Genetic analyses of eight complex diseases using predicted continuous representations of disease

  • Robert Chen
  • , Ghislain Rocheleau
  • , Ben Omega Petrazzini
  • , Iain S. Forrest
  • , Joshua K. Park
  • , Áine Duffy
  • , Ha My T. Vy
  • , Daniel Jordan
  • , Ron Do

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

We evaluated whether predicted continuous disease representations could enhance genetic discovery beyond case-control genome-wide association study (GWAS) phenotypes across eight complex diseases in up to 485,448 UK Biobank participants. Predicted phenotypes had high genetic correlations with case-control phenotypes (median rg = 0.66) but identified more independent associations (median 306 versus 125). While some predicted phenotype associations were spurious, multi-trait analysis of GWAS-boosted case-control phenotypes identified a median of 46 additional variants per disease, of which a median of 73% replicated in FinnGen, 37% reached genome-wide significance in a UK Biobank/FinnGen meta-analysis, and 45% had supporting evidence. Predicted phenotypes also identified 14 genes targeted by phase I–IV drugs not identified by case-control phenotypes, and combined polygenic risk scores (PRSs) using both phenotypes improved prediction performance, with a median 37% increase in Nagelkerke's R2. Predicted phenotypes represent composite biomarkers complementing case-control approaches in genetic discovery, drug target prioritization, and risk prediction, though efficacy varies across diseases.

Original languageEnglish
Article number101115
JournalCell Reports Methods
Volume5
Issue number8
DOIs
StatePublished - 18 Aug 2025

Keywords

  • CP: computational biology
  • CP: genetics
  • electronic health records
  • genome-wide association study
  • machine learning

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