Instability of high polygenic risk classification and mitigation by integrative scoring.

Abstract

Polygenic risk scores (PRS) continue to improve with novel methods and expanding genome-wide association studies. Healthcare and commercial laboratories are increasingly deploying PRS reports to patients, but it is unknown how the classification of high polygenic risk changes across individual PRS. Here, we assess the association and classification performance of cataloged PRS for three complex traits. We chronologically order all trait-related publications (Pub_n) and identify the single PRS Best(Pub_n) for each Pub_n that has the strongest association with the target outcome. While each Best(Pub_n) demonstrates generally consistent population-level strengths of associations, the classification of individuals in the top 10% of each Best(Pub_n) distribution varies widely. Using the PRSmix framework, which integrates information across several PRS to improve prediction, we generate corresponding ChronoAdd(Pub_n) scores for each Pub_n that combine all polygenic scores from all publications up to and including Pub_n. When compared with Best(Pub_n), ChronoAdd(Pub_n) scores demonstrate more consistent high-risk classification amongst themselves. This integrative scoring approach provides stable and reliable classification of high-risk individuals and is an adaptable framework into which new scores can be incorporated as they are introduced, integrating easily with current PRS implementation strategies.