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 third-party laboratories are increasingly deploying PRS reports to patients. Although new PRS show improving strengths of association with traits, it is unknown how the classification of high polygenic risk changes across individual PRS for the same trait. Here, we determined classification of high genetic risk from all cataloged PRS for three complex traits. While each PRS for each trait demonstrated generally consistent population-level strengths of associations, classification of individuals in the top 10% of each PRS distribution varied widely. Using the PRSMix framework, which incorporates information across several PRS to improve prediction, we generated sequential add-one-in (AOI) PRSMix_AOI scores based on order of publication. PRSMix_AOIₙ led to improved PRS performance and more consistent high-risk classification compared with the PRSₙ. The PRSMix_AOI approach provides more stable and reliable classification of high-risk as new PRS continue to be generated toward PRS standardization.