Structural and Nucleotide Variation as Genomic Risks for Venous Thrombosis: TOPMED and INVENT Collaboration

摘要 静脉血栓栓塞症(VTE)是一种遗传性疾病，包括深静脉血栓形成和肺 血栓形成，并与显著的发病率和死亡率有关。基因组的发现可能是 更好地了解人类生物学，众所周知，静脉血栓栓塞症具有重要的遗传决定因素。 随着技术和样本量的进步，支持新关联的证据越来越多。首字母 探索构成人类31亿个核苷酸的罕见和不寻常的变异 使用全基因组测序(WGS)的基因组正在为罕见的、潜在的损害提供有趣的结果 已知VTE危险基因座的单核苷酸变异(SNV)。此外，WGS可以用来完全表征 全基因组的结构变异(SVS)。我们来自社区动脉粥样硬化风险的初步数据 使用TOPMed WGS数据的研究显示，多个已知与风险相关的基因中罕见的SV缺失 对于VTE，包括影响纤维蛋白原结构基因(FGB)的缺失，与2倍 静脉血栓栓塞症风险增加。与静脉血栓栓塞症相关的系统性和不可知性全基因组搜索尚未 已经进行了，如果没有专门的资源，将不可能进行，如本报告中提议的 申请。这项提议是TOPMed VTE工作组内部领导层之间的合作 TOPMed结构变异工作组和国际抗静脉血栓网络 (发明)联合体。以下具体目标旨在发现新的基因组变异(SVS和SNVS) 使用来自17项研究的数据来增加VTE风险，这些研究包括46,200例VTE病例和超过380,000名对照。 目标1：在TOPMed和发明参与者中，使用新的方法来产生高质量和更多 比目前可用于WGS和现有阵列数据的SV调用算法更准确的SV调用。这个 新的呼叫将被过滤和管理，并将可用于AIM 2。SV呼叫也将在 TOPM，并由工作组应用于其他心血管、肺和睡眠表型。目标2：行为 室上性心动过速与VTE的相关性分析发现：使用AIM 1中派生的SV调用进行基于基因的 多达32,545例VTE病例和170,000名对照的节段性关联分析。验证和复制： 来自有统计学意义的基于基因的关联发现的SVS将使用定量PCR和 将在多达13,655例VTE病例和210,000名对照人群/研究中重复使用 发现阶段。目的3：进行SNV基因与静脉血栓栓塞症的关联分析。发现：使用SNV 从WGS和WGS派生的数据-推算数据(包括插入和删除)，进行聚合变量 MAF&lt；0.05与VTE相关的SNV基因及调控区分析 使用负荷测试和序列核关联测试(SKAT)的病例和17万个对照。我们也 建议结合SV和SNV数据分析复合杂合子的功能丧失 突变及其与静脉血栓栓塞症的关联。复制：将复制目标2中的重要关联。

ABSTRACT Venous thromboembolism (VTE) is a heritable condition that includes deep vein thrombosis and pulmonary embolism and is associated with marked morbidity and mortality. Genomic discovery can be a first step to better understand human biology and it is well established that VTE has important genetic determinants. Evidence supporting new associations has grown with advances in technology and sample size. Initial exploration of rare and uncommon variation across the 3.1 billion nucleotides that constitute the human genome using whole-genome sequencing (WGS) is providing interesting results for rare, potentially-damaging single nucleotide variants (SNVs) in known VTE risk loci. Further, WGS can be used to fully characterizing structural variants (SVs) genome-wide. Our preliminary data from the Atherosclerosis Risk in Communities study using TOPMed WGS data reveals rare SV deletions in multiple genes known to be associated with risk for VTE, including a deletion affecting a fibrinogen structural gene (FGB) that is associated with a 2-fold increase in VTE risk. A systematic and agnostic genome-wide search for SVs associated with VTE has not been conducted and will not be possible without dedicated resources, such as those proposed in this application. This proposal is a collaboration between leadership within the TOPMed VTE Working Group, the TOPMed Structural Variation Working Group, and the International Network Against Venous Thrombosis (INVENT) Consortium. The following specific aims seek to uncover novel genomic variation (SVs and SNVs) contributing to VTE risk using data from 17 studies that include 46,200 VTE cases and over 380,000 controls. Aim 1: In TOPMed and INVENT participants, use novel methodology to generate high-quality and more accurate SV calls than the SV calling algorithms currently available for both WGS and existing array data. The new calls will be filtered, curated, and will be available for Aim 2. The SV call will also be shared within TOPMed and applied by working groups to other cardiovascular, lung, and sleep phenotypes. Aim 2: Conduct association analyses of SVs with VTE. Discovery: Using SV calls derived in Aim 1 to conduct gene-based, segmental association analyses in up to 32,545 VTE cases and 170,000 controls. Validation and Replication: SVs from statistically-significant gene-based association findings will be validated using quantitative PCR and will be replicated in up to 13,655 VTE cases and 210,000 controls in populations/studies not used in the discovery phase. Aim 3: Conduct association analyses of SNV genotypes with VTE. Discovery: Using SNV data derived from WGS and WGS-imputed data (including insertions and deletions), conduct aggregate-variant analyses of genes and regulatory regions for SNVs with MAF <0.05 for association with VTE in up to 32,454 cases and 170,000 controls using burden tests and the Sequence Kernel Association Test (SKAT). We also propose analyses that combine SV and SNV data to analyze compound heterozygotes for loss-of-function mutations and their association with VTE. Replication: Significant associations will be replicated as in Aim 2.