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

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.

Predicting warfarin dosage in European-Americans and African-Americans using DNA samples linked to an electronic health record.

AIM: Warfarin pharmacogenomic algorithms reduce dosing error, but perform poorly in non-European-Americans. Electronic health record (EHR) systems linked to biobanks may allow for pharmacogenomic analysis, but they have not yet been used for this purpose. PATIENTS & METHODS: We used BioVU, the Vanderbilt EHR-linked DNA repository, to identify European-Americans (n = 1022) and African-Americans (n = 145) on stable warfarin therapy and evaluated the effect of 15 pharmacogenetic variants on stable warfarin dose. RESULTS: Associations between variants in VKORC1, CYP2C9 and CYP4F2 with weekly dose were observed in European-Americans as well as additional variants in CYP2C9 and CALU in African-Americans. Compared with traditional 5 mg/day dosing, implementing the US FDA recommendations or the International Warfarin Pharmacogenomics Consortium (IWPC) algorithm reduced error in weekly dose in European-Americans (13.5-12.4 and 9.5 mg/week, respectively) but less so in African-Americans (15.2-15.0 and 13.8 mg/week, respectively). By further incorporating associated variants specific for European-Americans and African-Americans in an expanded algorithm, dose-prediction error reduced to 9.1 mg/week (95% CI: 8.4-9.6) in European-Americans and 12.4 mg/week (95% CI: 10.0-13.2) in African-Americans. The expanded algorithm explained 41 and 53% of dose variation in African-Americans and European-Americans, respectively, compared with 29 and 50%, respectively, for the IWPC algorithm. Implementing these predictions via dispensable pill regimens similarly reduced dosing error. CONCLUSION: These results validate EHR-linked DNA biorepositories as real-world resources for pharmacogenomic validation and discovery.

Portability of an algorithm to identify rheumatoid arthritis in electronic health records.

OBJECTIVES: Electronic health records (EHR) can allow for the generation of large cohorts of individuals with given diseases for clinical and genomic research. A rate-limiting step is the development of electronic phenotype selection algorithms to find such cohorts. This study evaluated the portability of a published phenotype algorithm to identify rheumatoid arthritis (RA) patients from EHR records at three institutions with different EHR systems. MATERIALS AND METHODS: Physicians reviewed charts from three institutions to identify patients with RA. Each institution compiled attributes from various sources in the EHR, including codified data and clinical narratives, which were searched using one of two natural language processing (NLP) systems. The performance of the published model was compared with locally retrained models. RESULTS: Applying the previously published model from Partners Healthcare to datasets from Northwestern and Vanderbilt Universities, the area under the receiver operating characteristic curve was found to be 92% for Northwestern and 95% for Vanderbilt, compared with 97% at Partners. Retraining the model improved the average sensitivity at a specificity of 97% to 72% from the original 65%. Both the original logistic regression models and locally retrained models were superior to simple billing code count thresholds. DISCUSSION: These results show that a previously published algorithm for RA is portable to two external hospitals using different EHR systems, different NLP systems, and different target NLP vocabularies. Retraining the algorithm primarily increased the sensitivity at each site. CONCLUSION: Electronic phenotype algorithms allow rapid identification of case populations in multiple sites with little retraining.