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

Abstract

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

Evaluating electronic health record data sources and algorithmic approaches to identify hypertensive individuals

“…Using automated phenotyping algorithms, investigators have identified cases and controls for diseases of interest to replicate known phenotype-genotype associations and make novel discoveries, [12][13][14][15][16][17] potentially with decreased cost 18 and faster execution than traditional trials.…”

Phenome-wide association studies (PheWASs) for functional variants

“…For rare ICD9 codes where cell counts for a genotype fell below five in a contingency table, Fisher's exact test was used, similar to methods described in previous PheWASs. 4,6 Q-Q plots were generated for every SNP (data not shown) and at the study-wide level (Supplementary Material and Supplementary Figure S3) to measure systematic confounding or bias in the SNP-phenotype associations. The PheWAS results from the discovery set and validation set were combined by meta-analysis.…”

“…The common approach to account for multiple testing in PheWAS is the use of a Bonferroni correction. 1,4,6,[23][24][25] With 4841 phenotypes, 110 SNPs, and a studywide a of 0.05, an association with Po9.4 Â 10 À8 would be required to identify a statistically significant association assuming independence. The only association that meets this criterion is that between the PheWAS control SNP rs1061170 in CFH and ICD9 codes that define AMD (Figure 1e).…”

“…[5][6][7][8] The use of SNPs identified by GWAS for PheWAS has the advantage of leveraging known association data when interpreting PheWAS results. For example, PheWAS results demonstrate that multiple sclerosis (MS) and erythematous conditions, including rosacea, may share a common genetic etiology (ie, HLA-DRB1*1501), 4,6 demonstrating the capacity of PheWAS to identify pleiotropic effects for GWAS SNPs.…”

Pathway and network-based strategies to translate genetic discoveries into effective therapies

“…Another major source of phenotyping data is the electronic health record (44,45). The breadth of these data presents both challenges and opportunities (46).…”