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/摘要 大多数常见复杂疾病的病因不仅涉及离散的遗传和环境因素， 还有它们之间的相互作用。然而，在全基因组关联研究（GWAS）中， 大多数研究遗传因素的边际效应，而没有考虑基因-环境相互作用 （GxE）。该提案的核心假设是，将GxE纳入GWAS将增强以下能力： 检测遗传关联的变异赋予疾病易感性受暴露于环境 危险因素这项研究的目标是开发出强有力的方法来识别遗传关联， 将非线性GxE通过半参数方法纳入GWAS的统一框架中， 在可伸缩软件中实现了这些方法。第一个具体目标是同时检测基因和GxE， 受试者之间的相关性可以忽略不计;第二个具体目标是同时检测基因和GxE， 对象之间的相关性被明确地建模。有效性，功率和计算效率的建议 方法将通过模拟和真实的数据分析来检验。建议的研究完成后， 一种计算效率高的工具，它是用一种强大的方法来同时测试基因和 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 检测与环境因素相互作用的变异以影响GWAS中的特征的能力。检测 这种遗传变异将为未来的机制、药理学和临床研究提供焦点。