Alzheimer’s disease and related dementias (ADRD) are complex, polygenic conditions with substantial public health impact. Accurate genetic risk prediction may enable earlier identification and stratif Show more
Alzheimer’s disease and related dementias (ADRD) are complex, polygenic conditions with substantial public health impact. Accurate genetic risk prediction may enable earlier identification and stratification of individuals at elevated risk. To evaluate the predictive performance of polygenic risk scores (PRS) for ADRD using a Bayesian variational autoencoders approach and to assess the modifying effects of age and We analyzed data from 276,566 unrelated individuals of European ancestry in the UK Biobank, with a median follow-up of 9.19 years. PRS and polygenic hazard scores (PHS) were constructed using genome-wide association study summary statistics, with PHS incorporating age-at-onset information. Three PRS methods were compared: DDML (Bayesian variational autoencoders), SBayesR (Bayesian multiple regression), and clumping and thresholding (C + T). Models were stratified by age and Among the participants (mean age 56.8 ± 8.0 years; 46.7% male), 1,328 (0.48%) developed ADRD. In covariate-adjusted models, DDML_PRS achieved the highest predictive accuracy (AUC = 0.847) in individuals aged 65–70 years. PHS models showed peak performance at 7 years of follow-up. DDML_PRS significantly improved classification in The DDML_PRS framework showed consistently higher discrimination than standard PRS baselines in this UK Biobank setting, particularly in older adults and The online version contains supplementary material available at 10.1186/s13195-026-02011-w. Show less