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 最常见的复杂疾病的病因学不仅涉及离散的遗传和环境因素， 还包括它们之间的相互作用。然而，在全基因组关联研究(GWAS)中，科学家们 主要考察了遗传因素的边际效应，没有考虑基因与环境的相互作用 (GxE)。提案中的中心假设是，将GxE纳入GWAS将增强以下能力 检测使疾病易感性易受环境影响的变异的遗传关联 风险因素。这项拟议研究的目标是开发强大的方法，通过以下方式识别遗传关联 通过半参数方法将非线性GxE引入统一的GWAS框架中，并对GXE进行了改进。 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测试基因和GxE，其中 对象之间的相关性被显式建模。建议的有效性、功率和计算效率 方法将通过模拟和实际数据分析进行检验。在完成建议的研究后， 一种计算高效的工具，它通过一种强大的方法来同时测试基因和 将提供基于人口和以家庭为基础的研究的GxE。方法和工具应增加 检测与环境因素相互作用的变异的能力，以影响GWAS中的一个特征。检测 这些遗传变异将为未来的机械学、药理学和临床研究提供一个焦点。