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.
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Acknowledgements
This work was supported by the eMERGE Network, initiated and funded by the National Human Genome Research Institute (NHGRI), with additional funding from the National Institute of General Medical Sciences (NIGMS), through the following grants: U01-HG004610 and U01-HG006375 (Group Health Cooperative/University of Washington); U01-HG004608 (Marshfield Clinic); U01-HG004599 and U01-HG006379 (Mayo Clinic); U01-HG004609 and U01-HG006388 (Northwestern University); U01-HG006389 (Essentia Institute of Rural Health/Marshfield Clinic); U01-HG004603 and U01-HG006378 (Vanderbilt University); and U01-HG006385 (Vanderbilt University serving as the Coordinating Center). Funding support for eMERGE genotyping was provided by NHGRI through the grants: U01-HG004424 (The Broad Institute) and U01-HG004438 (Johns Hopkins University, Center for Inherited Disease Research). Replication genotypes were derived from a pharmacogenomics resource supported by NIGMS RC2-GM092318. Development of the PheWAS method is also supported by R01-LM010685 from the National Library of Medicine. BioVU received and continues to receive support through the National Center for Research Resources UL1 RR024975, which is now the National Center for Advancing Translational Sciences, 2 UL1 TR000445. Additional support for this work at the University of Washington was partially provided by the National Center for Advancing Translational Sciences grant UL1TR000427.
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The experiment was conceived by J.C.D., L.B., D.R.M., and D.M.R. J.C.D. and L.B. designed the final PheWAS algorithm, phenotype classification and matching to NHGRI Catalog phenotypes. L.B. performed the PheWAS. Statistical analysis was performed by J.C.D., L.B., R.J.C. and J.D.M. eMERGE Phenotype algorithms were developed primarily by D.S.C., A.N.K. and J.C.D. Novel phenotype algorithms for skin phenotypes were generated and executed by J.C.D., L.B. and R.Z. and evaluated by J.D.M. S.A.P. performed power calculations. J.R.F., J.C.D. and L.B. reviewed the literature for previous publications for each SNP. Genetic quality control and the merged data set were performed by M.D.R. with input from D.C.C., D.R.C. and J.L.H. Data were provided by D.S.C., P.L.P., A.N.K., J.A.P., L.V.R., D.R.C., P.K.C., J.P., S.J.B. and M.A.B. J.C.D., L.B. and D.M.R. drafted the manuscript, with substantial revision and direction by D.R.M., J.L.H., D.C.C., M.D.R. and J.R.F. Guidance and critical revision were provided by T.A.M. and L.A.H. All authors edited the manuscript.
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Denny, J., Bastarache, L., Ritchie, M. et al. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat Biotechnol 31, 1102–1111 (2013). https://doi.org/10.1038/nbt.2749
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DOI: https://doi.org/10.1038/nbt.2749
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