# Patent

## Title
Polymorphisms in genes affecting ace-related disorders and uses thereof

## Abstract

A method for predicting a subject's risk factors for ACE-related disorders includes detecting the allelic status of one or more polymorphisms in a nucleic acid sample of the subject.

## Background
RELATED APPLICATIONS
This application claims the benefit of the PCT/US2008/005558 filed Apr. 30, 2008, which claims priority to U.S. Provisional Patent Application 60/926,932 filed Apr. 30, 2007, the disclosure of which is incorporated herein by reference, in its entirety.


GOVERNMENT SUPPORT
The invention was made with government support from the National Institutes of Health research grants HL 74730, HL 69758 and RR017568. The government may have certain rights in the invention.


BACKGROUND
Single nucleotide polymorphisms (SNPs) are useful as biomarkers for predicting disease susceptibility or progression, or as a guide for individualized therapy, including drug therapy.
ACE—
Angiotensin I-converting enzyme (ACE) plays a key role in cardiovascular biology. Its functions include formation of angiotensin II and inactivation of bradykinin, resulting in vasoconstriction and increased blood pressure. ACE inhibitors are recommended as first-line treatment of hypertension and heart failure. Expressed in many tissues, ACE further affects a broad spectrum of physiological processes. As a result, the ACE gene has been implicated in susceptibility to hypertension, myocardial infarction, renal pathophysiology, diabetes, and Alzheimer's disease.
In particular, angiotensin I-converting enzyme (ACE1) is expressed with a wide tissue distribution including plasma, endothelial cells, kidney, heart and lungs. This enzyme hydrolyzes a number of substrates, including conversion of angiotensin I to angiotensin II (as part of the renin-angiotensin system). Angiotensin II (AngII) is a potent vasoconstrictor and pro-hypertrophic factor. Ang II induces production of superoxide free radicals (O2−) that scavenge available nitric oxide and reduce endothelial vasodilatation. ACE1 has even greater affinity for bradykinin, thus hydrolyzing and inactivating a potent vasodilator. Through these pathways, ACE1 exerts potent physiological influence over salt balance, blood volume and blood pressure levels with significant implications for cardiovascular disease in particular.
Targeted reduction of ACE1 via the blockbuster drug class of ACE inhibitors that directly bind the active site of the ACE protein is a first line anti-hypertensive treatment for heart disease. ACE inhibitors decrease the release of aldosterone and retention of salt and water, significantly lowering blood pressure. Drugs in this class have been shown to reduce mortality in many large clinical trials. These drugs are often administered immediately following myocardial infarction. They currently represent a major pharmaceutical class with millions of prescriptions worldwide, with additional indications in hypertension or renal crisis in relation to scleroderma, and prevention of kidney damage in some diabetics. Furthermore, recent literature indicates that ACE1 may play a role in the degradation of Alzheimer's plaques making it a possible disease factor (26,39).
It has been determined that there is variability in patient responses to ACE inhibitor treatment. Family-based studies over the last two decades indicate that ACE1 levels as a quantitative phenotype are strongly influenced by a genetic component that maps to the ACE1 locus; however, well-supported functional variants remain to be identified (40, 1, 11). Nonetheless, this has been considered one of the most compelling examples in human genetics of a single gene contributing to variability in a complex human trait. Intolerance for ACE inhibitors is as high as 20%, with the most common side effect being a severe cough, especially in Asian patients (41).
Moreover, studies in African-American patients on ACE inhibitors indicated they received less benefit (16) and increased risk of side effects (4-5 fold) (18) and mortality from angioedema (42-45), suggesting a possible pharmacogenetic influence on drug response. An intron 16 ALU insertion-deletion polymorphism of 287 bp has been extensively studied, because it revealed significant associations in a number of studies. However, several research groups have shown that this polymorphism is unlikely to have any direct functional role (5,4) and, instead, is likely in linkage to one or more true, and as yet undetermined, functional variants. Studies employing diverse populations and public data from the HapMap project indicate the ALU polymorphism alone is an inadequate proxy for the genetic diversity at this gene locus. However, there are thousands of studies genotyping solely the ALU polymorphism in a variety of clinical populations. These demonstrate both positive and negative associations, as reflected in metanalyses of this variant (3). Since these previous studies rely on the assumption that the ALU polymorphism is completely or highly linked to true functional variants, they may be missing critical information if this assumption is incorrect or only partially correct. For example, one study of outcomes in 38,000 individuals receiving ACE inhibitor treatment genotyped only the ALU polymorphism and found no significant association (2).
The suggestion of a heritable component to serum ACE activity (1) led to extensive phenotype-genotype studies with ACE-related pathophysiology and response to ACE inhibitors (2). Numerous studies have focused on an insertion/deletion (I/D) polymorphism in intron 15. However, meta-analyses of phenotypic associations largely failed to confirm a role for I/D (3), and in vitro experiments did not reveal any effect on transcription (4) or splicing (5). Therefore, genetic factors contributing to differential ACE expression remain uncertain.
What are lacking are tools for predicting the likelihood that a particular patient will be responsive to a therapeutic ACE, and in particular, identifying agents to which the ACE will be sensitive or resistant. Also lacking are tools for profiling genetic factors influencing sensitivity and resistance of patients to ACE therapeutic agents. Such tools, and the resulting gene expression profiles, would be predictive of treatment response of a patient to a particular drug, and would allow for increased predictability regarding chemosensitivity or chemoresistance of such patients to enable the design of optimal treatment regimens for patients.
SOD2—
Oxidative stress and damage play a role in the pathogenesis of a number of diseases. In particular, mitochondrial-derived oxidants play an important role in the pathogenesis of many human disorders.
SOD2 is an antioxidant, the mitochondrial form of SOD and an important defense against oxidative damage. The SOD2 gene is a member of the iron/manganese superoxide dismutase family. The mitochondrial superoxide dismutase protein (SOD2) serves a critical cellular role in protecting from harmful reactive species by reducing these species to hydrogen peroxide (H2O2) which is then processed to hydroxide (OH) and then water (H2O). This is a normal cellular process that is critical to life and protects the integrity of cellular genomes. Under conditions of stress including disease and environmental conditions (e.g., toxins) reactive species can accumulate to a degree that overwhelms the capacity of endogenous protectors including SOD2. Thus, if common alleles exist that affect SOD2 production these alleles may contribute to many diseases, but may only be important under conditions of accumulated oxidative stress.
What are lacking are tools for predicting the likelihood that a particular patient will be responsive to a therapeutic SOD2 agent, and in particular, identifying agents to which the SOD2 agent will be sensitive or resistant.
Also lacking are tools for profiling genetic factors influencing sensitivity and resistance of patients to SOD2-caused oxidative damage.
SLC6A3—
Dopamine active transporter (SLC6A3, formerly) is a membrane-spanning protein that binds the neurotransmitter dopamine. SLC6A3 provides the primary mechanism through which dopamine is cleared from synapses. SLC6A3 works by transporting dopamine from the synapse into a neuron. SLC6A3 is present in the peri-synaptic area of dopaminergic neurons in areas of the brain where dopamine signaling is common. SLC6A3 terminates the dopamine signal and is implicated in a number of dopamine-related disorders, including alcoholism, attention deficit hyperactivity disorder, bipolar disorder, clinical depression, drug abuse, Parkinson disease, Tourette syndrome and Schizophrenia. Stimulant medications, such as those used to treat ADHD, and drugs of abuse such as amphetamine bind to SLC6A3 and inhibit reuptake of dopamine. Genetic variants of SLC6A3 may influence levels of gene expression and/or ability of drugs to bind to SLC6A3 protein. The gene that encodes the SLC6A3 protein is located on human chromosome 5, consists of 15 coding exons, and is roughly 64 kpb long. It is believed that the associations between SLC6A3 and dopamine related disorders has come from a genetic polymorphism in the SLC6A3 gene, which influences the amount of protein expressed.
What are lacking are tools for predicting the likelihood that a particular patient will be responsive to a therapeutic SLC6A3 agent, and in particular, identifying agents to which the SLC6A3 therapeutic agent will be sensitive or resistant.
CYP2C9—
CYP2C9 (encoding cytochrome P450 2C9) is a liver drug metabolizing enzyme, involved in metabolism of  ̃20% of pharmaceuticals. CYP2C9 is a member of the cytochrome P450 mixed-function oxidase system and is involved in the metabolism of xenobiotics in the body. CYP2C9 is involved in the metabolism of several groups of drugs, such as, for example, non-steroidal anti-inflammatory drugs (NSAIDs). Genetic polymorphism exists for CYP2C9 expression and there is a belief that approximately 1-3% of Caucasian populations are poor metabolizers with no CYP2C9 function.
What are lacking are tools for predicting the likelihood that a particular patient will be responsive to a therapeutic CYP2C9 agent, and in particular, identifying agents to which the CYP2C9 agent will be sensitive or resistant.
Such tools would likewise enable the identification of new drugs that modulate expression of genes that affect chemosensitivity, particularly new agents that alter expression of these genes to overcome drug resistance or enhance chemosensitivity.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objects and advantages of the invention may be realized and attained as particularly pointed out in the appended claims.
SUMMARY
In a very broad aspect, the disclosure provides for a method for predicting a subject's risk factors for an ACE-related disorder, such as, but not limited to cardiovascular diseases and/or a subject's responsiveness to a therapeutic agent targeting the subject's renin-angiotension system (for example, ACE inhibitors angiotension receptor blockers (ARBS) and the like). The method includes detecting the allelic status of one or more polymorphisms in a nucleic acid sample of the subject, wherein the polymorphism is selected from the group of (i) ACE-associated SNPs rs4290, rs7214530, rs7213516, rs4309, rs4343 or combinations thereof; or, (ii) a SNP in linkage disequilibrium with one or more SNPs listed in (i). In such a method, the allelic status of the polymorphism in the subject is predictive of the subject's risk factors for an ACE-related disorder.
In one embodiment, the method further includes the step of correlating the allelic status of the polymorphism in the subject with the allelic status of the polymorphism in a reference population to predict the subject's risk factors for an ACE-related disorder.
In another embodiment, the method further includes the step of correlating the allelic status of the polymorphism in the subject with the allelic status of the polymorphism in a reference population to predict whether the subject has risk factors for an ACE-related disorder.
In a particular embodiment, the disclosure provides for a method of screening a subject for a prognostic biomarker, comprising detecting the allelic status of one or more polymorphisms in a nucleic acid sample of the subject, wherein the polymorphism is one or more of:

    
    
        (i) ACE-associated SNPs rs4290, rs7214530, rs7213516 or combinations thereof; or,
        (ii) a SNP in linkage disequilibrium with one or more SNPs listed in (i).

In this method, the allelic status of the polymorphism in the subject is predictive of the prognostic outcome of the subject.

    
    


In a particular embodiment, the disclosure provides for a method of screening a subject for a prognostic biomarker, comprising detecting the allelic status of one or more polymorphisms in a nucleic acid sample of the subject, wherein the polymorphism is one or more of:

    
    
        (i) SOD2-associated SNPs rs4880, rs5746092 or combinations thereof; or,
        (ii) a SNP in linkage disequilibrium with one or more SNPs listed in (i), wherein the allelic status of the polymorphism in the subject is predictive of the subject's risk for having or developing the SOD2-related disorder.
    
    


In a particular embodiment, the disclosure provides for a method of screening a subject for a prognostic biomarker, comprising detecting the allelic status of one or more polymorphisms in a nucleic acid sample of the subject wherein the polymorphism is one or more of:

    
    
        (i) SLC6A3-associated rs27072, rs6347 or combinations thereof; or,
        (ii) a SNP in linkage disequilibrium with one or more SNPs listed in (i), wherein the allelic status of the polymorphism in the subject is predictive of the subject's risk for having or developing the SLC6A3-related disorder.
    
    


In a particular embodiment, the disclosure provides for a method of screening a subject for a prognostic biomarker, comprising detecting the allelic status of one or more polymorphisms in a nucleic acid sample of the subject, wherein the polymorphism is one or more of:

    
    
        (i) CYP2C9-associated rs1057911, rs9332242, rs2017319 or combinations thereof; or,
        (ii) a SNP in linkage disequilibrium with one or more SNPs listed in (i), wherein the allelic status of the polymorphism in the subject is predictive of the subject's risk for having or developing the CYP2C9-related disorder.
    
    


In one embodiment, the method further includes the step of correlating the allelic status of the polymorphism in the subject with the allelic status of the polymorphism in a reference population to predict the prognostic outcome of the subject.
In another embodiment, the method further includes the step of correlating the allelic status of the polymorphism in the subject with the allelic status of the polymorphism in a reference population to predict whether the subject has a greater or lesser risk factors for an ACE-related disorder.
In another embodiment, the method further includes the step of correlating the allelic status of the polymorphism in the subject with the allelic status of the polymorphism in a reference population to predict the subject's response to treatment.
In one embodiment, the disorders for which a therapeutic ACE inhibitor may be indicated includes, but is not limited to, one or more of the following: hypertensive treatment for heart disease, lowering blood pressure, myocardial infarction, hypertension or renal crisis in relation to scleroderma, prevention of kidney damage in diabetics, and Alzheimer's disease.
The SNPs identified herein can be used in combination with additional predictive tests including, but not limited to, additional SNPs, mutations, and clinical tests.
The disclosure also provides for a method for finding a functional polymorphism in a target gene implicated a in subject's risk factors for an ACE-related disorder, comprising: (i) providing a sample of a target tissue expressing the target gene; (ii) measuring the target gene's allelic mRNA expression imbalance (AEI) by quantitatively measuring the relative amounts of mRNA generated from each of two alleles in a transcribed region of the target gene and comparing the mRNA expression of one allele against the other allele to obtain an AEI ratio; and (iii) using the AEI ratio as a phenotype to scan the target gene for regions containing polymorphisms. Accordingly, a significant association between the AEI ratio and the polymorphism indicates that the polymorphism is a functional polymorphism that can serve as a biomarker for assessing a subject's risk factors for an ACE-related disorder.
The present disclosure also relates to a kit comprising useful components for practicing the present method. A useful kit can contain oligonucleotide probes specific for ACE alleles. The kit can also include instructions for correlating the assay results with the subject's responsiveness to a therapeutic agent, the subject's prognostic outcome, or the probability of success or failure of a particular drug treatment in the subject.



BRIEF DESCRIPTION OF THE DRAWINGS
The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.
The invention can be more fully understood from the following detailed description, the drawings and the Sequence Descriptions that form a part of this application. The sequence descriptions and Sequence Listing attached hereto comply with the rules governing nucleotide and/or amino acid sequence disclosures in patent applications as set forth in 37 CFR §§1.821-1.825. The Sequence Descriptions contain the three letter codes for amino acids as defined in 37 CFR §§1.821-1.825, which are incorporated herein by reference.
FIGS. 1a and 1b. ACE Allelic mRNA expression in left ventricular heart tissues from African-Americans (FIG. 1a) and Caucasian-Americans (FIG. 1b). Allelic mRNA expression ratios (major/minor allele for marker SNPs rs4309 (C/T), rs4343 (A/G)) are averages of results using both markers. AEI was prevalent in African-American (FIG. 1a) but not Caucasian-American (FIG. 1b) heart tissues. Genotypes for the promoter SNPs are indicated above the African-American samples. Data are mean±SD, ***P<0.001 versus pooled DNA ratios.
FIGS. 2a and 2b. Total mRNA expression levels of ACE in 65 heart tissues. The boxplots display the median plus or minus one quartile. Results are grouped by genotype of I/D (rs13447447) (P=0.93)(FIG. 2a) and carriers of the promoter rs4290 T allele (FIG. 2b). ACE mRNA levels are relative to β-actin. *P<0.05 versus CC genotype (t-test for mean differences).
FIGS. 3a and 3b. Luciferase reporter gene assay of the ACE promoter in bovine aortic endothelial cells (BAEC) FIG. 3a) and HEK293 cells (FIG. 3b). An ACE promoter DNA fragment spanning from −4,335 to +1 was cloned into the pGL3-Basic vector, containing various combinations of the promoter SNPs rs7213516 (G/A), rs7214530 (T/G) and rs4290 (C/T).
The reference haplotype is G-T-C, while the variant constructs contain 1-3 minor alleles (G-T-T, G-G-C, G-G-T, A-G-C, A-G-T). In BAEC, 0.8 μg plasmids were co-transfected with 40 ng Renilla luciferase plasmid using either Lipofectamine or Fugen reagent, and activity was measured by Dual-Glo luciferase assay kit (Promega). Luciferase activities from fused-pGL3 vector were normalized using Renilla luciferase activity as an internal control. *P<0.05; **P<0.001 compared to reference haplotype G-T-C. In HEK293 cells, various amounts of plasmid were transfected using Lipofectamine, with no differences observed between all conditions.
FIG. 4. Odds ratios of three polymorphisms for primary outcome in the overall population and within each race/ethnicity group. The three polymorphisms are promoter SNPs rs7213516 and rs4290, and intron 15 SNP rs13447447 (I/D). Odds ratios were adjusted for age, sex, race/ethnicity, BMI, smoking, INVEST treatment strategy, previous myocardial infarction, previous stroke, heart failure, diabetes, renal insufficiency, baseline SBP, diuretic use, and ACE inhibitor use.
FIG. 5. ACE gene structure (UCSC genome browser) and location of polymorphisms tested in this study. The boxes indicate the exons coding for the two peptidase domains in the full length ACE isoform. Overviews of HapMap LD in the gene region for individuals from Utah of Northern-European ancestry (CEU) and from Yoruba, Nigeria (YRI) are shown at bottom (Haploview).
FIGS. 6a and 6b. Schematics of the allelic expression imbalance (AEI) assay used to uncover cis-acting functional alleles. Shown here, marker SNP rs4309 (C/T) is used in the SNaPshot reaction for both gDNA and mRNA. Peak area ratios represent allelic ratios in gDNA and mRNA (after conversion to cDNA).
FIG. 7. LD structure of polymorphisms in the INVEST-GENES clinical genetic association study. Values for D′ and r2 are provided and color coded; the light blue boxes indicate very low allele abundance preventing calculation of D′.
FIG. 8. Promoter sequence alignments and TF binding sites. The three promoter SNPs rs7213516 (G/A), rs7214530 (T/G) and rs4290 (C/T) are located −2883, −2828 and −2306 bp upstream of the transcription start site (+1). The predicted MEF2A transcription factor binding sites based on the JASPAR database position-weight matrices are shown in detail. Sequence alignments (CLUSTALW) are based on genomic matches identified by BLAST of the human promoter region (* indicates a 1 bp insert in rhesus, dog, elephant, and armadillo sequences; \ indicates a 9 bp insert in dog and armadillo sequences). Human [SEQ ID NOS 70, 34 and 278, respectively, in order of appearance], Chimp [SEQ ID NOS 71, 270 and 279, respectively, in order of appearance], Rhesus [SEQ ID NOS 72, 271 and 280, respectively, in order of appearance], Bushbaby [SEQ ID NOS 76, 272 and 281, respectively, in order of appearance], Shrew [SEQ ID NOS 73, 273 and 282, respectively, in order of appearance], Dog [SEQ ID NOS 74, 274 and 283, respectively, in order of appearance], Elephant [SEQ ID NOS 75, 275 and 284, respectively, in order of appearance], Squirrel [SEQ ID NOS 77, 276 and 285, respectively, in order of appearance], Armadillo [SEQ ID NOS 78, 277 and 286, respectively, in order of appearance].
FIG. 9. Schematic illustration of ACE gene structure and relevant genetic polyporphism (chromosome 17q.23.3) (not to scale).
FIG. 10. Table 1. Unadjusted and adjusted odds ratios and 95% confidence intervals for secondary outcomes by genotype
FIGS. 11a and 11b. Tables 2A and 2B. Polymorphisms analyzed in this study, and minor allele frequencies observed in the 65 heart tissues (Table 2A, FIG. 11a) and in the INVEST-GENES cohort (Table 2B, FIG. 11b), sorted by race/ethnicity. The P values indicate the level of significance for interethnic differences in minor allele frequencies.
FIG. 12. Table 3. Baseline characteristics for the INVEST-GENES case and control patients.
FIG. 13. Table 4. Oligonucleotide sequences used in genotyping and allelic expression imbalance (AEI) assays for ACE that employed primer extension technology. Underlined nucleotides were intentionally mismatched against the reference sequence.
FIG. 14. Table 5. Oligonucleotide sequences employed in genotyping ACE SNPs by the GC-clamp method described in Papp et al.
FIG. 15. Table 6. Oligonucleotides sequences employed in ACE Pyrosequencing genotyping.
FIG. 16. Table 7. Oligonucleotide primers used in the amplification and direct sequencing of the ACE upstream gene region and cDNA.
FIG. 17. Table 8. FAM-labeled oligos and related oligos used in genotyping ACE polymorphisms.
FIG. 18. Table 9. Oligonucleotides used in the measurement of ACE expression by RT-PCR, including one that spans cDNA exons.
FIG. 19. Table 10. Oligonucleotide sequences used in genotyping and allelic expression imbalance (AEI) assays for SOD2 that employed primer extension technology.
FIG. 20. Table 11. A list of SNPs used in the SLC6A3 example herein.
FIG. 21. Table 12. A list of SNPs used in the CYP2C9 example herein.
FIG. 22—Results of AEI analysis for ACE, SOD2, NOS3 and CCL2, in heart left ventricular tissues. Each peak represents a distinct allele measured in genomic DNA or cDNA from a single heterozygous individual. The selected samples (columns, left to right) represent the typical genomic DNA ratio observed, a cDNA showing insignificant deviation from the expected ratio and a cDNA sample showing highly significant deviation from unity. Normalization to the average genomic DNA is used in the calculation of AEI values (cDNA values listed as major:minor allele on a log 2 scale) and accounts for differences in fluorescent dideoxynucleotide incorporation efficiencies and fluorescence yields. See FIG. 26—Table 13, for a list of genes reported here and FIG. 27—Table 14 for marker SNPs and genes showing significant AEI results.
FIG. 23. Allelic mRNA expression ratios (major allele over minor allele, normalized to the mean allelic ratio in genomic DNA) measured in heart failure samples for 12 cardiovascular candidate genes. Results for individual samples are displayed with the magnitude and direction of AEI indicated on a log 2 scale (y-axis). Potential AEI in individual samples is indicated by ratios >(+0.3) log 2 or <(−0.3) log 2, a cutoff arrived at by analysis of the extent of variation in genomic DNA ratios. For the present survey study we considered ratios >(+0.5) log 2 or <(−0.5) log 2 to represent significant AEI.
FIG. 24. Lack of correlation between SOD2 allelic mRNA expression ratios and allelic CpG methylation ratios in 34 heart tissue samples. Allelic methylation ratios were determined from triplicate assays using Hpa II digestion of the genomic DNA region containing rs4880 (only non-methylated DNA is cut), followed by SNaPshot analysis of the allelic ratios for uncut genomic DNA.
FIG. 25. Computed changes of mRNA folding (minimum free energy conformations) induced by all transitions (SNP generated by C< >T and G< >A substitutions) in the transcribed exonic domains of OPRM1 mRNA. The arrow indicates the location of the functional SNP A118G, affecting mRNA levels in human brain (18). The x-axis denotes the nucleotide position in the mature OPRM1 mRNA (cDNA), while the y-axis represent a scale of the extent by which predicted mRNA folding is affected by any given transition. Conformations were calculated for wild-type and mutant sequences using Mfold, and then the sum of the differences in the Mfold single-strandedness count measure at each nucleotide was computed both globally (across the full mRNA structure, each point shown here) and in more regional sliding windows of different sizes. Sliding windows and analysis of both types of transversions at each position (pyrimidine< >Opurine), as well as A>G transitions alone all gave very similar results (data not shown).
FIG. 26. Table 13. A list of candidate genes tested for the presence of AEI in Example II herein.
FIG. 27. Table 14. Gene showing significant allelic mRNA expression ratios (at least one sample showing minimally ±0.20.5, or  ̃40% AEI in either direction.
FIG. 28. Table 15. Genotyping of suspected functional polymorphisms compared with AEI data.
FIG. 29. Table 16. List of candidate genes analyzed in Example II, grouped by indication (disease or pharmacology). List of candidate genes analyzed in this study, grouped by indication (disease or pharmacology). Marker SNPs are all located in transcribed regions of the mature mRNA, or a splice variant. For some genes more than one marker SNP and tissue were used.
FIG. 30. Table 17. List of oligonucleotide primers used for PCT amplification and SNaPshot primer extension reactions.
FIG. 31. mRNA sequence of the ACE gene [SEQ ID NO: 261 (DNA) and SEQ ID NO: 287 (protein)].
FIG. 32. mRNA sequence of the SOD2 gene[SEQ ID NO: 262 (DNA) and SEQ ID NO: 288 (protein)].
FIG. 33. mRNA sequence of the SLC6A3 gene[SEQ ID NO: 263 (DNA) and SEQ ID NO: 289 (protein)].
FIG. 34. mRNA sequence of the CYP2C9 gene [SEQ ID NO: 264 (DNA) and SEQ ID NO: 290 (protein)].



DETAILED DESCRIPTION OF THE INVENTION
The present invention will now be described with occasional reference to the specific embodiments of the invention. This invention may, however, be embodied in different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety. The disclosure of all patents, patent applications (and any patents that issue thereon, as well as any corresponding published foreign patent applications), GenBank and other accession numbers and associated data, and publications mentioned throughout this description are hereby incorporated by reference herein. It is expressly not admitted, however, that any of the documents incorporated by reference herein teach or disclose the present invention.
The present invention may be understood more readily by reference to the following detailed description of the embodiments of the invention and the Examples included herein. However, before the present methods, compounds and compositions are disclosed and described, it is to be understood that this invention is not limited to specific methods, specific cell types, specific host cells or specific conditions, etc., as such may, of course, vary, and the numerous modifications and variations therein will be apparent to those skilled in the art. It is also to be understood that the terminology used herein is for the purpose of describing specific embodiments only and is not intended to be limiting.
Accordingly, the disclosure provides diagnostic and prognostic methods, compositions, assays, and kits useful for predicting the phenotype of a subject's risk factors for cardiovascular diseases and/or a subject's responsiveness to therapeutic ACE inhibitors. The methods also include predicting the prognostic outcome of the subject, as well as the subject's responsiveness to drug treatments. The methods and kits include determining the allelic status of polymorphisms in the ACE genes.
The disclosure also provides methods for identifying functional polymorphisms using an allele-specific mRNA expression imbalance (AEI) assay combined with SNP scanning of a target gene locus with allelic mRNA ratios as a quantitative phenotype, together with in vitro molecular genetic analysis to identify the functional polymorphisms. Also provided are a number of functional single nucleotide polymorphisms (SNPs) in the ACE gene.
AEI Assay
The question of how genetic processes interact to regulate gene expression can be addressed by measuring allelic expression imbalance (AEI). Measuring allelic mRNA expression compares one allele against the other in a relevant target tissue of the same individual. The assay quantitatively measures the relative amounts of mRNA generated from each of two alleles in physiologically relevant target tissues (e.g., specific cardiac regions) from subjects that are heterozygous for a marker SNP in the transcribed region of the gene in question. AEI indicates the presence of cis-acting factors in gene regulation and/or mRNA processing. AEI results provide a quantitative measure of the allelic differences in each individual, one allele serving as the control for the other, while canceling out any trans-acting factors. The allelic expression ratios are then used as the phenotype to scan a gene locus for regions containing functional polymorphisms. If cis-acting polymorphisms contribute to the measured AEI ratios, significant correlations should be detectable. For this analysis it is helpful to know the phasing of each SNP with the marker SNPs. As disclosed in the Examples, the inventors conducted a single locus association test between SNP genotype and allelic expression phenotype. The AEI phenotype can be represented either as present/absent; or absent/present low/present high, or as a continuous quantitative trait. Significant associations indicate that a SNP, or one closely linked, contributes to AEI, by affecting mRNA expression levels. These candidate polymorphisms, or haplotypes, are then cloned into expression vectors to determine the molecular mechanisms underlying the genetic changes where this is possible. A goal of this assay is to identify the polymorphisms that most closely account for any genetically based phenotypic differences between individuals.
Polymorphisms Linked to Function (AEI)
Using the above method, we were able to designate specific polymorphisms as biological biomarkers, used either alone or in combination with each other or with already established biomarkers. For each polymorphism in the candidate genes, we have established a link with allelic expression in human biopsy cardiac tissues as the phenotype. Obtained by scanning the entire gene in a number of individuals for polymorphisms that correlate with AEI, these polymorphisms are either directly responsible for altering mRNA expression, or they are in linkage disequilibrium or strong linkage disequilibrium with a functional SNP or SNPs. The listed polymorphisms are frequent (>5%), and have already shown statistically significant associations with clinical phenotypes. These polymorphisms therefore represent biallelic biomarkers associated with functional variants of key genes conveying susceptibility to CNS disorders and treatment outcome.
We disclose the use of AEI analysis to screen the ACE gene for functional polymorphisms. We have discovered several AEI across a number of individuals, indicating the presence of previously unknown and yet frequent functional polymorphisms.
By scanning each gene in a number of individuals we have identified polymorphisms (SNPs) most closely related to the functional variation. Because these SNPs are linked to functional defects, and occur frequently in key candidate genes implicated in cardiovascular disorders, they represent strong biomarkers for predicting individual risk and response to ACE inhibitor therapy. Because their functional significance is established, one can also analyze combinations of gene variants as risk factors, without greatly increasing the required statistical stringency for multiple comparisons.
DEFINITIONS
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood to one of ordinary skill in the art to which this disclosure belongs.
As used herein and in the appended claims, the singular forms “a,” “and,” and “the” include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to “a polynucleotide” includes a plurality of such polynucleotides and reference to “the SNP” includes reference to one or more SNPs known to those skilled in the art, and so forth.
Unless otherwise indicated, all numbers expressing quantities of ingredients, properties such as molecular weight, reaction conditions, and so forth as used in the specification and claims are to be understood as being modified in all instances by the term “about.” Accordingly, unless otherwise indicated, the numerical properties set forth in the following specification and claims are approximations that may vary depending on the desired properties sought to be obtained in embodiments of the present invention. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the invention are approximations, the numerical values set forth in the specific examples are reported as precisely as possible. Any numerical values, however, inherently contain certain errors necessarily resulting from error found in their respective measurements.
The term “allele” is used herein to refer to variants of a nucleotide sequence. Alleles are identified with respect to one or more polymorphic positions, with the rest of the gene sequence unspecified. For example, an allele may be defined by the nucleotide present at a single SNP; or by the nucleotides present at a plurality of SNPs, also termed haplotypes. A biallelic polymorphism has two forms. Diploid organisms may be homozygous or heterozygous for an allelic form.
For convenience, the allele present at the higher or highest frequency in the population will be referred to as the “main” or “wild-type” allele; less frequent allele(s) will be referred to as “minor” or “variant” allele(s).
Assessing the “allelic status” of a polymorphism refers to determining whether a subject is heterozygous (has one minor allele and one main allele), homozygous for the minor allele or homozygous for the main allele.
A “gene” refers to a segment of genomic DNA that contains the coding sequence for a protein, wherein the segment may include promoters, exons, introns, and other untranslated regions that control expression.
A “genotype” is an unphased 5′ to 3′ sequence of nucleotide pair(s) found at a set of one or more polymorphic sites in a locus on a pair of homologous chromosomes in a subject.
The term “genotyping” a sample or a subject for a polymorphism involves determining the specific allele or the specific nucleotide(s) carried by an individual at a biallelic marker.
The term “haplotype” refers to a combination of alleles present in an individual or a sample on a single chromosome. In the context of the present disclosure, a haplotype refers to a combination of biallelic marker alleles found in a given individual and which may be associated with a phenotype.
“Haplotyping” is the process for determining one or more haplotypes in a subject and includes use of family pedigrees, molecular techniques and/or statistical inference.
The term “polymorphism” as used herein refers to the occurrence of two or more alternative genomic sequences or alleles between or among different genomes or individuals. “Polymorphic” refers to the condition in which two or more variants of a specific genomic sequence can be found in a population. A “polymorphic site” is the locus at which the variation occurs. A polymorphism may comprise a substitution, deletion or insertion of one or more nucleotides. A single nucleotide polymorphism (SNP) is a single base pair change. Typically, a single nucleotide polymorphism is the replacement of one nucleotide by another nucleotide at the polymorphic site. Deletion of a single nucleotide or insertion of a single nucleotide, also give rise to single nucleotide polymorphisms. In the context of the present disclosure, “single nucleotide polymorphism” refers to a single nucleotide substitution. Typically, between different genomes or between different individuals, the polymorphic site may be occupied by two different nucleotides.
The term “biallelic polymorphism,” “bialleleic marker,” or “biomarker” are used interchangeably and refer to a polymorphism having two alleles at a fairly high frequency in the population, sometimes a single nucleotide polymorphism. Typically, the frequency of the less common allele of the biallelic polymorphism of the present disclosure has been validated to be greater than 1%, sometimes the frequency is greater than 10%, 20% (i.e. heterozygosity rate of at least 0.32), or 30% (i.e. heterozygosity rate of at least 0.42).
The term “mutation” refers to a difference in DNA sequence between or among different genomes or individuals that causes a functional change and which can have a frequency below 1%. Sequence variants describe any alteration in DNA sequence regardless of function or frequency.
“Linkage Disequilibrium” (“LD”) refers to alleles at different loci that are not associated at random, i.e., not associated in proportion to their frequencies. If the alleles are in positive linkage disequilibrium, then the alleles occur together more often than expected assuming statistical independence. Conversely, if the alleles are in negative linkage disequilibrium, then the alleles occur together less often than expected assuming statistical independence. As used herein, “strong linkage disequilibrium” is defined by D′ of >0.8.
As used interchangeably herein, the terms “oligonucleotides”, and “polynucleotides” include RNA, DNA, or RNA/DNA hybrid sequences of more than one nucleotide in either single chain or duplex form. The term “nucleotide” as used herein as an adjective to describe molecules comprising RNA, DNA, or RNA/DNA hybrid sequences of any length in single-stranded or duplex form. The term “nucleotide” is also used herein as a noun to refer to individual nucleotides or varieties of nucleotides, meaning a molecule, or individual unit in a larger nucleic acid molecule, comprising a purine or pyrimidine, a ribose or deoxyribose sugar moiety, and a phosphate group, or phosphodiester linkage in the case of nucleotides within an oligonucleotide or polynucleotide.
The term “purified” is used herein to describe a polynucleotide or polynucleotide vector of the disclosure which has been separated from other compounds including, but not limited to other nucleic acids, carbohydrates, lipids and proteins (such as the enzymes used in the synthesis of the polynucleotide), or the separation of covalently closed polynucleotides from linear polynucleotides.
The term “isolated” requires that the material be removed from its original environment (e.g., the natural environment if it is naturally occurring). For example, a naturally-occurring polynucleotide or polypeptide present in a living animal is not isolated, but the same polynucleotide or DNA or polypeptide, separated from some or all of the coexisting materials in the natural system, is isolated. Such polynucleotide could be part of a vector and/or such polynucleotide or polypeptide could be part of a composition, and still be isolated in that the vector or composition is not part of its natural environment.
The term “heterozygosity rate” is used herein to refer to the incidence of individuals in a population, which are heterozygous at a particular allele. In a biallelic system, the heterozygosity rate is on average equal to 2 Pa(1-Pa), where Pa is the frequency of the least common allele. In order to be useful in genetic studies, a genetic biomarker should have an adequate level of heterozygosity to allow a reasonable probability that a randomly selected person will be heterozygous.
The term “upstream” refers to a location which, is toward the 5′ end of the polynucleotide from a specific reference point. The term “downstream” refers to a location which is toward the 3′ end of the polynucleotide from a specific reference point.
The terms “base paired” and “Watson & Crick base paired” are used interchangeably herein to refer to nucleotides which can be hydrogen bonded to one another be virtue of their sequence identities in a manner like that found in double-helical DNA with thymine or uracil residues linked to adenine residues by two hydrogen bonds and cytosine and guanine residues linked by three hydrogen bonds (See Stryer, L., Biochemistry, 4th edition, 1995; incorporated herein by reference).
The terms “complementary” or “complement thereof” are used herein to refer to the sequences of polynucleotides which are capable of forming Watson & Crick base pairing with another specified polynucleotide throughout the entirety of the complementary region. This term is applied to pairs of polynucleotides based solely upon their sequences and not any particular set of conditions under which the two polynucleotides would actually bind.
The term “primer” denotes a specific oligonucleotide sequence which is complementary to a target nucleotide sequence and used to hybridize to the target nucleotide sequence. A primer serves as an initiation point for nucleotide polymerization catalyzed by either DNA polymerase, RNA polymerase or reverse transcriptase, or in a single nucleotide extension reaction for the measurement of AEI.
The term “probe” denotes a defined nucleic acid segment (or nucleotide analog segment, e.g., polynucleotide as defined herein) which can be used to identify a specific polynucleotide sequence present in samples, said nucleic acid segment comprising a nucleotide sequence complementary of the specific polynucleotide sequence to be identified.
The primers and probes can be prepared by any suitable method, including, for example, cloning and restriction of appropriate sequences and direct chemical synthesis. The probes and primers can comprise nucleic acid analogs such as, for example, peptide nucleic acids, locked nucleic acid (LNA) analogs, and morpholino analogs. The 3′ end of the probe can be functionalized with a capture or detectable label to assist in detection of a polymorphism.
Any of the oligonucleotides or nucleic acid of the disclosure can be labeled by incorporating a detectable label measurable by spectroscopic, photochemical, biochemical, immunochemical, or chemical means. For example, such labels can comprise radioactive substances (32P, 35S, 3H, 125I) fluorescent dyes (5-bromodesoxyuridin, fluorescein, acetylaminofluorene, digoxigenin), biotin, nanoparticles, and the like. Such oligonucleotides are typically labeled at their 3′ and 5′ ends.
Probes can be used to detectably distinguish between target molecules differing in structure. Detection can be accomplished in a variety of different ways depending on the type of probe used and the type of target molecule. Thus, for example, detection may be based on discrimination of activity levels of the target molecule, but typically is based on detection of specific binding. Examples of such specific binding include antibody binding and nucleic acid probe hybridization. Thus, for example, probes can include enzyme substrates, antibodies and antibody fragments, and nucleic acid hybridization probes. Thus, in one embodiment, the detection of the presence or absence of the at least one variance involves contacting a target polymorphic site with a probe, typically an oligonucleotide probe, where the probe hybridizes with a form of the target nucleic acid containing a complementary base at the variance site as compared to hybridization to a form of the target nucleic acid having a non-complementary base at the variance site, where the hybridization is carried out under selective hybridization conditions. Such an oligonucleotide probe may span two or more variance sites. Unless otherwise specified, an oligonucleotide probe can include one or more nucleic acid analogs, labels or other substituents or moieties so long as the base-pairing function is retained.
A “control population” refers to a group of subjects or individuals who are predicted to be representative of the genetic variation found in the general population.
A “subject” comprises an individual (e.g., a mammalian subject or human) whose genotypes or haplotypes or response to treatment or disease state are to be determined.
A “nucleic acid sample” includes blood, serum, plasma, cerebrospinal fluid, urine, saliva, and tissue samples.
The term “phenotype” refers to any biochemically, anatomically, and clinically distinguishable, detectable or otherwise measurable property of an organism such as symptoms of, or susceptibility to a disease for example. Typically, the term “phenotype” is used herein to refer to symptoms of, or susceptibility to a cardiovascular disorder; or to refer to an individual's response to a therapeutic agent; or to refer to symptoms of, or susceptibility to side effects to a therapeutic agent. A “less severe phenotype” is defined as a less severe form of a cardiovascular disorder, or a form of the cardiovascular disorder that is more responsive to treatment, displays less side effects with treatment, has better prognosis, is not recurrent, or has a combination of these characteristics. A “more severe phenotype” is defined as a more severe form of a cardiovascular disorder, or a form of the disorder that is less responsive to treatment, displays more side effects with treatment, has worse prognosis, is recurrent, or has a combination of these characteristics. In general, the more severe phenotype is a disease state with profound consequences to the patient's life quality and requires more aggressive therapy.
A subject who is at risk for “having or developing a cardiovascular disorder” includes a subject with no clinical signs or symptoms of a cardiovascular disorder but with a strong family history of such disorders, a subject who exhibits clinical signs or symptoms associated with a cardiovascular disorder, or a subject who has been clinically diagnosed as having a cardiovascular disorder.
The term “prognosis” as used herein refers to predicting the course or outcome of a condition in a subject. This does not refer to the ability to predict the course or outcome of a condition with 100% accuracy, or even that a given course or outcome is predictably more or less likely to occur based on the pattern of biomarkers. Instead, the skilled artisan will understand that the term “prognosis” refers to an increased probability that a certain course or outcome will occur.
A “diagnostic” biomarker is a biallelic polymorphism, the allelic status of which is indicative of whether or not a subject has, or is at risk for developing, a cardiovascular disorder.
A “prognostic” biomarker is a biallelic polymorphism, the allelic status of which is predictive of the severity or prognosis of a cardiovascular disorder.
When one or more prognostic biomarkers exhibit a certain pattern in samples obtained from a subject, the pattern may signal that the subject is at an increased probability for experiencing a future event in comparison to a similar subject exhibiting a different pattern. For example, a certain pattern of prognostic biomarkers can predict an increased predisposition to an adverse outcome, or the chance of a person responding or not responding to a certain drug.
In some embodiments, a “prognostic biomarker” can predict the presence of a “prognostic indicator.” For example, the presence of a minor allele of a SNP (prognostic biomarker) is indicative of a lower mRNA expression (prognostic indicator) in a target tissue.
The term “ACE-related disorder” as used herein refers to any ACE-related disorder comprising one or more of the following: cardiovascular diseases, hypertension, myocardial infarction, angioedema, altered kidney function, Alzheimer's, and/or responsiveness to a therapeutic targeting the subject's renin-angiotensin system, including, but not limited to ACE inhibitors, beta blockers, angiotensin receptor blockers (ARBs).
The term “cardiovascular disorder” as used herein refers to any disorder in which an increase or decrease in ACE levels, which can lead to hypertension, heart disease, heart failure, myocardial infarction, renal pathophysiology, diabetes, and related pathologies.
All the above disorders have their usual meaning in the art, or are defined according to “The Merck Manual of Diagnosis and Therapy” Seventeenth Edition, 1999, Ed. Keryn A. G. Lane, pp. 1503-1598, incorporated herein by reference.
“Treatment” as used herein means the medical management of a subject, e.g., a human patient, with the intent to cure, ameliorate, stabilize, or prevent a disease, pathological condition, or disorder. This term includes active treatment, that is, treatment directed specifically toward the improvement or associated with the cure of a disease, pathological condition, or disorder, and also includes causal treatment, that is, treatment directed toward removal of the cause of the associated disease, pathological condition, or disorder. In addition, this term includes palliative treatment, that is, treatment designed for the relief of symptoms rather than the curing of the disease, pathological condition, or disorder; preventative treatment, that is, treatment directed to minimizing or partially or completely inhibiting the development of the associated disease, pathological condition, or disorder; and supportive treatment, that is, treatment employed to supplement another specific therapy directed toward the improvement of the associated disease, pathological condition, or disorder. “Treatment” also includes symptomatic treatment, that is, treatment directed toward constitutional symptoms of the associated disease, pathological condition, or disorder. “Treatment” also includes the act of not giving a subject a contra-indicated therapeutic agent.
The terms “correlating” as used herein refers to comparing the allelic status of a polymorphism in a subject to the allelic status of the polymorphism in a reference population. The reference population may be persons known to be free of a given condition, i.e., “normal individuals,” or may be persons known to suffer from, or to be at risk of developing, a given mental disorder, persons known to have a form of the mental disorder with better or worse outcome, or persons known to respond to or be resistant to a certain treatment. For example, a SNP pattern in a patient sample can be compared to a SNP pattern known to be associated with response to a certain depression medication. By correlating the sample's biomarker pattern with the reference pattern, the skilled artisan can predict whether the patient will respond to a certain medication, and prescribe accordingly.
In this method, the allelic status of the polymorphism in the subject is predictive of the prognostic outcome.
In one embodiment, the method further includes the step of correlating the allelic status of the polymorphism in the subject with the allelic status of the polymorphism in a reference population to predict the prognostic outcome of the subject.
In another embodiment, the method further includes the step of correlating the allelic status of the polymorphism in the subject with the allelic status of the polymorphism in a reference population to predict whether the subject has a greater or less severe risk factors for cardiovascular diseases and/or responsiveness to therapeutic agents.
In another embodiment, the method further includes the step of correlating the allelic status of the polymorphism in the subject with the allelic status of the polymorphism in a reference population to predict the subject's response to treatment.
The SNPs identified herein can be used in combination with additional predictive tests including, but not limited to, additional SNPs, mutations, and clinical tests. The SNPs can be those provided herein, and discussed in detail in the Examples. The SNPs can also be SNPs in positive linkage disequilibrium with any of the SNPs provided herein.
The present invention is further defined in the following Examples, in which parts and percentages are by weight and degrees are Celsius, unless otherwise stated. Techniques in molecular biology were typically performed as described in Ausubel, F. M. et al., In Current Protocols in Molecular Biology; John Wiley and Sons: New York, 1990 or Sambrook, J. et al., In Molecular Cloning: A Laboratory Manual; 2ed.; Cold Spring Harbor Laboratory Press: Cold Spring Harbor, N.Y., 1989). It should be understood that these Examples, while indicating preferred embodiments of the invention, are given by way of illustration only.
From the discussion and the Examples herein, one skilled in the art can ascertain the essential characteristics of this invention, and without departing from the spirit and scope thereof, can make various changes and modifications of the invention to adapt it to various usages and conditions. Thus, various modifications of the invention in addition to those shown and described herein, will be apparent to those skilled in the art from the foregoing description. Such modifications are also intended to fall within the scope of the appended claims. All publications, including patents and non-patent literature, referred to in this specification are expressly incorporated by reference herein.
EXAMPLE I
ACE
Accordingly, the disclosure provides for a method for predicting a subject's risk factors for cardiovascular diseases and/or a subject's responsiveness to therapeutic ACE inhibitors.
The method includes detecting the allelic status of one or more polymorphisms in a nucleic acid sample of the subject, wherein the polymorphism is selected from the group: (i) ACE-associated SNPs rs4290, rs7214530, rs7213516 or combinations thereof; or, (ii) a SNP in linkage disequilibrium with one or more SNPs listed in (i). In this method, the allelic status of the polymorphism in the subject is predictive of the prognostic outcome of the subject.
In such a method, the allelic status of the polymorphism in the subject is predictive of the subject's risk factors for cardiovascular diseases and/or a subject's responsiveness to therapeutic ACE inhibitors.
In one embodiment, the method further includes the step of correlating the allelic status of the polymorphism in the subject with the allelic status of the polymorphism in a reference population to predict the subject's risk factors for cardiovascular diseases and/or a subject's responsiveness to therapeutic ACE inhibitors.
In another embodiment, the method further includes the step of correlating the allelic status of the polymorphism in the subject with the allelic status of the polymorphism in a reference population to predict whether the subject has a more or less severe phenotype for cardiovascular diseases and/or responsiveness to therapeutic ACE inhibitors.
In another aspect, the disclosure provides for a method of screening a subject for a prognostic biomarker for determining a subject's risk factors for cardiovascular diseases and/or a subject's responsiveness to therapeutic ACE inhibitors, comprising detecting the allelic status of one or more polymorphisms in a nucleic acid sample of the subject, wherein the polymorphism is one or more of: (i) ACE-associated SNPs rs4290, rs7214530, rs7213516 or combinations thereof; or, (ii) a SNP in linkage disequilibrium with one or more SNPs listed in (i). In this method, the allelic status of the polymorphism in the subject is predictive of the prognostic outcome of the subject.
Allelic mRNA Expression Imbalance (AEI) is Useful for Finding Functional Polymorphisms.
How genetic processes interact to regulate gene expression can be addressed by measuring allelic expression imbalance (AEI). Measuring allelic mRNA expression compares one allele against the other in a relevant target tissue of the same individual. The relative amounts of mRNA generated from each of two alleles in subjects heterozygous for a marker SNP in the transcribed region of the gene in question are quantitatively measured. AEI indicates the presence of cis-acting factors in gene regulation and/or mRNA processing. AEI results provide a quantitative measure of the allelic differences in each individual, one allele serving as the control for the other, while canceling out any trans-acting factors. The allelic expression ratios are used as the phenotype to scan a gene locus for regions containing functional polymorphisms. If cis-acting polymorphisms contribute to the measured AEI ratios, significant correlations should be detectable. Also, a single locus association test between SNP genotype and allelic expression phenotype can be conducted. The AEI phenotype is represented either as present/absent; or absent/present low/present high. Significant associations indicate that a SNP, or one closely linked, contributes to AEI, by affecting mRNA expression levels.
ACE1 Polymorphisms are Linked to Differences in ACE Expression
FIG. 31 contains the mRNA sequence for the ACE gene [SEQ ID NO: 261]. The ACE gene consists of 25 exons spanning  ̃25 kb and encoding a soluble or a membrane-bound protein variant with two peptidase domains (FIG. 5). Also, FIG. 9 shows a schematic illustration of ACE gene structure and relevant genetic polymorphism (chromosome 17q.23.3) (not to scale).
ACE harbors a number of polymorphisms; however, frequent nonsynonymous SNPs that affect the protein sequence are lacking, suggesting that yet to be discovered regulatory polymorphisms may contribute to genetic susceptibility in cardiovascular diseases involving ACE. To search for regulatory polymorphisms, we measured allelic mRNA expression of ACE in human cardiac tissues. In contrast to total mRNA levels, allelic mRNA ratios cancel out trans-acting factors, so that any detectable allelic expression imbalance (AEI) is a strong indicator of cis-acting regulatory factors (6-10). We can then exploit the allelic mRNA ratios as the most proximate and accurate phenotype for SNP scanning in search of regulatory polymorphism(s), followed by molecular genetic studies to address underlying mechanisms (6, 7, 10).
Genetic family studies map the heritable contribution to ACE activity and blood pressure to the region of the ACE gene, particularly in subjects of African ancestry (11,12). Moreover, allele frequencies at the ACE locus vary greatly between African-Americans and European-Americans (13). African-Americans are at higher risk of hypertension (14) and its target organ sequelae (15), and less responsive to ACE inhibitors (16,17) while more likely to experience adverse drug effects (18). To test whether genetic variation in ACE accounts for differences among population groups, we measured allelic ACE mRNA expression in heart tissues from both Caucasians and African-Americans.
Here we report regulatory alleles affecting ACE expression that are common among African-Americans, discovered in a screen of human myocardial tissues. To assess the clinical relevance of these alleles, we conducted a clinical genetic association study in the INternational VErapamil SR Trandolapril STudy GENEtic Substudy (INVEST-GENES) (19).
Results
Allelic mRNA Expression of ACE and Association with Promoter SNPs in Heart Tissues
We selected two marker SNPs (rs4309, rs4343) located in exon 8 and exon 16 of ACE FIG. 5) to measure allelic ratios of genomic DNA (gDNA) and mRNA in heart tissues using SNaPshot (Applied Biosciences) (FIG. 6).
Standard curves performed with mixtures of DNA alleles were linear over the observed range (r2=0.996-0.999). Since gDNA ratios varied within a small range (<±2SD), no variable copy number polymorphisms were detectable (although the SNaPshot method used would have missed hemizygous subjects). Therefore, the mean allelic gDNA ratios were normalized to 1. Allelic ACE mRNA expression in heart tissues varied up to four-fold compared to gDNA ratios, indicating the presence of strong cis-acting regulatory factors (see FIG. 1; using a log2 scale).
Allelic expression ratios obtained with the two marker SNPs in compound heterozygotes (n=20), indicating that the results are reproducible. Allelic mRNA ratios deviated significantly from unity in five of 33 subjects. Strikingly, each of the five tissues showing strong AEI was obtained from African-American subjects even though only eight African-Americans were heterozygous for a marker SNP. In contrast, none of the Caucasian-Americans displayed significant AEI (FIG. 1), showing a significant difference between ethnic groups.
To ascertain the responsible regulatory polymorphisms, we sequenced the ACE locus in genomic DNA from the eight African-American subjects. No polymorphisms within the transcribed mRNA region (UTRs or protein-coding regions) matched the pattern of allelic expression. On the other hand, three polymorphisms (rs7213516, rs7214530, rs4290) (FIG. 11a—Table 2A) in a region 2-3 kb upstream of the ACE transcription start site were strongly associated with allelic mRNA expression imbalance in African-Americans (P<10−7). Moreover, these three SNPs were absent in Caucasian-American cardiac tissues that also failed to show detectable AEI (FIG. 1b).
In the cardiac tissues surveyed, rs7213516, rs7214530 and rs4290 were in extensive but incomplete LD, so that we cannot exclude any of the 3 SNPs from contributing to the AEI ratios. In the HapMap data for the Yuruba population in Ibadan, Nigeria, rs4290 was in complete LD with rs7214530 (D′=1.0, r2=1.0) but not with rs7213516 (D′=1.0, r2=0.55) (FIG. 5).
The incomplete LD in HapMap between rs4290 and rs7213516 motivated later selection of these two markers for the clinical association study.
We next genotyped additional ACE (rs4291, rs4292, rs4357, rs4363, rs13447447, rs4366) for all samples with allelic mRNA data (FIG. 11a—Table 2A). The only additional SNP showing significant association with AEI, rs4357 (P<10−7) located in intron 21 (FIG. 5), was in partial linkage disequilibrium (LD) with the upstream SNPs (with rs4290: D′=1.0, r2=0.77; with rs7213516: D′=0.71, r2=0.38). Since several subjects with AEI were homozygous for rs4357, this argues against a functional role. The commonly studied I/D variant (rs13447447) had a p-value of 0.09 for association with AEI, again owing to LD with the promoter SNPs, but it can also be ruled out as many I/D heterozygotes failed to show AEI.
The value of allelic mRNA ratios below 1 in the five African-American subjects showing AEI indicated that the less frequent allele had reduced mRNA expression (considering the inferred phasing between the marker SNP alleles and those of the promoter SNPs). To test this further we measured overall ACE mRNA levels by RT-PCR. Whereas no association with mRNA levels was observed with the I/D variant (FIG. 2a), carrying the minor allele of the promoter SNPs was associated with decreased ACE mRNA expression (rs4290 T; P<0.02 (FIG. 2b), rs7213516 A; P<0.04). This result indicates that the minor alleles of the promoter SNPs reduce expression.
Reporter Gene Analysis of Three ACE Promoter SNPs
To determine whether the promoter SNPs, rs7213516, rs7214530, and rs4290, affect transcription, we compared activities of a 4.3 kb fragment from the ACE promoter region, containing either the reference sequence (G-T-C) or different combinations of the three SNPs, using a reporter gene assay in HEK293 and bovine aortic endothelial cells (BAEC).
Shown in FIG. 3a, the expression constructs containing any of the minor alleles of the three promoter SNPs significantly reduced reporter gene expression in BAEC, using two different transfection reagents. While there were differences in the degree of reduction between the various constructs, no single SNP alone could account for all results. To test for cell context-dependent effects, we also measured promoter activity in HEK293 cells. In contrast to the results with BAEC, none of the SNPs had an effect on promoter activity in HEK293 cells regardless of plasmid amounts used for transfection (FIG. 3b).
As the experiments in BAEC and HEK293 cells were done side-by-side with the same plasmid preparations, the negative results in HEK293 cells further indicate that the plasmid preparations had similar transfection efficiencies, which can be a source of error if not controlled for. Taken together with the mRNA analysis in heart tissues (FIGS. 1, 2), we conclude that each of the three promoter SNPs appears to reduce ACE gene expression, although any effects are tissue-dependent.
Genetic Association of ACE with Adverse Cardiovascular Outcomes in INVEST-GENES
We genotyped rs7213516 and rs4290 and three additional polymorphisms (FIG. 11b—Table 2B) in 258 subjects experiencing a primary outcome event (first occurrence of all cause death, nonfatal myocardial infarction (MI), or nonfatal stroke) and 774 hypertensive controls lacking primary outcome events in the genetic substudy (INVEST-GENES) of the randomized controlled clinical trial INVEST. All genotype frequencies were in Hardy-Weinberg equilibrium in all three race/ethnicity groups and displayed substantial differences between ethnic groups.
Linkage disequilibrium is shown in FIG. 7, illustrating the relationships between the genotyped SNPs. For the five polymorphisms tested in the INVEST-GENES, allele frequencies in Hispanics were intermediate between Caucasians and African-Americans. Minor allele frequencies of both rs7213516 and rs4290 differed significantly between African-Americans (16%), Hispanics (4%) and Caucasians (<1%). Genotyping quality control checks showed >99.5% concordance between different assays for the same polymorphisms.
Consistent with our finding that the rs7213516 A allele and rs4290 T allele are associated with ACE expression differences, these alleles were also robustly associated in the INVEST-GENES cohort with increased risk of a primary outcome event (FIG. 4).
The main effect was strongest in African-Americans for both SNPs, with similar trends in Hispanics and Caucasians, despite limited power in these latter groups, because of low allele frequency. In African-Americans, rs7213516 A and rs4290 T carriers had 4 times higher odds of experiencing a primary outcome event (odds ratio (OR): 4.13, 95% confidence interval (CI): 1.52-11.21 (P=0.0054), and OR 3.91, 95% CI: 1.54-9.90 (P=0.0041)), respectively.
In secondary outcomes analysis, rs7213516 conferred highest risk for nonfatal myocardial infarction, OR 6.16, 95% CI: 2.43-15.60 (P=0.0001), whereas there was no significantly higher risk for all-cause mortality (P=0.92) or nonfatal stroke (P=0.19)(FIG. 10—Table 1).
Similarly, the association with rs4290 is also largely driven by nonfatal myocardial infarction (OR 2.34); however, it only reached marginal significance.
The ACE I/D polymorphism (rs13447447) was inconsistently associated with outcomes (FIG. 4). Associations were not directionally similar in the different racial/ethnic groups, nor was there a linear trend between I/D heterozygotes and I/I homozygotes. Finally, the I/D was not associated with any of the individual components of the composite outcome (FIG. 10—Table 1). While not wishing to be bound by theory, the inventors herein now believe that there is no meaningful association with clinical outcomes analyzed here, consistent with a lack of effects on ACE mRNA level in heart tissue (FIG. 2). There was also no evidence for association of the primary outcome with polymorphisms rs4291 and rs4366.
Discussion
This study employed allelic mRNA expression analysis of ACE in human heart tissues, followed by SNP scanning, to identify regulatory polymorphisms in the ACE locus, long suspected of conferring genetic risk for cardiovascular disease. This approach revealed strong effects on ACE mRNA expression attributable to three promoter SNPs, rs7213516, rs7214530, and rs4290, located in conserved regions 2-3 kb upstream of the transcription start site. The excellent congruence between AEI ratios and clearly identifiable polymorphisms, and a significant association between genotypes and total ACE mRNA expression in human heart tissues, support the notion that these promoter SNPs reduce ACE mRNA expression. This conclusion is further buttressed by results from reporter genes assays. The three ACE promoter SNPs are common in individuals of African-American ancestry (FIGS. 11a, 11b—Tables 2A, 2B), but rare in Caucasians, and intermediate in Hispanics. Consistent with our gene expression results, a clinical association study revealed a robust genetic effect on outcomes in hypertensive patients.
Three Promoter SNPs Linked to ACE Expression
Allelic mRNA expression ratios were strongly linked with the three promoter SNPs (P=<0.0001), but because of the extensive linkage disequilibrium among them, did not permit a conclusion on which polymorphism is functional. All three SNPs reside in conserved regions.
FIG. 8 shows the promoter sequence alignments and TF binding sites. The three promoter SNPs rs7213516 (G/A), rs7214530 (T/G) and rs4290 (C/T) are located −2883, −2828 and −2306 bp upstream of the transcription start site (+1). The predicted MEF2A transcription factor binding sites based on the JASPAR database position-weight matrices are shown in detail. Sequence alignments (CLUSTALW) are based on genomic matches identified by BLAST of the human promoter region (* indicates a 1 bp insert in rhesus, dog, elephant, and armadillo sequences; † indicates a 9 bp insert in dog and armadillo sequences). Human [SEQ ID NOS 70, 34 and 278, respectively, in order of appearance], Chimp [SEQ ID NOS 71, 270 and 279, respectively, in order of appearance], Rhesus [SEQ ID NOS 72, 271 and 280, respectively, in order of appearance], Bushbaby [SEQ ID NOS 76, 272 and 281, respectively, in order of appearance], Shrew [SEQ ID NOS 73, 273 and 282, respectively, in order of appearance], Dog [SEQ ID NOS 74, 274 and 283, respectively, in order of appearance], Elephant [SEQ ID NOS 75, 275 and 284, respectively, in order of appearance], Squirrel [SEQ ID NOS 77, 276 and 285, respectively, in order of appearance], Armadillo [SEQ ID NOS 78, 277 and 286, respectively, in order of appearance].
Moreover, rs7214530 is part of a predicted recognition site for MEF2, a cardiac transcription factor previously implicated in cardiovascular disease and myocardial infarction (20-22). It is therefore possible that all three SNPs have co-evolved as part of a haplotype block prevalent in subjects of African origin, each contributing to gene regulation, possibly to different extents in different tissues. Reporter genes assays with an ACE promoter fragment, containing various combinations of the three suspected SNPs demonstrated decreased promoter activity for each combination of variant alleles, compared to the reference sequence in endothelial cells (BAEC), but not in HEK293 cells, indicating that these effects can be tissue specific. It is therefore likely that these ACE promoter polymorphisms have different effects in different target tissues, and therefore, could be associated with different pathophysiologies.
Other Polymorphisms in ACE
We found no evidence for a functional effect of the ACE I/D polymorphism in intron 15 on mRNA expression in human heart tissue, consistent with previous negative in vitro studies (4-5). Additionally, our clinical association data did not support an effect of I/D on outcomes across the various ethnic populations, despite an allele frequency and power that was substantially higher than for the promoter SNPs. Yet, countless genetic association studies are based on the I/D polymorphism even though evidence for a physiological function is lacking, and clinical associations are borderline at best (3).
Association of ACE Promoter SNPs with Clinical Outcomes
We tested several ACE polymorphisms for association with clinical outcomes in hypertensive patients with coronary artery disease (INVEST-GENES). The promoter SNPs identified in our mechanism-based screen (rs7213516 and rs4290; rs7214530 was not genotyped because of strong LD with rs4290) were highly associated with cardiovascular disease outcomes (P<0.001) (FIG. 4), and in particular, with myocardial infarction (FIG. 10—Table 1). The odds ratios ranging from 4-6 suggest an unexpectedly strong genetic effect.
We also assessed relative risk of primary outcome as a function of drug treatment, showing a strong association in individuals receiving ACE inhibitor and/or beta-blocker therapy (data not shown). However, the INVEST-GENES design was not optimal for assessing the effects of genetic factors on drug treatment outcomes. Nevertheless, this association may have a biological basis given that trandolopril and atenolol target overlapping systems of blood pressure control where ACE is a critical component. Results from the Val-HeFT trial raise the possibility that excessive neurohormonal inhibition may contribute to adverse outcomes in heart failure treatment (23). Since the promoter alleles identified here are associated with decreased ACE expression we hypothesize that they may potentiate pharmacological ACE inhibition plus beta-blockade, resulting in higher event rates via excessive neurohormonal inhibition.
Since the promoter alleles are common in African-Americans, they may partially account for phenotypic variation in ACE levels, blood pressure (e.g., 11) and response to ACE inhibitors (16-18) in individuals of African ancestry. Excessive ACE inhibition in African-Americans carrying the minor alleles of these promoter SNPs could have accounted for the increased susceptibility to angioedema as a main adverse effect of ACE inhibitors (18). While these alleles were found at lower frequency in Hispanics and Caucasians, they could be clinically relevant in the population at large, although we had limited statistical power to address this question. While not wishing to be bound by theory, the inventors herein now believe that these alleles have clinical utility as biomarkers in the selection of therapeutic options for individual patients. The use of race in guiding treatment is controversial but does play a role in clinical practice (24). Ultimately, therapy may be best optimized for individual patients with tests for functional biomarkers instead of relying on assumptions related to apparent, or self-identified, race or ethnicity (25).
Physiological roles for ACE include blood pressure regulation, kidney function, processing of kinins and other peptides, and degradation of amyloid-beta protein (26,27), suggesting the new ACE promoter alleles may be relevant in other human pathologies. Among the heart tissues from 12 African-American heart transplant patients only 8 were eligible for AEI analysis. Among the twelve samples the minor allele frequency of rs7213516 and rs4290 (25-27%) were higher than expected (16.0% in INVEST-GENES), with one patient homozygous for the minor alleles, arguing for conducting a larger study of heart failure patients.
Thus, the discovery of regulatory alleles in key genes through allelic mRNA expression analysis, followed by clinical association studies, has broad potential for leading to viable biomarkers guiding an individual's therapy (28).
MATERIALS AND METHODS
Analysis of ACE mRNA Expression in Heart Tissues
Approval for use of human subjects was obtained from the OSU IRB. Left ventricle tissue from 65 heart transplant patients was obtained through The Cooperative Human Tissue Network: Midwestern Division at OSU and stored at −80° C. until extraction. Genomic DNA and RNA were isolated, and cDNA was prepared from 1.0 ug RNA in three independent preparations, using oligo dT and gene-specific primers close to the two marker SNPs to minimize the effects of mRNA decay in post-extract tissues.
Total mRNA Expression Levels
Overall ACE mRNA expression was measured by RT-PCR for each sample. Gene expression results by genotype were analyzed with SPSS 14.
Measurement of Allelic ACE mRNA Expression
We measured allelic mRNA expression, as described previously (6-10), amplifying short regions of gDNA and cDNA around ACE exonic marker SNPs from heart tissues of heterozygous individuals (rs4309, located in exon 8, n=28; rs4343, located in exon 16, n=24). Primer extension with fluorescent dideoxynucleotides by SNaPshot (Applied Biosciences) allowed quantitation of relative amounts of each allele by capillary electrophoresis on an ABI3730 (Applied Biosciences). Corrected allelic mRNA expression ratios for individual cDNAs were calculated by normalizing to the mean ratio of gDNA peaks (SD for gDNA: rs4309±12.4%, rs4343±8.6%).
Examples for assay results are shown in FIG. 6. Each sample was assayed from three independent cDNA syntheses, each performed at least in duplicate.
Scanning the ACE Locus for Functional Polymorphisms
To link SNPs to allelic mRNA expression ratios, we genotyped SNPs selected to represent the major haplotype blocks (FIGS. 11a, 11b—Tables 2A, 2B) in all 65 heart tissues. SNPs were genotyped as described herein. In addition, we sequenced full length cDNAs and the 5′-upstream region over 3 kb in eight African-Americans detecting five SNPs in the upstream region (FIG. 11a—Table 2A).
The presence of AEI was set at allelic mRNA ratios >1.5 or <1/1.5 as cutoff. Association between genotype status (heterozygous or homozygous) with AEI was determined using HelixTree (Golden Helix, Inc.). Linkage disequilibrium between SNPs (expressed as D′) and haplotypes were calculated using HelixTree (Golden Helix, Inc.).
ACE Reporter Gene Assay
A promoter fragment ranging from −4,335 to +1 (the major transcription start site) in PGL3 basic vector (Promega) was provided by Dr. Melanie Eyries (29). Various combinations of rs7213516/rs7214530/rs4290 haplotypes were obtained via site-directed mutagenesis or restriction digest of amplified genomic DNA with MscI and BstEII and subsequent cloning. All inserts were fully sequenced to verify the intended sequence. The constructs were transfected into HEK-293 and BAEC, cultured in DMEM/F12 media containing 10% fetal bovine serum, penicillin (0.10 units/ml), and streptomycin (10 μg/ml), at 37° C. with 5% CO2. Twenty four hours before transfection, 1-2×105 cells were plated into 24-well plates and transiently transfected with FuGENE HD Transfection Reagent (Roche Applied Science) or Lipofectamine (Invitrogen) in serum free medium for 5 hours. As a control, Renilla luciferase constructs were cotransfected with PGL3 basic fused constructs at a 1:20 ratio. Cells were harvested after 48 hours and transferred to 96-well plates, and luciferase activity was detected with Dual-Glo luciferase assays (Promega) on a fluorescence plate reader (PerkinElmer). Two independent transfections and triplicate luciferase assays were performed for each construct and cell line. Results were analyzed with Prism (GraphPad).
Clinical Genetic Association Study INVEST and INVEST-GENES
The INternational VErapamil SR Trandolapril STudy (INVEST) evaluated cardiovascular adverse outcomes in patients randomized to atenolol or verapamil SR hypertension treatment strategy in 22,576 patients with documented coronary artery disease (CAD) and hypertension (19). The primary outcome was the first occurrence of death (all cause), nonfatal MI, or nonfatal stroke. These events were taken separately as secondary outcomes. In the genetic substudy (INVEST-GENES), genomic DNA was collected from 5,979 patients using buccal cells from mouthwash samples (30). All patients provided written informed consent, as approved by the UF IRB. The present case-control study focused on the 258 INVEST-GENES patients who experienced a primary outcome event during study follow-up (cases), frequency matched 3:1 to cases for age, sex, and race/ethnicity with 774 individuals who were event-free during study follow-up (controls).
The patients had a mean age of 71 years, half were female, 25% were of Hispanic ethnicity, and 13% were African-Americans. Previous analyses showed that case-control analysis in this group match findings from the entire INVEST cohort, the inclusion of which increases only the number of controls (31).
We genotyped promoter SNPs rs7213516 and rs4290, and the tagging markers (rs4291, rs13447447, rs4366) (genotyping details below), to sample major haplotype blocks. Quality control procedures included blind duplicate genotyping of 5% of samples via the same or an alternative method, assessment of Hardy-Weinberg equilibrium, and assay validation using Coriell samples previously genotyped as part of HapMap. To address potential population stratification, we genotyped 87 autosomal ancestry informative markers (AIMS) interspaced with large interlocus distances across the genome in order to give independent association with the disease and genetic background (see detailed analysis below) (32,33).
Statistical Analysis of Clinical Genetic Associations
Baseline characteristics between case and controls in INVEST-GENES were compared using t-test for continuous and Chi-squared test for categorical variables, respectively. Hardy-Weinberg equilibrium (HWE) of genotype frequencies within each race/ethnic group was tested with Chi-squared test with one degree of freedom. Because of the low minor allele frequency for rs7213516 and rs4290 in the entire INVEST cohort, we decided a priori to combine heterozygous patients with those homozygous for the variant alleles for all analyses. Logistic regression was performed to assess the association of genotypes/haplotypes with the primary and secondary outcomes after adjusting for ancestry and pre-specified confounding factors, namely age (by decades), gender, race/ethnicity, and history of MI and heart failure, and drug treatments.
Detailed Analysis
Tissue Preparation and ACE Allelic Expression Analysis
Sixty-five heart failure tissue explants from left ventricles were isolated and frozen for later research under an OSU IRB approved protocol. The demographic breakdown of these samples was as follows: Caucasian male (n=42), African-American male (n=4), Caucasian female (n=13), African-American female (n=6). DNA was prepared by a standard salting-out method from heart tissue (34). For RNA isolation,  ̃100 mg tissue was pulverized over dry ice and suspended in Trizol reagent, followed by phenol-chloroform extraction, and filtration through an RNAeasy column (Qiagen) after treatment with DNAse I. RNA quantity and quality was confirmed by UV spectrophotometry and nanodrop analysis (Bioanalyzer, Agilent Biotechnologies). cDNA was synthesized following the manufacturer's protocol (Superscript RTII, Invitrogen) from 1.0 ug RNA in three independent preparations using oligo dT and ACE gene-specific reverse primers to increase specific yield. Negative controls (lacking RTII) and positive expression signals for ACE were confirmed by RT-PCR on an ABI7000 cycler followed by gel electrophoresis to confirm correctly sized products. The primers used for RT-PCR verification were the outer primers for the SNaPshot assay.
Marker SNPs (rs4309, rs4343) were genotyped at the Ohio State University Pharmacogenomics Core Laboratory in 65 heart failure samples by a melting curve dissociation approach on an ABI7000 real-time PCR instrument in order to determine heterozygotes for allelic expression assays (35). Allelic expression assays in genomic DNA and cDNA for each heterozygote were carried out in triplicate, and analyzed as previously described (6-10). For the rs4343 assay, due to the SNP location near an exon border, separate DNA and cDNA forward primers were used. Outside amplification primers for the assay were as follows (see FIG. 13—Table 4):
rs4309 forward primer: TGAGATGGGCCATATACAGTACTAC [SEQ ID NO: 1];
reverse primer: CCCGACGCAGGGAGAC [SEQ ID NO: 2], and
rs4343 DNA forward primer: CCCTTACAAGCAGGTGAGCTAA [SEQ ID NO: 3];
cDNA forward primer: ACCACCTACAGCGTGGCC [SEQ ID NO: 4];
The extension primers for ACE allelic expression assay were as follows:
for rs4309, CTGCAGTACAAGGATCTGCC [SEQ ID NO: 6];
for rs4343, GACGAATGTGATGGCCAC [SEQ ID NO: 7].
Measurement of Overall mRNA ACE Expression
Total ACE mRNA expression levels were measured in all heart tissues on two cDNA preparations by RT-PCR with cDNA specific primers that span the ACE exon 9/10 border (see FIG. 18—Table 9):
forward-primer: CCCCTTCCCGCTACAACTT [SEQ ID NO: 8];
reverse-primer: TCCCCTGATACTTGGTTCGAA [SEQ ID NO: 9].
RT-PCR was done with SYBR Green on an ABI7000 (30 cycles, 2 steps: 95° C., 60° C.); values were normalized to β-actin expression levels. The correct size products were verified by gel electrophoresis.
Generation of ACE Promoter Region Constructs for Reporter Gene Assays
An ACE upstream region construct (−4335 to the transcription start site) driving expression of a firefly Luciferase reporter gene (pGL3. Basic, Promega) was kindly provided by M. Eyries (29). Sequencing indicated this construct contained the major allele at all polymorphic sites in the region compared to the reference genome sequence, thus it was labeled (G-T-C). Site-directed mutagenesis (Stratagene) was employed to generate altered constructs with SNP combinations; for
rs4290 (G-T-T) sense primer: CTCTGCACCCTTCCTTTGATGAGGTTTTG CCCT [SEQ ID NO: 10];
antisense primer: AGGGCAAAACCTCATCAAAGGAAGGGTGCAGAG [SEQ ID NO: 11],
rs7214530 (G-G-C) sense primer: GAGCATATTTTTAAGGGCTGGTTTTCT CTCCTGTGGTAACT [SEQ ID NO: 12];
antisense primer: AGTTACCACAGGAGAGAAAACCAGCCCTTAAAAATATGCTC) [SEQ ID NO: 13], and
rs4290 and rs7214530 (G-G-T).
A fifth construct containing the three minor alleles for rs7213516, rs7214530 and rs4290, (A-G-T), was isolated by PCR of an individual genomic DNA
(forward primer, GAGACGGAGTTTTGCTCTTGTTG [SEQ ID NO: 14];
reverse primer, CAGAGACCTGACCCACGTGAG) [SEQ ID NO: 15],
restriction digest with MscI and BstEII and ligation with digested plasmid that contained the rs4290 T variant. (See FIG. 17—Table 8). All plasmid insert sequences were fully sequenced confirming the absence of additional genetic differences.
Genotyping
Genomic DNA isolation and genotyping for rs4290, rs4291 and rs7213516 in INVEST-GENES was performed at the University of Florida Center for Pharmacogenomics. Genomic DNA was isolated from buccal genetic samples using commercially available kits (PureGene, Gentra Systems Inc.) and adjusted to 20 ng/μl. Genotyping for rs4290 was performed by polymerase chain reaction (PCR) followed by pyrosequencing using a PSQ HS96A SNP reagent kit according to the manufacturer's protocol (Biotage AB) (36). SNPs rs7213516 and rs4291 were genotyped by Taqman assay. The PCR and sequencing primers used for ACE SNP rs4290 were as follows (see FIG. 15—Table 6):
forward biotinylated PCR primer, 5′-GAGTGTGGGTCATTTCCTCTTT-3′ [SEQ ID NO: 16];
reverse PCR primer, 5′-AGTTTAGCATGGTGCCTAGCA-3′ [SEQ ID NO: 17]; and
reverse sequencing primer, 5′-GGGCAAAACCTCATC-3′[SEQ ID NO: 18].
The PCR conditions were as follows: 95° C. for 15 min, 40 cycles consisting of denaturation at 94° C. for 30 s, annealing at 59° C. for 30 s, and extension at 72° C. for 1 min, followed by final extension at 72° C. for 7 min. The Applied Biosystems 7900 HT SNP genotyping platform was used for the Taqman assays. The SNP genotyping probes (Applied Biosystems IDs: C—32160109—10 and C—11942507—10) were used for ACE rs7213516 G>A and rs4291 A>T, respectively. Five μL reactions in 384-well plates were prepared, and the assays were performed and analyzed according to the manufacturer's recommendations.
The 287 bp insertion/deletion polymorphism (rs13447447) was genotyped at the Ohio State University Pharmacogenomics Core Laboratory by PCR with
FAM-labeled reverse primer (FAM-GTGGCCATCACATTCGTCAG), [SEQ ID NO: 19], and two unlabeled forward primers, one of which was insertion-specific both alleles forward primer, CCCATCCTTTCTCCCATTTCT [SEQ ID NO: 20];
insertion-specific forward primer, GACCTCGTGATCCGCCC [SEQ ID NO: 21],
and run on an ABI3730 capillary electrophoresis instrument to distinguish size products (insert peaks 191 by and 462 bp, deletion 175 bp).
The CT2/3 repeat polymorphism (rs4366) was similarly genotyped by PCR with a FAM-labeled forward primer
(FAM-TGGCTCCTGCCTGTACCAG) [SEQ ID NO: 22] and
reverse primer (CCAAGGCTGTTCACCCGA) [SEQ ID NO: 23],
and capillary electrophoresis. (See FIG. 17—Table 8).
The SNPs rs4291 and rs4292 were genotyped by multiplexed SNaPshot primer extension assay within one amplicon. Extension primers were:
rs4291 (TGGCTAGAAAGGGCCTCCTCTCTTT) [SEQ ID NO: 24] and
rs4292 (TTCAGGCGCCGCTGAGGACT) [SEQ ID NO: 25]. (see FIG. 14—Table 4)
An intentional mismatch was introduced into the rs4292 primer at the 6th position from the 3′ terminus to interrupt the poly G strng.
FIG. 13—Table 4 presenting the oligonucleotide sequences used in genotyping and allelic expression imbalance (AEI) assays for ACE that employed primer extension technology, showing [SEQ ID NOs: 1-7, 24, 25, 32-33]. Underlined nucleotides were intentionally mismatched against the reference sequence. The original primer sequences [SEQ ID NOs: 1-7, 24, 25, 32-33] are validated assays. In addition, a multiplex assay was developed using the primers [SEQ ID NOs: 265-269].
The SNPs rs4357 and rs4363 were genotyped by a melting curve dissociation approach as previously described (2) with the following primers:
rs4363 forward primer CTGCCCCGCACCCTTG [SEQ ID NO: 26];
rs4363 reverse primer G allele CCTTCTGAGCGAGCTGTGC [SEQ ID NO: 27];
rs4363 reverse primer A allele with GC clamp GGCGGCCGGCCCGCCCCGCCTTCT GAGCGAGCTGCGT [SEQ ID NO: 28];
rs4357 reverse primer TGACTTGAGGGAGGGTCCCT [SEQ ID NO: 29];
rs4357 forward primer C allele GCAGGAGAATGGGGTTCC [SEQ ID NO: 30];
rs4357 reverse primer T allele with GC clamp CGGGCCGCCGGGCCGCGGCAG GAGAATGGGGTACT [SEQ ID NO: 31].
INVEST and INVEST-GENE Cohort
The INternational VErapamil SR Trandolapril STudy (INVEST) evaluated blood pressure and cardiovascular adverse outcomes occurring with either an atenolol or verapamil SR hypertension treatment strategy in 22,576 patients with documented coronary artery disease (CAD) and hypertension (19). Race/ethnicity was based on patient self-report and interaction with the site investigator, choosing all that were applicable among: Caucasian, African-American, Asian, Hispanic, and “other”. Hispanic patients were defined as those who chose only ‘Hispanic’. Patients were seen every six weeks for six months and every six months thereafter until two years after the last patient was enrolled. Addition of trandolapril and hydrochlorothiazide were allowed in both arms, and were added as needed to meet JNC VI BP goals (37,38). The primary outcome was the first occurrence of one of three secondary outcomes: death (all cause), nonfatal MI, or nonfatal stroke. All events were adjudicated by an independent committee. Clinical Trial Registration Identifier: NCT00133692 L: clinicaltrials.gov/ct/gui/showNCT00133692?order=5.
Controlling for Population Stratification in INVEST-GENES
To control for potential population stratification in our racially and ethnically diverse population, we used a panel of 87 autosomal ancestry informative markers (AIMs) that show large allele frequency differences across three parental populations (West. Africans, Indigenous Americans, and Europeans) (32). The AIMs were selected to be distributed across the genome and to be distantly interspaced to give independent association with the disease and genetic background. These 87 AIMs were genotyped using either allele-specific PCR with universal energy transfer labeled primers or competitive allele specific PCR at Prevention Genetics (Marshfield, Wis.) (33). Results from this analysis were used in the adjusted genetic association analysis.
FIG. 12 shows Table 3 presenting the baseline characteristics for the INVEST-GENES case and control patients.
FIG. 14 shows Table 5 presenting the oligonucleotide sequences employed in genotyping ACE SNPs by the GC-clamp method described in Papp et al, showing [SEQ ID NOs: 35-43].
FIG. 15 shows Table 6 presenting the oligonucleotides sequences employed in ACE Pyrosequencing genotyping, showing [SEQ ID NOs: 16-18].
FIG. 16 shows Table 7 presenting the oligonucleotide primers used in the amplification and direct sequencing of the ACE upstream gene region and cDNA, showing [SEQ ID NOs: 45-69].
FIG. 17 shows Table 8 presenting the FAM-labeled oligos and related oligos used in genotyping ACE polymorphisms, \showing [SEQ ID NOs: 15-16, 20-23, 44].
FIG. 18 shows Table 9 presenting the oligonucleotides used in the measurement of ACE expression by RT-PCR, including one that spans cDNA exons, showing [SEQ ID NOs: 8-9].
In summary, described herein are novel insights into the molecular genetics and function of the ACE1 gene, uncovered using the AEI approach described. We have characterized three SNPs that define a subpopulation of samples that exhibit differences in the mRNA expression of ACE1. We further determined that these SNPs are found in high frequency within an African-American demographic. Due to the relatively high frequency of these SNPs are believed to be useful as predictive or diagnostic biomarkers. Lower frequency biomarkers (<5%) may be less likely to reach a threshold in terms of market size that makes them economically feasible to use in clinical testing. Due to the physiological importance of ACE1 and the considerable body of literature supporting ACE1 genetic variability as an influence on medically relevant traits, there is potential for these SNPs to eventually be used as biomarkers to assess disease risks (e.g., heart failure, Alzheimer's disease) or predict adverse responses to current and future therapeutics targeting the renin-angiotensin system (e.g., ACE inhibitors, angiotensin receptor blockers (ARBs)).
EXAMPLE II
SOD2
Genetic, epigenetic, and environmental factors determine phenotypic variability, including susceptibility to disease or treatment outcome. Polymorphisms that change the amino acid sequences in coding regions (cSNPs) are readily detectable. However, regulatory polymorphisms (rSNPs) appear to be more prevalent than functional nonsynonymous cSNPs [1-5]. Genome-wide surveys and SNP association analysis with mRNA expression trait mapping [5,6] indicate regulatory polymorphisms as major factors in human phenotypic evolution and variability [5,7]. A third type of functional polymorphism affects mRNA processing (splicing, maturation, stability, transport) and translation [8]. We refer to this class of polymorphisms as ‘structural RNA polymorphisms’ (srSNPs). However, the overall role of rSNPs and srSNP still requires systematic evaluation.
Whereas mRNA levels are subject to both cis- and trans-acting factors, measuring the relative allelic mRNA expression selectively detects only cis-acting factors. Allelic expression imbalance (AEI), i.e., a different number or type of mRNAs generated between alleles, is a robust and quantitative phenotype directly linked to cis-acting polymorphisms [3, 5, 8-21] and epigenetic regulation, including X-inactivation, imprinting, and gene silencing [4, 22, 23].
Genome-wide association studies continue to increase the number of candidate genes, while knowledge of the functional genetic variants is lagging. AEI analysis is a powerful tool for finding regulatory polymorphisms, but technical difficulties hamper broad usage.
Earlier AEI methods mostly targeted monoallelic expression, while polymorphisms resulting in relatively small changes, although potentially physiologically relevant, are more difficult to measure. Array- and RT-PCR-based methods with limited precision or sensitivity have been applied to detect partial regulatory changes, but have mostly been applied to small sets of candidate genes in lymphocytes. Results from these studies suggest that 20-50% of genes show detectable AEI [2, 3, 24-26]. Yet, because the impact of rSNPs and srSNPs strongly depends on the tissue context, AEI analysis should be performed in physiologically relevant tissues [27,28]. Systematic and accurate surveys of AEI in many genes applied to a variety of human target tissues are lacking. Yet, autopsy tissues present additional difficulties because of partial mRNA degradation.
Also disclosed herein is a method for the rapid detection of regulatory polymorphisms in multiple genes. The method described herein is a robust and fast methodology that is especially applicable to human autopsy tissues. The method described herein fills an important gap between large-scale candidate gene discovery and resolution of the functional variants.
In the examples described herein, is a study which surveyed AEI for 42 genes in human autopsy tissues, including brain, heart, liver, intestines, and kidney, as well as peripheral mononuclear cells, revealing frequent AEI in a large fraction of genes.
In one embodiment, in cardiovascular genes where regulatory polymorphisms had been reported previously, we tested whether the observed AEI ratios were compatible with any effects of these polymorphisms on allelic expression in relevant tissues. We also addressed the question of how srSNPs affect mRNA folding, and point to a number of genes where frequent srSNPs affect mRNA expression. The results provide insight into the prevalence of rSNPs and srSNPs.
Results
Methodology for AEI Analysis of Multiple Genes in Human Autopsy Tissues
We developed a rapid methodology for measuring allelic ratios in genomic DNA and mRNA (as cDNA) (AEI analysis) in human autopsy target tissues.
The assay relies on PCR/RT-PCR amplification, followed by a primer extension step with fluorescently labeled dideoxynucleotides, and analysis by capillary electrophoresis. Details of the assays applied to single genes have been published previously by us for several genes included in the present survey [9-16].
To facilitate application to multiple genes in human autopsy tissues, the method described herein includes several steps for obtaining reproducible allelic gDNA and mRNA ratios, including use of multiple gene-specific primers to maximize cDNA yields for the target genes. Assay throughput is  ̃150 samples/hour, or higher with multiplexing, with an error rate in the order of 5% (gDNA) and 10-15% (mRNA).
Application of AEI Analysis to Candidate Genes
AEI analysis was applied to 42 candidate genes in a variety of human tissues (FIG. 26—Table 13), divided into genes for cardiovascular and CNS disorders, and drug metabolism and transport.
This selection provides information on the frequency of cis-acting factors but was not designed to cover the much larger number of possible candidate genes. We first determined (by RT-PCR) all 42 genes were well expressed in the target tissues examined and then determined >4,200 individual genotypes for mRNA marker SNPs in the candidate genes. The 1,008 heterozygous samples suitable for use in AEI assays yielded relative allelic expression for an average of 23 subjects or 46 individual chromosomes per gene (average marker SNP heterozygosity  ̃24%). Results for four genes (ACE, SOD2, NOS3, CCL2) are shown in FIG. 22.
This example was well-powered to detect frequent functional polymorphisms (>5% minor allele frequency), similar to previous AEI studies [2, 25, 26]. Details on tissue source, number of samples, marker SNPs, and allele frequency are found in FIG. 27—Table 14.
As a conservative detection threshold for the presence of mRNA AEI ratios (major:minor allele), we used ±log 2 0.5 (1:1.4 or 1.4:1) corresponding to 3 SD or more relative to DNA ratios, similar to previous studies [24,25].
FIG. 27—Table 14 contains results for genes meeting the detection threshold in at least one sample, along with information on the marker SNPs, number of replicate analyses, frequency, magnitude and direction of AEI. If a suspected functional polymorphism is in near complete linkage disequilibrium with the marker SNP, most or all AEI ratios are unidirectional (either <1 or >1), as observed with SOD2 in heart tissues (FIG. 26).
In contrast, functional polymorphisms unlinked to the marker SNP are revealed by random distribution of ratios <1 and >1 (FIG. 26, FIG. 27—Table 14), indicating these are located in other haplotype blocks. Lesser AEI ratios may also be of physiological relevance but should be subject to more extensive analytical validation to exclude artifacts. The results reveal AEI above our threshold in 67% of the candidate genes, with AEI in two or more subjects in 55% of genes. Where genes lack significant AEI this argues against the presence of cis-acting factors in the tissues analyzed.
Several well-studied genes, such as ACE and SOD2, displayed substantial AEI that was unexpected from previous genetic analyses (FIG. 27—Table 14).
In some cases, the AEI data confirm previous studies, for example, the modest AEI ratios observed for COMT [17], and a similar frequency and extent of AEI for NQO2 in white blood cells [26] and DTNBP1 in the pons region [29]. We also failed to observe significant AEI in 5HT2A, as reported [30]; however, another study suggests the presence of AEI [31] but lacks rigorous validation of the results.
It is possible that AEI may be detectable only in certain ethnogeographic populations where regulatory alleles are sufficiently frequent (see ACE below), or in specific tissues, environmental conditions, and diseases. For example, AEI was observed for VKORC1 only in the liver but was undetectable in heart tissues and B-lymphoblasts (CEPH samples) (FIG. 27—Table 14).
Relationship Between AEI and mRNA Levels
We tested whether the presence of AEI is correlated with total mRNA levels, measured by RT-PCR, in a subset of genes (SOD2, CCL2, NOS3, FLT1, HIF1A, LPL, PTGDS, and MAOA). Borderline significant correlations between AEI and mRNA levels were observed for HIF1A (r=−0.45, p<0.06) and PTGDS (r=0.38, p<0.04). These moderate correlations reflect the greater variability of overall mRNA levels compared to allelic ratios.
Cardiovascular Disease Candidate Genes
AEI analysis was applied to 18 cardiovascular candidate genes that serve as drug targets and have roles in inflammation, coagulation, lipid metabolism, vasomotor tone, and heart contractility (FIG. 26—Table 13).
Target tissues included 65 heart failure explants from transplant recipients, livers, ex vivo monocytes, and peripheral blood monocyte-derived macrophages. AEI was detectable for 15 cardiovascular genes at a 20% imbalance threshold (FIG. 26—Table 13), while 9 genes displayed AEI when we set our more stringent threshold based on the typical error rates (±log 2 0.5). AEI ratios for genes surveyed in heart tissues are shown in FIG. 27—Table 14.
Allelic mRNA expression of ACE, CCL2, SOD2, CACNA1C, and KCNMB1 was validated using a second marker SNP, with cDNA derived from a different primer (FIG. 27—Table 14).
CCL2, PTGDS, and KCNMB1 showed allelic ratios below and above 1, suggesting multiple functional polymorphisms and/or incomplete linkage disequilibrium between the marker SNPs and functional alleles (FIG. 23).
In contrast, ACE displayed large unidirectional AEI ratios only in African-Americans, suggesting the presence of a cis-acting factor enriched in this population. AEI results for ACE were confirmed with use of a second marker SNP (r2=0.98 in compound heterozygotes). Standard curves were linear, obtained with homozygous DNA representing both alleles (r2=0.99).
Both SOD2 and NOS3 showed AEI largely in a single direction—suggestive of a functional polymorphism in a shared haplotype with the marker SNP, or that the marker SNP itself is functional. The results on the frequency and extent of NOS3 AEI are consistent with published AEI results in brain tissues [27].
A number of genes did not show any AEI, for example, the L-type channel CACNA1C—a gene featuring >55 exons across  ̃250 kB. Subsequent use of several marker SNPs and AEI analysis of splice variants failed to reveal any cis-acting factor that could have caused highly variable splicing observed for CACNA1C in human heart [15].
Relationship Between AEI and Previously Suggested Regulatory Polymorphisms
The frequency and directionality of AEI ratios enables us to investigate whether previously proposed regulatory polymorphisms in NOS3 (rs2070744), CCL2 (rs1024611), SOD2 (rs5746091), PTGDS (rs6926), and ACE (intron 16 I/D) contribute to this phenotype.
We genotyped the proposed regulatory polymorphisms and tested for association between genotype and AEI ratios. We analyzed AEI ratios with two discrete thresholds, and also as a continuous variable.
The results in FIG. 26—Table 13 indicate that the putative regulatory polymorphisms cannot account for or are only marginally associated with AEI. For example, a proposed promoter SNP (rs1024611) [32] in CCL2 was incompatible with AEI observed in two subjects, or for the absence of AEI in many samples where this SNP is heterozygous, in both heart tissues and macrophages (FIG. 26—Table 13).
Similarly, a putative regulatory SNP, T-786C (rs2070744) upstream of NOS3, and rs6296 in PTGDS, were not significantly associated with the AEI observed in human target tissues (FIG. 26—Table 13).
A marginal association between the intensely studied ACE intron 16 I/D was detectable when AEI was analyzed as a continuous variable, but there was no association with the large AEI ratios shown in FIG. 23.
Detailed Analysis of AEI Observed for SOD2
FIG. 32 contains the mRNA sequence for the SOD2 gene[SEQ ID NO: 262]. Allelic mRNA ratios for SOD2 were  ̃1.5-fold in 83% of heart tissues heterozygous for marker rs4880, indicating that the ‘major allele’ has  ̃50% greater expression (however, since allele frequency is close to 50% assignment of the minor allele is arbitrary).
A second marker, rs5746092 in the 5′UTR in modest LD with rs4880, gave similar results (r2=0.73, in 16 compound heterozygotes), supporting the accuracy of the assay. FIG. 19—Table 10 shows the forward PCR primer, the reverse PCR primer and the extension primer for rs4880 and rs5746092, showing [SEQ ID NOs: 79-84].
Neither rs5746092 (37% heterozygosity) nor rs4880 (52% heterozygosity) were completely associated with AEI, as several homozygotes or heterozygotes displayed significant or no AEI, respectively.
The results suggest one or more regulatory factors within a common haplotype block. Testing a proposed functional promoter SNP, rs5746091 [33] in 10 subjects, we found that 3 homozygous carriers had no AEI and 3 heterozygous carriers did show AEI (allelic ratio >1.4), but 4 homozygous carriers displayed significant AEI, indicating that rs5746091 could not have played a sole role in allelic expression.
Because the AEI ratios are substantiated for each individual by multiple replicates, each subject showing discrepancy between AEI and SNP heterozygosity is informative and, thus fails to support a putative functional role for that SNP.
Since epigenetic factors could affect allelic expression, methylation of a CpG island close to rs4880 was measured. Distant CpG islands outside this haplotype block were not expected to preferentially affect alleles marked by rs4880. To test allele-selective methylation, we digested DNA at a Hpa II methylation-sensitive restriction site near rs4880 and measured the DNA allelic ratios, in comparison to a standard curve from mixed ratios of digested and undigested reference DNA. CpG methylation differed detectably between alleles, but allele-specific methylation did not correlate with corresponding allele-specific mRNA expression ratios (Pearson r2=0.03) (FIG. 24), arguing against an effect on allelic mRNA expression.
Effect of srSNPs on Predicted mRNA Folding Structures
To assess the potential of SNP-induced changes in mRNA folding, we estimated changes in folding energies for all possible transitions (C< >U, G< >A) and transversions (C< >G, C< >A, G< >U, A< >U) in the mRNA coding regions of the μ, κ and δ opioid receptors (OPRM1, OPRK1, OPRD1), using Mfold.
We calculated both the minimum free energy structures (MFE) and the ensembles of suboptimal structures in varying sized windows around all nucleotide positions. A majority of SNPs showed the potential to alter mRNA folding, often predicting more profound changes than the known functional A118G SNP in OPRM1 [17] (see arrow in FIG. 25).
Approximately 60% of single nucleotide substitutions affected MFE structures, and  ̃90% altered the ensemble of suboptimal structures, with the potential to affect mRNA functions [34].
Because SOD2 allelic expression was consistently in a single direction in such a high proportion (>80%) of samples, the inventors now believe that the SOD2 marker SNPs might have a direct, functional effect on expression. Thus, the inventors further analyzed the predicted allelic effects on mRNA folding for the marker SNPs in SOD2 (rs4880, rs5646092).
Both SNPs are in regions that display highly stable structures, with rs5646092 positioned within an 18 bp helix near the transcription and translation initiation sites. These results suggest that one or more of these alleles could affect gene expression through a change in mRNA structure.
Discussion
Robust Assay of Allelic Ratios in Genomic DNA and mRNA
Described herein is a broadly applicable methodology for rapid and robust assays of allelic gene expression (AEI) in human autopsy tissues. Measuring allelic ratios circumvents at least in part problems arising from post-mortem mRNA degradation.
The AEI analysis can be scaled up to address multiple genes at a time, and thus, represents an intermediate tool for discovering functional polymorphisms affecting gene regulation (rSNPs) and RNA processing (srSNPs) in candidate genes. The effect of rSNPs and srSNPs is expected to vary with the cellular environment, so that studies on human genes in physiologically relevant target tissues are of critical importance, for example the pontine brainstem for SERT and TPH2 mRNA [11,13].
Factors other than rSNPs and srSNPs could contribute to AEI, including variable copy number (CNV) in germline DNA or more frequently as somatic mutations in cancer [35]. We observed deviations of the DNA ratios from unity only with TPH2 in two subjects [13], indicating that gene duplications are rare among the 42 genes studied. On the other hand, complete loss of one allele in germline DNA at the marker SNP locus cannot be assessed with the SNaPshot method as presented because hemizygous carriers would appear as homozygotes, unless the gene dosage is quantitated.
Another possible source of AEI, allele-selective epigenetic regulation of gene expression must be considered where SNP scanning fails to reveal regulatory polymorphisms. The relatively high precision by which the AEI ratios can be measured, facilitated the dissection of genetic and epigenetic regulation of the X-linked MAOA, with both processes contributing to AEI [12].
Prevalence of AEI in the Candidate Genes
The method described herein permits an estimation of the prevalence of cis-acting polymorphisms in multiple (in the example herein, 42) candidate genes in human target tissues, a larger, more diverse sampling than previous studies.
FIG. 27—Table 14 provides information on the magnitude, direction, and frequency of AEI, as guides for more detailed studies. Substantial AEI (>log 2 0.5) in more than one subject was observed for 55% of the surveyed genes (FIG. 27—Table 14), similar to previous studies [2, 3, 25]; however, the frequency is higher than estimates from other studies performed with a random selection of genes in cell lines and blood cells [24,26]. These differences may be attributable to the selection of strong candidate genes, or differences in methodology, tissue specificity, number of subjects, and stringency of AEI thresholds. The presence of frequent AEI was unexpected for some of the candidate genes that had already been intensely studied for genetic polymorphisms (e.g., SOD2, ACE, TPH2 [13], DRD2 [16]).
Differential post-mortem decay for alleles could represent a confounding factor that can be overcome by molecular genetic studies of the functional polymorphisms. Polymorphisms affecting alternative splicing may not be detectable if the splice isoforms have similar turnover rates. To address this issue, allelic mRNA expression can be performed after specific amplification of each splice variant, as we have demonstrated for DRD2 (intron 5 and 6 SNPs alter formation of D2S and D2L) [16].
Scanning for Regulatory Polymorphisms Using Allelic mRNA Expression Profiles
AEI patterns provide a means of determining the location of the functional polymorphism by SNP scanning or sequencing the gene locus, followed by molecular genetic analysis of the rSNP or srSNPs, as shown for OPRM1, MDR1, MAOA, SERT, TPH2, and DRD2 [9-13,16].
Reporter gene assays in heterologous tissues are commonly used to characterize regulatory polymorphisms. If these polymorphisms are functional in vivo, one expects corresponding changes in the AEI ratios. However, for the five genes tested (FIG. 26—Table 13) we have failed to detect significant linkage between the observed AEI ratios and the putative regulatory SNPs. Similarly, our genotype scanning with AEI did not support a role for a putative SERT promoter polymorphism (SERT-LPR), although we cannot rule out that this promoter polymorphism might be active in development, or under stress [11]. Previously suggested regulatory polymorphisms in DRD2 also failed to correlate with AEI ratios [16]. A separate study of 4 genes (MAOA, NOS3, PDYN, NPY) using AEI analysis again yielded results incompatible with reporter gene assays [27], corroborating our results for MAOA and NOS3. Similarly, the AEI observed with CCL2 (MCP1) was not associated with the putative promoter SNP rs1024611 [32]. Therefore, reporter gene assays are not always reliable indicators of regulatory polymorphisms. Combined use of AEI analysis and reporter gene assays can yield more definitive results regarding regulatory polymorphisms [16].
Relevance of Structural RNA SNPs (srSNPs)
For OPRM1, MDR1, TPH2, and DRD2, we have linked the AEI ratios to SNPs in the transcribed region of the gene, likely involved in mRNA processing, turnover, and splicing [9, 10, 13, 16]. srSNPs have been shown to affect mRNA stability [9,36] and alternative splicing [16,37]. Our AEI analysis of marker SNPs in SOD2 and NQO2 indicates they (or SNPs in tight LD with them) may also affect RNA structures. Taken together, these results support the notion that srSNPs can be at least as prevalent as rSNPs.
srSNPs could alter mRNA function through changed folding dynamics [15, 16, 34]. Using Mfold to predict mRNA structural changes resulting from systematic nucleotide exchanges in opioid receptor mRNAs (FIG. 25), we find that most SNPs affect the likely ensemble of structural conformations. Consistent with this, SNPs can be detected by a physical method based on ‘single-strand conformational polymorphisms’, with a 95% discovery rate.
srSNPs can further affect translation, as suggested for the OPRM1 SNP A118G [16], and COMT haplotypes with altered mRNA folding [38]. Measuring AEI ratios at the protein level with use of nonsynonymous marker SNPs can allow for the determination of quantitative effects of polymorphisms on translation and protein turnover.
Cardiovascular Disease Candidate Genes
Half of the 18 cardiovascular genes studied displayed AEI at a conservative cutoff, with ACE and SOD2 conspicuous examples. An intron 16 I/D polymorphism of ACE had been extensively tested in clinical association studies, but its functional role remained unclear [39]. Our results suggest strong cis-acting factors unrelated to the I/D variant in heart tissues, with high frequency in African-Americans.
SOD2 (mitochondrial manganese superoxide dismutase) is a key factor involved in metabolizing superoxide molecules and may have a role in failing human hearts [40]. Previous association studies of two variants in SOD2 with cardiomyopathy [41,42], cancers (e.g., [43], and other disorders have yielded inconsistent results. The nonsynonymous marker SNP used here lies in a leader sequence (rs4880, −9A>V) and was suggested to affect mitochondrial uptake of the mature protein [41], while a promoter region SNP (rs5746091) disrupts binding of AP-2[33].
Common AEI observed here in failed heart tissues (FIG. 23), with allelic mRNA ratios consistently >1, indicates presence of a frequent functional variant(s) in a haplotype block containing the marker SNPs. Limited genotype scanning of the SOD2 locus indicated that the two marker SNPs (rs4880, rs5746092) each taken alone cannot account for the observed AEI, but may interact with each other or merely represent tags for a functional srSNP in this region.
The promoter SNP rs5746091 did not appear to play a main role. Previous studies have implicated structural elements in SOD2 expression, including a GC-rich 5′ region upstream of the transcription start site that also extends into the 5′ end of the transcript [44] and regions in the 3′UTR of the mRNA [45]. Highly favorable RNA structures exist in the region of rs5746092 and rs4880 suggesting multiple structural states in SOD2 mRNA could affect functions. Alternatively, epigenetic regulation of SOD2 expression by CpG methylation [46] could have contributed to AEI, but our initial results argue against this possibility.
The measured AEI ratios clearly demonstrate functional variation of SOD2 mRNA expression.
FLT1, HIF1A, HMOX1, and LPL did not display common and large AEI. However, because the studied candidate genes all have important physiological roles, even relatively small AEI ratios, as observed for CCL2, NOS3, FLT1, HIF1A, HMOX1, HMGCR, and LPL, may be of clinical importance [35]. Even a small activity change of a critical gene such as HMGCR could affect cholesterol production over an individual's lifetime. Moreover, pravastatin response was associated with two intronic SNPs in HMGCR, with frequency >5% in the population [47], and a genome-wide association study for LDL cholesterol also revealed an association with an intronic HMGCR SNP [48].
Thus, as described herein, the inventors have applied mRNA AEI analysis to the detection of cis-acting variation for many candidate genes, revealing many instances of yet unrecognized functional polymorphisms or other cis-acting factors. The AEI methodology can be applied on a fairly large scale while maintaining high accuracy.
Materials and Methods
Human Tissue Selection and Sources
We obtained autopsy or biopsy tissue samples from liver, kidney, intestines, peripheral white blood cells, and various brain regions (prefrontal cortex, hippocampus, ventral tegmental area (VTA), amygdala, and nucleus accumbens, and pontine nuclei of the brain stem (for SERT and TPH2)). Specimens from up to  ̃100 subjects for each cell or tissue were obtained from various sources and tissue banks (OSU tissue procurement division, NIH Cooperative Human Tissue Network, 105 brain sections from the Stanley Foundation, Red Cross blood samples, and tissue banks at the University of Maryland and the National Disease Research Interchange). Left ventricular pieces were collected from the failed hearts of transplant recipients under an IRB-approved protocol at The Ohio State University. Ninety EBV-transformed B-lymphoblast cell lines were obtained from the Coriell cell repository, consisting of 30 Caucasian family trios. A majority of the tissues are from normal subjects, while some tissues included subjects diagnosed with schizophrenia, bipolar disorder, Alzheimer's disease, and cancer. Ethnic distributions varied between tissues repositories; no attempt was made to cover ethnic groups evenly. The objective of this study was to detect functional polymorphisms with allele frequencies of 5% or more.
Sample Preparation
Genomic DNA and RNA were prepared from peripheral lymphocytes, or B-lymphocyte pellets, and frozen tissue samples (brain, liver, etc) as described previously [9-16]. Monocytes and monocyte-derived macrophages were cultured as described [49]. For whole blood extractions, the buffy coat was harvested, then red cells were either lysed using ammonium chloride to yield a leukocyte pellet for RNA extraction, or red and white cells were lysed with a sucrose Triton solution, providing a nuclear pellet for DNA purification. Frozen tissue samples were pulverized under liquid nitrogen and portioned into aliquots for DNA and RNA extractions. DNA was prepared by digestion of the pellet or frozen powder with SDS and proteinase K followed by NaCl salting out of proteins. DNA was recovered by ethanol precipitation, and RNA was extracted in TrizolTM, chloroform extracted, and recovered by precipitation with isopropanol. RNA precipitates were dissolved in RNase-free water or Qiagen buffer, and then extracted using Qiagen RNeasy columns.
Analysis of Allelic mRNA Expression Ratios for Detection of Allelic Expression Imbalance (AEI)
Assay Design
Allelic ratios of genomic DNA and mRNA were measured with SNaPshot as reported [9-16]. Briefly, DNA or mRNA (after conversion to cDNA) regions containing a marker SNP (FIG. 29—Table 16) were PCR amplified, followed by SNaPshot primer extension analysis of each allele (FIG. 29—Table 16).
The procedure differs from earlier studies (e.g., [2]) by combining multiple gene-specific primers close to the marker SNP region for cDNA synthesis to compensate for mRNA degradation. Accurate AEI analysis requires robust expression (RT-PCR cycle threshold 27 or less). Selection criteria for a marker SNP were as follows: 1) location in the transcribed region, coding or non-coding, 2) high minor allele frequency (0.15-0.50), 3) position of marker SNPs preferably more than 20 bp from exon boundaries so that the same set of primers for PCR amplification can be used in both DNA and RNA.
Complementary DNA Synthesis
cDNA was generated from total RNA (1 ug) by Superscript II reverse transcriptase (Invitrogen). Because oligo-dT priming often fails in autopsy tissues, we used both oligo-dT and gene-specific oligonucleotide primers targeting a region immediately 3′ of the marker SNP (same oligonucleotide used for PCR). We have multiplexed up to 30 primers to permit 30 different AEI assays per cDNA preparation. Comparisons between single and multiple primers showed no significant differences where tested. cDNA was successfully extracted from autopsy tissues to yield reproducible results between independent cDNA preparations [9-16].
Quantitative PCR-Based mRNA Analysis
We determined the mRNA levels for each candidate gene in each tissue or cell line, using RT-PCR, to assure that expression is sufficient for accurate AEI analysis (cycle thresholds equal to or below 27 cycles). Primers used for RT-PCR were the same as those selected for the AEI analysis, with PCR conditions optimized for each primer pair on an ABI7000 cycler with SYBR-Green. Results were normalized to an internal standard (β-actin or GAPDH).
Computational Analysis of mRNA Folding
We used Mfold version 3.0 to estimate the effect of SNPs on mRNA folding [50]. Wild-type Refseq mRNA sequences of OPRM1, OPRD1 and OPRK1 were obtained without untranslated regions. A custom Unix program created every possible variant at each base position and fed sequences to Mfold for structure prediction, and subsequent automated analysis. Changes in minimum free energy, as well as pairwise comparisons in structural interactions (paired vs. unpaired) were calculated relative to the wild-type structure using sliding windows around the induced variants, and across the complete mRNA structure.
EXAMPLE III
SLC6A3
Polymorphisms in Genes Affecting Biogenic Amines as Biomarkers in CNS Disorders
SLC6A3 (encoding the dopamine transporter) (newly added gene) is associated with multiple mental disorders such as drug abuse, attention deficit disorder (ADHD/ADD), Parkinson disease, Tourette syndrome and Schizophrenia. FIG. 33 contains the mRNA sequence for the SLC6A3 gene [SEQ ID NO: 263]. Stimulant medications, such as those used to treat ADHD, and drugs of abuse such as amphetamine bind to SLC6A3 and inhibit reuptake of dopamine. Genetic variants of SLC6A3 may influence levels of gene expression and/or ability of drugs to bind to SLC6A3 protein.
Described herein is the determination that a synonymous SNP in Exon (rs6347) is associated with higher mRNA expression in both brain tissue and in a heterologous cell culture system. This is the first functional SNP occurring at high frequency in this key gene.
Polymorphisms in SLC6A3 are now believed by the inventors herein to be useful as biomarkers in numerous diseases and treatment outcomes including but not restricted to mental disorders and specifically drug addiction.
Role of SLC6A3 in Mental Disorders
Dopamine transporter is associated with multiple mental disorders such as drug abuse, attention deficit disorder (ADHD/ADD), Parkinson disease, Tourette syndrome and Schizophrenia (for review: see Bannon, 2005 and Sotnikova et al, 2006). Stimulant medications, such as those used to treat ADHD, and drugs of abuse such as amphetamine bind to SLC6A3 and inhibit reuptake of dopamine. Genetic variants of SLC6A3 may influence levels of gene expression and/or ability of drugs to bind to SLC6A3 protein. One SNP (rs27072) has been found to be significantly associated with inattention and hyperactivity/impulsivity in children with ADHD.
Polymorphisms Linked to AEI:
SLC6A3: SNP rs27072 located in the 3′UTR is associated with AEI in brain tissue. An additional synonymous SNP in Exon 9 rs6347 (not linked to rs27072) is associated with AEI in both brain tissue and cell culture. See FIG. 20 Table 11 which shows the sequences for rs27072 and rs6347, showing [SEQ ID NOs: 85-85].
FIG. 30—Table 17 shows the forward primer, the reverse primer and the extension primer for rs6437 [SEQ ID NOs: 87-89].
The rs6347 biomarker is based on molecular genetics and function. Also the frequency and penetrance are measurable by AEI. In addition, the combined use of two frequent functional polymorphisms can be used to assess disease risk and response to therapy (e.g., SSRIs).
EXAMPLE IV
CYP2C9
CYP2C9 (encoding cytochrome P450 2C9) is a liver drug metabolizing enzyme, involved in metabolism of  ̃20% of pharmaceuticals. FIG. 34 contains the mRNA sequence for the CYP2C9 gene [SEQ ID NO: 264].
The most common functional SNPs are CYP2C9*2 (430 C>T) and *3 (1075 A>C). These are non-synonymous SNPs with reduced enzyme activity (*2 50% and *3 25% of wild-type allele).
Described herein is a novel functional SNP, 1425 A>T, which is associated with 20-50% increased in mRNA level in human liver tissues, suggesting a “gain of function”. The frequency of SNP 1425 A>T is  ̃4% but may vary significantly in different populations. Because it represents a gain of function (dominant effect), a 4% frequency is pharmacologically relevant. SNP 1425 A>T is in partial linkage disequilbrium with *3 (and hence may affect 8# activity), but is never to link to *2 in >liver tissues.
Polymorphisms in CYP2C9 can be useful as biomarkers in optimizing drug treatment for personalized medicine. It is noted that CYP2C9*2 and *3 already comprise a drug biomarker test, FDA approved and commercialized. See FIG. 21 Table 12 which shows the sequence for sr1057911 [SEQ ID NO: 90].
FIG. 30—Table 17 shows the forward primer, the reverse primer and the extension primer for rs9332242 and rs2017319 [SEQ ID NOs: 91-96].
While the invention has been described with reference to various and preferred embodiments, it should be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the essential scope of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from the essential scope thereof. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed herein contemplated for carrying out this invention, but that the invention will include all embodiments falling within the scope of the claims.
The citation of any reference herein is not an admission that such reference is available as prior art to the instant invention. Any publications mentioned in this specification are herein incorporated by reference. Any discussion of documents, acts, materials, devices, articles or the like which has been included in the present specification is solely for the purpose of providing a context for the present invention. It is not to be taken as an admission that any or all of these matters form part of the prior art base or were common general knowledge in the field relevant to the present invention as it existed before the priority date of each claim of this application.
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## Claims

1. A method for determining whether or not a hypertensive human has increased risk of myocardial infarction, comprising:
a.) obtaining a nucleic acid-containing sample from a hypertensive human;
b.) conducting laboratory analysis of the sample so as to obtain genotype data of the human at rs4290 or at rs7213516, wherein the laboratory analysis is polymerase chain reaction with subsequent pyrosequencing or Taqman assay; and
c.) determining that the human has increased risk of myocardial infarction if the genotype data indicate that the sample comprises either of (i) the minor allele of rs4290 or (ii) the minor allele of rs7213516; or
d.) determining that the human has no increased risk of myocardial infarction if the genotype data indicate that the sample does not comprise either of (i) the minor allele of rs4290 or (ii) the minor allele of rs7213516.

2. The method of claim 1, further comprising the step of correlating the data with similar data from a reference population.

3. The method of claim 1, wherein the minor allele of rs4290 is present and the minor allele of rs7213516 is present.

4. A method to determine whether or not a hypertensive human has increased susceptibility for reduced ACE mRNA expression, comprising:
a.) obtaining a nucleic acid-containing sample from a hypertensive human;
b.) conducting laboratory analysis of the sample so as to obtain genotype data of the human at rs4290 or at rs7213516, wherein the laboratory analysis is polymerase chain reaction with subsequent pyrosequencing or Taqman assay; and
c.) determining the human as having increased risk of a susceptibility for reduced ACE mRNA expression if the genotype data indicate that the sample comprises either of (i) the minor allele of rs4290 or (ii) the minor allele of rs7213516; or
d.) determining the human as having no increased susceptibility for reduced ACE mRNA expression if the physical data indicate that the sample does not comprise either of (i) the minor allele of rs4290 or (ii) the minor allele of rs7213516.