Leveraging family-based association data to further explain the missing heritability for coronary artery disease and myocardial infarction

Atherosclerosis and the resulting sequelae coronary artery disease (CAD) and myocardial infarction (MI) are complex diseases and leading causes of death worldwide. Since 2007, genome-wide association studies have identified more than 280 common single-nucleotide polymorphisms that firmly associate with higher risk of CAD and MI.The genetic component reflected by hundreds of single-nucleotide polymorphisms identified in genome-wide association studies cannot explain the familial clustering of these diseases as indicated by a positive family history. Familial clustering of both CAD and MI is mediated by either rare variants with a more profound effect than common variants, or by specific interactions between rare and common variants.Here, we propose to search for rare and low-frequency variants in CAD and MI cases enriched for positive family history to discover targets relevant for therapy.We will use low-coverage whole-genome sequencing (1× coverage) to analyze the German MI family study, comprising 2,566 affected siblings presenting with premature MI.Low-coverage whole-genome sequencing effectively identifies new variants and costs as much as traditional sequencing approaches, but low-coverage whole-genome sequencing is more sensitive than traditional approaches. We built our project on the assumption that we will need fewer affected sibling pairs than unrelated case-control pairs to reach the same statistical power. For example, a conventional case-control cohort must comprise 27,000 cases and 24,300 controls to reach the power we achieve with our smaller German MI family study cohort. Thus, the same power is reached by a sample size reduced by a factor of nine by using a cohort comprising affected sibling pairs, and by modeling both linkage and association effects. By using this novel approach, we expect to discover rare variants that associate with CAD and MI.We anticipate one of our key findings to be the identification of genetic variants that profoundly affect CAD risk. Such discovery may lead to identification of novel druggable targets and to improvements in predicting risk in affected families.