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: 10809953
• 负责人: Kaixiong Ye
• 金额: $ 1.1万
• 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/10809953
• 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 (Unchanged) 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（不变） 遗传变异，环境暴露及其相互作用是所有人类疾病的病因。 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和 流行病学研究指出了与疾病相关的无数环境因素，其作用 相互作用的探索要少得多，这主要是由于缺乏非常大的人群队列，高质量 环境措施和有效的工具。该建议旨在表征基因环境相互作用 （GEI）在人类进化和复杂性状中。对本地环境的遗传和多基因适应 在人类进化过程中，基因环境关系和复杂的遗传结构 特质。利用不断增长的古代DNA，我们将首先开发并应用统计测试以识别 对农业革命的遗传和多基因反应。自适应遗传变异的发现和 多基因特征将为我们对当前复杂疾病的流行病的理解和研究，这是 当今的基因失误不匹配的可能结果。第二，直接识别和量化复杂的GEI 特征，我们将开发有效的计算管道，并在整个过程中进行大规模交互分析 英国生物库中的基因组，现象和精选的高质量环境因素。所有摘要统计数据将 在专用网站上以数据库的形式公开发行，以推动进一步的探索，例如荟萃分析 并测试跨人群和祖先的可复制性。第三，为了协助和指导未来的GEI研究，我们将 开发目标遗传变异的首个用于全现象相互作用研究（PHEWIS）的生物信息学工具和 环境暴露，使人们能够有效且无偏见地搜索可环境的表型效应。 此外，以大量暴露和临床状态减轻GEI研究中的多重测试负担 结果，我们将检查Mendelian随机分析是否与现象全关联研究结合 （Phewas-Mr）可能是优先考虑因果关系的有效方法， 可能会增加检测GEI并帮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的明确统计建模是否可以减轻 问题。同时，我们将检查PGS-环境相互作用分析是否是一种有效的方法 确定可行的环境暴露，以减轻遗传风险。总体而言，这项拟议的研究将 生成新方法，计算工具，数据库资源以及对一般模式的新颖见解 GEI在人类复杂性状中。

项目成果

Shared genetic basis informs the roles of polyunsaturated fatty acids in brain disorders.

共同的遗传基础揭示了多不饱和脂肪酸在大脑疾病中的作用。

• DOI: 10.1101/2023.10.03.23296500
• 发表时间: 2023
• 期刊: medRxiv : the preprint server for health sciences
• 作者: Xu,Huifang;Sun,Yitang;Francis,Michael;Cheng,ClaireF;Modulla,NityaTR;Brenna,JThomas;Chiang,CharlestonWK;Ye,Kaixiong
• 通讯作者: Ye,Kaixiong

The Protective Effect of Omega-3 Fatty Acids in Attenuating Lung Function Decline.

• DOI: 10.1164/rccm.202307-1222ed
• 发表时间: 2023-10-15
• 期刊: American journal of respiratory and critical care medicine
• 影响因子: 24.7

Fish oil supplementation modifies the genetic potential for blood lipids.

鱼油补充剂可以改变血脂的遗传潜力。

• DOI: 10.1101/2023.09.22.23295987
• 发表时间: 2023
• 期刊: medRxiv : the preprint server for health sciences
• 作者: Sun,Yitang;McDonald,Tryggvi;Baur,Abigail;Xu,Huifang;Bateman,NaveenBrahman;Shen,Ye;Li,Changwei;Ye,Kaixiong
• 通讯作者: Ye,Kaixiong

A metabolome-wide Mendelian randomization study prioritizes potential causal circulating metabolites for multiple sclerosis.

一项全代谢组孟德尔随机化研究优先考虑多发性硬化症的潜在因果循环代谢物。

• DOI: 10.1016/j.jneuroim.2023.578105
• 发表时间: 2023
• 期刊: Journal of neuroimmunology
• 影响因子: 3.3
• 作者: Ge,Angela;Sun,Yitang;Kiker,Thaddaeus;Zhou,Yanjiao;Ye,Kaixiong
• 通讯作者: Ye,Kaixiong