Systematic comparison of phenome-wide associa... [Nat Biotechnol. 2013] - PubMed - NCBI

Systematic comparison of phenome-wide associa... [Nat Biotechnol. 2013] - PubMed - NCBI

Nat Biotechnol. 2013 Nov 24. doi: 10.1038/nbt.2749. [Epub ahead of print]

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

Denny JC, Bastarache L, Ritchie MD, Carroll RJ, Zink R, Mosley JD, Field JR, Pulley JM, Ramirez AH, Bowton E, Basford MA, Carrell DS, Peissig PL, Kho AN, Pacheco JA, Rasmussen LV, Crosslin DR, Crane PK, Pathak J, Bielinski SJ, Pendergrass SA, Xu H, Hindorff LA, Li R, Manolio TA, Chute CG, Chisholm RL, Larson EB, Jarvik GP, Brilliant MH, McCarty CA, Kullo IJ, Haines JL, Crawford DC, Masys DR, Roden DM.

Source

1] Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, USA. [2] Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.

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.

PMID:

24270849

[PubMed - as supplied by publisher]