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.

项目总结/文摘