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UK Biobank

Summary

UK Biobank is a prospective cohort study that recruited 502,485 participants aged 37–73 across the United Kingdom between 2006 and 2010. It compiles extensive deep-phenotyping data, including electronic health records, physical measures, lifestyle questionnaires, multi-omic datasets (genomics, proteomics, metabolomics), and imaging. UK Biobank serves as a foundational resource for clinical informatics, epidemiology, and statistical genetics, supporting tools like mvSuSiE, SuShiE, RisQ, Delphi-2M, and Ret-AAE.

Data Sources and Access

  • Electronic Health Records: Linked inpatient hospital admissions, primary care GP data, cancer registries, and death registries.
  • Multi-Omics: Whole-genome and whole-exome sequencing, plasma proteomics (profiled via Olink), and metabolomics (profiled via Nightingale NMR).
  • Access Model: Available to approved researchers globally for health-related research in the public interest.

Metabolic-Flux Interaction Study

UK Biobank provided the discovery cohort for metabolic flux modulation of genetic risk. The analysis modeled 6,185 organ-specific reactions in 459,902 participants assigned European genetic ancestry and tested genetically predicted fluxes against 5,852 CAD risk variants using linked time-to-event records.[2]

Admixed-Sample GWAS Validation

A 9,220-person 2-way AFR-EUR admixed subset of UK Biobank (315 first-degree and 489 second-degree relative pairs by KING-robust) was used to validate Tractor-Mix, a local-ancestry- and relatedness-aware GWAS method. It replicated known hits for total cholesterol (rs7412, APOE) and sickle cell anemia (rs334, HBB), with well-controlled genomic inflation (λGC = 0.997 joint).[3]

Citations

[2] Foguet et al. (2025), UK Biobank gene-by-flux analysis [3] Tan, T. et al. (2025). Extending Genome-Wide Association Studies to admixed cohorts with high degrees of relatedness. medRxiv preprint. DOI: 10.1101/2025.05.27.25328444. Source: 2025.05.27.25328444v1.full.md. - Sudlow, C. et al. (2015). UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Medicine, 12(4), e1001779. DOI: 10.1371/journal.pmed.1001779 - Julian, T. H. et al. (2026). Multi-omic analysis of deep learning-derived phenotypes links ophthalmic imaging to cardiovascular and neurological traits. Nature Cardiovascular Research, 5, 541–554. DOI: 10.1038/s44161-026-00815-5. Source paper: s44161-026-00815-5.pdf