Evaluating the Impact of Principal Component and Mixed Model Approaches on Polygenic Risk Score Portability to Diverse Ancestries in the UK Biobank

Abstract

Polygenic risk scores (PRS) offer considerable potential for precision medicine. However, their predictive performance often attenuates when applied to populations that differ from the genome-wide association study (GWAS) training population. There are many potential sources of this portability problem, and one relatively under-explored contributor is the presence of residual confounding in GWAS summary statistics. In particular, confounding specific to the training population may contribute to predictive performance that does not transfer to other populations, such that improved control of population stratification could potentially improve PRS portability. Here, we investigated whether varying levels of population stratification adjustment, through the inclusion of principal components and the use of mixed models, altered PRS portability in three broad ancestry groups in the UK Biobank. The PRS were built using European training data for coronary artery disease and type 2 diabetes and subsequently evaluated in South Asian, African, and Latin American participants. We found that increasing PC adjustment did not produce a consistent trend in portability across ancestry groups or phenotypes, despite modest reductions in the LDSC intercept. However, substantial ancestry- and phenotype-specific effects on transferability were observed. Mixed-model association provided no significant change in PRS discrimination or portability. These findings highlight the need for a better understanding of the nature of residual confounding in PRS and whether improving the causal validity of GWAS results can ultimately improve the transferability of predictive accuracy between populations.