Statistical analysis of large genomic data sets

大型基因组数据集的统计分析

• 批准号: 10359127
• 负责人: XIAOFENG ZHU
• 金额: $ 38.95万
• 依托单位: CASE WESTERN RESERVE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-08 至 2024-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10359127
• 关键词: Address; Biological; Blood Pressure; Cardiovascular Diseases; Complement; Complex; Computer software; Data; Data Analyses; Data Set; Dementia; Detection; Development; Diabetes Mellitus; Disease; Disease Outcome; Environment; Epidemiologist; Family; Genes; Genetic; Genotype; Heart failure; Heritability; Individual; Knowledge; Literature; Mediation; Mendelian randomization; Methods; National Heart, Lung, and Blood Institute; Obesity; Observational epidemiology; Outcome; Phenotype; Publishing; Reporting; Request for Applications; Research Personnel; Risk Factors; SNP array; Sleep; Sleep Disorders; Software Tools; Specificity; Speed; Statistical Data Interpretation; Statistical Methods; Susceptibility Gene; Testing; Trans-Omics for Precision Medicine; Variant; analytical method; base; biobank; clinical application; cohort; cost; cost efficient; design; epidemiology study; experimental study; gene environment interaction; gene interaction; genetic architecture; genetic linkage analysis; genetic pedigree; genetic variant; genome sequencing; genome wide association study; genome-wide; genomic data; novel; pleiotropism; programs; rare variant; simulation; statistics; trait; user-friendly; web site; whole genome

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.

最大的全基因组序列(WGS)数据集NHLBI的遗传力分析 精准医学全基因组测序计划(TOPMed)， 强烈建议“遗漏遗传性”可以归因于罕见的变异 不能很好地被基于数组的基因变异体定位。大基因组大范围关联 由全基因组测序研究(WGS)补充的研究(GWAS)将是一个 识别遗传变异和了解遗传结构的经济高效的策略 具有复杂的特征。具有SNP阵列数据和全基因组的多个大型生物库 测序数据，例如用于精密医学整体的NHLBI反式组学 基因组测序计划(TOPMed)，提供了一种前所未有但具有挑战性的 有机会了解复杂疾病背后的遗传机制。我们 确定了在利用大型GWAS和WGS数据集方面面临的三个紧迫挑战 提出以下四个具体目标来应对挑战：1)差异化 使用GWAS汇总统计数据从调解中获得水平多效性，并应用 方法来处理公开存在的数据。2)优先考虑对相互作用敏感的遗传变异， 并估计交互作用对表型的总体贡献。3)合并族 连锁/地方祖先鉴定TOPMed全基因组中的遗传变异 测序数据。4)开发相应的软件，并公开发布 可用。我们将把我们的新分析方法应用于TOPMED WGS，UK Biobank数据 和许多现有的地理信息系统汇总统计数据。我们的数据分析将集中在血液上 压力、肥胖和睡眠障碍及其对疾病结局的影响，如 心血管疾病、糖尿病、心力衰竭和痴呆症。