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Published on: August 24, 2013
Genetic analyses of eight complex diseases using predicted continuous representations of disease
Robert Chen1, Ghislain Rocheleau2, Ben Omega Petrazzini2
1The Charles Bronfman Institute for Personalized Medicine, 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; Windreich Department of Artificial Intelligence and Human Health, 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.
None:
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.
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