Genome-wide study of sudden cardiac arrest in the community

描述(申请人提供)：从家族聚集性、候选基因和遗传性心律失常的分子遗传学研究中有越来越多的证据表明，遗传变异会影响心脏骤停(SCA)的易感性，但SCA在社区中的遗传结构仍不清楚。这是一份修订后的申请的最终提交，该申请使用病例对照、全基因组关联研究(GWAS)设计，确定跨基因组的常见遗传变异与SCA的新关联(主效应)。我们利用来自西雅图和华盛顿州金县的大规模、基于人群的SCA研究中可用的DNA；我们使用现有的全基因组扫描(WGS)数据进行对照。在欧洲美洲人(EA)中，我们将对2500例SCA病例和7500名对照进行GWA(步骤1)，随后在2500例SCA病例和2500名对照中精细绘制100个基因组区域(步骤2)；并在1000例SCA病例和1000名对照的独立样本中复制(步骤3)。我们将获得包括在心脏骤停血液研究资料库(CABS-R)中的2500例SCA患者和来自心脏骤停血液研究(CABS)的750例对照的WGS。我们使用了来自普吉特湾心脏病发作风险(HARPS)研究的600名对照(非病例)和社区动脉粥样硬化风险(ARIC)研究的6150名对照的现有WGS数据。使用来自Affymetrix 6.0阵列的WGS数据，我们将检查700k单核苷酸多态(SNPs)与SCA的关联，以识别EAs中的100个基因组区域，基于“最高命中”的p值(步骤1)。使用Illumina珠粒阵列，我们将在2500例EA病例和2500例EA对照(步骤2)中对1536个SNP进行分型，包括来自步骤1中100个基因组区域中每个区域的大约15个SNP。最后，我们将使用1,000个EA案例和1,000个EA对照(步骤3)的独立样本来重复步骤1和2(对于那些p值为0.0000014的SNPs)中的发现。我们将把GWAS(步骤1)中使用的对照的选择与SCA病例的种群(等位基因)结构进行频率匹配；并且，我们将在步骤2中使用基因组对照，以最大限度地减少种群分层带来的偏差。考虑到大样本量和三步设计，我们将拥有80%的统计能力来检测适度大小的遗传效应，同时限制预期的假阳性关联数量。虽然动力不足，但我们包括来自CABS-R的400个非裔美国人(AA)SCA案例的探索性WGS和ARIC的900个AA控制(具有可用WGS数据)，这也将为未来的合作努力提供资源。使用大规模的病例对照、GWAs和精细的作图和复制来识别与SCA相关的整个基因组的遗传变异，将最大限度地减少假阴性和阳性关联，并提供对SCA机制的洞察，这将有助于针对性地干预以降低SCA的死亡率。公共卫生相关性：越来越多的证据表明，家族史影响心脏骤停(SCA)的风险，SCA是一种毁灭性的心脏事件，占美国总死亡率的10%；然而，与SCA相关的人类基因组变异仍不清楚。我们将研究整个人类基因组的变异，以确定影响SCA的新基因。研究结果将为SCA的分子机制提供洞察力，并可能有助于开发新的药物治疗方法，以降低SCA的死亡率。

DESCRIPTION (provided by applicant): 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. PUBLIC HEALTH RELEVANCE: 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.