The Integrated Landscape of Biological Candidate Causal Genes in Coronary Artery Disease

The past decade has seen tremendous progress in understanding the genetic architecture of coronary artery disease (CAD). Khera and Kathiresan review research efforts that have improved our understanding of the genetic drivers of CAD, and discuss the promises and challenges of integrating genetic information into routine clinical practice.

Genetic factors contribute importantly to the risk of coronary artery disease (CAD), and in the past decade, there has been major progress in this area. The tools applied include genome-wide association studies encompassing >200,000 individuals complemented by bioinformatic approaches, including 1000 Genomes imputation, expression quantitative trait locus analyses, and interrogation of Encyclopedia of DNA Elements, Roadmap, and other data sets. close to 60 common SNPs (minor allele frequency>0.05) associated with CAD risk and reaching genome-wide significance (P<5 × 10(-8)) have been identified. Furthermore, a total of 202 independent signals in 109 loci have achieved a false discovery rate (q<0.05) and together explain 28% of the estimated heritability of CAD. These data have been used successfully to create genetic risk scores that can improve risk prediction beyond conventional risk factors and identify those individuals who will benefit most from statin therapy. Such information also has important applications in clinical medicine and drug discovery by using a Mendelian randomization approach to interrogate the causal nature of many factors found to associate with CAD risk in epidemiological studies. In contrast to genome-wide association studies, whole-exome sequencing has provided valuable information directly relevant to genes with known roles in plasma lipoprotein metabolism but has, thus far, failed to identify other rare coding variants linked to CAD. Overall, recent studies have led to a broader understanding of the genetic architecture of CAD and demonstrate that it largely derives from the cumulative effect of multiple common risk alleles individually of small effect size rather than rare variants with large effects on CAD risk. Despite this success, there has been limited progress in understanding the function of the novel loci; the majority of which are in noncoding regions of the genome.

Gene-based tests such as versatile gene-based association study (VEGAS) are commonly used following per-single nucleotide polymorphism (SNP) GWAS (genome-wide association studies) analysis. Two limitations of VEGAS were that the HapMap2 reference set was used to model the correlation between SNPs and only autosomal genes were considered. HapMap2 has now been superseded by the 1,000 Genomes reference set, and whereas early GWASs frequently ignored the X chromosome, it is now commonly included. Here we have developed VEGAS2, an extension that uses 1,000 Genomes data to model SNP correlations across the autosomes and chromosome X. VEGAS2 allows greater flexibility when defining gene boundaries. VEGAS2 offers both a user-friendly, web-based front end and a command line Linux version. The online version of VEGAS2 can be accessed through https://vegas2.qimrberghofer.edu.au/. The command line version can be downloaded from https://vegas2.qimrberghofer.edu.au/zVEGAS2offline.tgz. The command line version is developed in Perl, R and shell scripting languages; source code is available for further development.