Statistical analysis of large genomic data sets

大型基因组数据集的统计分析

基本信息

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
项目类别：
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

项目摘要

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跨组学全基因组测序计划（TOPMed），强烈建议“遗传性缺失”可归因于罕见变异基于阵列的基因型变体不能很好地靶向大基因组范围的关联。研究（GWAS），辅之以全基因组测序研究（WGS），将是识别遗传变异和了解遗传结构的成本效益策略具有 SNP 阵列数据和全基因组的多个大型生物库。测序数据，例如 NHLBI Trans-omics for Precision Medicine Whole 基因组测序计划（TOPMed），提供了前所未有但具有挑战性的有机会了解复杂疾病的遗传机制。确定了利用大型 GWAS 和 WGS 数据集的三个紧迫挑战，提出以下四个具体目标来应对挑战： 1）差异化使用 GWAS 汇总统计从中介中得出水平多效性，并应用 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) 合并家族。连锁/本地祖先来识别 TOPMed 全基因组中的遗传变异 4）开发相应的测序数据软件。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，我们的数据分析将重点关注血液。压力、肥胖和睡眠障碍及其对疾病结果的影响，例如心血管疾病、糖尿病、心力衰竭和痴呆。