Leveraging Multi-ethnic Evidence for Risk Assessment of Quantitative Traits in Minority Populations

Abstract

An essential component of precision medicine is the ability to predict an individual's risk of disease based on genetic and non-genetic factors. For complex traits and diseases, assessing the risk due to genetic factors is challenging because it requires knowledge of both the identity of variants that influence the trait and their corresponding allelic effects. Although the set of risk variants and their allelic effects may vary between populations, a large proportion of these variants were identified based on studies in populations of European descent. Heterogeneity in genetic architecture underlying complex traits and diseases, while broadly acknowledged, remains poorly characterized. Ignoring such heterogeneity likely reduces predictive accuracy for minority individuals. In this study, we propose an approach, called XP-BLUP, which ameliorates this ethnic disparity by combining trans-ethnic and ethnic-specific information. We build a polygenic model for complex traits that distinguishes candidate trait-relevant variants from the rest of the genome. The set of candidate variants are selected based on studies in any human population, yet the allelic effects are evaluated in a population-specific fashion. Simulation studies and real data analyses demonstrate that XP-BLUP adaptively utilizes trans-ethnic information and can substantially improve predictive accuracy in minority populations. At the same time, our study highlights the importance of the continued expansion of minority cohorts.

Generalizing polygenic risk scores from Europeans to Hispanics/Latinos

“…This is in agreement with the work of Coram, Fang, Candille, Assimes, and Tang (2017), who assumed different effect sizes between populations and found that estimating effect for risk prediction purposes is useful in ethnically matched population, whereas SNP selection using EA GWAS is generally appropriate. Based on simulation results, if the causal SNPs are the same and have the same effect sizes in all EA and Hispanic/Latino populations, we would expect that EA-based SNP selection combined with either EA or META weights would have optimal performance.…”

Theoretical and empirical quantification of the accuracy of polygenic scores in ancestry divergent populations

“…Instead, transancestry studies have been increasingly popular. They incorporate genotype data from different ancestries to boost statistic power with increasing sample sizes, which have the benefit to discover disease/trait-associated loci and fine-mapping causal variants associated with complex traits or diseases 13,[40][41][42][43] . However, the structure of the reference population still remains to be thoroughly explored, such as whether some specific populations with certain sample sizes are mostly useful in trans-ancestry studies.…”

Polygenic risk scores: from research tools to clinical instruments

“…Key methodological considerations for GWAS in ancestrally diverse populations have been recently discussed, including the choice between performing a meta-analysis stratified by ethnic groups and performing a joint mixed-model across all participants [23]. Novel methods for 'polyethnic' scores, like XP-BLUP and Multi-ethnic PRS, which improve predictive accuracy by combining transethnic with ethnicspecific information, are being developed [30][31][32].…”