Disease Prediction With Multi-Omics and Biomarkers Empowers Case-Control Genetic Discoveries in the UK Biobank

Authors

• Lawrence Middleton
• Oliver S Burren
• Fengyuan Hu
• Eleanor Wheeler
• Katherine R Smith
• Margarete A Fabre
• Jonathan Mitchell
• Amanda O'Neill
• Euan A Ashley
• Andrew R Harper
• Quanli Wang
• Ryan S Dhindsa
• Slavé Petrovski
• Dimitrios Vitsios

Language

English

Publication Date

9-1-2024

Journal

Nature Genetics

DOI

10.1038/s41588-024-01898-1

PMID

39261665

PMCID

PMC11390475

PubMedCentral® Posted Date

9-11-2024

PubMedCentral® Full Text Version

Post-print

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 <  1 × 10−8) gene–disease relationships alongside 182 gene–disease relationships that did not achieve genome-wide significance in the nonaugmented baseline cohorts. We validated these discoveries in the FinnGen biobank alongside two orthogonal machine-learning methods built for gene–disease prioritization. All extracted gene–disease associations and incident disease predictive biomarkers are publicly available (http://milton.public.cgr.astrazeneca.com).

Keywords

Humans, United Kingdom, Biological Specimen Banks, Genome-Wide Association Study, Biomarkers, Machine Learning, Case-Control Studies, Genetic Predisposition to Disease, Multifactorial Inheritance, Proteomics, Phenotype, Polymorphism, Single Nucleotide, Algorithms, Multiomics, UK Biobank, Genetics, Genetics research, Genomics, Computational biology and bioinformatics

Published Open-Access

yes

Recommended Citation

Middleton, Lawrence; Burren, Oliver S; Hu, Fengyuan; et al., "Disease Prediction With Multi-Omics and Biomarkers Empowers Case-Control Genetic Discoveries in the UK Biobank" (2024). Faculty, Staff and Students Publications. 8558.
https://digitalcommons.library.tmc.edu/baylor_docs/8558