ArticleNature genetics2025
Statistical construction of calibrated prediction intervals for polygenic score-based phenotype prediction.
Article in Nature genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Calibrated Prediction Intervals for Polygenic Scores: Updated Comparisons, Contextual Calibration, and Data Normalization.medRxiv : the preprint server for health sciences · 2026Article
- Characterizing the Uncertainty, Misclassification and Inconsistency of Polygenic Prediction.medRxiv : the preprint server for health sciences · 2026Article
- Optimizing phenotype scale improves genetic analyses in large-scale biobanks.bioRxiv : the preprint server for biology · 2026Article
- CalPred yields calibrated intervals for polygenic risk prediction.medRxiv : the preprint server for health sciences · 2026Article
- Statistical uncertainty explains the poor agreement in polygenic scoring for type 2 diabetes.medRxiv : the preprint server for health sciences · 2026Article
- Choice of phenotype scale is critical in biobank-based G×E tests.bioRxiv : the preprint server for biology · 2026Article
- Statistical construction of calibrated prediction intervals for polygenic score-based phenotype prediction.Nature genetics · 2025Article
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3 authors.
Funding
Abstract
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.
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