The influence of genetic ancestry and population-specific epidemiology on the transferability of genomic findings to diverse and admixed populations

项目总结 大多数基因组学研究是在欧洲血统的人群中进行的，留下了其他 随着我们从基因发现快速过渡到临床翻译，我们落后的群体，加剧了现有的 健康不平等。在混合种群中，研究结果的可转移性更加复杂，那些 来自两个或多个大陆的近亲祖先，因为两者都有很大的遗传异质性 团体之间和团体内部。因此，有必要全面地理解这些偏见 跨越多个祖先、研究设计和特征的方式，以更好地为未来的方法提供信息 发展和生物医学研究框架。我在这里提出的研究计划 应用程序将使用现有的个人级别的遗传数据和已发布的汇总统计数据来 解开基因和环境对人类健康的相对贡献 人口。这一多因素建议寻求(1)量化由于全球和 地方血统水平和(2)了解遗传血统与环境的相互作用 估计遗传效应大小时的变量。这些调查将在不同的层面上进行和 全基因组的多基因风险分数(PR)。变异水平分析，如全基因组关联 研究(Gwas)，试图找出构成特定特征的基因和调控机制。至 为了确定生物靶标，有必要确定缺乏可转移性是否是由于人口 遗传学(等位基因频率、连锁不平衡)或祖先特有的基因与环境 互动。通过对基因组中的PRS求和效应来估计某一性状和分层的遗传易感性 风险中的个体。通过扩大他们的范围，PR经常捕捉到脱离目标的研究特征， 无论是混杂还是真正的多效性，反过来又限制了种群之间的可携带性。这些 在混合种群中，在变异和全基因组水平上的关系更加复杂， 血统模式与特征、感兴趣的遗传变异以及 非遗传变量。拟议的研究计划将使用全球 来自个体水平数据的混合比例和地方血统单倍型特征良好 队列，以精确的方式分离遗传和非遗传因素，并提供 血统的全面目录--特征考虑因素和混杂意识框架 对更广泛研究的变异(GWAS)和全基因组(PRS)遗传效应估计的评估 社区。通过系统地探索这些关系，我们将更好地为未来的方法提供信息 发展和风险评估框架与财团正在进行的努力 增加基因组研究的多样性，建立下一代基因组 解决现有健康不平等问题的研究。

PROJECT SUMMARY The majority of genomics research is conducted in populations of European descent, leaving other groups behind as we rapidly move from genetic discovery to clinical translation, exacerbating existing health inequities. The transferability of findings is further complicated in admixed populations, those with recent ancestry from two or more continents, in that there is substantial genetic heterogeneity both between and within groups. It is therefore necessary to understand these biases in a comprehensive manner across multiple ancestries, study designs, and traits, to better inform future methodological developments and biomedical research frameworks. The research program I propose in this application would use existing individual-level genetic data and published summary statistics to disentangle the relative contributions of genetics and environment to human health in admixed populations. This multi-factorial proposal seeks to (1) quantify bias due to admixture on a global and local ancestry level and (2) deconvolute the interaction between genetic ancestry and environmental variables when estimating genetic effect sizes. These investigations will occur on both a variant-level and genome-wide with polygenic risk scores (PRS). Variant-level analyses, such as genome-wide association studies (GWAS), seek to pinpoint genes and regulatory mechanisms that underlie a particular trait. To identify biological targets, it is necessary to determine if a lack of transferability is due to population genetics (allele frequencies, linkage disequilibrium) or ancestry-specific gene-by-environment interactions. PRS sum effects across the genome to estimate the genetic liability of a trait and stratify individuals by risk. By expanding their scope, PRS often capture the off-target study characteristics, whether by confounding or true pleiotropy, in turn limiting the portability between populations. These relationships, both on a variant- and genome-wide level, are further complicated in admixed populations, with ancestry patterns being correlated with the trait, genetic variants of interest, and the prevalence of non-genetic variables. The proposed research program will examine these dynamics using both global admixture proportions and local ancestry haplotypes from individual-level data in well-characterized cohorts, disentangling of genetic and non-genetic factors in a precise manner, and providing a comprehensive catalog of ancestry-trait considerations and an admixture-aware framework for the evaluation of variant- (GWAS) and genome-wide (PRS) genetic effect estimates to the wider research community. By systematically exploring these relationships, we will better inform future method development and risk assessment frameworks in parallel with on-going consortia efforts to increase diverse representation in genomic studies, setting up the next generation of genomic research to address existing health inequities.