Genetics of coronary artery disease in the post‐GWAS era

Abstract

During the past decade, genome‐wide association studies (GWAS) have transformed our understanding of many heritable traits. Three recent large‐scale GWAS meta‐analyses now further markedly expand the knowledge on coronary artery disease (CAD) genetics in doubling the number of loci with genome‐wide significant signals. Here, we review the unprecedented discoveries of CAD GWAS on low‐frequency variants, underrepresented populations, sex differences and integrated polygenic risk. We present the milestones of CAD GWAS and post‐GWAS studies from 2007 to 2021, and the trend in identification of variants with smaller odds ratio by year due to the increasing sample size. We compile the 321 CAD loci discovered thus far and classify candidate genes as well as distinct functional pathways on the road to indepth biological investigation and identification of novel treatment targets. We draw attention to systems genetics in integrating these loci into gene regulatory networks within and across tissues. We review the traits, biomarkers and diseases scrutinized by Mendelian randomization studies for CAD. Finally, we discuss the potentials and concerns of polygenic scores in predicting CAD risk in patient care as well as future directions of GWAS and post‐GWAS studies in the field of precision medicine.

Risk Prediction of Cardiovascular Events by Exploration of Molecular Data with Explainable Artificial Intelligence

“…Figure 1 gives a brief overview on how molecular data can be used for the purpose of risk prediction of recurrent cardiovascular events. Genome-wide association studies (GWAS) have pinpointed hundreds of genetic loci and SNPs (Single Nucleotide Polymorphisms) contributing to the risk of developing CAD [14,16,61]. These SNPs also have value in predicting recurrent events as well as the efficacy of preventive treatments [17,[62][63][64][65][66].…”

“…Genome-wide association studies (GWAS), which associate genetic variants on the DNA to a phenotypic trait, such as CVD, have become a popular and successful tool within the past decade [13,14]. This has enabled researchers to identify hundreds of heritable genetic risk loci harboring CAD-associated DNA-variants, which affect CAD via additive and non-additive effects [15][16][17][18].…”

“…Lately, a multitude of reviews have covered the topics of classical risk prediction models [23], artificial intelligence (AI) applied to recognize CVD in various forms of imaging data [24][25][26], or AI for the purpose of electrocardiogram-based prediction and drug discovery [26]. Additionally, reviews considering the molecular and genetic aspects of CVDs have been published in recent years, mostly focusing on describing the complex processes underlying disease [14,27,28]. These reviews consider incident CVD rather than recurrent events.…”

Polygenic Risk for Coronary Artery Disease in the Scottish and English Population

“…Evolutionary genetics determine the allele frequency in a population, which is modulated by natural selection and stochastic forces such as genet drift [9]. These and other factors contribute to variation among individuals in the same population and to variation among populations [8][9][10]. While mutations causing monogenic disorders are under evolutionary pressure this applies to a lesser extent to common risk alleles with small effect sizes [10].…”

“…These and other factors contribute to variation among individuals in the same population and to variation among populations [8][9][10]. While mutations causing monogenic disorders are under evolutionary pressure this applies to a lesser extent to common risk alleles with small effect sizes [10]. Indeed, genome-wide association studies (GWAS) revealed that most common cardiometabolic conditions like hypertension, diabetes mellitus, or hyperlipidemia are affected by hundreds of risk alleles, most of which are common [11].…”