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

Journal: Cell reports methods
Published Date:

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 r = 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 R. Predicted phenotypes represent composite biomarkers complementing case-control approaches in genetic discovery, drug target prioritization, and risk prediction, though efficacy varies across diseases.

Authors

  • Robert Chen
    Research, Sutter Health Research, Walnut Creek, CA (R.C., X.Y.).
  • Ghislain Rocheleau
    The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Ben Omega Petrazzini
    The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Iain S Forrest
    The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
  • Joshua K Park
    The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Áine Duffy
    The Charles Bronfman Institute for Personalized Medicine, Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
  • Ha My T Vy
    The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Daniel Jordan
    The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Ron Do
    The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, USA.