Disease prediction with multi-omics and biomarkers empowers case-control genetic discoveries in the UK Biobank.

ABSTRACT: The emergence of biobank-level datasets offers new opportunities to discover novel biomarkers and develop predictive algorithms for human disease. Here, we present an ensemble machine-learning framework (machine learning with phenotype associations, MILTON) utilizing a range of biomarkers to predict 3,213 diseases in the UK Biobank. Leveraging the UK Biobank's longitudinal health record data, MILTON predicts incident disease cases undiagnosed at time of recruitment, largely outperforming available polygenic risk scores. We further demonstrate the utility of MILTON in augmenting genetic association analyses in a phenome-wide association study of 484,230 genome-sequenced samples, along with 46,327 samples with matched plasma proteomics data. This resulted in improved signals for 88 known (P 

SUBMITTER: Garg M 

PROVIDER: S-EPMC11390475 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature