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

By Joshua Charles Denny, L. Bastarache, Marylyn DeRiggi Ritchie, Robert J. Carroll, Raquel M. Zink, J. Mosley, Julie Field, J. Pulley, A. Ramirez, E. Bowton and 26 more. 2013

986 citations recorded by the source corpus.

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.

Subfields: Genetics, Molecular Biology

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Citations in the corpus

Natural Language Processing for EHR-Based Computational Phenotyping (2018, background): “Additional studies have established the efficiency of EHR-based PheWAS to detect genetic association [72-74].”
Clinical phenotyping in selected national networks: demonstrating the need for high-throughput, portable, and computational methods (2016, methodology): “These include groupings of billing codes into meanngful phenotypes, such as done by the Agency for Healthcare esearch and Quality’s Clinical Classifications Software [46,47] or he phenome-wide association study (PheWAS) tools [3,48–50].”
Rubik: Knowledge Guided Tensor Factorization and Completion for Health Data Analytics (2015, positive): “Successful pheno-typing eﬀorts on EHR data can enable many important applications, such as clinical predictive modeling [12, 27] and EHR-based genomic association studies [12, 17, 27].”
Extracting research-quality phenotypes from electronic health records to support precision medicine (2015, background): “This study also identified 63 new associations, some of which represent true pleiotropy, in which the genetic variant is associated with multiple distinct phenotypes [99].”
Detection and interpretation of shared genetic influences on 42 human traits (2016, positive): “A previous “phenome-wide association study” identified 44 genetic variants associated with multiple phenotypes 34 , so this represents an order-of-magnitude increase in the number of such loci.”
A survey of current trends in computational drug repositioning (2016, positive): “[36] performed a large-scale application of the PheWAS using electronic medical records (EMRs), and demonstrated that PheWAS is a useful tool to enhance the analysis of the genomic basis and to detect novel associations between genetic markers and human diseases.”
Open Targets Platform: new developments and updates two years on (2018, methodology): “Since our previous publication, we have added two new data sources as evidence for Genetic associations between targets and Mendelian and more common diseases: Genomics England PanelAPP and the PheWAS catalog, respectively.”