Coronary Heart Disease (CHD) is a leading cause of morbidity and mortality in the United States and the world. The broad, long-term goal of our research is to dissect the complex genetic architecture of Coronary Heart Disease (CHD), which will subsequently lead to better prediction and treatment of this devastating disease. We hypothesize that amino acid variants, their interactions with each other, and with the environment will play an important role in the complex etiology of CHD. We expect that in order to unravel the genetic architecture of CHD, we must go beyond the additive model. To determine the role of amino acid variants in CHD, we will use a novel approach to acquire genotype data for a comprehensive set of uncommon (0.010.01) amino acid variants in the full ARIC study. We will use whole- exome sequence data in a subset of 1500 ((1000 EA, ~500 AA) participants from the Atherosclerosis Risk in Communities (ARIC) study and an additional 7500 from the NHLBI Exome Sequencing Project (ESP) and the CHARGE-S project in conjunction with GWAS data to impute a comprehensive set of amino acid variants in the entire ARIC cohort. To analytically dissect the complex architecture of lipid profiles and CHD in this subset of SNPs, we will go beyond the simple models used in GWAS screening into more sophisticated models that fall into three categories. First, a single locus model that is generalized to allow the full range of genetic effects (not just additive). Second, two locus models that test for gene-by-gene interactions. Third, test for genotype- by-sex interactions. Sequence uncertainty and imputation probability will be incorporated into each analysis. Using this novel approach, for the first time, a comprehensive set of uncommon to common amino acid variants will be analyzed in a large sample and in multiple ethnic populations for their association with lipid profiles and incident CHD.
Public Health Relevance
Project Narrative The broad, long-term goal of our research is to dissect the complex genetic architecture of Coronary Heart Disease (CHD), which will subsequently lead to better prediction and treatment of this devastating disease. To accomplish this, we use a novel approach to acquire genotype data for a comprehensive set of uncommon (0.010.01) amino acid variants in the full ARIC study using imputation to leverage both genome-wide association (GWAS) data and whole-exome sequencing data. To analytically dissect the complex architecture of lipid profiles and CHD we will go beyond the additive model by testing a general single locus model, test for pairwise gene-by-gene interactions, and finally test for gene-by-sex interactions.
