Citations 2
PheKB: a catalog and workflow for creating electronic phenotype algorithms for transportability
Kirby, Jacqueline, Speltz, Peter, Rasmussen, Luke V. , Gottesman, Omri, Peissig, Peggy , Pacheco, Jennifer A. , Tromp, Gerard , Pathak, Jyotishman , Ellis, Stephen B. , Thompson, Will K, Savova, Guergana , Haines, Jonathan L. , Roden, Dan M. , Denny, Joshua C. , Basford, Melissa , Carrell, David , Lingren, Todd , Roden, Dan M. , Harris, Paul A. , Denny, Joshua C , Rasmussen, Luke V , Pacheco, Jennifer A
Journal of the American Medical Informatics Association | March 30, 2016 | Volume: 23 | Issue: 6 | Pages: 1046-1052
Abstract Objective Health care generated data have become an important source for clinical and genomic research. Often, investigators create and iteratively refine phenotype algorithms to achieve high positive predictive values (PPVs) or sensitivity, thereby identifying valid cases and controls. These algorithms achieve the greatest utility when validated and shared by multiple health care systems. Materials and Methods We report the current status and impact of the Phenotype KnowledgeBase (PheKB, http://phekb.org ), an online environment supporting the workflow of building, sharing, and validating electronic phenotype algorithms. We analyze the most frequent components used in algorithms and their performance at authoring institutions and secondary implementation sites. Results As of June 2015, PheKB contained 30 finalized phenotype algorithms and 62 algorithms in development spanning a range of traits and diseases. Phenotypes have had over 3500 unique views in a 6-month period and have been reused by other institutions. International Classification of Disease codes were the most frequently used component, followed by medications and natural language processing. Among algorithms with published performance data, the median PPV was nearly identical when evaluated at the authoring institutions (n = 44; case 96.0%, control 100%) compared to implementation sites (n = 40; case 97.5%, control 100%). Discussion These results demonstrate that a broad range of algorithms to mine electronic health record data from different health systems can be developed with high PPV, and algorithms developed at one site are generally transportable to others. Conclusion By providing a central repository, PheKB enables improved development, transportability, and validity of algorithms for research-grade phenotypes using health care generated data.
Genetic variation among 82 pharmacogenes: The PGRNseq data from the eMERGE network
Crosslin, DR, Owusu‐Obeng, Aniwaa, Wallace, John R. , Almoguera, Berta , Basford, MA, Bielinski, Suzette J. , Carrell, DS, Connolly, John J. , Doheny, KF, Gallego, CJ, Gordon, Allan , Kirby, Jacqueline, Kitchner, Terrie, Manzi, Shannon , Mejia, A R, Pan, Vivian , Perry, Cassandra, Peterson, JF, Prows, CA, Ralston, James D. , Scott, Stuart A. , Scrol, Aaron, Smith, Maureen E. , Stallings, SC, Veldhuizen, Tamra L., Wolf, W.A., Volpi, Simona , Wiley, Kenneth L , Manolio, Teri A. , Böttinger, Erwin P. , Brilliant, MH, Carey, David J. , Chisholm, Rex L. , Chute, CG, Jl, Haines, Håkonarson, Håkon , Harley, JB, Holm, Ingrid A. , Kullo, IJ, Jarvik, GP, Larson, EB, McCarty, Catherine A. , Williams, MS, Denny, Joshua C. , Rasmussen‐Torvik, LJ, Roden, DM, Ritchie, MD, Rasmussen‐Torvik, Laura J. , Jarvik, Gail P. , Peterson, Josh F. , Bush, William S. , Wallace, J, Crawford, Dana C. , Keating, Brendan J. , Perry, CL, Peterson, JF, Stallings, SC, Li, R, Roden, DM, Ritchie, M.D., Holm, Ingrid A , Hakonarson, Hakon , Denny, Joshua C , Jarvik, Gail P , Bielinski, Suzette , Gordon, Adam S , Connolly, John J , Peterson, Josh F , Scott, Stuart A , Rasmussen-Torvik, Laura J
Clinical Pharmacology & Therapeutics | February 9, 2016 | Volume: 100 | Issue: 2 | Pages: 160-169
Genetic variation can affect drug response in multiple ways, although it remains unclear how rare genetic variants affect drug response. The electronic Medical Records and Genomics (eMERGE) Network, collaborating with the Pharmacogenomics Research Network, began eMERGE‐PGx, a targeted sequencing study to assess genetic variation in 82 pharmacogenes critical for implementation of “precision medicine.” The February 2015 eMERGE‐PGx data release includes sequence‐derived data from ∼5,000 clinical subjects. We present the variant frequency spectrum categorized by variant type, ancestry, and predicted function. We found 95.12% of genes have variants with a scaled Combined Annotation‐Dependent Depletion score above 20, and 96.19% of all samples had one or more Clinical Pharmacogenetics Implementation Consortium Level A actionable variants. These data highlight the distribution and scope of genetic variation in relevant pharmacogenes, identifying challenges associated with implementing clinical sequencing for drug treatment at a broader level, underscoring the importance for multifaceted research in the execution of precision medicine.