New susceptibility locus for coronary artery disease on chromosome 3q22.3

Association of a polymorphism of BTN2A1 with myocardial infarction in East Asian populations

OBJECTIVE

We have performed a genome-wide association study (GWAS) to identify genetic variants that confer susceptibility to myocardial infarction (MI) in Japanese and Korean populations.

METHODS

A total of 17,447 Japanese or Korean individuals from four independent subject panels was examined. Japanese subject panels A, B, and C comprised 134 individuals with MI and 137 controls, 1431 individuals with MI and 3161 controls, and 643 individuals with MI and 1347 controls, respectively, whereas the Korean population comprised 1880 individuals with MI and 8714 controls. A GWAS for MI was performed in Japanese subject panel A with the use of the Affymetrix GeneChip Human Mapping 500K Array Set.

RESULTS

Seventy single nucleotide polymorphisms (SNPs) significantly (P<1.0×10(-7)) associated with MI by the GWAS were examined further in Japanese subject panel B, revealing two SNPs (rs6929846 of BTN2A1, rs2569512 of ILF3) to be significantly (P<0.0007) associated with MI. The rs6929846 SNP of BTN2A1, but not rs2569512 of ILF3, was also significantly associated with MI in Japanese subject panel C. However, the association of neither rs6929846 nor rs2569512 with MI was replicated in the Korean population.

CONCLUSION

BTN2A1 may be a susceptibility gene for MI in Japanese individuals.

Genetics of diabetes complications

A large body of evidence indicates that the risk for developing chronic diabetic complications is under the control of genetic factors. Previous studies using a candidate gene approach have uncovered a number of genetic loci that may shape this risk, such as the VEGF gene for retinopathy, the ELMO1 gene for nephropathy, and the ADIPOQ gene for coronary artery disease. Recently, a new window has opened on identifying these genes through genome-wide association studies. Such systematic approach has already led to the identification of a major locus for coronary artery disease on 9p21 as well three potential genes for nephropathy on 7p, 11p, and 13q. Further insights are expected from a broader application of this strategy. It is anticipated that the identification of these genes will provide novel insights on the etiology of diabetic complications, with crucial implications for the development of new drugs to prevent the adverse effects of diabetes.

Modeling interactions with known risk loci-a Bayesian model averaging approach

Genome-wide association studies (GWAS) are now clearly established as a powerful method for detecting loci involved in the etiology of common complex diseases. Most diseases and traits studied using the GWAS approach now have several loci that have been shown to be convincingly replicated. It is generally the case that these loci have been identified using single locus association scans of genotyped or imputed SNPs and very few loci have been identified by taking interactions into account. We propose a method that assesses the evidence of association at each SNP by modeling the effect of the locus in combination with other known loci. We use a Bayesian model averaging approach that combines the evidence across several different plausible models for the way in which the loci interact. We show that the method has good power both when the association is the result of marginal effects only, and when interaction with a known locus occurs. The method is implemented as an option in the program SNPTEST.

Effect of the rs2259816 polymorphism in the HNF1A gene on circulating levels of c-reactive protein and coronary artery disease (the ludwigshafen risk and cardiovascular health study)

Background

C-reactive protein is a well established marker of inflammation and has been used to predict future cardiovascular disease. It is still controversial if it plays an active role in the development of cardiovascular disease. Recently, polymorphisms in the gene for HNF1α have been linked to the levels of C-reactive protein and coronary artery disease.

Methods

We investigated the association of the rs2259816 polymorphism in the HNF1A gene with the circulating level of C-reactive protein and the hazard of coronary artery disease in the LURIC Study cohort.

Results

Compared to CC homozygotes, the level of C-reactive protein was decreased in carriers of at least one A-allele. Each A-allele decreased CRP by approximately 15%. The odds ratio for coronary artery disease was only very slightly increased in carriers of the A-allele and this association did not reach statistical significance.

Conclusions

In the LURIC Study cohort the A-allele of rs2259816 is associated with decreased CRP but not with coronary artery disease.

The genetics of type 2 diabetes: what have we learned from GWAS?

Type 2 diabetes mellitus has been at the forefront of human diseases and phenotypes studied by new genetic analyses. Thanks to genome-wide association studies, we have made substantial progress in elucidating the genetic basis of type 2 diabetes. This review summarizes the concept, history, and recent discoveries produced by genome-wide association studies for type 2 diabetes and glycemic traits, with a focus on the key notions we have gleaned from these efforts. Genome-wide association findings have illustrated novel pathways, pointed toward fundamental biology, confirmed prior epidemiological observations, drawn attention to the role of β-cell dysfunction in type 2 diabetes, explained ~10% of disease heritability, tempered our expectations with regard to their use in clinical prediction, and provided possible targets for pharmacotherapy and pharmacogenetic clinical trials. We can apply these lessons to future investigation so as to improve our understanding of the genetic basis of type 2 diabetes.

Stem cell models of cardiac development and disease

The past few years have witnessed remarkable advances in stem cell biology and human genetics, and we have arrived at an era in which patient-specific cell and tissue models are now practical. The recent identification of cardiovascular progenitor cells, as well as the identification of genetic variants underlying congenital heart disorders and adult disease, opens the door to the development of human models of human cardiovascular disease. We review the current understanding of the contribution of progenitor cells to cardiogenesis and outline how pluripotent stem cells can be applied to the modeling of cardiovascular disorders of genetic origin. A key challenge will be to implement these models in an efficient manner to develop a molecular understanding of how genes lead to disease and to screen for genes and drugs that modify the disease process.

A multilocus genetic risk score for coronary heart disease: case-control and prospective cohort analyses

BACKGROUND

Comparison of patients with coronary heart disease and controls in genome-wide association studies has revealed several single nucleotide polymorphisms (SNPs) associated with coronary heart disease. We aimed to establish the external validity of these findings and to obtain more precise risk estimates using a prospective cohort design.

METHODS

We tested 13 recently discovered SNPs for association with coronary heart disease in a case-control design including participants differing from those in the discovery samples (3829 participants with prevalent coronary heart disease and 48,897 controls free of the disease) and a prospective cohort design including 30,725 participants free of cardiovascular disease from Finland and Sweden. We modelled the 13 SNPs as a multilocus genetic risk score and used Cox proportional hazards models to estimate the association of genetic risk score with incident coronary heart disease. For case-control analyses we analysed associations between individual SNPs and quintiles of genetic risk score using logistic regression.

FINDINGS

In prospective cohort analyses, 1264 participants had a first coronary heart disease event during a median 10·7 years' follow-up (IQR 6·7-13·6). Genetic risk score was associated with a first coronary heart disease event. When compared with the bottom quintile of genetic risk score, participants in the top quintile were at 1·66-times increased risk of coronary heart disease in a model adjusting for traditional risk factors (95% CI 1·35-2·04, p value for linear trend=7·3×10(-10)). Adjustment for family history did not change these estimates. Genetic risk score did not improve C index over traditional risk factors and family history (p=0·19), nor did it have a significant effect on net reclassification improvement (2·2%, p=0·18); however, it did have a small effect on integrated discrimination index (0·004, p=0·0006). Results of the case-control analyses were similar to those of the prospective cohort analyses.

INTERPRETATION

Using a genetic risk score based on 13 SNPs associated with coronary heart disease, we can identify the 20% of individuals of European ancestry who are at roughly 70% increased risk of a first coronary heart disease event. The potential clinical use of this panel of SNPs remains to be defined.

FUNDING

The Wellcome Trust; Academy of Finland Center of Excellence for Complex Disease Genetics; US National Institutes of Health; the Donovan Family Foundation.

Translating genomic analyses into improved management of coronary artery disease

Human genetic variation can modulate pathophysiologic processes that alter susceptibility to complex disease. Recent genomic analyses have sought to identify how common genetic variation alters susceptibility to coronary artery disease (CAD). From genome-wide association studies (GWAS), common genetic variants in several novel chromosomal loci have been associated with CAD. GWAS identified the 9p21 locus as the strongest independent genetic CAD risk factor, along with 11 additional loci that harbor common genetic variants associated with increased CAD risk. A thorough understanding of human genetic variation and genomic analyses will be crucial to understand how GWAS-identified loci increase susceptibility to CAD. This article outlines the relevance of genetic variation in the elucidation of novel CAD risk factors and the clinical utility of genetic variants in the management and treatment of CAD.

Identifying disease polymorphisms from case-control genetic association data

In case-control association studies, it is typical to observe several associated polymorphisms in a gene region. Often the most significantly associated polymorphism is considered to be the disease polymorphism; however, it is not clear whether it is the disease polymorphism or there is more than one disease polymorphism in the gene region. Currently, there is no method that can handle these problems based on the linkage disequilibrium (LD) relationship between polymorphisms. To distinguish real disease polymorphisms from markers in LD, a method that can detect disease polymorphisms in a gene region has been developed. Relying on the LD between polymorphisms in controls, the proposed method utilizes model-based likelihood ratio tests to find disease polymorphisms. This method shows reliable Type I and Type II error rates when sample sizes are large enough, and works better with re-sequenced data. Applying this method to fine mapping using re-sequencing or dense genotyping data would provide important information regarding the genetic architecture of complex traits.

Genetic causes of myocardial infarction: new insights from genome-wide association studies

BACKGROUND

A positive family history for myocardial infarction (MI) is known to be a major cardiovascular risk factor. The current European guidelines therefore recommend intensified primary prevention for the siblings and children of persons who have had an MI. Although the genes underlying the heritable component of MI were largely unknown previously, the development of new molecular genetic methods, and particularly the advent of genome-wide association (GWA) studies, has led to the discovery of numerous genetic variants that are associated with an elevated risk of MI.

METHODS

In this article, we review GWA studies on MI and coronary heart disease (CHD) that were retrieved by a selective literature search from 2007 onward. We comment on their implications for clinical practice.

RESULTS

In the last three years, GWA studies have enabled the identification of many alleles that confer a higher risk of MI. A total of eleven chromosomal regions have been replicated and associated with the disease, and their functional significance has been studied. Furthermore, it has been shown that some of the manifestations of CHD, e.g., calcification, ectasia and main-stem stenosis, are more strongly inherited than others.

CONCLUSION

The results of recent GWA studies for MI and CHD will aid in individual risk prediction for MI by molecular biological means. They will also permit the development of new approaches to research on the pathophysiology of myocardial infarction.

Design of the Coronary ARtery DIsease Genome-Wide Replication And Meta-Analysis (CARDIoGRAM) Study: A Genome-wide association meta-analysis involving more than 22 000 cases and 60 000 controls

BACKGROUND

Recent genome-wide association studies (GWAS) of myocardial infarction (MI) and other forms of coronary artery disease (CAD) have led to the discovery of at least 13 genetic loci. In addition to the effect size, power to detect associations is largely driven by sample size. Therefore, to maximize the chance of finding novel susceptibility loci for CAD and MI, the Coronary ARtery DIsease Genome-wide Replication And Meta-analysis (CARDIoGRAM) consortium was formed.

METHODS AND RESULTS

CARDIoGRAM combines data from all published and several unpublished GWAS in individuals with European ancestry; includes >22 000 cases with CAD, MI, or both and >60 000 controls; and unifies samples from the Atherosclerotic Disease VAscular functioN and genetiC Epidemiology study, CADomics, Cohorts for Heart and Aging Research in Genomic Epidemiology, deCODE, the German Myocardial Infarction Family Studies I, II, and III, Ludwigshafen Risk and Cardiovascular Heath Study/AtheroRemo, MedStar, Myocardial Infarction Genetics Consortium, Ottawa Heart Genomics Study, PennCath, and the Wellcome Trust Case Control Consortium. Genotyping was carried out on Affymetrix or Illumina platforms followed by imputation of genotypes in most studies. On average, 2.2 million single nucleotide polymorphisms were generated per study. The results from each study are combined using meta-analysis. As proof of principle, we meta-analyzed risk variants at 9p21 and found that rs1333049 confers a 29% increase in risk for MI per copy (P=2×10⁻²⁰).

CONCLUSION

CARDIoGRAM is poised to contribute to our understanding of the role of common genetic variation on risk for CAD and MI.

Genetics of coronary artery disease

Coronary artery disease and its clinical manifestations, including myocardial infarction, are heritable traits, consistent with a role for inherited DNA sequence variation in conferring risk for disease. Knowledge of the new sequence variations in the genome that confer risk has the potential to illuminate new causal biologic pathways in humans and to thereby further improve diagnosis and treatment. Here, we review recent progress in mapping genetic loci related to coronary disease and risk factor phenotypes, including plasma lipoprotein concentrations. Genome-wide linkage (in families) and association (in populations) studies have identified more than a dozen genetic loci related to coronary disease. A key challenge now is to move from mapping loci to pinpointing causal genes and variants, and to develop a molecular understanding of how these genes lead to coronary disease.

Genome-wide association study of coronary artery disease

Coronary artery disease (CAD) is a multifactorial disease with environmental and genetic determinants. The genetic determinants of CAD have previously been explored by the candidate gene approach. Recently, the data from the International HapMap Project and the development of dense genotyping chips have enabled us to perform genome-wide association studies (GWAS) on a large number of subjects without bias towards any particular candidate genes. In 2007, three chip-based GWAS simultaneously revealed the significant association between common variants on chromosome 9p21 and CAD. This association was replicated among other ethnic groups and also in a meta-analysis. Further investigations have detected several other candidate loci associated with CAD. The chip-based GWAS approach has identified novel and unbiased genetic determinants of CAD and these insights provide the important direction to better understand the pathogenesis of CAD and to develop new and improved preventive measures and treatments for CAD.

Improved prediction of cardiovascular disease based on a panel of single nucleotide polymorphisms identified through genome-wide association studies

BACKGROUND

Genome-wide association studies (GWAS) have identified single-nucleotide polymorphisms (SNPs) at multiple loci that are significantly associated with coronary artery disease (CAD) risk. In this study, we sought to determine and compare the predictive capabilities of 9p21.3 alone and a panel of SNPs identified and replicated through GWAS for CAD.

METHODS AND RESULTS

We used the Ottawa Heart Genomics Study (OHGS) (3323 cases, 2319 control subjects) and the Wellcome Trust Case Control Consortium (WTCCC) (1926 cases, 2938 control subjects) data sets. We compared the ability of allele counting, logistic regression, and support vector machines. Two sets of SNPs, 9p21.3 alone and a set of 12 SNPs identified by GWAS and through a model-fitting procedure, were considered. Performance was assessed by measuring area under the curve (AUC) for OHGS using 10-fold cross-validation and WTCCC as a replication set. AUC for logistic regression using OHGS increased significantly from 0.555 to 0.608 (P=3.59×10⁻¹⁴) for 9p21.3 versus the 12 SNPs, respectively. This difference remained when traditional risk factors were considered in a subgroup of OHGS (1388 cases, 2038 control subjects), with AUC increasing from 0.804 to 0.809 (P=0.037). The added predictive value over and above the traditional risk factors was not significant for 9p21.3 (AUC 0.801 versus 0.804, P=0.097) but was for the 12 SNPs (AUC 0.801 versus 0.809, P=0.0073). Performance was similar between OHGS and WTCCC. Logistic regression outperformed both support vector machines and allele counting.

CONCLUSIONS

Using the collective of 12 SNPs confers significantly greater predictive capabilities for CAD than 9p21.3, whether traditional risks are or are not considered. More accurate models probably will evolve as additional CAD-associated SNPs are identified.

Emerging clinical applications in cardiovascular pharmacogenomics

Over one-fourth of the 36 million annual outpatient prescriptions filled in the United States are known to have human genomic biomarker information available that predicts drug safety and efficacy, or both. However, to date, we have not systematically implemented strategies to effectively use this data in clinical practice to improve patient outcomes. Part of the difficulty has stemmed from the only modest predictive capacity of previously identified gene variants, lack of replication of data in multiple studies, and the hesitancy of the clinical community to translate data gleaned from basic and translational research to routine clinical practice. Now, additional key variants that strongly impact drug absorption, metabolism, and excretion are rapidly surfacing through the use of genome-wide association technology. Most importantly, these variants are being validated in independent cohorts of thousands of cases and controls. In the near future, the dramatic reduction in the cost of DNA sequencing will lead to further insight into the common and rare genetic variants that strongly predict our individual response to commonly used medications. The clinical community will need to be prepared to utilize this vital data in aiding their selection of the right drug for the right patient if we expect to significantly reduce the ever increasing burden of societies' most common diseases. Herein, we detail the most clinically compelling and robust examples of pharmacogenomics emerging in the field of cardiovascular disease and hopefully foretell how cardiovascular disease might be treated in the era of genomic medicine.

FGF21 signalling pathway and metabolic traits - genetic association analysis

Fibroblast growth factor 21 (FGF21) is a novel master regulator of metabolic profile. The biological actions of FGF21 are elicited upon its klotho beta (KLB)-facilitated binding to FGF receptor 1 (FGFR1), FGFR2 and FGFR3. We hypothesised that common polymorphisms in the FGF21 signalling pathway may be associated with metabolic risk. At the screening stage, we examined associations between 63 common single-nucleotide polymorphisms (SNPs) in five genes of this pathway (FGF21, KLB, FGFR1, FGFR2, FGFR3) and four metabolic phenotypes (LDL cholesterol - LDL-C, HDL-cholesterol - HDL-C, triglycerides and body mass index) in 629 individuals from Silesian Hypertension Study (SHS). Replication analyses were performed in 5478 unrelated individuals of the Swiss CoLaus cohort (imputed genotypes) and in 3030 directly genotyped individuals of the German Myocardial Infarction Family Study (GerMIFS). Of 54 SNPs that met quality control criteria after genotyping in SHS, 4 (rs4733946 and rs7012413 in FGFR1; rs2071616 in FGFR2 and rs7670903 in KLB) showed suggestive association with LDL-C (P=0.0006, P=0.0013, P=0.0055, P=0.011, respectively) and 1 (rs2608819 in KLB) was associated with body mass index (P=0.011); all with false discovery rate q<0.5. Of these, only one FGFR2 polymorphism (rs2071616) showed replicated association with LDL-C in both CoLaus (P=0.009) and men from GerMIFS (P=0.017). The direction of allelic effect of rs2071616 upon LDL-C was consistent in all examined populations. These data show that common genetic variations in FGFR2 may be associated with LDL-C in subjects of white European ancestry.

Plasma bilirubin and UGT1A1*28 are not protective factors against first-time myocardial infarction in a prospective, nested case-referent setting

BACKGROUND

Bilirubin, an effective antioxidant, shows a large variation in levels between individuals and has been positively associated with reduced cardiovascular disease risk. A major reason for the variability is a common promoter polymorphism, UGT1A1*28, which reduces the transcription of the enzyme that conjugates bilirubin, UDP-glucuronosyltransferase 1A1. The aim of the study was to evaluate a possible protective effect of plasma bilirubin and the UGT1A1*28 polymorphism against myocardial infarction in a prospective case-referent setting.

METHODS AND RESULTS

Subjects (n=618) with a first-ever myocardial infarction (median event age, 60.5 years; median lag time, 3.5 years) and 1184 matched referents were studied. Plasma bilirubin was lower in cases versus referents. Despite a strong gene-dosage effect on bilirubin levels in both cases and referents, the UGT1A1*28 polymorphism did not influence the risk of myocardial infarction. Among multiple other variables, serum iron showed one of the strongest associations with bilirubin levels.

CONCLUSIONS

We found no evidence for a protective effect of the UGT1A1*28 polymorphism against myocardial infarction and consequently neither for bilirubin. The lower bilirubin levels in cases might be caused by decreased production, increased degradation, or increased elimination.

Association of genetic risk variants with expression of proximal genes identifies novel susceptibility genes for cardiovascular disease

BACKGROUND

Population-based genome-wide association studies have identified several single nucleotide polymorphisms (SNPs) associated with cardiovascular disease or its risk factors. Genes in close proximity to these risk-SNPs are often thought to be pathogenetically important based on their location alone. However, the actual connections between SNPs and disease mechanisms remain largely unknown.

METHODS AND RESULTS

To identify novel susceptibility genes, we investigated how 166 SNPs previously found to be associated with increased cardiovascular risk and/or predisposing metabolic traits relate to the expression of nearby genes. Gene expression in 577 samples of aorta, liver, mammary artery, and carotid atherosclerotic plaque was measured using expression arrays. For 47 SNPs, the expression levels of proximal genes (located within 200 kb) were affected (P<0.005). More than 20 of these genes had not previously been identified as candidate genes for cardiovascular or related metabolic traits. SNP-associated gene effects were tissue-specific and the tissue specificity was phenotype-dependent.

CONCLUSIONS

This study demonstrates several instances of association between risk-SNPs and genes immediately adjacent to them. It also demonstrates instances in which the associated gene is not the immediately proximal and obvious candidate gene for disease. This shows the necessity of careful studies of genetic marker data as a first step toward application of genome-wide association studies findings in a clinical setting.

Genetic regulation of serum phytosterol levels and risk of coronary artery disease

BACKGROUND

Phytosterols are plant-derived sterols that are taken up from food and can serve as biomarkers of cholesterol uptake. Serum levels are under tight genetic control. We used a genomic approach to study the molecular regulation of serum phytosterol levels and potential links to coronary artery disease (CAD).

METHODS AND RESULTS

A genome-wide association study for serum phytosterols (campesterol, sitosterol, brassicasterol) was conducted in a population-based sample from KORA (Cooperative Research in the Region of Augsburg) (n=1495) with subsequent replication in 2 additional samples (n=1157 and n=1760). Replicated single-nucleotide polymorphisms (SNPs) were tested for association with premature CAD in a metaanalysis of 11 different samples comprising 13 764 CAD cases and 13 630 healthy controls. Genetic variants in the ATP-binding hemitransporter ABCG8 and at the blood group ABO locus were significantly associated with serum phytosterols. Effects in ABCG8 were independently related to SNPs rs4245791 and rs41360247 (combined P=1.6 x 10(-50) and 6.2 x 10(-25), respectively; n=4412). Serum campesterol was elevated 12% for each rs4245791 T-allele. The same allele was associated with 40% decreased hepatic ABCG8 mRNA expression (P=0.009). Effects at the ABO locus were related to SNP rs657152 (combined P=9.4x10(-13)). Alleles of ABCG8 and ABO associated with elevated phytosterol levels displayed significant associations with increased CAD risk (rs4245791 odds ratio, 1.10; 95% CI, 1.06 to 1.14; P=2.2 x 10(-6); rs657152 odds ratio, 1.13; 95% CI, 1.07 to 1.19; P=9.4 x 10(-6)), whereas alleles at ABCG8 associated with reduced phytosterol levels were associated with reduced CAD risk (rs41360247 odds ratio, 0.84; 95% CI, 0.78 to 0.91; P=1.3 x 10(-5)).

CONCLUSION

Common variants in ABCG8 and ABO are strongly associated with serum phytosterol levels and show concordant and previously unknown associations with CAD.

A systems biology approach to understanding atherosclerosis

Atherosclerosis, a chronic inflammatory disease of the vascular system, presents significant challenges to developing effective molecular diagnostics and novel therapies. A systems biology approach integrating data from large-scale measurements (e.g. transcriptomics, proteomics and genomics) is successfully contributing to deciphering regulatory networks underlying the response of many different cellular systems to perturbations. Such a network analysis strategy using pathway information and data from multiple measurement platforms, tissues and species is a promising approach to elucidate the mechanistic underpinnings of complex diseases. Here, we present our views on the contributions that a systems approach can bring to the study of atherosclerosis, propose ways to tackle the complexity of the disease in a systems manner and review recent systems-level studies of the disease.

Genetics and beyond--the transcriptome of human monocytes and disease susceptibility

Background

Variability of gene expression in human may link gene sequence variability and phenotypes; however, non-genetic variations, alone or in combination with genetics, may also influence expression traits and have a critical role in physiological and disease processes.

Methodology/Principal Findings

To get better insight into the overall variability of gene expression, we assessed the transcriptome of circulating monocytes, a key cell involved in immunity-related diseases and atherosclerosis, in 1,490 unrelated individuals and investigated its association with >675,000 SNPs and 10 common cardiovascular risk factors. Out of 12,808 expressed genes, 2,745 expression quantitative trait loci were detected (P<5.78×10⁻¹²), most of them (90%) being cis-modulated. Extensive analyses showed that associations identified by genome-wide association studies of lipids, body mass index or blood pressure were rarely compatible with a mediation by monocyte expression level at the locus. At a study-wide level (P<3.9×10⁻⁷), 1,662 expression traits (13.0%) were significantly associated with at least one risk factor. Genome-wide interaction analyses suggested that genetic variability and risk factors mostly acted additively on gene expression. Because of the structure of correlation among expression traits, the variability of risk factors could be characterized by a limited set of independent gene expressions which may have biological and clinical relevance. For example expression traits associated with cigarette smoking were more strongly associated with carotid atherosclerosis than smoking itself.

Conclusions/Significance

This study demonstrates that the monocyte transcriptome is a potent integrator of genetic and non-genetic influences of relevance for disease pathophysiology and risk assessment.

Early identification of cardiovascular risk using genomics and proteomics

Coronary heart disease (CHD) will soon become the leading cause of death and morbidity in the world. Early detection and treatment of CHD is thus imperative to improve global health. Atherosclerosis of the coronary arteries is a complex multifactorial disease process involving multiple pathways that can be influenced by both genetic and environmental factors. With the recent advances in genomics and proteomics, many new risk factors with small-to-moderate effects are likely to be identified. Additionally, individualized risk stratification and targeted therapy may become feasible; each individual could potentially be assessed with a panel of tests for genomic and proteomic markers and, on the basis of the individual's composite risk profile, preventive and therapeutic steps could then be undertaken. With a multimarker approach, it may also be possible to identify alterations in pathways involved in atherogenesis, rather than focus on individual risk factors. In this article, we use the specific example of atherosclerosis to discuss the role of genomics and proteomics in cardiovascular risk assessment.

The past, present, and future of direct-to-consumer genetic tests

Technological advances in the field of human genetics have resulted in a wave of discoveries of common DNA sequence variants that are associated with a risk of common complex diseases, such as heart attack, that account for a substantial proportion of morbidity, mortality, and health care costs in most contemporary populations. The overall predictive power of these sequence variants can be considerable, due to the high incidence of these diseases and the sheer number of associations that have been discovered. Health care providers have been slow to utilize this knowledge for preventative medicine. However, several companies have taken on a translational role by offering genetic tests based on these discoveries direct to consumers. In this paper, we review the current state and future prospects of such genetic tests, as scientists involved both in the discovery of disease associations and the development of genetic tests.

Genome-wide association studies in atherothrombosis

Atherothrombotic diseases are complex diseases, arising from the interaction between several genetic and environmental factors. Until recently, the genetic basis of complex diseases in general, and of atherothrombosis in particular, were poorly characterized. Progress in DNA analysis techniques and the increasing level of characterization of the variability of the human genome has recently allowed to study comprehensively the association between genetic variants and diseases. To date, more than 400 genome-wide association studies have been conducted, allowing to identify more than 430 genomic regions at which common genetic variants influence the predisposition to complex diseases of great epidemiological relevance. This review article summarizes the progress achieved in the genetic basis of atherothrombotic diseases such as myocardial infarction and ischemic stroke. The advances achieved so far now await for clinical applications.

The genome-wide association study--a new era for common polygenic disorders

This review covers the advances made in the last decade utilizing the high-density single-nucleotide microarrays to screen the entire human genome for genetic risk variants and outlines future strategies to draw deeper into the human genetic front. The sequence of the human genome provides the blueprint for life, while its variation provides the spice of life. Approximately 99.5% of the human genome DNA sequence is identical among humans with 0.5% of the genome sequence (15 million bps) accounting for all individual differences including susceptibility for disease. The new technology of the computerized chip array containing up to millions of SNPs as DNA markers makes possible genome-wide association studies to detect genetic predisposition to common polygenic disorders such as coronary artery disease (CAD). The sample sizes required for these studies are massive and large; worldwide consortiums such as CARDIoGRAM have been formed to accommodate this requirement. The progress has been remarkable with the identification of 9p21 followed by several others within the past 2 years. It is expected that most of the common variants (minor allele frequency, MAF >5%) will be identified for CAD within the next 2 to 3 years. Rare variants (MAF <5%) will require direct sequencing which will be delayed somewhat. The ultimate objective for the future is the sequencing and functional analysis of the causative polymorphisms. This will require a new approach involving several disciplines, namely, bioinformatics, high-throughput cell expression, and animal models.

Genomics of heart failure

Cardiovascular disease is the leading cause of death in men and women, and heart failure (HF) is associated with high rates of morbidity and mortality. Most common forms of HF are non-mendelian and the evidence for heritability is modest. Study of the genetic susceptibility to HF has been limited to patients with rare familial forms of HF and candidate gene association studies in patients with distinct subtypes of HF. However, with the completion of the human genome project and the development of the HapMap template, new large-scale genome-wide association studies are possible. This article reviews the status of these and other important developments in genomics, in particular genome-wide sequencing, and other "omics".

Genetics of myocardial infarction: a progress report

A small region on chromosome 9p21.3, discovered in parallel by three groups in the year 2007, is typical of the new understanding of the genetic basis of myocardial infarction (MI). The finding emerged from the application of novel high-throughput genome-wide approaches, the risk-associated allele is frequent, acts independently of traditional risk factors, and confers a modest yet highly reproducible hazard. Since then, another 10 chromosomal regions have been identified to affect the risk of MI or coronary artery disease (CAD). Although the number of risk alleles is growing rapidly, several conclusions can already be drawn from the findings to date. First, it appears that multiple hitherto unknown molecular mechanisms--initiated by these chromosomal variants--ultimately precipitate CAD. Secondly, essentially all Caucasians carry a variable number of risk alleles such that disease manifestation is affected to some extent by these inherited factors in basically all individuals. This means that a better understanding of underlying functional genomic mechanisms may offer novel opportunities to neutralize a broadly based genetic susceptibility for CAD in a large proportion of the population. In parallel, the newly discovered genes open novel opportunities for disease prediction. In summary, modern MI genetics carries the promise to identify individuals at high risk and to improve prevention and therapy of this important disease.

[Advances in genome-wide association study of coronary heart disease]

As a new effective strategy for researching complex diseases, genome-wide association study (GWAS) has being developed rapidly in recent years worldwide. The world genomic study has been taken into a new stage, since a series of disease related genes or variants have been identified by GWAS strategy. Coronary heart disease (CHD) is a complex disease that is caused by both environmental and genetic factors, and has become one of the leading causes of death and disability worldwide. With the application of GWAS strategy, researchers from all over the world have identified many susceptibility loci or regions of CHD that were unable to be identified by candidate gene case-control study. The present paper reviewed the important progresses worldwide attained in GWAS of CHD in recent years. Then it is also expounded the challenges we are facing nowadays in GWAS as well as the future study direction. The information outlined in this paper provides us a valuable guidance upon further exploration into genetic mechanism of CHD.

Implications of genetic polymorphisms in inflammation-induced atherosclerosis

Inflammation is the mainstay of atherosclerosis and is an important governing factor at all stages of the disease process from lesion formation to plaque build-up and final end-stage rupture and thrombosis. An overview of the numerous clinico-epidemiological studies on the association between inflammatory gene polymorphisms and Cardiovascular disease (CVD) and its co-morbidities have shown that the risk associated with any single genotype is modest while the haplotypes, especially those defined on the basis of tag-SNP approach, have better coverage of the gene and show moderately higher impact on disease risk. Nevertheless, even these associations have been inconsistent with low cross-race repeatability. This has been attributed to many plausible causes such as clinical heterogeneity, sample selection criteria, variable genetic landscapes across different ethnic groups, confounding effect of co-morbidities etc. On the other hand, unbiased studies such as the family-based linkage and case-control based associations that have taken into account, thousands of genotypic markers spanning the whole genome, have had the ability to identify novel genetic loci for coronary artery disease. These studies have shown that many inflammatory genes are involved in the regulation of specific biomarkers of inflammation that collectively contribute to the disease-associated risk. In addition, there appears to be considerable cross talk between the different biochemical and metabolic processes. Therefore, consideration of all these factors can build towards an 'atherosclerotic bionetwork' that can refine our quest for developing a robust risk stratification tool for cardiovascular disease.

Association between a literature-based genetic risk score and cardiovascular events in women

CONTEXT

While multiple genetic markers associated with cardiovascular disease have been identified by genome-wide association studies, their aggregate effect on risk beyond traditional factors is uncertain, particularly among women.

OBJECTIVE

To test the predictive ability of a literature-based genetic risk score for cardiovascular disease.

DESIGN, SETTING, AND PARTICIPANTS

Prospective cohort of 19,313 initially healthy white women in the Women's Genome Health Study followed up over a median of 12.3 years (interquartile range, 11.6-12.8 years). Genetic risk scores were constructed from the National Human Genome Research Institute's catalog of genome-wide association study results published between 2005 and June 2009.

MAIN OUTCOME MEASURE

Incident myocardial infarction, stroke, arterial revascularization, and cardiovascular death.

RESULTS

A total of 101 single nucleotide polymorphisms reported to be associated with cardiovascular disease or at least 1 intermediate cardiovascular disease phenotype at a published P value of less than 10(-7) were identified and risk alleles were added to create a genetic risk score. During follow-up, 777 cardiovascular disease events occurred (199 myocardial infarctions, 203 strokes, 63 cardiovascular deaths, 312 revascularizations). After adjustment for age, the genetic risk score had a hazard ratio (HR) for cardiovascular disease of 1.02 per risk allele (95% confidence interval [CI], 1.00-1.03/risk allele; P = .006). This corresponds to an absolute cardiovascular disease risk of 3% over 10 years in the lowest tertile of genetic risk (73-99 risk alleles) and 3.7% in the highest tertile (106-125 risk alleles). However, after adjustment for traditional factors, the genetic risk score did not improve discrimination or reclassification (change in c index from Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults [ATP III] risk score, 0; net reclassification improvement, 0.5%; [P = .24]). The genetic risk score was not associated with cardiovascular disease risk (ATP III-adjusted HR/allele, 1.00; 95% CI, 0.99-1.01). In contrast, self-reported family history remained significantly associated with cardiovascular disease in multivariable models.

CONCLUSION

After adjustment for traditional cardiovascular risk factors, a genetic risk score comprising 101 single nucleotide polymorphisms was not significantly associated with the incidence of total cardiovascular disease.

Personalized genomic information: preparing for the future of genetic medicine

The falling cost of sequencing means that we are rapidly approaching an era in which access to personalized genomic information is likely to be widespread. Here, four experts with different insights into the field of genomic medicine answer questions about the prospects for using this type of information. Their responses highlight the diverse range of issues that must be addressed - ranging from scientific to ethical and logistical - to ensure that the potential benefits of personal genomic information outweigh the costs to both individuals and societies.

The interleukin-1 cluster, dyslipidaemia and risk of myocardial infarction

Coronary heart disease (CHD) is among the most serious worldwide health problems. Recent genetic studies have robustly identified a number of polymorphic loci throughout the genome that are associated with disease risk but it is certain that more remain to be discovered. It is well established that inflammation plays a key role in the pathophysiology of CHD. Determining whether or not polymorphisms in genes involved in the inflammatory cascade affect the risk of CHD is of considerable interest with respect to understanding the direction of the causal arrow between increased expression of inflammatory genes and CHD. Establishing an association between the variation in inflammatory genes and CHD would provide conceptual support for the use of appropriately specific anti-inflammatory agents in CHD prevention and, potentially, suggest new therapeutic targets. This month in BMC Medicine, Benjamin Brown and colleagues adopt a family-based case-control association study design to address this question. In a large number of CHD cases and healthy sibling controls genotyped for 51 mainly coding single nucleotide polymorphisms (SNPs), they find evidence for the association of a common haplotype at the Interleukin-1 (IL-1) cluster with CHD which appears to be stronger in younger cases without hypercholesterolaemia. They also find suggestive evidence for an association between this same haplotype and hypercholesterolaemia. If replicated in other cohorts, these results could be of substantial importance in advancing the understanding of the way in which inflammatory genes affect coronary heart disease risk.See the associated research paper by Brown et al: http://www.biomedcentral.com/1741-7015/8/5.

Genome-wide association studies in myocardial infarction and coronary artery disease

Myocardial infarction (MI) and its major determinant, coronary artery disease (CAD), are complex diseases arising from the interaction between several genetic and environmental factors. Until recently, the genetic basis of these diseases was poorly understood. Genome-wide genetic association studies have afforded a comprehensive insight into the association between genetic variants and diseases. To date, seven genome-wide association studies have been conducted in CAD/MI, identifying thirteen genomic regions at which common genetic variants influence the predisposition to these diseases. This review article summarizes the progress achieved in the genetic basis of MI and CAD by means of genome-wide association studies and the potential clinical applications of these findings.

ACPA: automated cluster plot analysis of genotype data

Genome-wide association studies have become standard in genetic epidemiology. Analyzing hundreds of thousands of markers simultaneously imposes some challenges for statisticians. One issue is the problem of multiplicity, which has been compared with the search for the needle in a haystack. To reduce the number of false-positive findings, a number of quality filters such as exclusion of single-nucleotide polymorphisms (SNPs) with a high missing fraction are employed. Another filter is exclusion of SNPs for which the calling algorithm had difficulties in assigning the genotypes. The only way to do this is the visual inspection of the cluster plots, also termed signal intensity plots, but this approach is often neglected. We developed an algorithm ACPA (automated cluster plot analysis), which performs this task automatically for autosomal SNPs. It is based on counting samples that lie too close to the cluster of a different genotype; SNPs are excluded when a certain threshold is exceeded. We evaluated ACPA using 1,000 randomly selected quality controlled SNPs from the Framingham Heart Study data that were provided for the Genetic Analysis Workshop 16. We compared the decision of ACPA with the decision made by two independent readers. We achieved a sensitivity of 88% (95% CI: 81%-93%) and a specificity of 86% (95% CI: 83%-89%). In a screening setting in which one aims at not losing any good SNP, we achieved 99% (95% CI: 98%-100%) specificity and still detected every second low-quality SNP.

Evaluation of single-nucleotide polymorphism imputation using random forests

Genome-wide association studies (GWAS) have helped to reveal genetic mechanisms of complex diseases. Although commonly used genotyping technology enables us to determine up to a million single-nucleotide polymorphisms (SNPs), causative variants are typically not genotyped directly. A favored approach to increase the power of genome-wide association studies is to impute the untyped SNPs using more complete genotype data of a reference population.

Random forests (RF) provides an internal method for replacing missing genotypes. A forest of classification trees is used to determine similarities of probands regarding their genotypes. These proximities are then used to impute genotypes of untyped SNPs.

We evaluated this approach using genotype data of the Framingham Heart Study provided as Problem 2 for Genetic Analysis Workshop 16 and the Caucasian HapMap samples as reference population. Our results indicate that RFs are faster but less accurate than alternative approaches for imputing untyped SNPs.

Association of genetic variants in SEMA3F, CLEC16A, LAMA3, and PCSK2 with myocardial infarction in Japanese individuals

OBJECTIVE

The purpose of the present study was to identify genetic variants that confer susceptibility to myocardial infarction (MI) in Japanese individuals.

METHODS

The study population comprised 5014 Japanese individuals, including 1444 subjects with MI and 3570 controls. The 150 polymorphisms examined in the present study were selected by a genome-wide association study for ischemic stroke with the use of the GeneChip Human Mapping 500K Array Set (Affymetrix), and were determined by a method that combines the polymerase chain reaction and sequence-specific oligonucleotide probes with suspension array technology.

RESULTS

An initial screen by the chi-square test revealed that the A-->G polymorphism of SEMA3F (rs12632110), the C-->T polymorphism of CLEC16A (rs9925481), the A-->G polymorphism of LAMA3 (rs12373237), and the C-->G polymorphism of PCSK2 (rs6080699) were significantly (false discovery rate for allele frequencies of <0.05) associated with MI. Subsequent multivariable logistic regression analysis with adjustment for covariates and a stepwise forward selection procedure revealed that the A-->G polymorphism of SEMA3F (dominant model; P=0.0014; odds ratio, 0.76), the C-->T polymorphism of CLEC16A (dominant model; P=0.0009; odds ratio, 0.75), the A-->G polymorphism of LAMA3 (recessive model; P=0.0099; odds ratio, 0.80), and the C-->G polymorphism of PCSK2 (recessive model; P=0.0155; odds ratio, 1.19) were significantly (P<0.05) associated with the prevalence of MI.

CONCLUSION

Determination of these genotypes may prove informative for assessment of the genetic risk for MI in Japanese individuals.

A genome-wide association study reveals variants in ARL15 that influence adiponectin levels

The adipocyte-derived protein adiponectin is highly heritable and inversely associated with risk of type 2 diabetes mellitus (T2D) and coronary heart disease (CHD). We meta-analyzed 3 genome-wide association studies for circulating adiponectin levels (n = 8,531) and sought validation of the lead single nucleotide polymorphisms (SNPs) in 5 additional cohorts (n = 6,202). Five SNPs were genome-wide significant in their relationship with adiponectin (P< or =5x10(-8)). We then tested whether these 5 SNPs were associated with risk of T2D and CHD using a Bonferroni-corrected threshold of P< or =0.011 to declare statistical significance for these disease associations. SNPs at the adiponectin-encoding ADIPOQ locus demonstrated the strongest associations with adiponectin levels (P-combined = 9.2x10(-19) for lead SNP, rs266717, n = 14,733). A novel variant in the ARL15 (ADP-ribosylation factor-like 15) gene was associated with lower circulating levels of adiponectin (rs4311394-G, P-combined = 2.9x10(-8), n = 14,733). This same risk allele at ARL15 was also associated with a higher risk of CHD (odds ratio [OR] = 1.12, P = 8.5x10(-6), n = 22,421) more nominally, an increased risk of T2D (OR = 1.11, P = 3.2x10(-3), n = 10,128), and several metabolic traits. Expression studies in humans indicated that ARL15 is well-expressed in skeletal muscle. These findings identify a novel protein, ARL15, which influences circulating adiponectin levels and may impact upon CHD risk.

Peroxisome proliferator-activated receptor gamma polymorphisms and coronary heart disease

Single nucleotide polymorphisms (SNPs) in the peroxisome proliferator-activated receptor γ (PPARG) gene have been associated with cardiovascular risk factors, particularly obesity and diabetes. We assessed the relationship between 4 PPARG SNPs (C-681G, C-689T, Pro12Ala, and C1431T) and coronary heart disease (CHD) in the PRIME (249 cases/494 controls, only men) and ADVANCE (1,076 cases/805 controls, men or women) studies. In PRIME, homozygote individuals for the minor allele of the PPARG C-689T, Pro12Ala, and C1431T SNPs tended to have a higher risk of CHD than homozygote individuals for the frequent allele (adjusted OR [95% CI] = 3.43 [0.96–12.27], P = .058, 3.41 [0.95–12.22], P = .060 and 5.10 [0.99–26.37], P = .050, resp.). No such association could be detected in ADVANCE. Haplotype distributions were similar in cases and control in both studies. A meta-analysis on the Pro12Ala SNP, based on our data and 11 other published association studies (6,898 CHD cases/11,287 controls), revealed that there was no evidence for a significant association under the dominant model (OR = 0.99 [0.92–1.07], P = .82). However, there was a borderline association under the recessive model (OR = 1.29 [0.99–1.67], P = .06) that became significant when considering men only (OR = 1.73 [1.20–2.48], P = .003). In conclusion, the PPARG Ala12Ala genotype might be associated with a higher CHD risk in men but further confirmation studies are needed.

Common polymorphisms influencing serum uric acid levels contribute to susceptibility to gout, but not to coronary artery disease

Background

Recently, a large meta-analysis including over 28,000 participants identified nine different loci with association to serum uric acid (UA) levels. Since elevated serum UA levels potentially cause gout and are a possible risk factor for coronary artery disease (CAD) and myocardial infarction (MI), we performed two large case-control association analyses with participants from the German MI Family Study. In the first study, we assessed the association of the qualitative trait gout and ten single nucleotide polymorphisms (SNP) markers that showed association to UA serum levels. In the second study, the same genetic polymorphisms were analyzed for association with CAD.

Methods and Findings

A total of 683 patients suffering from gout and 1,563 healthy controls from the German MI Family Study were genotyped. Nine SNPs were identified from a recently performed genome-wide meta-analysis on serum UA levels (rs12129861, rs780094, rs734553, rs2231142, rs742132, rs1183201, rs12356193, rs17300741 and rs505802). Additionally, the marker rs6855911 was included which has been associated with gout in our cohort in a previous study. SNPs rs734553 and rs6855911, located in SLC2A9, and SNP rs2231142, known to be a missense polymorphism in ABCG2, were associated with gout (p = 5.6*10⁻⁷, p = 1.1*10⁻⁷, and p = 1.3*10⁻³, respectively). Other SNPs in the genes PDZK1, GCKR, LRRC16A, SLC17A1-SLC17A3, SLC16A9, SLC22A11 and SLC22A12 failed the significance level. None of the ten markers were associated with risk to CAD in our study sample of 1,473 CAD cases and 1,241 CAD-free controls.

Conclusion

SNP markers in SLC2A9 and ABCG2 genes were found to be strongly associated with the phenotype gout. However, not all SNP markers influencing serum UA levels were also directly associated with the clinical manifestation of gout in our study sample. In addition, none of these SNPs showed association with the risk to CAD in the German MI Family Study.

Genetic determinants of circulating sphingolipid concentrations in European populations

Sphingolipids have essential roles as structural components of cell membranes and in cell signalling, and disruption of their metabolism causes several diseases, with diverse neurological, psychiatric, and metabolic consequences. Increasingly, variants within a few of the genes that encode enzymes involved in sphingolipid metabolism are being associated with complex disease phenotypes. Direct experimental evidence supports a role of specific sphingolipid species in several common complex chronic disease processes including atherosclerotic plaque formation, myocardial infarction (MI), cardiomyopathy, pancreatic beta-cell failure, insulin resistance, and type 2 diabetes mellitus. Therefore, sphingolipids represent novel and important intermediate phenotypes for genetic analysis, yet little is known about the major genetic variants that influence their circulating levels in the general population. We performed a genome-wide association study (GWAS) between 318,237 single-nucleotide polymorphisms (SNPs) and levels of circulating sphingomyelin (SM), dihydrosphingomyelin (Dih-SM), ceramide (Cer), and glucosylceramide (GluCer) single lipid species (33 traits); and 43 matched metabolite ratios measured in 4,400 subjects from five diverse European populations. Associated variants (32) in five genomic regions were identified with genome-wide significant corrected p-values ranging down to 9.08x10(-66). The strongest associations were observed in or near 7 genes functionally involved in ceramide biosynthesis and trafficking: SPTLC3, LASS4, SGPP1, ATP10D, and FADS1-3. Variants in 3 loci (ATP10D, FADS3, and SPTLC3) associate with MI in a series of three German MI studies. An additional 70 variants across 23 candidate genes involved in sphingolipid-metabolizing pathways also demonstrate association (p = 10(-4) or less). Circulating concentrations of several key components in sphingolipid metabolism are thus under strong genetic control, and variants in these loci can be tested for a role in the development of common cardiovascular, metabolic, neurological, and psychiatric diseases.