Genome-wide study of sudden cardiac arrest in the community

描述（由申请人提供）：从家族聚集、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，但社区中SCA的遗传结构仍然未知。这是一份最终提交的修订申请，旨在通过病例对照全基因组关联研究（GWAS）设计，确定SCA与基因组中常见遗传变异的新关联（主效应）。我们利用了来自西雅图和金县（华盛顿）的一项基于人群的大型SCA研究的可用DNA；并且，我们使用现有的全基因组扫描（WGS）数据作为对照。在欧洲裔美国人（EAs）中，我们将对2500例SCA病例和7500例对照进行GWAS（步骤1），然后对2500例SCA病例和2500例对照进行100个基因组区域的精细绘制（步骤2）；并在1000个SCA案例和1000个对照的独立样本中进行复制（步骤3）。我们将获得心脏骤停血液研究库（CABS- r）中的2500例SCA病例和心脏骤停血液研究库（CABS）中的750例对照的WGS。我们使用来自普吉特海湾心脏病发作风险（HARPS）研究的600名对照（非病例）和来自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Affymetrix 6.0阵列的WGS数据，我们将检查700k单核苷酸多态性（snp）与SCA的关联，以确定100个基因组区域，基于ea中的“顶击”p值（步骤1）。使用Illumina Bead Array，我们将在2500例EA病例和2500例EA对照（步骤2）中对1536个snp进行基因分型，其中包括步骤1中100个基因组区域中每个区域的约15个snp。最后，我们将使用1000例EA病例和1000例EA对照（步骤3）的独立样本重复步骤1和2的发现（对于p值< 0.0000014的snp）。我们将在GWAS（步骤1）中使用的对照选择频率匹配SCA病例的群体（等位基因）结构；并且，我们将在第二步中使用基因组控制来最小化来自群体分层的偏差。考虑到大样本量和三步设计，我们将有80%的统计能力来检测中等大小的遗传效应，同时限制假阳性关联的预期数量。虽然功能不足，但我们包括了来自cab - r的400例非裔美国人（AA） SCA病例和来自ARIC的900例AA对照（具有可用的WGS数据）的探索性WGS，这也将为未来的合作努力提供资源。通过大型病例对照GWAS，以及精细的定位和复制，识别与SCA相关的基因组遗传变异，将最大限度地减少假阴性和阳性关联，并提供对SCA机制的深入了解，这将有助于有针对性的干预措施，以降低SCA的死亡率。公共卫生相关性：越来越多的证据表明，家族史会影响心脏骤停（SCA）的风险，这是一种毁灭性的心脏事件，占美国总死亡率的10%；然而，人类基因组中与SCA相关的变异仍然未知。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。该研究结果将为SCA的分子机制提供深入的见解，并可能有助于开发新的药物疗法来降低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.