Large uncertainty in individual PRS estimation impacts PRS-based risk stratification

Large-scale genome-wide association studies have enabled polygenic risk scores (PRS), which estimate the genetic value of an individual for a given trait. Since PRS accuracy is typically assessed using cohort-level metrics (e.g., R 2 ), uncertainty in PRS estimates at individual level remains underexplored. Here we show that Bayesian PRS methods can estimate the variance of an individual’s PRS and can yield well-calibrated credible intervals for the genetic value of a single individual. For real traits in the UK Biobank (N=291,273 unrelated “white British”) we observe large variance in individual PRS estimates which impacts interpretation of PRS-based stratification; for example, averaging across 13 traits, only 0.8% (s.d. 1.6%) of individuals with PRS point estimates in the top decile have their entire 95% credible intervals fully contained in the top decile. We provide an analytical estimator for individual PRS variance—a function of SNP-heritability, number of causal SNPs, and sample size—and observe high concordance with individual variances estimated via posterior sampling. Finally as an example of the utility of individual PRS uncertainties, we explore a probabilistic approach to PRS-based stratification that estimates the probability of an individual’s genetic value to be above a prespecified threshold. Our results showcase the importance of incorporating uncertainty in individual PRS estimates into subsequent analyses.