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Drivers of genetic heterogeneity and their impact on polygenic risk prediction

遗传异质性的驱动因素及其对多基因风险预测的影响

基本信息

批准号：
10314395
负责人：
Dan Ju
金额：
$ 4.6万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-01 至 2022-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10314395
关键词：
Address Affect Africa African Automobile Driving Clinical Complex Data Disease Disease model Environment Ethnic group European Exhibits Future Gene Frequency Genes Genetic Genetic Heterogeneity Genetic Models Genetic Predisposition to Disease Genetic Risk Genetic Variation Genetic study Genome Genomic medicine Genomics Genotype Ghana Height Heterogeneity Human Genetics Individual Intervention Investments Linkage Disequilibrium Measures Meta-Analysis Methodology Modeling Niger Pattern Phenotype Population Population Heterogeneity Privatization Publishing Quantitative Trait Loci Reporting Research Risk Role Sample Size Sampling Scanning Single Nucleotide Polymorphism Map Structure Testing Variant base biobank causal variant cohort disorder risk ethnic diversity gene environment interaction gene interaction genetic architecture genetic variant genome wide association study genome-wide improved novel polygenic risk score risk prediction risk stratification risk variant rural environment standing height statistics trait urban setting

项目摘要

Abstract Genome-wide association studies (GWAS) have informed us about the genetic architecture of complex traits and diseases by identifying common genetic risk variants. Typically many genetic variants conferring small increases in risk underlie disease variation in a population. Summarizing the effects of these genetic risk variants can measure an individual’s genetic predisposition, which is done by calculating a polygenic risk score (PRS). PRS show clinical promise in identifying individuals at the extremes of the genetic risk distribution. However, the potential benefits of using PRS in risk stratification may be curtailed due to loss of predictive power of PRS in groups of non-European ancestry. PRS are commonly calculated from GWAS conducted in European populations because of large sample sizes. PRS derived from European GWAS suffer the greatest loss in predictive power in African populations. Currently we do not know the reasons for this loss of predictive power. Investigating the factors limiting the transferability of PRS will inform future avenues of research into developing more generalizable PRS. Since few GWAS have been conducted in African ancestry individuals, the proposal will perform a GWAS in Sub-Saharan African populations for height in Aim 1 to contribute to our knowledge of the genetic architecture of a complex trait in diverse African populations. Through the GWAS in Aim 1, genetic variation not present in non-African populations that affects height can be uncovered. In Aim 2, the proposal will investigate the role of differences in linkage disequilibrium (LD) structure across populations in limiting the transferability of PRS. GWAS rely on LD between a genotyped marker and the causal variant, so the variants included in a PRS are not necessarily causal. To test whether differences in the tagging of shared causal variants between populations limit the transferability of PRS, this proposal will identify instances of differential tagging of causal variants between Europeans and Africans and cases where multiple associations at a locus are obscured by high LD in Europeans, which can be interrogated with low LD in Africans. In Aim 3, this proposal will study the mechanisms driving differences in effect size of variants shared across ancestries, specifically the role of gene by gene (GxG) and gene by environment (GxE) interactions. Variants with the most disparate effect sizes between Europeans and Africans will be examined for evidence of interaction effects. GxG interactions for a candidate variant will be tested based on its local genomic ancestry. GxE interactions will be tested using two groups of similar ancestry but differing by status of living in a rural or urban environment. In all, these analyses will inform our understanding of the roles of LD structure and GxG and GxE interactions in limiting the transferability of PRS across populations.

摘要全基因组关联研究（GWAS）为我们提供了复杂性状的遗传结构信息通过识别常见的遗传风险变异来预防疾病。通常许多遗传变异赋予小风险的增加是人群中疾病变异的基础。总结这些遗传风险变异的影响可以测量个体的遗传倾向，这是通过计算多基因风险评分（PRS）完成的。 PRS在识别处于遗传风险分布极端的个体方面显示出临床前景。但在危险分层中使用PRS的潜在益处可能由于PRS在以下方面的预测能力的丧失而减少：非欧洲血统的群体。PRS通常根据在欧洲进行的GWAS计算因为样本量大。欧洲GWAS的PRS损失最大，预测非洲人口的能力。目前我们还不知道这种预测能力丧失的原因。调查限制PRS可转移性的因素将为未来的研究开发提供信息。更普遍的PRS。由于GWAS很少在非洲血统的个体中进行，将在撒哈拉以南非洲人群中进行目标1身高的GWAS，以帮助我们了解一个复杂性状在不同非洲人群中的遗传结构。通过目标1中的GWAS，遗传可以发现在非非洲人群中不存在的影响身高的变异。在目标2中，建议将研究不同群体间连锁不平衡（LD）结构的差异在限制 PRS的可转移性。GWAS依赖于基因型标记和致病变异之间的LD，因此变异 PRS中包含的信息不一定是因果关系。为了测试是否在标记共享的因果变异的差异限制了PRS的可转移性，本建议将确定欧洲人和非洲人之间的因果变异，以及在一个位点的多个关联被掩盖的情况欧洲人的LD高，非洲人的LD低。在目标3中，本建议将研究驱动不同祖先之间共享的变异效应大小差异的机制，特别是基因与基因（GxG）和基因与环境（GxE）的相互作用。效应量差异最大的变量欧洲人和非洲人之间的相互作用的证据将被检查。GxG相互作用候选变体将基于其本地基因组祖先进行测试。GxE相互作用将使用两个祖先相似但生活在农村或城市环境中的地位不同的群体。总之，这些分析将告知我们对LD结构以及GxG和GxE相互作用在限制 PRS在人群中的可转移性。