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.

项目摘要 遗传变异，环境暴露及其相互作用是所有人类病因的基础 疾病。虽然全基因组的关联研究揭示了许多与性状相关的遗传变异 流行病学研究已经指出了无数疾病相关的环境因素，即该作用 他们的相互作用的探索程度要少得多，这主要是由于缺乏非常大的人群队列，高 优质的环境措施和有效的工具。该建议旨在表征基因 人类进化和复杂性状中的环境相互作用（GEI）。遗传和多基因 人类进化过程中对当地环境的适应已经塑造了基因环境 关系和复杂特征的遗传结构。利用越来越多的古代 DNA，我们将首先开发和应用统计检验，以识别对遗传和多基因的反应 农业革命。自适应遗传变异和多基因性状的发现将告知我们 了解和研究当前复杂疾病的流行病，这可能是 当今的基因 - 叶片不匹配。其次，要直接识别和量化复杂性状中的GEI， 我们将开发有效的计算管道，并在跨越大规模的交互分析 英国生物库中的基因组，现象和精选的高质量环境因素。所有摘要 统计信息将在专用网站上公开以数据库的形式公开发行，以推动进一步的探索， 例如元分析和对跨同胞的可复制性的测试。第三，协助和 指导未来的GEI研究，我们将开发首个用于全现象互动研究的生物信息学工具 目标遗传变异和环境暴露的（PHEWIS），实现有效且无偏见 搜索可环境的表型效应。此外，减轻多个测试负担 在具有大量暴露和临床结果的GEI研究中，我们将检查Mendelian是否是否 随机分析与现象全面关联研究（PHEWAS-MR）结合可能是 优先考虑因果关系的有效方法，这可能会增加 检测GEI并协助下游搜索功能机制的统计能力。最后， 为了提高跨祖先和亚组的多基因评分（PG）的可移植性 同样的祖先，我们将使用模拟和经验数据来测试GEI的明确统计建模 可以减轻问题。同时，我们将检查PGS-环境的相互作用分析是否为 一种有效的方法来识别可行的环境暴露，以减轻遗传风险。 总体而言，这项提出的研究将生成新的方法，计算工具，数据库资源， 以及对人类复杂性状中GEI的一般模式的新颖见解。