Gene-Environment Interactions in Human Evolution and 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疾病的病因学基础。 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， 流行病学研究已经确定了无数与疾病相关的环境因素， 他们的相互作用是少得多的探索，主要是由于缺乏非常大的人口队列，高- 高质量的环境措施和有效的工具。该研究旨在揭示基因- 环境相互作用（GEI）在人类进化和复杂性状。遗传和多基因 人类进化过程中对局部环境的适应塑造了基因环境 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，以确定遗传和多基因反应， 农业革命。适应性遗传变异和多基因性状的发现将为我们的研究提供信息。 了解和研究目前流行的复杂疾病，这些疾病可能是 基因与生活方式的不匹配第二，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，并在整个 英国生物库中的基因组、表型组和精选的高质量环境因子。所有简要 统计数字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， 如荟萃分析和测试跨群体和祖先的可复制性。第三，协助和 为了指导未来的GEI研究，我们将开发第一个用于全表型相互作用研究的生物信息学工具 （PheWIS）的目标遗传变异和环境暴露，使有效和公正的 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GEI研究中，大量暴露和临床结果，我们将检查孟德尔 随机化分析结合全表型相关性研究（PheWAS-MR）可能是一种 一种有效的方法来优先考虑潜在的因果关系，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， 相同的祖先，我们将使用模拟和经验数据来测试，如果明确的统计模型的GEI 可以缓解这个问题。同时，我们将检查是否PGS-environment相互作用分析是 一个有效的方法来确定可采取行动的环境暴露，减少遗传风险。 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GEI在人类复杂特征中的一般模式的新见解。

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.