Cardiovascular Risk Factors and Ischemic Heart Disease: Is the Confluence of Risk Factors Greater Than the Parts? A Genetic Approach.

BACKGROUND: Cardiovascular risk factors tend to aggregate. The biological and predictive value of this aggregation is questioned and genetics could shed light on this debate. Our aims were to reappraise the impact of risk factor confluence on ischemic heart disease (IHD) risk by testing whether genetic risk scores (GRSs) associated with these factors interact on an additive or multiplicative scale, and to determine whether these interactions provide additional value for predicting IHD risk. METHODS AND RESULTS: We selected genetic variants associated with blood pressure, body mass index, waist circumference, triglycerides, type-2 diabetes mellitus, high-density lipoprotein and low-density lipoprotein cholesterol, and IHD to create GRSs for each factor. We tested and meta-analyzed the impact of additive (synergy index) and multiplicative (ßinteraction) interactions between each GRS pair in 1 case-control (n=6042) and 4 cohort studies (n=17 794) and evaluated the predictive value of these interactions. We observed 2 multiplicative interactions: GRSLDL·GRSTriglycerides (ßinteraction=-0.096; SE=0.028) and nonpleiotropic GRSIHD·GRSLDL (ßinteraction=0.091; SE=0.028). Inclusion of these interaction terms did not improve predictive capacity. CONCLUSIONS: The confluence of low-density lipoprotein cholesterol and triglycerides genetic risk load has an additive effect on IHD risk. The interaction between low-density lipoprotein cholesterol and IHD genetic load is more than multiplicative, supporting the hazardous impact on atherosclerosis progression of the combination of inflammation and increased lipid levels. The capacity of risk factor confluence to improve IHD risk prediction is questionable. Further studies in larger samples are warranted to confirm and expand our results.

Analysis of gene-gene interactions among common variants in candidate cardiovascular genes in coronary artery disease.

OBJECTIVE: Only a small fraction of coronary artery disease (CAD) heritability has been explained by common variants identified to date. Interactions between genes of importance to cardiovascular regulation may account for some of the missing heritability of CAD. This study aimed to investigate the role of gene-gene interactions in common variants in candidate cardiovascular genes in CAD. APPROACH AND RESULTS: 2,101 patients with CAD from the British Heart Foundation Family Heart Study and 2,426 CAD-free controls were included in the discovery cohort. All subjects were genotyped with the Illumina HumanCVD BeadChip enriched for genes and pathways relevant to the cardiovascular system and disease. The primary analysis in the discovery cohort examined pairwise interactions among 913 common (minor allele frequency >0.1) independent single nucleotide polymorphisms (SNPs) with at least nominal association with CAD in single locus analysis. A secondary exploratory interaction analysis was performed among all 11,332 independent common SNPs surviving quality control criteria. Replication analyses were conducted in 2,967 patients and 3,075 controls from the Myocardial Infarction Genetics Consortium. None of the interactions amongst 913 SNPs analysed in the primary analysis was statistically significant after correction for multiple testing (required P<1.2x10-7). Similarly, none of the pairwise gene-gene interactions in the secondary analysis reached statistical significance after correction for multiple testing (required P = 7.8x10-10). None of 36 suggestive interactions from the primary analysis or 31 interactions from the secondary analysis was significant in the replication cohort. Our study had 80% power to detect odds ratios > 1.7 for common variants in the primary analysis. CONCLUSIONS: Moderately large additive interactions between common SNPs in genes relevant to cardiovascular disease do not appear to play a major role in genetic predisposition to CAD. The role of genetic interactions amongst less common SNPs and with medium and small magnitude effects remain to be investigated.

Big data challenges in bone research: genome-wide association studies and next-generation sequencing.

Genome-wide association studies (GWAS) have been developed as a practical method to identify genetic loci associated with disease by scanning multiple markers across the genome. Significant advances in the genetics of complex diseases have been made owing to advances in genotyping technologies, the progress of projects such as HapMap and 1000G and the emergence of genetics as a collaborative discipline. Because of its great potential to be used in parallel by multiple collaborators, it is important to adhere to strict protocols assuring data quality and analyses. Quality control analyses must be applied to each sample and each single-nucleotide polymorphism (SNP). The software package PLINK is capable of performing the whole range of necessary quality control tests. Genotype imputation has also been developed to substantially increase the power of GWAS methodology. Imputation permits the investigation of associations at genetic markers that are not directly genotyped. Results of individual GWAS reports can be combined through meta-analysis. Finally, next-generation sequencing (NGS) has gained popularity in recent years through its capacity to analyse a much greater number of markers across the genome. Although NGS platforms are capable of examining a higher number of SNPs compared with GWA studies, the results obtained by NGS require careful interpretation, as their biological correlation is incompletely understood. In this article, we will discuss the basic features of such protocols.

Pathogenesis of coronary artery disease: focus on genetic risk factors and identification of genetic variants.

Coronary artery disease (CAD) is the leading cause of death and disability worldwide, and its prevalence is expected to increase in the coming years. CAD events are caused by the interplay of genetic and environmental factors, the effects of which are mainly mediated through cardiovascular risk factors. The techniques used to study the genetic basis of these diseases have evolved from linkage studies to candidate gene studies and genome-wide association studies. Linkage studies have been able to identify genetic variants associated with monogenic diseases, whereas genome-wide association studies have been more successful in determining genetic variants associated with complex diseases. Currently, genome-wide association studies have identified approximately 40 loci that explain 6% of the heritability of CAD. The application of this knowledge to clinical practice is challenging, but can be achieved using various strategies, such as genetic variants to identify new therapeutic targets, personal genetic information to improve disease risk prediction, and pharmacogenomics. The main aim of this narrative review is to provide a general overview of our current understanding of the genetics of coronary artery disease and its potential clinical utility.

DNA isolation method is a source of global DNA methylation variability measured with LUMA. Experimental analysis and a systematic review.

In DNA methylation, methyl groups are covalently bound to CpG dinucleotides. However, the assumption that methyl groups are not lost during routine DNA extraction has not been empirically tested. To avoid nonbiological associations in DNA methylation studies, it is essential to account for potential batch effect bias in the assessment of this epigenetic mechanism. Our purpose was to determine if the DNA isolation method is an independent source of variability in methylation status. We quantified Global DNA Methylation (GDM) by luminometric methylation assay (LUMA), comparing the results from 3 different DNA isolation methods. In the controlled analysis (n = 9), GDM differed slightly for the same individual depending on extraction method. In the population analysis (n = 580) there were significant differences in GDM between the 3 DNA isolation methods (medians, 78.1%, 76.5% and 75.1%; p<0.001). A systematic review of published data from LUMA GDM studies that specify DNA extraction methods is concordant with our findings. DNA isolation method is a source of GDM variability measured with LUMA. To avoid possible bias, the method used should be reported and taken into account in future DNA methylation studies.

Hypothesis-based analysis of gene-gene interactions and risk of myocardial infarction.

The genetic loci that have been found by genome-wide association studies to modulate risk of coronary heart disease explain only a fraction of its total variance, and gene-gene interactions have been proposed as a potential source of the remaining heritability. Given the potentially large testing burden, we sought to enrich our search space with real interactions by analyzing variants that may be more likely to interact on the basis of two distinct hypotheses: a biological hypothesis, under which MI risk is modulated by interactions between variants that are known to be relevant for its risk factors; and a statistical hypothesis, under which interacting variants individually show weak marginal association with MI. In a discovery sample of 2,967 cases of early-onset myocardial infarction (MI) and 3,075 controls from the MIGen study, we performed pair-wise SNP interaction testing using a logistic regression framework. Despite having reasonable power to detect interaction effects of plausible magnitudes, we observed no statistically significant evidence of interaction under these hypotheses, and no clear consistency between the top results in our discovery sample and those in a large validation sample of 1,766 cases of coronary heart disease and 2,938 controls from the Wellcome Trust Case-Control Consortium. Our results do not support the existence of strong interaction effects as a common risk factor for MI. Within the scope of the hypotheses we have explored, this study places a modest upper limit on the magnitude that epistatic risk effects are likely to have at the population level (odds ratio for MI risk 1.3-2.0, depending on allele frequency and interaction model).

Assessment of the value of a genetic risk score in improving the estimation of coronary risk.

BACKGROUND: The American Heart Association has established criteria for the evaluation of novel markers of cardiovascular risk. In accordance with these criteria, we assessed the association between a multi-locus genetic risk score (GRS) and incident coronary heart disease (CHD), and evaluated whether this GRS improves the predictive capacity of the Framingham risk function. METHODS AND RESULTS: Using eight genetic variants associated with CHD but not with classical cardiovascular risk factors (CVRFs), we generated a multi-locus GRS, and found it to be linearly associated with CHD in two population based cohorts: The REGICOR Study (n=2351) and The Framingham Heart Study (n=3537) (meta-analyzed HR [95%CI]: ~1.13 [1.01-1.27], per unit). Inclusion of the GRS in the Framingham risk function improved its discriminative capacity in the Framingham sample (c-statistic: 72.81 vs.72.37, p=0.042) but not in the REGICOR sample. According to both the net reclassification improvement (NRI) index and the integrated discrimination index (IDI), the GRS improved re-classification among individuals with intermediate coronary risk (meta-analysis NRI [95%CI]: 17.44 [8.04; 26.83]), but not overall. CONCLUSIONS: A multi-locus GRS based on genetic variants unrelated to CVRFs was associated with a linear increase in risk of CHD events in two distinct populations. This GRS improves risk reclassification particularly in the population at intermediate coronary risk. These results indicate the potential value of the inclusion of genetic information in classical functions for risk assessment in the intermediate risk population group.

Meta-analyses of the association between cytochrome CYP2C19 loss- and gain-of-function polymorphisms and cardiovascular outcomes in patients with coronary artery disease treated with clopidogrel.

AIMS: To perform a meta-analysis of the association between CYP2C19 loss- and gain-of-function variants and cardiovascular outcomes and bleeding in patients with coronary artery disease treated with clopidogrel, and to explore the causes of heterogeneity between studies. METHODS: A comprehensive literature search was conducted. A random-effects model was used to summarise the results. In the presence of between-study heterogeneity, a meta-regression analysis was performed to identify study characteristics explaining this heterogeneity. RESULTS: Patients who carried a loss-of-function allele, mainly CYP2C19*2, did not present an increased risk of a cardiovascular event, HR =1.23 (95% CI 0.97 to 1.55). Substantial heterogeneity was observed between studies (I(2) =35.6), which was partially explained by the study sample size: the pooled HR was higher among studies with a sample size <500 patients (HR =3.55; 95% CI 1.66 to 7.56) and lower among studies with a sample size ≥500 (HR =1.06; 95% CI 0.89 to 1.26). CYP2C19*2 was associated with an increased risk of a stent thrombosis (HR =2.24; 95% CI 1.52 to 3.30). The gain-of-function allele, mainly CYP2C19*17, was associated with a lower risk of cardiovascular events (HR =0.75; 95% CI 0.66 to 0.87) and a higher risk of major bleeding (HR =1.26; 95% CI 1.05 to 1.50). CONCLUSIONS: Not only CYP2C19 loss-of-function but also gain-of-function alleles should be considered to define the pharmacogenetic response to clopidogrel. The results question the relevance of the CYP2C19 loss-of-function alleles in the prediction of major cardiovascular events beyond stent thrombosis in coronary patients treated with clopidogrel. The gain-of-function variant is associated with a lower risk of cardiovascular events but a higher risk of bleeding.

Post-genomic update on a classical candidate gene for coronary artery disease: ESR1.

BACKGROUND: After age, sex is the most important risk factor for coronary artery disease (CAD). The mechanism through which women are protected from CAD is still largely unknown, but the observed sex difference suggests the involvement of the reproductive steroid hormone signaling system. Genetic association studies of the gene-encoding Estrogen Receptor α (ESR1) have shown conflicting results, although only a limited range of variation in the gene has been investigated. METHODS AND RESULTS: We exploited information made available by advanced new methods and resources in complex disease genetics to revisit the question of ESR1's role in risk of CAD. We performed a meta-analysis of 14 genome-wide association studies (CARDIoGRAM discovery analysis, N=≈87,000) to search for population-wide and sex-specific associations between CAD risk and common genetic variants throughout the coding, noncoding, and flanking regions of ESR1. In addition to samples from the MIGen (N=≈6000), WTCCC (N=≈7400), and Framingham (N=≈3700) studies, we extended this search to a larger number of common and uncommon variants by imputation into a panel of haplotypes constructed using data from the 1000 Genomes Project. Despite the widespread expression of ERα in vascular tissues, we found no evidence for involvement of common or low-frequency genetic variation throughout the ESR1 gene in modifying risk of CAD, either in the general population or as a function of sex. CONCLUSIONS: We suggest that future research on the genetic basis of sex-related differences in CAD risk should initially prioritize other genes in the reproductive steroid hormone biosynthesis system.

Genome-wide association study identifies six new loci influencing pulse pressure and mean arterial pressure.

Numerous genetic loci have been associated with systolic blood pressure (SBP) and diastolic blood pressure (DBP) in Europeans. We now report genome-wide association studies of pulse pressure (PP) and mean arterial pressure (MAP). In discovery (N = 74,064) and follow-up studies (N = 48,607), we identified at genome-wide significance (P = 2.7 × 10(-8) to P = 2.3 × 10(-13)) four new PP loci (at 4q12 near CHIC2, 7q22.3 near PIK3CG, 8q24.12 in NOV and 11q24.3 near ADAMTS8), two new MAP loci (3p21.31 in MAP4 and 10q25.3 near ADRB1) and one locus associated with both of these traits (2q24.3 near FIGN) that has also recently been associated with SBP in east Asians. For three of the new PP loci, the estimated effect for SBP was opposite of that for DBP, in contrast to the majority of common SBP- and DBP-associated variants, which show concordant effects on both traits. These findings suggest new genetic pathways underlying blood pressure variation, some of which may differentially influence SBP and DBP.