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

遗传血统和人群特异性流行病学对基因组发现向不同和混合人群的可转移性的影响

• 批准号: 10293941
• 负责人: Genevieve Lianne Wojcik
• 金额: $ 46.64万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-10 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10293941
• 关键词: Address; Admixture; Awareness; Biological; Biomedical Research; Catalogs; Communities; Data; Development; Disease; Environment; Epidemiology; European; Evaluation; Future; Gene Frequency; Genes; Genetic; Genetic Heterogeneity; Genome; Genomic medicine; Genomics; Haplotypes; Health; Human; Individual; Investigation; Linkage Disequilibrium; Methodology; Minority Groups; Modeling; Pathway interactions; Pattern; Population; Population Genetics; Prevalence; Publishing; Research; Research Design; Risk; Risk Assessment; Role; Sum; Translations; Variant; clinical translation; cohort; gene environment interaction; genetic epidemiology; genetic variant; genome wide association study; genome-wide; health inequalities; interest; method development; minority health; next generation; non-genetic; pleiotropism; polygenic risk score; portability; programs; research and development; statistics; study characteristics; tool; trait

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.

项目摘要 大多数基因组学研究是在欧洲血统的人群中进行的， 随着我们迅速从基因发现转向临床转化， 卫生不平等。结果的可转移性在混合人群中进一步复杂化， 最近的祖先来自两个或两个以上的大陆，因为有大量的遗传异质性， 群体之间和群体内部。因此，有必要全面了解这些偏见， 跨多个祖先，研究设计和特征的方式，以更好地为未来的方法学提供信息 发展和生物医学研究框架。我在这本书中提出的研究计划 应用程序将使用现有的个人遗传数据和已公布的汇总统计数据， 解开遗传学和环境对人类健康的相对贡献， 人口。这个多因素的建议旨在（1）量化由于混合物在全球范围内的偏倚， 地方祖先水平和（2）解卷积遗传祖先和环境之间的相互作用 估计遗传效应大小时的变量。这些调查将在变量级别进行， 多基因风险评分（PRS）。变异水平分析，如全基因组关联 研究（GWAS），寻求精确定位基因和调节机制，构成特定性状的基础。到 为了鉴定生物靶点，有必要确定缺乏可转移性是否是由于群体 遗传学（等位基因频率，连锁不平衡）或祖先特异性基因环境 交互. PRS对整个基因组的效应进行求和，以估计性状的遗传易感性， 个人风险。通过扩大其范围，PRS通常捕获脱靶研究特征， 无论是通过混淆还是真正的多效性，反过来又限制了群体之间的可移植性。这些 在变异和全基因组水平上的关系，在混合群体中进一步复杂化， 祖先模式与性状、感兴趣的遗传变异和遗传病的流行相关。 非遗传变量拟议的研究计划将使用全球和全球两种方法来研究这些动态。 混合比例和当地祖先单倍型从个人水平的数据，在良好的特点， 队列，以精确的方式解开遗传和非遗传因素，并提供一个 祖先特质考虑因素的全面目录和混合感知框架， 评估变异（GWAS）和全基因组（PRS）遗传效应估计，以进行更广泛的研究 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，同时正在进行的财团努力， 增加基因组研究的多样性，建立下一代基因组 研究以解决现有的卫生不平等问题。