Gene-Environment Interactions in Human Evolution and Complex Traits

人类进化和复杂性状中的基因-环境相互作用

• 批准号: 10621827
• 负责人: Kaixiong Ye
• 金额: $ 37.75万
• 依托单位: UNIVERSITY OF GEORGIA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-05 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10621827
• 关键词: Agriculture; Attenuated; Clinical; Complex; Coupled; DNA; Data; Databases; Dedications; Disease; Environment; Environmental Exposure; Environmental Risk Factor; Epidemic; Etiology; Evolution; Future; Genes; Genetic; Genetic Risk; Genetic Variation; Genome; Human; Intervention; Life Style; Measures; Mendelian randomization; Meta-Analysis; Methods; Outcome; Pattern; Phenotype; Polygenic Traits; Population; Research; Resources; Role; Shapes; Statistical Models; Subgroup; Testing; biobank; bioinformatics tool; cohort; computational pipelines; computerized tools; gene environment interaction; genetic architecture; genetic epidemiology; genetic predictors; genetic variant; genome wide association study; human disease; improved; insight; novel; phenome; portability; precision medicine; response; statistics; tool; trait; web site

Project Summary Genetic variations, environmental exposures, and their interactions underlie the etiology of all human diseases. While genome-wide association studies have revealed many trait-associated genetic variants and epidemiological studies have pinpointed myriad disease-associated environmental factors, the role of their interactions is much less explored, mainly due to the lack of very large population cohorts, high- quality environmental measures, and efficient tools. This proposal aims to characterize gene- environment interactions (GEI) in both human evolution and complex traits. Genetic and polygenic adaptations to local environments during human evolution have shaped the gene-environment relationship and the genetic architecture of complex traits. Leveraging the growing number of ancient DNA, we will first develop and apply statistical tests to identify genetic and polygenic responses to the Agricultural Revolution. The findings of adaptive genetic variants and polygenic traits will inform our understanding and study of the current epidemics of complex diseases, which are likely results of present-day gene-lifestyle mismatches. Second, to directly identify and quantify GEI in complex traits, we will develop an efficient computational pipeline and perform large-scale interaction analysis across the genome, phenome, and selected high-quality environmental factors in UK Biobank. All summary statistics will be released publicly as a database on a dedicated website to fuel further explorations, such as meta-analysis and testing for replicability across cohorts and ancestries. Third, to assist and guide future GEI studies, we will develop the first bioinformatics tool for phenome-wide interaction study (PheWIS) of target genetic variants and environmental exposures, enabling efficient and unbiased search for environment-modifiable phenotypic effects. Moreover, to alleviate the multiple testing burden in GEI studies with a large number of exposures and clinical outcomes, we will examine if Mendelian randomization analysis coupled with phenome-wide association study (PheWAS-MR) could be an effective way to prioritize potentially causal exposure-outcome relationships, which may increase the statistical power of detecting GEI and assist the downstream search for functional mechanisms. Lastly, as an effort to improve the portability of polygenic score (PGS) across ancestries and subgroups with the same ancestry, we will use simulated and empirical data to test if explicit statistical modeling of GEI could mitigate the problem. Concurrently, we will examine if PGS-environment interaction analysis is an effective approach to identify actionable environmental exposures that attenuate genetic risks. Overall, this proposed research will generate new methods, computational tools, database resources, and novel insights into the general patterns of GEI in human complex traits.

项目概要 遗传变异、环境暴露及其相互作用是所有人类病因学的基础 疾病。虽然全基因组关联研究揭示了许多与性状相关的遗传变异 流行病学研究已经确定了多种与疾病相关的环境因素， 他们的相互作用的研究较少，主要是由于缺乏非常大的人口群体，高 优质的环境措施和高效的工具。该提案旨在表征基因 人类进化和复杂特征中的环境相互作用（GEI）。遗传和多基因 人类进化过程中对当地环境的适应塑造了基因环境 关系和复杂性状的遗传结构。利用越来越多的古代 DNA，我们将首先开发并应用统计测试来识别对DNA的遗传和多基因反应 农业革命。适应性遗传变异和多基因性状的发现将为我们提供信息 了解和研究当前流行的复杂疾病，这些疾病可能是 当今的基因与生活方式不匹配。其次，直接识别和量化复杂性状中的GEI， 我们将开发高效的计算管道并跨领域进行大规模交互分析 英国生物银行的基因组、表型组和精选的优质环境因子。全部总结 统计数据将作为数据库在专门网站上公开发布，以推动进一步的探索， 例如跨群体和祖先的荟萃分析和可复制性测试。三、协助和 指导未来的 GEI 研究，我们将开发第一个用于全表组相互作用研究的生物信息学工具 （PheWIS）目标遗传变异和环境暴露，从而实现高效和公正 寻找环境可改变的表型效应。此外，为了减轻多重测试负担 在具有大量暴露和临床结果的 GEI 研究中，我们将检查孟德尔是否 随机化分析与全表组关联研究（PheWAS-MR）相结合可能是一种 优先考虑潜在因果暴露-结果关系的有效方法，这可能会增加 检测GEI的统计能力并协助下游寻找功能机制。最后， 作为提高多基因评分（PGS）跨祖先和亚组的可移植性的努力 同样的祖先，我们将使用模拟和经验数据来测试 GEI 的显式统计模型是否成立 可以缓解这个问题。同时，我们将检查 PGS-环境相互作用分析是否有效 识别可减轻遗传风险的可操作环境暴露的有效方法。 总的来说，这项研究将产生新的方法、计算工具、数据库资源、 以及对人类复杂特征中 GEI 一般模式的新颖见解。