PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene–disease associations

By Joshua Charles Denny, Marylyn DeRiggi Ritchie, Melissa A. Basford, J. Pulley, L. Bastarache, K. Brown-Gentry, Deede Wang, D. Masys, D. Roden, D. Crawford. 2010

1,141 citations recorded by the source corpus.

Motivation: Emergence of genetic data coupled to longitudinal electronic medical records (EMRs) offers the possibility of phenome-wide association scans (PheWAS) for disease–gene associations. We propose a novel method to scan phenomic data for genetic associations using International Classification of Disease (ICD9) billing codes, which are available in most EMR systems. We have developed a code translation table to automatically define 776 different disease populations and their controls using prevalent ICD9 codes derived from EMR data. As a proof of concept of this algorithm, we genotyped the first 6005 European–Americans accrued into BioVU, Vanderbilt's DNA biobank, at five single nucleotide polymorphisms (SNPs) with previously reported disease associations: atrial fibrillation, Crohn's disease, carotid artery stenosis, coronary artery disease, multiple sclerosis, systemic lupus erythematosus and rheumatoid arthritis. The PheWAS software generated cases and control populations across all ICD9 code groups for each of these five SNPs, and disease-SNP associations were analyzed. The primary outcome of this study was replication of seven previously known SNP–disease associations for these SNPs. Results: Four of seven known SNP–disease associations using the PheWAS algorithm were replicated with P-values between 2.8 × 10−6 and 0.011. The PheWAS algorithm also identified 19 previously unknown statistical associations between these SNPs and diseases at P < 0.01. This study indicates that PheWAS analysis is a feasible method to investigate SNP–disease associations. Further evaluation is needed to determine the validity of these associations and the appropriate statistical thresholds for clinical significance. Availability:The PheWAS software and code translation table are freely available at http://knowledgemap.mc.vanderbilt.edu/research. Contact: josh.denny@vanderbilt.edu

Subfields: Genetics, Molecular Biology

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Citations in the corpus

Natural Language Processing for EHR-Based Computational Phenotyping (2018, methodology): “[71] applied computational phenotyping on EHR to automatically detect 776 different disease populations and their matched controls.”
Understanding multicellular function and disease with human tissue-specific networks (2015, neutral): “This is particularly important in the domain of phenome-wide association studies that rely on endpoints gleaned from electronic health records (EHRs) 67 .”
Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data (2013, methodology): “Here, we expanded the PheWAS disease classifications to analyze the diverse spectrum of phenotypes in the NHGRI Catalog using EMR data and refined the statistical methods over previous publications 9,11–13 .”
Genome- and Phenome-Wide Analysis of Cardiac Conduction Identifies Markers of Arrhythmia Risk (2013, methodology): “8 in vagal and spinal afferents in gastrointestinal mucosa and myenteric plexes,41 and interestingly, SCN10A was also associated with cholecystitis in the PheWAS.”
The Matchmaker Exchange: A Platform for Rare Disease Gene Discovery (2015, background): “, 2008] or PheWASs [Denny et al., 2010], as well as targeted approaches in Mendelian diseases.”
Limestone: High-throughput candidate phenotype generation via tensor factorization (2014, background): “6 Note that other hierarchies, such as the PheWAS code groups [80] could have”
PheKB: a catalog and workflow for creating electronic phenotype algorithms for transportability (2016, background): “Critically, sharing phenotypic and genotypic data across sites allowed increased sample size and thus power for discovery and has been able to advance genomic discoveries based on this data.(15,19,20,28,29) For example, in the study of ventricular conduction in normal hearts, eMERGE leveraged existi”