We propose to leverage the full scope of the Atherosclerosis Risk in Communities (ARIC) study to investigate the interaction between genetic variation and modifiable environments in the determination of incident CHD. We are currently funded (HL087641) to type 600 incident CHD cases and 1,400 individuals in a cohort random sample (CRS) for 500,000 TagSNPs. For the proposed research, we will add an additional 1,100 incident CHD cases (a total of 1,700 CHD cases) and an additional 1,000 CRS individuals (a total of 2,400 CRS individuals) for the same genome-wide collection of SNPs to identify environment-specific genetic effects influencing incident CHD. The already-funded grant is only powered for main effects;this new opportunity allows the study to be powered for interaction effects. We will perform a combination of computer science and sliding window haplotype analyses which directly incorporate gene-environment interaction effects to identify SNP-environmental combinations that predict incident CHD. Replication of initial findings will be done by investigating the concordance of """"""""statistical significance and direction of effect"""""""" between the top 10,000 (~2%) SNPs from ARIC and the top 10,000 SNPs from analyses of gene-environment interaction effects in the Framingham Heart Study. For those SNPs that consistently replicate throughout, we will genotype the remainder of the ARIC cohort (-11,900 individuals) so that the full spectrum of environmental and phenotypic variation is represented in initial follow-up analyses. The CRS provides an ideal cost effective opportunity for initial studies to use GWA methods to investigate gene-environment interactions for other heart, lung and blood phenotypes. We will begin to identify genes influencing other heart, lung and blood phenotypes using the CRS and the 500,000 SNP dataset. Finally, we will support further analysis and discovery by sharing the data and analysis algorithms with other scientists in a timely fashion. This project will bring together appropriate population-based samples, good phenotyping, details on environmental measures and state-of-the art analyses to identify environment-specific genetic effects influencing CHD, along with other heart, lung and blood phenotypes. Identification of these environment-specific genetic effects will improve risk prediction and shed light on novel biological pathways bridging health and disease.
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