Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data

Joshua C. Denny, Lisa Bastarache, Marylyn D. Ritchie, Robert J. Carroll, Raquel Zink, Jonathan D. Mosley, Julie R. Field, Jill M. Pulley, Andrea H. Ramirez, Erica Bowton, Melissa A. Basford, David S. Carrell, Peggy L. Peissig, Abel N. Kho, Jennifer A. Pacheco, Luke V. Rasmussen, David R. Crosslin, Paul K. Crane, Jyotishman Pathak, Suzette J. BielinskiSarah A. Pendergrass, Hua Xu, Lucia A. Hindorff, Rongling Li, Teri A. Manolio, Christopher G. Chute, Rex L. Chisholm, Eric B. Larson, Gail P. Jarvik, Murray H. Brilliant, Catherine A. Mccarty, Iftikhar J. Kullo, Jonathan L. Haines, Dana C. Crawford, Daniel R. Masys, Dan M. Roden

Research output: Contribution to journalArticle

Abstract

Candidate gene and genome-wide association studies (GWAS) have identified genetic variants that modulate risk for human disease; many of these associations require further study to replicate the results. Here we report the first large-scale application of the phenome-wide association study (PheWAS) paradigm within electronic medical records (EMRs), an unbiased approach to replication and discovery that interrogates relationships between targeted genotypes and multiple phenotypes. We scanned for associations between 3,144 single-nucleotide polymorphisms (previously implicated by GWAS as mediators of human traits) and 1,358 EMR-derived phenotypes in 13,835 individuals of European ancestry. This PheWAS replicated 66% (51/77) of sufficiently powered prior GWAS associations and revealed 63 potentially pleiotropic associations with P < 4.6 × 10 -6 (false discovery rate < 0.1); the strongest of these novel associations were replicated in an independent cohort (n = 7,406). These findings validate PheWAS as a tool to allow unbiased interrogation across multiple phenotypes in EMR-based cohorts and to enhance analysis of the genomic basis of human disease.

Original languageEnglish (US)
Pages (from-to)1102-1110
Number of pages9
JournalNature biotechnology
Volume31
Issue number12
DOIs
StatePublished - Dec 1 2013
Externally publishedYes

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ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering
  • Applied Microbiology and Biotechnology
  • Molecular Medicine
  • Biomedical Engineering

Cite this

Denny, J. C., Bastarache, L., Ritchie, M. D., Carroll, R. J., Zink, R., Mosley, J. D., Field, J. R., Pulley, J. M., Ramirez, A. H., Bowton, E., Basford, M. A., Carrell, D. S., Peissig, P. L., Kho, A. N., Pacheco, J. A., Rasmussen, L. V., Crosslin, D. R., Crane, P. K., Pathak, J., ... Roden, D. M. (2013). Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nature biotechnology, 31(12), 1102-1110. https://doi.org/10.1038/nbt.2749