Multimodal deep learning approaches for improving polygenic risk scores with imaging data.

ABSTRACT: Enhancing the predictive accuracy of polygenic risk scores (PRS) can be achieved by incorporating imaging data, which captures disease-related phenotypic variations. This study aimed to evaluate the predictive performance of PRS, image-derived deep learning scores (IDS), imaging-derived phenotypes (IDPs), and clinical covariates. Additionally, IDP-proxy genetic scores (IGSs) were developed as substitutes for IDPs in cases where imaging data are unavailable. The study assessed the predictive performance of these variables for glaucoma using the XGBoost model. IDS was generated using a deep learning model trained on optical coherence tomography (OCT) images obtained from the UK Biobank. The dataset included 55,469 samples with OCT imaging data and 402,847 samples without OCT images. To accom

SUBMITTER: Kim H 

PROVIDER: S-EPMC12855870 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature