Transfer learning for genomic prediction in underrepresented populations

Polygenic risk scores (PRSs) are used to predict disease risk from genetic variants. They usually incorporate the influence of hundreds to millions of genetic variants. However, their adoption for clinical decision making is currently low, partly because historical genome-wide association studies (GWASs) have overwhelmingly evaluated European cohorts, resulting in severe accuracy drops when applied to non-European populations. These accuracy differences arise because of cross-population differences in genetic architectures, population structure, and variant allele frequencies.Moreover, conducting de novo GWASs across hundreds of thousands of individuals is cost-prohibitive for most healthcare systems. Transfer learning from existing European-centric GWAS, augmenting these large-scale results with target-population-specific GWAS, provides a potential solution.To that end, in this blog post we describe a study evaluating PRS performance in a target non-European population while varying the size of both the target population and European populations used to create the predictive model across eight clinical traits. Specifically, we evaluate transferability of PRS ascertained in hundreds of thousands of European individuals within the UK Biobank (UKB) to samples within Biobank Japan (BBJ), a deeply-phenotyped cohort of nearly 200 thousand Japanese individuals. Our primary objective is to provide systematic, empirical guidelines on how cross-population GWAS and PRS model training should be performed to optimize predictive performance in a target population.

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