Methods and Software for Large-Scale Gene-Environment Interaction Studies

大规模基因-环境相互作用研究的方法和软件

• 批准号: 9978093
• 负责人: Han Chen
• 金额: $ 79.58万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-15 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9978093
• 关键词: Accounting; Address; Age; Aging; Algorithms; All of Us Research Program; Aspirin; Benchmarking; Blood; Clinical Data; Cloud Computing; Collaborations; Communities; Complex; Computational algorithm; Computer software; Data; Data Commons; Disease; Environment; Environmental Exposure; Epidemiology; Ethnic Origin; Etiology; FAIR principles; Foundations; Future; Genes; Genetic; Genetic Variation; Genome Scan; Genomics; Habits; Heart; Heart Diseases; Hematological Disease; Intervention; Life Style; Lung; Lung diseases; Methods; Modeling; National Heart, Lung, and Blood Institute; Obesity; Pharmacology; Physiological; Play; Precision Health; Prevention; Prevention strategy; Procedures; Race; Research; Research Design; Research Personnel; Resources; Risk Factors; Role; Sample Size; Sampling; Scheme; Sleep Disorders; Smoking; Statistical Methods; Statistical Models; Technology; Testing; Toxin; Trans-Omics for Precision Medicine; United States National Institutes of Health; Variant; Veterans; Weight; analysis pipeline; analytical tool; base; biobank; biomedical resource; cardiometabolism; cloud based; cohort; cost; disorder prevention; disorder risk; flexibility; functional genomics; gene environment interaction; genetic architecture; genetic association; genetic variant; genome sequencing; genome wide association study; genomic data; genomic epidemiology; health disparity; health management; human disease; insight; non-genetic; open source; personalized intervention; phenotypic data; precision medicine; programs; racial and ethnic disparities; rare variant; scale up; sex; software development; tool; trait; treatment effect; treatment strategy; user friendly software; user-friendly; whole genome; working group

PROJECT SUMMARY/ABSTRACT Complex human diseases and related quantitative traits are the interplay of many risk factors, including genetic and environmental components. Gene-environment interaction studies are a general framework that can be used to identify genetic variations that modify environmental, physiological, lifestyle, or treatment effects, as well as those contributing to age, sex, racial/ethnic disparities on complex traits. Moreover, genetic association studies accounting for gene-environment interactions are conducted to enhance our understandings on the genetic architecture of complex diseases by allowing for different genetic effects in different exposure strata. With the recent advances in technology and lowering costs, genetic and genomic data are being generated on very large scales. However, commonly used statistical software programs for gene-environment interaction studies were generally developed many years ago, and their computational algorithms have not been optimized to analyze hundreds of thousands to millions of samples from possibly complex study designs. To fill in the gap between current and future analytical needs in large-scale gene-environment interaction studies and current analytical solutions, we plan to (Aim 1) develop efficient algorithms for common variant gene-environment interaction analyses that scale linearly with the sample size; (Aim 2) develop new statistical tests for rare variant gene- environment interaction analyses, in the mixed effects model framework for correlated samples; and (Aim 3) implement proposed statistical methods and computational algorithms in open-source new software programs. Our Aim 1 addresses current computational challenges in conducting gene-environment interaction studies in up to millions of samples. In Aim 2, we plan to solve statistical and computational challenges in gene-environment interaction analyses of large-scale whole genome sequencing data, accounting for relatedness, complex study designs, as well as model misspecification. Aim 3 focuses on software development and we will deliver well- documented and user-friendly software packages and analysis pipelines for large-scale gene-environment interaction studies. The methods and software programs will be applied to ongoing whole genome sequencing projects, as well as biobank-scale data, and they will significantly facilitate the use of large-scale genetic and genomic data for gene-environment interaction studies in upcoming years to better understand the genetic basis of complex cardio-metabolic, lung, blood, sleep diseases and their age, sex, racial/ethnic disparities, and promote personalized disease prevention and treatment strategies in precision health research.

项目概要/摘要 复杂的人类疾病和相关的数量性状是许多危险因素相互作用的结果，包括遗传因素 和环境成分。基因-环境相互作用研究是一个可以使用的通用框架 识别改变环境、生理、生活方式或治疗效果的遗传变异，以及 那些导致年龄、性别、种族/民族复杂特征差异的因素。此外，遗传关联研究 对基因与环境相互作用的解释是为了增强我们对遗传的理解 通过允许不同暴露层中的不同遗传效应来构建复杂疾病的结构。随着 随着技术的最新进步和成本的降低，遗传和基因组数据正在非常大的范围内生成 秤。然而，用于基因-环境相互作用研究的常用统计软件程序是 一般是很多年前开发的，他们的计算算法还没有经过优化来分析 来自可能复杂的研究设计的数十万至数百万样本。填补之间的空白 大规模基因-环境相互作用研究中当前和未来的分析需求以及当前的分析 解决方案，我们计划（目标 1）开发常见变异基因-环境相互作用的有效算法 分析与样本大小成线性比例； （目标 2）针对罕见变异基因开发新的统计测试 - 在相关样本的混合效应模型框架中进行环境相互作用分析；和（目标 3） 在开源新软件程序中实施提出的统计方法和计算算法。 我们的目标 1 解决了当前在进行基因-环境相互作用研究中的计算挑战 多达数百万个样本。在目标 2 中，我们计划解决基因环境中的统计和计算挑战 大规模全基因组测序数据的相互作用分析，考虑相关性，复杂的研究 设计以及型号规格错误。目标 3 专注于软件开发，我们将提供良好的服务 用于大规模基因环境的记录和用户友好的软件包和分析管道 相互作用研究。这些方法和软件程序将应用于正在进行的全基因组测序 项目以及生物库规模的数据，它们将极大地促进大规模遗传和 未来几年用于基因-环境相互作用研究的基因组数据，以更好地了解遗传基础 复杂的心脏代谢、肺、血液、睡眠疾病及其年龄、性别、种族/民族差异，以及 在精准健康研究中推动个性化疾病预防和治疗策略。