Benchmarking multi-ancestry prostate cancer polygenic risk scores in a real-world cohort.

Prostate cancer is a heritable disease with ancestry-biased incidence and mortality. Polygenic risk scores (PRSs) offer promising advancements in predicting disease risk, including prostate cancer. While their accuracy continues to improve, research aimed at enhancing their effectiveness within African and Asian populations remains key for equitable use. Recent algorithmic developments for PRS derivation have resulted in improved pan-ancestral risk prediction for several diseases. In this study, we benchmark the predictive power of six widely used PRS derivation algorithms, including four of which adjust for ancestry, against prostate cancer cases and controls from the UK Biobank and All of Us cohorts. We find modest improvement in discriminatory ability when compared with a simple method that prioritizes variants, clumping, and published polygenic risk scores. Our findings underscore the importance of improving upon risk prediction algorithms and the sampling of diverse cohorts.

PLoS computational biology. 2024 Apr 10 [Epub ahead of print]

Yajas Shah, Scott Kulm, Jones T Nauseef, Zhengming Chen, Olivier Elemento, Kevin H Kensler, Ravi N Sharaf

Englander Institute for Precision Medicine, Weill Cornell Medicine, New York City, New York, United States of America., Department of Population Health Sciences, Weill Cornell Medicine, New York City, New York, United States of America.