Statistical construction of calibrated prediction intervals for polygenic score-based phenotype prediction

Accurately quantifying uncertainty in predicted phenotypes from polygenic score (PGS)-based applications is essential for reliable clinical interpretation of PGS, supporting effective disease risk assessment and informed decision-making. Here, we present PredInterval, a nonparametric method for constructing well-calibrated prediction intervals. PredInterval is compatible with any PGS method, takes either individual-level data or summary statistics as input and relies on information from quantiles of phenotypic residuals through cross-validation to achieve well-calibrated coverage of true phenotypic values across diverse genetic architectures. We apply PredInterval to analyze 17 traits in real-data applications, where PredInterval not only represents the sole method achieving well-calibrated prediction coverage across traits, but it also offers a principled approach to identify high-risk individuals using prediction intervals, leading to an average improvement of identification rates by 8.7–830.4% compared with existing approaches. Overall, PredInterval represents a robust and versatile tool for enhancing the clinical utility of PGS. PredInterval quantifies phenotype prediction uncertainty in polygenic score-based applications, achieving well-calibrated prediction coverage across 17 traits tested and offering a principled approach to identify high-risk individuals.