Benchmarking Cross-ancestry Polygenic Score Methods in a Large Sample of Ancestrally Diverse Adolescents from the ABCD Study?

Using individuals? genetic data researchers can generate Polygenic Scores (PS) that are able to predict risk for diseases, variability in different behaviors as well as anthropomorphic measures. This is achieved by leveraging models learned from previously published large Genome-Wide Association Studies (GWASs) associating locations in the genome with a phenotype of interest. Previous GWASs have predominantly been performed in European ancestry individuals. This is of concern as PS generated in samples with a different ancestry to the original training GWAS have been shown to have lower performance and limited portability, and many efforts are now underway to collect genetic databases on individuals of diverse ancestries. In this study, we compare multiple methods of generating PS, including pruning and thresholding and bayesian continuous shrinkage models, to see which of them is best able to overcome these limitations. To do this we use the ABCD Study, a longitudinal cohort with deep phenotyping on individuals of diverse ancestry. We generate PS for anthropometric and psychiatric phenotypes using previously published GWA summary statistics and examine their performance in three subsamples of ABCD: African ancestry individuals (n=811), European ancestry Individuals (n=6,703), and admixed ancestry individuals (n=3,664). We find that the continuous shrinkage methods, PRScs and PRScsx Meta, show the best performance across ancestries and phenotypes.