CalPred yields calibrated intervals for polygenic risk prediction

Abstract Polygenic scores (PGS) have emerged as a useful biomarker for stratification of high-risk individuals in genomic medicine, with prediction intervals arising as a principled approach to incorporate statistical uncertainty in their individual-level predictions. In contrast to recent reports by Xu et al 7 , we show that CalPred 6 provides well-calibrated prediction intervals that contain the trait phenotypes at targeted confidence levels. CalPred maintains calibration when PGS performance varies across contextual factors (e.g., ancestry, age, sex, or socio-economic factors) whereas PredInterval 7 – a recently introduced method that focuses on marginal calibration across all individuals – exhibits miscalibration.