Statistical analysis of large genomic data sets

最大的全基因组序列（WGS）数据集NHLBI中的遗传力分析 精准医学全基因组测序计划（TOPMed）， 这强烈表明，“缺失遗传性”可以归因于罕见的变异， 不能被基于阵列的基因型变体很好地靶向。大基因组关联 研究（GWAS），辅之以全基因组测序研究（WGS），将是一个 识别遗传变异和了解遗传结构的成本效益策略 复杂的特征。拥有SNP阵列数据和全基因组的多个大型生物库 测序数据，如NHLBI Trans-omics for Precision Medicine 基因组测序计划（TOPMed），提供了前所未有的，但具有挑战性的 有机会了解复杂疾病的遗传机制。我们 已经确定了利用大型GWAS和WGS数据集的三个紧迫挑战， 为应对挑战，我提出以下四个具体目标：1）差异化 使用GWAS汇总统计量从中介中获得水平多效性，并应用 公开现有数据的方法。2)优先考虑对相互作用敏感的遗传变异， 并估计相互作用对表型的总体贡献。3)结合家庭 连锁/本地祖先，以识别TOPM标记的全基因组中的遗传变异 测序数据。4)开发相应的软件，并公开 available.我们将把我们的新分析方法应用于TOPMED WGS，英国生物银行数据 以及许多现有的GWAS汇总统计数据。我们的数据分析将集中在血液 压力，肥胖和睡眠障碍，以及它们对疾病结果的影响， 心血管疾病、糖尿病、心力衰竭和痴呆。

Heritability analysis in the largest whole genome sequence (WGS) dataset, the NHLBI Trans-omics for Precision Medicine Whole Genome Sequencing Program (TOPMed), strongly suggested that “missing heritability” can be attributed to rare variants that are not well targeted by array-based genotype variants. Large genome wide association studies (GWAS), complemented by whole genome sequencing studies (WGS), will be a cost efficient strategy to identify genetic variants and understand the genetic architecture of complex traits. Multiple large Biobanks with SNP-array data and whole genome sequencing data, such as the NHLBI Trans-omics for Precision Medicine Whole Genome Sequencing Program (TOPMed), provide an unprecedented but challenging opportunity to understand the genetic mechanisms underlying complex diseases. We have identified three pressing challenges in utilizing large GWAS and WGS datasets and propose the following four specific aims to meet the challenges: 1) Differentiate horizontal pleiotropy from mediation using GWAS summary statistics and apply the methods to publicly existing data. 2) Prioritize genetic variants sensitive to interactions, and estimate the overall contribution of interactions to a phenotype. 3) Incorporate family linkage/local ancestry to identify genetic variants in the TOPMed whole genome sequencing data. 4) Develop corresponding software that will be made publicly available. We will apply our new analytic methods to TOPMED WGS, UK Biobank data and many existing GWAS summary statistics. Our data analysis will focus on blood pressure, obesity and sleep disorders, and their effects on disease outcomes such as cardiovascular disease, diabetes, heart failure and dementia.