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

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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.