Instability of high polygenic risk classification and mitigation by integrative scoring

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 assessed association and classification performance of cataloged PRS for three complex traits. We chronologically ordered all trait-related publications (Pub _n ) and identified the single PRS Best(Pub _n ) for each Pub _n that had the strongest association with the target outcome. While each Best(Pub _n ) demonstrated generally consistent population-level strengths of associations, classification of individuals in the top 10% of each Best(Pub _n ) distribution varied 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 demonstrated 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.