Integrating electronic health records and GWAS summary statistics to predict the progression of autoimmune diseases from preclinical stages.

ABSTRACT: Autoimmune diseases often exhibit a preclinical stage before diagnosis. Electronic health record (EHR) based-biobanks contain genetic data and diagnostic information, which can identify preclinical individuals at risk for progression. Biobanks typically have small numbers of cases, which are not sufficient to construct accurate polygenic risk scores (PRS). Importantly, progression and case-control phenotypes may have shared genetic basis, which we can exploit to improve prediction accuracy. We propose a novel method Genetic Progression Score (GPS) that integrates biobank and case-control study to predict the disease progression risk. Via penalized regression, GPS incorporates PRS weights for case-control studies as prior and forces model parameters to be similar to the prior if the prior i

SUBMITTER: Wang C 

PROVIDER: S-EPMC11695684 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature