Estimating Disorder Probability Based on Polygenic Prediction Using the BPC Approach.

ABSTRACT: Polygenic Scores (PGSs) summarize an individual's genetic propensity for a given trait in a single value, based on SNP effect sizes derived from Genome-Wide Association Study (GWAS) results. Methods have been developed that apply Bayesian approaches to improve the prediction accuracy of PGSs through optimization of estimated effect sizes. While these methods are generally well-calibrated for continuous traits (implying the predicted values are on average equal to the true trait values), they are not well-calibrated for binary disorder traits in ascertained samples. This is a problem because well-calibrated PGSs are needed to reliably compute the absolute disorder probability for an individual to facilitate future clinical implementation. Here we introduce the Bayesian polygenic score Proba

SUBMITTER: Uffelmann E 

PROVIDER: S-EPMC10802765 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature