There is mounting evidence from studies of familial aggregation, candidate genes, and the molecular genetics of inherited arrhythmias that genetic variation influences susceptibility to sudden cardiac arrest (SCA), but the genetic architecture of SCA in the community remains unknown. This is a final submission of a revised application to identify novel associations (main effects) of common genetic variation across the genome with SCA, using a case-control, genome-wide association study (GWAS), design. We take advantage of available DNA from a large, population-based study of SCA from Seattle and King County (Washington);and, we use existing whole genome scan (WGS) data for controls. Among European Americans (EAs), we will conduct a GWAS of 2500 SCA cases and 7500 controls (step 1), followed by fine mapping of 100 genomic regions among 2500 SCA cases and 2500 controls (step 2);and replication in an independent sample of 1000 SCA cases and 1000 controls (step 3). We will obtain a WGS on 2500 SCA cases included in the Cardiac Arrest Blood Study - Repository (CABS-R) and 750 controls from the Cardiac Arrest Blood Study (CABS). We use existing WGS data from 600 controls (non-cases) from the Heart Attack Risk in Puget Sound (HARPS) Study and 6150 controls from the Atherosclerosis Risk in Communities (ARIC) study. Using WGS data from the Affymetrix 6.0 array, we will examine the associations of 700k single nucleotide polymorphisms (SNPs) with SCA to identify 100 genomic regions, based upon the """"""""top hit"""""""" p-values, in EAs (step 1). Using an Illumina Bead Array, we will genotype 1536 SNPs, including approximately 15 SNPs from each of the 100 genomic regions from step 1, in 2500 EA cases and 2500 EA controls (step 2). Finally, we will replicate the findings from steps 1 and 2 (for those SNPs with a p-value <0.0000014) using an independent sample of 1000 EA cases and 1000 EA controls (step 3). We will frequency match the selection of controls used in the GWAS (step 1) to the population (allelic) structure of the SCA cases;and, we will use genomic control in step 2 to minimize bias from population stratification. Given the large sample sizes and the three step design, we will have >80% statistical power to detect modest size genetic effects, while limiting the expected number of false positive associations. While underpowered, we include an exploratory WGS of 400 African-American (AA) SCA cases from the CABS-R and 900 AA controls (with available WGS data) from ARIC that also will provide a resource for future collaborative efforts. The identification of genetic variation across the genome associated with SCA using a large, case-control, GWAS followed by fine mapping and replication will minimize false negative and positive associations and provide insight into the mechanisms of SCA that will help to target interventions to reduce mortality from SCA.
There is mounting evidence that family history influences the risk of sudden cardiac arrest (SCA), a devastating cardiac event that accounts for 10% of total mortality in the US;however, the variation in the human genome associated with SCA remains unknown. We will investigate variation throughout the human genome to identify novel genes that influence SCA. The study results will provide insight into the molecular mechanisms of SCA and potentially help target the development of novel drug therapies to reduce mortality from SCA.
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