Key result
Bayesian polygenic risk scores improve BMI prediction by ~36% over pruning-and-thresholding methods.
Why the study?
Although racial and ethnic differences in polygenic risk score performance are documented, how demographic, lifestyle, and cardiometabolic factors influence BMI PRS performance within and across populations remains less understood.
Do Bayesian Polygenic Risk Scores improve body mass index prediction compared to pruning-and-thresholding methods across diverse populations?
Population
Up to 501,247 individuals across PAGE and eight additional cohorts and biobanks
Comparison
PRS-CS and PRS-CSx vs pruning-and-thresholding approaches across demographic and clinical strata
Design
Multi-cohort genetic prediction study
Authors
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Supports Bayesian PRS for BMI prediction research; leaves open ancestry- and context-specific validation before clinical adoption.
Observational (n=501,247)
Yes
Do Bayesian Polygenic Risk Scores improve body mass index prediction compared to pruning-and-thresholding methods across diverse populations?
Absolute Event Rate: 9% vs 6.6%
Bayesian polygenic risk scores improve BMI prediction compared to standard methods, but their accuracy remains significantly lower in non-Hispanic Black individuals and specific clinical subgroups, highlighting the need for context-specific evaluation.
KIM et al. (2026) conducted an observational in Obesity (n=501,247). Bayesian polygenic risk scores (PRS-CS) vs. Pruning-and-thresholding (P+T) polygenic risk scores was evaluated on Prediction performance (overall R²). Bayesian polygenic risk score methods outperformed pruning-and-thresholding for predicting body mass index (overall R² 9.0% vs. 6.6%), with performance varying by race and clinical context.
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