Genome-wide screen for dynamic gene-environment interactions

用于动态基因-环境相互作用的全基因组筛选

• 批准号: 10703433
• 负责人: Chao Xing
• 金额: $ 7.88万
• 依托单位: UT SOUTHWESTERN MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-13 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10703433
• 关键词: Algorithms; Applied Genetics; Biology; Chromosome Mapping; Clinical Research; Complex; Computer software; Data; Data Analyses; Data Set; Detection; Disease; Disease susceptibility; Environmental Exposure; Environmental Risk Factor; Etiology; Exposure to; Family; Future; Genes; Genetic; Genetic Models; Goals; Heart; Individual; International; Joints; Linear Regressions; Methods; Modeling; Non-linear Models; Persons; Pharmacology Study; Prevention; Prevention strategy; Prognosis; Public Health; Research; Sample Size; Scientist; Speed; Testing; Variant; biobank; family structure; flexibility; gene environment interaction; genetic association; genetic pedigree; genetic variant; genome wide association study; genome wide screen; genome-wide; large datasets; population based; population stratification; semiparametric; simulation; theories; tool; trait; treatment strategy; variant detection

Project Summary/Abstract The etiology of most common complex diseases involves not only discrete genetic and environmental factors, but also interactions between them. However, in genome-wide association studies (GWAS) scientists have mostly examined the marginal effects of genetic factors without incorporating gene-environment interaction (GxE). The central hypothesis in the proposal is that incorporating GxE in GWAS will enhance the power to detect genetic association for variants that confer disease susceptibility subject to exposure to environmental risk factors. The goal of the proposed study is to develop powerful methods to identify genetic associations by incorporating nonlinear GxE through a semiparametric approach in a unified framework for GWAS and to im- plement the methods in scalable software. The first specific aim is to simultaneously test gene and GxE when correlation among subjects is negligible; the second specific aim is to simultaneously test gene and GxE where correlation among subjects is explicitly modeled. Validity, power, and computational efficiency of the proposed methods will be examined by simulations and real data analyses. Upon completion of the proposed studies, a computationally efficient tool that is implemented with a powerful approach to simultaneously test gene and GxE for both population-based and family-based studies will be delivered. The method and tool shall increase the power of detecting variants interacting with environmental factors to influence a trait in GWAS. Detecting such genetic variants will provide a focal point for future mechanistic, pharmacological, and clinical studies.

项目摘要/摘要 最常见复杂疾病的病因不仅涉及离散的遗传和环境因素，而且还涉及 而且它们之间也相互作用。但是，在全基因组协会研究（GWAS）中，科学家有 主要检查了遗传因素的边际效应，而不结合基因环境相互作用 （GXE）。该提案中的中心假设是，将GXE纳入GWAS将增强到达的力量 检测遗传关联的变体，赋予疾病易感性的变体暴露于环境 风险因素。拟议的研究的目的是开发强大的方法来确定遗传关联 通过半参数方法将非线性GXE纳入GWAS的统一框架，并进行 填写可扩展软件中的方法。第一个具体目的是同时测试基因和GXE 受试者之间的相关性可以忽略不计；第二个具体目的是同时测试基因和GXE 受试者之间的相关性是明确建模的。提议的有效性，功率和计算效率 方法将通过模拟和实际数据分析来检查。拟议的研究完成后， 以强大的方法同时测试基因和 将提供基于人群和基于家庭的研究的GXE。方法和工具应增加 检测变体与环境因素相互作用以影响GWAS中性状的力量。检测 这种遗传变异将为未来的机械，药理和临床研究提供一个焦点。