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Am J Hum Genet. 2018 Apr 5;102(4):592-608. doi: 10.1016/j.ajhg.2018.02.017. Epub 2018 Mar 29.

PheWAS and Beyond: The Landscape of Associations with Medical Diagnoses and Clinical Measures across 38,662 Individuals from Geisinger.

Author information

1
Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA; The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA.
2
Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA.
3
Department of Statistics, The Pennsylvania State University, University Park, PA 16802, USA.
4
Biomedical and Translational Informatics Institute, Geisinger Health System, Danville, PA 17822, USA.
5
Mount Holyoke College, South Hadley, MA 01075, USA.
6
Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA; The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA; Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA 16802, USA.
7
Biomedical and Translational Informatics Institute, Geisinger Health System, Danville, PA 17822, USA. Electronic address: spendergrass@geisinger.edu.

Abstract

Most phenome-wide association studies (PheWASs) to date have used a small to moderate number of SNPs for association with phenotypic data. We performed a large-scale single-cohort PheWAS, using electronic health record (EHR)-derived case-control status for 541 diagnoses using International Classification of Disease version 9 (ICD-9) codes and 25 median clinical laboratory measures. We calculated associations between these diagnoses and traits with ∼630,000 common frequency SNPs with minor allele frequency > 0.01 for 38,662 individuals. In this landscape PheWAS, we explored results within diseases and traits, comparing results to those previously reported in genome-wide association studies (GWASs), as well as previously published PheWASs. We further leveraged the context of functional impact from protein-coding to regulatory regions, providing a deeper interpretation of these associations. The comprehensive nature of this PheWAS allows for novel hypothesis generation, the identification of phenotypes for further study for future phenotypic algorithm development, and identification of cross-phenotype associations.

KEYWORDS:

EHR; GWAS; PheWAS; bioinformatics; biorepository; genetic epidemiology; genomics; phenome-wide

PMID:
29606303
PMCID:
PMC5985339
DOI:
10.1016/j.ajhg.2018.02.017
[Indexed for MEDLINE]
Free PMC Article

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