ArticleNature communications2025
Estimating disorder probability based on polygenic prediction using the BPC approach.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Distinguishing different psychiatric disorders using DDx-PRS.Nature genetics · 2026Article
- Empirical evaluation of analytic validity of polygenic scores.Genome medicine · 2026Article
- Polygenic risk scores in routine genetic diagnostics: what lies ahead?Journal of community genetics · 2025Article
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6 authors.
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Abstract
Polygenic Scores (PGSs) summarize an individual's genetic propensity for a given trait. Bayesian methods, which improve the prediction accuracy of PGSs, are not well-calibrated for binary disorder traits in ascertained samples. This is a problem because well-calibrated PGSs are needed for future clinical implementation. We introduce the Bayesian polygenic score Probability Conversion (BPC) approach, which computes an individual's predicted disorder probability using genome-wide association study summary statistics, an existing Bayesian PGS method (e.g. PRScs, SBayesR), the individual's genotype data, and a prior disorder probability (which can be specified flexibly, based for example on literature, small reference samples, or prior elicitation). The BPC approach is practical in its application as it does not require a tuning sample with both genotype and phenotype data. Here, we show in simulated and empirical data of nine disorder traits that BPC yields well-calibrated results that are consistently better than the results of another recently published approach.
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