Investigating multi-locus epistatic interactions underlying coronary artery disease

Coronary artery disease (CAD) is a complex disease with a strong genetic component. Likewise, the response to treatment may be affected by the individual genetic disposition. Genome-wide association signals for CAD usually do not cover the higher order genetic architecture underlying the disease risk. Here we want to analyse whether epistasis effect, i.e., gene-gene interactions, may play a significant role in determining complex traits. Moreover, we want to study functionally relevant genetic variants that may mimic treatment responses. Coronary artery disease (CAD) is a complex disease which is a leading cause of death globally. So far no large-scale systematic investigation of epistasis had been made in the context of CAD, mainly due to the challenges in both computation power and sample size. The exploration and identification of epistasis is thus of high public interest. Moreover, genetic variants with known functional implications may be used to predict or correlate the effects of cardiovascular treatment modalities. Here we want to use these variants in specific applications to better understand potential primary or secondary cardiovascular effects of medications. We aim to identify epistasis between genomic loci affecting coronary artery disease (CAD) risk by calculating statistical interaction based on individual level genotype data of on a genome-wide scale. Moreover, we want to associate functional variants in candidate genes with cardiovascular related phenotypes as well other potential intermediate traits. All participants with individual-level genotype data available will be analysed. From these, cases with coronary artery disease (CAD) and phenotypes of interest will be extracted. We would like to perform analysis in the full cohort scale.