# Patent

## Title
Methods for identifying an individual at increased risk of developing coronary artery disease

## Abstract

The present invention provides methods of identifying a subject having an increased or decreased risk of developing cardiovascular disease, comprising:

    a) correlating the presence of one or more genetic markers in chromosome 3q13.31 with an increased or decreased risk of developing cardiovascular disease; and
    b) detecting the one or more genetic markers of step (a) in the subject, thereby identifying the subject as having an increased or decreased risk of developing cardiovascular disease. Also provided are methods of identifying subjects with cardiovascular disease as having a good or poor prognosis, as well as methods of identifying effective treatment regimens for cardiovascular disease, based on correlation with genetic markers in chromosome 3q13.31.

## Background
CROSS REFERENCE TO RELATED APPLICATIONS
This application is a continuation-in-part application of, and claims priority to, U.S. application Ser. No. 11/260,842, filed Oct. 27, 2005, which claims the benefit of U.S. Provisional Application Ser. No. 60/622,447, filed Oct. 27, 2004, the contents of each of which are herein incorporated by reference in their entireties.


GOVERNMENT SUPPORT
The present invention was made, in part, with the support of grant numbers HL073389, HL073042, HL73005, AG021547 and AG019757 from the National Institutes of Health/National Heart, Lung and Blood Institute. The United States Government has certain rights to this invention.


FIELD OF THE INVENTION
The present invention provides methods and compositions directed to identification of genetic markers in chromosome 3 and their correlation with cardiovascular disease.
BACKGROUND OF THE INVENTION
It is estimated that more than 13 million Americans are afflicted with clinically significant coronary artery disease (CAD) (American Heart Association 2004) and the care of these patients costs greater than $133 billion annually. Of those afflicted, 10% are less than 54 years old. Although a minority of the patient base, this group provides a valuable source for the investigation of the genetics underlying cardiac disease risk, because family history is known to be a robust predictor of cardiovascular disease, even after adjustment for known risk factors, which may be shared within families (Shea et al. 1984). Furthermore, these diseases inflict a high economic impact on this group of patients with early onset CAD. The identification of novel markers correlated with CAD is important in order to understand the pathophysiological mechanisms of this disease state and develop effective prevention and treatment regimens.
Cardiovascular disease is the leading killer in America today. Over 50 million Americans have heart and cardiovascular related problems. By the time that cardiovascular heart problems are usually detected, the disease is usually quite advanced, having progressed for decades, and often too advanced to allow successful prevention of major permanent disability.
Circulatory disease is caused by the normal flow of blood through the body being restricted or blocked as a result of arterial plaque. This may cause damage to the heart, brain, kidneys or other organs and tissues. Plaque build-up is a slow and progressive progress that is dependent on our environmental and genetic environment.
Cardiovascular disease refers to all disease, which involves the heart and/or blood vessels, arteries, and occasionally veins. These problems are most commonly due to consequences of arterial disease, atherosclerosis, atheroma, but also can be related to infection, valvular and clotting problems.
In humans, β1-adrenergic receptors (β1-ARs) are polymorphic at amino acid residue 389 (Arg/Gly). Mialet-Perez et al. (2003) Nat Med. 9:1260-1262, catecholamines stimulate cardiac contractility through reported that the human Arg389 variant predisposes to heart failure by instigating hyperactive signaling programs leading to depressed receptor coupling and ventricular dysfunction, and influences the therapeutic response to β-receptor blockade.
The present invention overcomes previous shortcomings in the art by providing methods and compositions for correlating genetic markers in a subject with various aspects of cardiovascular disease and its treatment.
SUMMARY OF THE INVENTION
The inventors have carried out a genome wide screening in 420 families with early-onset CAD disease (GENECARD study) and found significant linkage evidence (multipoint lod score=3.5) in chromosome 3q13 spanning over 60 mega bases. Systematic association analysis using single nucleotide polymorphism (SNP) was performed in case-control sets from the CATHGEN study. Subjects were selected based on their CAD index (CADi), a validated angiographical measure of the extent of CAD. CATHGEN included 301 young affected (YA: age ≦55, CADi >32), 168 older affected (OA: age >55, CADi >74), and 204 controls (ON: age >60, CADi <23). A two-stage approach was taken: a preliminary screening in pooled DNA followed by individual genotyping around significant markers at higher density to define the boundaries of the linkage disequilibrium (LD) block. Initial screening of 16 SNPs by DNA pooling revealed that the frequency of the G allele of rs1875518 is significantly higher in OA than ON (OA-ON=12.2%, p=0.001), which is confirmed by individual genotyping (OA=57.2%; ON=45.5%). Additional genotyping around rs1875518 defined an LD block extending  ̃100 kb that is highly associated with OA in Caucasians. Moreover, preliminary evidence supports the association of this block in the GENECARD probands versus Cathgen ON. Finally, a novel microsatellite marker (3M0238) within the block was identified, which breaks the LD and formed a significant risk haplotype (P<0.005) with rs1875518: rs1875518_G-3M0238—253 is twice as prevalent in OA (21.39%) as in ON (11.39%). In sum, the inventors have identified a 100 kb region in 3q13.31 containing genetic susceptibility for CAD. In particular, these data indicate that carriers of rs1875518_G-3M0238—253 are at higher risk of developing CAD.
The present invention provides a method of identifying a subject having an increased or decreased risk of developing cardiovascular disease, comprising detecting in the subject one or more genetic markers in chromosome 3q13.31 correlated with an increased or decreased risk of developing cardiovascular disease.
Further provided is a method of identifying a subject having an increased or decreased risk of developing cardiovascular disease, comprising: a) correlating the presence of one or more genetic markers in chromosome 3q13.31 with an increased or decreased risk of developing cardiovascular disease; and b) detecting the one or more genetic markers of step (a) in the subject, thereby identifying the subject as having an increased or decreased risk of developing cardiovascular disease.
In further embodiments, the present invention provides a method of correlating a genetic marker in chromosome 3q13.31 with an increased risk of developing cardiovascular disease, comprising: a) detecting in a subject with cardiovascular disease the presence of one or more genetic markers in chromosome 3q13.31; and b) correlating the presence of the one or more genetic markers of step (a) with cardiovascular disease in the subject.
Also provided is a method of correlating a genetic marker in chromosome 3q13.31 with a decreased risk of developing cardiovascular disease, comprising: a) detecting in a subject without cardiovascular disease the presence of one or more genetic markers in chromosome 3q13.31; and b) correlating the presence of the one or more genetic markers of step (a) with the absence of cardiovascular disease in the subject.
Additionally provided herein is a method of diagnosing cardiovascular disease in a subject, comprising detecting in the subject one or more genetic markers correlated with a diagnosis of cardiovascular disease, as well as a method of diagnosing cardiovascular disease in a subject, comprising: a) correlating the presence of one or more genetic markers in chromosome 3q13.31 with a diagnosis of cardiovascular disease; and b) detecting the one or more genetic markers of step (a) in the subject, thereby diagnosing cardiovascular disease in the subject.
A method is also provided of correlating a genetic marker in chromosome 3q13.31 with a diagnosis of cardiovascular disease, comprising: a) detecting in a subject diagnosed with cardiovascular disease the presence of one or genetic markers in chromosome 3q13.31; and b) correlating the presence of the one or more genetic markers of step (a) with a diagnosis of cardiovascular disease in a subject.
In yet further embodiments, the present invention provides a method of identifying a subject with cardiovascular disease as having a good or a poor prognosis, comprising detecting in the subject one or more markers genetic markers in chromosome 3q13.31 correlated with a good or a poor prognosis for cardiovascular disease.
Furthermore, the present invention provides a method of identifying a subject with cardiovascular disease as having a good or a poor prognosis, comprising: a) correlating the presence of one or more genetic markers in chromosome 3q13.31 with a good or a poor prognosis for cardiovascular disease; and b) detecting the one or more markers of step (a) in the subject, thereby identifying the subject as having a good or a poor prognosis.
In addition, the present invention provides a method of correlating a genetic marker in chromosome 3q13.31 with a good or a poor prognosis for cardiovascular disease, comprising: a) detecting in a subject with cardiovascular disease and having a good or a poor prognosis, the presence of one or more genetic markers in chromosome 3q13.31; and b) correlating the presence of the one or more genetic markers of step (a) with a good or a poor prognosis for cardiovascular disease.
Additionally provided herein is a method of identifying an effective treatment regimen for a subject with cardiovascular disease, comprising detecting one or more genetic markers in chromosome 3q13.31 in the subject correlated with an effective treatment regimen for cardiovascular disease.
Also provided is a method of identifying an effective treatment regimen for a subject with cardiovascular disease, comprising: a) correlating the presence of one or more genetic markers in chromosome 3q13.31 in a test subject with cardiovascular disease for whom an effective treatment regimen has been identified; and b) detecting the one or more markers of step (a) in the subject, thereby identifying an effective treatment regimen for the subject.
Further provided is a method of correlating a genetic marker of chromosome 3q13.31 with an effective treatment regimen for cardiovascular disease, comprising: a) detecting in a subject with cardiovascular disease and for whom an effective treatment regimen has been identified, the presence of one or more genetic markers in chromosome 3q13.31; and b) correlating the presence of the one or more genetic markers of step (a) with a an effective treatment regimen for cardiovascular disease.
The present invention additionally provides a method of identifying a Caucasian subject having an increased risk of developing coronary artery disease, comprising detecting in a nucleic acid sample of the subject an allele at a single nucleotide polymorphism in the LSAMP gene of the subject, selected from the group consisting of: a) an A allele at single nucleotide polymorphism rs1910040; b) an A allele at single nucleotide polymorphism ss70458782; c) a G allele at single nucleotide polymorphism rs1875518; d) an A allele at single nucleotide polymorphism rs1676232; e) an A allele at single nucleotide polymorphism rs4404477; and f) any combination of (a)-(e) above, wherein the detection of said allele(s) identifies the subject as having an increased risk of developing coronary artery disease.
Also provided herein is a method of identifying a Caucasian subject having an increased risk of developing coronary artery disease, comprising detecting in a nucleic acid sample of the subject a haplotype in the LSAMP gene of the subject comprising, consisting essentially of and/or consisting of an A allele at single nucleotide polymorphism ss70458782 and an A allele at single nucleotide polymorphism rs4404477, wherein the detection of said haplotype identifies the subject as having an increased risk of developing coronary artery disease.



BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 depicts linkage evidence of the susceptibility for CAD (multipoint lod score=3.5) in chromosome 3q13 spanning over 120 megabases (Mb).
FIG. 2 depicts the screening of 16 SNPs for linkage to the susceptibility for CAD.
FIG. 3 depicts association analysis of SNPs around rs1875518 with risk for CAD.
FIG. 4 depicts the quantitative trait loci (QTL) map for HDL cholesterol on chromosome 3.
FIG. 5 depicts chromosome 3 lod score curves using OSA that corroborate, strengthen and narrow the linkage peaks previously observed on chromosome 3q.
FIG. 6 depicts the genotypes of normal versus affected individuals with respect to three polymorphisms.
FIG. 7 depicts differences in allele frequency between affected versus control (normal) cases with exemplary SNPs within the region of human chromosome 3q13.31.
FIG. 8 depicts the frequency of genetic markers within the region of human chromosome 3q13.31 correlated with affected and control (normal cases) and the significance of the correlation of the G allele of rs1875518 and the 253 allele of 3M0238 with CAD.
FIG. 9 depicts additional SNPs associated with the risk for CAD on chromosome 3.



DETAILED DESCRIPTION OF THE INVENTION
As used herein, “a,” “an” or “the” can mean one or more than one. For example, “a” cell can mean a single cell or a multiplicity of cells.
Also as used herein, “and/or” refers to and encompasses any and all possible combinations of one or more of the associated listed items, as well as the lack of combinations when interpreted in the alternative (“or”).
Furthermore, the term “about,” as used herein when referring to a measurable value such as an amount of a compound or agent of this invention, dose, time, temperature, and the like, is meant to encompass variations of ±20%, ±10%, ±5%, ±1%, ±0.5%, or even ±0.1% of the specified amount.
The present invention is explained in greater detail below. This description is not intended to be a detailed catalog of all the different ways in which the invention may be implemented, or all the features that may be added to the instant invention. For example, features illustrated with respect to one embodiment may be incorporated into other embodiments, and features illustrated with respect to a particular embodiment may be deleted from that embodiment. In addition, numerous variations and additions to the various embodiments suggested herein will be apparent to those skilled in the art in light of the instant disclosure, which do not depart from the instant invention. Hence, the following specification is intended to illustrate some particular embodiments of the invention, and not to exhaustively specify all permutations, combinations and variations thereof.
Definitions
As used herein, the term “cardiovascular disease” includes any disease, disorder or pathological state or condition that involves the heart and/or blood vessels, arteries and veins. Examples of such diseases and disorders include, but are not limited to, arterial disease, atheroma, atherosclerosis, arteriosclerosis, coronary artery disease, arrhythmia, angina pectoris, congestive heart disease, myocardial infarction, stroke, transient ischemic attack (TIA), aortic aneurysm, cardiopericarditis, infection and/or inflammation of these tissues and/or organs, as well as valvular, vascular and clotting problems, insufficiencies and/or disorders, etc.
Also as used herein, “linked” describes a region of a chromosome that is shared more frequently in family members affected by a particular disease or disorder, than would be expected or observed by chance, thereby indicating that the gene or genes or other identified marker(s) within the linked chromosome region contain or are associated with an allele that is correlated with the presence of, or increased or decreased risk of the disease or disorder. Once linkage is established, association studies (linkage disequilibrium) can be used to narrow the region of interest or to identify the marker correlated with the disease or disorder.
The term “genetic marker” as used herein refers to a region of a nucleotide sequence (e.g., in a chromosome) that is subject to variability (i.e., the region can be polymorphic for a variety of alleles). For example, a single nucleotide polymorphism (SNP) in a nucleotide sequence is a genetic marker that is polymorphic for two alleles. Other examples of genetic markers of this invention can include but are not limited to microsatellites, restriction fragment length polymorphisms (RFLPs), repeats (i.e., duplications), insertions, deletions, etc.
A subject of this invention is any animal that is susceptible to cardiovascular disease as defined herein and can include mammals, birds and reptiles. Examples of subjects of this invention can include, but are not limited to, humans, non-human primates, dogs, cats, horses, cows, goats, guinea pigs, mice, rats and rabbits, as well as any other domestic or commercially valuable animal including animal models of cardiovascular disease.
As used herein, “nucleic acids” encompass both RNA and DNA, including cDNA, genomic DNA, mRNA, synthetic (e.g., chemically synthesized) DNA and chimeras of RNA and DNA. The nucleic acid can be double-stranded or single-stranded. Where single-stranded, the nucleic acid can be a sense strand or an antisense strand. The nucleic acid can be synthesized using oligonucleotide analogs or derivatives (e.g., inosine or phosphorothioate nucleotides). Such oligonucleotides can be used, for example, to prepare nucleic acids that have altered base-pairing abilities or increased resistance to nucleases.
An “isolated nucleic acid” is a DNA or RNA that is not immediately contiguous with nucleotide sequences with which it is immediately contiguous (one on the 5′ end and one on the 3′ end) in the naturally occurring genome of the organism from which it is derived. Thus, in one embodiment, an isolated nucleic acid includes some or all of the 5′ non-coding (e.g., promoter) sequences that are immediately contiguous to a coding sequence. The term therefore includes, for example, a recombinant DNA that is incorporated into a vector, into an autonomously replicating plasmid or virus, or into the genomic DNA of a prokaryote or eukaryote, or which exists as a separate molecule (e.g., a cDNA or a genomic DNA fragment produced by PCR or restriction endonuclease treatment), independent of other sequences. It also includes a recombinant DNA that is part of a hybrid nucleic acid encoding an additional polypeptide or peptide sequence.
The term “isolated” can refer to a nucleic acid or polypeptide that is substantially free of cellular material, viral material, or culture medium (when produced by recombinant DNA techniques), or chemical precursors or other chemicals (when chemically synthesized). Moreover, an “isolated fragment” is a fragment of a nucleic acid or polypeptide that is not naturally occurring as a fragment and would not be found in the natural state.
The term “oligonucleotide” refers to a nucleic acid sequence of at least about six nucleotides to about 100 nucleotides, for example, about 15 to 30 nucleotides, or about 20 to 25 nucleotides, which can be used, for example, as a primer in a PCR amplification or as a probe in a hybridization assay or in a microarray. Oligonucleotides can be natural or synthetic, e.g., DNA, RNA, modified backbones, etc.
The present invention further provides fragments or oligonucleotides of the nucleic acids of this invention, which can be used as primers or probes. Thus, in some embodiments, a fragment or oligonucleotide of this invention is a nucleotide sequence that is at least 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 100, 125, 150, 175, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 1000, 1500, 2000, 2500 or 3000 contiguous nucleotides of the nucleotide sequence set forth in SEQ ID NO:1, or the nucleotide sequence set forth from nucleotides 118500001 to 118761789 of the NCBI Build 35 sequence of human chromosome 3 (SEQ ID NO:1). Such fragments or oligonucleotides can be detectably labeled or modified, for example, to include and/or incorporate a restriction enzyme cleavage site when employed as a primer in an amplification (e.g., PCR) assay.
The present invention is based on the inventors' discovery of a correlation between genetic markers in chromosome 3q13.31 and various aspects of cardiovascular disease. Thus, in one aspect, the present invention provides a method of identifying a subject having either an increased or decreased risk of developing cardiovascular disease, comprising detecting in the subject one or more genetic markers in chromosome 3q13.31 correlated with an increased or decreased risk of developing cardiovascular disease.
Further provided is a method of identifying a subject having either an increased or decreased risk of developing cardiovascular disease, comprising: a) correlating the presence of one or more genetic markers in chromosome 3q13.31 with an increased or decreased risk of developing cardiovascular disease; and b) detecting the one or more genetic markers of step (a) in the subject, thereby identifying the subject as having an increased or decreased risk of developing cardiovascular disease.
In further embodiments, the present invention provides a method of correlating a genetic marker in chromosome 3q13.31 with an increased risk of developing cardiovascular disease, comprising: a) detecting in a subject with cardiovascular disease the presence of one or more genetic markers in chromosome 3q13.31; and b) correlating the presence of the one or more genetic markers of step (a) with cardiovascular disease in the subject.
Also provided is a method of correlating a genetic marker in chromosome 3q13.31 with a decreased risk of developing cardiovascular disease, comprising: a) detecting in a subject without cardiovascular disease the presence of one or more genetic markers in chromosome 3q13.31; and b) correlating the presence of the one or more genetic markers of step (a) with the absence of cardiovascular disease in the subject.
Additionally provided herein is a method of diagnosing cardiovascular disease in a subject, comprising detecting in the subject one or more genetic markers correlated with a diagnosis of cardiovascular disease, as well as a method of diagnosing cardiovascular disease in a subject, comprising: a) correlating the presence of one or more genetic markers in chromosome 3q13.31 with a diagnosis of cardiovascular disease; and b) detecting the one or more genetic markers of step (a) in the subject, thereby diagnosing cardiovascular disease in the subject.
A method is also provided of correlating a genetic marker in chromosome 3q13.31 with a diagnosis of cardiovascular disease, comprising: a) detecting in a subject diagnosed with cardiovascular disease the presence of one or genetic markers in chromosome 3q13.31; and b) correlating the presence of the one or more genetic markers of step (a) with a diagnosis of cardiovascular disease in a subject.
In the methods described herein, the detection of a genetic marker in a subject can be carried out according to methods well known in the art. For example DNA is obtained from any suitable sample from the subject that will contain DNA and the DNA is then prepared and analyzed according to well-established protocols for the presence of genetic markers according to the methods of this invention. In some embodiments, analysis of the DNA can be carried by amplification of the region of interest according to amplification protocols well known in the art (e.g., polymerase chain reaction, ligase chain reaction, strand displacement amplification, transcription-based amplification, self-sustained sequence replication (3SR), Qβ replicase protocols, nucleic acid sequence-based amplification (NASBA), repair chain reaction (RCR) and boomerang DNA amplification (BDA)). The amplification product can then be visualized directly in a gel by staining or the product can be detected by hybridization with a detectable probe. When amplification conditions allow for amplification of all allelic types of a genetic marker, the types can be distinguished by a variety of well-known methods, such as hybridization with an allele-specific probe, secondary amplification with allele-specific primers, by restriction endonuclease digestion, or by electrophoresis. Thus, the present invention further provides oligonucleotides for use as primers and/or probes for detecting and/or identifying genetic markers according to the methods of this invention.
The genetic markers of this invention are correlated with various aspects of cardiovascular disease as described herein according to methods well known in the art and as disclosed in the Examples provided herein for correlating genetic markers with various phenotypic traits, including disease states and pathological conditions and levels of risk associated with developing a disease or pathological condition. In general, identifying such correlation involves conducting analyses that establish a statistically significant association and/or a statistically significant correlation between the presence of a genetic marker or a combination of markers and the phenotypic trait in the subject. An analysis that identifies a statistical association (e.g., a significant association) between the marker or combination of markers and the phenotype establishes a correlation between the presence of the marker or combination of markers in a subject and the particular phenotype being analyzed.
The correlation can involve one or more than one genetic marker of this invention (e.g., two, three, four, five, or more) in any combination. In some embodiments of this invention, the genetic markers are located on chromosome 3 and can be localized to the region 3q13.31. However, in other embodiments, the methods of this invention can include correlations between genetic markers on chromosome 3 (e.g., at 3q13.31) in combination with genetic markers on other chromosomes (e.g., chromosome 1) and various aspects of cardiovascular disease as described herein. For example, the genetic markers of this invention can be combined with genetic markers in the ApoE gene on chromosome 19, genetic markers in the MEF21 gene on chromosome 15, genetic markers in the matrix metalloproteinase 3 gene on chromosome 11 and/or genetic markers in the β1-adrenergic receptor gene in chromosome 10 (e.g., the allele producing the Arg389 variant Perez et al., Nature Medicine 9:1300-1305 (2003); Bengtsson et al. Circulation 104:187-190 (2001)) in the methods of this invention and in establishing correlations between genetic markers and various aspects of cardiovascular disease as described herein.
Non-limiting examples of genetic markers of this invention are set forth in Tables 9, 10 and 11, which are located in the region from nucleotides 118500001 to 118761789 of human chromosome 3, NCBI Build 35 (SEQ ID NO:1).
In some embodiments, the genetic marker is a single nucleotide polymorphism (SNP). Exemplary single nucleotide polymorphisms include but are not limited to T for G, T for A, C for A, C for T, A for G, A for C, A for T, G for A and G for T substitutions. Other examples of genetic markers include insertions, deletions and duplications, including but not limited to an adenine deletion, a CAA insertion, and a 27-base pair duplication on human chromosome 3. Further examples of genetic markers of this invention include but are not limited to microsatellite markers such as 3M0238, which has a variety of alleles, such as alleles 245, 249, 250, 253 and 256, wherein each allele is defined by the length of the PCR product (245, 249, 250, 253 basepairs, etc.) produced using the 3M0238 primers (SEQ ID NOS:34 and 35) shown in Table 4. In a representative embodiment of the invention, the microsatellite marker is a tetranucleotide repeat, optionally, the tetranucleotide repeat sequence is GATA.
In the methods of this invention, particular alleles of the genetic markers are identified as being correlated with various aspects of cardiovascular disease. Thus, for example, an allele correlated with an increased risk of cardiovascular disease in a subject or with a diagnosis of cardiovascular disease in a subject can be a G allele at single nucleotide polymorphism rs1875518 (rs1875518_G), a T allele at single nucleotide polymorphism rs2937666 (rs2937666_T), a 253 allele at microsatellite marker 3M0238 (tetranucleotide GATA repeat, 253 basepair PCR product, 3M0238—253), a C allele at single nucleotide polymorphism hcv1602689 (hcv1602689_C), an A allele at single nucleotide polymorphism rs2272486 (rs2272486_A), an A allele at single nucleotide polymorphism rs1676232 (rs1676232_A), or an A allele at single nucleotide polymorphism rs4404477 (rs4404477_A), as well as any combination thereof. In some embodiments, a combination of genetic markers is provided that defines a haplotype that is correlated with an aspect of cardiovascular disease as described herein. Thus, for example, haplotypes correlated with increased risk of cardiovascular disease or with a diagnosis of cardiovascular disease include: rs1875518_G and G3M0238—253; rs1875518_G with G3M0238—253 and the A allele for rs2937666 (rs2937666_A); and/or the A allele for rs1875518 (rs1875518_A) with a non 253 allele of 3M0238 (3M0238_non253) and rs2937666_T.
Other examples of haplotypes correlated with cardiovascular disease are: the adenine deletion allele of the single nucleotide polymorphism of SEQ ID NO:15; the 27 basepair duplication allele of the polymorphism of SEQ ID NO:28; the CM insertion allele of the polymorphism of SEQ ID NO:29, and any combination thereof (Table 10). Still further examples of haplotypes correlated with cardiovascular disease are the A alleles for single nucleotide polymorphism rs1676232 or rs4404477 (rs1676232_A, rs4404477_A), or a combination thereof. Furthermore, rs4404477 appears to have an interaction with rs1676232 such that when both SNPs are homozygous for the A allele, the risk for CAD is significantly increased over that which is observed for a single SNP that is homozygous for the A allele, each of which is also associated with enhanced risk for CAD.
Additional alleles of this invention include an allele at a single nucleotide polymorphism in the LSAMP gene of the subject, which can be: a) an A allele at single nucleotide polymorphism rs1910040; b) an A allele at single nucleotide polymorphism ss70458782; c) a G allele at single nucleotide polymorphism rs1875518; d) an A allele at single nucleotide polymorphism rs1676232; e) an A allele at single nucleotide polymorphism rs4404477; and f) any combination of (a)-(e) above, wherein the detection of said allele(s) identifies the subject as having an increased risk of developing coronary artery disease.
Also provided herein is a method of identifying a Caucasian subject having an increased risk of developing coronary artery disease, comprising detecting in a nucleic acid sample of the subject a haplotype in the LSAMP gene of the subject comprising, consisting essentially of and/or consisting of an A allele at single nucleotide polymorphism ss70458782 and an A allele at single nucleotide polymorphism rs4404477, wherein the detection of said haplotype identifies the subject as having an increased risk of developing coronary artery disease.
An example of a haplotype correlated with decreased risk of cardiovascular disease is rs1875518_A with G3M0238_non253 and rs2937666_A.
Other genetic markers associated with cardiovascular disease are set forth in Tables 9, 10 and 11 and the Examples. The genetic markers of the invention can be used individually or in any combination.
In yet further embodiments, the present invention provides a method of identifying a subject with cardiovascular disease as having a good or a poor prognosis, comprising detecting in the subject one or more genetic markers in chromosome 3q13.31 correlated with a good or a poor prognosis for cardiovascular disease.
Furthermore, the present invention provides a method of identifying a subject with cardiovascular disease as having a good or a poor prognosis, comprising: a) correlating the presence of one or more genetic markers in chromosome 3q13.31 with a good or a poor prognosis for cardiovascular disease; and b) detecting the one or more markers of step (a) in the subject, thereby identifying the subject as having a good or a poor prognosis.
In addition, the present invention provides a method of correlating a genetic marker in chromosome 3q13.31 with a good or a poor prognosis for cardiovascular disease, comprising: a) detecting in a subject with cardiovascular disease and having a good or a poor prognosis, the presence of one or more genetic markers in chromosome 3q13.31; and b) correlating the present of the one or more genetic markers of step (a) with a good or a poor prognosis for cardiovascular disease.
A subject is identified as having cardiovascular disease according to diagnostic parameters well known in the art and can have a good or poor prognosis according to diagnostic and/or clinical parameters that are also known in the art. A correlation can be made between good and poor prognosis and a subject's genetic markers according to the methods of this invention, which can allow a clinician to determine the most effective treatment regimen for the subject.
The present invention further provides a method of identifying an effective treatment regimen for a subject with cardiovascular disease, comprising detecting one or more genetic markers in chromosome 3q13.31 in the subject correlated with an effective treatment regimen for cardiovascular disease.
Also provided is a method of identifying an effective treatment regimen for a subject with cardiovascular disease, comprising: a) correlating the presence of one or more genetic markers in chromosome 3q13.31 in a test subject with cardiovascular disease for whom an effective treatment regimen has been identified; and b) detecting the one or more markers of step (a) in the subject, thereby identifying an effective treatment regimen for the subject.
Further provided is a method of correlating a genetic marker of chromosome 3q13.31 with an effective treatment regimen for cardiovascular disease, comprising: a) detecting in a subject with cardiovascular disease and for whom an effective treatment regimen has been identified, the presence of one or more genetic markers in chromosome 3q13.31; and b) correlating the presence of the one or more genetic markers of step (a) with an effective treatment regimen for cardiovascular disease. Examples of treatment regimens for cardiovascular disease are well known in the art.
Patients who respond well to particular treatment protocols can be analyzed for specific genetic markers and a correlation can be established according to the methods provided herein. Alternatively, patients who respond poorly to a particular treatment regimen can also be analyzed for particular genetic markers correlated with the poor response. Then, a subject who is a candidate for treatment for cardiovascular disease can be assessed for the presence of the appropriate genetic markers and the most appropriate treatment regimen can be provided.
In some embodiments, the methods of correlating genetic markers with treatment regimens can be carried out using a computer database. Thus the present invention provides a computer-assisted method of identifying a proposed treatment for cardiovascular disease. The method involves the steps of (a) storing a database of biological data for a plurality of patients, the biological data that is being stored including for each of said plurality of patients (i) a treatment type, (ii) at least one genetic marker associated with cardiovascular disease and (iii) at least one disease progression measure for cardiovascular disease from which treatment efficacy can be determined; and then (b) querying the database to determine the dependence on said genetic marker of the effectiveness of a treatment type in treating cardiovascular disease, to thereby identify a proposed treatment as an effective treatment for a subject carrying a genetic marker correlated with cardiovascular disease.
In one embodiment, treatment information for a patient is entered into the database (through any suitable means such as a window or text interface), genetic marker information for that patient is entered into the database, and disease progression information is entered into the database. These steps are then repeated until the desired number of patients has been entered into the database. The database can then queried to determine whether a particular treatment is effective for patients carrying a particular marker, not effective for patients carrying a particular marker, etc. Such querying can be carried out prospectively or retrospectively on the database by any suitable means, but is generally done by statistical analysis in accordance with known techniques, as described herein.
The present invention is more particularly described in the following examples that are intended as illustrative only since numerous modifications and variations therein will be apparent to those skilled in the art.
EXAMPLES
Example 1
Overall summary: Using linkage analysis and association studies in families and isolated patients with cardiovascular disease (CAD), a 400 kb region in 3q13.31 was identified, containing a DNA region that affects susceptibility for CAD. A specific DNA haplotype was identified that is highly associated with CAD (p=0.0001) in Caucasians. This haplotype is defined by three markers: the single nucleotide polymorphism (SNP) marker rs1875518; a previously unidentified tetranucleotide GATA repeat, named 3M0238, and a third SNP, rs2937666. The actual alleles that are associated with susceptibility are shown in Tables 2 and 3. Both young onset and old onset CAD are affected by these haplotypes.
A genome wide screening in 420 families (GENECARD study Table 1) found the most significant linkage evidence (multipoint lod score=3.5) in chromosome 3q13 spanning over 120 megabases (Mb). This is shown in FIG. 1. Within this region is a genetic entity that influences the susceptibility for CAD. The present study was carried out to narrow the critical region and identify genetic variants conferring susceptibility to CAD in 3q13.
METHODS: Systematic association analysis using SNPs was performed in the 60 mB centered around the peak area of FIG. 1. A modified DNA pooling method was used to screen 16 SNPs, 100 kb apart, to look for association with CAD. To do this, another data set was used, different from the GENECARD data set, the CATHGEN samples, from a study of the Duke Catheterization Laboratory Database. Subjects were selected according to their CAD index (CADi), a validated angiographical measure of the extent of CAD. CATHGEN included 301 young affected (YA: age ≦55, CADi >32), 168 older affected (OA: age >55, CADi >74), and 204 controls (ON: age >60, CADi <23). Association analysis was performed separately by ethnicity and adjusting for gender.
Initial screening of 16 SNPs revealed that the frequency of the G allele of rs1875518 (A/G) is significantly higher in OA than ON (OA-ON=12.2%, p=0.001) in Caucasians (FIG. 2), which is confirmed by individual genotyping (OA=57.2%; ON=45.5%). Additional genotyping flanking rs1875518 defined a linkage disequilibrium (LD) block extending  ̃60 kb that is highly associated with OA in Caucasians. Moreover, evidence supports the association of this block in the GENECARD probands versus Cathgen ON (FIG. 3). Finally, a novel microsatellite marker (3M0238) was identified within the block, which broke the LD and formed a significant risk haplotype (P<0.005) with rs1875518: rs1875518_G-3M0238—253 is twice as prevalent in OA (21.39%) as in ON (11.39%).
Additional markers surrounding this region were genotyped and a further haplotype was obtained that defines the risks and protection, as seen in Tables 2 and 3. Multiple risk haplotypes exist, which could represent different alleles of the actual causal change. Primers and probes used in the analysis are shown in Table 4.
Example 2
Coronary artery disease (CAD) is the leading cause of death in the United States and approximately 8% of CAD occurs in Americans under 50 years of age (AHA website). It is well established that CAD and death from CAD have a hereditary component (Marenberg, Zradkovic). The strong genetic predisposition of CAD may be partially explained by the heritability of disease related intermediate traits such as dyslipidemia. Dyslipidemia is a well-recognized risk factor for CAD, and abnormalities in serum lipids have been shown to have a genetic component (Breslow). Further, there is an increased incidence of familial lipoprotein abnormalities in family members of patients with premature CAD (Genest). Twin and adoption studies suggest that at least 50% of the observed variation in low-density lipoprotein (LDL) cholesterol is genetically determined (Austin, Rice) and segregation analysis has shown evidence for a major gene for high-density lipoprotein (HDL) cholesterol (Mahaney 1995). The Family Heart Study has found evidence for a common major gene accounting for mild elevations of LDL cholesterol (Coon, 1999), although the exact gene has yet to be identified. Familial combined hyperlipidemia (FCH) has been mapped to chromosome 1q (Pajukanta Nat Gen 1998), with subsequent identification of the USF1 gene (Pajukanta 2004). Linkage of HDL cholesterol to chromosomes 5 and 13 has been reported (Peacock 2000), and recently, a pooled analysis of patients with FCH has revealed a susceptibility locus for low HDL on chromosome 16q (Pajukanta 2003).
Many candidate genes have been implicated in the development of coronary heart disease (CHD) and dyslipidemia, but none have been shown to account for even a modest fraction of the burden of CHD in the general population. One reason is that CHD is likely an oligogenic disease with multiple genetic loci conferring susceptibility to the disease, with the phenotype determined by complex gene-gene and gene-environment interactions. One approach to unraveling these complex relationships is to examine intermediate traits. Methods to map genes for complex traits that explicitly take into account the presence of such heterogeneity are likely to have greater power to identify subtle changes. Two such methods for incorporation of covariates into linkage mapping include examination of the extremes of the covariate distribution to find genes that cause gross perturbations (ordered subset analysis (OSA)), or examination of the entire covariate distribution to find genes for trait variability (quantitative trait loci (QTL) analysis).
The Genetics of Early Onset Cardiovascular Disease (GENECARD) linkage study was designed to conduct affected sibling pair (ASP) analysis for the identification of genes contributing to early onset CAD. Linkage studies employ an unbiased, genome-wide approach to identify genetic regions shared in excess between affected relative pairs. This strategy for gene mapping has been widely used and has led to the discovery of many disease susceptibility genes. Strong evidence has been provided for linkage to early onset CAD in GENECARD families to chromosome 3q13 in the overall population (lod 3.50), and in stratified analyses by families presenting with acute coronary syndrome (ACS; lod 3.16) and non-diabetic (NDIA) families (lod 2.42; Hauser 2004). Chromosome 1 q25 was significant in ACS families (lod 2.17); other regions showing evidence for linkage included 5q13, 7p14 and 19p13. Previous studies have also implicated regions on chromosome 3q26-27 in CAD (over 60 cM distal to the peak in the GENECARD analysis) (Francke 2001, Broeckel 2002, Harrap 2002), metabolic syndrome (Kissebah 2000), and type II diabetes mellitus (DM) (Vionett 2000, Mori 2002). There is also evidence of QTL for triglyceride-HDL cholesterol ratio (Shearman 2000), HDL cholesterol (Imperatore 2000, Coon 2001) and fractionated low-density lipoprotein (LDL) particles (Rainwater 1999) in the region of the GENECARD 3q peak. These results suggest potential interactions between CAD genes and intermediate lipid traits.
To incorporate disease-related risk factors, lipid phenotypes in the GENECARD study were examined. Incorporation of lipid phenotypes increases the power to map CAD susceptibility genes; uncovers additional regions of linkage, narrows linkage peaks, and identifies phenotypic subsets for further study. Since it is well known that lipid phenotypes themselves have a high heritability, QTL analysis was performed to identify chromosomal regions linked to variability in lipid values within high-risk CAD families. OSA was also performed using subclassification by lipid phenotypes to reduce etiologic heterogeneity.
Clinical data collection. The GENECARD study enrolled 900 families with early onset CAD to perform an ASP genetic linkage study for identification of genetic variants. The study design has been previously reported. Briefly, families with at least two siblings having early onset CAD were recruited from multiple sites. Individuals were recruited if they met the diagnosis of CAD and if the qualifying event occurred before the age of 51 years for men and 56 years for women. For the diagnosis of CAD, a sentinel event or diagnostic study was required that was verified by primary medical documents. Subjects were required to have myocardial infarction (Ml) or unstable angina, significant CAD on coronary angiography, coronary revascularization procedure, or a functional test documenting reversible ischemia with imaging. Medical history was confirmed by inspection of medical records. A system of periodic review was implemented to establish quality control and to ensure consistency among all clinical sites in diagnostic criteria. A genome-wide linkage analysis for early onset CAD was undertaken on the first 420 families enrolled in GENECARD, and these families form the basis for the analyses presented in this study
Laboratory methods. Blood samples were obtained by study staff primarily at the medical center or clinic, or by field trip to participants' homes. DNA was extracted using the Puregene system (Gentra Systems, Minneapolis, Minn.). Quality control (QC) samples were incorporated into specified slots in the genotyping lists. Laboratory technicians were blinded to the identity of the QC samples, and to affection status and family composition of all samples. Genotyping was performed using the gel-based FAAST method (Vance and Ben Othmane 1998). Quality control checks were implemented to maximize data quality during genotyping (Hauser 2004). A total of 395 (98.3%) markers out of 402 attempted passed the QC tests and were included in these analyses. The mean genotyping efficiency (proportion of non-zero genotypes) over the 395 markers was 97.6%. Using data from several large studies performed in the Duke Center for Human Genetics, we estimated an error rate in sample processing and allocation in 0.14% and we estimated the genotyping error rate to be approximately 0.8%. Given that GENECARD families were collected from six sites in the US and Europe, it is possible that they represent genetically distinct subpopulations. To test for population substructure Structure (Pritchard 2000) and Arlequin (Arlequin) were employed, using an indicator for each site. There was no evidence from either analysis that the sites could be distinguished on the basis of allele frequencies at the 395 markers in the genome scan. Based on these results, estimated allele frequencies were estimated from the family members in the entire sample (Broman 2001).
Serum lipoprotein measurements were done in the fasting state for 229 of the 420 families (54.5%) using a centralized core laboratory. Levels of plasma total cholesterol (TC) and triglycerides were measured as reported previously (Vega). Briefly, plasma lipids were measured enzymatically using the Boehringer Mannheim cholesterol enzymatic kit (Roche Diagnostics, Indianapolis, Ind.) and the Sigma-Aldrich kit for triglycerides (St. Louis, Mo.). HDL cholesterol was measured after precipitation of non-HDL cholesterol with dextran sulfate (Sigma-Aldrich, St. Louis, Mo.) (Warnick). The coefficients of inter- and intra-assay variation were ≦3%. The remaining 191 families, consisting mostly of United States participants, had lipoprotein measurements abstracted from the medical records. Adjustment for treatment with medications for dyslipidemia was done when creating the polygenic model used for quantitative trait loci analyses. 27 families were excluded for missing values. Reported results include all 393 families for the lipid parameters of TC, LDL, HDL cholesterol, and HDL/TC ratio, which has been shown to be an independent risk factor for CAD (Jeppesen). Reported results for triglycerides are restricted to the 229 families with measured lipid parameters, since serum triglyceride levels are highly affected by the non-fasting state. There were fewer than 10 families who would potentially meet broad diagnostic criteria for FCH; the family-specific lod scores did not identify specific FCH loci nor did these families appear to contribute an excess amount to the overall CAD genome scan, and therefore these families were included in all further analyses.
Analytic methods. Descriptive analysis for lipid values and for all covariates were performed using SAS software (SAS, Cary N.C.).
Quantitative trait loci (QTL). To identify genetic loci associated with lipid phenotypes, QTL linkage analysis was performed using a genome wide scan of 395 microsatellite markers. All lipoprotein subgroups had an approximately normal distribution, except serum triglycerides, which were log-transformed to approximate a normal distribution. QTL analysis was performed using the variance components approach as implemented in the Sequential Oligogenic Linkage Analysis Routines (SOLAR) software package, which uses maximum likelihood methods to estimate the genetic variance components (Almasy). The SOLAR package utilizes multipoint identical-by-descent (IBD) methods where the proportion of alleles shared IBD at genotyped loci are used to estimate IBD sharing at arbitrary points along a chromosome for each relative pair (Almasy, 1998). IBD and multipoint IBD matrices were constructed using the observed family pedigrees. An initial polygenic model was constructed adjusting for sex, age at exam, and treatment with dyslipidemia medications for each quantitative trait and used as the foundation for two-point and multipoint linkage analyses. Use of dyslipidemia medications was a binary, self-reported variable coded yes/no. A lod adjustment was calculated (lodadj=0.61) and used for analysis of TC because of a high residual kurtosis of 1.6. Although the GENECARD probands were not ascertained on lipid values, the relationship between CAD and lipid values does not reflect normal population values, implying an ascertainment bias. As a result, analyses were done with and without adjustment for proband lipid values and the results did not differ appreciably. Therefore, only results with proband ascertainment are presented. Empirical p-values were calculated using models with 10000 simulations in each of which a fully-informative marker, unlinked to the trait, is simulated and trait linkage is then tested at that marker (SOLAR). QTL mapping results that achieved a multipoint lod score of greater than 1.2 (corresponding to an empirical p-value of 0.007-0.03 depending on the covariate analyzed) were flagged for further study.
Ordered subset analysis (OSA). OSA examines evidence for linkage in a more homogeneous subset of families defined by a trait-related covariate. The average lipid values in the affected individuals from each family were chosen as trait-related covariates. In addition to the family-specific covariate values, a matrix of linkage statistics Zi(d,γ) is required as input, where d represents the disease location parameter and γ represents the genetic model, and the maximum ordered subset statistic for each family is calculated at a set of values for d and y. OSA begins by ordering N number of families by the covariate value xi, both in an ascending and a descending order, where Z(j)(d,γ) is the linkage statistic matrix for ordered family j. The maximum lod score is calculated for the jth family, as well as the estimates of d(j) and γ(j) at which the maximum occurs. Then, element-wise addition is used to add the matrix for the next ordered family Z(j+1)(d,γ) to the matrix for family 1 through j. In summary, the jth partial sum is created by adding each element of the linkage statistic matrix for each family up to and including ordered family j. The maximum subset lod score (the highest lod score using subsets of families with the highest or lowest mean covariate) represents the linkage evidence in a subset of families defined by that covariate. OSA also provides an estimate of the disease location on the specified chromosome. A permutation procedure, randomly ordering families and recalculating the OSA test statistic, provides an empirical p-value to assess the significance of the increase in the maximum lod score using the ordered subset of families compared to the overall lod score using all families. Significance was defined as a p-value <0.05 for an increase in the maximum subset lod when compared to the overall lod score. To further characterize subsets of families with significant results, the family-specific means of each covariate comparing families comprising the maximum subset lod score and the remainder of the GENECARD families. Mean family values for quantitative traits were compared using a univariate t-test (SAS).
Table 5 outlines baseline characteristics in the 420 GENECARD families, overall and by affection status, comprising a total of 1129 individuals, 952 affected with early onset CAD and 177 unaffected family members. Consistent with other studies, there was a high prevalence of cardiovascular risk factors among affected individuals, including hypertension (55.2%), diabetes (21.0%), tobacco use (32.9% currently smoking), dyslipidemia (82.3%) and metabolic syndrome (46.8%). As expected, these risk factors were more prevalent in affected individuals than in unaffected individuals. However, the mean values of total cholesterol, LDL and systolic blood pressure were higher in the unaffected group, consistent with the 14-year increase in the mean age of the unaffected family members and increased use of medications for dyslipidemia in the affected group. Heritability estimates revealed strong heritability of all lipid subgroups (Table 6), consistent with previous reports.
QTL results. The overall results of the QTL analysis are shown in Table 6. The largest lod score for a QTL was for HDL cholesterol on chromosome 3p (FIG. 4), with weaker evidence on chromosomes 7 and 15. QTLs for TC were found on chromosome 18p and 5p, and for LDL cholesterol on chromosomes 6 and 16. There was evidence for QTL for triglycerides on chromosome 13, 14, and 18, and there was evidence for loci for HDL/TC ratio on chromosome 3q, 7q and 8q. Three regions showing evidence for linkage in the overall genome scan (3q, 7p and 19p) also showed evidence for lipid QTLs (HDL/TC ratio, triglycerides and LDL cholesterol, respectively).
OSA results. Significant OSA results are shown in Table 7. FIG. 5 shows chromosome 3 lod score curves using OSA that corroborate, strengthen and narrow the linkage peaks previously observed on chromosome 3q. The increase in the lod score is intriguing because it occurs on top of already strong linkage evidence in this region. The 167 families in the OSA subset represent 39.7% of the GENECARD families. These families appear to have a different phenotypic profile with significantly fewer CAD risk factors than the remainder of the families (Table 8). FIG. 5 also shows a lod score curve using OSA showing a strong linkage peak on chromosome 5q, but more distal to the linkage peak observed on the overall genome scan. This set of 54 (12.8%) families represents a high-risk lipid phenotype with elevated TC, high LDL and triglycerides and having a significantly lower average age of onset. However, these families cannot be distinguished on the basis of other CAD risk factors such as BMI, gender, or smoking. The chromosome 5 subset of families is a distinct set of CAD families from the chromosome 3 subset, with the two subsets of families representing the two tails of the lipid distributions among these CAD families. OSA also revealed significant LOD scores in subsets of families on chromosomal regions not previously found to be significant in this sample, including peaks on 9p, 10q, 12q, 14p, 17q, and 22p. The subsets identified in these regions are smaller, ranging from 22 to 80 families (5.2% to 19.0%).
These results reveal evidence for several QTL for lipid subgroups in families with early onset CAD. OSA results corroborated and strengthened areas of strong linkage in the overall population on chromosome 3q and 5q, helped narrow the linkage peaks, identified new regions for further analysis, and defined phenotypic subsets comprising the peaks.
Specifically, QTL mapping of lipid phenotypes in the GENECARD population revealed multiple chromosomal areas with significant lod scores for lipid subtypes, with the strongest lod score for HDL cholesterol on chromosome 3p (lod 2.43). Evidence was also found for linkage for HDL cholesterol to chromosome 7q (156 cM), a region also found to link to HDL/TC ratio (143 cM). This area has previously been linked to TG and TG/HDL ratio (Shearman 2000), and is proximal to another reported peak for TG (186 cM) (Duggirala). This locus contains several candidate genes, including ABC28 (ATP-binding cassette subfamily F, member 2, similar to ABC1 which causes Tangier's disease, characterized by HDL deficiency and premature atherosclerosis). A QTL for LDL cholesterol was identified on chromosome 6q, which contains the gene for apolipoprotein (a) (Lp(a)), a well recognized cardiovascular risk factor (Murai), and has previously been linked to small LDL particles in the San Antonio Family Heart Study (Imperatore). There was evidence for linkage to triglycerides on chromosome 18 (near QTL for total cholesterol at 55 cM); though not as strongly linked, this region is interesting because it contains the gene for Niemann-Pick disease type C1 (NPC1), an autosomal recessive lipid storage disorder. These results did not corroborate previous results on chromosomes 4 (TG, LDL) (Arnett 2001), 15 (HDL, TG) (Almasy, Duggirala, Arnett), and 2 (TG HDL) (Pajukanata, Imperatore, Almasy).
To understand the impact of heterogeneity, it is useful to compare these results to the OSA analysis. At least two phenotypically distinct sets of families with early-onset CAD were identified that contributed to linkage evidence. On chromosome 3q, evidence was found for linkage to early onset CAD in families with lower TC and triglycerides, higher HDL cholesterol and overall lesser prevalence of metabolic syndrome, when compared to families not included in the OSA peak. These results were corroborated by the finding of a QTL for HDL/TC ratio in the same region. Therefore, it appears that the previously reported strong linkage peak on chromosome 3q is comprised of families without a preponderance of traditional cardiovascular risk factors. A recent meta-analysis of four genome-wide scans for CAD revealed strongest evidence for linkage on chromosome 3q26-27 (Chiodini), and this region has shown linkage to metabolic syndrome (Kissebah 2000) and type II diabetes mellitus (Vionett 2000, Mori 2002, Hegele 1999). However, in each of these genome scans the evidence for linkage to CAD is over 60 cM distal to the peak in the GENECARD analyses. In QTL analysis of plasma lipids, there is evidence of linkage with triglyceride-high density lipoprotein (HDL) cholesterol ratio in the peak 3q13 region (Shearman et al. 2000). There is also evidence for linkage to HDL cholesterol itself (Imperatore et al. 2000; Coon et al. 2001) and fractionated low-density lipoprotein (LDL) particles (Rainwater et al. 1999) in this region. A genome scan of lipid traits in Pima Indians found a locus on chromosome 3, but more distal to this peak (182 cM) (Imperatore 2000). The 3q26-qter region harbors several candidate genes involved in glucose homeostasis and lipid metabolism. The 3q13 region, however, is an area of relative paucity of genes. This area may harbor a previously undiscovered gene, represent a genetic area exerting a downstream influence, or may be in linkage disequilibrium with more distal candidate genes.
A linkage peak for early onset CAD was again observed on chromosome 5q using OSA, but more distal on the chromosome than seen in the overall genome scan, and is comprised of a subset of families who are younger with higher total cholesterol values. This area contains many genes, including HNRPAB (apolipoprotein B mRNA-editing enzyme) and F12 (factor XII deficiency), though none have been previously implicated in the pathogenesis of dyslipidemia or CAD.
OSA and QTL mapping are alternate methods for incorporating phenotypic data in linkage studies. Overall it was found that OSA and QTL results did not overlap, except on chromosome 3q. This is most likely related to the fact that QTL and OSA analyses model different aspects of lipid phenotypes and address different issues. The lod score for the OSA analysis is still linkage to CAD and the phenotype data are used as a measure of similarity to help identify homogeneous subsets. QTL mapping models the quantitative traits of lipid phenotypes specifically, in attempts to identify chromosomal regions that may harbor genes for normal variation in lipid phenotypes. OSA was used to identify and narrow chromosomal regions harboring candidate genes for the phenotype of early onset CAD, using lipid subtypes to create more etiologic homogeneity and potentially concentrate the genetic effect.
The study population consists of those who remain alive despite early onset CAD, a so-called “survivor effect.” Therefore, inferences drawn about genetic effects will be confined to familial early onset CAD, and may not be applicable to premature sudden cardiac death. Because the GENECARD families were ascertained on the basis of early onset CAD, their lipid values may not represent the normal distribution of lipid values. The phenotypic differences in the GENECARD sample compared to samples of unselected families, or families ascertained on the basis of hypertension or metabolic syndrome, may explain why QTL analysis did not identify the regions identified in other studies. Although genome-wide linkage studies may be superior in determining significant genetic loci, affected sibling pair studies only provide a general view of the true gene location. The permutation test employed by OSA analyses controls for the inflation in the false positive rate induced by examining multiple family subsets for a given covariate, and appears to give the proper type I error rate in previously done simulations (Hauser). However, these analyses do not control for OSA over multiple trait-related covariates, but the strong correlation between the lipid parameters makes it difficult to appropriately correct for multiple comparisons.
Regardless, the GENECARD cohort is an ideal population for genetic studies. Setting an age criteria for CAD selects for patients with a strong genetic predisposition and enriches the sample for CAD caused by genetic etiologies. It is also an ideal population for primary prevention, an eventual goal of the utilization of genetics in clinical cardiology. Furthermore, GENECARD represents a model database for evaluation of genotype-phenotype interactions in the pathogenesis of CAD, by virtue of its sibling pair approach; international population allowing for ethnic heterogeneity; relatively large sample size; and genome-wide methodology. The combined approach of using QTL and OSA analysis for incorporation of disease-related lipid phenotypes in a genome scan of CAD is unique. Such modeling of genotype-phenotype interactions in a multi-analytic approach will enhance discovery of genetic loci and aid in the eventual goal in creation of a comprehensive cardiovascular risk assessment model.
These results show strong evidence of linkage to chromosomal region 3q13 in families with early onset CAD but with more favorable lipid profiles, possibly due to a concentrated non-lipid-related genetic effect on CAD, and to chromosome 5q in families with early onset CAD but with higher total and LDL cholesterol values, possibly representing a hereditary lipid phenotype predisposing to early onset CAD. QTL mapping identified multiple loci for lipid phenotypes and overall corroborated results from the initial genome scan. These results suggest presence of etiologic heterogeneity in families with early onset CAD, potentially due to differential lipid phenotypes.
Example 3
Sequences of exemplary polymorphisms within the region of human chromosome 3q13.31 are depicted in Table 10. Of particular note are: the single nucleotide polymorphism as set forth by an adenine deletion in SEQ ID NO:15; the polymorphism as set forth by a 27 basepair duplication in SEQ ID NO:28; and the polymorphism as set forth by a CM insertion in SEQ ID NO:29. FIG. 6 depicts the genotypes of normal versus affected individuals with respect to these three variations.
FIG. 7 depicts differences in allele frequency between affected versus control (normal) cases with exemplary SNPs within the region of human chromosome 3q13.31.
FIG. 8 depicts the frequency of genetic markers within the region of human chromosome 3q13.31 correlated with affected and control (normal cases) and the significance of the correlation of the G allele of rs1875518 and the 253 allele of 3M0238 with CAD.
Example 4
Association analysis of additional SNPs with risk for CAD is depicted in FIG. 9. Of particular note are the SNPs rs2272486 and hcv1602689 in Huntington-associated protein-interacting protein (HAPIP) and myosin light chain kinase (MLCK), respectively. The locations of these SNPs on human chromosome 3 are listed in Table 11. Particularly, the C allele for hcv1602689 (SNP is C/G) and/or the A allele for rs2272486 (SNP is A/G) is associated with increased risk for CAD.
Additional SNPs associated with risk for CAD are the A alleles for rs1676232 and rs4404477 found in the gene for the limbic system-associated membrane protein (LSAMP; both SNPs are A/G). Furthermore, rs4404477 appears to have an interaction with rs1676232 so that when both SNPs are homozygous for the A allele, the risk for CAD is significantly increased over that which is observed for a single SNP that is homozygous for the A allele.
Example 5
Initial and Validation Datasets: Subjects in the initial and validation datasets were ascertained through the cardiac catheterization laboratories at Duke University Hospital and have been previously described (CATHGEN) (Wang et al. 2007). All subjects undergoing catheterization were offered participation in the study. To reduce confounding by population substructure, only Caucasians were used for the association analyses. Subjects were chronologically divided into sequential initial and validation datasets. The initial dataset included old affecteds, left main cases, and controls. The validation dataset included left main cases and controls. Briefly, the old affected has age-at-onset ≧51 in male and ≧56 in female and CAD index (Table 16), a numerical summary of angiographic data, greater than 72. Subjects with 75% or greater stenosis in the left main coronary artery were defined as left main cases regardless of age-at-onset. Controls were >60 years old at the time of angiography, and had no diseased vessels, history of myocardial infarction (MI) or interventional cardiac procedures. The major indications for cardiac catheterization for controls were possible ischemic heart disease (66%), valvular heart disease (8%), congenital heart disease (<1%), and “other” (25%, including evaluation for fatigue, pre-operative clearance, and asymptomatic decreased ejection fraction).
Third Control Dataset: Additional control subjects were recruited from community meetings and unrelated family members (e.g., spouses) of Alzheimer patients in an ongoing study of Alzheimer Disease (Margaret A. Pericak-Vance, P. I.). All members were self-reported Caucasians >60 years old, and had no history of Ml, diabetes, stroke, or peripheral vascular disease based on a detailed questionnaire for medical history. Their mental status was normal as evaluated by the Modified Mini-Mental Status exam (Teng & Chui 1987). Unlike the CATHGEN controls, no angiographic data were available for a definite phenotypic classification for this dataset. It is possible that some subjects have subclinical undiagnosed CAD. However, this dataset matched the phenotypic definition of controls in most of other genetic epidemiologic studies on CAD and provided an independent set of controls to validate associations in the CATHGEN subjects.
GENECARD Dataset: The sample collection and study design of the GENECARD study have been reported (Hauser et al. 2004). The family-based GENECARD dataset was composed of families with at least two affected siblings who met the criteria for early-onset CAD. The majority (>90%) of the GENECARD subjects were Caucasians. Unlike the CATHGEN samples, angiographic data in GENECARD samples was not available and left main CAD status was not determined.
The Duke Institutional Review Board approved all studies, and all subjects signed informed consent.
SNP Selection, Genotyping, and Sequencing Non-redundant SNPs (r2<0.7) were chosen across the LSAMP gene using the software program SNPSelector (Xu et al. 2005). SNPgenotyping and sequencing were performed using reagents and instruments from Applied Biosystems (Foster City, Calif.). SNP genotyping was performed using the TaqMan® Allelic Discrimination assay in 384-well format, and quality control was implemented as described previously (Connelly et al. 2006). Duplicated quality-control samples were placed within and across plates to identify potential sample-plating error and genotype-calling inconsistency. Hardy-Weinberg equilibrium (HWE) testing was performed for all markers. SNPs with mismatches on quality-control samples or failed HWE test (p<0.05) in white controls were reviewed by an independent genotyping supervisor for potential genotyping errors. All examined SNPs had a calling rate >95% in the studied population. On the basis of 26,000 duplicate genotypes, genotyping error-rate estimates for SNPs meeting the quality-control benchmarks were <0.2%. Direct PCR sequencing was performed using the Big Dye 3.1 and ABI 3730 automated sequencer. Sequences derived from nine patients with CAD and seven controls were assembled using Sequencher 4.7 (Gene Codes, Ann Arbor, Mich., United States) to discover novel polymorphisms.
Stepwise Validations To minimize false positive findings attendant to the multiple SNPs tested, we applied stepwise validations in the SNP association study. First, all SNPs were screened in the initial dataset. Then, promising SNPs (p<0.1) were further analyzed in the validation dataset. Joint analysis using the combined initial and validation dataset were performed to maximize the statistical power. Significant SNPs derived from this analysis were further examined in the third control dataset and the family-based GENECARD dataset. Finally, we performed pairwise haplotype analysis in our largest case-control dataset consisting of the initial, validation, and the third control datasets.
Gene Expression Analysis Human aortic endothelial cells and smooth muscle cells (SMCs) were purchased from Cambrex Bio Science, Inc. (Walkersville, Md.), and cultured following the manufacturer's instructions. Human aortas were collected from heart transplant donors and graded for atherosclerosis as previously described (Seo et al. 2004). Total RNAs were extracted from cells or aortas and were used to synthesize first strand cDNA using AdvantageTM RT-for-PCR Kit (BD Biosciences, Palo Alto, Calif.). Gene expression was measured by TaqMan® real-time, reverse-transcriptase PCR (RT-PCR) in triplicate and normalized to glyceraldehyde-3-phosphate dehydrogenase (GAPDH) expression.
RNA Interference Small interfering RNA (siRNA) specific for LSAMP and a negative control siRNA targeting no known gene were purchased from Silencers Pre-designed siRNAs (Ambion/Applied Biosystems). SMCs were plated at a density of 1.3×104 cells/cm2 two days before transfection. Cells were then transfected with LSAMP or negative control siRNA (25 nmol/L) using the LipofectamineTM RNAiMax transfection reagent (Invitrogen), following the manufacturer's instructions. Twenty-four hours after siRNA transfection, SMCs were made quiescent for 72 hours with serum-free SmGM-2 medium, and then subjected to thymidine incorporation, quantitative RT-PCR, or immunoblotting of SMC membrane fractions, as described (Zhang et al. 2007) with anti-LSAMP IgG (the kind gift of Dr. A. F. Pimenta) (Levitt 1984).
Thymidine Incorporation Quiescent SMCs were then challenged with SmGM-2 containing 5% fetal bovine serum for 20 hours before [3H]thymidine was added to the medium (1 μCi/ml). Incorporation of thymidine into SMC DNA was determined as we reported previously (Peppel et al. 2000).
Statistical Analysis The association between CAD and SNPs was examined using multivariable logistic regression analyses that adjusted for (a) gender (the “basic model”) or (b) gender, age-at-exam, hypertension, diabetes mellitus, body mass index, dyslipidemia, and smoking history (the “full” model). The genotype case-control statistic provided by SAS 9.0 was used to perform the association analysis, which tests both dominance genotypic effects and additive allelic effects. The Association in the Presence of Linkage (APL) (Martin et al. 2003b) test, Pedigree Disequilibrium Test (PDT) (Martin et al. 2003b) and GenoPDT (Martin et al. 2003a) were used to evaluate family-based association in the GENECARD samples. Each of the three analytic approaches offers distinct merits. The APL test takes into account for linkage and correctly infers missing parental genotypes in regions of linage by estimating identity-by-descent parameters. The PDT allows incorporation of extended pedigrees. Both APL and PDT are allele-based tests while GenoPDT examine the association between genotypes and disease status. The Graphical Overview of Linkage Disequilibrium (GOLD) program was used to assess linkage disequilibrium (LD) between SNPs (Abecasis & Cookson 2000). Haplotype association was performed using HaploStats 1.1.0 (Mayo Clinic, Rochester, Minn.).
To increase statistical power, we analyzed all the available aorta samples for the haplotype-specific gene expression. In some cases, two pieces of sample from the same aorta were assayed for gene expression. Therefore, a random effect was used for each aorta along with fixed effects for atherosclerosis burden and haplotype in a mixed model for the haplotype-specific gene expression analysis. An F-test was used to test for differences in gene expression for the atherosclerosis and haplotype. For the SMC proliferation assay, two-way ANOVA was performed. SAS 9.0 (SAS, Cary, N.C.) was used for statistical analyses.
Datasets for Association Studies The initial dataset included 168 old affecteds, 102 left main cases, and 149 controls. The validation dataset included an additional 141 left main cases and 215 controls. The third control dataset comprised 255 individuals. Baseline clinical characteristics for each dataset are given in Table 12. In general, the case groups had a higher prevalence of clinical CAD risk factors than the controls. The GENECARD samples have been described elsewhere (Hauser et al. 2004; Connelly et al. 2006). In brief, this dataset consisted of 2954 individuals, among which were 966 affected sibling pairs and 825 discordant sibling pairs.
Selected SNPs for Screening LSAMP It was recently reported that the mouse lsamp gene has an alternative first exon 1a located 1.5 megabases from the originally described first exon (now exon 1b) (Pimenta & Levitt 2004). Using RT-PCR, we confirmed the existence of these LSAMP alternative transcripts generated by exon 1a (LSAMP—1a) and exon 1b (LSAMP—1b) in several human tissues, including aorta. Ninety tagSNPs across both LSAMP transcripts were examined in the initial analysis (Table 17).
Association Tests in the Initial Dataset To test our hypothesis that association in LSAMP was driven by severe CAD as represented by left main cases, subset analysis in the old affected and the left main cases was performed in the initial dataset. Despite the smaller sample size of the left main CAD subgroup, this analysis revealed stronger SNP associations in the left main cases than in the old affecteds, supporting our hypothesis that left main CAD was the major phenotype underlying the association at LSAMP.
The strongest association was found at rs1875518 (p=0.008, OR=1.7, Table 17). Additional genotyping surrounding rs1875518 and linkage disequilibrium analysis found that LD surrounding rs1875518 extends over 40 kb, from rs1501885 to rs2937673. Therefore, novel SNPs were sought to partition this LD block by resequencing this 40 kb region. Two novel SNPs (ss70458781 and ss70458782) and one novel 27 bp duplication (ss70458783) were identified through this effort. However, only ss70458782 was not highly correlated with rs1875518 (r2=0.27). As a single marker, ss70458782 was marginally associated with left main CAD (p=0.091) (Table 17).
Validation of the Association in Multiple Additional Datasets To validate the left main CAD-associated LSAMP SNPs identified in the initial analysis, we tested the promising SNPs (p<0.1 in the initial dataset) in an independent validation dataset of left main CAD cases and controls ascertained by the same criteria as the initial dataset. Odds ratio (OR) estimates were compared between the initial and validation datasets to identify consistent trends of association. Since analyzing genetic markers in large datasets may be more effective in identifying true-positive associations for complex traits than replicating analyses in two smaller datasets (Shephard et al. 2005), joint analysis of both the initial and validation datasets was also performed. Among the ten SNPs tested in the validation dataset, five SNPs were designated as “significant SNPs,” as they displayed the same risk allele in both the initial and validation datasets and met the significant level of 0.05 in the joint analysis adjusting for gender (p=0.005 to 0.028, Table 13). In the full model analysis, which includes additional CAD risk factors as covariates, three of the five SNPs remained significant (p=0.021 to 0.044, listed in Table 13).
To avoid potential ascertainment bias with control subjects identified through the cardiac catheterization laboratory, and to provide an independent control dataset, we then studied the five significant LSAMP SNPs by analyzing the independent third control dataset along with the combined left main CAD cases from the initial and validation datasets. This analysis demonstrated significant association of rs4404477 with left main CAD (p=0.006) (Table 14). To maximize the statistical power and the precision of OR estimate, we then compared the combined left main CAD cases with all control subjects from the initial, validation, and third control datasets. This analysis found that four LSAMP SNPs were significantly associated with left main CAD, with rs4404477 being the most significant (p=0.003, OR=1.7) (Table 14). Finally, we evaluated association of the five significant SNP in the family-based GENECARD samples. Both SNP rs1676232 (p=0.020, 0.087 and 0.285, evaluated by APL, PDT, and GenoPDT, respectively) and rs4404477 (p=0.091, 0.011 and 0.044, evaluated by APL, PDT, and GenoPDT, respectively) displayed evidence for association in the GENECARD dataset.
The LSAMP Risk Haplotype Associates Strongly with Left Main CAD Haplotype analysis using more than one SNP at a time can greatly increase information generated through each SNP genotype by itself. Hence, we performed pairwise haplotype analyses using the five significant SNPs in our largest case-control dataset (comprising the initial and validation datasets, as well as the third control dataset). This analysis found that the ss70458782A_rs4404477A haplotype (HAP L) was highly significantly associated with left main CAD (p=0.00004, Table 15), and accounted for 35% of the risk for left main CAD, as estimated by the population attributable risk in our largest dataset (95% CI: 13 to 52%). In addition, HAP L demonstrated significant association with left main CAD in all independent subsets that composed the largest datasets (p=0.0001 to 0.021, Table 15).
The Reduced LSAMP Expression in Human Aortas: Association with Increased Atherosclerosis and Dosage of Risk Haplotype Since LSAMP has been shown to function as a tumor suppressor gene (Chen et al. 2003), we reasoned that diminished expression or function of LSAMP could promote atherogenesis by potentiating smooth muscle cells (SMC) and/or macrophage proliferation in atherosclerotic plaques (Hansson 2005). Alternatively, enhanced LSAMP expression or function could diminish endothelial cell proliferation, and thereby promote atherosclerosis (Hansson 2005). To begin testing these possibilities, we first examined LSAMP expression in cultured human aortic endothelial cells and SMCs. We found that neither LSAMP—1a nor LSAMP—1b was expressed in the endothelial cells, while both LSAMP isoforms were expressed in the SMCs. Thus, we inferred that the genetic risk conferred by the LSAMP SNPs was most likely playing out through LSAMP's potentially pro-atherogenic role in SMCs, and not endothelial cells.
Within the aortic SMCs, LSAMP—1a was the more abundant transcript. Interestingly, all the significant SNPs and haplotype also reside in the intron 1 of the LSAMP—1a. To determine whether LSAMP expression in arterial tissue correlates with human atherosclerosis, we measured LSAMP—1a mRNA in 28 human thoracic aortas with varying amounts of atherosclerosis (Seo et al. 2004). Quantitative RT-PCR revealed that aortas with severe atherosclerosis (N=7) contained 2.7-fold less LSAMP—1a transcript than those with mild or no atherosclerosis (N=21) (p=0.0001). As the haplotype HAP L is strongly associated with risk for CAD, we examined whether the decreased expression of LSAMP—1a mRNA was correlated with the presence of this risk haplotype. Indeed, we found that LSAMP—1a mRNA levels correlated inversely not only with the extent of aortic atherosclerosis, but also with the “dosage” of HAP L; i.e., mRNA levels for LSAMP—1a were twice as low in aortas with two copies (N=17) of the risk haplotype HAP L as they were in aortas with zero or one copy (N=11) of HAP L (p=0.0002), thus tying the risk genotype directly with the LSAMP atherosclerotic expression changes.
Down-regulation of LSAMP Promotes SMC Proliferation Data from our human aortas displayed  ̃2-3-fold LSAMP—1a down-regulation with atherogenesis. To test directly whether LSAMP down-regulation could promote SMC proliferation and thereby conceivably aggravate atherogenesis (Boucher et al. 2003), we used siRNA to achieve a 2-3 fold knockdown of total LSAMP expression in human aortic SMCs. In response to serum, SMCs with reduced LSAMP expression demonstrated a 2-fold increase in cell proliferation as measured by thymidine incorporation. Thus, the magnitude of LSAMP down-regulation observed in aortas from subjects with two copies of LSAMP HAP L might indeed be expected to potentiate atherogenic SMC proliferation.
The foregoing is illustrative of the present invention, and is not to be construed as limiting thereof. The invention is defined by the following claims, with equivalents of the claims to be included therein.
All publications, patent applications, patents, patent publications, sequences identified by Genbank and/or SNP accession numbers, NCBI Build 35 of human chromosome 3 and other references cited herein are incorporated by reference in their entireties for the teachings relevant to the sentence and/or paragraph in which the reference is presented.
REFERENCES


    Hauser et al. “A genomewide scan for early-onset coronary artery disease in 438 families: the GENECARD study” Am. J Hum. Genet. 75:436-447 (2004)
    Marenberg M, Risch N, Berkman L F, Floderus B, de Faire U. Genetic susceptibility to death from coronary heart disease in a study of twins. New Engl J Med 1994; 330:1041-46.
    Zdravkovic S, Wienke A, Pedersen N L, Marenberg M E, Yashin A I, de Faire U. Heritability of death from coronary heart disease: a 36-year follow-up of 20 966 Swedish twins. J Int Med 2002; 252:247-254.
    Sorensen T I, Nielsen G G, Anderson P K, Teasdale T W. Genetic and environmental influences on premature death in adult adoptees. New Engl J Med 1988; 318:727-32.
    Shearman A M. Ordovas J M. Cupples L A. Schaefer E J. Harmon M D. Shao Y. Keen J D. DeStefano A L. Joost O. Wilson P W. Housman D E. Myers R H. Evidence for a gene influencing the TG/HDL-C ratio on chromosome 7q32.3-qter: a genome-wide scan in the Framingham study. Hum Mol Genet. 9(9):1315-20, 2000 May 22.
    Mahaney M C, Blangero J, Rainwater D L, Comuzzie A G, VandeBerg J L, Stern M P, MacCluer J W, Hixson J E. A major locus influencing plasma high-density lipoprotein cholesterol levels in the San Antonio Family Heart Study: segregation and linkage analyses. Arterioscler Thromb Vasc Biol 1995; 15:1730-1739.
    Peacock J M, Arnett D K, Atwood L D, Myers R H, Coon H, Rich S S, Province M A, Heiss G. Genome scan for quantitative trait loci linked to high-density lipoprotein cholesterol: the NHLBI Family Heart Study. Arterioscler Thromb Vasc Biol 2001; 21:1823-1828.
    Imperatore G, Knowler W C, Pettitt D J, Kobes S, Fuller J H, Bennett P H, Hanson R L. A locus influencing total serum cholesterol on chromosome 19p: results from an autosomal genomic scan of serum lipid concentrations in Pima Indians. Arterioscler Thromb Vasc Biol 2000; 12:2651-2656.
    Duggirala R, Blangero J, Almasy L, Dyer T D, Williams K L, Leach R J, O'Connell P, Stern M. A major susceptibility locus influencing plasma triglyceride concentration is located on chromosome 15q in Mexican Americans. Am J Hum Genet 2000; 66:1237-1245.
    Almasy L, Hixson J E, Rainwater D L, Cole S, Williams J T, Mahaney M C, VandeBerg J L, Stern M P, MacCluer J W, Blangero J. Human pedigree-based quantitative-trait-locus mapping: localization of two genes influencing HDL-cholesterol metabolism. Am J Hum Genet 1999; 64:1686-1693.


Pajukanata P, Terwilliger D, Perola M, Hiekkalinna T, Nuotio I, Ellonen P, Parkkonen M, Hartiala J, Ylitalo K, Pihlajamaki J, et al. Genomewide scan for familial combined hyperlipidemia genes in Finnish families, suggesting multiple susceptibility loci influencing triglyceride, cholesterol, and apolipoprotein B levels. Am J Hum Genet 1999; 64:1453-1463.

    Arnett 2001
    J. J. Genest, Jr, S. S. Martin-Munley, J. R. McNamara et al., Familial lipoprotein disorders in patients with premature coronary heart disease. Circulation 85 (1992), pp. 2025-2033.
    Pajukanta P. Allayee H. Krass K L. Kuraishy A. Soro A. Lilja H E. Mar R. Taskinen M R. Nuotio I. Laakso M. Rotter J I. de Bruin T W. Cantor R M. Lusis A J. Peltonen L. Combined analysis of genome scans of dutch and finnish families reveals a susceptibility locus for high-density lipoprotein cholesterol on chromosome 16q. [Journal Article] American Journal of Human Genetics. 72(4):903-17, 2003 Apr.
    Pajukanta P. Nuotio I. Terwilliger J D. Porkka K V. Ylitalo K. Pihlajamaki J. Suomalainen A J. Syvanen A C. Lehtimaki T. Viikari J S. Laakso M. Taskinen M R. Ehnholm C. Peltonen L. Linkage of familial combined hyperlipidaemia to chromosome 1q21-q23. Nat Genet 1998; 18:369-373.
    Pritchard J K, Stephens M, Rosenberg N A, et al. Association mapping in structured populations. Am J Hum Genet 2000; 67:170-181.
    Ariquin ver. 2.000: a software for population genetics data analysis. Genetics and Biometry Laboratory, University of Geneva, Switzerland: 2000.
    Broman K W. Estimation of allele frequencies with data on sibships. Genet Epidemiol. 2001; 20:307-315.
    Chiodini B D, Lewis C M. Meta-analysis of 4 coronary heart disease genome-wide linkage studies confirms a susceptibility locus on chromosome 3q. Arterioscler Thromb Vasc Biol. 2003; 23:1863-1868.
    Vionnet N, Hani E, Dupont S, Gallina S, Francke S, Dofte S, De Matos F, Durand E, Lepretre F, Lecoeur C, Gallina P, Zekiri L, Dina C, Froguel P. Genome-wide search for type 2 diabetes-susceptibility genes in French whites: evidence for a novel susceptibility locus for early-onset diabetes on chromosome 3q27-qter and independent replication of a type 2-diabetes locus on chromosome 1q21-q24. Am J Hum Genet 2000; 67:1470-1480.
    Mori Y, Otabe S, Dina C, Yasuda K, Populaire C, Lecoeur C, Vatin V, Durand E, Hara K, Okada T, To be K, Boutin P, Kadowaki T, Froguel P. Genome-wide search for type 2 diabetes in Japanese affected sib-pairs confirms susceptibility genes on 3q, 15q, and 20q and identifies two new candidate loci on 7p and 11p. Diabetes. 2002; 51:1247-1255.
    Hegele R A, Sun F, Harris S B, Anderson C, Hanley A J G, Zinman B. Genome-wide scanning for type 2 diabetes susceptibility in Canadian Oji-Cree, using 190 microsatellite markers. J Hum Genet. 1999; 44:10-14.
    Kissebah A H, Sonnenberg G E, Myklebust J, Goldstein M, Broman K, James R G, marks J A, Krakower G R, Jacob H J, Weber A, Martin L, Blangero J, Comuzzie A G. Quantitative trait loci on chromosomes 3 and 17 influence phenotypes of the metabolic syndrome. Proc Natl Acad Sci USA 2000; 97:14478-144783.
    Schellenberg G D, Bird T D, Wijsman E M, et al. Genetic linkage evidence for a familial Alzheimer's disease locus on chromosome 14. Science. 1992; 258:668-671.
    Horikawa Y. Oda N. Cox N J. Li X. Orho-Melander M. Hara M. Hinokio Y. Lindner T H. Mashima H. Schwarz P E. del Bosque-Plata L. Horikawa Y. Oda Y. Yoshiuchi I. Colilla S. Polonsky K S. Wei S. Concannon P. Iwasaki N. Schulze J. Baier L J. Bogardus C. Groop L. Boerwinkle E. Hanis CL. Bell G I. Genetic variation in the gene encoding calpain-10 is associated with type 2 diabetes mellitus. Nat Genetics. 26(2):163-75, 2000 Oct.
    Breslow J L. Genetics of lipoprotein disorders. Circulation. 1993; 87(suppl III):III-16-III-21.
    Austin M A. King M C. Bawol R D. Hulley S B. Friedman G D. Risk factors for coronary heart disease in adult female twins. Genetic heritability and shared environmental influences. American Journal of Epidemiology. 125(2):308-18, 1987 Feb.
    Rice T. Vogler G P. Perry T S. Laskarzewski P M. Rao D C. Familial aggregation of lipids and lipoproteins in families ascertained through random and nonrandom probands in the Iowa Lipid Research Clinics family study. Human Heredity. 41(2):107-21, 1991.
    Murai A, Miyahara T, Fujimoto N, Matsuda M, Kameyama M. Lp(a) lipoprotein as a risk factor for coronary heart disease and cerebral infarction. Atherosclerosis 1986; 59 (2): 199-204.
    Jeppesen J. Hein H O. Suadicani P. Gyntelberg F. Relation of high TG-low HDL cholesterol and LDL cholesterol to the incidence of ischemic heart disease. An 8-year follow-up in the Copenhagen Male Study. [Journal Article] Arteriosclerosis, Thrombosis & Vascular Biology. 17(6):1114-20, 1997 Jun. Abecasis G. R. & Cookson W. O. (2000) GOLD—graphical overview of linkage disequilibrium. BioInformatics 16, 182-183.
    Boucher P., Gotthardt M., Li W. P., Anderson R. G. & Herz J. (2003) LRP: role in vascular wall integrity and protection from atherosclerosis. Science 300, 329-332.
    Chen J., Lui W. O., Vos M. D., Clark G. J., Takahashi M., Schoumans J., Khoo S. K., Petillo D., Layery T., Sugimura J., Astuti D., Zhang C., Kagawa S., Maher E. R., Larsson C., Alberts A. S., Kanayama H. O. & Teh B. T. (2003) The t(1; 3) breakpoint-spanning genes LSAMP and NORE1 are involved in clear cell renal cell carcinomas. Cancer Cell 4, 405-413.
    Connelly J. J., Wang T., Cox J. E., Haynes C., Wang L., Shah S. H., Crosslin D. R., Hale A. B., Nelson S., Crossman D. C., Granger C. B., Haines J. L., Jones C. J., Vance J. M., Goldschmidt-Clermont P. J., Kraus W. E., Hauser E. R. & Gregory S. G. (2006) GATA2 Is Associated with Familial Early-Onset Coronary Artery Disease. PLoS Genet 2.
    Hansson G. K. (2005) Inflammation, atherosclerosis, and coronary artery disease. N Engl J Med 352, 1685-1695.
    Levitt P. (1984) A monoclonal antibody to limbic system neurons. Science 223, 299-301.
    Martin E. R., Bass M. P., Gilbert J. R., Pericak-Vance M. A. & Hauser E. R. (2003a) Genotype-based association test for general pedigrees: the genotype-PDT. Genet Epidemiol 25, 203-213.
    Martin E. R., Bass M. P., Hauser E. R. & Kaplan N. L. (2003b) Accounting for linkage in family-based tests of association with missing parental genotypes. Am J Hum Genet 73, 1016-1026.
    Peppel K., Jacobson A., Huang X., Murray J. P., Oppermann M. & Freedman N. J. (2000) Overexpression of G protein-coupled receptor kinase-2 in smooth muscle cells attenuates mitogenic signaling via G protein-coupled and platelet-derived growth factor receptors. Circulation 102, 793-799.
    Pimenta A. F. & Levitt P. (2004) Characterization of the genomic structure of the mouse limbic system-associated membrane protein (Lsamp) gene. Genomics 83, 790-801.
    Seo D., Wang T., Dressman H., Herderick E. E., Iversen E. S., Dong C., Vata K., Milano C. A., Rigat F., Pittman J., Nevins J. R., West M. & Goldschmidt-Clermont P. J. (2004) Gene Expression Phenotypes of Atherosclerosis. Arterioscler Thromb Vasc Biol 24, 1922-1927.
    Shephard N., John S., Cardon L., McCarthy M. I. & Zeggini E. (2005) Will the real disease gene please stand up? BMC Genet 6 Suppl 1, S66.
    Teng E. L. & Chui H. C. (1987) The modified Mini-Mental State (3MS) examination. Journal of Clinical Psychiatry 48, 314-318.
    Wang L., Hauser E. R., Shah S. H., Pericak-Vance M. A., Haynes C., Crosslin D., Harris M., Nelson S., Hale A. B., Granger C. B., Haines J. L., Jones C. J., Crossman D., Seo D., Gregory S. G., Kraus W. E., Goldschmidt-Clermont P. J. & Vance J. M. (2007) Peakwide mapping on chromosome 3q13 identifies the kalirin gene as a novel candidate gene for coronary artery disease. Am J Hum Genet 80, 650-663.
    Xu H., Gregory S. G., Hauser E. R., Stenger J. E., Pericak-Vance M. A., Vance J. M., Zuchner S. & Hauser M. A. (2005) SNPselector: a web tool for selecting SNPs for genetic association studies. BioInformatics 21, 4181-4186.
    Zhang L., Peppel K., Sivashanmugam P., Orman E. S., Brian L., Exum S. T. & Freedman N. J. (2007) Expression of tumor necrosis factor receptor-1 in arterial wall cells promotes atherosclerosis. Arterioscler Thromb Vasc Biol 27, 1087-1094.









TABLE 1





GENECARD Study


















Families ascertained
438



Sampled individuals
1174



Number of affected individuals
976



Total affected sib pairs
491



Number of microsatellite markers
395



Distance between markers
~10 cM

















TABLE 2







Haplotypes for maximum hap scores (from Table 3)











Comparison
Effect
3M0238
RS1875518
RS2937666





YA vs ON
Protective
NON 253
A
A



RISK
NON 253
A
T


OA vs ON
Protective
NON 253
A
A



RISK
253
G
A


All Affected
Protective
NON 253
A
A


vs Control
RISK 1
NON 253
A
T



RISK 2
253
G
A
















TABLE 3







Haplotype table showing protective and risk effects for all age groups.


Negative hap score is protective, positive hapscore is risk


CAUCASIANS
















hap#
Hap. Score
p.val
sim. p. val
Hap. Freq
CONTROL
CASE
3M0238
RS1875518
RS2937666










CATHGEN Young Affecteds vs. CATHGEN Old Normals
















Protective
−3.038
0.00238
0.0022
0.2296
0.30747
0.17375
NON 253
A
A


2
−0.55983
0.57559
0.5787
0.22209
0.22007
0.22444
NON 253
G
A


3
−0.2186
0.82696
0.8293
0.0595
0.05444
0.06302
253
G
A


4
−0.07475
0.94042
0.9414
0.01434
0.01889
0.01105
253
A
T


5
0.46021
0.64537
0.6533
0.02893
0.01689
0.03616
253
A
A


6
0.55006
0.58228
0.582
0.06217
0.0628
0.06363
253
G
T


7
0.7818
0.43433
0.4331
0.16742
0.1594
0.17108
NON253
G
T


RISK
2.67549
0.00746
0.0066
0.21595
0.16004
0.25688
NON253
A
T







CATHGEN Old Affecteds vs. CATHGEN Old Normals
















Protective
−3.34905
0.00081
0.0011
0.25059
0.30747
0.18609
NON 253
A
A


2
−0.35638
0.72155
0.733
0.01108
0.01689
0.00742
253
A
A


3
−0.13402
0.89339
0.8899
0.16355
0.16004
0.16955
NON 253
A
T


4
0.2043
0.83812
0.8432
0.01813
0.01889
0.01702
253
A
T


5
0.4506
0.65227
0.6599
0.21883
0.22007
0.22307
NON 253
G
A


6
0.48243
0.6295
0.62
0.16897
0.1594
0.17521
NON 253
G
T


7
1.59332
0.11109
0.1092
0.06765
0.0628
0.07454
253
G
T


RISK
2.55689
0.01056
0.0098
0.1012
0.05444
0.1471
253
G
A







CATHGEN Young Affecteds, Old Affecteds and GENECARD-DNC Affected probands vs. CATHGEN Old Normals
















Protective
−3.87691
0.00011
0.0003
0.2123
0.30747
0.17659
NON 253
A
A


2
0.14011
0.88858
0.8886
0.02028
0.01689
0.02209
253
A
A


3
0.15602
0.87602
0.8759
0.22737
0.22007
0.232
NON 253
G
A


4
0.18761
0.85118
0.8515
0.01902
0.01889
0.01876
253
A
T


5
1.0031
0.31581
0.3225
0.06158
0.0628
0.06134
253
G
T1


6
1.09965
0.27149
0.2792
0.08415
0.05444
0.09424
253
G
A


7
1.27078
0.20381
0.206
0.17844
0.1594
0.18358
NON 253
G
T


RISK
1.29849
0.19412
0.1927
0.19687
0.16004
0.2114
NON 253
A
T
















TABLE 4







Primer and probe information of genetic markers









Marker
PCR Primers
Probe*













rs1875518
Forward:
A allele = FAM-




GGGCCTAGTGTGCTAATCTCTT
AGGTATTACTtAATCT



(SEQ ID NO: 30)
AGTTCA-MGB




(SEQ ID NO: 36)


 



Reverse:
G allele = TET-



TTATTTTACACTTAAGGGTGCTCA
AGGTATTACTcAATCT



(SEQ ID NO: 31)
AGTTCA-MGB




(SEQ ID NO: 37)


 


rs2937666
Forward:
A allele = TET-



GCAGTTTTTGTAGCTGCTGTTG
CCATCAACaATTGCAT



(SEQ ID NO: 32)
C-MGB




(SEQ ID NO: 38)


 



Reverse:
T allele = FAM-



TTTATAGTCCATTTTGGCTTGCTT
TCCATCAACtATTGCA



(SEQ ID NO: 33)
TC-MGB




(SEQ ID NO: 39)


 


3M0238
Forward:
N/A



CTTGCACCTGGGAGGTAGAG



(SEQ ID NO: 34)


 



Reverse:
N/A



CACAACTGTTGCTTTTCCAT



(SEQ ID NO: 35)





*The polymorphic site is in lower letter bold case.













TABLE 5







Baseline characteristics of GENECARD individuals (420 families).











Affected
Unaffected
All


Variable
(N = 952)
(N = 177)
(N = 1129)





Mean age (SD)
51.4 (7.1) 
65.3 (11.3)
53.6 (9.4) 


Mean age of onset
43.7 (5.8) 
—
—


(SD)


Sex (%)


Male
71.4%
36.0%
65.8%


Female
28.6%
64.0%
34.2%


Dyslipidemia
82.3%
57.1%
78.4%


Meds for dyslipidemia
84.7%
60.6%
81.9%


Lipids (mean, SD)


TC
205.7 (57.3) 
220.6 (50.3) 
206.9 (56.9) 


TG
222.1 (167.1)
213.8 (142.9)
221.5 (165.2)


HDL
39.1 (19.0)
48.1 (34.9)
39.9 (20.9)


LDL
117.7 (49.5) 
124.7 (40.0) 
118.3 (48.8) 


Hypertension
55.2%
49.1%
54.2%


Blood pressure


(mean, SD)


Systolic
141.1 (22.7) 
151.8 (26.3) 
146.1 (24.7) 


Diastolic
81.2 (12.2)
81.4 (9.8) 
81.3 (11.0)


Diabetes mellitus
21.0%
15.4%
20.1%


(DM)


Waist circumference
99.0 (14.2)
96.4 (16.4)
98.6 (14.6)


(SD)


Obesity


BMI < 25
19.6%
35.0%
22.1%


BMI 25-29
38.3%
37.3%
38.2%


BMI ≧ 30
42.0%
27.7%
39.8%


Metabolic
46.8%
30.3%
44.2%


syndrome***


Pack-years smoked
34.8 (23.4)
42.7 (36.7)
35.7 (25.3)


Currently smoking
32.9%
28.3%
32.4%


Post-menopausal
55.8%
82.1%
63.4%


History of MI
62.9%
—
59.8%


Multiple vessel CAD
66.0%
—
66.0%





TC = total cholesterol,


TG = triglycerides,


HDL = high density lipoprotein,


MI = myocardial infarction.


***Presence of 3 out of 5 of the following: history of DM; HTN or BP > 130/85; HDL < 40 in men and <50 in women; waist circumference >88 in women, >102 in men; TG ≧ 150.













TABLE 6







Quantitative trait loci mapping results, lipid phenotypes.















Locus
Multipoint
Empirical


Quantitative Trait
Heritability (SD)
Chrom
(cM)*
LOD
p-value**















Total cholesterol (TC)
71.1% (8.9%)***
5
98
1.28
0.03




6
10
1.28
0.03




13
15
1.19
0.03




18
55
1.32
0.02


Low density lipoprotein
67.3% (9.7%)***
6
164
1.65
<0.01


(LDL) cholesterol

16
0
1.41




19
52
1.25




21
16
1.39


High density lipoprotein
67.7% (11.9%)***
3
87
2.43
0.002


(HDL) cholesterol

7
156
1.73
<0.01




15
103
1.79
0.004


Triglycerides
63.7% (12.5%)***
4
119
1.30




7
80
1.35




13
18
1.55
<0.01




14
76
1.22




18
94
2.09
0.002


HDL/TC ratio
64.6% (9.8%)***
3
153
1.44
<0.01




7
143
1.44
<0.01




8
148
1.68





*Kosambi map locus; cM: centimorgans;


**using 10000 simulated repetitions;


***p-value<0.00001













TABLE 7







Ordered subset analysis (OSA) results.


















Mean covariate
Mean covariate



No. fams



Pos

value (SD) in
value (SD) in
Max
Overall

in


Chromosome
cM
Covariate
subset
others*
OSA LOD
LOD
p-value
subset


















3
146.9
Low TG
161.1 (49.3)
372.7 (137.9)
4.14
2.64
0.04
167


5
171.7
High TC
302.4 (78.9)
192.8 (30.1)
4.42
0.36
0.001
54


9
23.5
Low TG
 99.3 (21.8)
248.9 (121.0)
2.51
0.12
0.03
49


10
127.7
Low HDL
 24.8 (4.5)
 39.8 (8.2)
2.49
0.00
0.007
44


12
61.0
High HDL
 50.6 (8.2)
 34.3 (5.6)
2.43
0.35
0.03
80


14
0.0
High LDL
225.5 (36.1)
113.0 (32.0)
2.63
0.66
0.03
22


17
120.6
High TG
340.9 (133.8)
152.1 (44.0)
2.10
0.19
0.04
77


22
0.0
High LDL
225.5 (36.1)
113.0 (32.0)
2.52
0.001
0.02
22





*mean value of OSA covariate in families not included in the subset;













TABLE 8







Phenotypic characteristics of families in OSA subsets.











No.





families

Lipid



in
Phenotypic characteristics of
phenotypes


Chromosome
subset
subset*
of subset*













3
167
Older at time of exam, older age
Lower TC




of onset
Lower LDL




Less metabolic syndrome,
Higher HDL




diabetes




Lower BMI




Lower waist circumference and




weight


5
54
Younger age of onset
Higher LDL





Higher TG


9
49
Less diabetes
Lower TC




Lower weight, waist
Higher HDL




circumference, BMI




Less metabolic syndrome




Fewer pack-years smoked


10
44
More metabolic syndrome
Higher TG




More pack-years smoked




More diabetes




More male




Higher height, weight, waist




circumference


12
80
Lower waist, weight, BMI
Higher TC




Older at time of exam, older age
Lower TG




of onset




Less metabolic syndrome




More female


14
22
Younger at time of exam, younger
Higher TC




age of onset


17
77
More metabolic syndrome
Lower LDL





Lower HDL


22
22
Younger at time of exam, younger
Higher TC




age of onset





*when compared to family means of affected individuals in families not within the OSA subset; all comparisons statistically significant at p < 0.05. BMI: body-mass index













TABLE 9







Genetic Markers in Chromosome 3*













Basepair



SNP/Polymorphism
Basepair
location on SEQ


Chr
id
location on Ch 3
ID NO: 1





3
rs2927275
118666759
166759


3
rs1698042
118667838
167838


3
rs1501881
118672530
172530


3
rs1698041
118682441
182441


3
3M0238
118690772 to 118690975
190772 to 190975


3
rs2055426
118703034
203034


3
rs2937675
118706580
206580


3
27 bp Insertion
118711341 to 118711342
211341 to 211342


3
rs1875518
118712470
212470


3
rs2937673
118715077
215077


3
rs1676232
118717529
217529


3
3I0320
118719088
219088


3
3I0311
118719132 to 118719133
219132 to 219133


3
rs1381801
118723585
223585


3
rs2937666
118729388
229388


3
rs1910044
118733409
233409


3
rs6778437
118726628
226628


3
rs6795971
118751683
251683


3
rs1466416
118753496
253496


3
rs6795971
118751683
251683


3
rs2937673
118715077
215077


3
rs1698041
118682441
182441


3
rs4356827
118661434
161434


3
rs6790819
118659480
159480


3
rs7427839
118648013
148013


3
rs725154
117992940


3
rs1875516
118805109


3
rs1501882
118774319


3
rs1401951
119708716


3
rs1968010
119551910


3
rs1486336
119386693


3
rs843855
119239225


3
rs1456186
119110095


3
rs553070
119637627


3
rs1499989
119483894


3
rs39688
120225538


3
rs812824
120037336


3
rs705233
119952613


3
rs483349
120827383


3
rs2282171
120665288


3
rs834855
82731159


3
rs4404477
118857458 





*SNP basepair location on Ch 3 is based on the NCBI build 35 sequence of human chromosome 3.













TABLE 10







Additional Nucleotide Polymorphisms*













Polymorphism
Polymorphism



SEQ
Flanking Sequence
basepair
basepair


ID
(polymorphism in
position on
position on


NO:
brackets)
Ch 3**
SEQ ID NO: 1





 2
TGCGCGTGT[G/T]TGGTGTGTG
118664719
164719



 


 3
AAATAAATTAAC[G/A]TTTATCATCA
118670801
170801


 


 4
ATTTCTC[G/A]TTAAAATTT
118673682
173682


 


 5
ATTTCATATCT[-/A]GGAAAAAAC
118673698
173698




to
to




118673699
173699


 


 6
CCACCTAG[T/C]TTTTTTAATGAACA
118699111
199111


 


 7
ATCTTGATT[C/A]TATTTATGACTGC
118699690
199690


 


 8
GCTTAGTTGG[T/A]TAGACCAGCT
118708380
208380


 


 9
CCTCACTCT[A/C]TTCTCCTCCTT
118708990
208990


 


10
GGTGCAG[T/A]GGCATGAGCC
118713130
213130


 


11
AACCCTCCTCAATTGT[A/G]GAAAGATGGAA
118717982
217982



CA


 


12
GGAACAGCAACATTCTTA[A/G]ATGCTCATG
118718008
218008



TACC


 


13
ATTCTTAAATGCTCATGTA[C/A]CTTTATTAA
118718020
218020



AGTAT


 


14
ATGTGCATTTCTACA[T/A]TCATTCAAATAGT
118718327
218327



CTTTG


 


15
AATGATAAAAT[A/-]TTTTTTAAAG (3I0320)
118719088
219088


 


16
TCCCACCG[T/G]ACCCAGCCCT
118720122
220122


 


17
TTATATCAA[T/G]GCCTCCAAC
118720142
220142


 


18
ACTTGCAGAA[A/G]TTTTATATC
118720154
220154


 


19
GGTTGACTAG[T/A]CCATGCCTT
118720228
220228


 


20***
AACAGAACTKA[A/G]CACTCT
118720249
220249


 


21
GTCCAAAACA[T/C]ATGCTAAAGA
118722980
222980


 


22
TTATTTAC[A/G]TGAAGTTGT
118722998
222998


 


23
ACATCTT[A/G]TGAAATT
118723379
223379


 


24
TTGTTGGGGG[G/A]ACTATAGTAATC
118727468
227468


 


25
GACCCTCCAACAAA[T/G]GCCATTT
118728575
228575


 


26
AGTTTGGA[G/A]TTTCCTCA
118730282
230282


 


27
TCAGAGAAATG[C/A]AAATCAA
118730459
230459


 


28
CTGGAGGAGATAATCATTAAGTGGGAATTT
118711341
211341



GAATATTATAACAGATCCT
to
to



[---------------------------/
118711341
211342



GGGAATTTGAATATTATAACAGATCCT]GT



AATCACCTGACCACTGCACAGA (27 bp



duplication)


 


29
ATAAGCAAGTATAAAAA[---/CAA]
118719132
219132



TTTCCAGTAGATG (3I0311)
to
to




118719133
219133





*The polymorphism is indicated in bold text. The first nucleotide/sequence listed of the polymorphism is the nucleotide/sequence present in the NCBI build 35 sequence of human chromosome 3, the second nucleotide/sequence listed is the variant.


**SNP basepair position on Ch 3 is based on the NCBI build 35 sequence of human chromosome 3.


***K in SEQ ID NO: 20 represents a G/T polymorphism.













TABLE 11







SNPs in HAPIP and MLCK*













SNP basepair


Ch
SNP id
Gene
location





3
rs2272486
HAPIP
125470729


3
HCV1602689
MLCK
125024094





*SNP basepair location is based on the NCBI build 35 sequence of human chromosome 3.













TABLE 12







Clinical characteristics of patient datasets










Initial Dataset
Validation Dataset















Left Main

Left Main

Alzheimer



Old Affected
Case
Control
Case
Control
Control

















Number of individuals
167
102
149
141
215
255


Age-at-catheterization,
66.1 (10.5)*
66.1 (10.7)*
70.9 (7.2) 
68.5 (9.6) 
69.9 (6.6) 
73.8 (6.0)†


mean (SD)


Age-of-onset, mean
60.5 (8.9) 
56.8 (12.1) 
N/A
59.1 (10.8) 
N/A
N/A


(SD)


CAD index, mean (SD)
72.1 (19.2)*
89.1 (8.8)* 
10.9 (10.9) 
88.5 (8.7)* 
8.8 (10.7)
N/A


Gender: Male, %
83.8%*
74.51%* 
47.7%
85.8%*
44.7%
28.7%


BMI, Mean (SD)
29.2 (6.6)* 
28.9 (5.8) 
27.6 (5.9) 
28.4 (5.9) 
28.4 (5.9) 
N/A


Ever-smoked, %
59.3%*
57.8%*
43.6%
62.4%*
40.0%
N/A


Diabetes, %
32.9%*
31.4%*
11.4%
26.2% 
21.9%
 0.0%


Hypertension, %
73.7% 
82.4%*
66.4%
68.8% 
67.4%
46.4%


Dyslipidemia, %
73.1%*
77.5%*
40.3%
74.5%*
54.9%
43.8%





*P < 0.05 for the comparison of cases with controls.


Chi-square tests were performed for categorical variables and t-tests were performed for continuous variables.


BMI, body mass index.


N/A, not applicable.













TABLE 13







Promising SNP association with left main CAD in the initial, validation, and


combined datasets














Initial
Validation
Combined
Combined




Dataset
Dataset
Dataset*
Dataset




Basic
Basic
Basic
Full



Location
Model#
Model
Model
Model#

















SNP
Chr
(NCBI35)
p value
OR
p value
OR
p value
OR
p value
OR




















rs10934326
3
117,469,033
0.012
2.2
0.707
0.9
0.226
1.2
0.256
1.2


rs1106851
3
117,943,999
0.088
1.6
0.673
1.1
0.125
1.3
0.208
1.3


rs1513172
3
118,494,578
0.092
1.4
0.754
0.9
0.291
1.2
0.932
1.0


rs4075039
3
118,645,474
0.057
1.8
0.342
0.8
0.675
1.1
0.923
1.0


rs6790819
3
118,659,480
0.098
6.8
0.726
1.8
0.068
5.1
0.071
5.6


rs1910040
3
118,673,682
0.100
1.5
0.061
1.5
0.013
1.5
0.034
1.4


ss70458782
3
118,709,990
0.091
1.6
0.083
1.5
0.015
1.5
0.044
1.4


rs1875518
3
118,712,470
0.008
1.8
0.168
1.3
0.005
1.5
0.057
1.3


rs1676232
3
118,717,529
0.022
1.7
0.315
1.2
0.022
1.4
0.110
1.3


rs4404477
3
118,857,458
0.106
1.6
0.039
1.7
0.007
1.7
0.021
1.6





Subset analysis in the initial dataset identified ten promising LSAMP SNPs that displayed evidence for association with left main CAD. These SNPs were further examined in the validation dataset composed of left main affected and control. Logistic regression analysis was performed to evaluate SNP association with left main CAD using genotype case-control statistic provided by SAS 9.0. OR, odds ratio estimates. P-values less than 0.05 are shown in bold.


*The “combined dataset” consists of both the initial and validation datasets.


#In the basic model, gender was included as covariable; in the full model, gender, age, hypertension, diabetes mellitus, body mass index, dyslipidemia, and smoking history were included as covariable.













TABLE 14







Association of five “significant” SNPs in multiple additional datasets












Combined






Left



Main



Case*
Third Control
All Control*
GENECARD Dataset (N = 2954)















(N = 243)
(N = 255)
(N = 619)
Freq
APL
PDT
GenoPDT




















SNP
Allele
Freq
Freq
P value
OR
Freq
P value
OR
Affected
Unaffected
P value
P value
P value





rs1910040
A
78%
74%
0.539
1.1
72%
0.033
1.4
76%

0.225
0.333
0.488


ss70458782
A
85%
81%
0.243
1.3
80%
0.017
1.5
81%

0.624
0.476
0.690


rs1875518
G
63%
56%
0.062
1.4
54%
0.005
1.4
55%

0.468
0.435
0.607


rs1676232
A
68%
64%
0.633
1.1
61%
0.083
1.3
61%

0.020
0.087
0.285


rs4404477
A
87%
82%
0.006
1.9
82%
0.003
1.7
85%

0.091
0.012
0.044





Evaluation of promising SNPs in the validation dataset identified five LSAMP SNPs as significant SNPs. These SNPs were further examined in multiple additional datasets.


*“Combined Left Main Case” comprises all of the left main CAD cases in the initial and validation datasets; “All Control” denotes all of the controls reported in this study (from the initial, validation, and third control datasets). Freq, frequency of the displayed allele. OR, odds ratio estimates for the displayed allele. Logistic regression analyses were performed adjusting for gender for the case-control dataset using genotype case-control statistic provided by SAS 9.0. APL, PDT, and GenoPDT were performed for the family-based GENECARD samples. P values less than 0.05 are shown in bold.













TABLE 15







Association of HAP L with left main CAD in multiple independent datasets









Initial, Validation datasets and Third Control












Initial Dataset
Validation Dataset
Combined

















Left Main

Left Main

Left Main
Combined
Third




Case
Control
Case
Control
Case*
Control*
Control
All Control*


Haplotype
(N = 102)
(N = 149)
(N = 141)
(N = 215)
(N = 243)
(N = 364)
(N = 255)
(N = 619)





















ss70458782
rs4404477
Freq
Freq
P value
Freq
Freq
P value
Freq
Freq
P value
Freq
P value
Freq
P value





A
A
77%
67%
0.0205
76%
64%
0.0012
77%
65%
0.0001
65%
0.0022
65%
4.00E−05


A
G
 8%
12%
0.1384
10%
16%
0.0297
 9%
14%
0.0095
16%
0.0032
15%
0.0026


C
A
10%
17%
0.1284
11%
17%
0.0601
11%
17%
0.0299
17%
0.2736
17%
0.0302


C
G
 5%
 4%
0.5311
3%
 3%
0.4239
 4%
 3%
0.2348
 2%
0.685 
 3%
0.2765





*“Combined Left Main Case” comprises all of the left main CAD cases in the initial and validation datasets; “Combined Control” denotes all of the controls from both the initial and validation datasets; “All Control” denotes all of the controls reported in this study (from the initial, validation, and third control datasets). Freq, frequency of the displayed haplotype. Haplotype association tests were performed adjusting for gender. LSAMP haplotype ss70458782A_rs4404477A was designated as HAP L. P-values less than 0.05 are shown in bold.













TABLE 16







Definition of the coronary artery disease index (CADi)21










Extent of CAD
CADi














No CAD ≧ 50%
0



One-VD 50% to 74%
19



One-VD 75%
23



One-VD ≧ 95%
32



Two-VD
37



Two-VD (both ≧ 95%)
42



One-VD ≧ 95%, proximal (LAD)
48



Two-VD ≧ 95% LAD
48



Two-VD ≧ 95% proximal LAD
56



Three-VD
56



Three-VD ≧ 95% in at least one
63



vessel



Three-VD 75% proximal LAD
67



Three-VD ≧ 95% proximal LAD
74



Left main (75%)
82



Left main (≧95%)
100







CAD = coronary artery disease;



LAD = left anterior descending coronary artery;



VD = vessel disease.













TABLE 17







Association tests in the initial dataset










Old Affected
Left Main Case













SNP
Chr
NCBI35
p value
OR
p value
OR





rs9822311
3
117,021,341
0.623
1.1
0.509
1.2


rs3821560
3
117,054,054
0.062
0.5
0.390
0.7


rs11719516
3
117,067,985
0.252
0.7
0.886
1.0


rs10511352
3
117,125,967
0.886
1.0
0.530
0.8


rs9872913
3
117,183,885
0.389
1.2
0.360
1.2


rs1920384
3
117,250,752
0.270
1.3
0.274
1.3


rs9866658
3
117,331,906
0.703
1.1
0.952
1.0


rs7641464
3
117,368,960
0.047
1.5
0.112
1.4


rs10934326
3
117,469,033
0.130
1.6
0.012
2.2


rs1461131
3
117,483,362
0.280
1.3
0.850
1.0


rs9809878
3
117,515,131
0.254
0.8
0.580
0.9


rs2033406
3
117,547,823
0.506
0.9
0.382
1.2


rs9822445
3
117,644,224
0.501
1.2
0.229
1.3


rs10934345
3
117,692,305
0.721
1.1
0.912
1.0


rs9834065
3
117,730,574
0.184
1.3
0.259
1.3


rs1795293
3
117,761,199
0.474
1.2
0.805
1.1


rs1467213
3
117,805,164
0.424
1.2
0.288
1.2


rs9847048
3
117,838,700
0.709
1.1
0.838
0.9


rs10934364
3
117,896,600
0.598
1.1
0.981
1.0


rs1106851
3
117,943,999
0.919
1.0
0.088
0.6


rs1835856
3
117,974,362
0.682
0.9
0.936
1.0


rs6785331
3
117,990,316
0.784
1.1
0.415
0.8


rs7433070
3
118,042,925
0.644
0.9
0.199
0.8


rs6438359
3
118,071,957
0.933
1.0
0.985
1.0


rs2037009
3
118,110,689
0.185
0.8
0.149
0.7


rs1133603
3
118,133,470
0.777
0.9
0.327
1.3


rs1589182
3
118,186,282
0.751
0.9
0.688
0.9


rs1518898
3
118,211,238
0.389
1.2
0.492
0.8


rs4855909
3
118,212,508
0.717
1.1
0.743
0.9


rs938115
3
118,257,964
0.928
1.0
0.495
1.1


rs1850719
3
118,284,215
0.967
1.0
0.538
1.1


rs7633227
3
118,313,302
0.357
1.2
0.818
1.1


rs733527
3
118,347,424
0.306
1.2
0.822
1.0


rs6788787
3
118,353,538
0.767
1.1
0.745
1.1


rs1915585
3
118,391,522
0.432
0.8
0.396
0.8


rs1462845
3
118,425,700
0.619
0.9
0.136
0.7


rs4855900
3
118,477,957
0.331
0.8
0.247
0.7


rs1513172
3
118,494,578
0.700
1.1
0.092
1.4


rs6438389
3
118,532,507
0.548
0.9
0.788
0.9


rs1513156
3
118,549,311
0.345
0.8
0.254
0.7


rs11716267
3
118,586,537
0.603
1.1
0.312
1.3


rs1398626
3
118,616,293
0.178
1.3
0.544
1.1


rs1513162
3
118,617,776
0.519
1.1
0.669
1.1


rs4075039
3
118,645,474
0.361
0.8
0.057
0.5


rs7427839
3
118,648,013
0.218
1.3
0.245
1.3


rs6790819
3
118,659,480
0.073
7.6
0.098
6.8


rs4356827
3
118,661,434
0.284
0.8
0.314
0.8


rs2927275
3
118,666,759
0.421
0.8
0.851
1.0


rs1698042
3
118,667,838
0.110
0.5
0.433
0.7


rs1910040
3
118,673,682
0.203
0.7
0.100
0.7


rs11713954
3
118,699,690
0.580
0.8
0.117
0.5


ss70458782
3
118,709,990
0.062
0.6
0.091
0.6


rs1875518
3
118,712,470
0.079
1.4
0.008
1.8


rs1676232
3
118,717,529
0.044
0.7
0.022
0.6


rs4855952
3
118,717,715
0.506
1.5
0.410
1.6


rs1501874
3
118,720,007
0.501
0.8
0.426
0.7


rs2937670
3
118,720,251
0.844
1.1
0.122
0.6


rs1979868
3
118,722,031
0.744
1.1
0.760
1.1


rs1381801
3
118,723,585
0.750
0.9
0.929
1.0


rs2937666
3
118,729,388
0.231
1.3
0.910
1.0


rs1910044
3
118,733,409
0.504
1.2
0.917
1.0


rs4855955
3
118,738,784
0.434
0.8
0.399
0.8


rs6778437
3
118,746,628
0.552
2.3
0.534
2.4


rs6795971
3
118,751,683
0.552
2.3
0.534
2.4


rs1393192
3
118,752,560
0.418
0.8
0.581
0.9


rs1466416
3
118,753,496
0.979
0.0
0.175
0.2


rs2869787
3
118,791,508
0.645
1.1
0.738
1.1


rs869851
3
118,804,008
0.998
1.0
0.707
1.1


rs2904196
3
118,829,308
0.524
0.9
0.968
1.0


rs6774738
3
118,849,617
0.291
0.8
0.165
0.7


rs4234669
3
118,851,827
0.583
0.9
0.448
0.9


rs4290831
3
118,856,228
0.447
0.7
0.731
0.9


rs4404477
3
118,857,458
0.206
0.7
0.106
0.6


rs9877923
3
118,862,230
0.635
1.1
0.522
1.2


rs4440150
3
118,863,334
0.843
1.0
0.635
1.1


rs6784348
3
118,892,675
0.499
0.9
0.719
0.9


rs7646668
3
118,914,350
0.075
1.4
0.290
1.2


rs6438404
3
118,918,128
0.449
0.8
0.634
0.9


rs4367097
3
118,922,408
0.207
0.4
0.255
0.5


rs9861188
3
118,932,645
0.023
1.6
0.111
1.4


rs7647501
3
118,939,388
0.212
0.8
0.556
0.9


rs4687991
3
118,947,921
0.246
0.8
0.339
0.8


rs4687996
3
118,956,667
0.186
0.8
0.356
0.8


rs6796552
3
118,967,152
0.281
0.8
0.378
0.8


rs4687889
3
119,020,129
0.295
1.2
0.586
1.1


rs7427162
3
119,069,371
0.191
1.3
0.285
1.3


rs1378834
3
119,102,092
0.149
0.5
0.575
0.8


rs1456186
3
119,110,095
0.763
0.9
0.715
1.1


rs817508
3
119,168,266
0.978
1.0
0.571
1.2


rs17723301
3
119,198,278
0.452
1.2
0.569
1.1





Logistic regression analyses were performed adjusting for gender using genotype case-control statistics provided by SAS 9.0.


OR = odds ratio estimates.


P-values less than 0.05 are shown in bold.

## Claims

1. A method of identifying a human subject having an increased risk of developing coronary artery disease, comprising detecting in a nucleic acid sample of the subject a haplotype in the LSAMP gene of the subject comprising an A allele at single nucleotide polymorphism ss70458782 and an A allele at single nucleotide polymorphism rs4404477, wherein the detection of said haplotype identifies the subject as having an increased risk of developing coronary artery disease.

2. The method of claim 1, wherein detecting is carried out by a hybridization reaction.

3. The method of claim 2, wherein the hybridization reaction is carried out with hybridization probes in a microarray.

4. The method of claim 1, wherein detecting is carried out by electrophoresis.

5. The method of claim 1, wherein detecting is carried out by restriction endonuclease digestion analysis.

6. The method of claim 1, wherein detecting is carried out by an amplification reaction.

7. The method of claim 6, wherein the amplification reaction is a polymerase chain reaction.