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: 10459529
• 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/10459529
• 关键词: Agriculture; Attenuated; Clinical; Complex; Coupled; DNA; Data; Databases; 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; Statistical Models; Subgroup; Testing; biobank; bioinformatics tool; cohort; computational pipelines; computerized tools; epidemiology study; gene environment interaction; genetic architecture; 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 基因变异、环境暴露及其相互作用构成了所有人类的病因学基础 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 流行病学研究已经准确地指出了无数与疾病相关的环境因素， 它们相互作用的研究要少得多，主要是因为缺乏非常大的种群队列，高- 高质量的环境措施和有效的工具。这项提议旨在描述基因-- 人类进化和复杂性状中的环境交互作用(GEI)。遗传和多基因 在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，我们将首先开发和应用统计测试来确定对 农业革命。适应性遗传变异和多基因性状的发现将告诉我们 了解和研究当前复杂疾病的流行情况，这可能是 当今的基因和生活方式不匹配。第二，为了直接识别和量化复杂性状中的GEI， 我们将开发一个高效的计算管道并执行大规模的相互作用分析 英国生物库中的基因组、表现组和精选的高质量环境因素。所有摘要 统计数据将作为数据库在一个专门的网站上公开发布，以推动进一步的探索， 例如荟萃分析和跨队列和祖先的可复制性测试。第三，协助和 指导未来的GEI研究，我们将开发第一个生物信息学工具，用于整个物候群的相互作用研究 (PheWIS)目标基因变异和环境暴露，使高效和公正 寻找环境可改变的表型效应。此外，为了减轻多次测试的负担， 在有大量暴露和临床结果的GEI研究中，我们将检查孟德尔 随机化分析结合全基因组关联研究(Phewas-MR)可能是一种 有效的方法来区分潜在的因果暴露-结果关系的优先顺序，这可能会增加 检测GEI的统计能力，并协助下游寻找作用机制。最后， 作为一项改善多基因评分(PGS)在祖先和亚组之间可移植性的努力 同样，我们将使用模拟数据和经验数据来检验GEI的显式统计建模 可以缓解这个问题。同时，我们将检查PGS-环境交互分析是否 一种有效的方法来确定可操作的环境暴露，以降低遗传风险。 总的来说，这项拟议的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的一般模式的新见解。