from the target SNP site). During the primer extension reaction, a primer is typically not able to extend past a target SNP site if a particular nucleotide (allele) is present at that target SNP site, and the primer extension product can be detected in order to determine which SNP allele is present at the target SNP site. For example, particular ddNTPs are typically used in the primer extension reaction to terminate primer extension once a ddNTP is incorporated into the extension product (a primer extension product which includes a ddNTP at the 3′-most end of the primer extension product, and in which the ddNTP is a nucleotide of a SNP disclosed herein, is a composition that is specifically contemplated by the present invention). Thus, reagents that bind to a nucleic acid molecule in a region adjacent to a SNP site and that are used for assaying the SNP site, even though the bound sequences do not necessarily include the SNP site itself, are also contemplated by the present invention.
SNP Detection Kits and Systems
A person skilled in the art will recognize that, based on the SNP and associated sequence information disclosed herein, detection reagents can be developed and used to assay any SNP of the present invention individually or in combination, and such detection reagents can be readily incorporated into one of the established kit or system formats which are well known in the art. The terms “kits” and “systems,” as used herein in the context of SNP detection reagents, are intended to refer to such things as combinations of multiple SNP detection reagents, or one or more SNP detection reagents in combination with one or more other types of elements or components (e.g., other types of biochemical reagents, containers, packages such as packaging intended for commercial sale, substrates to which SNP detection reagents are attached, electronic hardware components, etc.). Accordingly, the present invention further provides SNP detection kits and systems, including but not limited to, packaged probe and primer sets (e.g. TaqMan probe/primer sets), arrays/microarrays of nucleic acid molecules, and beads that contain one or more probes, primers, or other detection reagents for detecting one or more SNPs of the present invention. The kits/systems can optionally include various electronic hardware components; for example, arrays (“DNA chips”) and microfluidic systems (“lab-on-a-chip” systems) provided by various manufacturers typically comprise hardware components. Other kits/systems (e.g., probe/primer sets) may not include electronic hardware components, but may be comprised of, for example, one or more SNP detection reagents (along with, optionally, other biochemical reagents) packaged in one or more containers.
In some embodiments, a SNP detection kit typically contains one or more detection reagents and other components (e.g. a buffer, enzymes such as DNA polymerases or ligases, chain extension nucleotides such as deoxynucleotide triphosphates, and in the case of Sanger-type DNA sequencing reactions, chain terminating nucleotides, positive control sequences, negative control sequences, and the like) necessary to carry out an assay or reaction, such as amplification and/or detection of a SNP-containing nucleic acid molecule. A kit may further contain means for determining the amount of a target nucleic acid, and means for comparing the amount with a standard, and can comprise instructions for using the kit to detect the SNP-containing nucleic acid molecule of interest. In one embodiment of the present invention, kits are provided which contain the necessary reagents to carry out one or more assays to detect one or more SNPs disclosed herein. In a preferred embodiment of the present invention, SNP detection kits/systems are in the form of nucleic acid arrays, or compartmentalized kits, including microfluidic/lab-on-a-chip systems.
SNP detection kits/systems may contain, for example, one or more probes, or pairs of probes, that hybridize to a nucleic acid molecule at or near each target SNP position. Multiple pairs of allele-specific probes may be included in the kit/system to simultaneously assay large numbers of SNPs, at least one of which is a SNP of the present invention. In some kits/systems, the allele-specific probes are immobilized to a substrate such as an array or bead. For example, the same substrate can comprise allele-specific probes for detecting at least 1; 10; 100; 1000; 10,000; 100,000 (or any other number in-between) or substantially all of the SNPs shown in Table 1 and/or Table 2.
The terms “arrays,” “microarrays,” and “DNA chips” are used herein interchangeably to refer to an array of distinct polynucleotides affixed to a substrate, such as glass, plastic, paper, nylon or other type of membrane, filter, chip, or any other suitable solid support. The polynucleotides can be synthesized directly on the substrate, or synthesized separate from the substrate and then affixed to the substrate. In one embodiment, the microarray is prepared and used according to the methods described in Chee et al., U.S. Pat. No. 5,837,832 and PCT application WO95/11995; D. J. Lockhart et al., Nat Biotech 14:1675-1680 (1996); and M. Schena et al., Proc Natl Acad Sci 93:10614-10619 (1996), all of which are incorporated herein in their entirety by reference. In other embodiments, such arrays are produced by the methods described by Brown et al., U.S. Pat. No. 5,807,522.
Nucleic acid arrays are reviewed in the following references: Zammatteo et al., “New chips for molecular biology and diagnostics,” Biotechnol Annu Rev 8:85-101 (2002); Sosnowski et al., “Active microelectronic array system for DNA hybridization, genotyping and pharmacogenomic applications,” Psychiatr Genet 12(4):181-92 (December 2002); Heller, “DNA microarray technology: devices, systems, and applications,” Annu Rev Biomed Eng 4:129-53 (2002); Epub Mar. 22, 2002; Kolchinsky et al., “Analysis of SNPs and other genomic variations using gel-based chips,” Hum Mutat 19(4):343-60 (April 2002); and McGall et al., “High-density genechip oligonucleotide probe arrays,” Adv Biochem Eng Biotechnol 77:21-42 (2002).
Any number of probes, such as allele-specific probes, may be implemented in an array, and each probe or pair of probes can hybridize to a different SNP position. In the case of polynucleotide probes, they can be synthesized at designated areas (or synthesized separately and then affixed to designated areas) on a substrate using a light-directed chemical process. Each DNA chip can contain, for example, thousands to millions of individual synthetic polynucleotide probes arranged in a grid-like pattern and miniaturized (e.g., to the size of a dime). Preferably, probes are attached to a solid support in an ordered, addressable array.
A microarray can be composed of a large number of unique, single-stranded polynucleotides, usually either synthetic antisense polynucleotides or fragments of cDNAs, fixed to a solid support. Typical polynucleotides are preferably about 6-60 nucleotides in length, more preferably about 15-30 nucleotides in length, and most preferably about 18-25 nucleotides in length. For certain types of microarrays or other detection kits/systems, it may be preferable to use oligonucleotides that are only about 7-20 nucleotides in length. In other types of arrays, such as arrays used in conjunction with chemiluminescent detection technology, preferred probe lengths can be, for example, about 15-80 nucleotides in length, preferably about 50-70 nucleotides in length, more preferably about 55-65 nucleotides in length, and most preferably about 60 nucleotides in length. The microarray or detection kit can contain polynucleotides that cover the known 5′ or 3′ sequence of a gene/transcript or target SNP site, sequential polynucleotides that cover the full-length sequence of a gene/transcript; or unique polynucleotides selected from particular areas along the length of a target gene/transcript sequence, particularly areas corresponding to one or more SNPs disclosed in Table 1 and/or Table 2. Polynucleotides used in the microarray or detection kit can be specific to a SNP or SNPs of interest (e.g., specific to a particular SNP allele at a target SNP site, or specific to particular SNP alleles at multiple different SNP sites), or specific to a polymorphic gene/transcript or genes/transcripts of interest.
Hybridization assays based on polynucleotide arrays rely on the differences in hybridization stability of the probes to perfectly matched and mismatched target sequence variants. For SNP genotyping, it is generally preferable that stringency conditions used in hybridization assays are high enough such that nucleic acid molecules that differ from one another at as little as a single SNP position can be differentiated (e.g., typical SNP hybridization assays are designed so that hybridization will occur only if one particular nucleotide is present at a SNP position, but will not occur if an alternative nucleotide is present at that SNP position). Such high stringency conditions may be preferable when using, for example, nucleic acid arrays of allele-specific probes for SNP detection. Such high stringency conditions are described in the preceding section, and are well known to those skilled in the art and can be found in, for example, Current Protocols in Molecular Biology 6.3.1-6.3.6, John Wiley & Sons, N.Y. (1989).
In other embodiments, the arrays are used in conjunction with chemiluminescent detection technology. The following patents and patent applications, which are all hereby incorporated by reference, provide additional information pertaining to chemiluminescent detection. U.S. patent applications Ser. Nos. that describe chemiluminescent approaches for microarray detection: 10/620332 and 10/620333. U.S. patents that describe methods and compositions of dioxetane for performing chemiluminescent detection: Nos. 6,124,478; 6,107,024; 5,994,073; 5,981,768; 5,871,938; 5,843,681; 5,800,999 and 5,773,628. And the U.S. published application that discloses methods and compositions for microarray controls: US2002/0110828.
In one embodiment of the invention, a nucleic acid array can comprise an array of probes of about 15-25 nucleotides in length. In further embodiments, a nucleic acid array can comprise any number of probes, in which at least one probe is capable of detecting one or more SNPs disclosed in Table 1 and/or Table 2, and/or at least one probe comprises a fragment of one of the sequences selected from the group consisting of those disclosed in Table 1, Table 2, the Sequence Listing, and sequences complementary thereto, said fragment comprising at least about 8 consecutive nucleotides, preferably 10, 12, 15, 16, 18, 20, more preferably 22, 25, 30, 40, 47, 50, 55, 60, 65, 70, 80, 90, 100, or more consecutive nucleotides (or any other number in-between) and containing (or being complementary to) a novel SNP allele disclosed in Table 1 and/or Table 2. In some embodiments, the nucleotide complementary to the SNP site is within 5, 4, 3, 2, or 1 nucleotide from the center of the probe, more preferably at the center of said probe.
A polynucleotide probe can be synthesized on the surface of the substrate by using a chemical coupling procedure and an ink jet application apparatus, as described in PCT application WO95/251116 (Baldeschweiler et al.) which is incorporated herein in its entirety by reference. In another aspect, a “gridded” array analogous to a dot (or slot) blot may be used to arrange and link cDNA fragments or oligonucleotides to the surface of a substrate using a vacuum system, thermal, UV, mechanical or chemical bonding procedures. An array, such as those described above, may be produced by hand or by using available devices (slot blot or dot blot apparatus), materials (any suitable solid support), and machines (including robotic instruments), and may contain 8, 24, 96, 384, 1536, 6144 or more polynucleotides, or any other number which lends itself to the efficient use of commercially available instrumentation.
Using such arrays or other kits/systems, the present invention provides methods of identifying the SNPs disclosed herein in a test sample. Such methods typically involve incubating a test sample of nucleic acids with an array comprising one or more probes corresponding to at least one SNP position of the present invention, and assaying for binding of a nucleic acid from the test sample with one or more of the probes. Conditions for incubating a SNP detection reagent (or a kit/system that employs one or more such SNP detection reagents) with a test sample vary. Incubation conditions depend on such factors as the format employed in the assay, the detection methods employed, and the type and nature of the detection reagents used in the assay. One skilled in the art will recognize that any one of the commonly available hybridization, amplification and array assay formats can readily be adapted to detect the SNPs disclosed herein.
A SNP detection kit/system of the present invention may include components that are used to prepare nucleic acids from a test sample for the subsequent amplification and/or detection of a SNP-containing nucleic acid molecule. Such sample preparation components can be used to produce nucleic acid extracts (including DNA and/or RNA), proteins or membrane extracts from any bodily fluids (such as blood, serum, plasma, urine, saliva, phlegm, gastric juices, semen, tears, sweat, etc.), skin, hair, cells (especially nucleated cells), biopsies, buccal swabs or tissue specimens. The test samples used in the above-described methods will vary based on such factors as the assay format, nature of the detection method, and the specific tissues, cells or extracts used as the test sample to be assayed. Methods of preparing nucleic acids, proteins, and cell extracts are well known in the art and can be readily adapted to obtain a sample that is compatible with the system utilized. Automated sample preparation systems for extracting nucleic acids from a test sample are commercially available, and examples are Qiagen's BioRobot 9600, Applied Biosystems' PRISMTM 6700 sample preparation system, and Roche Molecular Systems' COBAS AmpliPrep System.
Another form of kit contemplated by the present invention is a compartmentalized kit. A compartmentalized kit includes any kit in which reagents are contained in separate containers. Such containers include, for example, small glass containers, plastic containers, strips of plastic, glass or paper, or arraying material such as silica. Such containers allow one to efficiently transfer reagents from one compartment to another compartment such that the test samples and reagents are not cross-contaminated, or from one container to another vessel not included in the kit, and the agents or solutions of each container can be added in a quantitative fashion from one compartment to another or to another vessel. Such containers may include, for example, one or more containers which will accept the test sample, one or more containers which contain at least one probe or other SNP detection reagent for detecting one or more SNPs of the present invention, one or more containers which contain wash reagents (such as phosphate buffered saline, Tris-buffers, etc.), and one or more containers which contain the reagents used to reveal the presence of the bound probe or other SNP detection reagents. The kit can optionally further comprise compartments and/or reagents for, for example, nucleic acid amplification or other enzymatic reactions such as primer extension reactions, hybridization, ligation, electrophoresis (preferably capillary electrophoresis), mass spectrometry, and/or laser-induced fluorescent detection. The kit may also include instructions for using the kit. Exemplary compartmentalized kits include microfluidic devices known in the art. See, e.g., Weigl et al., “Lab-on-a-chip for drug development,” Adv Drug Deliv Rev 55(3):349-77 (February 2003). In such microfluidic devices, the containers may be referred to as, for example, microfluidic “compartments,” “chambers,” or “channels.”
Microfluidic devices, which may also be referred to as “lab-on-a-chip” systems, biomedical micro-electro-mechanical systems (bioMEMs), or multicomponent integrated systems, are exemplary kits/systems of the present invention for analyzing SNPs. Such systems miniaturize and compartmentalize processes such as probe/target hybridization, nucleic acid amplification, and capillary electrophoresis reactions in a single functional device. Such microfluidic devices typically utilize detection reagents in at least one aspect of the system, and such detection reagents may be used to detect one or more SNPs of the present invention. One example of a microfluidic system is disclosed in U.S. Pat. No. 5,589,136, which describes the integration of PCR amplification and capillary electrophoresis in chips. Exemplary microfluidic systems comprise a pattern of microchannels designed onto a glass, silicon, quartz, or plastic wafer included on a microchip. The movements of the samples may be controlled by electric, electroosmotic or hydrostatic forces applied across different areas of the microchip to create functional microscopic valves and pumps with no moving parts. Varying the voltage can be used as a means to control the liquid flow at intersections between the micro-machined channels and to change the liquid flow rate for pumping across different sections of the microchip. See, for example, U.S. Pat. No. 6,153,073, Dubrow et al., and U.S. Pat. No. 6,156,181, Parce et al.
For genotyping SNPs, an exemplary microfluidic system may integrate, for example, nucleic acid amplification, primer extension, capillary electrophoresis, and a detection method such as laser induced fluorescence detection. In a first step of an exemplary process for using such an exemplary system, nucleic acid samples are amplified, preferably by PCR. Then, the amplification products are subjected to automated primer extension reactions using ddNTPs (specific fluorescence for each ddNTP) and the appropriate oligonucleotide primers to carry out primer extension reactions which hybridize just upstream of the targeted SNP. Once the extension at the 3′ end is completed, the primers are separated from the unincorporated fluorescent ddNTPs by capillary electrophoresis. The separation medium used in capillary electrophoresis can be, for example, polyacrylamide, polyethyleneglycol or dextran. The incorporated ddNTPs in the single nucleotide primer extension products are identified by laser-induced fluorescence detection. Such an exemplary microchip can be used to process, for example, at least 96 to 384 samples, or more, in parallel.
Uses of Nucleic Acid Molecules
The nucleic acid molecules of the present invention have a variety of uses, especially for the diagnosis, prognosis, treatment, and prevention of CHD (particularly MI) and aneurysm/dissection, and for predicting drug response, particularly response to statins. For example, the nucleic acid molecules of the invention are useful for predicting an individual's risk for developing CHD (particularly the risk for experiencing a first or recurrent MI) or aneurysm/dissection, for prognosing the progression of CHD (e.g., the severity or consequences of MI) or aneurysm/dissection in an individual, in evaluating the likelihood of an individual who has CHD or aneurysm/dissection (or who is at increased risk for CHD or aneurysm/dissection) of responding to treatment (or prevention) of CHD or aneurysm/dissection with statin, and/or predicting the likelihood that the individual will experience toxicity or other undesirable side effects from the statin treatment, etc. For example, the nucleic acid molecules are useful as hybridization probes, such as for genotyping SNPs in messenger RNA, transcript, cDNA, genomic DNA, amplified DNA or other nucleic acid molecules, and for isolating full-length cDNA and genomic clones encoding the variant peptides disclosed in Table 1 as well as their orthologs.
A probe can hybridize to any nucleotide sequence along the entire length of a nucleic acid molecule referred to in Table 1 and/or Table 2. Preferably, a probe of the present invention hybridizes to a region of a target sequence that encompasses a SNP position indicated in Table 1 and/or Table 2. More preferably, a probe hybridizes to a SNP-containing target sequence in a sequence-specific manner such that it distinguishes the target sequence from other nucleotide sequences which vary from the target sequence only by which nucleotide is present at the SNP site. Such a probe is particularly useful for detecting the presence of a SNP-containing nucleic acid in a test sample, or for determining which nucleotide (allele) is present at a particular SNP site (i.e., genotyping the SNP site).
A nucleic acid hybridization probe may be used for determining the presence, level, form, and/or distribution of nucleic acid expression. The nucleic acid whose level is determined can be DNA or RNA. Accordingly, probes specific for the SNPs described herein can be used to assess the presence, expression and/or gene copy number in a given cell, tissue, or organism. These uses are relevant for diagnosis of disorders involving an increase or decrease in gene expression relative to normal levels. In vitro techniques for detection of mRNA include, for example, Northern blot hybridizations and in situ hybridizations. In vitro techniques for detecting DNA include Southern blot hybridizations and in situ hybridizations. Sambrook and Russell, Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Press, N.Y. (2000).
Probes can be used as part of a diagnostic test kit for identifying cells or tissues in which a variant protein is expressed, such as by measuring the level of a variant protein-encoding nucleic acid (e.g., mRNA) in a sample of cells from a subject or determining if a polynucleotide contains a SNP of interest.
Thus, the nucleic acid molecules of the invention can be used as hybridization probes to detect the SNPs disclosed herein, thereby determining whether an individual with the polymorphism(s) is at risk for developing CHD or aneurysm/dissection (or has already developed early stage CHD or aneurysm/dissection), or the likelihood that an individual will respond positively to statin treatment (including preventive treatment) of CHD or aneurysm/dissection. Detection of a SNP associated with a disease phenotype provides a diagnostic tool for an active disease and/or genetic predisposition to the disease.
Furthermore, the nucleic acid molecules of the invention are therefore useful for detecting a gene (gene information is disclosed in Table 2, for example) which contains a SNP disclosed herein and/or products of such genes, such as expressed mRNA transcript molecules (transcript information is disclosed in Table 1, for example), and are thus useful for detecting gene expression. The nucleic acid molecules can optionally be implemented in, for example, an array or kit format for use in detecting gene expression.
The nucleic acid molecules of the invention are also useful as primers to amplify any given region of a nucleic acid molecule, particularly a region containing a SNP identified in Table 1 and/or Table 2.
The nucleic acid molecules of the invention are also useful for constructing recombinant vectors (described in greater detail below). Such vectors include expression vectors that express a portion of, or all of, any of the variant peptide sequences referred to in Table 1. Vectors also include insertion vectors, used to integrate into another nucleic acid molecule sequence, such as into the cellular genome, to alter in situ expression of a gene and/or gene product. For example, an endogenous coding sequence can be replaced via homologous recombination with all or part of the coding region containing one or more specifically introduced SNPs.
The nucleic acid molecules of the invention are also useful for expressing antigenic portions of the variant proteins, particularly antigenic portions that contain a variant amino acid sequence (e.g., an amino acid substitution) caused by a SNP disclosed in Table 1 and/or Table 2.
The nucleic acid molecules of the invention are also useful for constructing vectors containing a gene regulatory region of the nucleic acid molecules of the present invention.
The nucleic acid molecules of the invention are also useful for designing ribozymes corresponding to all, or a part, of an mRNA molecule expressed from a SNP-containing nucleic acid molecule described herein.
The nucleic acid molecules of the invention are also useful for constructing host cells expressing a part, or all, of the nucleic acid molecules and variant peptides.
The nucleic acid molecules of the invention are also useful for constructing transgenic animals expressing all, or a part, of the nucleic acid molecules and variant peptides. The production of recombinant cells and transgenic animals having nucleic acid molecules which contain the SNPs disclosed in Table 1 and/or Table 2 allows, for example, effective clinical design of treatment compounds and dosage regimens.
The nucleic acid molecules of the invention are also useful in assays for drug screening to identify compounds that, for example, modulate nucleic acid expression.
The nucleic acid molecules of the invention are also useful in gene therapy in patients whose cells have aberrant gene expression. Thus, recombinant cells, which include a patient's cells that have been engineered ex vivo and returned to the patient, can be introduced into an individual where the recombinant cells produce the desired protein to treat the individual.
SNP Genotyping Methods
The process of determining which specific nucleotide (i.e., allele) is present at each of one or more SNP positions, such as a SNP position in a nucleic acid molecule disclosed in Table 1 and/or Table 2, is referred to as SNP genotyping. The present invention provides methods of SNP genotyping, such as for use in evaluating an individual's risk for developing CHD (particularly MI) or aneurysm/dissection, for evaluating an individual's prognosis for disease severity and recovery, for predicting the likelihood that an individual who has previously had CHD (e.g., MI) or aneurysm/dissection will have CHD or aneurysm/dissection again in the future (e.g., one or more recurrent MI's or aneurysms/dissections), for implementing a preventive or treatment regimen for an individual based on that individual having an increased susceptibility for developing CHD (e.g., increased risk for MI) or aneurysm/dissection, in evaluating an individual's likelihood of responding to statin treatment (particularly for treating or preventing CHD or aneurysm/dissection), in selecting a treatment or preventive regimen (e.g., in deciding whether or not to administer statin treatment to an individual having CHD or aneurysm/dissection, or who is at increased risk for developing CHD or aneurysm/dissection in the future), or in formulating or selecting a particular statin-based treatment or preventive regimen such as dosage and/or frequency of administration of statin treatment or choosing which form/type of statin to be administered, such as a particular pharmaceutical composition or compound, etc.), determining the likelihood of experiencing toxicity or other undesirable side effects from statin treatment, or selecting individuals for a clinical trial of a statin (e.g., selecting individuals to participate in the trial who are most likely to respond positively from the statin treatment, and/or excluding individuals from the trial who are unlikely to respond positively from the statin treatment), etc.
Nucleic acid samples can be genotyped to determine which allele(s) is/are present at any given genetic region (e.g., SNP position) of interest by methods well known in the art. The neighboring sequence can be used to design SNP detection reagents such as oligonucleotide probes, which may optionally be implemented in a kit format. Exemplary SNP genotyping methods are described in Chen et al., “Single nucleotide polymorphism genotyping: biochemistry, protocol, cost and throughput,” Pharmacogenomics J 3(2):77-96 (2003); Kwok et al., “Detection of single nucleotide polymorphisms,” Curr Issues Mol Biol 5(2):43-60 (April 2003); Shi, “Technologies for individual genotyping: detection of genetic polymorphisms in drug targets and disease genes,” Am J Pharmacogenomics 2(3):197-205 (2002); and Kwok, “Methods for genotyping single nucleotide polymorphisms,” Annu Rev Genomics Hum Genet 2:235-58 (2001). Exemplary techniques for high-throughput SNP genotyping are described in Marnellos, “High-throughput SNP analysis for genetic association studies,” Curr Opin Drug Discov Devel 6(3):317-21 (May 2003). Common SNP genotyping methods include, but are not limited to, TaqMan assays, molecular beacon assays, nucleic acid arrays, allele-specific primer extension, allele-specific PCR, arrayed primer extension, homogeneous primer extension assays, primer extension with detection by mass spectrometry, pyrosequencing, multiplex primer extension sorted on genetic arrays, ligation with rolling circle amplification, homogeneous ligation, OLA (U.S. Pat. No. 4,988,167), multiplex ligation reaction sorted on genetic arrays, restriction-fragment length polymorphism, single base extension-tag assays, and the Invader assay. Such methods may be used in combination with detection mechanisms such as, for example, luminescence or chemiluminescence detection, fluorescence detection, time-resolved fluorescence detection, fluorescence resonance energy transfer, fluorescence polarization, mass spectrometry, and electrical detection.
Various methods for detecting polymorphisms include, but are not limited to, methods in which protection from cleavage agents is used to detect mismatched bases in RNA/RNA or RNA/DNA duplexes (Myers et al., Science 230:1242 (1985); Cotton et al., PNAS 85:4397 (1988); and Saleeba et al., Meth. Enzymol 217:286-295 (1992)), comparison of the electrophoretic mobility of variant and wild type nucleic acid molecules (Orita et al., PNAS 86:2766 (1989); Cotton et al., Mutat Res 285:125-144 (1993); and Hayashi et al., Genet Anal Tech Appl 9:73-79 (1992)), and assaying the movement of polymorphic or wild-type fragments in polyacrylamide gels containing a gradient of denaturant using denaturing gradient gel electrophoresis (DGGE) (Myers et al., Nature 313:495 (1985)). Sequence variations at specific locations can also be assessed by nuclease protection assays such as RNase and S1 protection or chemical cleavage methods.
In a preferred embodiment, SNP genotyping is performed using the TaqMan assay, which is also known as the 5′ nuclease assay (U.S. Pat. Nos. 5,210,015 and 5,538,848). The TaqMan assay detects the accumulation of a specific amplified product during PCR. The TaqMan assay utilizes an oligonucleotide probe labeled with a fluorescent reporter dye and a quencher dye. The reporter dye is excited by irradiation at an appropriate wavelength, it transfers energy to the quencher dye in the same probe via a process called fluorescence resonance energy transfer (FRET). When attached to the probe, the excited reporter dye does not emit a signal. The proximity of the quencher dye to the reporter dye in the intact probe maintains a reduced fluorescence for the reporter. The reporter dye and quencher dye may be at the 5′ most and the 3′ most ends, respectively, or vice versa. Alternatively, the reporter dye may be at the 5′ or 3′ most end while the quencher dye is attached to an internal nucleotide, or vice versa. In yet another embodiment, both the reporter and the quencher may be attached to internal nucleotides at a distance from each other such that fluorescence of the reporter is reduced.
During PCR, the 5′ nuclease activity of DNA polymerase cleaves the probe, thereby separating the reporter dye and the quencher dye and resulting in increased fluorescence of the reporter. Accumulation of PCR product is detected directly by monitoring the increase in fluorescence of the reporter dye. The DNA polymerase cleaves the probe between the reporter dye and the quencher dye only if the probe hybridizes to the target SNP-containing template which is amplified during PCR, and the probe is designed to hybridize to the target SNP site only if a particular SNP allele is present.
Preferred TaqMan primer and probe sequences can readily be determined using the SNP and associated nucleic acid sequence information provided herein. A number of computer programs, such as Primer Express (Applied Biosystems, Foster City, Calif.), can be used to rapidly obtain optimal primer/probe sets. It will be apparent to one of skill in the art that such primers and probes for detecting the SNPs of the present invention are useful in, for example, screening for individuals who are susceptible to developing CHD (particularly MI), aneurysm/dissection, and related pathologies, or in screening individuals who have CHD or aneurysm/dissection (or who are susceptible to CHD or aneurysm/dissection) for their likelihood of responding to statin treatment. These probes and primers can be readily incorporated into a kit format. The present invention also includes modifications of the Taqman assay well known in the art such as the use of Molecular Beacon probes (U.S. Pat. Nos. 5,118,801 and 5,312,728) and other variant formats (U.S. Pat. Nos. 5,866,336 and 6,117,635).
Another preferred method for genotyping the SNPs of the present invention is the use of two oligonucleotide probes in an OLA (see, e.g., U.S. Pat. No. 4,988,617). In this method, one probe hybridizes to a segment of a target nucleic acid with its 3′ most end aligned with the SNP site. A second probe hybridizes to an adjacent segment of the target nucleic acid molecule directly 3′ to the first probe. The two juxtaposed probes hybridize to the target nucleic acid molecule, and are ligated in the presence of a linking agent such as a ligase if there is perfect complementarity between the 3′ most nucleotide of the first probe with the SNP site. If there is a mismatch, ligation would not occur. After the reaction, the ligated probes are separated from the target nucleic acid molecule, and detected as indicators of the presence of a SNP.
The following patents, patent applications, and published international patent applications, which are all hereby incorporated by reference, provide additional information pertaining to techniques for carrying out various types of OLA. The following U.S. patents describe OLA strategies for performing SNP detection: U.S. Pat. Nos. 6,027,889; 6,268,148; 5,494,810; 5,830,711 and 6,054,564. WO 97/31256 and WO 00/56927 describe OLA strategies for performing SNP detection using universal arrays, wherein a zipcode sequence can be introduced into one of the hybridization probes, and the resulting product, or amplified product, hybridized to a universal zip code array. U.S. application Ser. No. 01/17329 (and Ser. No. 09/584,905) describes OLA (or LDR) followed by PCR, wherein zipcodes are incorporated into OLA probes, and amplified PCR products are determined by electrophoretic or universal zipcode array readout. U.S. applications 60/427818, 60/445636, and 60/445494 describe SNPlex methods and software for multiplexed SNP detection using OLA followed by PCR, wherein zipcodes are incorporated into OLA probes, and amplified PCR products are hybridized with a zipchute reagent, and the identity of the SNP determined from electrophoretic readout of the zipchute. In some embodiments, OLA is carried out prior to PCR (or another method of nucleic acid amplification). In other embodiments, PCR (or another method of nucleic acid amplification) is carried out prior to OLA.
Another method for SNP genotyping is based on mass spectrometry. Mass spectrometry takes advantage of the unique mass of each of the four nucleotides of DNA. SNPs can be unambiguously genotyped by mass spectrometry by measuring the differences in the mass of nucleic acids having alternative SNP alleles. MALDI-TOF (Matrix Assisted Laser Desorption Ionization —Time of Flight) mass spectrometry technology is preferred for extremely precise determinations of molecular mass, such as SNPs. Numerous approaches to SNP analysis have been developed based on mass spectrometry. Preferred mass spectrometry-based methods of SNP genotyping include primer extension assays, which can also be utilized in combination with other approaches, such as traditional gel-based formats and microarrays.
Typically, the primer extension assay involves designing and annealing a primer to a template PCR amplicon upstream (5′) from a target SNP position. A mix of dideoxynucleotide triphosphates (ddNTPs) and/or deoxynucleotide triphosphates (dNTPs) are added to a reaction mixture containing template (e.g., a SNP-containing nucleic acid molecule which has typically been amplified, such as by PCR), primer, and DNA polymerase. Extension of the primer terminates at the first position in the template where a nucleotide complementary to one of the ddNTPs in the mix occurs. The primer can be either immediately adjacent (i.e., the nucleotide at the 3′ end of the primer hybridizes to the nucleotide next to the target SNP site) or two or more nucleotides removed from the SNP position. If the primer is several nucleotides removed from the target SNP position, the only limitation is that the template sequence between the 3′ end of the primer and the SNP position cannot contain a nucleotide of the same type as the one to be detected, or this will cause premature termination of the extension primer. Alternatively, if all four ddNTPs alone, with no dNTPs, are added to the reaction mixture, the primer will always be extended by only one nucleotide, corresponding to the target SNP position. In this instance, primers are designed to bind one nucleotide upstream from the SNP position (i.e., the nucleotide at the 3′ end of the primer hybridizes to the nucleotide that is immediately adjacent to the target SNP site on the 5′ side of the target SNP site). Extension by only one nucleotide is preferable, as it minimizes the overall mass of the extended primer, thereby increasing the resolution of mass differences between alternative SNP nucleotides. Furthermore, mass-tagged ddNTPs can be employed in the primer extension reactions in place of unmodified ddNTPs. This increases the mass difference between primers extended with these ddNTPs, thereby providing increased sensitivity and accuracy, and is particularly useful for typing heterozygous base positions. Mass-tagging also alleviates the need for intensive sample-preparation procedures and decreases the necessary resolving power of the mass spectrometer.
The extended primers can then be purified and analyzed by MALDI-TOF mass spectrometry to determine the identity of the nucleotide present at the target SNP position. In one method of analysis, the products from the primer extension reaction are combined with light absorbing crystals that form a matrix. The matrix is then hit with an energy source such as a laser to ionize and desorb the nucleic acid molecules into the gas-phase. The ionized molecules are then ejected into a flight tube and accelerated down the tube towards a detector. The time between the ionization event, such as a laser pulse, and collision of the molecule with the detector is the time of flight of that molecule. The time of flight is precisely correlated with the mass-to-charge ratio (m/z) of the ionized molecule. Ions with smaller m/z travel down the tube faster than ions with larger m/z and therefore the lighter ions reach the detector before the heavier ions. The time-of-flight is then converted into a corresponding, and highly precise, m/z. In this manner, SNPs can be identified based on the slight differences in mass, and the corresponding time of flight differences, inherent in nucleic acid molecules having different nucleotides at a single base position. For further information regarding the use of primer extension assays in conjunction with MALDI-TOF mass spectrometry for SNP genotyping, see, e.g., Wise et al., “A standard protocol for single nucleotide primer extension in the human genome using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry,” Rapid Commun Mass Spectrom 17(11):1195-202 (2003).
The following references provide further information describing mass spectrometry-based methods for SNP genotyping: Bocker, “SNP and mutation discovery using base-specific cleavage and MALDI-TOF mass spectrometry,” Bioinformatics 19 Suppl 1:144-153 (July 2003); Storm et al., “MALDI-TOF mass spectrometry-based SNP genotyping,” Methods Mol Biol 212:241-62 (2003); Jurinke et al., “The use of Mass ARRAY technology for high throughput genotyping,” Adv Biochem Eng Biotechnol 77:57-74 (2002); and Jurinke et al., “Automated genotyping using the DNA MassArray technology,” Methods Mol Biol 187:179-92 (2002).
SNPs can also be scored by direct DNA sequencing. A variety of automated sequencing procedures can be utilized (e.g. Biotechniques 19:448 (1995)), including sequencing by mass spectrometry. See, e.g., PCT International Publication No. WO 94/16101; Cohen et al., Adv Chromatogr 36:127-162 (1996); and Griffin et al., Appl Biochem Biotechnol 38:147-159 (1993). The nucleic acid sequences of the present invention enable one of ordinary skill in the art to readily design sequencing primers for such automated sequencing procedures. Commercial instrumentation, such as the Applied Biosystems 377, 3100, 3700, 3730, and 3730x1 DNA Analyzers (Foster City, Calif.), is commonly used in the art for automated sequencing.
Other methods that can be used to genotype the SNPs of the present invention include single-strand conformational polymorphism (SSCP), and denaturing gradient gel electrophoresis (DGGE). Myers et al., Nature 313:495 (1985). SSCP identifies base differences by alteration in electrophoretic migration of single stranded PCR products, as described in Orita et al., Proc. Nat. Acad. Single-stranded PCR products can be generated by heating or otherwise denaturing double stranded PCR products. Single-stranded nucleic acids may refold or form secondary structures that are partially dependent on the base sequence. The different electrophoretic mobilities of single-stranded amplification products are related to base-sequence differences at SNP positions. DGGE differentiates SNP alleles based on the different sequence-dependent stabilities and melting properties inherent in polymorphic DNA and the corresponding differences in electrophoretic migration patterns in a denaturing gradient gel. PCR Technology: Principles and Applications for DNA Amplification Chapter 7, Erlich, ed., W.H. Freeman and Co, N.Y. (1992).
Sequence-specific ribozymes (U.S. Pat. No. 5,498,531) can also be used to score SNPs based on the development or loss of a ribozyme cleavage site. Perfectly matched sequences can be distinguished from mismatched sequences by nuclease cleavage digestion assays or by differences in melting temperature. If the SNP affects a restriction enzyme cleavage site, the SNP can be identified by alterations in restriction enzyme digestion patterns, and the corresponding changes in nucleic acid fragment lengths determined by gel electrophoresis.
SNP genotyping can include the steps of, for example, collecting a biological sample from a human subject (e.g., sample of tissues, cells, fluids, secretions, etc.), isolating nucleic acids (e.g., genomic DNA, mRNA or both) from the cells of the sample, contacting the nucleic acids with one or more primers which specifically hybridize to a region of the isolated nucleic acid containing a target SNP under conditions such that hybridization and amplification of the target nucleic acid region occurs, and determining the nucleotide present at the SNP position of interest, or, in some assays, detecting the presence or absence of an amplification product (assays can be designed so that hybridization and/or amplification will only occur if a particular SNP allele is present or absent). In some assays, the size of the amplification product is detected and compared to the length of a control sample; for example, deletions and insertions can be detected by a change in size of the amplified product compared to a normal genotype.
SNP genotyping is useful for numerous practical applications, as described below. Examples of such applications include, but are not limited to, SNP-disease association analysis, disease predisposition screening, disease diagnosis, disease prognosis, disease progression monitoring, determining therapeutic strategies based on an individual's genotype (“pharmacogenomics”), developing therapeutic agents based on SNP genotypes associated with a disease or likelihood of responding to a drug, stratifying a patient population for clinical trial for a treatment regimen, predicting the likelihood that an individual will experience toxic side effects from a therapeutic agent, and human identification applications such as forensics.
Analysis of Genetic Association Between SNPs and Phenotypic Traits
SNP genotyping for disease diagnosis, disease predisposition screening, disease prognosis, determining drug responsiveness (pharmacogenomics), drug toxicity screening, and other uses described herein, typically relies on initially establishing a genetic association between one or more specific SNPs and the particular phenotypic traits of interest.
Different study designs may be used for genetic association studies. Modern Epidemiology 609-622, Lippincott, Williams & Wilkins (1998). Observational studies are most frequently carried out in which the response of the patients is not interfered with. The first type of observational study identifies a sample of persons in whom the suspected cause of the disease is present and another sample of persons in whom the suspected cause is absent, and then the frequency of development of disease in the two samples is compared. These sampled populations are called cohorts, and the study is a prospective study. The other type of observational study is case-control or a retrospective study. In typical case-control studies, samples are collected from individuals with the phenotype of interest (cases) such as certain manifestations of a disease, and from individuals without the phenotype (controls) in a population (target population) that conclusions are to be drawn from. Then the possible causes of the disease are investigated retrospectively. As the time and costs of collecting samples in case-control studies are considerably less than those for prospective studies, case-control studies are the more commonly used study design in genetic association studies, at least during the exploration and discovery stage.
In both types of observational studies, there may be potential confounding factors that should be taken into consideration. Confounding factors are those that are associated with both the real cause(s) of the disease and the disease itself, and they include demographic information such as age, gender, ethnicity as well as environmental factors. When confounding factors are not matched in cases and controls in a study, and are not controlled properly, spurious association results can arise. If potential confounding factors are identified, they should be controlled for by analysis methods explained below.
In a genetic association study, the cause of interest to be tested is a certain allele or a SNP or a combination of alleles or a haplotype from several SNPs. Thus, tissue specimens (e.g., whole blood) from the sampled individuals may be collected and genomic DNA genotyped for the SNP(s) of interest. In addition to the phenotypic trait of interest, other information such as demographic (e.g., age, gender, ethnicity, etc.), clinical, and environmental information that may influence the outcome of the trait can be collected to further characterize and define the sample set. In many cases, these factors are known to be associated with diseases and/or SNP allele frequencies. There are likely gene-environment and/or gene-gene interactions as well. Analysis methods to address gene-environment and gene-gene interactions (for example, the effects of the presence of both susceptibility alleles at two different genes can be greater than the effects of the individual alleles at two genes combined) are discussed below.
After all the relevant phenotypic and genotypic information has been obtained, statistical analyses are carried out to determine if there is any significant correlation between the presence of an allele or a genotype with the phenotypic characteristics of an individual. Preferably, data inspection and cleaning are first performed before carrying out statistical tests for genetic association. Epidemiological and clinical data of the samples can be summarized by descriptive statistics with tables and graphs. Data validation is preferably performed to check for data completion, inconsistent entries, and outliers. Chi-squared tests and t-tests (Wilcoxon rank-sum tests if distributions are not normal) may then be used to check for significant differences between cases and controls for discrete and continuous variables, respectively. To ensure genotyping quality, Hardy-Weinberg disequilibrium tests can be performed on cases and controls separately. Significant deviation from Hardy-Weinberg equilibrium (HWE) in both cases and controls for individual markers can be indicative of genotyping errors. If HWE is violated in a majority of markers, it is indicative of population substructure that should be further investigated. Moreover, Hardy-Weinberg disequilibrium in cases only can indicate genetic association of the markers with the disease. B. Weir, Genetic Data Analysis, Sinauer (1990).
To test whether an allele of a single SNP is associated with the case or control status of a phenotypic trait, one skilled in the art can compare allele frequencies in cases and controls. Standard chi-squared tests and Fisher exact tests can be carried out on a 2×2 table (2 SNP alleles×2 outcomes in the categorical trait of interest). To test whether genotypes of a SNP are associated, chi-squared tests can be carried out on a 3×2 table (3 genotypes×2 outcomes). Score tests are also carried out for genotypic association to contrast the three genotypic frequencies (major homozygotes, heterozygotes and minor homozygotes) in cases and controls, and to look for trends using 3 different modes of inheritance, namely dominant (with contrast coefficients 2, −1, −1), additive or allelic (with contrast coefficients 1, 0, −1) and recessive (with contrast coefficients 1, 1, −2). Odds ratios for minor versus major alleles, and odds ratios for heterozygote and homozygote variants versus the wild type genotypes are calculated with the desired confidence limits, usually 95%.
In order to control for confounders and to test for interaction and effect modifiers, stratified analyses may be performed using stratified factors that are likely to be confounding, including demographic information such as age, ethnicity, and gender, or an interacting element or effect modifier, such as a known major gene (e.g., APOE for Alzheimer's disease or HLA genes for autoimmune diseases), or environmental factors such as smoking in lung cancer. Stratified association tests may be carried out using Cochran-Mantel-Haenszel tests that take into account the ordinal nature of genotypes with 0, 1, and 2 variant alleles. Exact tests by StatXact may also be performed when computationally possible. Another way to adjust for confounding effects and test for interactions is to perform stepwise multiple logistic regression analysis using statistical packages such as SAS or R. Logistic regression is a model-building technique in which the best fitting and most parsimonious model is built to describe the relation between the dichotomous outcome (for instance, getting a certain disease or not) and a set of independent variables (for instance, genotypes of different associated genes, and the associated demographic and environmental factors). The most common model is one in which the logit transformation of the odds ratios is expressed as a linear combination of the variables (main effects) and their cross-product terms (interactions). Hosmer and Lemeshow, Applied Logistic Regression, Wiley (2000). To test whether a certain variable or interaction is significantly associated with the outcome, coefficients in the model are first estimated and then tested for statistical significance of their departure from zero.
In addition to performing association tests one marker at a time, haplotype association analysis may also be performed to study a number of markers that are closely linked together. Haplotype association tests can have better power than genotypic or allelic association tests when the tested markers are not the disease-causing mutations themselves but are in linkage disequilibrium with such mutations. The test will even be more powerful if the disease is indeed caused by a combination of alleles on a haplotype (e.g., APOE is a haplotype formed by 2 SNPs that are very close to each other). In order to perform haplotype association effectively, marker-marker linkage disequilibrium measures, both D′ and r2, are typically calculated for the markers within a gene to elucidate the haplotype structure. Recent studies in linkage disequilibrium indicate that SNPs within a gene are organized in block pattern, and a high degree of linkage disequilibrium exists within blocks and very little linkage disequilibrium exists between blocks. Daly et al, Nature Genetics 29:232-235 (2001). Haplotype association with the disease status can be performed using such blocks once they have been elucidated.
Haplotype association tests can be carried out in a similar fashion as the allelic and genotypic association tests. Each haplotype in a gene is analogous to an allele in a multi-allelic marker. One skilled in the art can either compare the haplotype frequencies in cases and controls or test genetic association with different pairs of haplotypes. It has been proposed that score tests can be done on haplotypes using the program “haplo.score.” Schaid et al, Am J Hum Genet 70:425-434 (2002). In that method, haplotypes are first inferred by EM algorithm and score tests are carried out with a generalized linear model (GLM) framework that allows the adjustment of other factors.
An important decision in the performance of genetic association tests is the determination of the significance level at which significant association can be declared when the P value of the tests reaches that level. In an exploratory analysis where positive hits will be followed up in subsequent confirmatory testing, an unadjusted P value<0.2 (a significance level on the lenient side), for example, may be used for generating hypotheses for significant association of a SNP with certain phenotypic characteristics of a disease. It is preferred that a p-value<0.05 (a significance level traditionally used in the art) is achieved in order for a SNP to be considered to have an association with a disease. It is more preferred that a p-value<0.01 (a significance level on the stringent side) is achieved for an association to be declared. When hits are followed up in confirmatory analyses in more samples of the same source or in different samples from different sources, adjustment for multiple testing will be performed as to avoid excess number of hits while maintaining the experiment-wide error rates at 0.05. While there are different methods to adjust for multiple testing to control for different kinds of error rates, a commonly used but rather conservative method is Bonferroni correction to control the experiment-wise or family-wise error rate. Westfall et al., Multiple comparisons and multiple tests, SAS Institute (1999). Permutation tests to control for the false discovery rates, FDR, can be more powerful. Benjamini and Hochberg, Journal of the Royal Statistical Society, Series B 57:1289-1300 (1995); Westfall and Young, Resampling-based Multiple Testing, Wiley (1993). Such methods to control for multiplicity would be preferred when the tests are dependent and controlling for false discovery rates is sufficient as opposed to controlling for the experiment-wise error rates.
In replication studies using samples from different populations after statistically significant markers have been identified in the exploratory stage, meta-analyses can then be performed by combining evidence of different studies. Modern Epidemiology 643-673, Lippincott, Williams & Wilkins (1998). If available, association results known in the art for the same SNPs can be included in the meta-analyses.
Since both genotyping and disease status classification can involve errors, sensitivity analyses may be performed to see how odds ratios and p-values would change upon various estimates on genotyping and disease classification error rates.
It has been well known that subpopulation-based sampling bias between cases and controls can lead to spurious results in case-control association studies when prevalence of the disease is associated with different subpopulation groups. Ewens and Spielman, Am J Hum Genet 62:450-458 (1995). Such bias can also lead to a loss of statistical power in genetic association studies. To detect population stratification, Pritchard and Rosenberg suggested typing markers that are unlinked to the disease and using results of association tests on those markers to determine whether there is any population stratification. Pritchard et al., Am J Hum Gen 65:220-228 (1999). When stratification is detected, the genomic control (GC) method as proposed by Devlin and Roeder can be used to adjust for the inflation of test statistics due to population stratification. Devlin et al., Biometrics 55:997-1004(1999). The GC method is robust to changes in population structure levels as well as being applicable to DNA pooling designs. Devlin et al., Genet Epidem 21:273-284 (2001).
While Pritchard's method recommended using 15-20 unlinked microsatellite markers, it suggested using more than 30 biallelic markers to get enough power to detect population stratification. For the GC method, it has been shown that about 60-70 biallelic markers are sufficient to estimate the inflation factor for the test statistics due to population stratification. Bacanu et al., Am J Hum Genet 66:1933-1944 (2000). Hence, 70 intergenic SNPs can be chosen in unlinked regions as indicated in a genome scan. Kehoe et al., Hum Mol Genet 8:237-245 (1999).
Once individual risk factors, genetic or non-genetic, have been found for the predisposition to disease, the next step is to set up a classification/prediction scheme to predict the category (for instance, disease or no-disease) that an individual will be in depending on his genotypes of associated SNPs and other non-genetic risk factors. Logistic regression for discrete trait and linear regression for continuous trait are standard techniques for such tasks. Draper and Smith, Applied Regression Analysis, Wiley (1998). Moreover, other techniques can also be used for setting up classification. Such techniques include, but are not limited to, MART, CART, neural network, and discriminant analyses that are suitable for use in comparing the performance of different methods. The Elements of Statistical Learning, Hastie, Tibshirani & Friedman, Springer (2002).
Disease Diagnosis and Predisposition Screening
Information on association/correlation between genotypes and disease-related phenotypes can be exploited in several ways. For example, in the case of a highly statistically significant association between one or more SNPs with predisposition to a disease for which treatment is available, detection of such a genotype pattern in an individual may justify immediate administration of treatment, or at least the institution of regular monitoring of the individual. Detection of the susceptibility alleles associated with serious disease in a couple contemplating having children may also be valuable to the couple in their reproductive decisions. In the case of a weaker but still statistically significant association between a SNP and a human disease, immediate therapeutic intervention or monitoring may not be justified after detecting the susceptibility allele or SNP. Nevertheless, the subject can be motivated to begin simple life-style changes (e.g., diet, exercise) that can be accomplished at little or no cost to the individual but would confer potential benefits in reducing the risk of developing conditions for which that individual may have an increased risk by virtue of having the risk allele(s).
The SNPs of the invention may contribute to the development of CHD (e.g., MI) or aneurysm/dissection, or to responsiveness of an individual to statin treatment, in different ways. Some polymorphisms occur within a protein coding sequence and contribute to disease phenotype by affecting protein structure. Other polymorphisms occur in noncoding regions but may exert phenotypic effects indirectly via influence on, for example, replication, transcription, and/or translation. A single SNP may affect more than one phenotypic trait. Likewise, a single phenotypic trait may be affected by multiple SNPs in different genes.
As used herein, the terms “diagnose,” “diagnosis,” and “diagnostics” include, but are not limited to, any of the following: detection of CHD or aneurysm/dissection that an individual may presently have, predisposition/susceptibility/predictive screening (i.e., determining whether an individual has an increased or decreased risk of developing CHD or aneurysm/dissection in the future), prognosing the future course of CHD or aneurysm/dissection or recurrence of CHD or aneurysm/dissection in an individual, determining a particular type or subclass of CHD or aneurysm/dissection in an individual who currently or previously had CHD or aneurysm/dissection, confirming or reinforcing a previously made diagnosis of CHD or aneurysm/dissection, evaluating an individual's likelihood of responding positively to a particular treatment or therapeutic agent such as statin treatment (particularly treatment or prevention of CHD or aneurysm/dissection using statins), determining or selecting a therapeutic or preventive strategy that an individual is most likely to positively respond to (e.g., selecting a particular therapeutic agent such as a statin, or combination of therapeutic agents, or determining a dosing regimen, etc.), classifying (or confirming/reinforcing) an individual as a responder/non-responder (or determining a particular subtype of responder/non-responder) with respect to the individual's response to a drug treatment such as statin treatment, and predicting whether a patient is likely to experience toxic effects from a particular treatment or therapeutic compound. Such diagnostic uses can be based on the SNPs individually or in a unique combination or SNP haplotypes of the present invention.
Haplotypes are particularly useful in that, for example, fewer SNPs can be genotyped to determine if a particular genomic region harbors a locus that influences a particular phenotype, such as in linkage disequilibrium-based SNP association analysis.
Linkage disequilibrium (LD) refers to the co-inheritance of alleles (e.g., alternative nucleotides) at two or more different SNP sites at frequencies greater than would be expected from the separate frequencies of occurrence of each allele in a given population. The expected frequency of co-occurrence of two alleles that are inherited independently is the frequency of the first allele multiplied by the frequency of the second allele. Alleles that co-occur at expected frequencies are said to be in “linkage equilibrium.” In contrast, LD refers to any non-random genetic association between allele(s) at two or more different SNP sites, which is generally due to the physical proximity of the two loci along a chromosome. LD can occur when two or more SNPs sites are in close physical proximity to each other on a given chromosome and therefore alleles at these SNP sites will tend to remain unseparated for multiple generations with the consequence that a particular nucleotide (allele) at one SNP site will show a non-random association with a particular nucleotide (allele) at a different SNP site located nearby. Hence, genotyping one of the SNP sites will give almost the same information as genotyping the other SNP site that is in LD.
Various degrees of LD can be encountered between two or more SNPs with the result being that some SNPs are more closely associated (i.e., in stronger LD) than others. Furthermore, the physical distance over which LD extends along a chromosome differs between different regions of the genome, and therefore the degree of physical separation between two or more SNP sites necessary for LD to occur can differ between different regions of the genome.
For diagnostic purposes and similar uses, if a particular SNP site is found to be useful for, for example, predicting an individual's susceptibility to CHD or aneurysm/dissection or an individual's response to statin treatment, then the skilled artisan would recognize that other SNP sites which are in LD with this SNP site would also be useful for the same purposes. Thus, polymorphisms (e.g., SNPs and/or haplotypes) that are not the actual disease-causing (causative) polymorphisms, but are in LD with such causative polymorphisms, are also useful. In such instances, the genotype of the polymorphism(s) that is/are in LD with the causative polymorphism is predictive of the genotype of the causative polymorphism and, consequently, predictive of the phenotype (e.g., CHD, aneurysm/dissection, or responder/non-responder to statin treatment) that is influenced by the causative SNP(s). Therefore, polymorphic markers that are in LD with causative polymorphisms are useful as diagnostic markers, and are particularly useful when the actual causative polymorphism(s) is/are unknown.
Examples of polymorphisms that can be in LD with one or more causative polymorphisms (and/or in LD with one or more polymorphisms that have a significant statistical association with a condition) and therefore useful for diagnosing the same condition that the causative/associated SNP(s) is used to diagnose, include other SNPs in the same gene, protein-coding, or mRNA transcript-coding region as the causative/associated SNP, other SNPs in the same exon or same intron as the causative/associated SNP, other SNPs in the same haplotype block as the causative/associated SNP, other SNPs in the same intergenic region as the causative/associated SNP, SNPs that are outside but near a gene (e.g., within 6 kb on either side, 5′ or 3′, of a gene boundary) that harbors a causative/associated SNP, etc. Such useful LD SNPs can be selected from among the SNPs disclosed in Tables 1 and 2, for example.
Linkage disequilibrium in the human genome is reviewed in Wall et al., “Haplotype blocks and linkage disequilibrium in the human genome,” Nat Rev Genet 4(8):587-97 (August 2003); Garner et al., “On selecting markers for association studies: patterns of linkage disequilibrium between two and three diallelic loci,” Genet Epidemiol 24(1):57-67 (January 2003); Ardlie et al., “Patterns of linkage disequilibrium in the human genome,” Nat Rev Genet 3(4):299-309 (April 2002); erratum in Nat Rev Genet 3(7):566 (July 2002); and Remm et al., “High-density genotyping and linkage disequilibrium in the human genome using chromosome 22 as a model,” Curr Opin Chem Biol 6(1):24-30 (February 2002); J. B. S. Haldane, “The combination of linkage values, and the calculation of distances between the loci of linked factors,” J Genet 8:299-309 (1919); G. Mendel, Versuche über Pflanzen-Hybriden. Verhandlungen des naturforschenden Vereines in Brünn (Proceedings of the Natural History Society of Brünn) (1866); Genes IV, B. Lewin, ed., Oxford University Press, N.Y. (1990); D. L. Hartl and A. G. Clark Principles of Population Genetics 2nd ed., Sinauer Associates, Inc., Mass. (1989); J. H. Gillespie Population Genetics: A Concise Guide. 2nd ed., Johns Hopkins University Press (2004); R. C. Lewontin, “The interaction of selection and linkage. I. General considerations; heterotic models,” Genetics 49:49-67 (1964); P. G. Hoel, Introduction to Mathematical Statistics 2nd ed., John Wiley & Sons, Inc., N.Y. (1954); R. R. Hudson, “Two-locus sampling distributions and their application,” Genetics 159:1805-1817 (2001); A. P. Dempster, N. M. Laird, D. B. Rubin, “Maximum likelihood from incomplete data via the EM algorithm,” J R Stat Soc 39:1-38 (1977); L. Excoffier, M. Slatkin, “Maximum-likelihood estimation of molecular haplotype frequencies in a diploid population,” Mol Biol Evol 12(5):921-927 (1995); D. A. Tregouet, S. Escolano, L. Tiret, A. Mallet, J. L. Golmard, “A new algorithm for haplotype-based association analysis: the Stochastic-EM algorithm,” Ann Hum Genet 68(Pt 2):165-177 (2004); A. D. Long and C. H. Langley C H, “The power of association studies to detect the contribution of candidate genetic loci to variation in complex traits,” Genome Research 9:720-731 (1999); A. Agresti, Categorical Data Analysis, John Wiley & Sons, Inc., N.Y. (1990); K. Lange, Mathematical and Statistical Methods for Genetic Analysis, Springer-Verlag New York, Inc., N.Y. (1997); The International HapMap Consortium, “The International HapMap Project,” Nature 426:789-796 (2003); The International HapMap Consortium, “A haplotype map of the human genome,” Nature 437:1299-1320 (2005); G. A. Thorisson, A. V. Smith, L. Krishnan, L. D. Stein, “The International HapMap Project Web Site,” Genome Research 15:1591-1593 (2005); G. McVean, C. C. A. Spencer, R. Chaix, “Perspectives on human genetic variation from the HapMap project,” PLoS Genetics 1(4):413-418 (2005); J. N. Hirschhorn, M. J. Daly, “Genome-wide association studies for common diseases and complex traits,” Nat Genet 6:95-108(2005); S. J. Schrodi, “A probabilistic approach to large-scale association scans: a semi-Bayesian method to detect disease-predisposing alleles,” SAGMB 4(1):31 (2005); W. Y. S. Wang, B. J. Barratt, D. G. Clayton, J. A. Todd, “Genome-wide association studies: theoretical and practical concerns,” Nat Rev Genet 6:109-118 (2005); J. K. Pritchard, M. Przeworski, “Linkage disequilibrium in humans: models and data,” Am J Hum Genet 69:1-14 (2001).
As discussed above, one aspect of the present invention is the discovery that SNPs that are in certain LD distance with an interrogated SNP can also be used as valid markers for determining whether an individual has an increased or decreased risk of having or developing CHD or aneurysm/dissection, or an individual's likelihood of benefiting from a drug treatment such as statin treatment. As used herein, the term “interrogated SNP” refers to SNPs that have been found to be associated with an increased or decreased risk of disease using genotyping results and analysis, or other appropriate experimental method as exemplified in the working examples described in this application. As used herein, the term “LD SNP” refers to a SNP that has been characterized as a SNP associating with an increased or decreased risk of diseases due to their being in LD with the “interrogated SNP” under the methods of calculation described in the application. Below, applicants describe the methods of calculation with which one of ordinary skilled in the art may determine if a particular SNP is in LD with an interrogated SNP. The parameter r2 is commonly used in the genetics art to characterize the extent of linkage disequilibrium between markers (Hudson, 2001). As used herein, the term “in LD with” refers to a particular SNP that is measured at above the threshold of a parameter such as r2 with an interrogated SNP.
It is now common place to directly observe genetic variants in a sample of chromosomes obtained from a population. Suppose one has genotype data at two genetic markers located on the same chromosome, for the markers A and B. Further suppose that two alleles segregate at each of these two markers such that alleles A1 and A2 can be found at marker A and alleles B1 and B2 at marker B. Also assume that these two markers are on a human autosome. If one is to examine a specific individual and find that they are heterozygous at both markers, such that their two-marker genotype is A1A2B1B2 then there are two possible configurations: the individual in question could have the alleles A1B1 on one chromosome and A2B2 on the remaining chromosome; alternatively, the individual could have alleles A1B2 on one chromosome and A2B1 on the other. The arrangement of alleles on a chromosome is called a haplotype. In this illustration, the individual could have haplotypes A1B1/A2B2 or A1B2/A2B1 (see Hartl and Clark (1989) for a more complete description). The concept of linkage equilibrium relates the frequency of haplotypes to the allele frequencies.
Assume that a sample of individuals is selected from a larger population. Considering the two markers described above, each having two alleles, there are four possible haplotypes: A1B1, A1B2, A2/B1 and A2B2 . Denote the frequencies of these four haplotypes with the following notation

P11=freq(A1B1)   (1)

P12=freq(A1B2)   (2)

P21=freq(A2B1)   (3)

P22=freq(A2B2)   (4)

The allele frequencies at the two markers are then the sum of different haplotype frequencies, it is straightforward to write down a similar set of equations relating single-marker allele frequencies to two-marker haplotype frequencies:

p1=freq(A1)=P11+P12   (5)

p2=freq(A2)=P21+P22   (6)

q1=freq(B1)=P11+P21   (7)

q2=freq(B2)=P12+P22   (8)

Note that the four haplotype frequencies and the allele frequencies at each marker must sum to a frequency of 1.

P11+P12+P21+P22=1   (9)

p1+p2=1   (10)

q1q2=1   (11)

If there is no correlation between the alleles at the two markers, one would expect that the frequency of the haplotypes would be approximately the product of the composite alleles. Therefore,

P11≈p1q1   (12)

P12≈p1q2   (13)

P21≈p2q1   (14)

P22≈p2q2   (15)

These approximating equations (12)-(15) represent the concept of linkage equilibrium where there is independent assortment between the two markers—the alleles at the two markers occur together at random. These are represented as approximations because linkage equilibrium and linkage disequilibrium are concepts typically thought of as properties of a sample of chromosomes; and as such they are susceptible to stochastic fluctuations due to the sampling process. Empirically, many pairs of genetic markers will be in linkage equilibrium, but certainly not all pairs.

Having established the concept of linkage equilibrium above, applicants can now describe the concept of linkage disequilibrium (LD), which is the deviation from linkage equilibrium. Since the frequency of the A1B1 haplotype is approximately the product of the allele frequencies for A1 and B1 under the assumption of linkage equilibrium as stated mathematically in (12), a simple measure for the amount of departure from linkage equilibrium is the difference in these two quantities, D,

D=P11−p1g1   (16)

D=0 indicates perfect linkage equilibrium. Substantial departures from D=0 indicates LD in the sample of chromosomes examined. Many properties of D are discussed in Lewontin (1964) including the maximum and minimum values that D can take. Mathematically, using basic algebra, it can be shown that D can also be written solely in terms of haplotypes:

D=P11P22−P12P21   (17)

If one transforms D by squaring it and subsequently dividing by the product of the allele frequencies of A1, A2, B1 and B2, the resulting quantity, called r2, is equivalent to the square of the Pearson's correlation coefficient commonly used in statistics (e.g. Hoel, 1954).





  
    
      
        
          r
          2
        
        =
        
          
            D
            2
          
          
            
              p
              1
            
            ⁢
            
              p
              2
            
            ⁢
            
              q
              1
            
            ⁢
            
              q
              2
            
          
        
      
    
    
      
        (
        18
        )
      
    
  




As with D, values of r2 close to 0 indicate linkage equilibrium between the two markers examined in the sample set. As values of r2 increase, the two markers are said to be in linkage disequilibrium. The range of values that r2 can take are from 0 to 1. r2=1 when there is a perfect correlation between the alleles at the two markers.
In addition, the quantities discussed above are sample-specific. And as such, it is necessary to formulate notation specific to the samples studied. In the approach discussed here, three types of samples are of primary interest: (i) a sample of chromosomes from individuals affected by a disease-related phenotype (cases), (ii) a sample of chromosomes obtained from individuals not affected by the disease-related phenotype (controls), and (iii) a standard sample set used for the construction of haplotypes and calculation pairwise linkage disequilibrium. For the allele frequencies used in the development of the method described below, an additional subscript will be added to denote either the case or control sample sets.

P1,cs=freq(A1 in cases)   (19)

P2,cs=freq(A2 in cases)   (20)

q1,cs=freq(B1 in cases)   (21)

q2,cs=freq(B2 in cases)   (22)

Similarly,

P1,ct=freq(A1 in controls)   (23)

P2,ct=freq(A2 in controls)   (24)

q1,ct=freq(B1 in controls)   (25)

q2,ct=freq(B2 in controls)   (26)

As a well-accepted sample set is necessary for robust linkage disequilibrium calculations, data obtained from the International HapMap project (The International HapMap Consortium 2003, 2005; Thorisson et al, 2005; McVean et al, 2005) can be used for the calculation of pairwise r2 values. Indeed, the samples genotyped for the International HapMap Project were selected to be representative examples from various human sub-populations with sufficient numbers of chromosomes examined to draw meaningful and robust conclusions from the patterns of genetic variation observed. The International HapMap project website (hapmap.org) contains a description of the project, methods utilized and samples examined. It is useful to examine empirical data to get a sense of the patterns present in such data.
Haplotype frequencies were explicit arguments in equation (18) above. However, knowing the 2-marker haplotype frequencies requires that phase to be determined for doubly heterozygous samples. When phase is unknown in the data examined, various algorithms can be used to infer phase from the genotype data. This issue was discussed earlier where the doubly heterozygous individual with a 2-SNP genotype of A1A2B1B2 could have one of two different sets of chromosomes: A1B1/A2 B2 or A1B2/A2B1. One such algorithm to estimate haplotype frequencies is the expectation-maximization (EM) algorithm first formalized by Dempster et al. (1977). This algorithm is often used in genetics to infer haplotype frequencies from genotype data (e.g. Excoffier and Slatkin (1995); Tregouet et al. (2004)). It should be noted that for the two-SNP case explored here, EM algorithms have very little error provided that the allele frequencies and sample sizes are not too small. The impact on r2 values is typically negligible.
As correlated genetic markers share information, interrogation of SNP markers in LD with a disease-associated SNP marker can also have sufficient power to detect disease association (Long and Langley (1999)). The relationship between the power to directly find disease-associated alleles and the power to indirectly detect disease-association was investigated by Pritchard and Przeworski (2001). In a straight-forward derivation, it can be shown that the power to detect disease association indirectly at a marker locus in linkage disequilibrium with a disease-association locus is approximately the same as the power to detect disease-association directly at the disease-association locus if the sample size is increased by a factor of




  1
  
    r
    2
  




(the reciprocal of equation 18) at the marker in comparison with the disease-association locus.

Therefore, if one calculated the power to detect disease-association indirectly with an experiment having N samples, then equivalent power to directly detect disease-association (at the actual disease-susceptibility locus) would necessitate an experiment using approximately r2N samples. This elementary relationship between power, sample size and linkage disequilibrium can be used to derive an r2 threshold value useful in determining whether or not genotyping markers in linkage disequilibrium with a SNP marker directly associated with disease status has enough power to indirectly detect disease-association.
To commence a derivation of the power to detect disease-associated markers through an indirect process, define the effective chromosomal sample size as




  
    
      
        
          n
          =
          
            
              4
              ⁢
              
                N
                cs
              
              ⁢
              
                N
                ct
              
            
            
              
                N
                cs
              
              +
              
                N
                ct
              
            
          
        
        ;
      
    
    
      
        (
        27
        )
      
    
  




where Ncs and Nct are the numbers of diploid cases and controls, respectively. This is necessary to handle situations where the numbers of cases and controls are not equivalent. For equal case and control sample sizes, Ncs=Nct=N, the value of the effective number of chromosomes is simply n=2N—as expected. Let power be calculated for a significance level α (such that traditional P-values below α will be deemed statistically significant). Define the standard Gaussian distribution function as Φ(•). Mathematically,





  
    
      
        
          Φ
          ⁡
          
            (
            x
            )
          
        
        =
        
          
            1
            
              
                2
                ⁢
                π
              
            
          
          ⁢
          
            
              ∫
              
                -
                ∞
              
              x
            
            ⁢
            
              
                e
                
                  -
                  
                    
                      θ
                      2
                    
                    2
                  
                
              
              ⁢
              
                
              
              ⁢
              
                d
                θ
              
            
          
        
      
    
    
      
        (
        28
        )
      
    
  




Alternatively, the following error function notation (Erf) may also be used,





  
    
      
        
          Φ
          ⁡
          
            (
            x
            )
          
        
        =
        
          
            1
            2
          
          ⁡
          
            [
            
              1
              +
              
                Erf
                ⁡
                
                  (
                  
                    x
                    
                      2
                    
                  
                  )
                
              
            
            ]
          
        
      
    
    
      
        (
        29
        )
      
    
  




For example, Φ(1.644854)=0.95. The value of r2 may be derived to yield a pre-specified minimum amount of power to detect disease association though indirect interrogation. Noting that the LD SNP marker could be the one that is carrying the disease-association allele, therefore that this approach constitutes a lower-bound model where all indirect power results are expected to be at least as large as those interrogated.
Denote by β the error rate for not detecting truly disease-associated markers. Therefore, 1−β is the classical definition of statistical power. Substituting the Pritchard-Pzreworski result into the sample size, the power to detect disease association at a significance level of α is given by the approximation




  
    
      
        
          
            1
            -
            β
          
          ≅
          
            Φ
            [
            
              
                
                  
                  
                    
                      q
                      
                        1
                        ,
                        cs
                      
                    
                    -
                    
                      q
                      
                        1
                        ,
                        ct
                      
                    
                  
                  
                
                
                  
                    
                      
                        
                          q
                          
                            1
                            ,
                            cs
                          
                        
                        ⁡
                        
                          (
                          
                            1
                            -
                            
                              q
                              
                                1
                                ,
                                cs
                              
                            
                          
                          )
                        
                      
                      +
                      
                        
                          q
                          
                            1
                            ,
                            ct
                          
                        
                        ⁡
                        
                          (
                          
                            1
                            -
                            
                              q
                              
                                1
                                ,
                                ct
                              
                            
                          
                          )
                        
                      
                    
                    
                      
                        r
                        2
                      
                      ⁢
                      n
                    
                  
                
              
              -
              
                Z
                
                  1
                  -
                  
                    α
                    /
                    2
                  
                
              
            
            ]
          
        
        ;
      
    
    
      
        (
        30
        )
      
    
  




where Zu is the inverse of the standard normal cumulative distribution evaluated at u (u ε (0,1)). Zu=Φ−1(u), where Φ(Φ−1(u))=Φ−1(Φ(u))=u. For example, setting α=0.05, and therefore 1−α/2=0.975, one obtains Z0.975=1.95996. Next, setting power equal to a threshold of a minimum power of T,





  
    
      
        T
        =
        
          Φ
          [
          
            
              
                
                
                  
                    q
                    
                      1
                      ,
                      cs
                    
                  
                  -
                  
                    q
                    
                      1
                      ,
                      ct
                    
                  
                
                
              
              
                
                  
                    
                      
                        q
                        
                          1
                          ,
                          cs
                        
                      
                      ⁡
                      
                        (
                        
                          1
                          -
                          
                            q
                            
                              1
                              ,
                              cs
                            
                          
                        
                        )
                      
                    
                    +
                    
                      
                        q
                        
                          1
                          ,
                          ct
                        
                      
                      ⁡
                      
                        (
                        
                          1
                          -
                          
                            q
                            
                              1
                              ,
                              ct
                            
                          
                        
                        )
                      
                    
                  
                  
                    
                      r
                      2
                    
                    ⁢
                    n
                  
                
              
            
            -
            
              Z
              
                1
                -
                
                  α
                  /
                  2
                
              
            
          
          ]
        
      
    
    
      
        (
        31
        )
      
    
  




and solving for r2, the following threshold r2 is obtained:





  
    
      
        
          
            r
            T
            2
          
          =
          
            
              
                
                  [
                  
                    
                      
                        q
                        
                          1
                          ,
                          cs
                        
                      
                      ⁡
                      
                        (
                        
                          1
                          -
                          
                            q
                            
                              1
                              ,
                              cs
                            
                          
                        
                        )
                      
                    
                    +
                    
                      
                        q
                        
                          1
                          ,
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                      ⁡
                      
                        (
                        
                          1
                          -
                          
                            q
                            
                              1
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                        )
                      
                    
                  
                  ]
                
                
                  
                    n
                    ⁡
                    
                      (
                      
                        
                          q
                          
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                        -
                        
                          q
                          
                            1
                            ,
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                      )
                    
                  
                  2
                
              
              ⁡
              
                [
                
                  
                    
                      Φ
                      
                        -
                        1
                      
                    
                    ⁡
                    
                      (
                      T
                      )
                    
                  
                  +
                  
                    Z
                    
                      1
                      -
                      
                        α
                        /
                        2
                      
                    
                  
                
                ]
              
            
            2
          
        
        ⁢
        
          

        
        ⁢
        
          Or
          ,
        
      
    
    
      
        (
        32
        )
      
    
  
  
    
      
        
          r
          T
          2
        
        =
        
          
            
              
                (
                
                  
                    Z
                    T
                  
                  +
                  
                    Z
                    
                      1
                      -
                      
                        α
                        /
                        2
                      
                    
                  
                
                )
              
              2
            
            n
          
          ⁡
          
            [
            
              
                
                  q
                  
                    1
                    ,
                    cs
                  
                
                -
                
                  
                    (
                    
                      q
                      
                        1
                        ,
                        cs
                      
                    
                    )
                  
                  2
                
                +
                
                  q
                  
                    1
                    ,
                    ct
                  
                
                -
                
                  
                    (
                    
                      q
                      
                        1
                        ,
                        ct
                      
                    
                    )
                  
                  2
                
              
              
                
                  (
                  
                    
                      q
                      
                        1
                        ,
                        cs
                      
                    
                    -
                    
                      q
                      
                        1
                        ,
                        ct
                      
                    
                  
                  )
                
                2
              
            
            ]
          
        
      
    
    
      
        (
        33
        )
      
    
  




Suppose that r2 is calculated between an interrogated SNP and a number of other SNPs with varying levels of LD with the interrogated SNP. The threshold value rT2 is the minimum value of linkage disequilibrium between the interrogated SNP and the potential LD SNPs such that the LD SNP still retains a power greater or equal to T for detecting disease-association. For example, suppose that SNP rs200 is genotyped in a case-control disease-association study and it is found to be associated with a disease phenotype. Further suppose that the minor allele frequency in 1,000 case chromosomes was found to be 16% in contrast with a minor allele frequency of 10% in 1,000 control chromosomes. Given those measurements one could have predicted, prior to the experiment, that the power to detect disease association at a significance level of 0.05 was quite high—approximately 98% using a test of allelic association. Applying equation (32) one can calculate a minimum value of r2 to indirectly assess disease association assuming that the minor allele at SNP rs200 is truly disease-predisposing for a threshold level of power. If one sets the threshold level of power to be 80%, then rT2=0.489 given the same significance level and chromosome numbers as above. Hence, any SNP with a pairwise r2 value with rs200 greater than 0.489 is expected to have greater than 80% power to detect the disease association. Further, this is assuming the conservative model where the LD SNP is disease-associated only through linkage disequilibrium with the interrogated SNP rs200.
The contribution or association of particular SNPs and/or SNP haplotypes with disease phenotypes, such as CHD or aneurysm/dissection, enables the SNPs of the present invention to be used to develop superior diagnostic tests capable of identifying individuals who express a detectable trait, such as CHD or aneurysm/dissection, as the result of a specific genotype, or individuals whose genotype places them at an increased or decreased risk of developing a detectable trait at a subsequent time as compared to individuals who do not have that genotype. As described herein, diagnostics may be based on a single SNP or a group of SNPs. Combined detection of a plurality of SNPs (for example, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 24, 25, 30, 32, 48, 50, 64, 96, 100, or any other number in-between, or more, of the SNPs provided in Table 1 and/or Table 2) typically increases the probability of an accurate diagnosis. For example, the presence of a single SNP known to correlate with CHD might indicate a probability of 20% that an individual has or is at risk of developing CHD, whereas detection of five SNPs, each of which correlates with CHD, might indicate a probability of 80% that an individual has or is at risk of developing CHD. To further increase the accuracy of diagnosis or predisposition screening, analysis of the SNPs of the present invention can be combined with that of other polymorphisms or other risk factors of CHD or aneurysm/dissection, such as disease symptoms, pathological characteristics, family history, diet, environmental factors or lifestyle factors.
It will, of course, be understood by practitioners skilled in the treatment or diagnosis of CHD or aneurysm/dissection that the present invention generally does not intend to provide an absolute identification of individuals who are at risk (or less at risk) of developing CHD or aneurysm/dissection, and/or pathologies related to CHD or aneurysm/dissection, but rather to indicate a certain increased (or decreased) degree or likelihood of developing the disease based on statistically significant association results. However, this information is extremely valuable as it can be used to, for example, initiate preventive treatments or to allow an individual carrying one or more significant SNPs or SNP haplotypes to foresee warning signs such as minor clinical symptoms, or to have regularly scheduled physical exams to monitor for appearance of a condition in order to identify and begin treatment of the condition at an early stage. Particularly with diseases that are extremely debilitating or fatal if not treated on time, the knowledge of a potential predisposition, even if this predisposition is not absolute, would likely contribute in a very significant manner to treatment efficacy.
The diagnostic techniques of the present invention may employ a variety of methodologies to determine whether a test subject has a SNP or a SNP pattern associated with an increased or decreased risk of developing a detectable trait or whether the individual suffers from a detectable trait as a result of a particular polymorphism/mutation, including, for example, methods which enable the analysis of individual chromosomes for haplotyping, family studies, single sperm DNA analysis, or somatic hybrids. The trait analyzed using the diagnostics of the invention may be any detectable trait that is commonly observed in pathologies and disorders related to CHD or aneurysm/dissection.
Another aspect of the present invention relates to a method of determining whether an individual is at risk (or less at risk) of developing one or more traits or whether an individual expresses one or more traits as a consequence of possessing a particular trait-causing or trait-influencing allele. These methods generally involve obtaining a nucleic acid sample from an individual and assaying the nucleic acid sample to determine which nucleotide(s) is/are present at one or more SNP positions, wherein the assayed nucleotide(s) is/are indicative of an increased or decreased risk of developing the trait or indicative that the individual expresses the trait as a result of possessing a particular trait-causing or trait-influencing allele.
In another embodiment, the SNP detection reagents of the present invention are used to determine whether an individual has one or more SNP allele(s) affecting the level (e.g., the concentration of mRNA or protein in a sample, etc.) or pattern (e.g., the kinetics of expression, rate of decomposition, stability profile, Km, Vmax, etc.) of gene expression (collectively, the “gene response” of a cell or bodily fluid). Such a determination can be accomplished by screening for mRNA or protein expression (e.g., by using nucleic acid arrays, RT-PCR, TaqMan assays, or mass spectrometry), identifying genes having altered expression in an individual, genotyping SNPs disclosed in Table 1 and/or Table 2 that could affect the expression of the genes having altered expression (e.g., SNPs that are in and/or around the gene(s) having altered expression, SNPs in regulatory/control regions, SNPs in and/or around other genes that are involved in pathways that could affect the expression of the gene(s) having altered expression, or all SNPs could be genotyped), and correlating SNP genotypes with altered gene expression. In this manner, specific SNP alleles at particular SNP sites can be identified that affect gene expression.
Pharmacogenomics and Therapeutics/Drug Development
The present invention provides methods for assessing the pharmacogenomics of a subject harboring particular SNP alleles or haplotypes to a particular therapeutic agent or pharmaceutical compound, or to a class of such compounds. Pharmacogenomics deals with the roles which clinically significant hereditary variations (e.g., SNPs) play in the response to drugs due to altered drug disposition and/or abnormal action in affected persons. See, e.g., Roses, Nature 405, 857-865 (2000); Gould Rothberg, Nature Biotechnology 19, 209-211 (2001); Eichelbaum, Clin Exp Pharmacol Physiol 23(10-11):983-985 (1996); and Linder, Clin Chem 43(2):254-266 (1997). The clinical outcomes of these variations can result in severe toxicity of therapeutic drugs in certain individuals or therapeutic failure of drugs in certain individuals as a result of individual variation in metabolism. Thus, the SNP genotype of an individual can determine the way a therapeutic compound acts on the body or the way the body metabolizes the compound. For example, SNPs in drug metabolizing enzymes can affect the activity of these enzymes, which in turn can affect both the intensity and duration of drug action, as well as drug metabolism and clearance.
The discovery of SNPs in drug metabolizing enzymes, drug transporters, proteins for pharmaceutical agents, and other drug targets has explained why some patients do not obtain the expected drug effects, show an exaggerated drug effect, or experience serious toxicity from standard drug dosages. SNPs can be expressed in the phenotype of the extensive metabolizer and in the phenotype of the poor metabolizer. Accordingly, SNPs may lead to allelic variants of a protein in which one or more of the protein functions in one population are different from those in another population. SNPs and the encoded variant peptides thus provide targets to ascertain a genetic predisposition that can affect treatment modality. For example, in a ligand-based treatment, SNPs may give rise to amino terminal extracellular domains and/or other ligand-binding regions of a receptor that are more or less active in ligand binding, thereby affecting subsequent protein activation. Accordingly, ligand dosage would necessarily be modified to maximize the therapeutic effect within a given population containing particular SNP alleles or haplotypes.
As an alternative to genotyping, specific variant proteins containing variant amino acid sequences encoded by alternative SNP alleles could be identified. Thus, pharmacogenomic characterization of an individual permits the selection of effective compounds and effective dosages of such compounds for prophylactic or therapeutic uses based on the individual's SNP genotype, thereby enhancing and optimizing the effectiveness of the therapy. Furthermore, the production of recombinant cells and transgenic animals containing particular SNPs/haplotypes allow effective clinical design and testing of treatment compounds and dosage regimens. For example, transgenic animals can be produced that differ only in specific SNP alleles in a gene that is orthologous to a human disease susceptibility gene.
Pharmacogenomic uses of the SNPs of the present invention provide several significant advantages for patient care, particularly in predicting an individual's predisposition to CHD (e.g., MI) or aneurysm/dissection and in predicting an individual's responsiveness to the use of statin (particularly for treating or preventing CHD or aneurysm/dissection). Pharmacogenomic characterization of an individual, based on an individual's SNP genotype, can identify those individuals unlikely to respond to treatment with a particular medication and thereby allows physicians to avoid prescribing the ineffective medication to those individuals. On the other hand, SNP genotyping of an individual may enable physicians to select the appropriate medication and dosage regimen that will be most effective based on an individual's SNP genotype. This information increases a physician's confidence in prescribing medications and motivates patients to comply with their drug regimens. Furthermore, pharmacogenomics may identify patients predisposed to toxicity and adverse reactions to particular drugs or drug dosages. Adverse drug reactions lead to more than 100,000 avoidable deaths per year in the United States alone and therefore represent a significant cause of hospitalization and death, as well as a significant economic burden on the healthcare system (Pfost et al., Trends in Biotechnology, August 2000.). Thus, pharmacogenomics based on the SNPs disclosed herein has the potential to both save lives and reduce healthcare costs substantially.
Pharmacogenomics in general is discussed further in Rose et al., “Pharmacogenetic analysis of clinically relevant genetic polymorphisms,” Methods Mol Med 85:225-37 (2003). Pharmacogenomics as it relates to Alzheimer's disease and other neurodegenerative disorders is discussed in Cacabelos, “Pharmacogenomics for the treatment of dementia,” Ann Med 34(5):357-79 (2002); Maimone et al., “Pharmacogenomics of neurodegenerative diseases,” Eur J Pharmacol 413(1):11-29 (February 2001); and Poirier, “Apolipoprotein E: a pharmacogenetic target for the treatment of Alzheimer's disease,” Mol Diagn 4(4):335-41 (December 1999). Pharmacogenomics as it relates to cardiovascular disorders is discussed in Siest et al., “Pharmacogenomics of drugs affecting the cardiovascular system,” Clin Chem Lab Med 41(4):590-9 (April 2003); Mukherjee et al., “Pharmacogenomics in cardiovascular diseases,” Prog Cardiovasc Dis 44(6):479-98 (May-June 2002); and Mooser et al., “Cardiovascular pharmacogenetics in the SNP era,” J Thromb Haemost 1(7):1398-402 (July 2003). Pharmacogenomics as it relates to cancer is discussed in McLeod et al., “Cancer pharmacogenomics: SNPs, chips, and the individual patient,” Cancer Invest 21(4):630-40 (2003); and Watters et al., “Cancer pharmacogenomics: current and future applications,” Biochim Biophys Acta 1603(2):99-111 (March 2003).
The SNPs of the present invention also can be used to identify novel therapeutic targets for CHD or aneurysm/dissection. For example, genes containing the disease-associated variants (“variant genes”) or their products, as well as genes or their products that are directly or indirectly regulated by or interacting with these variant genes or their products, can be targeted for the development of therapeutics that, for example, treat the disease or prevent or delay disease onset. The therapeutics may be composed of, for example, small molecules, proteins, protein fragments or peptides, antibodies, nucleic acids, or their derivatives or mimetics which modulate the functions or levels of the target genes or gene products.
The SNP-containing nucleic acid molecules disclosed herein, and their complementary nucleic acid molecules, may be used as antisense constructs to control gene expression in cells, tissues, and organisms. Antisense technology is well established in the art and extensively reviewed in Antisense Drug Technology: Principles, Strategies, and Applications, Crooke, ed., Marcel Dekker, Inc., N.Y. (2001). An antisense nucleic acid molecule is generally designed to be complementary to a region of mRNA expressed by a gene so that the antisense molecule hybridizes to the mRNA and thereby blocks translation of mRNA into protein. Various classes of antisense oligonucleotides are used in the art, two of which are cleavers and blockers. Cleavers, by binding to target RNAs, activate intracellular nucleases (e.g., RNaseH or RNase L) that cleave the target RNA. Blockers, which also bind to target RNAs, inhibit protein translation through steric hindrance of ribosomes. Exemplary blockers include peptide nucleic acids, morpholinos, locked nucleic acids, and methylphosphonates. See, e.g., Thompson, Drug Discovery Today 7(17): 912-917 (2002). Antisense oligonucleotides are directly useful as therapeutic agents, and are also useful for determining and validating gene function (e.g., in gene knock-out or knock-down experiments).
Antisense technology is further reviewed in: Lavery et al., “Antisense and RNAi: powerful tools in drug target discovery and validation,” Curr Opin Drug Discov Devel 6(4):561-9 (July 2003); Stephens et al., “Antisense oligonucleotide therapy in cancer,” Curr Opin Mol Ther 5(2):118-22 (April 2003); Kurreck, “Antisense technologies. Improvement through novel chemical modifications,” Eur J Biochem 270(8):1628-44 (April 2003); Dias et al., “Antisense oligonucleotides: basic concepts and mechanisms,” Mol Cancer Ther 1(5):347-55 (March 2002); Chen, “Clinical development of antisense oligonucleotides as anti-cancer therapeutics,” Methods Mol Med 75:621-36 (2003); Wang et al., “Antisense anticancer oligonucleotide therapeutics,” Curr Cancer Drug Targets 1(3):177-96 (November 2001); and Bennett, “Efficiency of antisense oligonucleotide drug discovery,” Antisense Nucleic Acid Drug Dev 12(3):215-24 (June 2002).
The SNPs of the present invention are particularly useful for designing antisense reagents that are specific for particular nucleic acid variants. Based on the SNP information disclosed herein, antisense oligonucleotides can be produced that specifically target mRNA molecules that contain one or more particular SNP nucleotides. In this manner, expression of mRNA molecules that contain one or more undesired polymorphisms (e.g., SNP nucleotides that lead to a defective protein such as an amino acid substitution in a catalytic domain) can be inhibited or completely blocked. Thus, antisense oligonucleotides can be used to specifically bind a particular polymorphic form (e.g., a SNP allele that encodes a defective protein), thereby inhibiting translation of this form, but which do not bind an alternative polymorphic form (e.g., an alternative SNP nucleotide that encodes a protein having normal function).
Antisense molecules can be used to inactivate mRNA in order to inhibit gene expression and production of defective proteins. Accordingly, these molecules can be used to treat a disorder, such as CHD or aneurysm/dissection, characterized by abnormal or undesired gene expression or expression of certain defective proteins. This technique can involve cleavage by means of ribozymes containing nucleotide sequences complementary to one or more regions in the mRNA that attenuate the ability of the mRNA to be translated. Possible mRNA regions include, for example, protein-coding regions and particularly protein-coding regions corresponding to catalytic activities, substrate/ligand binding, or other functional activities of a protein.
The SNPs of the present invention are also useful for designing RNA interference reagents that specifically target nucleic acid molecules having particular SNP variants. RNA interference (RNAi), also referred to as gene silencing, is based on using double-stranded RNA (dsRNA) molecules to turn genes off. When introduced into a cell, dsRNAs are processed by the cell into short fragments (generally about 21, 22, or 23 nucleotides in length) known as small interfering RNAs (siRNAs) which the cell uses in a sequence-specific manner to recognize and destroy complementary RNAs. Thompson, Drug Discovery Today 7(17): 912-917 (2002). Accordingly, an aspect of the present invention specifically contemplates isolated nucleic acid molecules that are about 18-26 nucleotides in length, preferably 19-25 nucleotides in length, and more preferably 20, 21, 22, or 23 nucleotides in length, and the use of these nucleic acid molecules for RNAi. Because RNAi molecules, including siRNAs, act in a sequence-specific manner, the SNPs of the present invention can be used to design RNAi reagents that recognize and destroy nucleic acid molecules having specific SNP alleles/nucleotides (such as deleterious alleles that lead to the production of defective proteins), while not affecting nucleic acid molecules having alternative SNP alleles (such as alleles that encode proteins having normal function). As with antisense reagents, RNAi reagents may be directly useful as therapeutic agents (e.g., for turning off defective, disease-causing genes), and are also useful for characterizing and validating gene function (e.g., in gene knock-out or knock-down experiments).
The following references provide a further review of RNAi: Reynolds et al., “Rational siRNA design for RNA interference,” Nat Biotechnol 22(3):326-30 (March 2004); Epub Feb. 1, 2004; Chi et al., “Genomewide view of gene silencing by small interfering RNAs,” PNAS 100(11):6343-6346(2003); Vickers et al., “Efficient Reduction of Target RNAs by Small Interfering RNA and RNase H-dependent Antisense Agents,” J Biol Chem 278:7108-7118 (2003); Agami, “RNAi and related mechanisms and their potential use for therapy,” Curr Opin Chem Biol 6(6):829-34 (December 2002); Lavery et al., “Antisense and RNAi: powerful tools in drug target discovery and validation,” Curr Opin Drug Discov Devel 6(4):561-9 (July 2003); Shi, “Mammalian RNAi for the masses,” Trends Genet 19(1):9-12 (January 2003); Shuey et al., “RNAi: gene-silencing in therapeutic intervention,” Drug Discovery Today 7(20):1040-1046 (October 2002); McManus et al., Nat Rev Genet 3(10):737-47 (October 2002); Xia et al., Nat Biotechnol 20(10):1006-10 (October 2002); Plasterk et al., Curr Opin Genet Dev 10(5):562-7 (October 2000); Bosher et al., Nat Cell Biol 2(2):E31-6(February 2000); and Hunter, Curr Biol 17; 9(12):R440-2 (June 1999).
A subject suffering from a pathological condition ascribed to a SNP, such as CHD or aneurysm/dissection, may be treated so as to correct the genetic defect. See Kren et al., Proc Natl Acad Sci USA 96:10349-10354 (1999). Such a subject can be identified by any method that can detect the polymorphism in a biological sample drawn from the subject. Such a genetic defect may be permanently corrected by administering to such a subject a nucleic acid fragment incorporating a repair sequence that supplies the normal/wild-type nucleotide at the position of the SNP. This site-specific repair sequence can encompass an RNA/DNA oligonucleotide that operates to promote endogenous repair of a subject's genomic DNA. The site-specific repair sequence is administered in an appropriate vehicle, such as a complex with polyethylenimine, encapsulated in anionic liposomes, a viral vector such as an adenovirus, or other pharmaceutical composition that promotes intracellular uptake of the administered nucleic acid. A genetic defect leading to an inborn pathology may then be overcome, as the chimeric oligonucleotides induce incorporation of the normal sequence into the subject's genome. Upon incorporation, the normal gene product is expressed, and the replacement is propagated, thereby engendering a permanent repair and therapeutic enhancement of the clinical condition of the subject.
In cases in which a cSNP results in a variant protein that is ascribed to be the cause of, or a contributing factor to, a pathological condition, a method of treating such a condition can include administering to a subject experiencing the pathology the wild-type/normal cognate of the variant protein. Once administered in an effective dosing regimen, the wild-type cognate provides complementation or remediation of the pathological condition.
The invention further provides a method for identifying a compound or agent that can be used to treat CHD or aneurysm/dissection. The SNPs disclosed herein are useful as targets for the identification and/or development of therapeutic agents. A method for identifying a therapeutic agent or compound typically includes assaying the ability of the agent or compound to modulate the activity and/or expression of a SNP-containing nucleic acid or the encoded product and thus identifying an agent or a compound that can be used to treat a disorder characterized by undesired activity or expression of the SNP-containing nucleic acid or the encoded product. The assays can be performed in cell-based and cell-free systems. Cell-based assays can include cells naturally expressing the nucleic acid molecules of interest or recombinant cells genetically engineered to express certain nucleic acid molecules.
Variant gene expression in a CHD or aneurysm/dissection patient can include, for example, either expression of a SNP-containing nucleic acid sequence (for instance, a gene that contains a SNP can be transcribed into an mRNA transcript molecule containing the SNP, which can in turn be translated into a variant protein) or altered expression of a normal/wild-type nucleic acid sequence due to one or more SNPs (for instance, a regulatory/control region can contain a SNP that affects the level or pattern of expression of a normal transcript).
Assays for variant gene expression can involve direct assays of nucleic acid levels (e.g., mRNA levels), expressed protein levels, or of collateral compounds involved in a signal pathway. Further, the expression of genes that are up- or down-regulated in response to the signal pathway can also be assayed. In this embodiment, the regulatory regions of these genes can be operably linked to a reporter gene such as luciferase.
Modulators of variant gene expression can be identified in a method wherein, for example, a cell is contacted with a candidate compound/agent and the expression of mRNA determined. The level of expression of mRNA in the presence of the candidate compound is compared to the level of expression of mRNA in the absence of the candidate compound. The candidate compound can then be identified as a modulator of variant gene expression based on this comparison and be used to treat a disorder such as CHD or aneurysm/dissection that is characterized by variant gene expression (e.g., either expression of a SNP-containing nucleic acid or altered expression of a normal/wild-type nucleic acid molecule due to one or more SNPs that affect expression of the nucleic acid molecule) due to one or more SNPs of the present invention. When expression of mRNA is statistically significantly greater in the presence of the candidate compound than in its absence, the candidate compound is identified as a stimulator of nucleic acid expression. When nucleic acid expression is statistically significantly less in the presence of the candidate compound than in its absence, the candidate compound is identified as an inhibitor of nucleic acid expression.
The invention further provides methods of treatment, with the SNP or associated nucleic acid domain (e.g., catalytic domain, ligand/substrate-binding domain, regulatory/control region, etc.) or gene, or the encoded mRNA transcript, as a target, using a compound identified through drug screening as a gene modulator to modulate variant nucleic acid expression. Modulation can include either up-regulation (i.e., activation or agonization) or down-regulation (i.e., suppression or antagonization) of nucleic acid expression.
Expression of mRNA transcripts and encoded proteins, either wild type or variant, may be altered in individuals with a particular SNP allele in a regulatory/control element, such as a promoter or transcription factor binding domain, that regulates expression. In this situation, methods of treatment and compounds can be identified, as discussed herein, that regulate or overcome the variant regulatory/control element, thereby generating normal, or healthy, expression levels of either the wild type or variant protein.
The SNP-containing nucleic acid molecules of the present invention are also useful for monitoring the effectiveness of modulating compounds on the expression or activity of a variant gene, or encoded product, in clinical trials or in a treatment regimen. Thus, the gene expression pattern can serve as an indicator for the continuing effectiveness of treatment with the compound, particularly with compounds to which a patient can develop resistance, as well as an indicator for toxicities. The gene expression pattern can also serve as a marker indicative of a physiological response of the affected cells to the compound. Accordingly, such monitoring would allow either increased administration of the compound or the administration of alternative compounds to which the patient has not become resistant. Similarly, if the level of nucleic acid expression falls below a desirable level, administration of the compound could be commensurately decreased.
In another aspect of the present invention, there is provided a pharmaceutical pack comprising a therapeutic agent (e.g., a small molecule drug, antibody, peptide, antisense or RNAi nucleic acid molecule, etc.) and a set of instructions for administration of the therapeutic agent to humans diagnostically tested for one or more SNPs or SNP haplotypes provided by the present invention.
The SNPs/haplotypes of the present invention are also useful for improving many different aspects of the drug development process. For instance, an aspect of the present invention includes selecting individuals for clinical trials based on their SNP genotype. For example, individuals with SNP genotypes that indicate that they are likely to positively respond to a drug can be included in the trials, whereas those individuals whose SNP genotypes indicate that they are less likely to or would not respond to the drug, or who are at risk for suffering toxic effects or other adverse reactions, can be excluded from the clinical trials. This not only can improve the safety of clinical trials, but also can enhance the chances that the trial will demonstrate statistically significant efficacy. Furthermore, the SNPs of the present invention may explain why certain previously developed drugs performed poorly in clinical trials and may help identify a subset of the population that would benefit from a drug that had previously performed poorly in clinical trials, thereby “rescuing” previously developed drugs, and enabling the drug to be made available to a particular CHD or aneurysm/dissection patient population that can benefit from it.
SNPs have many important uses in drug discovery, screening, and development. A high probability exists that, for any gene/protein selected as a potential drug target, variants of that gene/protein will exist in a patient population. Thus, determining the impact of gene/protein variants on the selection and delivery of a therapeutic agent should be an integral aspect of the drug discovery and development process. Jazwinska, A Trends Guide to Genetic Variation and Genomic Medicine S30-S36 (March 2002).
Knowledge of variants (e.g., SNPs and any corresponding amino acid polymorphisms) of a particular therapeutic target (e.g., a gene, mRNA transcript, or protein) enables parallel screening of the variants in order to identify therapeutic candidates (e.g., small molecule compounds, antibodies, antisense or RNAi nucleic acid compounds, etc.) that demonstrate efficacy across variants. Rothberg, Nat Biotechnol 19(3):209-11 (March 2001). Such therapeutic candidates would be expected to show equal efficacy across a larger segment of the patient population, thereby leading to a larger potential market for the therapeutic candidate.
Furthermore, identifying variants of a potential therapeutic target enables the most common form of the target to be used for selection of therapeutic candidates, thereby helping to ensure that the experimental activity that is observed for the selected candidates reflects the real activity expected in the largest proportion of a patient population. Jazwinska, A Trends Guide to Genetic Variation and Genomic Medicine S30-S36 (March 2002).
Additionally, screening therapeutic candidates against all known variants of a target can enable the early identification of potential toxicities and adverse reactions relating to particular variants. For example, variability in drug absorption, distribution, metabolism and excretion (ADME) caused by, for example, SNPs in therapeutic targets or drug metabolizing genes, can be identified, and this information can be utilized during the drug development process to minimize variability in drug disposition and develop therapeutic agents that are safer across a wider range of a patient population. The SNPs of the present invention, including the variant proteins and encoding polymorphic nucleic acid molecules provided in Tables 1 and 2, are useful in conjunction with a variety of toxicology methods established in the art, such as those set forth in Current Protocols in Toxicology, John Wiley & Sons, Inc., N.Y.
Furthermore, therapeutic agents that target any art-known proteins (or nucleic acid molecules, either RNA or DNA) may cross-react with the variant proteins (or polymorphic nucleic acid molecules) disclosed in Table 1, thereby significantly affecting the pharmacokinetic properties of the drug. Consequently, the protein variants and the SNP-containing nucleic acid molecules disclosed in Tables 1 and 2 are useful in developing, screening, and evaluating therapeutic agents that target corresponding art-known protein forms (or nucleic acid molecules). Additionally, as discussed above, knowledge of all polymorphic forms of a particular drug target enables the design of therapeutic agents that are effective against most or all such polymorphic forms of the drug target.
Pharmaceutical Compositions and Administration Thereof
Any of the CHD, aneurysm/dissection, and/or statin response-associated proteins, and encoding nucleic acid molecules, disclosed herein can be used as therapeutic targets (or directly used themselves as therapeutic compounds) for treating or preventing CHD, aneurysm/dissection, or related pathologies, and the present disclosure enables therapeutic compounds (e.g., small molecules, antibodies, therapeutic proteins, RNAi and antisense molecules, etc.) to be developed that target (or are comprised of) any of these therapeutic targets.
In general, a therapeutic compound will be administered in a therapeutically effective amount by any of the accepted modes of administration for agents that serve similar utilities. The actual amount of the therapeutic compound of this invention, i.e., the active ingredient, will depend upon numerous factors such as the severity of the disease to be treated, the age and relative health of the subject, the potency of the compound used, the route and form of administration, and other factors.
Therapeutically effective amounts of therapeutic compounds may range from, for example, approximately 0.01-50 mg per kilogram body weight of the recipient per day; preferably about 0.1-20 mg/kg/day. Thus, as an example, for administration to a 70-kg person, the dosage range would most preferably be about 7 mg to 1.4g per day.
In general, therapeutic compounds will be administered as pharmaceutical compositions by any one of the following routes: oral, systemic (e.g., transdermal, intranasal, or by suppository), or parenteral (e.g., intramuscular, intravenous, or subcutaneous) administration. The preferred manner of administration is oral or parenteral using a convenient daily dosage regimen, which can be adjusted according to the degree of affliction. Oral compositions can take the form of tablets, pills, capsules, semisolids, powders, sustained release formulations, solutions, suspensions, elixirs, aerosols, or any other appropriate compositions.
The choice of formulation depends on various factors such as the mode of drug administration (e.g., for oral administration, formulations in the form of tablets, pills, or capsules are preferred) and the bioavailability of the drug substance. Recently, pharmaceutical formulations have been developed especially for drugs that show poor bioavailability based upon the principle that bioavailability can be increased by increasing the surface area, i.e., decreasing particle size. For example, U.S. Pat. No. 4,107,288 describes a pharmaceutical formulation having particles in the size range from 10 to 1,000 nm in which the active material is supported on a cross-linked matrix of macromolecules. U.S. Pat. No. 5,145,684 describes the production of a pharmaceutical formulation in which the drug substance is pulverized to nanoparticles (average particle size of 400 nm) in the presence of a surface modifier and then dispersed in a liquid medium to give a pharmaceutical formulation that exhibits remarkably high bioavailability.
Pharmaceutical compositions are comprised of, in general, a therapeutic compound in combination with at least one pharmaceutically acceptable excipient. Acceptable excipients are non-toxic, aid administration, and do not adversely affect the therapeutic benefit of the therapeutic compound. Such excipients may be any solid, liquid, semi-solid or, in the case of an aerosol composition, gaseous excipient that is generally available to one skilled in the art.
Solid pharmaceutical excipients include starch, cellulose, talc, glucose, lactose, sucrose, gelatin, malt, rice, flour, chalk, silica gel, magnesium stearate, sodium stearate, glycerol monostearate, sodium chloride, dried skim milk and the like. Liquid and semisolid excipients may be selected from glycerol, propylene glycol, water, ethanol and various oils, including those of petroleum, animal, vegetable or synthetic origin, e.g., peanut oil, soybean oil, mineral oil, sesame oil, etc. Preferred liquid carriers, particularly for injectable solutions, include water, saline, aqueous dextrose, and glycols.
Compressed gases may be used to disperse a compound of this invention in aerosol form. Inert gases suitable for this purpose are nitrogen, carbon dioxide, etc.
Other suitable pharmaceutical excipients and their formulations are described in Remington's Pharmaceutical Sciences 18th ed., E. W. Martin, ed., Mack Publishing Company (1990).
The amount of the therapeutic compound in a formulation can vary within the full range employed by those skilled in the art. Typically, the formulation will contain, on a weight percent (wt %) basis, from about 0.01-99.99 wt % of the therapeutic compound based on the total formulation, with the balance being one or more suitable pharmaceutical excipients. Preferably, the compound is present at a level of about 1-80% wt.
Therapeutic compounds can be administered alone or in combination with other therapeutic compounds or in combination with one or more other active ingredient(s). For example, an inhibitor or stimulator of a CHD or aneurysm/dissection-associated protein can be administered in combination with another agent that inhibits or stimulates the activity of the same or a different CHD or aneurysm/dissection-associated protein to thereby counteract the effects of CHD or aneurysm/dissection.
For further information regarding pharmacology, see Current Protocols in Pharmacology, John Wiley & Sons, Inc., N.Y.
Human Identification Applications
In addition to their diagnostic, therapeutic, and preventive uses in CHD, aneurysm/dissection, and related pathologies, as well as in predicting response to drug treatment, particularly statin treatment, the SNPs provided by the present invention are also useful as human identification markers for such applications as forensics, paternity testing, and biometrics. See, e.g., Gill, “An assessment of the utility of single nucleotide polymorphisms (SNPs) for forensic purposes,” Int J Legal Med 114(4-5):204-10 (2001). Genetic variations in the nucleic acid sequences between individuals can be used as genetic markers to identify individuals and to associate a biological sample with an individual. Determination of which nucleotides occupy a set of SNP positions in an individual identifies a set of SNP markers that distinguishes the individual. The more SNP positions that are analyzed, the lower the probability that the set of SNPs in one individual is the same as that in an unrelated individual. Preferably, if multiple sites are analyzed, the sites are unlinked (i.e., inherited independently). Thus, preferred sets of SNPs can be selected from among the SNPs disclosed herein, which may include SNPs on different chromosomes, SNPs on different chromosome arms, and/or SNPs that are dispersed over substantial distances along the same chromosome arm.
Furthermore, among the SNPs disclosed herein, preferred SNPs for use in certain forensic/human identification applications include SNPs located at degenerate codon positions (i.e., the third position in certain codons which can be one of two or more alternative nucleotides and still encode the same amino acid), since these SNPs do not affect the encoded protein. SNPs that do not affect the encoded protein are expected to be under less selective pressure and are therefore expected to be more polymorphic in a population, which is typically an advantage for forensic/human identification applications. However, for certain forensics/human identification applications, such as predicting phenotypic characteristics (e.g., inferring ancestry or inferring one or more physical characteristics of an individual) from a DNA sample, it may be desirable to utilize SNPs that affect the encoded protein.
For many of the SNPs disclosed in Tables 1 and 2 (which are identified as “Applera” SNP source), Tables 1 and 2 provide SNP allele frequencies obtained by re-sequencing the DNA of chromosomes from 39 individuals (Tables 1 and 2 also provide allele frequency information for “Celera” source SNPs and, where available, public SNPs from dbEST, HGBASE, and/or HGMD). The allele frequencies provided in Tables 1 and 2 enable these SNPs to be readily used for human identification applications. Although any SNP disclosed in Table 1 and/or Table 2 could be used for human identification, the closer that the frequency of the minor allele at a particular SNP site is to 50%, the greater the ability of that SNP to discriminate between different individuals in a population since it becomes increasingly likely that two randomly selected individuals would have different alleles at that SNP site. Using the SNP allele frequencies provided in Tables 1 and 2, one of ordinary skill in the art could readily select a subset of SNPs for which the frequency of the minor allele is, for example, at least 1%, 2%, 5%, 10%, 20%, 25%, 30%, 40%, 45%, or 50%, or any other frequency in-between. Thus, since Tables 1 and 2 provide allele frequencies based on the re-sequencing of the chromosomes from 39 individuals, a subset of SNPs could readily be selected for human identification in which the total allele count of the minor allele at a particular SNP site is, for example, at least 1, 2, 4, 8, 10, 16, 20, 24, 30, 32, 36, 38, 39, 40, or any other number in-between.
Furthermore, Tables 1 and 2 also provide population group (interchangeably referred to herein as ethnic or racial groups) information coupled with the extensive allele frequency information. For example, the group of 39 individuals whose DNA was re-sequenced was made-up of 20 Caucasians and 19 African-Americans. This population group information enables further refinement of SNP selection for human identification. For example, preferred SNPs for human identification can be selected from Tables 1 and 2 that have similar allele frequencies in both the Caucasian and African-American populations; thus, for example, SNPs can be selected that have equally high discriminatory power in both populations. Alternatively, SNPs can be selected for which there is a statistically significant difference in allele frequencies between the Caucasian and African-American populations (as an extreme example, a particular allele may be observed only in either the Caucasian or the African-American population group but not observed in the other population group); such SNPs are useful, for example, for predicting the race/ethnicity of an unknown perpetrator from a biological sample such as a hair or blood stain recovered at a crime scene. For a discussion of using SNPs to predict ancestry from a DNA sample, including statistical methods, see Frudakis et al., “A Classifier for the SNP-Based Inference of Ancestry,” Journal of Forensic Sciences 48(4):771-782 (2003).
SNPs have numerous advantages over other types of polymorphic markers, such as short tandem repeats (STRs). For example, SNPs can be easily scored and are amenable to automation, making SNPs the markers of choice for large-scale forensic databases. SNPs are found in much greater abundance throughout the genome than repeat polymorphisms. Population frequencies of two polymorphic forms can usually be determined with greater accuracy than those of multiple polymorphic forms at multi-allelic loci. SNPs are mutationally more stable than repeat polymorphisms. SNPs are not susceptible to artifacts such as stutter bands that can hinder analysis. Stutter bands are frequently encountered when analyzing repeat polymorphisms, and are particularly troublesome when analyzing samples such as crime scene samples that may contain mixtures of DNA from multiple sources. Another significant advantage of SNP markers over STR markers is the much shorter length of nucleic acid needed to score a SNP. For example, STR markers are generally several hundred base pairs in length. A SNP, on the other hand, comprises a single nucleotide, and generally a short conserved region on either side of the SNP position for primer and/or probe binding. This makes SNPs more amenable to typing in highly degraded or aged biological samples that are frequently encountered in forensic casework in which DNA may be fragmented into short pieces.
SNPs also are not subject to microvariant and “off-ladder” alleles frequently encountered when analyzing STR loci. Microvariants are deletions or insertions within a repeat unit that change the size of the amplified DNA product so that the amplified product does not migrate at the same rate as reference alleles with normal sized repeat units. When separated by size, such as by electrophoresis on a polyacrylamide gel, microvariants do not align with a reference allelic ladder of standard sized repeat units, but rather migrate between the reference alleles. The reference allelic ladder is used for precise sizing of alleles for allele classification; therefore alleles that do not align with the reference allelic ladder lead to substantial analysis problems. Furthermore, when analyzing multi-allelic repeat polymorphisms, occasionally an allele is found that consists of more or less repeat units than has been previously seen in the population, or more or less repeat alleles than are included in a reference allelic ladder. These alleles will migrate outside the size range of known alleles in a reference allelic ladder, and therefore are referred to as “off-ladder” alleles. In extreme cases, the allele may contain so few or so many repeats that it migrates well out of the range of the reference allelic ladder. In this situation, the allele may not even be observed, or, with multiplex analysis, it may migrate within or close to the size range for another locus, further confounding analysis.
SNP analysis avoids the problems of microvariants and off-ladder alleles encountered in STR analysis. Importantly, microvariants and off-ladder alleles may provide significant problems, and may be completely missed, when using analysis methods such as oligonucleotide hybridization arrays, which utilize oligonucleotide probes specific for certain known alleles. Furthermore, off-ladder alleles and microvariants encountered with STR analysis, even when correctly typed, may lead to improper statistical analysis, since their frequencies in the population are generally unknown or poorly characterized, and therefore the statistical significance of a matching genotype may be questionable. All these advantages of SNP analysis are considerable in light of the consequences of most DNA identification cases, which may lead to life imprisonment for an individual, or re-association of remains to the family of a deceased individual.
DNA can be isolated from biological samples such as blood, bone, hair, saliva, or semen, and compared with the DNA from a reference source at particular SNP positions. Multiple SNP markers can be assayed simultaneously in order to increase the power of discrimination and the statistical significance of a matching genotype. For example, oligonucleotide arrays can be used to genotype a large number of SNPs simultaneously. The SNPs provided by the present invention can be assayed in combination with other polymorphic genetic markers, such as other SNPs known in the art or STRs, in order to identify an individual or to associate an individual with a particular biological sample.
Furthermore, the SNPs provided by the present invention can be genotyped for inclusion in a database of DNA genotypes, for example, a criminal DNA databank such as the FBI's Combined DNA Index System (CODIS) database. A genotype obtained from a biological sample of unknown source can then be queried against the database to find a matching genotype, with the SNPs of the present invention providing nucleotide positions at which to compare the known and unknown DNA sequences for identity. Accordingly, the present invention provides a database comprising novel SNPs or SNP alleles of the present invention (e.g., the database can comprise information indicating which alleles are possessed by individual members of a population at one or more novel SNP sites of the present invention), such as for use in forensics, biometrics, or other human identification applications. Such a database typically comprises a computer-based system in which the SNPs or SNP alleles of the present invention are recorded on a computer readable medium.
The SNPs of the present invention can also be assayed for use in paternity testing. The object of paternity testing is usually to determine whether a male is the father of a child. In most cases, the mother of the child is known and thus, the mother's contribution to the child's genotype can be traced. Paternity testing investigates whether the part of the child's genotype not attributable to the mother is consistent with that of the putative father. Paternity testing can be performed by analyzing sets of polymorphisms in the putative father and the child, with the SNPs of the present invention providing nucleotide positions at which to compare the putative father's and child's DNA sequences for identity. If the set of polymorphisms in the child attributable to the father does not match the set of polymorphisms of the putative father, it can be concluded, barring experimental error, that the putative father is not the father of the child. If the set of polymorphisms in the child attributable to the father match the set of polymorphisms of the putative father, a statistical calculation can be performed to determine the probability of coincidental match, and a conclusion drawn as to the likelihood that the putative father is the true biological father of the child.
In addition to paternity testing, SNPs are also useful for other types of kinship testing, such as for verifying familial relationships for immigration purposes, or for cases in which an individual alleges to be related to a deceased individual in order to claim an inheritance from the deceased individual, etc. For further information regarding the utility of SNPs for paternity testing and other types of kinship testing, including methods for statistical analysis, see Krawczak, “Informativity assessment for biallelic single nucleotide polymorphisms,” Electrophoresis 20(8):1676-81 (June 1999).
The use of the SNPs of the present invention for human identification further extends to various authentication systems, commonly referred to as biometric systems, which typically convert physical characteristics of humans (or other organisms) into digital data. Biometric systems include various technological devices that measure such unique anatomical or physiological characteristics as finger, thumb, or palm prints; hand geometry; vein patterning on the back of the hand; blood vessel patterning of the retina and color and texture of the iris; facial characteristics; voice patterns; signature and typing dynamics; and DNA. Such physiological measurements can be used to verify identity and, for example, restrict or allow access based on the identification. Examples of applications for biometrics include physical area security, computer and network security, aircraft passenger check-in and boarding, financial transactions, medical records access, government benefit distribution, voting, law enforcement, passports, visas and immigration, prisons, various military applications, and for restricting access to expensive or dangerous items, such as automobiles or guns. See, for example, O'Connor, Stanford Technology Law Review, and U.S. Pat. No. 6,119,096.
Groups of SNPs, particularly the SNPs provided by the present invention, can be typed to uniquely identify an individual for biometric applications such as those described above. Such SNP typing can readily be accomplished using, for example, DNA chips/arrays. Preferably, a minimally invasive means for obtaining a DNA sample is utilized. For example, PCR amplification enables sufficient quantities of DNA for analysis to be obtained from buccal swabs or fingerprints, which contain DNA-containing skin cells and oils that are naturally transferred during contact.
Further information regarding techniques for using SNPs in forensic/human identification applications can be found, for example, in Current Protocols in Human Genetics 14.1-14.7, John Wiley & Sons, N.Y. (2002).
Variant Proteins, Antibodies, Vectors, Host Cells, & Uses Thereof
Variant Proteins Encoded by SNP-Containing Nucleic Acid Molecules
The present invention provides SNP-containing nucleic acid molecules, many of which encode proteins having variant amino acid sequences as compared to the art-known (i.e., wild-type) proteins. Amino acid sequences encoded by the polymorphic nucleic acid molecules of the present invention are referred to as SEQ ID NO:2 in Table 1 and provided in the Sequence Listing. These variants will generally be referred to herein as variant proteins/peptides/polypeptides, or polymorphic proteins/peptides/polypeptides of the present invention. The terms “protein,” “peptide,” and “polypeptide” are used herein interchangeably.
A variant protein of the present invention may be encoded by, for example, a nonsynonymous nucleotide substitution at any one of the cSNP positions disclosed herein. In addition, variant proteins may also include proteins whose expression, structure, and/or function is altered by a SNP disclosed herein, such as a SNP that creates or destroys a stop codon, a SNP that affects splicing, and a SNP in control/regulatory elements, e.g. promoters, enhancers, or transcription factor binding domains.
As used herein, a protein or peptide is said to be “isolated” or “purified” when it is substantially free of cellular material or chemical precursors or other chemicals. The variant proteins of the present invention can be purified to homogeneity or other lower degrees of purity. The level of purification will be based on the intended use. The key feature is that the preparation allows for the desired function of the variant protein, even if in the presence of considerable amounts of other components.
As used herein, “substantially free of cellular material” includes preparations of the variant protein having less than about 30% (by dry weight) other proteins (i.e., contaminating protein), less than about 20% other proteins, less than about 10% other proteins, or less than about 5% other proteins. When the variant protein is recombinantly produced, it can also be substantially free of culture medium, i.e., culture medium represents less than about 20% of the volume of the protein preparation.
The language “substantially free of chemical precursors or other chemicals” includes preparations of the variant protein in which it is separated from chemical precursors or other chemicals that are involved in its synthesis. In one embodiment, the language “substantially free of chemical precursors or other chemicals” includes preparations of the variant protein having less than about 30% (by dry weight) chemical precursors or other chemicals, less than about 20% chemical precursors or other chemicals, less than about 10% chemical precursors or other chemicals, or less than about 5% chemical precursors or other chemicals.
An isolated variant protein may be purified from cells that naturally express it, purified from cells that have been altered to express it (recombinant host cells), or synthesized using known protein synthesis methods. For example, a nucleic acid molecule containing SNP(s) encoding the variant protein can be cloned into an expression vector, the expression vector introduced into a host cell, and the variant protein expressed in the host cell. The variant protein can then be isolated from the cells by any appropriate purification scheme using standard protein purification techniques. Examples of these techniques are described in detail below. Sambrook and Russell, Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Laboratory Press, N.Y. (2000).
The present invention provides isolated variant proteins that comprise, consist of or consist essentially of amino acid sequences that contain one or more variant amino acids encoded by one or more codons that contain a SNP of the present invention.
Accordingly, the present invention provides variant proteins that consist of amino acid sequences that contain one or more amino acid polymorphisms (or truncations or extensions due to creation or destruction of a stop codon, respectively) encoded by the SNPs provided in Table 1 and/or Table 2. A protein consists of an amino acid sequence when the amino acid sequence is the entire amino acid sequence of the protein.
The present invention further provides variant proteins that consist essentially of amino acid sequences that contain one or more amino acid polymorphisms (or truncations or extensions due to creation or destruction of a stop codon, respectively) encoded by the SNPs provided in Table 1 and/or Table 2. A protein consists essentially of an amino acid sequence when such an amino acid sequence is present with only a few additional amino acid residues in the final protein.
The present invention further provides variant proteins that comprise amino acid sequences that contain one or more amino acid polymorphisms (or truncations or extensions due to creation or destruction of a stop codon, respectively) encoded by the SNPs provided in Table 1 and/or Table 2. A protein comprises an amino acid sequence when the amino acid sequence is at least part of the final amino acid sequence of the protein. In such a fashion, the protein may contain only the variant amino acid sequence or have additional amino acid residues, such as a contiguous encoded sequence that is naturally associated with it or heterologous amino acid residues. Such a protein can have a few additional amino acid residues or can comprise many more additional amino acids. A brief description of how various types of these proteins can be made and isolated is provided below.
The variant proteins of the present invention can be attached to heterologous sequences to form chimeric or fusion proteins. Such chimeric and fusion proteins comprise a variant protein operatively linked to a heterologous protein having an amino acid sequence not substantially homologous to the variant protein. “Operatively linked” indicates that the coding sequences for the variant protein and the heterologous protein are ligated in-frame. The heterologous protein can be fused to the N-terminus or C-terminus of the variant protein. In another embodiment, the fusion protein is encoded by a fusion polynucleotide that is synthesized by conventional techniques including automated DNA synthesizers. Alternatively, PCR amplification of gene fragments can be carried out using anchor primers which give rise to complementary overhangs between two consecutive gene fragments which can subsequently be annealed and re-amplified to generate a chimeric gene sequence. See Ausubel et al., Current Protocols in Molecular Biology (1992). Moreover, many expression vectors are commercially available that already encode a fusion moiety (e.g., a GST protein). A variant protein-encoding nucleic acid can be cloned into such an expression vector such that the fusion moiety is linked in-frame to the variant protein.
In many uses, the fusion protein does not affect the activity of the variant protein. The fusion protein can include, but is not limited to, enzymatic fusion proteins, for example, beta-galactosidase fusions, yeast two-hybrid GAL fusions, poly-His fusions, MYC-tagged, HI-tagged and Ig fusions. Such fusion proteins, particularly poly-His fusions, can facilitate their purification following recombinant expression. In certain host cells (e.g., mammalian host cells), expression and/or secretion of a protein can be increased by using a heterologous signal sequence. Fusion proteins are further described in, for example, Terpe, “Overview of tag protein fusions: from molecular and biochemical fundamentals to commercial systems,” Appl Microbiol Biotechnol 60(5):523-33 (January 2003); Epub Nov. 7, 2002; Graddis et al., “Designing proteins that work using recombinant technologies,” Curr Pharm Biotechnol 3(4):285-97 (December 2002); and Nilsson et al., “Affinity fusion strategies for detection, purification, and immobilization of recombinant proteins,” Protein Expr Purif 11(1):1-16 (October 1997).
The present invention also relates to further obvious variants of the variant polypeptides of the present invention, such as naturally-occurring mature forms (e.g., allelic variants), non-naturally occurring recombinantly-derived variants, and orthologs and paralogs of such proteins that share sequence homology. Such variants can readily be generated using art-known techniques in the fields of recombinant nucleic acid technology and protein biochemistry. It is understood, however, that variants exclude those known in the prior art before the present invention.
Further variants of the variant polypeptides disclosed in Table 1 can comprise an amino acid sequence that shares at least 70-80%, 80-85%, 85-90%, 91%, 92%, 93%, 94%, 95%, 96%, 97, 98%, or 99% sequence identity with an amino acid sequence disclosed in Table 1 (or a fragment thereof) and that includes a novel amino acid residue (allele) disclosed in Table 1 (which is encoded by a novel SNP allele). Thus, an aspect of the present invention that is specifically contemplated are polypeptides that have a certain degree of sequence variation compared with the polypeptide sequences shown in Table 1, but that contain a novel amino acid residue (allele) encoded by a novel SNP allele disclosed herein. In other words, as long as a polypeptide contains a novel amino acid residue disclosed herein, other portions of the polypeptide that flank the novel amino acid residue can vary to some degree from the polypeptide sequences shown in Table 1.
Full-length pre-processed forms, as well as mature processed forms, of proteins that comprise one of the amino acid sequences disclosed herein can readily be identified as having complete sequence identity to one of the variant proteins of the present invention as well as being encoded by the same genetic locus as the variant proteins provided herein.
Orthologs of a variant peptide can readily be identified as having some degree of significant sequence homology/identity to at least a portion of a variant peptide as well as being encoded by a gene from another organism. Preferred orthologs will be isolated from non-human mammals, preferably primates, for the development of human therapeutic targets and agents. Such orthologs can be encoded by a nucleic acid sequence that hybridizes to a variant peptide-encoding nucleic acid molecule under moderate to stringent conditions depending on the degree of relatedness of the two organisms yielding the homologous proteins.
Variant proteins include, but are not limited to, proteins containing deletions, additions and substitutions in the amino acid sequence caused by the SNPs of the present invention. One class of substitutions is conserved amino acid substitutions in which a given amino acid in a polypeptide is substituted for another amino acid of like characteristics. Typical conservative substitutions are replacements, one for another, among the aliphatic amino acids Ala, Val, Leu, and Ile; interchange of the hydroxyl residues Ser and Thr; exchange of the acidic residues Asp and Glu; substitution between the amide residues Asn and Gln; exchange of the basic residues Lys and Arg; and replacements among the aromatic residues Phe and Tyr. Guidance concerning which amino acid changes are likely to be phenotypically silent are found, for example, in Bowie et al., Science 247:1306-1310 (1990).
Variant proteins can be fully functional or can lack function in one or more activities, e.g. ability to bind another molecule, ability to catalyze a substrate, ability to mediate signaling, etc. Fully functional variants typically contain only conservative variations or variations in non-critical residues or in non-critical regions. Functional variants can also contain substitution of similar amino acids that result in no change or an insignificant change in function. Alternatively, such substitutions may positively or negatively affect function to some degree. Non-functional variants typically contain one or more non-conservative amino acid substitutions, deletions, insertions, inversions, truncations or extensions, or a substitution, insertion, inversion, or deletion of a critical residue or in a critical region.
Amino acids that are essential for function of a protein can be identified by methods known in the art, such as site-directed mutagenesis or alanine-scanning mutagenesis, particularly using the amino acid sequence and polymorphism information provided in Table 1. Cunningham et al., Science 244:1081-1085 (1989). The latter procedure introduces single alanine mutations at every residue in the molecule. The resulting mutant molecules are then tested for biological activity such as enzyme activity or in assays such as an in vitro proliferative activity. Sites that are critical for binding partner/substrate binding can also be determined by structural analysis such as crystallization, nuclear magnetic resonance or photoaffinity labeling. Smith et al., J Mol Biol 224:899-904 (1992); de Vos et al., Science 255:306-312 (1992).
Polypeptides can contain amino acids other than the 20 amino acids commonly referred to as the 20 naturally occurring amino acids. Further, many amino acids, including the terminal amino acids, may be modified by natural processes, such as processing and other post-translational modifications, or by chemical modification techniques well known in the art. Accordingly, the variant proteins of the present invention also encompass derivatives or analogs in which a substituted amino acid residue is not one encoded by the genetic code, in which a substituent group is included, in which the mature polypeptide is fused with another compound, such as a compound to increase the half-life of the polypeptide (e.g., polyethylene glycol), or in which additional amino acids are fused to the mature polypeptide, such as a leader or secretory sequence or a sequence for purification of the mature polypeptide or a pro-protein sequence.
Known protein modifications include, but are not limited to, acetylation, acylation, ADP-ribosylation, amidation, covalent attachment of flavin, covalent attachment of a heme moiety, covalent attachment of a nucleotide or nucleotide derivative, covalent attachment of a lipid or lipid derivative, covalent attachment of phosphotidylinositol, cross-linking, cyclization, disulfide bond formation, demethylation, formation of covalent crosslinks, formation of cystine, formation of pyroglutamate, formylation, gamma carboxylation, glycosylation, GPI anchor formation, hydroxylation, iodination, methylation, myristoylation, oxidation, proteolytic processing, phosphorylation, prenylation, racemization, selenoylation, sulfation, transfer-RNA mediated addition of amino acids to proteins such as arginylation, and ubiquitination.
Such protein modifications are well known to those of skill in the art and have been described in great detail in the scientific literature. Particularly common modifications, for example glycosylation, lipid attachment, sulfation, gamma-carboxylation of glutamic acid residues, hydroxylation and ADP-ribosylation, are described in most basic texts, such as Proteins—Structure and Molecular Properties 2nd Ed., T. E. Creighton, W.H. Freeman and Company, N.Y. (1993); F. Wold, Posttranslational Covalent Modification of Proteins 1-12, B. C. Johnson, ed., Academic Press, N.Y. (1983); Seifter et al., Meth Enzymol 182:626-646 (1990); and Rattan et al., Ann NY Acad Sci 663:48-62 (1992).
The present invention further provides fragments of the variant proteins in which the fragments contain one or more amino acid sequence variations (e.g., substitutions, or truncations or extensions due to creation or destruction of a stop codon) encoded by one or more SNPs disclosed herein. The fragments to which the invention pertains, however, are not to be construed as encompassing fragments that have been disclosed in the prior art before the present invention.
As used herein, a fragment may comprise at least about 4, 8, 10, 12, 14, 16, 18, 20, 25, 30, 50, 100 (or any other number in-between) or more contiguous amino acid residues from a variant protein, wherein at least one amino acid residue is affected by a SNP of the present invention, e.g., a variant amino acid residue encoded by a nonsynonymous nucleotide substitution at a cSNP position provided by the present invention. The variant amino acid encoded by a cSNP may occupy any residue position along the sequence of the fragment. Such fragments can be chosen based on the ability to retain one or more of the biological activities of the variant protein or the ability to perform a function, e.g., act as an immunogen. Particularly important fragments are biologically active fragments. Such fragments will typically comprise a domain or motif of a variant protein of the present invention, e.g., active site, transmembrane domain, or ligand/substrate binding domain. Other fragments include, but are not limited to, domain or motif-containing fragments, soluble peptide fragments, and fragments containing immunogenic structures. Predicted domains and functional sites are readily identifiable by computer programs well known to those of skill in the art (e.g., PROSITE analysis). Current Protocols in Protein Science, John Wiley & Sons, N.Y. (2002).
Uses of Variant Proteins
The variant proteins of the present invention can be used in a variety of ways, including but not limited to, in assays to determine the biological activity of a variant protein, such as in a panel of multiple proteins for high-throughput screening; to raise antibodies or to elicit another type of immune response; as a reagent (including the labeled reagent) in assays designed to quantitatively determine levels of the variant protein (or its binding partner) in biological fluids; as a marker for cells or tissues in which it is preferentially expressed (either constitutively or at a particular stage of tissue differentiation or development or in a disease state); as a target for screening for a therapeutic agent; and as a direct therapeutic agent to be administered into a human subject. Any of the variant proteins disclosed herein may be developed into reagent grade or kit format for commercialization as research products. Methods for performing the uses listed above are well known to those skilled in the art. See, e.g., Molecular Cloning: A Laboratory Manual, Sambrook and Russell, Cold Spring Harbor Laboratory Press, N.Y. (2000), and Methods in Enzymology: Guide to Molecular Cloning Techniques, S. L. Berger and A. R. Kimmel, eds., Academic Press (1987).
In a specific embodiment of the invention, the methods of the present invention include detection of one or more variant proteins disclosed herein. Variant proteins are disclosed in Table 1 and in the Sequence Listing as a#1-a#1. Detection of such proteins can be accomplished using, for example, antibodies, small molecule compounds, aptamers, ligands/substrates, other proteins or protein fragments, or other protein-binding agents. Preferably, protein detection agents are specific for a variant protein of the present invention and can therefore discriminate between a variant protein of the present invention and the wild-type protein or another variant form. This can generally be accomplished by, for example, selecting or designing detection agents that bind to the region of a protein that differs between the variant and wild-type protein, such as a region of a protein that contains one or more amino acid substitutions that is/are encoded by a non-synonymous cSNP of the present invention, or a region of a protein that follows a nonsense mutation-type SNP that creates a stop codon thereby leading to a shorter polypeptide, or a region of a protein that follows a read-through mutation-type SNP that destroys a stop codon thereby leading to a longer polypeptide in which a portion of the polypeptide is present in one version of the polypeptide but not the other.
In another specific aspect of the invention, the variant proteins of the present invention are used as targets for diagnosing CHD or aneurysm/dissection or for determining predisposition to CHD or aneurysm/dissection in a human, for treating and/or preventing CHD or aneurysm/dissection, or for predicting an individual's response to statin treatment (particularly treatment or prevention of CHD or aneurysm/dissection using statins), etc. Accordingly, the invention provides methods for detecting the presence of, or levels of, one or more variant proteins of the present invention in a cell, tissue, or organism. Such methods typically involve contacting a test sample with an agent (e.g., an antibody, small molecule compound, or peptide) capable of interacting with the variant protein such that specific binding of the agent to the variant protein can be detected. Such an assay can be provided in a single detection format or a multi-detection format such as an array, for example, an antibody or aptamer array (arrays for protein detection may also be referred to as “protein chips”). The variant protein of interest can be isolated from a test sample and assayed for the presence of a variant amino acid sequence encoded by one or more SNPs disclosed by the present invention. The SNPs may cause changes to the protein and the corresponding protein function/activity, such as through non-synonymous substitutions in protein coding regions that can lead to amino acid substitutions, deletions, insertions, and/or rearrangements; formation or destruction of stop codons; or alteration of control elements such as promoters. SNPs may also cause inappropriate post-translational modifications.
One preferred agent for detecting a variant protein in a sample is an antibody capable of selectively binding to a variant form of the protein (antibodies are described in greater detail in the next section). Such samples include, for example, tissues, cells, and biological fluids isolated from a subject, as well as tissues, cells and fluids present within a subject.
In vitro methods for detection of the variant proteins associated with CHD or aneurysm/dissection that are disclosed herein and fragments thereof include, but are not limited to, enzyme linked immunosorbent assays (ELISAs), radioimmunoassays (RIA), Western blots, immunoprecipitations, immunofluorescence, and protein arrays/chips (e.g., arrays of antibodies or aptamers). For further information regarding immunoassays and related protein detection methods, see Current Protocols in Immunology, John Wiley & Sons, N.Y., and Hage, “Immunoassays,” Anal Chem 15;71(12):294R-304R (June 1999).
Additional analytic methods of detecting amino acid variants include, but are not limited to, altered electrophoretic mobility, altered tryptic peptide digest, altered protein activity in cell-based or cell-free assay, alteration in ligand or antibody-binding pattern, altered isoelectric point, and direct amino acid sequencing.
Alternatively, variant proteins can be detected in vivo in a subject by introducing into the subject a labeled antibody (or other type of detection reagent) specific for a variant protein. For example, the antibody can be labeled with a radioactive marker whose presence and location in a subject can be detected by standard imaging techniques.
Other uses of the variant peptides of the present invention are based on the class or action of the protein. For example, proteins isolated from humans and their mammalian orthologs serve as targets for identifying agents (e.g., small molecule drugs or antibodies) for use in therapeutic applications, particularly for modulating a biological or pathological response in a cell or tissue that expresses the protein. Pharmaceutical agents can be developed that modulate protein activity.
As an alternative to modulating gene expression, therapeutic compounds can be developed that modulate protein function. For example, many SNPs disclosed herein affect the amino acid sequence of the encoded protein (e.g., non-synonymous cSNPs and nonsense mutation-type SNPs). Such alterations in the encoded amino acid sequence may affect protein function, particularly if such amino acid sequence variations occur in functional protein domains, such as catalytic domains, ATP-binding domains, or ligand/substrate binding domains. It is well established in the art that variant proteins having amino acid sequence variations in functional domains can cause or influence pathological conditions. In such instances, compounds (e.g., small molecule drugs or antibodies) can be developed that target the variant protein and modulate (e.g., up- or down-regulate) protein function/activity.
The therapeutic methods of the present invention further include methods that target one or more variant proteins of the present invention. Variant proteins can be targeted using, for example, small molecule compounds, antibodies, aptamers, ligands/substrates, other proteins, or other protein-binding agents. Additionally, the skilled artisan will recognize that the novel protein variants (and polymorphic nucleic acid molecules) disclosed in Table 1 may themselves be directly used as therapeutic agents by acting as competitive inhibitors of corresponding art-known proteins (or nucleic acid molecules such as mRNA molecules).
The variant proteins of the present invention are particularly useful in drug screening assays, in cell-based or cell-free systems. Cell-based systems can utilize cells that naturally express the protein, a biopsy specimen, or cell cultures. In one embodiment, cell-based assays involve recombinant host cells expressing the variant protein. Cell-free assays can be used to detect the ability of a compound to directly bind to a variant protein or to the corresponding SNP-containing nucleic acid fragment that encodes the variant protein.
A variant protein of the present invention, as well as appropriate fragments thereof, can be used in high-throughput screening assays to test candidate compounds for the ability to bind and/or modulate the activity of the variant protein. These candidate compounds can be further screened against a protein having normal function (e.g., a wild-type/non-variant protein) to further determine the effect of the compound on the protein activity. Furthermore, these compounds can be tested in animal or invertebrate systems to determine in vivo activity/effectiveness. Compounds can be identified that activate (agonists) or inactivate (antagonists) the variant protein, and different compounds can be identified that cause various degrees of activation or inactivation of the variant protein.
Further, the variant proteins can be used to screen a compound for the ability to stimulate or inhibit interaction between the variant protein and a target molecule that normally interacts with the protein. The target can be a ligand, a substrate or a binding partner that the protein normally interacts with (for example, epinephrine or norepinephrine). Such assays typically include the steps of combining the variant protein with a candidate compound under conditions that allow the variant protein, or fragment thereof, to interact with the target molecule, and to detect the formation of a complex between the protein and the target or to detect the biochemical consequence of the interaction with the variant protein and the target, such as any of the associated effects of signal transduction.
Candidate compounds include, for example, 1) peptides such as soluble peptides, including Ig-tailed fusion peptides and members of random peptide libraries (see, e.g., Lam et al., Nature 354:82-84 (1991); Houghten et al., Nature 354:84-86 (1991)) and combinatorial chemistry-derived molecular libraries made of D- and/or L-configuration amino acids; 2) phosphopeptides (e.g., members of random and partially degenerate, directed phosphopeptide libraries, see, e.g., Songyang et al., Cell 72:767-778 (1993)); 3) antibodies (e.g., polyclonal, monoclonal, humanized, anti-idiotypic, chimeric, and single chain antibodies as well as Fab, F(ab′)2, Fab expression library fragments, and epitope-binding fragments of antibodies); and 4) small organic and inorganic molecules (e.g., molecules obtained from combinatorial and natural product libraries).
One candidate compound is a soluble fragment of the variant protein that competes for ligand binding. Other candidate compounds include mutant proteins or appropriate fragments containing mutations that affect variant protein function and thus compete for ligand. Accordingly, a fragment that competes for ligand, for example with a higher affinity, or a fragment that binds ligand but does not allow release, is encompassed by the invention.
The invention further includes other end point assays to identify compounds that modulate (stimulate or inhibit) variant protein activity. The assays typically involve an assay of events in the signal transduction pathway that indicate protein activity. Thus, the expression of genes that are up or down-regulated in response to the variant protein dependent signal cascade can be assayed. In one embodiment, the regulatory region of such genes can be operably linked to a marker that is easily detectable, such as luciferase. Alternatively, phosphorylation of the variant protein, or a variant protein target, could also be measured. Any of the biological or biochemical functions mediated by the variant protein can be used as an endpoint assay. These include all of the biochemical or biological events described herein, in the references cited herein, incorporated by reference for these endpoint assay targets, and other functions known to those of ordinary skill in the art.
Binding and/or activating compounds can also be screened by using chimeric variant proteins in which an amino terminal extracellular domain or parts thereof, an entire transmembrane domain or subregions, and/or the carboxyl terminal intracellular domain or parts thereof, can be replaced by heterologous domains or subregions. For example, a substrate-binding region can be used that interacts with a different substrate than that which is normally recognized by a variant protein. Accordingly, a different set of signal transduction components is available as an end-point assay for activation. This allows for assays to be performed in other than the specific host cell from which the variant protein is derived.
The variant proteins are also useful in competition binding assays in methods designed to discover compounds that interact with the variant protein. Thus, a compound can be exposed to a variant protein under conditions that allow the compound to bind or to otherwise interact with the variant protein. A binding partner, such as ligand, that normally interacts with the variant protein is also added to the mixture. If the test compound interacts with the variant protein or its binding partner, it decreases the amount of complex formed or activity from the variant protein. This type of assay is particularly useful in screening for compounds that interact with specific regions of the variant protein. Hodgson, Bio/technology, 10(9), 973-80 (September 1992).
To perform cell-free drug screening assays, it is sometimes desirable to immobilize either the variant protein or a fragment thereof, or its target molecule, to facilitate separation of complexes from uncomplexed forms of one or both of the proteins, as well as to accommodate automation of the assay. Any method for immobilizing proteins on matrices can be used in drug screening assays. In one embodiment, a fusion protein containing an added domain allows the protein to be bound to a matrix. For example, glutathione-S-transferase/125I fusion proteins can be adsorbed onto glutathione sepharose beads (Sigma Chemical, St. Louis, Mo.) or glutathione derivatized microtitre plates, which are then combined with the cell lysates (e.g., 35S-labeled) and a candidate compound, such as a drug candidate, and the mixture incubated under conditions conducive to complex formation (e.g., at physiological conditions for salt and pH). Following incubation, the beads can be washed to remove any unbound label, and the matrix immobilized and radiolabel determined directly, or in the supernatant after the complexes are dissociated. Alternatively, the complexes can be dissociated from the matrix, separated by SDS-PAGE, and the level of bound material found in the bead fraction quantitated from the gel using standard electrophoretic techniques.
Either the variant protein or its target molecule can be immobilized utilizing conjugation of biotin and streptavidin. Alternatively, antibodies reactive with the variant protein but which do not interfere with binding of the variant protein to its target molecule can be derivatized to the wells of the plate, and the variant protein trapped in the wells by antibody conjugation. Preparations of the target molecule and a candidate compound are incubated in the variant protein-presenting wells and the amount of complex trapped in the well can be quantitated. Methods for detecting such complexes, in addition to those described above for the GST-immobilized complexes, include immunodetection of complexes using antibodies reactive with the protein target molecule, or which are reactive with variant protein and compete with the target molecule, and enzyme-linked assays that rely on detecting an enzymatic activity associated with the target molecule.
Modulators of variant protein activity identified according to these drug screening assays can be used to treat a subject with a disorder mediated by the protein pathway, such as CHD or aneurysm/dissection. These methods of treatment typically include the steps of administering the modulators of protein activity in a pharmaceutical composition to a subject in need of such treatment.
The variant proteins, or fragments thereof, disclosed herein can themselves be directly used to treat a disorder characterized by an absence of, inappropriate, or unwanted expression or activity of the variant protein. Accordingly, methods for treatment include the use of a variant protein disclosed herein or fragments thereof.
In yet another aspect of the invention, variant proteins can be used as “bait proteins” in a two-hybrid assay or three-hybrid assay to identify other proteins that bind to or interact with the variant protein and are involved in variant protein activity. See, e.g., U.S. Pat. No. 5,283,317; Zervos et al., Cell 72:223-232 (1993); Madura et al., J Biol Chem 268:12046-12054 (1993); Bartel et al., Biotechniques 14:920-924 (1993); Iwabuchi et al., Oncogene 8:1693-1696 (1993); and Brent, WO 94/10300. Such variant protein-binding proteins are also likely to be involved in the propagation of signals by the variant proteins or variant protein targets as, for example, elements of a protein-mediated signaling pathway. Alternatively, such variant protein-binding proteins are inhibitors of the variant protein.
The two-hybrid system is based on the modular nature of most transcription factors, which typically consist of separable DNA-binding and activation domains. Briefly, the assay typically utilizes two different DNA constructs. In one construct, the gene that codes for a variant protein is fused to a gene encoding the DNA binding domain of a known transcription factor (e.g., GAL-4). In the other construct, a DNA sequence, from a library of DNA sequences, that encodes an unidentified protein (“prey” or “sample”) is fused to a gene that codes for the activation domain of the known transcription factor. If the “bait” and the “prey” proteins are able to interact, in vivo, forming a variant protein-dependent complex, the DNA-binding and activation domains of the transcription factor are brought into close proximity. This proximity allows transcription of a reporter gene (e.g., LacZ) that is operably linked to a transcriptional regulatory site responsive to the transcription factor. Expression of the reporter gene can be detected, and cell colonies containing the functional transcription factor can be isolated and used to obtain the cloned gene that encodes the protein that interacts with the variant protein.
Antibodies Directed to Variant Proteins
The present invention also provides antibodies that selectively bind to the variant proteins disclosed herein and fragments thereof. Such antibodies may be used to quantitatively or qualitatively detect the variant proteins of the present invention. As used herein, an antibody selectively binds a target variant protein when it binds the variant protein and does not significantly bind to non-variant proteins, i.e., the antibody does not significantly bind to normal, wild-type, or art-known proteins that do not contain a variant amino acid sequence due to one or more SNPs of the present invention (variant amino acid sequences may be due to, for example, nonsynonymous cSNPs, nonsense SNPs that create a stop codon, thereby causing a truncation of a polypeptide or SNPs that cause read-through mutations resulting in an extension of a polypeptide).
As used herein, an antibody is defined in terms consistent with that recognized in the art: they are multi-subunit proteins produced by an organism in response to an antigen challenge. The antibodies of the present invention include both monoclonal antibodies and polyclonal antibodies, as well as antigen-reactive proteolytic fragments of such antibodies, such as Fab, F(ab)′2, and Fv fragments. In addition, an antibody of the present invention further includes any of a variety of engineered antigen-binding molecules such as a chimeric antibody (U.S. Pat. Nos. 4,816,567 and 4,816,397; Morrison et al., Proc Natl Acad Sci USA 81:6851 (1984); Neuberger et al., Nature 312:604 (1984)), a humanized antibody (U.S. Pat. Nos. 5,693,762; 5,585,089 and 5,565,332), a single-chain Fv (U.S. Pat. No. 4,946,778; Ward et al., Nature 334:544 (1989)), a bispecific antibody with two binding specificities (Segal et al., J Immunol Methods 248:1 (2001); Carter, J Immunol Methods 248:7 (2001)), a diabody, a triabody, and a tetrabody (Todorovska et al., J Immunol Methods 248:47 (2001)), as well as a Fab conjugate (dimer or trimer), and a minibody.
Many methods are known in the art for generating and/or identifying antibodies to a given target antigen. Harlow, Antibodies, Cold Spring Harbor Press, N.Y. (1989). In general, an isolated peptide (e.g., a variant protein of the present invention) is used as an immunogen and is administered to a mammalian organism, such as a rat, rabbit, hamster or mouse. Either a full-length protein, an antigenic peptide fragment (e.g., a peptide fragment containing a region that varies between a variant protein and a corresponding wild-type protein), or a fusion protein can be used. A protein used as an immunogen may be naturally-occurring, synthetic or recombinantly produced, and may be administered in combination with an adjuvant, including but not limited to, Freund's (complete and incomplete), mineral gels such as aluminum hydroxide, surface active substance such as lysolecithin, pluronic polyols, polyanions, peptides, oil emulsions, keyhole limpet hemocyanin, dinitrophenol, and the like.
Monoclonal antibodies can be produced by hybridoma technology, which immortalizes cells secreting a specific monoclonal antibody. Kohler and Milstein, Nature 256:495 (1975). The immortalized cell lines can be created in vitro by fusing two different cell types, typically lymphocytes, and tumor cells. The hybridoma cells may be cultivated in vitro or in vivo. Additionally, fully human antibodies can be generated by transgenic animals. He et al., J Immunol 169:595 (2002). Fd phage and Fd phagemid technologies may be used to generate and select recombinant antibodies in vitro. Hoogenboom and Chames, Immunol Today 21:371 (2000); Liu et al., J Mol Biol 315:1063 (2002). The complementarity-determining regions of an antibody can be identified, and synthetic peptides corresponding to such regions may be used to mediate antigen binding. U.S. Pat. No. 5,637,677.
Antibodies are preferably prepared against regions or discrete fragments of a variant protein containing a variant amino acid sequence as compared to the corresponding wild-type protein (e.g., a region of a variant protein that includes an amino acid encoded by a nonsynonymous cSNP, a region affected by truncation caused by a nonsense SNP that creates a stop codon, or a region resulting from the destruction of a stop codon due to read-through mutation caused by a SNP). Furthermore, preferred regions will include those involved in function/activity and/or protein/binding partner interaction. Such fragments can be selected on a physical property, such as fragments corresponding to regions that are located on the surface of the protein, e.g., hydrophilic regions, or can be selected based on sequence uniqueness, or based on the position of the variant amino acid residue(s) encoded by the SNPs provided by the present invention. An antigenic fragment will typically comprise at least about 8-10contiguous amino acid residues in which at least one of the amino acid residues is an amino acid affected by a SNP disclosed herein. The antigenic peptide can comprise, however, at least 12, 14, 16, 20, 25, 50, 100 (or any other number in-between) or more amino acid residues, provided that at least one amino acid is affected by a SNP disclosed herein.
Detection of an antibody of the present invention can be facilitated by coupling (i.e., physically linking) the antibody or an antigen-reactive fragment thereof to a detectable substance. Detectable substances include, but are not limited to, various enzymes, prosthetic groups, fluorescent materials, luminescent materials, bioluminescent materials, and radioactive materials. Examples of suitable enzymes include horseradish peroxidase, alkaline phosphatase, β-galactosidase, or acetylcholinesterase; examples of suitable prosthetic group complexes include streptavidin/biotin and avidin/biotin; examples of suitable fluorescent materials include umbelliferone, fluorescein, fluorescein isothiocyanate, rhodamine, dichlorotriazinylamine fluorescein, dansyl chloride or phycoerythrin; an example of a luminescent material includes luminol; examples of bioluminescent materials include luciferase, luciferin, and aequorin, and examples of suitable radioactive material include 125I, 131I, 35S or 3H.
Antibodies, particularly the use of antibodies as therapeutic agents, are reviewed in: Morgan, “Antibody therapy for Alzheimer's disease,” Expert Rev Vaccines (1):53-9 (February 2003); Ross et al., “Anticancer antibodies,” Am J Clin Pathol 119(4):472-85 (April 2003); Goldenberg, “Advancing role of radiolabeled antibodies in the therapy of cancer,” Cancer Immunol Immunother 52(5):281-96 (May 2003); Epub Mar. 11, 2003; Ross et al., “Antibody-based therapeutics in oncology,” Expert Rev Anticancer Ther 3(1):107-21 (February 2003); Cao et al., “Bispecific antibody conjugates in therapeutics,” Adv Drug Deliv Rev 55(2):171-97 (February 2003); von Mehren et al., “Monoclonal antibody therapy for cancer,” Annu Rev Med 54:343-69 (2003); Epub Dec. 3, 2001; Hudson et al., “Engineered antibodies,” Nat Med 9(1):129-34 (January 2003); Brekke et al., “Therapeutic antibodies for human diseases at the dawn of the twenty-first century,” Nat Rev Drug Discov 2(1):52-62 (January 2003); Erratum in: Nat Rev Drug Discov 2(3):240 (March 2003); Houdebine, “Antibody manufacture in transgenic animals and comparisons with other systems,” Curr Opin Biotechnol 13(6):625-9 (December 2002); Andreakos et al., “Monoclonal antibodies in immune and inflammatory diseases,” Curr Opin Biotechnol 13(6):615-20 (December 2002); Kellermann et al., “Antibody discovery: the use of transgenic mice to generate human monoclonal antibodies for therapeutics,” Curr Opin Biotechnol 13(6):593-7 (December 2002); Pini et al., “Phage display and colony filter screening for high-throughput selection of antibody libraries,” Comb Chem High Throughput Screen 5(7):503-10 (November 2002); Batra et al., “Pharmacokinetics and biodistribution of genetically engineered antibodies,” Curr Opin Biotechnol 13(6):603-8 (December 2002); and Tangri et al., “Rationally engineered proteins or antibodies with absent or reduced immunogenicity,” Curr Med Chem 9(24):2191-9 (December 2002).
Uses of Antibodies
Antibodies can be used to isolate the variant proteins of the present invention from a natural cell source or from recombinant host cells by standard techniques, such as affinity chromatography or immunoprecipitation. In addition, antibodies are useful for detecting the presence of a variant protein of the present invention in cells or tissues to determine the pattern of expression of the variant protein among various tissues in an organism and over the course of normal development or disease progression. Further, antibodies can be used to detect variant protein in situ, in vitro, in a bodily fluid, or in a cell lysate or supernatant in order to evaluate the amount and pattern of expression. Also, antibodies can be used to assess abnormal tissue distribution, abnormal expression during development, or expression in an abnormal condition, such as in CHD or aneurysm/dissection, or during statin treatment. Additionally, antibody detection of circulating fragments of the full-length variant protein can be used to identify turnover.
Antibodies to the variant proteins of the present invention are also useful in pharmacogenomic analysis. Thus, antibodies against variant proteins encoded by alternative SNP alleles can be used to identify individuals that require modified treatment modalities.
Further, antibodies can be used to assess expression of the variant protein in disease states such as in active stages of the disease or in an individual with a predisposition to a disease related to the protein's function, such as CHD or aneurysm/dissection, or during the course of a treatment regime, such as during statin treatment. Antibodies specific for a variant protein encoded by a SNP-containing nucleic acid molecule of the present invention can be used to assay for the presence of the variant protein, such as to diagnose CHD or aneurysm/dissection or to predict an individual's response to statin treatment or predisposition/susceptibility to CHD or aneurysm/dissection, as indicated by the presence of the variant protein.
Antibodies are also useful as diagnostic tools for evaluating the variant proteins in conjunction with analysis by electrophoretic mobility, isoelectric point, tryptic peptide digest, and other physical assays well known in the art.
Antibodies are also useful for tissue typing. Thus, where a specific variant protein has been correlated with expression in a specific tissue, antibodies that are specific for this protein can be used to identify a tissue type.
Antibodies can also be used to assess aberrant subcellular localization of a variant protein in cells in various tissues. The diagnostic uses can be applied, not only in genetic testing, but also in monitoring a treatment modality. Accordingly, where treatment is ultimately aimed at correcting the expression level or the presence of variant protein or aberrant tissue distribution or developmental expression of a variant protein, antibodies directed against the variant protein or relevant fragments can be used to monitor therapeutic efficacy.
The antibodies are also useful for inhibiting variant protein function, for example, by blocking the binding of a variant protein to a binding partner. These uses can also be applied in a therapeutic context in which treatment involves inhibiting a variant protein's function. An antibody can be used, for example, to block or competitively inhibit binding, thus modulating (agonizing or antagonizing) the activity of a variant protein. Antibodies can be prepared against specific variant protein fragments containing sites required for function or against an intact variant protein that is associated with a cell or cell membrane. For in vivo administration, an antibody may be linked with an additional therapeutic payload such as a radionuclide, an enzyme, an immunogenic epitope, or a cytotoxic agent. Suitable cytotoxic agents include, but are not limited to, bacterial toxin such as diphtheria, and plant toxin such as ricin. The in vivo half-life of an antibody or a fragment thereof may be lengthened by pegylation through conjugation to polyethylene glycol. Leong et al., Cytokine 16:106 (2001).
The invention also encompasses kits for using antibodies, such as kits for detecting the presence of a variant protein in a test sample. An exemplary kit can comprise antibodies such as a labeled or labelable antibody and a compound or agent for detecting variant proteins in a biological sample; means for determining the amount, or presence/absence of variant protein in the sample; means for comparing the amount of variant protein in the sample with a standard; and instructions for use.
Vectors and Host Cells
The present invention also provides vectors containing the SNP-containing nucleic acid molecules described herein. The term “vector” refers to a vehicle, preferably a nucleic acid molecule, which can transport a SNP-containing nucleic acid molecule. When the vector is a nucleic acid molecule, the SNP-containing nucleic acid molecule can be covalently linked to the vector nucleic acid. Such vectors include, but are not limited to, a plasmid, single or double stranded phage, a single or double stranded RNA or DNA viral vector, or artificial chromosome, such as a BAC, PAC, YAC, or MAC.
A vector can be maintained in a host cell as an extrachromosomal element where it replicates and produces additional copies of the SNP-containing nucleic acid molecules. Alternatively, the vector may integrate into the host cell genome and produce additional copies of the SNP-containing nucleic acid molecules when the host cell replicates.
The invention provides vectors for the maintenance (cloning vectors) or vectors for expression (expression vectors) of the SNP-containing nucleic acid molecules. The vectors can function in prokaryotic or eukaryotic cells or in both (shuttle vectors).
Expression vectors typically contain cis-acting regulatory regions that are operably linked in the vector to the SNP-containing nucleic acid molecules such that transcription of the SNP-containing nucleic acid molecules is allowed in a host cell. The SNP-containing nucleic acid molecules can also be introduced into the host cell with a separate nucleic acid molecule capable of affecting transcription. Thus, the second nucleic acid molecule may provide a trans-acting factor interacting with the cis-regulatory control region to allow transcription of the SNP-containing nucleic acid molecules from the vector. Alternatively, a trans-acting factor may be supplied by the host cell. Finally, a trans-acting factor can be produced from the vector itself. It is understood, however, that in some embodiments, transcription and/or translation of the nucleic acid molecules can occur in a cell-free system.
The regulatory sequences to which the SNP-containing nucleic acid molecules described herein can be operably linked include promoters for directing mRNA transcription. These include, but are not limited to, the left promoter from bacteriophage λ, the lac, TRP, and TAC promoters from E. coli, the early and late promoters from SV40, the CMV immediate early promoter, the adenovirus early and late promoters, and retrovirus long-terminal repeats.
In addition to control regions that promote transcription, expression vectors may also include regions that modulate transcription, such as repressor binding sites and enhancers. Examples include the SV40 enhancer, the cytomegalovirus immediate early enhancer, polyoma enhancer, adenovirus enhancers, and retrovirus LTR enhancers.
In addition to containing sites for transcription initiation and control, expression vectors can also contain sequences necessary for transcription termination and, in the transcribed region, a ribosome-binding site for translation. Other regulatory control elements for expression include initiation and termination codons as well as polyadenylation signals. A person of ordinary skill in the art would be aware of the numerous regulatory sequences that are useful in expression vectors. See, e.g., Sambrook and Russell, Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Laboratory Press, N.Y. (2000).
A variety of expression vectors can be used to express a SNP-containing nucleic acid molecule. Such vectors include chromosomal, episomal, and virus-derived vectors, for example, vectors derived from bacterial plasmids, from bacteriophage, from yeast episomes, from yeast chromosomal elements, including yeast artificial chromosomes, from viruses such as baculoviruses, papovaviruses such as SV40, Vaccinia viruses, adenoviruses, poxviruses, pseudorabies viruses, and retroviruses. Vectors can also be derived from combinations of these sources such as those derived from plasmid and bacteriophage genetic elements, e.g., cosmids and phagemids. Appropriate cloning and expression vectors for prokaryotic and eukaryotic hosts are described in Sambrook and Russell, Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Laboratory Press, N.Y. (2000).
The regulatory sequence in a vector may provide constitutive expression in one or more host cells (e.g., tissue specific expression) or may provide for inducible expression in one or more cell types such as by temperature, nutrient additive, or exogenous factor, e.g., a hormone or other ligand. A variety of vectors that provide constitutive or inducible expression of a nucleic acid sequence in prokaryotic and eukaryotic host cells are well known to those of ordinary skill in the art.
A SNP-containing nucleic acid molecule can be inserted into the vector by methodology well-known in the art. Generally, the SNP-containing nucleic acid molecule that will ultimately be expressed is joined to an expression vector by cleaving the SNP-containing nucleic acid molecule and the expression vector with one or more restriction enzymes and then ligating the fragments together. Procedures for restriction enzyme digestion and ligation are well known to those of ordinary skill in the art.
The vector containing the appropriate nucleic acid molecule can be introduced into an appropriate host cell for propagation or expression using well-known techniques. Bacterial host cells include, but are not limited to, Escherichia coli, Streptomyces spp., and Salmonella typhimurium. Eukaryotic host cells include, but are not limited to, yeast, insect cells such as Drosophila spp., animal cells such as COS and CHO cells, and plant cells.
As described herein, it may be desirable to express the variant peptide as a fusion protein. Accordingly, the invention provides fusion vectors that allow for the production of the variant peptides. Fusion vectors can, for example, increase the expression of a recombinant protein, increase the solubility of the recombinant protein, and aid in the purification of the protein by acting, for example, as a ligand for affinity purification. A proteolytic cleavage site may be introduced at the junction of the fusion moiety so that the desired variant peptide can ultimately be separated from the fusion moiety. Proteolytic enzymes suitable for such use include, but are not limited to, factor Xa, thrombin, and enterokinase. Typical fusion expression vectors include pGEX (Smith et al., Gene 67:31-40 (1988)), pMAL (New England Biolabs, Beverly, Mass.) and pRIT5 (Pharmacia, Piscataway, N.J.) which fuse glutathione S-transferase (GST), maltose E binding protein, or protein A, respectively, to the target recombinant protein. Examples of suitable inducible non-fusion E. coli expression vectors include pTrc (Amann et al., Gene 69:301-315 (1988)) and pET 11d (Studier et al., Gene Expression Technology: Methods in Enzymology 185:60-89 (1990)).
Recombinant protein expression can be maximized in a bacterial host by providing a genetic background wherein the host cell has an impaired capacity to proteolytically cleave the recombinant protein (S. Gottesman, Gene Expression Technology: Methods in Enzymology 185:119-128, Academic Press, Calif. (1990)). Alternatively, the sequence of the SNP-containing nucleic acid molecule of interest can be altered to provide preferential codon usage for a specific host cell, for example, E. coli. Wada et al., Nucleic Acids Res 20:2111-2118 (1992).
The SNP-containing nucleic acid molecules can also be expressed by expression vectors that are operative in yeast. Examples of vectors for expression in yeast (e.g., S. cerevisiae) include pYepSec1 (Baldari et al., EMBO J 6:229-234 (1987)), pMFa (Kurjan et al., Cell 30:933-943 (1982)), pJRY88 (Schultz et al., Gene 54:113-123 (1987)), and pYES2 (Invitrogen Corporation, San Diego, Calif.).
The SNP-containing nucleic acid molecules can also be expressed in insect cells using, for example, baculovirus expression vectors. Baculovirus vectors available for expression of proteins in cultured insect cells (e.g., Sf 9 cells) include the pAc series (Smith et al., Mol Cell Biol 3:2156-2165 (1983)) and the pVL series (Lucklow et al., Virology 170:31-39 (1989)).
In certain embodiments of the invention, the SNP-containing nucleic acid molecules described herein are expressed in mammalian cells using mammalian expression vectors. Examples of mammalian expression vectors include pCDM8 (B. Seed, Nature 329:840(1987)) and pMT2PC (Kaufman et al., EMBO J 6:187-195 (1987)).
The invention also encompasses vectors in which the SNP-containing nucleic acid molecules described herein are cloned into the vector in reverse orientation, but operably linked to a regulatory sequence that permits transcription of antisense RNA. Thus, an antisense transcript can be produced to the SNP-containing nucleic acid sequences described herein, including both coding and non-coding regions. Expression of this antisense RNA is subject to each of the parameters described above in relation to expression of the sense RNA (regulatory sequences, constitutive or inducible expression, tissue-specific expression).
The invention also relates to recombinant host cells containing the vectors described herein. Host cells therefore include, for example, prokaryotic cells, lower eukaryotic cells such as yeast, other eukaryotic cells such as insect cells, and higher eukaryotic cells such as mammalian cells.
The recombinant host cells can be prepared by introducing the vector constructs described herein into the cells by techniques readily available to persons of ordinary skill in the art. These include, but are not limited to, calcium phosphate transfection, DEAE-dextran-mediated transfection, cationic lipid-mediated transfection, electroporation, transduction, infection, lipofection, and other techniques such as those described in Sambrook and Russell, Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Laboratory, Cold Spring Harbor Laboratory Press, N.Y. (2000).
Host cells can contain more than one vector. Thus, different SNP-containing nucleotide sequences can be introduced in different vectors into the same cell. Similarly, the SNP-containing nucleic acid molecules can be introduced either alone or with other nucleic acid molecules that are not related to the SNP-containing nucleic acid molecules, such as those providing trans-acting factors for expression vectors. When more than one vector is introduced into a cell, the vectors can be introduced independently, co-introduced, or joined to the nucleic acid molecule vector.
In the case of bacteriophage and viral vectors, these can be introduced into cells as packaged or encapsulated virus by standard procedures for infection and transduction. Viral vectors can be replication-competent or replication-defective. In the case in which viral replication is defective, replication can occur in host cells that provide functions that complement the defects.
Vectors generally include selectable markers that enable the selection of the subpopulation of cells that contain the recombinant vector constructs. The marker can be inserted in the same vector that contains the SNP-containing nucleic acid molecules described herein or may be in a separate vector. Markers include, for example, tetracycline or ampicillin-resistance genes for prokaryotic host cells, and dihydrofolate reductase or neomycin resistance genes for eukaryotic host cells. However, any marker that provides selection for a phenotypic trait can be effective.
While the mature variant proteins can be produced in bacteria, yeast, mammalian cells, and other cells under the control of the appropriate regulatory sequences, cell-free transcription and translation systems can also be used to produce these variant proteins using RNA derived from the DNA constructs described herein.
Where secretion of the variant protein is desired, which is difficult to achieve with multi-transmembrane domain containing proteins such as G-protein-coupled receptors (GPCRs), appropriate secretion signals can be incorporated into the vector. The signal sequence can be endogenous to the peptides or heterologous to these peptides.
Where the variant protein is not secreted into the medium, the protein can be isolated from the host cell by standard disruption procedures, including freeze/thaw, sonication, mechanical disruption, use of lysing agents, and the like. The variant protein can then be recovered and purified by well-known purification methods including, for example, ammonium sulfate precipitation, acid extraction, anion or cationic exchange chromatography, phosphocellulose chromatography, hydrophobic-interaction chromatography, affinity chromatography, hydroxylapatite chromatography, lectin chromatography, or high performance liquid chromatography.
It is also understood that, depending upon the host cell in which recombinant production of the variant proteins described herein occurs, they can have various glycosylation patterns, or may be non-glycosylated, as when produced in bacteria. In addition, the variant proteins may include an initial modified methionine in some cases as a result of a host-mediated process.
For further information regarding vectors and host cells, see Current Protocols in Molecular Biology, John Wiley & Sons, N.Y.
Uses of Vectors and Host Cells, and Transgenic Animals
Recombinant host cells that express the variant proteins described herein have a variety of uses. For example, the cells are useful for producing a variant protein that can be further purified into a preparation of desired amounts of the variant protein or fragments thereof. Thus, host cells containing expression vectors are useful for variant protein production.
Host cells are also useful for conducting cell-based assays involving the variant protein or variant protein fragments, such as those described above as well as other formats known in the art. Thus, a recombinant host cell expressing a variant protein is useful for assaying compounds that stimulate or inhibit variant protein function. Such an ability of a compound to modulate variant protein function may not be apparent from assays of the compound on the native/wild-type protein, or from cell-free assays of the compound. Recombinant host cells are also useful for assaying functional alterations in the variant proteins as compared with a known function.
Genetically-engineered host cells can be further used to produce non-human transgenic animals. A transgenic animal is preferably a non-human mammal, for example, a rodent, such as a rat or mouse, in which one or more of the cells of the animal include a transgene. A transgene is exogenous DNA containing a SNP of the present invention which is integrated into the genome of a cell from which a transgenic animal develops and which remains in the genome of the mature animal in one or more of its cell types or tissues. Such animals are useful for studying the function of a variant protein in vivo, and identifying and evaluating modulators of variant protein activity. Other examples of transgenic animals include, but are not limited to, non-human primates, sheep, dogs, cows, goats, chickens, and amphibians. Transgenic non-human mammals such as cows and goats can be used to produce variant proteins which can be secreted in the animal's milk and then recovered.
A transgenic animal can be produced by introducing a SNP-containing nucleic acid molecule into the male pronuclei of a fertilized oocyte, e.g., by microinjection or retroviral infection, and allowing the oocyte to develop in a pseudopregnant female foster animal. Any nucleic acid molecules that contain one or more SNPs of the present invention can potentially be introduced as a transgene into the genome of a non-human animal.
Any of the regulatory or other sequences useful in expression vectors can form part of the transgenic sequence. This includes intronic sequences and polyadenylation signals, if not already included. A tissue-specific regulatory sequence(s) can be operably linked to the transgene to direct expression of the variant protein in particular cells or tissues.
Methods for generating transgenic animals via embryo manipulation and microinjection, particularly animals such as mice, have become conventional in the art and are described, for example, in U.S. Pat. Nos. 4,736,866 and 4,870,009, both by Leder et al.; U.S. Pat. No. 4,873,191 by Wagner et al., and in B. Hogan, Manipulating the Mouse Embryo, Cold Spring Harbor Laboratory Press, N.Y. (1986). Similar methods are used for production of other transgenic animals. A transgenic founder animal can be identified based upon the presence of the transgene in its genome and/or expression of transgenic mRNA in tissues or cells of the animals. A transgenic founder animal can then be used to breed additional animals carrying the transgene. Moreover, transgenic animals carrying a transgene can further be bred to other transgenic animals carrying other transgenes. A transgenic animal also includes a non-human animal in which the entire animal or tissues in the animal have been produced using the homologously recombinant host cells described herein.
In another embodiment, transgenic non-human animals can be produced which contain selected systems that allow for regulated expression of the transgene. One example of such a system is the cre/loxP recombinase system of bacteriophage P1. Lakso et al., PNAS 89:6232-6236 (1992). Another example of a recombinase system is the FLP recombinase system of S. cerevisiae. O'Gorman et al., Science 251:1351-1355 (1991). If a cre/loxP recombinase system is used to regulate expression of the transgene, animals containing transgenes encoding both the Cre recombinase and a selected protein are generally needed. Such animals can be provided through the construction of “double” transgenic animals, e.g., by mating two transgenic animals, one containing a transgene encoding a selected variant protein and the other containing a transgene encoding a recombinase.
Clones of the non-human transgenic animals described herein can also be produced according to the methods described, for example, in I. Wilmut et al., Nature 385:810-813 (1997) and PCT International Publication Nos. WO 97/07668 and WO 97/07669. In brief, a cell (e.g., a somatic cell) from the transgenic animal can be isolated and induced to exit the growth cycle and enter Go phase. The quiescent cell can then be fused, e.g., through the use of electrical pulses, to an enucleated oocyte from an animal of the same species from which the quiescent cell is isolated. The reconstructed oocyte is then cultured such that it develops to morula or blastocyst and then transferred to pseudopregnant female foster animal. The offspring born of this female foster animal will be a clone of the animal from which the cell (e.g., a somatic cell) is isolated.
Transgenic animals containing recombinant cells that express the variant proteins described herein are useful for conducting the assays described herein in an in vivo context. Accordingly, the various physiological factors that are present in vivo and that could influence ligand or substrate binding, variant protein activation, signal transduction, or other processes or interactions, may not be evident from in vitro cell-free or cell-based assays. Thus, non-human transgenic animals of the present invention may be used to assay in vivo variant protein function as well as the activities of a therapeutic agent or compound that modulates variant protein function/activity or expression. Such animals are also suitable for assessing the effects of null mutations (i.e., mutations that substantially or completely eliminate one or more variant protein functions).
For further information regarding transgenic animals, see Houdebine, “Antibody manufacture in transgenic animals and comparisons with other systems,” Curr Opin Biotechnol 13(6):625-9 (December 2002); Petters et al., “Transgenic animals as models for human disease,” Transgenic Res 9(4-5):347-51, discussion 345-6 (2000); Wolf et al., “Use of transgenic animals in understanding molecular mechanisms of toxicity,” J Pharm Pharmacol 50(6):567-74 (June 1998); Echelard, “Recombinant protein production in transgenic animals,” Curr Opin Biotechnol 7(5):536-40 (October 1996); Houdebine, “Transgenic animal bioreactors,” Transgenic Res 9(4-5):305-20 (2000); Pirity et al., “Embryonic stem cells, creating transgenic animals,” Methods Cell Biol 57:279-93 (1998); and Robl et al., “Artificial chromosome vectors and expression of complex proteins in transgenic animals,” Theriogenology 59(1):107-13 (January 2003).
EXAMPLES
The following examples are offered to illustrate, but not limit, the claimed invention.
Example 1
Genetic Polymorphism Designated hCV3054799 in KIF6 Gene is Associated with Risk of CHD and Benefit from Statin Therapy for Reduction of Coronary Events
In order to identify genetic markers associated with CHD (particularly MI, including recurrent MI) or the effect of statin treatment on CHD, samples were genotyped in two clinical trials: the Cholesterol and Recurrent Events (CARE) study (a randomized multicentral double-blinded trial on secondary prevention of MI with pravastatin (Pravachol®); Sacks et al., Am. J. Cardiol. 68: 1436-1446 [1991]), and the West of Scotland Coronary Prevention Study (WOSCOPS) study (a randomized multicentral double-blinded trial on primary prevention of CHD with pravastatin (Pravachol®); Shepherd et al., N Eng J Med 333 (20), pp. 1301-7, Nov. 16, 1995; Packard et al., N Eng J Med 343 (16), pp. 1148-55, Oct. 19, 2000).
A well-documented MI was one of the enrollment criteria for entry into the CARE study. Patients were enrolled in the CARE trial from 80 participating study centers. Men and post-menopausal women were eligible for the trial if they had had an acute MI between 3 and 20 months prior to randomization, were 21 to 75 years of age, and had plasma total cholesterol levels of less than 240 mg/deciliter, LDL cholesterol levels of 115 to 174 mg/deciliter, fasting triglyceride levels of less than 350 mg/deciliter, fasting glucose levels of no more than 220 mg/deciliter, left ventricular ejection fractions of no less than 25%, and no symptomatic congestive heart failure. Patients were randomized to receive either 40 mg of pravastatin once daily or a matching placebo. The primary endpoint of the trial was death from CHD or nonfatal MI and the median duration of follow-up was 5.0 years (range, 4.0 to 6.2 years). For this genetic study of CARE, a composite endpoint comprised of fatal or nonfatal recurrent MI was used.
The designs of the original WOSCOPS cohort and the nested case-control study have been described (Shepherd et al., N Eng J Med 333 (20), pp. 1301-7, Nov. 16, 1995; Packard et al., N Eng J Med 343 (16), pp. 1148-55, Oct. 19, 2000). The objective of the WOSCOPS trial was to assess pravastatin efficacy at reducing risk of primary MI or coronary death among Scottish men with hypercholesterolemia (fasting LDL cholesterol>155 mg/dl). Participants in the WOSCOPS study were 45-64 years of age and followed for an average of 4.9 years for coronary events. The nested case-control study included as cases all WOSCOPS patients who experienced a coronary event (confirmed nonfatal MI, death from CHD, or a revascularization procedure; N=580). Controls were age- and smoking-matched to unaffected patients.
For genotyping SNPs in CARE patient samples, DNA was extracted from blood samples using conventional DNA extraction methods such as the QIAamp kit from Qiagen. Genotypes were obtained by a method similar to that described by Iannone (M. A. Iannone et al., Cytometry 39(2):131-140 [Feb. 1, 2000]; also described in section “SNP DETECTION REAGENTS,” supra). Briefly, target DNA was amplified by multiplex PCR, the amplified product was submitted to a multiplex oligo ligation assay (OLA), the specific ligated products were hybridized to unique universal “ZIP code” oligomer sequences covalently attached to Luminex xMAP® Multi-Analyte COOH Microspheres, and these hybridization products/microspheres were detected in a rapid automated multiplex system (Luminex 100 instrument).
References are made to Tables 5 and 6 for data supporting the conclusion that individuals carrying certain hCV3054799 Arg allele had an increased risk of developing recurrent MI in CARE and CHD events in WOSCOPS, and that they responded to statin treatment by showing a decrease in CHD events. In the placebo arm of CARE, carriers of the KIF6 719Arg risk allele had a hazard ratio for recurrent MI of 1.50 (95% CI 1.05 to 2.15, Table 5) in a model adjusted for age, sex, smoking, history of hypertension, history of diabetes, body mass index, LDL-C, and HDL-C. In the placebo arm of WOSCOPS, it was found that carriers of the KIF6 719Arg risk allele had an odds ratio for CHD of 1.55 (95% CI 1.14 to 2.09, Table 5) in a model adjusted for history of hypertension, history of diabetes, body mass index, LDL-C, and HDL-C (cases and controls were matched for age and smoking status).
In CARE, pravastatin treatment reduced the relative risk of recurrent MI by 37% among carriers of the 719Arg risk allele (adjusted HR 0.63, 95% CI 0.46 to 0.87, Table 6), and among carriers in WOSCOPS pravastatin treatment resulted in an odds ratio for primary CHD of 0.50 (95% CI 0.38 to 0.68, Table 6). The genotype frequencies for KIF6 Trp719Arg were 40.6%, 46.5%, and 12.8%, for TrpTrp, ArgTrp, and ArgArg, respectively, in the CARE cohort. In the WOSCOPS control group, the frequencies were 44.2%, 43.6%, and 12.2%, respectively. Data in Tables 5 and 6 were derived as follows:
In analyzing the CARE patients, statistical analysis was performed with SAS version 9. Cox proportional hazard models (Wald tests) were used in CARE to assess the association of genotype with both the risk of incident MI in the placebo arm and the effect of pravastatin on MI compared to placebo in subgroups defined by genotypes. In analyzing the WOSCOPS patients, a conditional logistic regression model was used in WOSCOPS given that the controls had been previously matched to the cases. The column entitled “On-trial MI” lists the number of patients who suffered a recurrent MI during the clinical trial period.
HR stands for Hazard Ratio, which is a concept similar to Odds Ratio (OR). The HR in event-free survival analysis is the effect of an explanatory variable on the hazard or risk of an event. For examples, it describes the likelihood of developing MI based on comparison of rate of coronary events between carriers of the certain allele and noncarriers; therefore, HR=1.5 would mean that carriers of the certain allele have 50% higher risk of coronary events during the study follow up than non-carriers. HR can also describe the effect of statin therapy on coronary events based on comparison of rate of coronary events between patient treated with statin and patient treated with placebo (or other statin) in subgroups defined by SNP genotype; therefore, HR=0.5 would mean that, for example, carriers of the certain allele, had 50% reduction of coronary events by statin therapy as compared to placebo. In Table 6, p interaction values were calculated. An interaction (or effect modification) is formed when a third variable modifies the relation between an exposure and outcome. A p interaction <0.05 indicates that a third variable (genotype) modifies the relation between an exposure (statin treatment) and outcome (MI). Genotype and drug interaction is present when the effect of statins (incidence rate of disease in statin-treated group, as compared to placebo) differs in patients with different genotypes. The hCV3054799 risk allele predicted risk of MI in CARE and was associated with odds of CHD in WOSCOPS.
In both trials, carriers of the KIF6 719Arg risk allele (about 60% of population) received significant benefit from pravastatin therapy in terms of reduction of coronary events whereas non-carriers did not receive significant benefit. Moreover, the nominal risk reduction was greater in carriers of the KIF6 719Arg allele than in non-carriers: in CARE, a significant risk reduction of 37% was observed in carriers whereas an insignificant risk reduction of 20% was observed in noncarriers, and in WOSCOPS, a significant risk reduction of 50% was observed in carriers whereas an insignificant risk reduction of 9% was observed in noncarriers. A significant interaction between genotype and treatment was observed in WOSCOPS (p=0.01 interaction) and did not reach significance in CARE (p=0.39) (Table 6) (Iakoubova et al., “Association of the Trp719Arg polymorphism in kinesin-like protein 6 with myocardial infarction and coronary heart disease in 2 prospective trials: the CARE and WOSCOPS trials”, J Am Coll Cardiol. 2008 Jan 29;51(4):435-43, which is incorporated herein by reference in its entirety).
Example 2
Carriers of the Risk Allele of hCV3054799 Benefited From Atorvastatin (Lipitor®) Treatment In Addition To Pravastatin (Pravachol®) Treatment
As discussed earlier in Example 1, carriers of the KIF6 719Arg risk allele had a greater reduction of recurrent MI and primary CHD in the CARE and WOSCOPS trials (respectively) by pravastatin (Pravachol®) treatment, as compared to placebo, than did non-carriers (in the placebo arms of these studies, it was observed that carriers of the KIF6 719Arg allele had a 50% greater incidence of CHD compared to non-carriers). Given the previous observation that in CARE and WOSCOPS, carriers of the KIF6 719Arg risk variant had a greater reduction of CHD events from pravastatin therapy, as compared to placebo, than non-carriers, it was determined whether in the PROVE-IT (“Pravastatin or Atorvastatin Evaluation and Infection Therapy “) study, intensive therapy with high-dose atorvastatin (Lipitor®), as compared with standard-dose pravastatin treatment, would result in a greater reduction of recurrent CHD events in carriers than in non-carriers. In addition, with this trial, it was determined whether carriers of the KIF6 719Arg risk allele would benefit from intensive therapy with high-dose atorvastatin as compared with standard-dose pravastatin, just as carriers of the KIF6 719Arg risk allele benefited from standard-dose pravastation as compared with placebo.
To further study the effect of statin treatment on KIF6 719Arg carriers, a genetic association study was conducted in a population derived from the study referred to as “Pravastatin or Atorvastatin Evaluation and Infection Therapy-Thrombolysis in Myocardial Infraction” (PROVE-IT-TIME) that assessed the effect of atorvastatin as compared to pravastatin in the prevention of death or major cardiovascular events in patients with an acute coronary syndrome. The design of PROVE-IT protocol has been described previously (Christopher P. Cannon., et al., Intensive versus moderate lipid lowering with statins after acute coronary syndromes. N Engl J Med, 2004, 350 (15): pp. 1495-04).
Briefly, it was a randomized trial that used a two-by-two factorial design to compare the effect of intensive statin therapy (80 mg of atorvastatin per day) and moderate statin therapy (40 mg of pravastatin per day) on the risk of recurrent coronary events after acute coronary syndromes. Patients were followed for 18 to 36 months, with an average follow-up of 24 months. This genetic study comprised 2,061 patients who provided written informed consent for genetic analysis among 4,162 patients in PROVE-IT cohort.
For this genetic analysis, the evaluation was limited to PROVE-IT patients who (1) underwent successful DNA extraction and genotyping as outlined herein, 32 patients were excluded due to inadequate quantity or quality of DNA; (2) are white (since non-whites comprised only 10.4% of the population that did not provide sufficient power for a separate analysis, and population stratification might have compromised a combined analysis); (3) had information for all covariates adjusted in model 1 and 2, 93 patients were excluded because of the missing information. Of the 1,724 patients who met the criteria listed above, 1344 (78.0%) were male. For this genetic study, a composite end point of death from any cause or major cardiovascular events was used, which included MI, documented unstable angina requiring hospitalization, revascularization (performed at least 30 days after randomization), and stroke (Iakoubova et al., “Polymorphism in KIF6 gene and benefit from statins after acute coronary syndromes: results from the PROVE IT-TIMI 22 study”, J Am Coll Cardiol. 2008 Jan. 29; 51(4):449-55). This study was approved by institutional review boards of the Brigham and Women's Hospital and all participating centers.
KIF6 genotypes were determined using allele—specific real-time PCR as previously described. Cox proportional hazard models (Wald tests) were used to assess the effect of atorvastatin-treatment on incident CHD in the KIF6 719Arg carriers and noncarriers. Continuous variables tested were age, baseline LDL-C, and baseline HDL-C levels. Categorical variables tested were smoking (current versus non-current), hypertension, and baseline diabetes. All reported p values are two-sided.
The high-dose atorvastatin therapy, as compared to standard-dose pravastatin therapy, resulted in substantial and significant benefit in carriers of the KIF6 719Arg allele, but not in non-carriers. In carriers (58.7% of the PROVE-IT population) therapy with high-dose atorvastatin, as compared to standard-dose of pravastatin, reduced the relative risk of coronary events by 44%. The hazard ratio was 0.56 (95% confidence interval 0.42 to 0.75; P<0.0001) after adjustment for age, sex, smoking, history of hypertension, history of diabetes, base-line levels of LDL-C and HDL-C. In contrast, in non-carriers, high-dose atorvastatin therapy, as compared to standard-dose pravastatin, resulted in the adjusted hazard ratio of 0.97 (95% confidence interval 0.72 to 1.31; P=0.84). This difference between carriers and non-carriers in benefit from high-dose of atorvastatin was significant (P=0.01 for interaction between genotype and treatment) (Table 7).
Thus, data in Table 7 shows the effect of Atorvastatin, as compared to Pravastatin, on incidence of coronary events in the PROVE-IT cohort. As seen from Table 7, carriers of the KIF6 719Arg allele benefited from the atorvastatin therapy, as compared to pravastatin, and noncarriers did not receive significant benefit from atorvastatin. This finding, together with the previous observation in the CARE and WOSCOPS studies that carriers of the KIF6 719Arg risk allele received greater benefit from pravastatin than non-carriers, as compared to placebo, indicate that both statins (pravastatin and atorvastatin) substantially and significantly reduce coronary events in carriers of the KIF6 719Arg allele but do not substantially and significantly reduce coronary events in non-carriers. Furthermore, high-dose atorvastatin is more effective than pravastatin in reducing coronary events in carriers of the KIF6 719Arg allele (Iakoubova et al., “Polymorphism in KIF6 gene and benefit from statins after acute coronary syndromes: results from the PROVE IT-TIMI 22 study”, J Am Coll Cardiol. 2008 Jan. 29; 51(4):449-55, which is incorporated herein by reference in its entirety).
Thus, because it has been shown herein in Example 2 that carriers of the KIF6 719Arg allele benefit from multiple different types of statins (e.g., both pravastatin, which is a hydrophilic statin, and atorvastatin, which is a lipophilic statin), this SNP, as well as the other statin-response associated SNPs disclosed herein, is expected to have similar utilities across the entire class of statins, particularly since it has specifically been shown to be useful for both hydrophilic and lipophilic statins. Therefore, the KIF6 Trp719Arg SNP is broadly useful in predicting the therapeutic effect for the entire class of statins, such as for determining whether an individual will benefit from any of the statins, including, but not limited to, fluvastatin (Lescol®), lovastatin (Mevacor®), rosuvastatin (Crestor®), and simvastatin (Zocor®), as well as combination therapies that include a statin such as simvastatin+ezetimibe (Vytorin®), lovastatin +niacin extended-release (Advicor®), and atorvastatin+amlodipine besylate (Caduet®).
Moreover, in carriers of the KIF6 719Arg allele, the substantial and significant benefit from high-dose atorvastatin therapy, compared with standard-dose pravastatin therapy, was evident as early as 30 days after randomization and remained significant throughout the trial: the unadjusted hazard ratio at 30 days was 0.18 (95% CI, 0.04 to 0.81). In contrast to the early benefit seen in carriers of the KIF6 719Arg allele, in noncarriers there was no significant benefit of intensive therapy, compared with moderate therapy, at any time point.
Since LDL-C cholesterol, CRP, and triglyceride levels are risk factors for cardiovascular events and can be reduced by intensive statin therapy, it was analyzed whether the changes in the levels of these risk factors in response to treatment with either high-dose atorvastatin or with standard-dose pravastatin differed between carriers and noncarriers of the KIF6 719Arg allele. No evidence was found in either the high-dose atorvastatin group or in the standard-dose pravastatin group that these risk factors differed between carriers and noncarriers at any time during therapy. Specifically, in both treatment groups, no significant differences in median LDL-C, CRP, or triglyceride levels were found when comparing carriers and noncarriers of the KIF6 719Arg allele at baseline or at any scheduled visit during the study (p≧0.3).
These data suggest that this early superiority of intensive statin therapy for reduction of coronary event in carriers may be due to an early, plaque-stabilizing effect of the intensive treatment regimen, a pleiotropic effect that has been proposed to explain the early benefit from statin therapy that appears not to be due to LDL-lowering. A pleiotropic mechanism would be consistent with the observation described herein that on-treatment median LDL-C levels did not differ between carriers and noncarriers. Additionally, this early benefit from intensive statin therapy in carriers of the KIF6 719Arg allele is likely to be distinct from anti-inflammatory mechanisms related to the reduction of CRP levels since carriers and noncarriers of the KIF6 719Arg allele did not differ in median CRP levels at baseline or during the trial (Iakoubova et al., “Polymorphism in KIF6 gene and benefit from statins after acute coronary syndromes: results from the PROVE IT-TIMI 22 study”, J Am Coll Cardiol. 2008 Jan. 29; 51(4):449-55, which is incorporated herein by reference in its entirety).
Example 3
Elderly Carriers of the KIF6 719Arg Variant are at Greater Risk for CHD and also Benefited from Statin Therapy Whereas Noncarriers Did Not Benefit: Results from the PROSPER Study
The Arg variant of the Trp719Arg polymorphism (rs20455/hCV3054799) in KIF6 is shown in Examples 1 and 2 above to be associated with greater risk for CHD, particularly MI, and greater benefit from statin therapy. The analysis described here in Example 3 relates to confirming whether elderly carriers of this variant are at greater risk for coronary events, and whether elderly carriers receive significant benefit from statin therapy.
In this analysis, the KIF6 Trp719Arg polymorphism was analyzed in samples from the “Prospective Study of Pravastatin in the Elderly at Risk” (PROSPER) trial (PROSPER is described further in Shepherd et al., Lancet. 2002 Nov. 23; 360(9346):1623-30). PROSPER was both a secondary and primary trial of elderly men and women (ages 70-82). A substantial fraction of the patients in the PROSPER trial had an MI or other vascular disease prior to enrollment into the trial, and the rest of the patients did not have a vascular event prior to enrollment. Since an interaction has been observed between KIF6 carrier status and prior vascular events, groups with and without prior vascular events were genotyped separately.
5752 patients were genotyped within the PROSPER trial, and Cox proportional hazards models were used to investigate (1) the risk for coronary events in 719Arg carriers compared with noncarriers in a placebo arm, and (2) the effect of statin therapy according to 719Arg carrier status in a pravastatin arm.
Table 8 shows the number of patients of the three genotypes and carriers (minor homozygote+heterozygote) in the placebo arm of the PROSPER trial. Among those in the placebo group with prior vascular disease, 719Arg carriers (59.0%) were at nominally greater risk for coronary events compared with noncarriers: hazard ratio =1.25 (95% CI 0.95 to 1.64; Table 9). For heterozygotes compared with noncarriers, the hazard ratio =1.33 (95% CI 1.01 to 1.77; Table 9).
Table 10 shows the number of patients of the three genotypes and carriers (minor homozygote+heterozygote) in the placebo arm of the PROSPER trial. Table 11 shows the number of patients of the three genotypes and carriers (minor homozygote +heterozygote) in the pravastatin arm of the PROSPER trial. To assess the effect of pravastatin, risk estimates were used to compare the pravastatin arm with the placebo arm in subgroups defined by genotypes.
Among 719Arg carriers with prior vascular disease, a substantial and significant benefit from pravastatin therapy was observed (hazard ratio 0.67, 95% CI 0.52 to 0.87; Table 12), whereas no significant benefit was observed in noncarriers (hazard ratio 0.92, 95% CI 0.68 to 1.25; Table 12). The interaction between 719Arg carrier status and pravastatin treatment is shown in Table 13 (p=0.12).
Thus, elderly carriers of the KIF6 719Arg allele are at greater risk for CHD (e.g., MI) compared with noncarriers, and elderly carriers of the KIF6 719Arg allele receive significant benefit from statin therapy whereas noncarriers do not.
Example 4
Identification of SNPs in Linkage Disequilibrium with the KIF6 SNP—Analysis of 27 Tagging SNPs in CARE and WOSCOPS
To identify SNPs in linkage disequilibrium with the KIF6 Trp719Arg SNP (rs20455/hCV3054799) that may also be associated with CHD, 27 SNPs (which may be referred to herein as “tagging SNPs”) in the genomic region around the KIF6 Trp719Arg SNP were analyzed in samples from both the CARE and WOSCOPS trials (the CARE and WOSCOPS trials are described further in Example 1) (Iakoubova et al., J Am Coll Cardiol. 2008 Jan. 29; 51(4):435-43, particularly the Online Appendix, which is incorporated herein by reference in its entirety).
To test for association between CHD and other SNPs that may be in linkage disequilibrium with the KIF6 Trp719Arg SNP, 27 tagging SNPs were selected, including Trp719Arg, in genomic regions flanking the Trp719Arg SNP using pairwise tagging in Tagger (de Bakker et al., Nat Genet 2005;37:1217-23) as implemented in Haploview (Barrett et al., Bioinformatics 2005;21:263-5). These flanking genomic regions span 95.5 kb in the KIF6 gene (from nucleotide positions 39,347,330 to 39,442,863 on chromosome 6 (nucleotide positions based on HapMap release #19, October 2005; The International HapMap Project. Nature 2003;426:789-96) and contain 148 SNPs that have allele frequencies greater than 2% in HapMap public release #19. The 27 tagging SNPs (including the Trp719Arg SNP) tagged 117 of these 148 SNPs with a mean r2 of 0.93 (91% of SNPs were tagged with r2>0.8; the minimum tagging r2 was 0.7). The other 31 of the 148 SNPs are less likely to be responsible for the observed association of the KIF6 Trp719Arg SNP and disease because they were not in strong linkage disequilibrium with the Trp719Arg SNP (r2<0.25). These 27 tagging SNPs were genotyped in CARE and WOSCOPS samples and linkage disequilibrium was assessed between KIF6 Trp719Arg and the other tagging SNPs using r2 values from the placebo arms of both studies.
Based on this analysis, it was found that five of these 27 tagging SNPs (KIF6 Trp719Arg, as well as rs9471077, rs9394584, rs11755763, and rs9471080) were associated with recurrent MI in the placebo arm of CARE and with CHD in the placebo arm of WOSCOPS (p<0.15; meta-analysis p<0.05), with KIF6 Trp719Arg and rs9471077 being particularly strongly associated (Table 14). The risk estimate and the p-value were calculated based on the genetic model presented in Table 14 for each SNP. The genotype counts in both CARE and WOSCOPS are also presented in Table 14. The results of meta-analysis provided in Table 14 show the risk estimate and p-value from either the genotypic or genetic models (additive, dominant, recessive) that gave the lowest p-value in an analysis of CARE and WOSCOPS combined.
The SNPs in Table 14 (other than the KIF6 Trp719Arg SNP, which is shown elsewhere herein to be associated with drug response, particularly benefit from statin treatment) were further analyzed to determine whether individuals with increased risk of CHD will benefit from pravastatin treatment. Besides the KIF6 Trp719Arg SNP, four other SNPs in Table 14 (rs9471080/hCV29992177, rs9394584/hCV30225864, rs9471077/hCV3054813, and rs9462535/hCV3054808, which is described below in the “Example 4—Supplemental Analysis” section below) were found to be associated with benefit from pravastatin treatment in CARE and WOSCOPS samples (this is shown in Table 22), in addition to their association with CHD risk (as shown in Table 14). Thus, these four SNPs are associated with both CHD risk (particulary MI) and statin benefit.
rs9471077 is in strong linkage disequilibrium with the Trp719Arg SNP (r2=0.79 in placebo —treated patients), and the risk ratios for Trp719Arg and rs9471077 were similar in the placebo arms of CARE and WOSCOPS. After adjusting for conventional risk factors, the hazard ratios were 1.57 and 1.54 (p=0.01 and 0.02) in CARE for Trp719Arg and rs9471077, respectively, and the adjusted odds ratios were 1.59 and 1.46 (p=0.003 and 0.01) in WOSCOPS for Trp719Arg and rs9471077, respectively. No haplotype in the KIF6 region was more significantly associated with both recurrent MI in CARE and with CHD in WOSCOPS than the KIF6 Trp719Arg SNP alone. The association of the rs9471077 SNP (an intronic SNP) with CHD is most likely explained by its linkage disequilibrium (r2=0.79) with the missense Trp719Arg SNP, which may play a functional role in the pathogenesis of CHD and MI.
Example 4
Supplemental Analysis: Fine-Mapping Analysis of SNPs in the Genomic Region around the KIF6 SNP
In the analysis described here, other SNPs around the KIF6 SNP were analyzed in CARE (the CARE trial is described further in Example 1) in order to identify SNPs associated with CHD (particularly RMI) and/or drug response, in addition to the SNPs described in Example 4 above. These markers were located by fine-mapping of the genomic region around the KIF6 SNP rs20455. The genomic region around the KIF6 SNP rs20455 that was analyzed was from nucleotide positions 39,335,279 to 39,679,743 of chromosome 6 (nucleotide positions based on Hapmap position build 36).
CARE was a secondary prevention trial. All the patients in the CARE trial had MI within 10 months prior to enrollment into the trial. During the 5 years of follow-up, 264 patients had experienced another MI and 2649 patients had not developed another MI. In a placebo-treated group, 150 patients among these 264 patients had experienced another MI and 1290 patients among these 2649 patients had not developed another MI. In a pravastatin-treated group, 114 patients had experienced another MI and 1359 patients in this group had not developed another MI.
It was analyzed whether or not individuals with different genotypes in certain genetic polymorphisms were associated with recurrent MI (RMI) in the placebo arm of CARE. For this analysis, the following were compared using Fisher's exact test (Table 15): the proportion of RMI events among minor homozygote individuals compared with the proportion of RMI events among major homozygote individuals; the proportion of RMI events among heterozygote individuals compared with the proportion of RMI events among major homozygote individuals; and the proportion of RMI events among minor homozygote or heterozygote individuals compared with the proportion of RMI events among major homozygote individuals. The association results for recurrent RMI and the genotype counts for different genotypes in the placebo arm of CARE of six markers are shown in Table 15.
To determine association with drug response, Fisher's exact test was used to assess the effect of pravastatin compared with placebo in CARE subgroups defined by genotypes, as follows: the proportion of RMI events among minor homozygote individuals treated with pravastatin compared with the proportion of RMI events among minor homozygote individuals treated with placebo; the proportion of RMI events among heterozygote individuals treated with pravastatin compared with the proportion of RMI events among heterozygote individuals treated with placebo; the proportion of RMI events among major homozygote individuals treated with pravastatin compared with the proportion of RMI events among major homozygote individuals treated with placebo; and the proportion of RMI events among minor homozygote or heterozygote individuals treated with pravastatin compared with the proportion of RMI events among minor homozygote or heterozygote individuals treated with placebo. Estimates of odds ratios and corresponding 95% confidence intervals were calculated (Table 16). Deviation from Hardy—Weinberg expectations were assessed using an exact test in the CARE cohort. Analysis in the CARE population was performed using RMI as an endpoint. Genetic polymorphisms showing a significant (p-value of <0.05) association with an effect of pravastatin treatment on risk of RMI in any of the models or modes described above are listed in Table 16.
In a further analysis, CARE and WOSCOPS samples were combined and logistic regression models were used to calculate the risk, 95% confidence intervals (95%CI), and 2-sided p-values (see “Meta-Analysis” in Table 14) for the association of each SNP with CHD when adjusted for each study. For example, the rs9462535 SNP (hCV3054808), which was identified by fine mapping of the genomic region surrounding the Trp719Arg SNP and analyzed in samples from the CARE trial as just described (and presented in Tables 15-16), was further analyzed in samples from the WOSCOPS trial, and in a meta-analysis combining CARE and WOSCOPS. Based on this supplemental analysis in WOSCOPS and the meta-analysis combining CARE and WOSCOPS, the rs9462535 SNP (hCV3054808) was further confirmed to be associated with CHD (Table 14).
Example 5
Genetic Markers for Predisposition to Aneurysm or Dissection
The analysis described here in Example 5 relates to determining whether or not the KIF6 SNP (hCV3054799/rs20455) is associated with aortic aneurysm and dissection.
To determine whether the KIF6 SNP (hCV3054799) is associated with aortic aneurysm or dissection, this SNP was analyzed using the following two endpoints: (1) using aortic aneurysm or aortic dissection as an endpoint, which compared patients with aneurysm or dissection against patients without aneurysm or dissection (shown in Table 17), and (2) using aortic dissection as an endpoint, which compared patients with dissection against patients without dissection (shown in Table 18).
The study populations were as follows. 555 Caucasian patients with thoracic aortic aneurysm (TAA) or thoracic aortic dissection were genotyped for the aneurysm or dissection endpoint analysis. 128 Caucasian patients with thoracic aortic dissection were genotyped for the dissection endpoint analysis. 180 patients without aneurysm or dissection were used as controls for both analyses. All samples were from the U.S. and Hungary (the U.S. samples may be referred to herein as the “Yale University” samples).
Based on this analysis, the KIF6 SNP (hCV3054799) was found to be associated (p-value≦0.05) with aneurysm or dissection and with dissection only, and the risk allele in both instances was the same allele that was previously associated with MI for this SNP (genotype counts for both endpoints are shown in Tables 19 and 20). Also, the genotypes of this SNP in controls did not deviate from the distributions expected under Hardy-Weinberg equilibrium (p-value>0.01).
Example 5
Supplemental Analysis
The analysis described here in this “Example 5—Supplemental Analysis” section relates to determining whether or not the KIF6 SNP (hCV3054799/rs20455) is associated with aortic aneurysm and dissection independently of CHD.
For this analysis, using the Yale University samples from Example 5 above (which included individuals with thoracic aortic aneurysm or dissection, along with controls, as described above in Example 5), individuals who had a CHD event were removed from both case and control sample sets. For this analysis, CHD was defined as MI, percutaneous transluminal coronary angioplasty (PTCA), or coronary artery bypass graft (CABG).
The KIF6 SNP was then analyzed in this sample set (which excluded individuals with CHD) for association in the following two endpoints: 1) dissection only, and 2) aneurysm or dissection. The results of this analysis are presented in Table 21.
As shown in Table 21, the KIF6 SNP (hCV3054799/rs20455) is associated with dissection, as well as aneurysm or dissection, among patients without CHD. Therefore, the association of the KIF6 SNP (hCV3054799/rs20455) with aneurysm/dissection is independent of CHD.
Thus, the KIF6 SNP (hCV3054799/rs20455) is associated with risk for aortic aneurysm and aortic dissection (as shown here in Example 5 and the “Example 5—Supplemental Analysis” section), as well as risk for other coronary events such as CHD (including MI). Therefore, this SNP has been shown to be associated with multiple different coronary events, and is therefore expected to have similar utilities in other coronary events. Consequently, the KIF6 SNP (hCV3054799/rs20455), as well as the other SNPs disclosed herein, is broadly useful with respect to the full spectrum of coronary events. Furthermore, the KIF6 SNP (hCV3054799/rs20455) has also been associated with other cardiovascular events such as stroke (see, e.g., U.S. provisional patent application 61/066,584, Luke et al., filed Feb. 20, 2008). Therefore, this SNP has been shown to be associated with multiple different cardiovascular events, and is therefore expected to have similar utilities in other cardiovascular events. Consequently, the KIF6 SNP (hCV3054799/rs20455), as well as the other SNPs disclosed herein, is broadly useful with respect to the full spectrum of cardiovascular events. Moreover, this SNP has been specifically shown to be associated with benefit from statin treatment for reduction of MI, unstable angina, revascularization, and stroke events (see, e.g., Example 2). The KIF6 SNP (hCV3054799/rs20455), as well as the other SNPs disclosed herein, is also particularly useful for determining an individual's risk for vulnerable plaque.
Example 6
Calculated LD SNPs associated with CHD and Drug Response
Another investigation was conducted to identify additional SNPs that are calculated to be in linkage disequilibrium (LD) with certain “interrogated SNPs” that have been found to be associated with CHD (particularly MI), aneurysm/dissection, and/or drug response (particularly statin response), as described herein and shown in the tables. The interrogated SNPs are shown in column 1 (which indicates the hCV identification numbers of each interrogated SNP) and column 2 (which indicates the public rs identification numbers of each interrogated SNP) of Table 4. The methodology is described earlier in the instant application. To summarize briefly, the power threshold (T) was set at an appropriate level, such as 51%, for detecting disease association using LD markers. This power threshold is based on equation (31) above, which incorporates allele frequency data from previous disease association studies, the predicted error rate for not detecting truly disease-associated markers, and a significance level of 0.05. Using this power calculation and the sample size, a threshold level of LD, or r 2 value, was derived for each interrogated SNP (rT2, equations (32) and (33) above). The threshold value rT2 is the minimum value of linkage disequilibrium between the interrogated SNP and its LD SNPs possible such that the non-interrogated SNP still retains a power greater or equal to T for detecting disease association.
Based on the above methodology, LD SNPs were found for the interrogated SNPs. Several exemplary LD SNPs for the interrogated SNPs are listed in Table 4; each LD SNP is associated with its respective interrogated SNP. Also shown are the public SNP IDs (rs numbers) for the interrogated and LD SNPs, when available, and the threshold r2 value and the power used to determine this, and the r2 value of linkage disequilibrium between the interrogated SNP and its corresponding LD SNP. As an example in Table 4, the interrogated SNP rs20455 (hCV3054799) (the KIF6 SNP which is shown herein to be associated with CHD, particularly MI, as well as aneurysm/dissection and drug response, particularly statin response) was calculated to be in LD with rs6924090 (hCV29161261) at an r2 value of 1, based on a 51% power calculation, thus establishing the latter SNP as a marker associated with CHD, aneurysm/dissection, or drug response as well.
All publications and patents cited in this specification are herein incorporated by reference in their entirety. Modifications and variations of the described compositions, methods and systems of the invention will be apparent to those skilled in the art without departing from the scope and spirit of the invention. Although the invention has been described in connection with specific preferred embodiments and certain working examples, it should be understood that the invention as claimed should not be unduly limited to such specific embodiments. Indeed, various modifications of the above-described modes for carrying out the invention that are obvious to those skilled in the field of molecular biology, genetics and related fields are intended to be within the scope of the following claims.









TABLE 1







Gene Number:
1



Celera Gene:
hCG1647070-84000313730920


Celera Transcript:
hCT1647197-84000313730921


Public Transcript Accession:
NM_145027


Celera Protein:
hCP1617236-197000069366336


Public Protein Accession:
NP_659464


Gene Symbol:
KIF6


Protein Name:
kinesin family member 6


Celera Genomic Axis:
GA_x5YUV32W6W6 (12311520 . . . 12426711)


Chromosome:
6


OMIM NUMBER:


OMIM Information:


 








Transcript Sequence (SEQ ID NO: 1):



 


Protein Sequence (SEQ ID NO: 2):


 


SNP Information


Context (SEQ ID NO: 3):


GTGGGCAGAGGAGGCCACCAACCTGCAGGTAAATTCTCCAGCAGTGAATTCACTCGATCACACGAAGCCATTTCTCCAGA


CATCTGACTCCCAGCATGAA


Y


GGTCCCAACTCCTCTCTAACAAAAGTTCTGGAGGCTGGGAAGTCCAAGATCAAGGCACTGGCAGATTCGATGTCTGTGAT


GTGAATGCCAGGAAAATCCT


 









Celera SNP ID:
hCV3054799



Public SNP ID:
rs20455


SNP in Transcript Sequence
SEQ ID NO: 1


SNP Position Transcript:
694


SNP Source:
dbSNP; Celera; HapMap; ABI_Val; HGBASE


Population (Allele, Count) :
caucasian (T, 77|C, 43)


SNP Type:
Missense Mutation


Protein Coding:
SEQ ID NO: 2, at position 170, (W, TGG) (R, CGG)


















TABLE 2







Gene Number:
1



Celera Gene:
hCG1647070-84000313730920


Gene Symbol:
KIF6


Protein Name:
kinesin family member 6


Celera Genomic Axis:
GA_x5YUV32W6W6 (12311520 . . . 12426711)


Chromosome:
6


OMIM NUMBER:


OMIM Information:


 


Genomic Sequence (SEQ ID NO: 4):


 


SNP Information


 








Context (SEQ ID NO: 10):



ACAGGAATAGGTTAAACAGAAAGGTAGGGAGCCTTTTCTGGGAACTCTAACACCTCCGGTGAGTTCTCACCTTACCTTTT


GTTAGAGAGGAGTTGGGACC


R


TTCATGCTGGGAGTCAGATGTCTGGAGAAATGGCTTCGTGTGATCGAGTGAATTCACTGCTGGAGAATTTACCTGTTGGC


CCCAGAAGGAGTTTCACAGT









Celera SNP ID:
hCV3054799



Public SNP ID:
rs20455


SNP in Genomic Sequence:
SEQ ID NO: 4


SNP Position Genomic:
31149


SNP Source:
dbSNP; Celera; HapMap; ABI_Val; HGBASE


Population (Allele, Count):
caucasian (A, 77|G, 43)


SNP Type:
MISSENSE MUTATION


 








Context (SEQ ID NO: 11):



GCTCCACTGATCCCAGGTGGATTGCTGGAAAGTCTTTTCAAAACATTGCATCAATTTCTCCCATTTGCAGAGGCCAGTGA


ACTCTGGGAAGGTGCACTGC


Y


GTCAAGCATGTGTCCATATCAAAGGCTGGGCTTCCATTAGGGGATGTTCGGAGCAAAAGGCCCCTTGGCCAGTTGCTGCA


CACACTTTGCATATGCTCTG









Celera SNP ID:
hCV792699



Public SNP ID:
rs728218


SNP in Genomic Sequence:
SEQ ID NO: 4


SNP Position Genomic:
7857


SNP Source:
dbSNP; Celera


Population (Allele, Count):
caucasian (C, 55|T, 65)


SNP Type:
INTRON


 








Context (SEQ ID NO: 12):



TTTCAACATCCAACATTCACAGCCAACCTCTCTACACCTGCTCTGCCTCGGAGCCCTGGCAGTCCCTGGACACGTGTTAT


GGAAGGAACACTGTCTCCCC


S


ACATGCTCACATGGCCCAAATCCCCACAAGTCCCCTTTTTGCCAAGCTCCACGTCCTTTGCCTCTTCCCATCACCCAATA


CCCAACCTGCCATCATTGTC









Celera SNP ID
hCV1650850



Public SNP ID:
rs35268572


SNP in Genomic Sequence:
SEQ ID NO: 4


SNP Position Genomic:
4809


SNP Source:
Celera


Population (Allele, Count):
no_pop (C,-|G,-)


SNP Type:
INTRON


 








Context (SEQ ID NO: 13):



TCTAATTTAATTTTAATAACATCTTTGTTTAAAAACGGCTCCCCACATTCCTGGAGTACTAACAGTTTATGCTTTTATGG


ACTGAGCCAGCTCAGCACAC


M


ACTGGGAATGGCTTATCATCATCCATGAATGATTTCTTCCAAATGGTGCCTCTCACATTACCACTAAATCTTTGTTCCTT


TGGGATATGGGATTGCCTTT









Celera SNP ID
hCV3054766



Public SNP ID:
rs9394587


SNP in Genomic Sequence:
SEQ ID NO: 4


SNP Position Genomic:
54872


SNP Source:
dbSNP; Celera; HapMap


Population (Allele, Count):
caucasian (C, 57|A, 63)


SNP Type:
INTRON; REPEATS


 








Context (SEQ ID NO: 14):



CAACTAATGATTCATTTGCTGAATACCCACTCTGGGCCTCCTTGGGGCTCAGCTGTCTCACACAACCATAGATCACTTCA


GCCTCACCTTGTTTGAGAAG


W


CAAAGTGTCTATTCTTACGGAGATTGATGGTGTTCATAGCTCAATTACCACAATGGTCAGCAGCTCAGAGGCAAAGATTC


TGGCTTCCCAATCAGACCAT









Celera SNP ID
hCV3054789



Public SNP ID:
rs4711595


SNP in Genomic Sequence:
SEQ ID NO: 4


SNP Position Genomic:
42930


SNP Source:
dbSNP; Celera; HGBASE


Population (Allele, Count):
caucasian (A, 33|T, 87)


SNP Type:
INTRON








Context (SEQ ID NO: 15):



GTCTTTGAAGCCAAAGGAAAAATGAGAGACAGGGCTTTCTACTCGCTGAGCAGGATATTGCTACCAGTAGAAGCTACCCA


AGAGATCAGAAAAGAGGGTA


S


AGAAATAGCCAGGGGTTCTGAGCCTCACAGCTGAAAGGAGAGCTTATTCTCATGGGGCAGATACACAAGGGCTCAGCACA


GAGCCAGAGCAATGAT TGAG









Celera SNP ID
hCV3054805



Public SNP ID:
rs2894424


SNP in Genomic Sequence:
SEQ ID NO: 4


SNP Position Genomic:
25606


SNP Source:
dbSNP; Celera; HapMap; HGBASE


Population (Allele, Count):
caucasian (C, 67|G, 51)


SNP Type:
INTRON


 








Context (SEQ ID NO: 16):



AGCAGTGGAGAGACTTTCCACGAGGTGCCCTTCATGGTGGGAAAGCAGAGATCCCTGTGCTGGGTCTAGTGAGGACCGAT


GTAGGACCAGAGGTAGCCAC


M


AGGTGGCAGGCTCTGCACGCTTTTCTTCAGAGAACAGTAACCAATTCTCAAGGCTCCTGCTGAGTGGTTCCTGGTGCAGC


CTGGAGAAGGGAAGGAGAAA









Celera SNP ID
hCV3054808



Public SNP ID:
rs9462535


SNP in Genomic Sequence:
SEQ ID NO: 4


SNP Position Genomic:
21875


SNP Source:
dbSNP; Celera; HapMap


Population (Allele, Count):
caucasian (A, 50|C, 74)


SNP Type:
INTRON


 








Context (SEQ ID NO: 17):



GGTAGATGTCATCGTCTCCATTTTATACATGAGAAAACTGAGGCTTAGACAGATTAAAGAAATTCTAAAGTTATCCAGCT


GGTAAGTGGCAGAATCGGGA


R


CTACTCAGGATTCTTTGAGGCTTCAGAACCTGTGCTCTCAGCCACAATGCCTTCTAACTGGGCTTGACTCTGTGGCATTC


TAGGAACACACGCTGGCCTA









Celera SNP ID
hCV3054809



Public SNP ID:
rsll755763


SNP in Genomic Sequence:
SEQ ID NO: 4


SNP Position Genomic:
21059


SNP Source:
dbSNP; Celera; HapMap


Population (Allele, Count):
caucasian (A, 51|G, 69)


SNP Type:
INTRON; REPEATS


 








Context (SEQ ID NO: 18):



GGAGGAATGATGGTTGGTTTCTATGGATTTGTTACAGGGGAAGGGATATCTCAGCTCAAAGGATATCCTGCGTATCCAAG


CCTTCCTCCACTTGTGCCCC


R


CACACTGTCCCCTCTACTTTCTCAAGGAAGTTTTTCCTGCAGCTCTGCTCCCCTCTCCCACCACAGCATCAGTTTCCCTC


TCTATGCATGTTTCAGCACG









Celera SNP ID
hCV3054813



Public SNP ID:
rs9471077


SNP in Genomic Sequence:
SEQ ID NO: 4


SNP Position Genomic:
14825


SNP Source:
dbSNP; Celera; HapMap


Population (Allele, Count) :
caucasian (A, 73|G, 47)


SNP Type:
INTRON; REPEATS


 








Context (SEQ ID NO: 19):



TCAGAAACTGCATCTTTCTTTTTTTTAAGGAGCAGGATTCCTCTTGCACATGAAATCTTTCCCAGAGCCCCAATCTGAAG


CCTACTTAAGAGTGGAGCTC


W


GGTTGAAGTAGGGGCAGAGGGCTCCCATTTCCACCAGCCCAGAGAGTGTGATTTCAAGAGCCCTTACCCGTAATTCTGAA


ATGGATGCCCTTGTCTAAGT









Celera SNP ID
hCV3054822



Public SNP ID:
rs11751357


SNP in Genomic Sequence:
SEQ ID NO: 4


SNP Position Genomic:
2345


SNP Source:
dbSNP; Celera; HapMap


Population (Allele, Count):
caucasian (T, 90|A, 28)


SNP Type:
INTRON


 








Context (SEQ ID NO: 20):



GTAGAGGCTGCGGTCACTCCCCTCGTCAATGCTGGTTCCTGTTCCTGAGGCCGAGAGAACTCCTGACAGCAGAGTGGGCA


TATCTTGGTAGTTGCAGCTT


Y


TCAAGACAGTGTGGCCCAGTGGGGAGAGAGCAGAAAACCTGGGTTATGCTGGCTCTGCCATTTATCAGCTGTGTAACCTT


GGGCAAGTGATACAACCTCT









Celera SNP ID
hCV15876373



Public SNP ID:
rs2281686


SNP in Genomic Sequence:
SEQ ID NO: 4


SNP Position Genomic:
14290


SNP Source:
dbSNP; HapMap; HGBASE


Population (Allele, Count):
caucasian (T, 105|C, 15)


SNP Type:
INTRON


 








Context (SEQ ID NO: 21):



TCCAAAAATAGCCTTATGGCTGATACCTAATTGCATTTCTAACAGAGCTATTCTTCATGTGCAGATAACAGTCTCTGTAA


CCTGCTGGATCTTGCAGCTT


Y


ATGGGTTTTGGCAAAAAAAGGAGTTGAGGGGGATTGCAGAATTCACTCAGCATGCAGCTTGCTGTCATTACCACGGTGAT


AAATTTGCTGGTTTTGGCCT









Celera SNP ID
hCV31340487



Public SNP ID:
rs12175497


SNP in Genomic Sequence:
SEQ ID NO: 4


SNP Position Genomic:
62509


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (T, 106|C, 12)


SNP Type:
HUMAN-MOUSE SYNTENIC REGION; INTRON


 








Context (SEQ ID NO: 22):



TTCCCCCAGTGCTGTGTTCAGACTCCATGAGTCACTCCATGAGGGTGACTCAGTGCCTCGCATGTTTGCTGGCCTTGCCT


TCTTTGAAAGTCATCAGACA


Y


GATAACTGAAGGAAGTATCTTAATATGAAACCGACAATGGTAGGACTTTCAATGGACAAACAATTCCTTTTTAAAAAGT


TTCAGAAATGGGCCCAGGTA









Celera SNP ID
hCV30388472



Public SNP ID:
rs9357303


SNP in Genomic Sequence:
SEQ ID NO: 4


SNP Position Genomic:
39408


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (T, 62|C, 52)


SNP Type:
INTRON


 








Context (SEQ ID NO: 23):



TATCCAAATATTCTTCTCCATTGTCCTGCCGCGTGGCCACTTGCTATCACACTGGTTATTGCTTGGCTATGTCCTTGGCA


AATCATCTTCTCTGGCTCTC


R


GTTTTCTTGTCCTTAAAATGAAGGGATCAGCCTGTCAGATAATCAGTAAGCATTCTGCCAGCCAGAAAAGGGATCTGATT


GTGCGTATTTTAGCACTTCC









Celera SNP ID
hCV30225864



Public SNP ID:
rs9394584


SNP in Genomic Sequence:
SEQ ID NO: 4


SNP Position Genomic:
33000


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (A, 74|G, 36)


SNP Type:
INTRON


 








Context (SEQ ID NO: 24):



AGCAGGCTGGCGGCCAAGCAAGGCGAGTACAGGACCAAGGCCGGCTCTCAGTTGCGGCGCTCCATCCATGCACAAACCTC


TTCCTGCCCAAACTGCACAC


R


GCTGGTGGAGAAGCTGAGTGCAGGCGCCACAGGGCAGGCATCAGTCATTATACATCGAATCTGCCAGCCCATACCATCAC


GGTGGGGGCGCCTTTCTGGC









Celera SNP ID
hCV30478067



Public SNP ID:
rs9471078


SNP in Genomic Sequence:
SEQ ID NO: 4


SNP Position Genomic:
16718


SNP Source:
dbSNP; HapMap


Population (Allele, Count) :
caucasian (G, 108|A, 12)


SNP Type:
INTRON


 








Context (SEQ ID NO: 25):



ATTATTAACATTTTGTATTAGTGTGATAAATTTGTTATAACTGGTGAATGAATATAGATACATTATTATGAACTAAAGTC


CATGGTTTATGCAGGGGTTC


R


CTCTTTGTGCTGTACAGCTCTATGGATTTTGACAAATGCATGACATCATCTATGCAACATTACAATATCATACAGAATAG


TTTCACTGCCCCCAAAATTC









Celera SNP ID
hCV29992177



Public SNP ID:
rs9471080


SNP in Genomic Sequence:
SEQ ID NO: 4


SNP Position Genomic:
26159


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (A, 94|G, 24)


SNP Type:
INTRON; REPEATS


 








Context (SEQ ID NO: 26):



ATGGCAGTAGGTCTACTGGCTGTCAGAGTGAGGGACTGAAGGGGTGTGCACTCCAACCAACTTGAAAGCCACTGTCTTGA


GTATCTACGACTAATTAAAC


R


GTAAAAGGAATTAATCCTGCTGGATCATATGGCTTATCAAGAGACATGAGAGCCACAGGAATAGGTTAAACAGAAAGGTA


GGGAGCCTTTTCTGGGAACT









Celera SNP ID
hCV2946524



Public SNP ID:
rs20456


SNP in Genomic Sequence:
SEQ ID NO: 4


SNP Position Genomic:
30994


Related Interrogated SNP:
hCV30225864 (Power = .51)


SNP Source:
Applera


Population (Allele, Count):
caucasian (A, 23|G, 15) african american



(A, 11|G, 27) total (A, 34|G, 42)


 


SNP Type:
INTRON


SNP Source:
dbSNP; Celera; HapMap; HGBASE


Population (Allele, Count):
caucasian (A, 67|G, 53)


SNP Type:
INTRON


 








Context (SEQ ID NO: 27):



CTGGGCTTCCATTAGGGGATGTTCGGAGCAAAAGGCCCCTTGGCCAGTTGCTGCACACACTTTGCATATGCTCTGGACAC


CAGTGGCCCCAGATCCCATG


Y


TGTGTGTTTTTCTGTCTTCATTTCCTTTCCTGTCTTAGTGATTGCCCCAGGAGGCTGGTTCACACCCCGCATGGGGCCAT


GCCACACATCTCTGTCAGTA









Celera SNP ID
hCV792698



Public SNP ID:
rs728217


SNP in Genomic Sequence:
SEQ ID NO: 4


SNP Position Genomic:
7983


Related Interrogated SNP:
hCV3054822 (Power = .7)


SNP Source:
dbSNP; Celera; HapMap; ABI_Val; HGBASE


Population (Allele, Count):
caucasian (T, 63|C, 55)


SNP Type:
INTRON


 








Context (SEQ ID NO: 28):



TCAGTCAGAGCCTGGGGGAAGTCAGAGTGACACCAGCCTGAGAAACACATCTGGGTTCTGGCTCGTCTACTCATGAACTG


AAGGACCGTTTACTGAACAG


Y


GACTCTGTGCTTGGGACACAGCAATGAGCAAGACAAACAGGTCCCTGTTGCATTGGAGCTCTTAGTATTATGGGGGGTTG


CAGGTATTTCAAAAATAGGC









Celera SNP ID
hCV11606396



Public SNP ID:
rs1887716


SNP in Genomic Sequence:
SEQ ID NO: 4


SNP Position Genomic:
8897


Related Interrogated SNP:
hCV3054822 (Power = .8)


SNP Source:
dbSNP; Celera; HapMap


Population (Allele, Count):
caucasian (C, 69|T, 51)


SNP Type:
INTRON


 








Context (SEQ ID NO: 29):



GATTGGCCTTCAGGATGCCCTGCATTCCACTGCCTTGCCTGTGGCTGGGACATCCTGCCTGGACCTGTCATGGCCATTTC


TGGGCTTCCAAACCAGAGCA


Y


GGGCCAAGGGAGAGAGCTGGCAAATCGTTAAAAATAAAAATATTCCCCCCACATTCCACTGGTGTGAGCATCCTCTGACA


TTATATTTTGATGGTGCTAT









Celera SNP ID
hCV27495641



Public SNP ID:
rs3823213


SNP in Genomic Sequence:
SEQ ID NO: 4


SNP Position Genomic:
9935


Related Interrogated SNP:
hCV3054822 (Power = .9)


SNP Source:
dbSNP; HapMap; HGBASE


Population (Allele, Count):
caucasian (C, 89|T, 31)


SNP Type:
INTERGENIC; UNKNOWN


 








Context (SEQ ID NO: 30):



TCTAGAAAGGGGTTAACCTCATTTCCTCAGACATAAAAACTGTCCTTGGCAGTTGGAAACAGCAGAGTTGACTCCACGGA


TGACTGATGCCCCTTACGGG


K


GTTATATACCTGTCCACGCCTGCCCCTGGCAGGGATTTAAACATTCCATGCTGTTCCATGAATCCTAAATCAAATAGATT


CATCCTGGCCCTTGGTGAAG









Celera SNP ID
hCV27505675



Public SNP ID:
rs3818308


SNP in Genomic Sequence:
SEQ ID NO: 4


SNP Position Genomic:
13597


Related Interrogated SNP:
hCV3054822 (Power = .6)


SNP Source:
dbSNP; HapMap; ABI_Val; HGBASE


Population (Allele, Count):
caucasian (T, 84|G, 36)


SNP Type:
INTRON


 








Context (SEQ ID NO: 31):



ATGTCCTTAGACCCTGCAGGGCAGATGGCTCCCTGGAGACACAGCTGTTCAGGCACTGCCTCCAGGTCATCCTGGCCAAG


AGGCTGATGGTGAAAGATGT


R


GTCTGGGAGATCAGGATTGGCCTTCAGGATGCCCTGCATTCCACTGCCTTGCCTGTGGCTGGGACATCCTGCCTGGACCT


GTCATGGCCATTTCTGGGCT









Celera SNP ID
hCV29161257



Public SNP ID:
rs6904582


SNP in Genomic Sequence:
SEQ ID NO: 4


SNP Position Genomic:
9820


Related Interrogated SNP:
hCV3054822 (Power = .8)


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (G, 69|A, 51)


SNP Type:
INTERGENIC; UNKNOWN


 








Context (SEQ ID NO: 32):



GACAGAGAGCCTGAGGGGTGAGGCTGCTCAGGGAACAGGTGAGCAGGCAGCTGCAGCCCTCCAGGTTCGGCCACAGGAAC


CTGGTACTGGGTGACGAGTG


TAGCTTGGTTGGCTTGATATTGCTGTGGAAGAAAGGGTAATTCTTGGTGATACATTGAATGGCCAGCTAGTGCGGAGGAA


GAGATTGGAAATAATTCACG









Celera SNP ID
hCV29161258



Public SNP ID:
rs6901022


SNP in Genomic Sequence:
SEQ ID NO: 4


SNP Position Genomic:
12146


Related Interrogated SNP:
hCV3054822 (Power = .9)


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (T, 81|G, 39)


SNP Type:
INTRON


 








Context (SEQ ID NO: 33):



TGGCAGGAGGCGTGCTTCCTCGGTGCTACCCCCTGGGTTGTGTGGTTGGAAAGAGACAATAACATGGTTGTGGGGAAGTA


GAGTCCCTGCTGGTCATCCC


R


TCATGTAGGGAGACTGGCTCTGGGCCATCCTCATGTGGTGTTTTTGGAGGCTACCTGGATCCTGGCTAGGACGAAGAGCT


CCCCTGTGTCTCAGTAGGCG









Celera SNP ID
hCV29161261



Public SNP ID:
rs6924090


SNP in Genomic Sequence:
SEQ ID NO: 4


SNP Position Genomic:
30652


Related Interrogated SNP:
hCV30225864 (Power = .6)


Related Interrogated SNP:
hCV3054799 (Power = .51)


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (A, 76|G, 42)


SNP Type:
INTRON


 








Context (SEQ ID NO: 34):



CAGGTGATCAGCCTTCTGAAGTGCTGGGATTACAGGCATGAGCCACCGTGCCCAGCCTGATTCTATTTGTACAAAAACAT


ATGGATATATATGCATAGAA


R


TCAAGGTAAAAGAATATAATCAAACATGTTAATATCAATTACATTTAAGTAGAGGAATTATGGTGATTTTTACTTTCTTC


TTTGTGTTTTTCTGCCTAGT









Celera SNP ID
hCV29576755



Public SNP ID:
rs6899653


SNP in Genomic Sequence:
SEQ ID NO: 4


SNP Position Genomic:
33779


Related Interrogated SNP:
hCV30225864 (Power = .7)


Related Interrogated SNP:
hCV29992177 (Power = .51)


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (A, 83|G, 37)


SNP Type:
INTRON; REPEATS


 








Context (SEQ ID NO: 35):



AATTTGGTCTTCCTCTGGTTCTACAGACTTTACAGAGGGCTTACTATGTTCCATTGAACTGTGAGGGCCAGGAGTGGACC


AGAGATGCTGGCCCCTGTCC


Y


GATGGGATTCAGTCAGAGCCTGGGGGAAGTCAGAGTGACACCAGCCTGAGAAACACATCTGGGTTCTGGCTCGTCTACTC


ATGAACTGAAGGACCGTTTA









Celera SNP ID
hCV30532403



Public SNP ID:
rs7754225


SNP in Genomic Sequence:
SEQ ID NO: 4


SNP Position Genomic:
8788


Related Interrogated SNP:
hCV3054822 (Power = .7)


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (C, 65|T, 55)


SNP Type:
INTRON


 








Context (SEQ ID NO: 36):



ACACAGGATGTTGTGTACCTTGTCATGTACTTATTTGCCATCTGTATCTCTTCTTTGGTGAAGTGGCTGTTCAGATCTTT


TGCCCACTTTTAAACTGAGT


K


GTTTATTTTCTTATTGTTGAATTTTTAGAGCTCTTTGTATACTTCAGATACAAGTCCTTTATCAGATATATGTTTTGCAA


ATATTTTCTCCCAGTCTGTA









Celera SNP ID
hCV30280062



Public SNP ID:
rs7772430


SNP in Genomic Sequence:
SEQ ID NO: 4


SNP Position Genomic:
27267


Related Interrogated SNP:
hCV30225864 (Power = .51)


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (G, 65|T, 51)


SNP Type:
INTRON; REPEATS


 








Context (SEQ ID NO: 37):



TGAGGTATGGTCGCACCACTGCACCCCAGCCTGGCTGACAGAGACCCCCCCCATCTCAAAAAATAAATAAAAGAAAGTAA


AATAAGAAAATAAAAGT


Y


AGTAGACTCTACCCAGCTCTAGAGGGCTGAGGGCACTAAGGCATGCCAAGAATACTGCTGCTGCTACATAGGGGACTGGG


GCAAATGCCATGCCATCTTC









Celera SNP ID
hCV30190183



Public SNP ID:
rs7774046


SNP in Genomic Sequence:
SEQ ID NO: 4


SNP Position Genomic:
8497


Related Interrogated SNP:
hCV3054822 (Power = .7)


SNP Source:
dbSNP


Population (Allele, Count):
caucasian (T, 65|C, 55)


SNP Type:
INTRON


 








Context (SEQ ID NO: 38):



AAAGAAAGTAAAAATAAAAGAAAATAAAAGTTAGTAGACTCTACCCAGCTCTAGAGGGCTGAGGGCACTAAGGCATGCCA


AGAATACTGCTGCTGCTACA


Y


AGGGGACTGGGGCAAATGCCATGCCATCTTCAGGGCTCTTGAGAGTGTGTGTGTGTATGTGTGTGTGTGTGTGCAGTTAC


ATGTGTGTTCCATGGACTCT









Celera SNP ID
hCV30082078



Public SNP ID:
rs7774204


SNP in Genomic Sequence:
SEQ ID NO: 4


SNP Position Genomic:
8566


Related Interrogated SNP:
hCV3054822 (Power = .7)


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (T, 65|C, 55)


SNP Type:
INTRON


 








Context (SEQ ID NO: 39):



TGGCTGTTGGAAACATGAACTCTTCCCAACACAGTGTTATCTCAGGAAATTATTCCACCTGCTTCCTCCTGGTGGTTCTT


TCCCCTGCCTTGGGTAATTT


S


TTCACATGCAGATGTTGATCGGTAGTTAGCTGAAGACTCAAGGGTAACCCTCTGAAGATCTCCAGAGCATGCTCTCTCTC


TTTCTCTCTCTTTTTTCTCC









Celera SNP ID
hCV30586596



Public SNP ID:
rs9369112


SNP in Genomic Sequence:
SEQ ID NO: 4


SNP Position Genomic:
6540


Related Interrogated SNP:
hCV3054822 (Power = .9)


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (C, 88|G, 32)


SNP Type:
INTRON


 








Context (SEQ ID NO: 40):



CAAATGCCATGCCATCTTCAGGGCTCTTGAGAGTGTGTGTGTGTATGTGTGTGTGTGTGTGCAGTTACATGTGTGTTCCA


TGGACTCTTGGGGAGGCTTC


Y


AGAATCAGAATTTGGTCTTCCTCTGGTTCTACAGACTTTACAGAGGGCTTACTATGTTCCATTGAACTGTGAGGGCCAGG


AGTGGACCAGAGATGCTGGC









Celera SNP ID
hCV29902034



Public SNP ID:
rs9380848


SNP in Genomic Sequence:
SEQ ID NO: 4


SNP Position Genomic:
8679


Related Interrogated SNP:
hCV3054822 (Power = .9)


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (T, 89|C, 31)


SNP Type:
INTRON


 








Context (SEQ ID NO: 41):



CCCCCAGGCAGCTATGTTTTTCCCTGTGGAAGTGGGGAGTGTCAGCCCAAATCCTCCCAAGTGGCTGGGTGAAGGTTGTG


AGGCCCAAGGTACCCACACT


R


CCCTCTCTCAAGCTCTCAGCAGAGCCCCTGGGAGCTGCCCACTGTTCTCTGGGTGCTATGATGTCCTTAGACCCTGCAGG


GCAGATGGCTCCCTGGAGAC









Celera SNP ID
hCV29937959



Public SNP ID:
rs9462531


SNP in Genomic Sequence:
SEQ ID NO: 4


SNP Position Genomic:
9659


Related Interrogated SNP:
hCV3054822 (Power = .8)


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (G, 77|A, 41)


SNP Type:
INTERGENIC; UNKNOWN


 








Context (SEQ ID NO: 42):



TGGCCATGGCTGAAACTCACTGCTTTTGTCTCTCTAGTACAAGGACTGGCAGGGCAAGAGCTGGGGGCCCGTGGGCACGT


GGCTGCTGATGAGATCTGGG


S


TCTGATGTTTCTGCTGCACTGTTGAAATGCAGCCTGGGCCAGGATTGTGAACGCTGCCGGTGGTAGGGAGCATGGAGTCT


GAGCCATCTCCCACCCAGGA









Celera SNP ID
hCV30478066



Public SNP ID:
rs9462533


SNP in Genomic Sequence:
SEQ ID NO: 4


SNP Position Genomic:
12662


Related Interrogated SNP:
hCV3054822 (Power = .9)


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (C, 76|G, 44)


SNP Type:
INTRON


 








Context (SEQ ID NO: 43):



AGAGCAATGATTGAGGAAGCTGAATCCTCCTCGTTGTGTTTTTCAGATGTGCTGTGTGGAGTCCTGGAGTTTCAGCGAGA


GGGCTCCGTGTGTGGAAGGT


S


CAGGTATACAGGGGTTGGAATTCAAATTTACTGGCCTCTAGAGGTTGGCAGGTGCCCAGGTGAATGCATGGAGTGGGCCA


GGTGCAGGGCACAAATGCTA









Celera SNP ID
hCV29703356



Public SNP ID:
rs9471079


SNP in Genomic Sequence:
SEQ ID NO: 4


SNP Position Genomic:
25792


Related Interrogated SNP:
hCV29992177 (Power = .7)


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (G, 94|C, 24)


SNP Type:
INTRON


 








Context (SEQ ID NO: 44):



TCAGTTTTTCACAAACCACCCAAGCCCAATAAGACTTCACCTGGCTGGGGTTTAGGATAGAGTCTTTCTCAGAAGCATGT


TCAGTTTAATGGGGGACTTC


Y


GAAAACCCTAGGGCCACAAAAGCCACAGAAAGGGCAACATAATGGTGGACTTTGGGGGAGATCTCAGCTCAAGAGGCCCA


CAGACTGTGCTGTAGGAACT









Celera SNP ID
hDV70715992



Public SNP ID:
rs16891930


SNP in Genomic Sequence:
SEQ ID NO: 4


SNP Position Genomic:
594


Related Interrogated SNP:
hCV3054822 (Power = .9)


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (T, 95|C, 23)


SNP Type:
INTRON


 








Context (SEQ ID NO: 45):



GGACCCCGTGTCAACCTGTCTTTGGCTTCCTTTGTGGCCCCGTGGTCACTGAAAAGCCAGAATGAATATTCTTCCTTTCG


GAATAAAAATTGAGCTGTGG


R


AGTTTTGTTTGCTTTGATGAATTACTTCCAGGCTGCTGTTTATTTGGAGAGCAAAGCTCCCCAGCTGCAGGGTGGGTAGA


GGCTGCGGTCACTCCCCTCG









Celera SNP ID
hDV70716012



Public SNP ID:
rs16891961


SNP in Genomic Sequence:
SEQ ID NO: 4


SNP Position Genomic:
14114


Related Interrogated SNP:
hCV3054822 (Power = .6)


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (A, 83|G, 35)


SNP Type:
INTRON


 


Gene Number:
2


Celera Gene:
hCG33195-84000313730962


Gene Symbol:
KIF6


Protein Name:
kinesin family member 6


Celera GenomicAxis:
GA_x5YUV32W6W6 (12492363 . . . 12669871)


Chromosome:
6


OMIM NUMBER:


OMIM Information:


 


Genomic Sequence (SEQ ID NO: 5):


 


SNP Information


 








Context (SEQ ID NO: 46):



ACAGGAATAGGTTAAACAGAAAGGTAGGGAGCCTTTTCTGGGAACTCTAACACCTCCGGTGAGTTCTCACCTTACCTTTT


GTTAGAGAGGAGTTGGGACC


R


TTCATGCTGGGAGTCAGATGTCTGGAGAAATGGCTTCGTGTGATCGAGTGAATTCACTGCTGGAGAATTTACCTGTTGGC


CCCAGAAGGAGTTTCACAGT









Celera SNP ID
hCV3054799



Public SNP ID:
rs20455


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
149694


SNP Source:
dbSNP; Celera; HapMap; ABI_Val; HGBASE


Population (Allele, Count):
caucasian (A, 77|G, 43)


SNP Type:
MISSENSE MUTATION


 








Context (SEQ ID NO: 47):



TCTAATTTAATTTTAATAACATCTTTGTTTAAAAACGGCTCCCCACATTCCTGGAGTACTAACAGTTTATGCTTTTATGG


ACTGAGCCAGCTCAGCACAC


M


ACTGGGAATGGCTTATCATCATCCATGAATGATTTCTTCCAAATGGTGCCTCTCACATTACCACTAAATCTTTGTTCCTT


TGGGATATGGGATTGCCTTT









Celera SNP ID
hCV3054766



Public SNP ID:
rs9394587


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
125971


SNP Source:
dbSNP; Celera; HapMap


Population (Allele, Count):
caucasian (C, 57|A, 63)


SNP Type:
INTRON; REPEATS


 








Context (SEQ ID NO: 48):



CAACTAATGATTCATTTGCTGAATACCCACTCTGGGCCTCCTTGGGGCTCAGCTGTCTCACACAACCATAGATCACTTCA


GCCTCACCTTGTTTGAGAAG


W


CAAAGTGTCTATTCTTACGGAGATTGATGGTGTTCATAGCTCAATTACCACAATGGTCAGCAGCTCAGAGGCAAAGATTC


TGGCTTCCCAATCAGACCAT









Celera SNP ID
hCV3054789



Public SNP ID:
rs4711595


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
137913


SNP Source:
dbSNP; Celera; HGBASE


Population (Allele, Count):
caucasian (A, 33|T, 87)


SNP Type:
INTRON


 








Context (SEQ ID NO: 49):



GTCTTTGAAGCCAAAGGAAAAATGAGAGACAGGGCTTTCTACTCGCTGAGCAGGATATTGCTACCAGTAGAAGCTACCCA


AGAGATCAGAAAAGAGGGTA


S


AGAAATAGCCAGGGGTTCTGAGCCTCACAGCTGAAAGGAGAGCTTATTCTCATGGGGCAGATACACAAGGGCTCAGCACA


GAGCCAGAGCAATGATTGAG









Celera SNP ID
hCV3054805



Public SNP ID:
rs2894424


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
155237


SNP Source:
dbSNP; Celera; HapMap; HGBASE


Population (Allele, Count):
caucasian (C, 67|G, 51)


SNP Type:
INTRON


 








Context (SEQ ID NO: 50):



AGCAGTGGAGAGACTTTCCACGAGGTGCCCTTCATGGTGGGAAAGCAGAGATCCCTGTGCTGGGTCTAGTGAGGACCGAT


GTAGGACCAGAGGTAGCCAC


M


AGGTGGCAGGCTCTGCACGCTTTTCTTCAGAGAACAGTAACCAATTCTCAAGGCTCCTGCTGAGTGGTTCCTGGTGCAGC


CTGGAGAAGGGAAGGAGAAA









Celera SNP ID
hCV3054808



Public SNP ID:
rs9462535


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
158968


SNP Source:
dbSNP; Celera; HapMap


Population (Allele, Count):
caucasian (A, 50|C, 74)


SNP Type:
INTRON


 








Context (SEQ ID NO: 51):



GGTAGATGTCATCGTCTCCATTTTATACATGAGAAAACTGAGGCTTAGACAGATTAAAGAAATTCTAAAGTTATCCAGCT


GGTAAGTGGCAGAATCGGGA


R


CTACTCAGGATTCTTTGAGGCTTCAGAACCTGTGCTCTCAGCCACAATGCCTTCTAACTGGGCTTGACTCTGTGGCATTC


TAGGAACACACGCTGGCCTA









Celera SNP ID
hCV3054809



Public SNP ID:
rs11755763


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
159784


SNP Source:
dbSNP; Celera; HapMap


Population (Allele, Count):
caucasian (A, 51|G, 69)


SNP Type:
INTRON; REPEATS


 








Context (SEQ ID NO: 52):



GGAGGAATGATGGTTGGTTTCTATGGATTTGTTACAGGGGAAGGGATATCTCAGCTCAAAGGATATCCTGCGTATCCAAG


CCTTCCTCCACTTGTGCCCC


R


CACACTGTCCCCTCTACTTTCTCAAGGAAGTTTTTCCTGCAGCTCTGCTCCCCTCTCCCACCACAGCATCAGTTTCCCTC


TCTATGCATGTTTCAGCACG









Celera SNP ID
hCV3054813



Public SNP ID:
rs9471077


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
166018


SNP Source:
dbSNP; Celera; HapMap


Population (Allele, Count):
caucasian (A, 73|G, 47)


SNP Type:
INTRON; REPEATS


 








Context (SEQ ID NO: 53):



GTAGAGGCTGCGGTCACTCCCCTCGTCAATGCTGGTTCCTGTTCCTGAGGCCGAGAGAACTCCTGACAGCAGAGTGGGCA


TATCTTGGTAGTTGCAGCTT


Y


TCAAGACAGTGTGGCCCAGTGGGGAGAGAGCAGAAAACCTGGGTTATGCTGGCTCTGCCATTTATCAGCTGTGTAACCTT


GGGCAAGTGATACAACCTCT









Celera SNP ID
hCV15876373



Public SNP ID:
rs2281686


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
166553


SNP Source:
dbSNP; HapMap; HGBASE


Population (Allele, Count):
caucasian (T, 105|C, 15)


SNP Type:
INTRON


 








Context (SEQ ID NO: 54):



TTTTTCAATCACTTTGTTATCTTGGAGTAAAATATCTCGACAGGAATTTCCACTAATCTAGAATAGATCAAATAAAGGAA


ATCTCTTTTCGATTGAACAG


S


ACATTATAAAGCATATGCCTGAGACTCCTTAAAACAAACCAACTAAGAACAACAATAGCAACCACAGGATCTAGAATTCA


GCTTTCCACTTCAGTTCTCC









Celera SNP ID
hCV32202303



Public SNP ID:
rs11751690


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
96995


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (G, 106|C, 12)


SNP Type:
INTRON


 








Context (SEQ ID NO: 55):



TCCAAAAATAGCCTTATGGCTGATACCTAATTGCATTTCTAACAGAGCTATTCTTCATGTGCAGATAACAGTCTCTGTAA


CCTGCTGGATCTTGCAGCTT


Y


ATGGGTTTTGGCAAAAAAAGGAGTTGAGGGGGATTGCAGAATTCACTCAGCATGCAGCTTGCTGTCATTACCACGGTGAT


AAATTTGCTGGTTTTGGCCT









Celera SNP ID
hCV31340487



Public SNP ID:
rs12175497


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
118334


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (T, 106|C, 12)


SNP Type:
HUMAN-MOUSE SYNTENIC REGION; INTRON


 








Context (SEQ ID NO: 56):



TTCCCCCAGTGCTGTGTTCAGACTCCATGAGTCACTCCATGAGGGTGACTCAGTGCCTCGCATGTTTGCTGGCCTTGCCT


TCTTTGAAAGTCATCAGACA


Y


GATAAACTGAAGGAAGTATCTTAATATGAAACCGACAATGGTAGGACTTTCAATGGACAAACAATTCCTTTTTAAAAAGT


TTCAGAAATGGGCCCAGGTA









Celera SNP ID
hCV30388472



Public SNP ID:
rs9357303


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
141435


SNP Source:
dbSNP; HapMap


Population (Allele, Count) :
caucasian (T, 62|C, 52)


SNP Type:
INTRON


 








Context (SEQ ID NO: 57):



TATCCAAATATTCTTCTCCATTGTCCTGCCGCGTGGCCACTTGCTATCACACTGGTTATTGCTTGGCTATGTCCTTGGCA


AATCATCTTCTCTGGCTCTC


R


GTTTTCTTGTCCTTAAAATGAAGGGATCAGCCTGTCAGATAATCAGTAAGCATTCTGCCAGCCAGAAAAGGGATCTGATT


GTGCGTATTTTAGCACTTCC









Celera SNP ID
hCV30225864



Public SNP ID:
rs9394584


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
147843


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (A, 74|G, 36)


SNP Type:
INTRON


 








Context (SEQ ID NO: 58):



AGCAGGCTGGCGGCCAAGCAAGGCGAGTACAGGACCAAGGCCGGCTCTCAGTTGCGGCGCTCCATCCATGCACAAACCTC


TTCCTGCCCAAACTGCACAC


R


GCTGGTGGAGAAGCTGAGTGCAGGCGCCACAGGGCAGGCATCAGTCATTATACATCGAATCTGCCAGCCCATACCATCAC


GGTGGGGGCGCCTTTCTGGC









Celera SNP ID
hCV30478067



Public SNP ID:
rs9471078


SNP in Genomic
Sequence: SEQ ID NO: 5


SNP Position Genomic:
164125


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (G, 108|A, 12)


SNP Type:
INTRON


 








Context (SEQ ID NO: 59):



ATTATTAACATTTTGTATTAGTGTGATAAATTTGTTATAACTGGTGAATGAATATAGATACATTATTATGAACTAAAGTC


CATGGTTTATGCAGGGGTTC


R


CTCTTTGTGCTGTACAGCTCTATGGATTTTGACAAATGCATGACATCATCTATGCAACATTACAATATCATACAGAATAG


TTTCACTGCCCCCAAAATTC









Celera SNP ID
hCV29992177



Public SNP ID:
rs9471080


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
154684


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (A, 94|G, 24)


SNP Type:
INTRON; REPEATS


 








Context (SEQ ID NO: 60):



ATGGCAGTAGGTCTACTGGCTGTCAGAGTGAGGGACTGAAGGGGTGTGCACTCCAACCAACTTGAAAGCCACTGTCTTGA


GTATCTACGACTAATTAAAC


R


GTAAAAGGAATTAATCCTGCTGGATCATATGGCTTATCAAGAGACATGAGAGCCACAGGAATAGGTTAAACAGAAAGGTA


GGGAGCCTTTTCTGGGAACT









Celera SNP ID
hCV2946524



Public SNP ID:
rs20456


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
149849


Related Interrogated SNP:
hCV30225864 (Power = .51)


SNP Source:
Applera


Population (Allele, Count):
caucasian (A, 23|G, 15) african american



(A, 11|G, 27) total (A, 34|G, 42)


 


SNP Type:
INTRON


SNP Source:
dbSNP; Celera; HapMap; HGBASE


Population (Allele, Count):
caucasian (A, 67|G, 53)


SNP Type:
INTRON


 








Context (SEQ ID NO: 61):



AGGGTCTGGCTCACTCAAAAAGTGCTCTAGGTGTTGCCTTTAGTGAGTCCTCCTCCCTTAGAAATAGTATATCAATATGC


AAATCTGTATTACCTTCTGC


Y


TCTGGCTGTTTGAATAAGGGTAGGCTTTGAGCACCCTCCCCAAACCAACCATATGAGGGCATTGTAGATGTCACCTATAA


TTCACACCTGTAGTTCATTA









Celera SNP ID
hCV814553



Public SNP ID:
rs302573


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
163798


Related Interrogated SNP:
hCV32202303 (Power = .51)


SNP Source:
dbSNP; HapMap; ABI_Val; HGBASE


Population (Allele, Count):
caucasian (C, 13|T, 107)


SNP Type:
INTRON


 








Context (SEQ ID NO: 62):



ACATTGGTTTTGTTATACGTTTTGCTTAAAGCCATAGTTTCCAAGAACATATCAATGACTTTGAGGACTTAATGAATACT


ACTACTAATAATATAACAAT


Y


ATTTGTTGAAAATTTACCATGTGACAGATAATATACATAATCTCAGTTAATATTTACAACAACCCTATGAACTTTTCAGA


ATTCTTTTCAGAACCAACTC









Celera SNP ID
hCV814557



Public SNP ID:
rs302575


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
165411


Related Interrogated SNP:
hCV32202303 (Power = .51)


SNP Source:
dbSNP; HapMap; HGBASE


Population (Allele, Count):
caucasian (T, 13|C, 105)


SNP Type:
INTRON


 








Context (SEQ ID NO: 63):



TTCATTAGAGAAGAGTATACTGTACTTAATATAAAAAAGCCTCACTTTTAAAAGTCATATAGCAATGCACCTGCTAGTTA


GGATAGAAATGAAGTAGCCT


Y


TCAGCATCAAAAATATAACTATCATAAACAACTCTCACCTATCAATTCCCTCAGGGAAGAATGGATTCACTTAGGTGGTA


ATGAAAGGGCATGACTCAGT









Celera SNP ID
hCV814585



Public SNP ID:
rs160023


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
185932


Related Interrogated SNP:
hCV32202303 (Power = .51)


SNP Source:
dbSNP; HapMap; HGBASE


Population (Allele, Count):
caucasian (C, 13|T, 107)


SNP Type:
INTRON


 








Context (SEQ ID NO: 64):



TTGGCTTGCTACTTAAATGTTACATTATTCTTGTGGCTTCCTTGGAAATCCAGATTTTCATGTACTGATAGAAATACTTC


CTCTACCAGATACATAAACA


Y


AGCATAGCAGCTTTCAGCACAATTTCTCTCATTAAAGAAGCAATTCCATTTTTTAAAAAGAATCATACAAAAGAAGTTTG


TTATTTTATTTAAAAAGGAT









Celera SNP ID
hCV814588



Public SNP ID:
rs159958


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
191237


Related Interrogated SNP:
hCV32202303 (Power = .51)


SNP Source:
dbSNP; HapMap; HGBASE


Population (Allele, Count):
caucasian (C, 13|T, 107)


SNP Type:
TRANSCRIPTION FACTOR BINDING SITE; INTRON


 








Context (SEQ ID NO: 65):



CCAGTCAATTATAAAGTCAAATAACTATAGTAAAAATCTTGCCAGTTGTAATCAATCACTACACATTTGTATAGGAAGGA


TTTTCCAGAACACCTTGCTT


Y


CTACTTGTGTTCTTTCTATATCTCTCTTTTGGAGGCTATCAAGGTGAGAAATAATAACTAGAAATATCTTTGGTTTATTT


GTAAGTGTATCCTCAAGTCC









Celera SNP ID
hCV814594



Public SNP ID:
rs302596


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
196892


Related Interrogated SNP:
hCV32202303 (Power = .51)


SNP Source:
dbSNP; HapMap; HGBASE


Population (Allele, Count):
caucasian (C, 13|T, 107)


SNP Type:
INTRON


 








Context (SEQ ID NO: 66):



CTGTACGTGAAAATCACATGCCCCAGTATATGTGATTGCTCAGTAAGTACATAGTGCATGCTCAATGAGTATTAGCTATC


ATCCATTTTTGGCTCCCCAC


Y


GACATTTTTATAGAAAAGGATTTTCATCCCAATTCAGAATTTTACCAAGTTATGAATGTTTTTCTGTATGATTTTTTTCC


CCCTTAACTCTTTTTAGATT









Celera SNP ID
hDV71111070



Public SNP ID:
rs302595


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
197163


Related Interrogated SNP:
hCV32202303 (Power = .51)


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (T, 13|C, 107)


SNP Type:
INTRON


 








Context (SEQ ID NO: 67):



AAGAATAGACTTTTTCATTTCCTAGAAATGAAAAAATAATAGCAAAATTAAAAAATTTAGAGGGATAAAGAAGAATTCAC


CACTGAAAAGCAAATTACCA


Y


GCTGGAAGTCTGTGCTGACAAATTCTTGCAGAACTCAGAACCACAGGGCAAAGAGAGGAGAACTATGAATGGGAAGTTGT


AATATTTGAAGGATAGGACT









Celera SNP ID
hCV814600



Public SNP ID:
rs302588


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
203632


Related Interrogated SNP:
hCV32202303 (Power = .51)


SNP Source:
dbSNP; HapMap; HGBASE


Population (Allele, Count):
caucasian (T, 13|C, 107)


SNP Type:
INTRON


 








Context (SEQ ID NO: 68):



TGTTTTGTTTTGTTTTGAGACTCAGAATCTAAATCTAAAACAAATGGTTATAGGAACAGAAGGTATTAATGCCATCATCA


TTACATGTCCTGCAGCCTAA


R


TATATAATTGATACACTTAAGCACACTACCACAGTTATCTGAGAATTGAAGGCAACATGAAAAACCACGAGCCCACTGAA


CAAAATATGGGAAATCCTAC









Celera SNP ID
hCV814607



Public SNP ID:
rs303701


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
207580


Related Interrogated SNP:
hCV32202303 (Power = .51)


SNP Source:
dbSNP; HapMap; HGBASE


Population (Allele, Count):
caucasian (G, 13|A, 107)


SNP Type:
HUMAN-MOUSE SYNTENIC REGION; INTRON


 








Context (SEQ ID NO: 69):



GTTGAATATTTTATTAATTTTTAATTTTGAATATTAAAACCATTATTTTCAATTCAGAGATTTAAAAATAAGCCAATGGT


GCTTTTACAACAATTGCACT


Y


TGTTTGTATCTATATAAAGAATACTTTGGGGCCAGGTGCAGTGGTTCACACCTGTAATCCTAGCACTTTGGGAGGCCGAG


GCGGGCAGATCACTCGAGGT









Celera SNP ID
hCV814610



Public SNP ID:
rs159957


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
210926


Related Interrogated SNP:
hCV32202303 (Power = .51)


SNP Source:
dbSNP; HapMap; HGBASE


Population (Allele, Count)
caucasian (T, 13|C, 107)


SNP Type:
INTRON


 








Context (SEQ ID NO: 70):



CTTGTTTAGTGTAATATTGGCTTTCTATCCTTATTCATAAAAATATTATTTGAGATAGTGAAGTTTTTTTTTTAAGAAGA


CAGAACAAGTGCAAGGATGA


W


TATGAAAATGTTTTGAAATGTATAAAGCACTGTACAAATGGAAAGCATTACCACTGTTAAAACTATCAAAGATTCATTTT


ATATATCCTTTTTATTAATC









Celera SNP ID
hCV814619



Public SNP ID:
rs303693


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
215471


Related Interrogated SNP:
hCV32202303 (Power = .51)


SNP Source:
dbSNP; HapMap; ABI_Val; HGBASE


Population (Allele, Count):
caucasian (T, 12|A, 104)


SNP Type:
INTRON


 








Context (SEQ ID NO: 71):



TAAAGCACCTGTCAGGTGGCCATTTCTCAAGATACATAAGAATGACTATGCTCCCTTCTAGGCAGAAGAGAGGATGTTTT


GAAATATTTAAGAAGCATAA


R


AAGTGCTGGGCGCGGTGGCTCACGCCTGTAATCCCAGCACTTTGGGAGGCCGAGGCGGGCGGATCACAAGATCAGGAGGA


GATCGAGACCATCCTAGCTA









Celera SNP ID
hCV814621



Public SNP ID:
rs303653


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
217178


Related Interrogated SNP:
hCV32202303 (Power = .51)


SNP Source:
dbSNP; HGBASE


Population (Allele, Count):
caucasian (A, 13|G, 107)


SNP Type:
INTRON


 








Context (SEQ ID NO: 72):



TACGCATATGCCCCTGGGAGGTTGGTACTATGAGAATATCCTCCAGGAGCCACTTTGTTTTAGCTCGTCTGACTGAGGCC


TTAAACAAGGAAAGGAAGAG


M


GCTGTTAAGGGCACCACAGTAGCACAAATCCAGAGATATTAAGACTTTCCTAACTCAAGTCACAAGGTCTTCCAGCTCAA


TAACTAATGCTTGATGCATA









Celera SNP ID
hCV1416399



Public SNP ID:
rs11752840


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
186181


Related Interrogated SNP:
hCV32202303 (Power = .51)


SNP Source:
dbSNP; Celera; HapMap


Population (Allele, Count):
caucasian (A, 107|C, 13)


SNP Type:
INTRON


 








Context (SEQ ID NO: 73):



TAGTTGTGTTGTCAATGTCCCTTCGGAGGTTTTGAAGTTATAGCTTGCTAAGTTCAGATACATAAAATGCTCACACAATC


TTGTCAATGAAGCTTTCCTT


M


AACAGCAGAAATCCTCTACCTTTTTTTTCTGGGTCATCCAAAACAGTCTTTCAAAAGCTAAATTTTTTTTTGAGCTAAGG


AGCCCCAATATTCAATTTAT









Celera SNP ID
hCV1703050



Public SNP ID:
rs2499450


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
153546


Related Interrogated SNP:
hCV32202303 (Power = .51)


SNP Source:
dbSNP; Celera; HapMap; ABI_Val; HGBASE


Population (Allele, Count):
caucasian (C, 107|A, 13)


SNP Type:
INTRON


 








Context (SEQ ID NO: 74):



GAACATGTCACTGTTCCTAGAATTTTACTGAAATGTATCGGATCATGTGGGTTTTGTTTGCTTCTTTTTTAAAAATATTC


TTAGGATCATCAATCTATAC


R


GAATGAAAATCCTGAGATAGTAACTACATTGGCACCCTTCTCTATTAAACTAATATTGATGCCATATTTTCTATTTTTAC


TAAGGCTTTTAAGTCAAATC









Celera SNP ID
hCV2487638



Public SNP ID:
rs11758101


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
75076


Related Interrogated SNP:
hCV32202303 (Power = .6)


SNP Source:
dbSNP; Celera; HapMap


Population (Allele, Count):
caucasian (A, 99|G, 9)


SNP Type:
INTRON


 








Context (SEQ ID NO: 75):



AGTCTCTAGACCATCAAGAAGTCGGCTTCCTGTTTTTAGGTATGACGAGAGATCCATGTGTTTATCCCAGTGCTATAATT


CTCTAGATTTGATACAATAG


R


GGGAACCTGCAGCACACTCAGTCTGACATACTGAAACCACCATATTCAAAATCTTACCCTTAAACTAAGCCTCCTGAAAA


ACATACAGGATACACAGGAA









Celera SNP ID
hCV2487644



Public SNP ID:
rs11754132


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
69018


Related Interrogated SNP:
hCV32202303 (Power = .6)


SNP Source:
dbSNP; Celera; HapMap


Population (Allele, Count):
caucasian (A, 102|G, 8)


SNP Type:
INTRON


 








Context (SEQ ID NO: 76):



CCCACTATAGCTGTCCAGAAAGCCTAAAATCTCTCCTTCTAAAATCTACTTAGAGATTCTGGATAAAATATAATACATAC


TCCTTTTAATGCATAAATTT


R


CAAAAATGTAAGGAAAATATCTAAGGACCCAAAACAAAGAAGTAAATGAAAATCGAAATAAAAGCTTATGAGCTAATCCT


GGGGCACACATAGGAGTGTG









Celera SNP ID
hCV3078072



Public SNP ID:
rs303675


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
168081


Related Interrogated SNP:
hCV32202303 (Power = .51)


SNP Source:
dbSNP; Celera; HapMap; HGBASE


Population (Allele, Count):
caucasian (G, 13|A, 107)


SNP Type:
INTRON; INTERGENIC; UNKNOWN; REPEATS


 








Context (SEQ ID NO: 77):



AATGTGTTATATGGTCTCCCCCAATTATATCTTTAGAGAAAATGTTCAGCTTTCTGAAAATAATCTGTTAAGCACCTAAC


TTAGTCTAAATAGTTTCAGA


Y


TTCTCTTAACTTGTAACGTTCCTGTGAATGTTAACTATAGTGCTTTAAATCAAGAGTGGGTCCCTTCTGAGCATGGGGCC


CTGTGTTATGACATAGGTCT









Celera SNP ID
hCV3078083



Public SNP ID:
rs302593


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
197833


Related Interrogated SNP:
hCV32202303 (Power = .51)


SNP Source:
dbSNP; Celera; HapMap; HGBASE


Population (Allele, Count):
caucasian (T, 13|C, 107)


SNP Type:
INTRON


 








Context (SEQ ID NO: 78):



GTGCGTGGCCTGGGGCTTTGGGGACCCCTGCTACATAGTCATGCACATAATATCACTGGTTTGGACGAAGTAGAAAAGAT


GTCTAAATTGGTGAGATTCC


W


GAATTACAGAATTTTGGACAGAAATGTGATTAGTTTTATTTAGCCTTAATTGCATACAGTAACCAGAATTAAGTTACCAT


GTGTTAACCCAGTAGAATAA









Celera SNP ID
hCV3078087



Public SNP ID:
rs11754307


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
202631


Related Interrogated SNP:
hCV32202303 (Power = .51)


SNP Source:
dbSNP; Celera; HapMap


Population (Allele, Count):
caucasian (T, 107|A, 13)


SNP Type:
INTRON


 








Context (SEQ ID NO: 79):



TGGGAAAGATTCATGAACCTGCAATATATTTTTGTGTTAGTGTTGACCTAGACAAAATGGGCTAAAAGCAATGGACGTTT


TGTCCATTACTGAGATATTT


M


AAAAGCTCTTTCTAGTAGCTTGAAAACCGTATCTTTTCTTTGTAGTTTTATCTAACATCATATCTTTTCACTGACAATGA


AGAATATAGCTGATCACATG









Celera SNP ID
hCV8948950



Public SNP ID:
rs964116


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
111237


Related Interrogated SNP:
hCV32202303 (Power = .51)


SNP Source:
dbSNP; Celera; HapMap; HGBASE


Population (Allele, Count):
caucasian (C, 107|A, 13)


SNP Type:
INTRON


 








Context (SEQ ID NO: 80):



AAAAAATAATAACATAACAAGACACTATGAAACACAAAAACAGATAGGAAAAAAAAATGACCAAGTAGAACTTCTAAAAA


TGAAGATATCACCATTGAAA


Y


TTTAAAAACTCAATATAAGGGTTAAATGGAAGATTAGACATAGGTGAGAAAAGGCTTAATGAACTAGAAGAAATATTTGA


AGAAACTACTCAAAATGCAA









Celera SNP ID
hCV8948982



Public SNP ID:
rs303678


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
168820


Related Interrogated SNP:
hCV32202303 (Power = .6)


SNP Source:
dbSNP; HapMap; ABI_Val; HGBASE


Population (Allele, Count):
caucasian (C, 11|T, 105)


SNP Type:
INTRON; INTERGENIC; UNKNOWN; REPEATS


 








Context (SEQ ID NO: 81):



TTAGTCAGGTATGAGTGGACATTAATAGTTCACTGCCAATACCTCACCTGTGGAGTTAATTTTCCTGGCCCAAGAGGGCA


AATCAGAGATGTTTAGCTAT


Y


AGATAGAGAGAAATGAGAGAAATGTTTGTGGTGGGTCATTGCTGAAATGCAATGACATAGGCATCAATGTAATGAGGCTA


AAAGTAAAGGTGCCTGGTCC









Celera SNP ID
hCV16029234



Public SNP ID:
rs2499452


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
128596


Related Interrogated SNP:
hCV32202303 (Power = .51)


SNP Source:
dbSNP; HapMap; HGBASE


Population (Allele, Count):
caucasian (T, 107|C, 13)


SNP Type:
INTRON


 








Context (SEQ ID NO: 82):



TCTAGAAAGGGGTTAACCTCATTTCCTCAGACATAAAAACTGTCCTTGGCAGTTGGAAACAGCAGAGTTGACTCCACGGA


TGACTGATGCCCCTTACGGG


K


GTTATATACCTGTCCACGCCTGCCCCTGGCAGGGATTTAAACATTCCATGCTGTTCCATGAATCCTAAATCAAATAGATT


CATCCTGGCCCTTGGTGAAG









Celera SNP ID
hCV27505675



Public SNP ID:
rs3818308


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
167246


Related Interrogated SNP:
hCV3054822 (Power = .6)


SNP Source:
dbSNP; HapMap; ABI_Val; HGBASE


Population (Allele, Count):
caucasian (T, 84|G, 36)


SNP Type:
INTRON


 








Context (SEQ ID NO: 83):



GACAGAGAGCCTGAGGGGTGAGGCTGCTCAGGGAACAGGTGAGCAGGCAGCTGCAGCCCTCCAGGTTCGGCCACAGGAAC


CTGGTACTGGGTGACGAGTG


K


TAGCTTGGTTGGCTTGATATTGCTGTGGAAGAAAGGGTAATTCTTGGTGATACATTGAATGGCCAGCTAGTGCGGAGGAA


GAGAT TGGAAATAATTCACG









Celera SNP ID
hCV29161258



Public SNP ID:
rs6901022


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
168697


Related Interrogated SNP:
hCV3054822 (Power = .9)


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (T, 81|G, 39)


SNP Type:
INTRON


 








Context (SEQ ID NO: 84):



TGGCAGGAGGCGTGCTTCCTCGGTGCTACCCCCTGGGTTGTGTGGTTGGAAAGAGACAATAACATGGTTGTGGGGAAGTA


GAGTCCCTGCTGGTCATCCC


R


TCATGTAGGGAGACTGGCTCTGGGCCATCCTCATGTGGTGTTTTTGGAGGCTACCTGGATCCTGGCTAGGACGAAGAGCT


CCCCTGTGTCTCAGTAGGCG









Celera SNP ID
hCV29161261



Public SNP ID:
rs6924090


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
150191


Related Interrogated SNP:
hCV30225864 (Power = .6)


Related Interrogated SNP:
hCV3054799 (Power = .51)


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (A, 76|G, 42)


SNP Type:
INTRON


 








Context (SEQ ID NO: 85):



CAGGTGATCAGCCTTCTGAAGTGCTGGGATTACAGGCATGAGCCACCGTGCCCAGCCTGATTCTATTTGTACAAAAACAT


ATGGATATATATGCATAGAA


R


TCAAGGTAAAAGAATATAATCAAACATGTTAATATCAATTACATTTAAGTAGAGGAATTATGGTGATTTTTACTTTCTTC


TTTGTGTTTTTCTGCCTAGT









Celera SNP ID
hCV29576755



Public SNP ID:
rs6899653


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
147064


Related Interrogated SNP:
hCV30225864 (Power = .7)


Related Interrogated SNP:
hCV29992177 (Power = .51)


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (A, 83|G, 37)


SNP Type:
INTRON; REPEATS


 








Context (SEQ ID NO: 86):



ACACAGGATGTTGTGTACCTTGTCATGTACTTATTTGCCATCTGTATCTCTTCTTTGGTGAAGTGGCTGTTCAGATCTTT


TGCCCACTTTTAAACTGAGT


K


GTTTATTTTCTTATTGTTGAATTTTTAGAGCTCTTTGTATACTTCAGATACAAGTCCTTTATCAGATATATGTTTTGCAA


ATATTTTCTCCCAGTCTGTA









Celera SNP ID
hCV30280062



Public SNP ID:
rs7772430


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
153576


Related Interrogated SNP:
hCV30225864 (Power = .51)


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (G, 65|T, 51)


SNP Type:
INTRON; REPEATS


 








Context (SEQ ID NO: 87):



TGGCCATGGCTGAAACTCACTGCTTTTGTCTCTCTAGTACAAGGACTGGCAGGGCAAGAGCTGGGGGCCCGTGGGCACGT


GGCTGCTGATGAGATCTGGG


S


TCTGATGTTTCTGCTGCACTGTTGAAATGCAGCCTGGGCCAGGATTGTGAACGCTGCCGGTGGTAGGGAGCATGGAGTCT


GAGCCATCTCCCACCCAGGA









Celera SNP ID
hCV30478066



Public SNP ID:
rs9462533


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
168181


Related Interrogated SNP:
hCV3054822 (Power = .9)


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (C, 76|G, 44)


SNP Type:
INTRON


 








Context (SEQ ID NO: 88):



AGAGCAATGATTGAGGAAGCTGAATCCTCCTCGTTGTGTTTTTCAGATGTGCTGTGTGGAGTCCTGGAGTTTCAGCGAGA


GGGCTCCGTGTGTGGAAGGT


S


CAGGTATACAGGGGTTGGAATTCAAATTTACTGGCCTCTAGAGGTTGGCAGGTGCCCAGGTGAATGCATGGAGTGGGCCA


GGTGCAGGGCACAAATGCTA









Celera SNP ID
hCV29703356



Public SNP ID:
rs9471079


SNP in Genomic Sequence:
SEQ ID NO: 5


SNP Position Genomic:
155051


Related Interrogated SNP:
hCV29992177 (Power = .7)


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (G, 94|C, 24)


SNP Type:
INTRON


 


Gene Number:
3


Celera Gene:
hCG1999777-84000313730953


Gene Symbol:
KIF6


Protein Name:
kinesin family member 6


Celera GenomicAxis:
GA_x5YUV32W6W6 (12301147 . . . 12331055)


Chromosome:
6


OMIM NUMBER:


OMIM Information:


 


Genomic Sequence (SEQ ID NO: 6):


 


SNP Information


 








Context (SEQ ID NO: 89):



GCTCCACTGATCCCAGGTGGATTGCTGGAAAGTCTTTTCAAAACATTGCATCAATTTCTCCCATTTGCAGAGGCCAGTGA


ACTCTGGGAAGGTGCACTGC


Y


GTCAAGCATGTGTCCATATCAAAGGCTGGGCTTCCATTAGGGGATGTTCGGAGCAAAAGGCCCCTTGGCCAGTTGCTGCA


CACACTTTGCATATGCTCTG









Celera SNP ID
hCV792699



Public SNP ID:
rs728218


SNP in Genomic Sequence:
SEQ ID NO: 6


SNP Position Genomic:
18230


SNP Source:
dbSNP; Celera


Population (Allele, Count):
caucasian (C, 55|T, 65)


SNP Type:
INTRON


 








Context (SEQ ID NO: 90):



TTTCAACATCCAACATTCACAGCCAACCTCTCTACACCTGCTCTGCCTCGGAGCCCTGGCAGTCCCTGGACACGTGTTAT


GGAAGGAACACTGTCTCCCC


S


ACATGCTCACATGGCCCAAATCCCCACAAGTCCCCTTTTTGCCAAGCTCCACGTCCTTTGCCTCTTCCCATCACCCAATA


CCCAACCTGCCATCATTGTC









Celera SNP ID
hCV1650850



Public SNP ID:
rs35268572


SNP in Genomic Sequence:
SEQ ID NO: 6


SNP Position Genomic:
15182


SNP Source:
Celera


Population (Allele, Count):
no_pop (C, -|G, -)


SNP Type:
INTRON


 








Context (SEQ ID NO: 91):



GGAGGAATGATGGTTGGTTTCTATGGATTTGTTACAGGGGAAGGGATATCTCAGCTCAAAGGATATCCTGCGTATCCAAG


CCTTCCTCCACTTGTGCCCC


R


CACACTGTCCCCTCTACTTTCTCAAGGAAGTTTTTCCTGCAGCTCTGCTCCCCTCTCCCACCACAGCATCAGTTTCCCTC


TCTATGCATGTTTCAGCACG









Celera SNP ID
hCV3054813



Public SNP ID:
rs9471077


SNP in Genomic Sequence:
SEQ ID NO: 6


SNP Position Genomic:
25198


SNP Source:
dbSNP; Celera; HapMap


Population (Allele, Count)
caucasian (A, 73|G, 47)


SNP Type:
INTRON; REPEATS


 








Context (SEQ ID NO: 92):



TCAGAAACTGCATCTTTCTTTTTTTTAAGGAGCAGGATTCCTCTTGCACATGAAATCTTTCCCAGAGCCCCAATCTGAAG


CCTACTTAAGAGTGGAGCTC


W


GGTTGAAGTAGGGGCAGAGGGCTCCCATTTCCACCAGCCCAGAGAGTGTGATTTCAAGAGCCCTTACCCGTAATTCTGAA


ATGGATGCCCTTGTCTAAGT









Celera SNP ID
hCV3054822



Public SNP ID:
rs11751357


SNP in Genomic Sequence:
SEQ ID NO: 6


SNP Position Genomic:
12718


SNP Source:
dbSNP; Celera; HapMap


Population (Allele, Count):
caucasian (T, 90|A. 28)


SNP Type:
INTRON


 








Context (SEQ ID NO: 93):



AGACTGTGTGCTGTAGGAACTGGGGAGCAGCCAGGGTTCTGGAGCCTCCTAGCTTTCCACCAGCAGCGTGGCCTGACCAC


TCTCTACTCGCCCCAGGTGC


Y


TCCCATCCGCCGTTTCTCTGCCTTGGGGCTCCACTCCTGGCTGACCTCCTGGCATCTGGTCAGATCCATGCCCATTGCCC


TCTGCTGCTTTATCTTGGTA









Celera SNP ID
hCV3504829



Public SNP ID:
rs4535541


SNP in Genomic Sequence:
SEQ ID NO: 6


SNP Position Genomic:
9422


SNP Source:
dbSNP; Celera; HapMap; HGBASE


Population (Allele, Count):
caucasian (C, 52|T, 44)


SNP Type:
INTERGENIC; UNKNOWN


 








Context (SEQ ID NO: 94):



GTAGAGGCTGCGGTCACTCCCCTCGTCAATGCTGGTTCCTGTTCCTGAGGCCGAGAGAACTCCTGACAGCAGAGTGGGCA


TATCTTGGTAGTTGCAGCTT


Y


TCAAGACAGTGTGGCCCAGTGGGGAGAGAGCAGAAAACCTGGGTTATGCTGGCTCTGCCATTTATCAGCTGTGTAACCTT


GGGCAAGTGATACAACCTCT









Celera SNP ID
hCV15586373



Public SNP ID:
rs2281686


SNP in Genomic Sequence:
SEQ ID NO: 6


SNP Position Genomic:
24663


SNP Source:
dbSNP; HapMap; HGBASE


Population (Allele, Count):
caucasian (T, 105|C. 15)


SNP Type:
INTRON


 








Context (SEQ ID NO: 95):



AGCAGGCTGGCGGCCAAGCAAGGCGAGTACAGGACCAAGGCCGGCTCTCAGTTGCGGCGCTCCATCCATGCACAAACCTC


TTCCTGCCCAAACTGCACAC


R


GCTGGTGGAGAAGCTGAGTGCAGGCGCCACAGGGCAGGCATCAGTCATTATACATCGAATCTGCCAGCCCATACCATCAC


GGTGGGGGCGCCTTTCTGGC









SNP ID:
hCV30478067



Public SNP ID:
rs9471078


SNP in Genomic Sequence:
SEQ ID NO: 6


SNP Position Genomic:
27091


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (G, 108|A, 12)


SNP Type:
INTRON


 








Context (SEQ ID NO: 96):



CTGGGCTTCCATTAGGGGATGTTCGGAGCAAAAGGCCCCTTGGCCAGTTGCTGCACACACTTTGCATATGCTCTGGACAC


CAGTGGCCCCAGATCCCATG


Y


TGTGTGTTTTTCTGTCTTCATTTCCTTTCCTGTCTTAGTGATTGCCCCAGGAGGCTGGTTCACACCCCGCATGGGGCCAT


GCCACACATCTCTGTCAGTA









Celera SNP ID
hCV792698



Public SNP ID:
rs728217


SNP in Genomic Sequence:
SEQ ID NO: 6


SNP Position Genomic:
18356


Related Interrogated SNP:
hCV3054822 (Power = .7)


SNP Source:
dbSNP; Celera; HapMap; ABI_Val; HGBASE


Population (Allele, Count):
caucasian (T, 63|C, 55)


SNP Type:
INTRON


 








Context (SEQ ID NO: 97):



TCCTTTCCCTCCTCCCCAGCTAGCAGAACCCCGGCTTTGTAAGTGTTAACCCCAGGTAATTGAGCATAATTGGTCTAAGT


CATAGTGCTAATCTTGCTCC


M


CTCTGTCTCAGCCCCTGAGAGCAGTGAAGTCACTGAAGACCCATAAGACAGTGGGCAAGAGGGCCAGGCTGAGGAGAAAG


CCAAACAAGGCACCACAGTC









Celera SNP ID
hCV8948890



Public SNP ID:
rs1328384


SNP in Genomic Sequence:
SEQ ID NO: 6


SNP Position Genomic:
8245


Related Interrogated SNP:
hCV3054822 (Power = .6)


SNP Source:
dbSNP; Celera; HapMap


Population (Allele, Count):
caucasian (C, 73|A, 45)


SNP Type:
INTERGENIC; UNKNOWN


 








Context (SEQ ID NO: 98):



TCAGTCAGAGCCTGGGGGAAGTCAGAGTGACACCAGCCTGAGAAACACATCTGGGTTCTGGCTCGTCTACTCATGAACTG


AAGGACCGTTTACTGAACAG


Y


GACTCTGTGCTTGGGACACAGCAATGAGCAAGACAAACAGGTCCCTGTTGCATTGGAGCTCTTAGTATTATGGGGGGTTG


CAGGTATTTCAAAAATAGGC









Celera SNP ID
hCV11606396



Public SNP ID:
rs1887716


SNP in Genomic Sequence:
SEQ ID NO: 6


SNP Position Genomic:
19270


Related Interrogated SNP:
hCV3054822 (Power = .8)


SNP Source:
dbSNP; Celera; HapMap


Population (Allele, Count):
caucasian (C, 69|T, 51)


SNP Type:
INTRON


 








Context (SEQ ID NO: 99):



GCTGTTTCCTTTAATGAGCTGTGGCTGCAGACACTGTGAGTTGCCTGCTGAGTATCCTTTCCCTCCTCCCCAGCTAGCAG


AACCCCGGCTTTGTAAGTGT


Y


AACCCCAGGTAATTGAGCATAATTGGTCTAAGTCATAGTGCTAATCTTGCTCCCCTCTGTCTCAGCCCCTGAGAGCAGTG


AAGTCACTGAAGACCCATAA









Celera SNP ID
hCV26547610



Public SNP ID:
rs5006081


SNP in Genomic
Sequence: SEQ ID NO: 6


SNP Position Genomic:
8191


Related Interrogated SNP:
hCV3054822 (Power = .6)


SNP Source:
Celera; HapMap


Population (Allele, Count):
caucasian (T, 73|C, 43)


SNP Type:
INTERGENIC; UNKNOWN


 








Context (SEQ ID NO: 100):



GATTGGCCTTCAGGATGCCCTGCATTCCACTGCCTTGCCTGTGGCTGGGACATCCTGCCTGGACCTGTCATGGCCATTTC


TGGGCTTCCAAACCAGAGCA


Y


GGGCCAAGGGAGAGAGCTGGCAAATCGTTAAAAATAAAAATATTCCCCCCACATTCCACTGGTGTGAGCATCCTCTGACA


TTATATTTTGATGGTGCTAT









Celera SNP ID
hCV27495641



Public SNP ID:
rs3823213


SNP in Genomic Sequence:
SEQ ID NO: 6


SNP Position Genomic:
20308


Related Interrogated SNP:
hCV3054822 (Power = .9)


SNP Source:
dbSNP; HapMap; HGBASE


Population (Allele, Count):
caucasian (C, 89|T, 31)


SNP Type:
INTERGENIC; UNKNOWN


 








Context (SEQ ID NO: 101):



TCTAGAAAGGGGTTAACCTCATTTCCTCAGACATAAAAACTGTCCTTGGCAGTTGGAAACAGCAGAGTTGACTCCACGGA


TGACTGATGCCCCTTACGGG


K


GTTATATACCTGTCCACGCCTGCCCCTGGCAGGGATTTAAACATTCCATGCTGTTCCATGAATCCTAAATCAAATAGATT


CATCCTGGCCCTTGGTGAAG









Celera SNP ID
hCV27505675



Public SNP ID:
rs3818308


SNP in Genomic Sequence:
SEQ ID NO: 6


SNP Position Genomic:
23970


Related Interrogated SNP:
hCV3054822 (Power = .6)


SNP Source:
dbSNP; HapMap; ABI_Val; HGBASE


Population (Allele, Count):
caucasian (T, 84|G, 36)


SNP Type:
INTRON


 








Context (SEQ ID NO: 102):



ATGTCCTTAGACCCTGCAGGGCAGATGGCTCCCTGGAGACACAGCTGTTCAGGCACTGCCTCCAGGTCATCCTGGCCAAG


AGGCTGATGGTGAAAGATGT


R


GTCTGGGAGATCAGGATTGGCCTTCAGGATGCCCTGCATTCCACTGCCTTGCCTGTGGCTGGGACATCCTGCCTGGACCT


GTCATGGCCATTTCTGGGCT









Celera SNP ID
hCV29161257



Public SNP ID:
rs6904582


SNP in Genomic Sequence:
SEQ ID NO: 6


SNP Position Genomic:
20193


Related Interrogated SNP:
hCV3054822 (Power = .8)


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (G, 69|A, 51)


SNP Type:
INTERGENIC; UNKNOWN


 








Context (SEQ ID NO: 103):



GACAGAGAGCCTGAGGGGTGAGGCTGCTCAGGGAACAGGTGAGCAGGCAGCTGCAGCCCTCCAGGTTCGGCCACAGGAAC


CTGGTACTGGGTGACGAGTG


K


TAGCTTGGTTGGCTTGATATTGCTGTGGAAGAAAGGGTAATTCTTGGTGATACATTGAATGGCCAGCTAGTGCGGAGGAA


GAGATTGGAAATAATTCACG









Celera SNP ID
hCV29161258



Public SNP ID:
rs6901022


SNP in Genomic Sequence:
SEQ ID NO: 6


SNP Position Genomic:
22519


Related Interrogated SNP:
hCV3054822 (Power = .9)


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (T, 81|G, 39)


SNP Type:
INTRON


 








Context (SEQ ID NO: 104):



AATTTGGTCTTCCTCTGGTTCTACAGACTTTACAGAGGGCTTACTATGTTCCATTGAACTGTGAGGGCCAGGAGTGGACC


AGAGATGCTGGCCCCTGTCC


Y


GATGGGATTCAGTCAGAGCCTGGGGGAAGTCAGAGTGACACCAGCCTGAGAAACACATCTGGGTTCTGGCTCGTCTACTC


ATGAACTGAAGGACCGTTTA









Celera SNP ID
hCV30532403



Public SNP ID:
rs7754225


SNP in Genomic Sequence:
SEQ ID NO: 6


SNP Position Genomic:
19161


Related Interrogated SNP:
hCV3054822 (Power = .7)


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (C, 65|T, 55)


SNP Type:
INTRON


 








Context (SEQ ID NO: 105)



TGAGGTATGGTCGCACCACTGCACCCCAGCCTGGCTGACAGAGACCCCCCCCATCTCAAAAAATAAATAAAAGAAAGTAA


AAATAAGAAAATAAAAGT


Y


AGTAGACTCTACCCAGCTCTAGAGGGCTGAGGGCACTAAGGCATGCCAAGAATACTGCTGCTGCTACATAGGGGACTGGG


GCAAATGCCATGCCATCTTC









Celera SNP ID
hCV30190183



Public SNP ID:
rs7774046


SNP in Genomic Sequence:
SEQ ID NO: 6


SNP Position Genomic:
18870


Related Interrogated SNP:
hCV3054822 (Power = .7)


SNP Source:
dbSNP


Population (Allele, Count):
caucasian (T, 65|C, 55)


SNP Type:
INTRON


 








Context (SEQ ID NO: 106):



AAAGAAAGTAAAAATAAAAGAAAATAAAAGTTAGTAGACTCTACCCAGCTCTAGAGGGCTGAGGGCACTAAGGCATGCCA


AGAATACTGCTGCTGCTACA


Y


AGGGGACTGGGGCAAATGCCATGCCATCTTCAGGGCTCTTGAGAGTGTGTGTGTGTATGTGTGTGTGTGTGTGCAGTTAC


ATGTGTGTTCCATGGACTCT









Celera SNP ID
hCV30082078



Public SNP ID:
rs7774204


SNP in Genomic Sequence:
SEQ ID NO: 6


SNP Position Genomic:
18939


Related Interrogated SNP:
hCV3054822 (Power = .7)


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (T, 65|C, 55)


SNP Type:
INTRON


 








Context (SEQ ID NO: 107):



TGGCTGTTGGAAACATGAACTCTTCCCAACACAGTGTTATCTCAGGAAATTATTCCACCTGCTTCCTCCTGGTGGTTCTT


TCCCCTGCCTTGGGTAATTT


S


TTCACATGCAGATGTTGATCGGTAGTTAGCTGAAGACTCAAGGGTAACCCTCTGAAGATCTCCAGAGCATGCTCTCTCTC


TTTCTCTCTCTTTTTTCTCC









Celera SNP ID
hCV30586596



Public SNP ID:
rs9369112


SNP in Genomic Sequence:
SEQ ID NO: 6


SNP Position Genomic:
16913


Related Interrogated SNP:
hCV3054822 (Power = .9)


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (C, 88|G, 32)


SNP Type:
INTRON


 








Context (SEQ ID NO: 108):



CAAATGCCATGCCATCTTCAGGGCTCTTGAGAGTGTGTGTGTGTATGTGTGTGTGTGTGTGCAGTTACATGTGTGTTCCA


TGGACTCTTGGGGAGGCTTC


Y


AGAATCAGAATTTGGTCTTCCTCTGGTTCTACAGACTTTACAGAGGGCTTACTATGTTCCATTGAACTGTGAGGGCCAGG


AGTGGACCAGAGATGCTGGC









Celera SNP ID
hCV29902034



Public SNP ID:
rs9380848


SNP in Genomic Sequence:
SEQ ID NO: 6


SNP Position Genomic:
19052


Related Interrogated SNP:
hCV3054822 (Power = .9)


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (T, 89|C, 31)


SNP Type:
INTRON


 








Context (SEQ ID NO: 109):



CCCCCAGGCAGCTATGTTTTTCCCTGTGGAAGTGGGGAGTGTCAGCCCAAATCCTCCCAAGTGGCTGGGTGAAGGTTGTG


AGGCCCAAGGTACCCACACT


R


CCCTCTCTCAAGCTCTCAGCAGAGCCCCTGGGAGCTGCCCACTGTTCTCTGGGTGCTATGATGTCCTTAGACCCTGCAGG


GCAGATGGCTCCCTGGAGAC









Celera SNP ID
hCV29937959



Public SNP ID:
rs9462531


SNP in Genomic Sequence:
SEQ ID NO: 6


SNP Position Genomic:
20032


Related Interrogated SNP:
hCV3054822 (Power = .8)


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (G, 77|A, 41)


SNP Type:
INTERGENIC; UNKNOWN


 








Context (SEQ ID NO: 110):



TGGCCATGGCTGAAACTCACTGCTTTTGTCTCTCTAGTACAAGGACTGGCAGGGCAAGAGCTGGGGGCCCGTGGGCACGT


GGCTGCTGATGAGATCTGGG


S


TCTGATGTTTCTGCTGCACTGTTGAAATGCAGCCTGGGCCAGGATTGTGAACGCTGCCGGTGGTAGGGAGCATGGAGTCT


GAGCCATCTCCCACCCAGGA









Celera SNP ID
hCV30478066



Public SNP ID:
rs9462533


SNP in Genomic Sequence:
SEQ ID NO: 6


SNP Position Genomic:
23035


Related Interrogated SNP:
hCV3054822 (Power = .9)


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (C, 76|G, 44)


SNP Type:
INTRON


 








Context (SEQ ID NO: 111):



TCAGTTTTTCACAAACCACCCAAGCCCAATAAGACTTCACCTGGCTGGGGTTTAGGATAGAGTCTTTCTCAGAAGCATGT


TCAGTTTAATGGGGGACTTC


Y


GAAAACCCTAGGGCCACAAAAGCCACAGAAAGGGCAACATAATGGTGGACTTTGGGGGAGATCTCAGCTCAAGAGGCCCA


CAGACTGTGCTGTAGGAACT









Celera SNP ID
hDV70715992



Public SNP ID:
rs16891930


SNP in Genomic Sequence:
SEQ ID NO: 6


SNP Position Genomic:
10967


Related Interrogated SNP:
hCV3054822 (Power = .9)


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (T, 95|C, 23)


SNP Type:
INTRON


 








Context (SEQ ID NO: 112):



GGACCCCGTGTCAACCTGTCTTTGGCTTCCTTTGTGGCCCCGTGGTCACTGAAAAGCCAGAATGAATATTCTTCCTTTCG


GAATAAAAATTGAGCTGTGG


R


AGTTTTGTTTGCTTTGATGAATTACTTCCAGGCTGCTGTTTATTTGGAGAGCAAAGCTCCCCAGCTGCAGGGTGGGTAGA


GGCTGCGGTCACTCCCCTCG









Celera SNP ID
hDV70716012



Public SNP ID:
rs16891961


SNP in Genomic Sequence:
SEQ ID NO: 6


SNP Position Genomic:
24487


Related Interrogated SNP:
hCV3054822 (Power = .6)


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (A, 83|G, 35)


SNP Type:
INTRON


 


Gene Number:
4


Celera Gene:
hCG2041191-209000073583183


Gene Symbol:
KIF6


Protein Name:
kinesin family member 6


Celera GenomicAxis:
GA_x5YUV32W6W6 (12667048 . . . 12720760)


Chromosome:
6


OMIM NUMBER:


OMIM Information:


 


Genomic Sequence (SEQ ID NO: 7):


 


SNP Information


 








Context (SEQ ID NO: 113):



TTCATTAGAGAAGAGTATACTGTACTTAATATAAAAAAGCCTCACTTTTAAAAGTCATATAGCAATGCACCTGCTAGTTA


GGATAGAAATGAAGTAGCC T


Y


TCAGCATCAAAAATATAACTATCATAAACAACTCTCACCTATCAATTCCCTCAGGGAAGAATGGATTCACTTAGGTGGTA


ATGAAAGGGCATGACTCAGT









Celera SNP ID
hCV814585



Public SNP ID:
rs160023


SNP in Genomic Sequence:
SEQ ID NO: 7


SNP Position Genomic:
11247


Related Interrogated SNP:
hCV32202303 (Power = .51)


SNP Source:
dbSNP; HapMap; HGBASE


Population (Allele, Count):
caucasian (C, 13|T, 107)


SNP Type:
INTRON


 








Context (SEQ ID NO: 114):



TTGGCTTGCTACTTAAATGTTACATTATTCTTGTGGCTTCCTTGGAAATCCAGATTTTCATGTACTGATAGAAATACTTC


CTCTACCAGATACATAAACA


Y


AGCATAGCAGCTTTCAGCACAATTTCTCTCATTAAAGAAGCAATTCCATTTTTTAAAAAGAATCATACAAAAGAAGTTTG


TTATTTTATTTAAAAAGGAT









Celera SNP ID
hCV814588



Public SNP ID:
rs159958


SNP in Genomic Sequence:
SEQ ID NO: 7


SNP Position Genomic:
16552


Related Interrogated SNP:
hCV32202303 (Power = .51)


SNP Source:
dbSNP; HapMap; HGBASE


Population (Allele, Count):
caucasian (C, 13|T, 107)


SNP Type:
TRANSCRIPTION FACTOR BINDING SITE; INTRON


 








Context (SEQ ID NO: 115):



CCAGTCAATTATAAAGTCAAATAACTATAGTAAAAATCTTGCCAGTTGTAATCAATCACTACACATTTGTATAGGAAGGA


TTTTCCAGAACACCTTGCTT


Y


CTACTTGTGTTCTTTCTATATCTCTCTTTTGGAGGCTATCAAGGTGAGAAATAATAACTAGAAATATCTTTGGTTTATTT


GTAAGTGTATCCTCAAGTCC









Celera SNP ID
hCV814594



Public SNP ID:
rs302596


SNP in Genomic Sequence:
SEQ ID NO: 7


SNP Position Genomic:
22207


Related Interrogated SNP:
hCV32202303 (Power = .51)


SNP Source:
dbSNP; HapMap; HGBASE


Population (Allele, Count):
caucasian (C, 13|T, 107)


SNP Type:
INTRON


 








Context (SEQ ID NO: 116):



CTGTACGTGAAAATCACATGCCCCAGTATATGTGATTGCTCAGTAAGTACATAGTGCATGCTCAATGAGTATTAGCTATC


ATCCATTTTTGGCTCCCCAC


Y


GACATTTTTATAGAAAAGGATTTTCATCCCAATTCAGAATTTTACCAAGTTATGAATGTTTTTCTGTATGATTTTTTTCC


CCCTTAACTCTTTTTAGATT









Celera SNP ID
hDV71111070



Public SNP ID:
rs302595


SNP in Genomic Sequence:
SEQ ID NO: 7


SNP Position Genomic:
22478


Related Interrogated SNP:
hCV32202303 (Power = .51)


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (T, 13|C, 107)


SNP Type:
INTRON


 








Context (SEQ ID NO: 117):



AAGAATAGACTTTTTCATTTCCTAGAAATGAAAAATAATAGCAAAAATTAAAAAATTTAGAGGGATAAAGAAGAATTCAC


CACTGAAAAGCAAATTACCA


Y


GCTGGAAGTCTGTGCTGACAAATTCTTGCAGAACTCAGAACCACAGGGCAAAGAGAGGAGAACTATGAATGGGAAGTTGT


AATATTTGAAGGATAGGACT









Celera SNP ID
hCV814600



Public SNP ID:
rs302588


SNP in Genomic Sequence:
SEQ ID NO: 7


SNP Position Genomic:
28947


Related Interrogated SNP:
hCV32202303 (Power = .51)


SNP Source:
dbSNP; HapMap; HGBASE


Population (Allele, Count)
caucasian (T, 13|C, 107)


SNP Type:
INTRON


 








Context (SEQ ID NO: 118):



TGTTTTGTTTTGTTTTGAGACTCAGAATCTAAATCTAAAACAAATGGTTATAGGAACAGAAGGTATTAATGCCATCATCA


TTACATGTCCTGCAGCCTAA


R


TATATAATTGATACACTTAAGCACACTACCACAGTTATCTGAGAATTGAAGGCAACATGAAAAACCACGAGCCCACTGAA


CAAAATATGGGAAATCCTAC









Celera SNP ID
hCV814607



Public SNP ID:
rs303701


SNP in Genomic Sequence:
SEQ ID NO: 7


SNP Position Genomic:
32895


Related Interrogated SNP:
hCV32202303 (Power = .51)


SNP Source:
dbSNP; HapMap; HGBASE


Population (Allele, Count):
caucasian (G, 13|A, 107)


SNP Type:
HUMAN-MOUSE SYNTENIC REGION; INTRON


 








Context (SEQ ID NO: 119):



GTTGAATATTTTATTAATTTTTAATTTTGAATATTAAAACCATTATTTTCAATTCAGAGATTTAAAAATAAGCCAATGGT


GCTTTTACAACAATTGCACT


Y


TGTTTGTATCTATATAAAGAATACTTTGGGGCCAGGTGCAGTGGTTCACACCTGTAATCCTAGCACTTTGGGAGGCCGAG


GCGGGCAGATCACTCGAGGT









Celera SNP ID
hCV814610



Public SNP ID:
rs159957


SNP in Genomic Sequence:
SEQ ID NO: 7


SNP Position Genomic:
36241


Related Interrogated SNP:
hCV32202303 (Power | .10


SNP Source:
dbSNP; HapMap; HGBASE


Population (Allele, Count):
caucasian (T,13|C, 107)


SNP Type:
INTRON


 








Context (SEQ ID NO: 120):



CTTGTTTAGTGTAATATTGGCTTTCTATCCTTATTCATAAAAATATTATTTGAGATAGTGAAGTTTTTTTTTTAAGAAGA


CAGAACAAGTGCAAGGATGA


W


TATGAAAATGTTTTGAAATGTATAAAGCACTGTACAAATGGAAAGCATTACCACTGTTAAAACTATCAAAGATTCATTTT


ATATATCCTTTTTATTAATC









Celera SNP ID
hCV814619



Public SNP ID:
rs303693


SNP in Genomic Sequence:
SEQ ID NO: 7


SNP Position Genomic:
40786


Related Interrogated SNP:
hCV32202303 (Power = .51)


SNP Source:
dbSNP; HapMap; ABI_Val; HGBASE


Population (Allele, Count):
caucasian (T, 12|A, 104)


SNP Type:
INTRON


 








Context (SEQ ID NO: 121):



TAAAGCACCTGTCAGGTGGCCATTTCTCAAGATACATAAGAATGACTATGCTCCCTTCTAGGCAGAAGAGAGGATGTTTT


GAAATATTTAAGAAGCATAA


R


AAGTGCTGGGCGCGGTGGCTCACGCCTGTAATCCCAGCACTTTGGGAGGCCGAGGCGGGCGGATCACAAGATCAGGAGGA


GATCGAGACCATCCTAGCTA









Celera SNP ID
hCV814621



Public SNP ID:
rs303653


SNP in Genomic Sequence:
SEQ ID NO: 7


SNP Position Genomic:
42493


Related Interrogated SNP:
hCV32202303 (Power = .51)


SNP Source:
dbSNP; HGBASE


Population (Allele, Count):
caucasian (A, 13|G, 107)


SNP Type:
INTRON


 








Context (SEQ ID NO: 122):



CCTTTGCTGCTTCCTCTCTCTAGCATATCTACATCCAAATAGTTTCCAAGTCCTGGTTATGCTGCCTCCTGAATAATTTT


TTCCTCCCTCCCCTCAACTT


Y


GTCCTCACTACTTACTACTTGCTTTAGTTTGGCTCTTAGTCTCTTTGCCAGAACTAGCAAAGTTCTCCTAACTGCTTTTC


CTGCCTCTAATCCCGCCCTC









Celera SNP ID
hCV814633



Public SNP ID:
rs303649


SNP in Genomic Sequence:
SEQ ID NO: 7


SNP Position Genomic:
50875


Related Interrogated SNP:
hCV32202303 (Power = .51)


SNP Source:
dbSNP; HapMap; HGBASE


Population (Allele, Count):
caucasian (T, 13|C, 107)


SNP Type:
INTERGENIC; UNKNOWN; REPEATS


 








Context (SEQ ID NO: 123):



TACGCATATGCCCCTGGGAGGTTGGTACTATGAGAATATCCTCCAGGAGCCACTTTGTTTTAGCTCGTCTGACTGAGGCC


TTAAACAAGGAAAGGAAGAG


M


GCTGTTAAGGGCACCACAGTAGCACAAATCCAGAGATATTAAGACTTTCCTAACTCAAGTCACAAGGTCTTCCAGCTCAA


TAACTAATGCTTGATGCATA









Celera SNP ID
hCV1416399



Public SNP ID:
rs11752840


SNP in Genomic Sequence:
SEQ ID NO: 7


SNP Position Genomic:
11496


Related Interrogated SNP:
hCV32202303 (Power = .51)


SNP Source:
dbSNP; Celera; HapMap


Population (Allele, Count):
caucasian (A, 107|C, 13)


SNP Type:
INTRON


 








Context (SEQ ID NO: 124):



AATGTGTTATATGGTCTCCCCCAATTATATCTTTAGAGAAAATGTTCAGCTTTCTGAAAATAATCTGTTAAGCACCTAAC


TTAGTCTAAATAGTTTCAGA


Y


TTCTCTTAACTTGTAACGTTCCTGTGAATGTTAACTATAGTGCTTTAAATCAAGAGTGGGTCCCTTCTGAGCATGGGGCC


CTGTGTTATGACATAGGTCT









Celera SNP ID
hCV3078083



Public SNP ID:
rs302593


SNP in Genomic Sequence:
SEQ ID NO: 7


SNP Position Genomic:
23148


Related Interrogated SNP:
hCV32202303 (Power = .51)


SNP Source:
dbSNP; Celera; HapMap; HGBASE


Population (Allele, Count):
caucasian (T, 13|C, 107)


SNP Type:
INTRON


 








Context (SEQ ID NO: 125):



GTGCGTGGCCTGGGGCTTTGGGGACCCCTGCTACATAGTCATGCACATAATATCACTGGTTTGGACGAAGTAGAAAAGAT


GTCTAAATTGGTGAGATTCC


W


GAATTACAGAATTTTGGACAGAAATGTGATTAGTTTTATTTAGCCTTAATTGCATACAGTAACCAGAATTAAGTTACCAT


GTGTTAACCCAGTAGAATAA









Celera SNP ID
hCV3078087



Public SNP ID:
rs11754307


SNP in Genomic Sequence:
SEQ ID NO: 7


SNP Position Genomic:
27946


Related Interrogated SNP:
hCV32202303 (Power = .51)


SNP Source:
dbSNP; Celera; HapMap


Population (Allele, Count):
caucasian (T, 107|A, 13)


SNP Type:
INTRON


 


Gene Number:
5


Celera Gene:
Chr6:40760720 . . . 40800720


Gene Symbol:


Protein Name:


Celera Genomic Axis:
GA_x5YUV32W6W6 (12225041 . . . 12265041)


Chromosome:
6


OMIM NUMBER:


OMIM Information:


 


Genomic Sequence (SEQ ID NO: 8):


 


SNP Information


 








Context (SEQ ID NO: 126):



CTAATGTATTTTTTATTCTGTGGAGATGGGTAGCTAGAACCACAGACACGCACCATCATATCCTGCTAATTTTTAAATCT


TTTGTGGAGATGAGGTCTCT


Y


GATGCTGCCCAGGAAGTCTCAAACTCCTGGCCTCAAGAGATCCTCTCACCTTAGGCTTTTGAAGTGCTGGGATTATAGGC


ATGAGCCTCTGCACCTGCCC









Celera SNP ID
hCV29649182



Public SNP ID:
rs9471032


SNP in Genomic Sequence:
SEQ ID NO: 8


SNP Position Genomic:
20000


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (C, 104|T, 16)


SNP Type:
INTERGENIC; UNKNOWN; REPEATS


 


Gene Number:
6


Celera Gene:
Chr6:41237467 . . . 41290799


Gene Symbol:


Protein Name:


Celera Genomic Axis:
GA_x5YUV32W6W6 (12701788 . . . 12755120)


Chromosome:
6


OMIM NUMBER:


OMIM Information:


 


Genomic Sequence (SEQ ID NO: 9):


 


SNP Information


 








Context (SEQ ID NO: 127):



GTTGAATATTTTATTAATTTTTAATTTTGAATATTAAAACCATTATTTTCAATTCAGAGATTTAAAAATAAGCCAATGGT


GCTTTTACAACAATTGCACT


Y


TGTTTGTATCTATATAAAGAATACTTTGGGGCCAGGTGCAGTGGTTCACACCTGTAATCCTAGCACTTTGGGAGGCCGAG


GCGGGCAGATCACTCGAGGT









Celera SNP ID
hCV814610



Public SNP ID:
rs159957


SNP in Genomic Sequence:
SEQ ID NO: 9


SNP Position Genomic:
1501


Related Interrogated SNP:
hCV32202303 (Power = .51)


SNP Source:
dbSNP; HapMap; HGBASE


Population (Allele, Count):
caucasian (T, 13|C, 107)


SNP Type:
INTRON


 








Context (SEQ ID NO: 128):



CTTGTTTAGTGTAATATTGGCTTTCTATCCTTATTCATAAAAATATTATTTGAGATAGTGAAGTTTTTTTTTTAAGAAGA


CAGAACAAGTGCAAGGATGA


W


TATGAAAATGTTTTGAAATGTATAAAGCACTGTACAAATGGAAAGCATTACCACTGTTAAAACTATCAAAGATTCATTTT


ATATATCCTTTTTATTAATC









Celera SNP ID
hCV814619



Public SNP ID:
rs303693


SNP in Genomic Sequence:
SEQ ID NO: 9


SNP Position Genomic:
6046


Related Interrogated SNP:
hCV32202303 (Power = .51)


SNP Source:
dbSNP; HapMap; ABI_Val; HGBASE


Population (Allele, Count):
caucasian (T, 12|A, 104)


SNP Type:
INTRON


 








Context (SEQ ID NO: 129):



TAAAGCACCTGTCAGGTGGCCATTTCTCAAGATACATAAGAATGACTATGCTCCCTTCTAGGCAGAAGAGAGGATGTTTT


GAAATATTTAAGAAGCATAA


R


AAGTGCTGGGCGCGGTGGCTCACGCCTGTAATCCCAGCACTTTGGGAGGCCGAGGCGGGCGGATCACAAGATCAGGAGGA


GATCGAGACCATCCTAGCTA









Celera SNP ID
hCV814621



Public SNP ID:
rs303653


SNP in Genomic Sequence:
SEQ ID NO: 9


SNP Position Genomic:
7753


Related Interrogated SNP:
hCV32202303 (Power = .51)


SNP Source:
dbSNP; HGBASE


Population (Allele, Count):
caucasian (A, 13|G, 107)


SNP Type:
INTRON


 








Context (SEQ ID NO: 130):



CCTTTGCTGCTTCCTCTCTCTAGCATATCTACATCCAAATAGTTTCCAAGTCCTGGTTATGCTGCCTCCTGAATAATTTT


TTCCTCCCTCCCCTCAACTT


Y


GTCCTCACTACTTACTACTTGCTTTAGTTTGGCTCTTAGTCTCTTTGCCAGAACTAGCAAAGTTCTCCTAACTGCTTTTC


CTGCCTCTAATCCCGCCCTC









Celera SNP ID
hCV814633



Public SNP ID:
rs303649


SNP in Genomic Sequence:
SEQ ID NO: 9


SNP Position Genomic:
16135


Related Interrogated SNP:
hCV32202303 (Power = .51)


SNP Source:
dbSNP; HapMap; HGBASE


Population (Allele, Count):
caucasian (T, 13|C, 107)


SNP Type:
INTERGENIC; UNKNOWN; REPEATS


 








Context (SEQ ID NO: 131):



GTGCATAATCAAGTACTCTAAGACTTAGTGTCATAAAGCAACCATTTATTTTGTTCATGGATTCTCTGGGTCGGGAATCC


AGAAAGGACAGAGGGGTCAA


R


GCTGGTCTCTGTGTCACAATGTCAGGAGCCTCAGCTGGAAGACTGGAAGCTGGGGATAGGAATACTCATTGGAAGCTCCT


TTACTCACATGTCTGGCTAT









Celera SNP ID
hCV1416391



Public SNP ID:
rs11751730


SNP in Genomic Sequence:
SEQ ID NO: 9


SNP Position Genomic:
33332


Related Interrogated SNP:
hCV32202303 (Power = .51)


SNP Source:
dbSNP; Celera; HapMap


Population (Allele, Count):
caucasian (G, 106|A, 10)


SNP Type:
INTERGENIC; UNKNOWN; REPEATS


 








Context (SEQ ID NO: 132):



CCACTGGAAATATTTCAGAATTCCAGATGCTCTCTATCCTCATCAGCACTTGGTATTGACACTTAAGAAATTTTTTAGCC


ATTTAGTAAGTACTAGTGGG


R


GTGGAGCCAAGATGGCCGAATAGGAACAGCTCCAGTCTACAGCTCCCAGCATAAGCAACACAGAAGACGAATGATTTCTG


CATTTCCAACTGAGGTACTG









Celera SNP ID
hCV32203040



Public SNP ID:
rs11755914


SNP in Genomic Sequence:
SEQ ID NO: 9


SNP Position Genomic:
20000


Related Interrogated SNP:
hCV32202303 (Power = .51)


SNP Source:
dbSNP; HapMap


Population (Allele, Count):
caucasian (G, 106|A, 14)


SNP Type:
INTERGENIC; UNKNOWN; REPEATS


















TABLE 3







Marker
Alleles
Primer 1 (Allele-specific Primer)














hCV15876373
C/T
GGCCACACTGTCTTGAG
(SEQ ID NO: 133)



 


hCV1650850
C/G
GGCCATGTGAGCATGTG
(SEQ ID NO: 136)


 


hCV29649182
C/T
TTGTGGAGATGAGGTCTCTC
(SEQ ID NO: 139)


 


hCV29992177
A/G
TGAATCGTAGACGGAGCTTAGGACGCGTTTATGCAGGGGTTCA
(SEQ ID NO: 142)


 


hCV30225864
A/G
GCCCAAGCGGAAGGTACGACTTTCCATTTTAAGGACAAGAAAACT
(SEQ ID NO: 145)


 


hCV30388472
C/T
CCTTCTTTGAAAGTCATCAGACAC
(SEQ ID NO: 148)


 


hCV30478067
A/G
GCCCAAACTGCACACA
(SEQ ID NO: 151)


 


hCV3054766
A/C
GATGATAAGCCATTCCCAGTT
(SEQ ID NO: 154)


 


hCV3054789
A/T
CCTCACCTTGTTTGAGAAGA
(SEQ ID NO: 157)


 


hCV3054799
A/G
CGGGTAGAAGCCGACCAAGTGTCTAGACTCCCAGCATGAAT
(SEQ ID NO: 160)


 


hCV3054805
C/G
GAACCCCTGGCTATTTCTG
(SEQ ID NO: 163)


 


hCV3054808
A/C
GACCAGAGGTAGCCACA
(SEQ ID NO: 166)


 


hCV3054809
A/G
GCAGATTAGCTTCGAGAGAGACGCCTGGCAGAATCGGGAA
(SEQ ID NO: 169)


 


hCV3054813
A/G
CGGTGTTGAGCCCATACCTAGTGGTGAGGGGACAGTGTGT
(SEQ ID NO: 172)


 


hCV3054822
A/T
CTGCCCCTACTTCAACCT
(SEQ ID NO: 175)


 


hCV3054829
C/T
ACGGCGGATGGGAG
(SEQ ID NO: 178)


 


hCV31340487
C/T
GCTGGATCTTGCAGCTTC
(SEQ ID NO: 181)


 


hCV32202303
C/G
GAAATCTCTTTTCGATTGAACAGC
(SEQ ID NO: 184)


 


hCV792699
C/T
ATGGACACATGCTTGACG
(SEQ ID NO: 187)












Marker
Alleles
Primer 2 (Allele-specific Primer)














hCV15876373
C/T
GGCCACACTGTCTTGAA
(SEQ ID NO: 134)



 


hCV1650850
C/G
GGCCATGTGAGCATGTC
(SEQ ID NO: 137)


 


hCV29649182
C/T
TTTGTGGAGATGAGGTCTCTT
(SEQ ID NO: 140)


 


hCV29992177
A/G
CACGAGTTGCCACGGACTTCATTGTTTTATGCAGGGGTTCG
(SEQ ID NO: 143)


 


hCV30225864
A/G
CACGGGAGAGATTGTTTTGCCAGAGAATTTTAAGGACAAGAAAACC
(SEQ ID NO: 146)


 


hCV30388472
C/T
CCTTCTTTGAAAGTCATCAGACAT
(SEQ ID NO: 149)


 


hCV30478067
A/G
GCCCAAACTGCACACG
(SEQ ID NO: 152)


 


hCV3054766
A/C
GATGATAAGCCATTCCCAGTG
(SEQ ID NO: 155)


 


hCV3054789
A/T
CCTCACCTTGTTTGAGAAGT
(SEQ ID NO: 158)


 


hCV3054799
A/G
CACGGGTTCCGAGCCCAGTCTATAGACTCCCAGCATGAAC
(SEQ ID NO: 161)


 


hCV3054805
C/G
GAACCCCTGGCTATTTCTC
(SEQ ID NO: 164)


 


hCV3054808
A/C
GACCAGAGGTAGCCACC
(SEQ ID NO: 167)


 


hCV3054809
A/G
CGAGCGAGGTCAATAGGTTCCAGCTTGGCAGAATCGGGAG
(SEQ ID NO: 170)


 


hCV3054813
A/G
CGGGTAGAAGCCGACCAAGTGTCTAAGGGGACAGTGTGC
(SEQ ID NO: 173)


 


hCV3054822
A/T
TGCCCCTACTTCAACCA
(SEQ ID NO: 176)


 


hCV3054829
C/T
AACGGCGGATGGGAA
(SEQ ID NO: 179)


 


hCV31340487
C/T
GCTGGATCTTGCAGCTTT
(SEQ ID NO: 182)


 


hCV32202303
C/G
GAAATCTCTTTTCGATTGAACAGG
(SEQ ID NO: 185)


 


hCV792699
C/T
GATATGGACACATGCTTGACA
(SEQ ID NO: 188)












Marker
Alleles
Common Primer














hCV15876373
C/T
AGCTGCAGGGTGGGTAGAG
(SEQ ID NO: 135)



 


hCV1650850
C/G
AACATTCACAGCCAACCTCTCTACA
(SEQ ID NO: 138)


 


hCV29649182
C/T
AGGATTGGGAGAGGGAGGTG
(SEQ ID NO: 141)


 


hCV29992177
A/G
CTCTTTGTGCTGTACAG
(SEQ ID NO: 144)


 


hCV30225864
A/G
GAGAGCCAGAGAAGATG
(SEQ ID NO: 147)


 


hCV30388472
C/T
CCTGGGCCCATTTCTGAAACT
(SEQ ID NO: 150)


 


hCV30478067
A/G
AGGCTTAACATTGCCTTCACATTG
(SEQ ID NO: 153)


 


hCV3054766
A/C
CCCCACATTCCTGGAGTACTAACAGTTTAT
(SEQ ID NO: 156)


 


hCV3054789
A/T
TGATTGGGAAGCCAGAATCTTTG
(SEQ ID NO: 159)


 


hCV3054799
A/G
GGTCCCAACTCCTCT
(SEQ ID NO: 162)


 


hCV3054805
C/G
ACACACACACACATGCACATGTAC
(SEQ ID NO: 165)


 


hCV3054808
A/C
AAGCCCTTTCTCCTTCCCTTCTC
(SEQ ID NO: 168)


 


hCV3054809
A/G
CTACTCAGGATTCTTTGAG
(SEQ ID NO: 171)


 


hCV3054813
A/G
GGGGCACAAGTGGA
(SEQ ID NO: 174)


 


hCV3054822
A/T
GAAACAGACAAACAAACACACCTGTTAC
(SEQ ID NO: 177)


 


hCV3054829
C/T
AGAACACACTCCAGGTATTTCTGGTAATT
(SEQ ID NO: 180)


 


hCV31340487
C/T
CATACCTCTGGATGGGGATGTGT
(SEQ ID NO: 183)


 


hCV32202303
C/G
ACTGAAGTGGAAAGCTGAATTCTAGA
(SEQ ID NO: 186)


 


hCV792699
C/T
TCATCCGTGGCTCCACTGAT
(SEQ ID NO: 189)






















TABLE 4





Interrogated SNP
Interrogated rs
LD SNP
LD SNP rs
Power
Threshold r2
r2





















hCV29992177
rs9471080
hCV29576755
rs6899653
0.51
0.524353992
0.55894


hCV29992177
rs9471080
hCV29703356
rs9471079
0.51
0.524353992
1


hCV29992177
rs9471080
hCV30225864
rs9394584
0.51
0.524353992
0.54118


hCV30225864
rs9394584
hCV29161261
rs6924090
0.51
0.604351053
0.88462


hCV30225864
rs9394584
hCV2946524
rs20456
0.51
0.604351053
0.62838


hCV30225864
rs9394584
hCV29576755
rs6899653
0.51
0.604351053
1


hCV30225864
rs9394584
hCV30280062
rs7772430
0.51
0.604351053
0.64299


hCV30225864
rs9394584
hCV3054799
rs20455
0.51
0.604351053
0.85135


hCV30225864
rs9394584
hCV3054805
rs2894424
0.51
0.604351053
0.67391


hCV30225864
rs9394584
hCV3054813
rs9471077
0.51
0.604351053
0.66022


hCV3054799
rs20455
hCV29161261
rs6924090
0.51
0.905231457
1


hCV3054822
rs11751357
hCV11606396
rs1887716
0.51
0.202258224
0.4381


hCV3054822
rs11751357
hCV26547610
rs5006081
0.51
0.202258224
0.26881


hCV3054822
rs11751357
hCV27495641
rs3823213
0.51
0.202258224
0.66707


hCV3054822
rs11751357
hCV27505675
rs3818308
0.51
0.202258224
0.27141


hCV3054822
rs11751357
hCV29161257
rs6904582
0.51
0.202258224
0.4381


hCV3054822
rs11751357
hCV29161258
rs6901022
0.51
0.202258224
0.65497


hCV3054822
rs11751357
hCV29902034
rs9380848
0.51
0.202258224
0.66707


hCV3054822
rs11751357
hCV29937959
rs9462531
0.51
0.202258224
0.41097


hCV3054822
rs11751357
hCV30082078
rs7774204
0.51
0.202258224
0.38155


hCV3054822
rs11751357
hCV30190183
rs7774046
0.51
0.202258224
0.38155


hCV3054822
rs11751357
hCV30478066
rs9462533
0.51
0.202258224
0.54264


hCV3054822
rs11751357
hCV30532403
rs7754225
0.51
0.202258224
0.38155


hCV3054822
rs11751357
hCV30586596
rs9369112
0.51
0.202258224
0.63465


hCV3054822
rs11751357
hCV792698
rs728217
0.51
0.202258224
0.37822


hCV3054822
rs11751357
hCV8948890
rs1328384
0.51
0.202258224
0.26881


hCV3054822
rs11751357
hDV70715992
rs16891930
0.51
0.202258224
0.59752


hCV3054822
rs11751357
hDV70716012
rs16891961
0.51
0.202258224
0.25712


hCV32202303
rs11751690
hCV1416391
rs11751730
0.51
0.668241315
0.77328


hCV32202303
rs11751690
hCV1416399
rs11752840
0.51
0.668241315
0.82307


hCV32202303
rs11751690
hCV16029234
rs2499452
0.51
0.668241315
0.82307


hCV32202303
rs11751690
hCV1703050
rs2499450
0.51
0.668241315
0.82293


hCV32202303
rs11751690
hCV2487638
rs11758101
0.51
0.668241315
0.88


hCV32202303
rs11751690
hCV2487644
rs11754132
0.51
0.668241315
1


hCV32202303
rs11751690
hCV3078072
rs303675
0.51
0.668241315
0.82307


hCV32202303
rs11751690
hCV3078083
rs302593
0.51
0.668241315
0.82293


hCV32202303
rs11751690
hCV3078087
rs11754307
0.51
0.668241315
0.82307


hCV32202303
rs11751690
hCV32203040
rs11755914
0.51
0.668241315
0.75058


hCV32202303
rs11751690
hCV814553
rs302573
0.51
0.668241315
0.82293


hCV32202303
rs11751690
hCV814557
rs302575
0.51
0.668241315
0.82259


hCV32202303
rs11751690
hCV814585
rs160023
0.51
0.668241315
0.82307


hCV32202303
rs11751690
hCV814588
rs159958
0.51
0.668241315
0.82307


hCV32202303
rs11751690
hCV814594
rs302596
0.51
0.668241315
0.82307


hCV32202303
rs11751690
hCV814600
rs302588
0.51
0.668241315
0.82307


hCV32202303
rs11751690
hCV814607
rs303701
0.51
0.668241315
0.82307


hCV32202303
rs11751690
hCV814610
rs159957
0.51
0.668241315
0.82307


hCV32202303
rs11751690
hCV814619
rs303693
0.51
0.668241315
0.80889


hCV32202303
rs11751690
hCV814621
rs303653
0.51
0.668241315
0.82307


hCV32202303
rs11751690
hCV814633
rs303649
0.51
0.668241315
0.82307


hCV32202303
rs11751690
hCV8948950
rs964116
0.51
0.668241315
0.82307


hCV32202303
rs11751690
hCV8948982
rs303678
0.51
0.668241315
0.90035


hCV32202303
rs11751690
hDV71111070
rs302595
0.51
0.668241315
0.82307
















TABLE 5





Association of KIF6Trp719Arg (hCV3054799) with MI and CHD


in the placebo arms of CARE and WOSCOPS


















Unadjusted
Adjusted†
















Study
Genotype
On-trial MI*
Total*
HR
95% Cl
p Value
HR
95% Cl
p Value





CARE
GG
16
155
1.33
0.75-2.35
0.33 
1.33
0.75-2.36
0.33



GA
82
636
1.63
1.13-2.35
0.009
1.54
1.07-2.23
0.02



GG + GA
98
791
1.57
1.10-2.25
0.013
1.50
1.05-2.15
0.03



AA
44
542
1.00
ref

1.00
ref













Matched‡
Adjusted†‡
















Study
Genotype
Case*
Control*
OR
95% Cl
p Value
OR
95% Cl
p Value





WOSCOPS
GG
 35
 59
1.49
0.92-2.40
0.10 
1.48
0.91-2.41
0.11 



GA
137
204
1.61
1.18-2.21
0.003
1.56
1.14-2.15
0.006



GG + GA
172
263
1.59
1.18-2.14
0.003
1.55
1.14-2.09
0.005



AA
104
256
1.00
ref

1.00
ref





*Data for patients presented as number of patients


†Adjusted for age (continuous), sex, smoking (current versus non-current) (except in WOSCOPS, where no adjustments for age and smoking were made because cases and controls were matched), history of hypertension, history of diabetes, BMI (continuous), baseline LDL-C level (continuous), and baseline HDL-C level (continuous).


‡Cases and controls were matched for age (in two-year age groups) and smoking (current versus non-current), all were men.













TABLE 6





Effect of pravastatin on MI and CHD in KIF6 Trp719Arg (hCV3054799) subgroups: CARE and WOSCOPS


















Unadjusted
Adjusted†

















Study
Genotype
Treatment
On-trial MI*
Total*
HR
95% CI
p Value
HR
95% CI
p Value





CARE
GG + GA
Pravastatin
64
810
0.63
0.46-0.86
0.004
0.63
0.46-0.87
0.005




Placebo
98
791
1.00
ref

1.00
ref



AA
Pravastatin
39
554
0.86
0.56-1.33
0.50
0.80
0.52-1.24
0.32




Placebo
44
542
1.00
ref

1.00
ref










p interaction 0.25§
p interaction 0.39§














Matched‡
Adjusted†‡

















Study
Genotype
Treatment
Case*
Control*
OR†
95% CI†
p Value
OR†
95% CI
p Value





WOSCOPS
GG + GA
Pravastatin
108
330
0.50
0.38-0.67
<0.0001
0.50
0.38-0.67
<0.0001




Placebo
172
263
1.00
ref

1.00
ref



AA
Pravastatin
81
213
0.94
0.67-1.33
0.73
0.91
0.64-1.28
0.58




Placebo
104
256
1.00
ref

1.00
ref










p interaction 0.006§
p interaction 0.011§







*Data for patients presented as number of patients



†Adjusted for age (continuous), smoking (current versus non-current) (except in WOSCOPS, where no adjustment for age and smoking were made because cases and controls were matched), history of hypertension, history of diabetes, BMI (continuous), baseline LDL-C level (continuous), and baseline HDL-C level (continuous).



‡Cases and controls were matched for age (in two-year age groups) and smoking (current versus non-current), all were men.



§Likelihood ratio test of interaction between KIF6 hCV3054799 genotype and treatment.













TABLE 7







Effect of Atorvastatin vs Pravastatin on CHD according to KIF6 Trp719Arg genotype












On-trial

Unadjusted
Model 1†
















Subgroup
Treatment
CHD*
Total*
HR
95% CI
P Value
HR
95% CI
P Value



















ArgArg + ArgTrp
Atorvastatin
76
509
0.57
0.43-0.76
0.0001
0.56
0.42-0.75
<0.0001



Pravastatin
123
502
1.00
ref

1.00
ref


TrpTrp
Atorvastatin
81
359
0.99
0.73-1.35
0.96
0.97
0.70-1.32
0.82



Pravastatin
80
352
1.00
ref

1.00
ref










P interaction = 0.01§
P interaction = 0.01§







HR denotes hazard ratio,



CI: confidence interval,



ref: reference group;



ArgArg + ArgTrp = GG + GA;



TrpTrp = AA, respectively



*Number of patients.



†Model 1: adjusted for sex, age (continuous), smoker versus past or never smoker, hypertension, baseline of diabetes, baseline LDL-C level (continuous), baseline HDL-C level (continuous), and antibiotic.



§Likelihood ratio test of interaction between KIF6 carrier and treatment.






Association of the KIF6 SNP (rs20455) with Risk of CHD in Placebo Arm of PROSPER (Elderly Population)













TABLE 8







Genotype
event count
no event count
total count





















GG
25
134
159



AG
119
454
573



AA
83
431
514



carrier
144
588
732






















TABLE 9







Hazard
95% CI
95% CI



Group
Model*
ratio
Lower
Upper
p value







Carrier vs noncarrier
1
1.27
0.97
1.66
0.082



2
1.24
0.94
1.62
0.121



3
1.25
0.95
1.64
0.104


Het vs noncarrier
1
1.36
1.03
1.80
0.033



2
1.33
1.00
1.75
0.049



3
1.33
1.01
1.77
0.045


Minor hom vs noncarrier
1
0.97
0.62
1.52
0.911



2
0.95
0.61
1.48
0.816



3
0.97
0.62
1.52
0.896





Model 1 - unadjusted


Model 2 - adjusted for age, sex and smoking status


Model 3 - adjusted for age, sex, smoking status, history of hypertension, LDL, HDL and history of diabetes






Effect of Pravastatin Compared with Placebo on Risk of CHD in PROSPER Subgroups Defined by KIF6 SNP (rs20455) Genotypes (Elderly Population)








TABLE 10







(Placebo arm)












genotype
event
no event
total
















GG
25
134
159



AG
119
454
573



AA
83
431
514



carrier
144
588
732

















TABLE 11







(Pravastatin arm)












genotype
event
no event
total
















GG
18
156
174



AG
85
501
586



AA
80
456
536



carrier
103
657
760






















TABLE 12







Hazard
95% CI
95% CI



Group
Model*
ratio
Lower
Upper
p value







minor homozyote
1
0.65
0.35
1.18
0.156



2
0.65
0.35
1.19
0.160



3
0.63
0.34
1.17
0.143


heterozygote
1
0.67
0.51
0.89
0.005



2
0.67
0.51
0.89
0.005



3
0.68
0.51
0.90
0.007


major homozyote
1
0.93
0.68
1.26
0.639



2
0.92
0.68
1.25
0.607



3
0.92
0.68
1.25
0.604


carrier
1
0.67
0.52
0.86
0.002



2
0.67
0.52
0.86
0.002



3
0.67
0.52
0.87
0.002





Model 1 - unadjusted


Model 2 - adjusted for age, sex and smoking status


Model 3 - adjusted for age, sex, smoking status, history of hypertension, LDL, HDL and history of diabetes

















TABLE 13







p_model1
p_model2
p_model3



















Interaction between
0.100203045
0.108490165
0.122604071


carrier status and


treatment


Interaction between het
0.123178705
0.129901043
0.150787067


vs noncarrier and


treatment
















TABLE 14





Association of SNPs in the KIF6 fine-mapping region with CHD, particularly MI and RMI


















CARE
WOSCOPS















rs
hCV
r2a
Riskb
p valueb
Modelc
Riskb
p valueb
Modelc





rs20455
hCV3054799
original
1.66
0.010
Heterozygote
1.65
0.002
Heterozygote




hit


rs9462535
hCV3054808
0.814
1.63
0.014
Heterozygote
1.57
0.004
Dominant


rs9471077
hCV3054813
0.797
1.77
0.006
Heterozygote
1.51
0.008
Dominant


rs9394584
hCV30225864
0.797
1.42
0.055
Dominant
1.52
0.007
Heterozygote


rs11755763
hCV3054809
0.402
1.47
0.126
Recessive
1.37
0.101
Recessive


rs9471080
hCV29992177
0.364
1.65
0.012
Heterozygote
1.29
0.149
Heterozygote












Genotype in CARE


















Case
Control

Case
Control

Case
Control


rs
Genot
Count
Count
Genot
Count
Count
Genot
Count
Count





rs20455
AA
44
495
GA
81
548
GG
16
138


rs9462535
CC
44
494
AC
81
559
AA
18
174


rs9471077
TT
38
450
CT
78
521
CC
18
172


rs9394584
TT
55
564
CT
68
492
CC
14
99


rs11755763
GG
45
363
AG
75
571
AA
20
229


rs9471080
AA
77
751
GA
47
278
GG
 2
31












Genotype in WOSCOPS


















Case
Control

Case
Control

Case
Control


rs
Genot
Count
Count
Genot
Count
Count
Genot
Count
Count





rs20455
TT
104
256
CT
137
204
CC
35
59


rs9462535
CC
96
228
AC
141
213
AA
43
66


rs9471077
TT
94
231
CT
136
218
CC
39
66


rs9394584
TT
128
290
CT
125
186
CC
22
43


rs11755763
GG
83
144
AG
145
264
AA
46
113 


rs9471080
AA
185
371
GA
 72
112
GG
 3
12












Meta-Analysisd












rs
p value
Risk
Model







rs20455
0.000047
1.659509397
Heterozygote



rs9462535
0.000348
1.577165825
Heterozygote



rs9471077
0.000155
1.626493396
Heterozygote



rs9394584
0.001318
1.476188836
Heterozygote



rs11755763
0.025282
0.709869659
Recessive



rs9471080
0.006167
1.435129278
Heterozygote







The risk estimate and p value were calculated by logistic regression among placebo-treated patients



aR2 with hCV3054799 based on CARE placebo-treated patients.



bThe risk estimate and p value was from either the genotypic or genetic models (additive, dominant, recessive) which gave the lowest p value.



cGenetic model which the risk estimate and the p value were calculated



dThe risk estimate and p value was from either the genotypic or genetic models (additive, dominant, recessive) which gave the lowest p value in combined CARE and WOSCOPS.























TABLE 15














GLM


GLM









Ref

Control
Non-Ref


hCV
rs number
model
P value
OddsRatio
OR95l
OR95u
Allele
Case Freq
Freq
Allele





hCV3054805
rs2894424
GC
0.051808295
1.482242
0.99692
2.203829
C
0.566434
0.5846906
G


hCV3054808
rs9462535
AC
0.013593462
1.62685
1.105336
2.394422
C
0.590909
0.6303993
A


hCV3054808
rs9462535
dominant
0.028697195
1.516371
1.044277
2.201888
C
0.590909
0.6303993
A


hCV3054822
rs11751357
AA
0.008349315
2.338246
1.243867
4.395482
T
0.65035
0.7597561
A


hCV3054822
rs11751357
AT
0.000134125
2.045113
1.416585
2.952513
T
0.65035
0.7597561
A


hCV3054822
rs11751357
dominant
4.46E−05
2.087719
1.466207
2.972685
T
0.65035
0.7597561
A


hCV3054822
rs11751357
additive
7.23E−05
1.698221
1.3074
2.20587
T
0.65035
0.7597561
A


hCV3054822
rs11751357
recessive
0.092804388
1.671318
0.918227
3.042062
T
0.65035
0.7597561
A


hCV3054829
rs4535541
CC
0.088634233
0.621377
0.359319
1.074558
T
0.597902
0.5461601
C


hCV3054829
rs4535541
additive
0.084241704
0.795789
0.614026
1.031356
T
0.597902
0.5461601
C


hCV31340487
rs12175497
dominant
0.083273158
0.592614
0.32782
1.07129
T
0.954545
0.9241181
C


hCV31340487
rs12175497
additive
0.086498869
0.584648
0.329551
1.037209
T
0.954545
0.9241181
C


hCV792699
rs728218
CC
0.093985146
1.536276
0.929485
2.539196
T
0.524476
0.5771429
C


hCV792699
rs728218
additive
0.086254401
1.242667
0.969514
1.592778
T
0.524476
0.5771429
C






















Case
Control

Case
Case
Control
Control

Case

Control


hCV
Freq
Freq
Genot
Count
Freq
Count
Freq
Genot
Count
Case Freq
Count





hCV3054805
0.433566
0.4153094
CC
41
0.286713
433
0.3526059
GC
80
0.559441
570


hCV3054808
0.409091
0.3696007
CC
44
0.307692
494
0.402608
AC
81
0.566434
559


hCV3054808
0.409091
0.3696007
CC
44
0.307692
494
0.402608
AC
81
0.566434
559


hCV3054822
0.34965
0.2402439
TT
57
0.398601
714
0.5804878
AT
72
0.503497
441


hCV3054822
0.34965
0.2402439
TT
57
0.398601
714
0.5804878
AT
72
0.503497
441


hCV3054822
0.34965
0.2402439
TT
57
0.398601
714
0.5804878
AT
72
0.503497
441


hCV3054822
0.34965
0.2402439
TT
57
0.398601
714
0.5804878
AT
72
0.503497
441


hCV3054822
0.34965
0.2402439
TT
57
0.398601
714
0.5804878
AT
72
0.503497
441


hCV3054829
0.402098
0.4538399
TT
48
0.335664
343
0.2802288
CT
75
0.524476
651


hCV3054829
0.402098
0.4538399
TT
48
0.335664
343
0.2802288
CT
75
0.524476
651


hCV31340487
0.045455
0.0758819
TT
130
0.909091
1043
0.8556194
CT
13
0.090909
167


hCV31340487
0.045455
0.0758819
TT
130
0.909091
1043
0.8556194
CT
13
0.090909
167


hCV792699
0.475524
0.4228571
TT
38
0.265734
403
0.3289796
CT
74
0.517483
608


hCV792699
0.475524
0.4228571
TT
38
0.265734
403
0.3289796
CT
74
0.517483
608



















Control

Case
Case
Control
Control



hCV
Freq
Genot
Count
Freq
Count
Freq







hCV3054805
0.4641694
GG
22
0.153846
225
0.1832248



hCV3054808
0.4555827
AA
18
0.125874
174
0.1418093



hCV3054808
0.4555827
AA
18
0.125874
174
0.1418093



hCV3054822
0.3585366
AA
14
0.097902
75
0.0609756



hCV3054822
0.3585366
AA
14
0.097902
75
0.0609756



hCV3054822
0.3585366
AA
14
0.097902
75
0.0609756



hCV3054822
0.3585366
AA
14
0.097902
75
0.0609756



hCV3054822
0.3585366
AA
14
0.097902
75
0.0609756



hCV3054829
0.5318627
CC
20
0.13986
230
0.1879085



hCV3054829
0.5318627
CC
20
0.13986
230
0.1879085



hCV31340487
0.1369975
CC
0
0
9
0.0073831



hCV31340487
0.1369975
CC
0
0
9
0.0073831



hCV792699
0.4963265
CC
31
0.216783
214
0.1746939



hCV792699
0.4963265
CC
31
0.216783
214
0.1746939







OR95l = lower 95% confidence interval for the Odds Ratio



OR95lu = upper 95% confidence interval for the Odds Ratio



GLM = Generalize Linear Model


























TABLE 16









total















count





case
control

case
control



Marker
Strata
(ALL)
Allele
ALL frq
Allele
ALL frq
Genot
count
count
Genot
count
count
Genot





hCV15876373
ALL
2777
C
0.106
T
0.8945
CC
4
28
CT
43
479
TT


hCV15876373
placebo
1369
C
0.102
T
0.8977
CC
0
13
CT
23
231
TT


hCV15876373
statin
1408
C
0.109
T
0.8913
CC
4
15
CT
20
248
TT


hCV1650850
ALL
2779
G
0.007
C
0.993
GG
0
0
GC
3
36
CC


hCV1650850
placebo
1369
G
0.007
C
0.9934
GG
0
0
GC
1
17
CC


hCV1650850
statin
1410
G
0.007
C
0.9926
GG
0
0
GC
2
19
CC


hCV29649182
ALL
2781
T
0.134
C
0.8664
TT
3
53
TC
69
562
CC


hCV29649182
placebo
1371
T
0.14
C
0.8596
TT
1
25
TC
41
292
CC


hCV29649182
statin
1410
T
0.127
C
0.873
TT
2
28
TC
28
270
CC


hCV30388472
ALL
2763
C
0.459
T
0.5413
CC
48
539
CT
124
1237
TT


hCV30388472
placebo
1363
C
0.464
T
0.5363
CC
31
254
CT
75
619
TT


hCV30388472
statin
1400
C
0.454
T
0.5461
CC
17
285
CT
49
618
TT


hCV30478067
ALL
2781
A
0.065
G
0.9351
AA
3
12
AG
24
307
GG


hCV30478067
placebo
1370
A
0.065
G
0.935
AA
0
5
AG
16
152
GG


hCV30478067
statin
1411
A
0.065
G
0.9352
AA
3
7
AG
8
155
GG


hCV3054766
ALL
2782
A
0.499
C
0.5007
AA
54
647
AC
130
1246
CC


hCV3054766
placebo
1370
A
0.499
C
0.5011
AA
35
296
AC
75
630
CC


hCV3054766
statin
1412
A
0.5
C
0.5004
AA
19
351
AC
55
616
CC


hCV3054789
ALL
2766
A
0.332
T
0.6676
AA
32
267
AT
120
1121
TT


hCV3054789
placebo
1364
A
0.331
T
0.669
AA
14
123
AT
69
560
TT


hCV3054789
statin
1402
A
0.334
T
0.6662
AA
18
144
AT
51
561
TT


hCV3054805
ALL
2779
G
0.413
C
0.5867
GG
42
446
GC
129
1192
CC


hCV3054805
placebo
1371
G
0.417
C
0.5828
GG
22
225
GC
80
570
CC


hCV3054805
statin
1408
G
0.409
C
0.5906
GG
20
221
GC
49
622
CC


hCV3054808
ALL
2778
A
0.38
C
0.6197
AA
39
369
AC
125
1172
CC


hCV3054808
placebo
1370
A
0.374
C
0.6263
AA
18
174
AC
81
559
CC


hCV3054808
statin
1408
A
0.387
C
0.6133
AA
21
195
AC
44
613
CC


hCV3054822
ALL
2782
A
0.255
T
0.7451
AA
27
159
AT
111
935
TT


hCV3054822
placebo
1373
A
0.252
T
0.7484
AA
14
75
AT
72
441
TT


hCV3054822
statin
1409
A
0.258
T
0.742
AA
13
84
AT
39
494
TT


hCV3054829
ALL
2778
C
0.455
T
0.5455
CC
41
500
CT
133
1310
TT


hCV3054829
placebo
1367
C
0.448
T
0.5516
CC
20
230
CT
75
651
TT


hCV3054829
statin
1411
C
0.46
T
0.5397
CC
21
270
CT
58
659
TT


hCV31340487
ALL
2767
C
0.074
T
0.9265
CC
1
16
CT
28
345
TT


hCV31340487
placebo
1362
C
0.073
T
0.9273
CC
0
9
CT
13
167
TT


hCV31340487
statin
1405
C
0.074
T
0.9256
CC
1
7
CT
15
178
TT


hCV32202303
ALL
2771
C
0.095
G
0.9054
CC
1
18
CG
35
451
GG


hCV32202303
placebo
1365
C
0.097
G
0.9029
CC
1
7
CG
22
227
GG


hCV32202303
statin
1406
C
0.092
G
0.9079
CC
0
11
CG
13
224
GG


hCV792699
ALL
2772
C
0.435
T
0.5648
CC
60
467
CT
122
1237
TT


hCV792699
placebo
1368
C
0.428
T
0.5716
CC
31
214
CT
74
608
TT


hCV792699
statin
1404
C
0.442
T
0.558
CC
29
253
CT
48
629
TT


hCV3054799
ALL
2677
G
0.36
A
0.6401
GG
34
308
GA
127
1116
AA


hCV3054799
placebo
1322
G
0.354
A
0.6456
GG
16
138
GA
81
548
AA


hCV3054799
statin
1355
G
0.365
A
0.6347
GG
18
170
GA
46
568
AA













Minor Homozygotes












OR for

Heterozygotes


















case
control
minor



OR for





Marker
count
count
hom
95% CI_L
95% CI_U
p
Het
95% CI_L
95% CI_U
p





hCV15876373
204
2019


hCV15876373
120
982
N/A
N/A
N/A
0.128
0.8241
0.46414
1.46336
0.528


hCV15876373
84
1037


hCV1650850
248
2492


hCV1650850
141
1210
N/A
N/A
N/A
1
1.7143
0.16906
17.3826
1


hCV1650850
107
1282


hCV29649182
179
1915


hCV29649182
101
911
1.7333
0.16658
18.0364 
1
0.7631
0.48447
1.2021
0.253


hCV29649182
78
1004


hCV30388472
75
740


hCV30388472
35
349
0.5175
0.29298
0.91415
0.024
0.6798
0.48192
0.95887
0.03


hCV30388472
40
391


hCV30478067
225
2210


hCV30478067
127
1070
N/A
N/A
N/A
0.505
0.5153
0.22677
1.17112
0.138


hCV30478067
98
1140


hCV3054766
67
638


hCV3054766
32
302
0.4856
0.28343
0.83209
0.01
0.7705
0.55309
1.07334
0.14


hCV3054766
35
336


hCV3054789
98
1128


hCV3054789
58
540
1.0873
0.56177
2.10445
0.853
0.7597
0.53833
1.072
0.125


hCV3054789
40
588


hCV3054805
81
889


hCV3054805
41
433
0.9317
0.52221
1.66238
0.872
0.5933
0.42282
0.8326
0.002


hCV3054805
40
456


hCV3054808
88
985


hCV3054808
44
494
1.037 
0.56982
1.88734
1
0.5292
0.37268
0.75132
3E−04


hCV3054808
44
491


hCV3054822
114
1436


hCV3054822
57
714
0.852 
0.42395
1.71219
0.682
0.5213
0.35998
0.75503
4E−04


hCV3054822
57
722


hCV3054829
78
716


hCV3054829
48
343
0.9021
0.50074
1.62503
0.747
0.783 
0.56474
1.08572
0.146


hCV3054829
30
373


hCV31340487
223
2154


hCV31340487
130
1043
N/A
N/A
N/A
0.471
1.0761
0.52675
2.19848
1


hCV31340487
93
1111


hCV32202303
216
2050


hCV32202303
120
988
N/A
N/A
N/A
0.421
0.6208
0.32018
1.2038
0.164


hCV32202303
96
1062


hCV792699
69
817


hCV792699
38
403
0.8127
0.50466
1.30891
0.412
0.6534
0.4617 
0.9248
0.017


hCV792699
31
414


hCV3054799
83
1009


hCV3054799
44
495
0.9215
0.48648
1.7457 
0.857
0.5818
0.41229
0.82093
0.002


hCV3054799
39
514













Minor Homozygotes + Heterozygotes




(i.e., carriers of at least one copy of



minor allele)












OR for

Major Homozygotes



















carrier



OR for








(Min +



major



HW (ALL)



Marker
Het)
95% CI_L
95% CI_U
p
hom
95% CI_L
95% CI_U
p
pExact
BDpvalue





hCV15876373








0.84


hCV15876373
0.9708
0.56171
1.67771
1
0.6881
0.52734
0.89797
0.006
0.883
0.0989


hCV15876373








0.492
0.0989


hCV1650850








1


hCV1650850
1.7143
0.16906
17.3826
1
0.7381
0.58069
0.9382
0.014
1


hCV1650850








1


hCV29649182








0.287


hCV29649182
0.7818
0.50151
1.21872
0.319
0.7223
0.54442
0.95834
0.028
0.911
0.692


hCV29649182








0.0913
0.692


hCV30388472








0.674


hCV30388472
0.6291
0.46873
0.84425
0.002
1.0182
0.66085
1.56888
1
0.414
0.0511


hCV30388472








0.146
0.0511


hCV30478067








0.276


hCV30478067
0.6875
0.32861
1.43835
0.423
0.7461
0.58032
0.95923
0.025
1
0.675


hCV30478067








0.0781
0.675


hCV3054766








0.57


hCV3054766
0.6695
0.50506
0.88747
0.005
0.9847
0.62409
1.55359
1
0.305
0.0504


hCV3054766








0.0626
0.0504


hCV3054789








0.578


hCV3054789
0.8227
0.6075
1.11423
0.232
0.6567
0.44599
0.967
0.035
0.142
0.217


hCV3054789








0.509
0.217


hCV3054805








0.309


hCV3054805
0.6653
0.49722
0.89032
0.006
0.9323
0.61443
1.41473
0.817
0.374
0.675


hCV3054805








0.582
0.675


hCV3054808








0.629


hCV3054808
0.6257
0.46442
0.84308
0.002
1.0056
0.67383
1.50075
1
0.954
0.497


hCV3054808








0.537
0.497


hCV3054822








0.583


hCV3054822
0.5778
0.41718
0.80021
8E−04
0.9897
0.6951 
1.40925
1
0.774
0.125


hCV3054822








0.676
0.125


hCV3054829








0.0129


hCV3054829
0.8052
0.60541
1.07087
0.153
0.6064
0.39272
0.93632
0.024
0.00737
0.282


hCV3054829








0.422
0.282


hCV31340487








0.575


hCV31340487
1.1573
0.57217
2.34076
0.704
0.697 
0.54076
0.89829
0.006
0.422
0.125


hCV31340487








0.847
0.125


hCV32202303








0.223


hCV32202303
0.5857
0.30354
1.13025
0.121
0.7655
0.59291
0.98822
0.045
0.164
0.378


hCV32202303








0.874
0.378


hCV792699








0.877


hCV792699
0.7089
0.53582
0.9378
0.016
0.8085
0.51256
1.27518
0.382
0.544
0.962


hCV792699








0.417
0.962


hCV3054799








0.706


hCV3054799
0.6442
0.47726
0.86943
0.005
0.8639
0.57082
1.30753
0.496
0.167
0.717


hCV3054799








0.412
0.717





N/A indicates that a value could not be calculated because at least one of the strata had a 0 count for minor homozygotes in cases and/or controls


OR = Odds ratio


95% CI_L = Lower 95% confidence interval


95% CI_U = upper 95% confidence interval


HW (ALL) pExact = Hardy-Weinberg expectations using an exact test


BDpvalue = Breslow-Day p-value













TABLE 17





Patients with Aneurysm or Dissection vs Control (555 cases and 180 controls)

















Genotypic results


























OR (min





Count
Count
Count
Risk


Control
hom vs



Marker
Gene
(ALL)
(cases)
(controls)
allele
ALL frq
Case frq
frq
maj hom)
P value





hCV3054799
KIF6
735
555
180
G
0.385
0.4009
0.3361
1.68
0.06













Genotypic results
Allelic results




















OR (het





allelicA

OR95
OR95




vs maj

OR

OR

sc

CI
CI



Marker
hom)
P value
(Dom)
P value
(Rec)
P value
pExact
OR
lower
upper







hCV3054799
1.36
0.12
1.43
0.04
1.43
0.19
0.029
1.32
1.0299
1.7

















TABLE 18





Patients with Dissection vs Control (128 cases and 180 controls)

















Genotypic results


























OR (Min





Count
Count
Count
Risk


Control
hom vs



Marker
Gene
(ALL)
(cases)
(controls)
allele
ALL frq
Case frq
frq
maj hom)
P value





hCV3054799
KIF6
308
128
180
G
0.3701
0.418
0.3361
1.75
0.14













Genotypic results
Allelic results




















OR (het





allelicA

OR95
OR95




vs maj

OR

OR

sc

CI
CI



Marker
hom)
P value
(Dom)
P value
(Rec)
P value
pExact
OR
lower
upper







hCV3054799
1.81
0.02
1.80
0.02
1.25
0.50
0.042
1.42
1.0188
1.97







frq = frequency



“Min” indicates minor allele homozygote



“maj” indicates major allele homozygote



“het” indicates heterozygote



“Dom” indicates dominant model (minor homozygote plus heterozygote vs major homozygote)



“Rec” indicates recessive model (minor homozygote vs heterozygote plus major homozygote)













TABLE 19







Genotype Counts in Aneurysm or Dissection Endpoint





























HW





Case
Control

Case
Control


Control
(Control)


Marker
Endpoint
Genot
cnt
cnt
Genot
cnt
cnt
Genot
Case cnt
cnt
pExact





hCV3054799
Aneurysm
GG
92
22
GA
261
77
AA
202
81
0.616



or



Dissection
















TABLE 20







Genotype Counts in Dissection Endpoint





























HW





Case
Control

Case
Control


Control
(Control)


Marker
Endpoint
Genot
cnt
cnt
Genot
cnt
cnt
Genot
Case cnt
cnt
pExact





hCV3054799
Dissection
GG
19
22
GA
69
77
AA
40
81
0.616

























TABLE 21











Odds



Case
Control


Endpoint
hCV
Model
P Value
Ratio
OR95l
OR95u
Genotype
Count
Count





Dissection
hCV3054799
GA
0.009
2.77
1.29
5.97
AA
25
29


Dissection
hCV3054799
GG
0.637
1.27
0.48
3.36
AA
25
29


Dissection
hCV3054799
dominant
0.027
2.20
1.09
4.43
AA
25
29


Dissection
hCV3054799
additive
0.231
1.34
0.83
2.18
AA
25
29


Dissection
hCV3054799
recessive
0.538
0.75
0.31
1.85
AA
25
29


Aneurysm or
hCV3054799
GA
0.046
1.91
1.01
3.59
AA
131
29


dissection


Aneurysm or
hCV3054799
GG
0.907
1.05
0.49
2.25
AA
131
29


dissection


Aneurysm or
hCV3054799
dominant
0.109
1.58
0.90
2.76
AA
131
29


dissection


Aneurysm or
hCV3054799
additive
0.448
1.17
0.78
1.74
AA
131
29


dissection


Aneurysm or
hCV3054799
recessive
0.492
0.78
0.38
1.60
AA
131
29


dissection






















Case
Control

Case
Control
Total
Total



Endpoint
Genotype
Count
Count
Genotype
Count
Count
Cases
Controls







Dissection
GA
43
18
GG
12
11
80
58



Dissection
GA
43
18
GG
12
11
80
58



Dissection
GA
43
18
GG
12
11
80
58



Dissection
GA
43
18
GG
12
11
80
58



Dissection
GA
43
18
GG
12
11
80
58



Aneurysm or
GA
155
18
GG
52
11
338
58



dissection



Aneurysm or
GA
155
18
GG
52
11
338
58



dissection



Aneurysm or
GA
155
18
GG
52
11
338
58



dissection



Aneurysm or
GA
155
18
GG
52
11
338
58



dissection



Aneurysm or
GA
155
18
GG
52
11
338
58



dissection







AA genotype was used as reference



“OR95l” = lower 95% confidence interval for the odds ratio



“OR95u” = upper 95% confidence interval for the odds ratio













TABLE 22





Effect of pravastatin on MI and CHD: CARE and WOSCOPS

















Genotype Counts






















Case
Control
Geno-
Case
Control

Case
Control


Study
Subgroup
HCV
Genotype
Count
Count
type
Count
Count
Genotype
Count
Count





CARE
Pravastatin
hCV3054808
CC
44
491
AC
44
613
AA
21
195


CARE
Placebo
hCV3054808
CC
44
494
AC
81
559
AA
18
174


WOSCOPS
Pravastatin
hCV3054808
CC
72
193
AC
83
248
AA
28
83


WOSCOPS
Placebo
hCV3054808
CC
94
224
AC
134
207
AA
39
64


CARE
Pravastatin
hCV29992177
AA
58
798
GA
27
281
GG
4
33


CARE
Placebo
hCV29992177
AA
77
751
GA
47
278
GG
2
31


WOSCOPS
Pravastatin
hCV29992177
AA
125
354
GA
51
148
GG
7
11


WOSCOPS
Placebo
hCV29992177
AA
185
371
GA
72
112
GG
3
12


CARE
Pravastatin
hCV30225864
TT
43
604
CT
44
503
CC
13
117


CARE
Placebo
hCV30225864
TT
55
564
CT
68
492
CC
14
99


WOSCOPS
Pravastatin
hCV30225864
TT
89
260
CT
85
234
CC
16
47


WOSCOPS
Placebo
hCV30225864
TT
128
290
CT
125
186
CC
22
43


CARE
Pravastatin
hCV3054813
TT
40
448
CT
38
570
CC
19
186


CARE
Placebo
hCV3054813
TT
38
450
CT
78
521
CC
18
172


WOSCOPS
Pravastatin
hCV3054813
TT
76
196
CT
82
258
CC
31
82


WOSCOPS
Placebo
hCV3054813
TT
94
231
CT
136
218
CC
39
66












Effect of Pravastatin on MI and CHD




























Geno-





Study
Genotype
OR
95% CI
p value
Genotype
OR
95% CI
p value
type
OR
95% CI
p value





CARE
CC
1.01
0.65-1.55
1.00000
AC
0.50
0.33-0.72
0.00032
AA
1.04
0.53-2.01
1.00000


CARE


WOSCOPS
CC
0.89
0.61-1.27
0.58041
AC
0.52
0.37-0.71
0.00010
AA
0.55
 0.3-0.99
0.05540


WOSCOPS


CARE
AA
0.71
0.50-1.01
0.05971
GA
0.57
0.34-0.94
0.02648
GG
1.88
 0.32-10.99
0.67667


CARE


WOSCOPS
AA
0.71
0.54-0.93
0.01422
GA
0.54
0.35-0.83
0.00608
GG
2.55
 0.52-12.37
0.28280


WOSCOPS


CARE
TT
0.73
0.48-1.11
0.14200
CT
0.63
0.42-0.94
0.02803
CC
0.79
0.35-1.75
0.68302


CARE


WOSCOPS
TT
0.78
0.56-1.07
0.12642
CT
0.54
0.39-0.76
0.00038
CC
0.67
0.31-1.43
0.33663


WOSCOPS


CARE
TT
1.06
0.67-1.68
0.81479
CT
0.45
0.30-0.67
0.00008
CC
0.98
0.50-1.92
1.00000


CARE


WOSCOPS
TT
0.95
0.67-1.36
0.85556
CT
0.51
0.37-0.71
0.00006
CC
0.64
0.36-1.13
0.14713


WOSCOPS





OR = odd ratio


95% CI = 95% confidence interval

## Claims

1. A method of determining whether a human has an increased risk for myocardial infarction, comprising:
a) testing nucleic acid from said human to determine the nucleotide content at a polymorphism in gene KIF6 as represented by position 101 of SEQ ID NO:16 or its complement; and
b) correlating the presence of A at position 101 of SEQ ID NO:16 or T at position 101of its complement with said human having said increased risk for myocardial infarction, or the absence of said A or said T with said human having no said increased risk for myocardial infarction.

2. The method of claim 1, wherein said correlating is performed by computer software.

3. The method of claim 1, wherein said nucleic acid is a nucleic acid extract from a biological sample from said human.

4. The method of claim 3, wherein said biological sample is blood, saliva, or buccal cells.

5. The method of claim 3, further comprising preparing said nucleic acid extract from said biological sample prior to said testing.

6. The method of claim 5, further comprising obtaining said biological sample from said human prior to said preparing.

7. The method of claim 1, wherein said testing comprises nucleic acid amplification.

8. The method of claim 7, wherein said nucleic acid amplification is carried out by polymerase chain reaction.

9. The method of claim 1, wherein said testing is performed using sequencing, 5′ nuclease digestion, molecular beacon assay, oligonucleotide ligation assay, size analysis, single-stranded conformation polymorphism analysis, or denaturing gradient gel electrophoresis (DGGE).

10. The method of claim 1, wherein said testing is performed using an allele-specific method.

11. The method of claim 10, wherein said allele-specific method is allele-specific probe hybridization, allele-specific primer extension, or allele-specific amplification.

12. The method of claim 10, wherein said allele-specific method detects said A or said T.

13. The method of claim 1 which is an automated method.

14. The method of claim 1, wherein said human is homozygous for said A or said T.

15. The method of claim 1, wherein said human is heterozygous for said A or said T.

16. The method of claim 1, wherein said human did not have myocardial infarction prior to said testing.

17. The method of claim 1, wherein said human had myocardial infarction prior to said testing.