Biomedical and Translational Informatics Laboratory

  • Lewis, J. P., Backman, J. D., Reny, J.-L., Bergmeijer, T. O., Mitchell, B. D., Ritchie, M. D., Déry, J.-P., Pakyz, R. E., Gong, L., Ryan, K., Kim, E.-Y., Aradi, D., Fernandez-Cadenas, I., Lee, M. T., Whaley, R. M., Montaner, J., Gensini, G. F., Cleator, J. H., Chang, K., … Shuldiner, A. R. (2019). Pharmacogenomic polygenic response score predicts ischaemic events and cardiovascular mortality in clopidogrel-treated patients. European Heart Journal - Cardiovascular Pharmacotherapy, 6(4), 203–210. https://doi.org/10.1093/ehjcvp/pvz045
  • Sangkuhl, K., Whirl‐Carrillo, M., Whaley, R. M., Woon, M., Lavertu, A., Altman, R. B., Carter, L., Verma, A., Ritchie, M. D., & Klein, T. E. (2019). Pharmacogenomics clinical annotation tool (PharmCat). Clinical Pharmacology & Therapeutics, 107(1), 203–210. https://doi.org/10.1002/cpt.1568
  • Verma, A., Bang, L., Miller, J. E., Zhang, Y., Lee, M. T., Zhang, Y., Byrska-Bishop, M., Carey, D. J., Ritchie, M. D., Pendergrass, S. A., & Kim, D. (2019). Human-disease phenotype map derived from Phewas across 38,682 individuals. The American Journal of Human Genetics, 104(1), 55–64. https://doi.org/10.1016/j.ajhg.2018.11.006
  • Stanaway, I. B., Hall, T. O., Rosenthal, E. A., Palmer, M., Naranbhai, V., Knevel, R., Namjou-Khales, B., Carroll, R. J., Kiryluk, K., Gordon, A. S., Linder, J., Gharavi, A. G., Pendergrass, S. A., Ritchie, M. D., de Andrade, M., Croteau-Chonka, D. C., Raychaudhuri, S., Weiss, S. T., Lebo, M., … Crosslin, D. R. (2019). The eMERGE genotype set of 83,717 subjects imputed to ~40 million variants genome wide and association with the herpes zoster medical record phenotype. Genetic Epidemiology , 43(1), 63–81. https://doi.org/https://doi.org/10.1002/gepi.22167
  • Safarova, M.S., Satterfield, B.A., Fan, X. et al. A phenome-wide association study to discover pleiotropic effects of PCSK9APOB, and LDLR. npj Genomic Med 4, 3 (2019). https://doi.org/10.1038/s41525-019-0078-7
  • Moore, J. H., Boland, M. R., Camara, P. G., Chervitz, H., Gonzalez, G., Himes, B. E., Kim, D., Mowery, D. L., Ritchie, M. D., Shen, L., Urbanowicz, R. J., & Holmes, J. H. (2019). Preparing next-generation scientists for Biomedical Big Data: Artificial Intelligence Approaches. Personalized Medicine, 16(3), 247–257. https://doi.org/10.2217/pme-2018-0145
  • Li, R., Kim, D., Wheeler, H.E. et al. Integration of genetic and functional genomics data to uncover chemotherapeutic induced cytotoxicity. Pharmacogenomics J 19, 178–190 (2019). https://doi.org/10.1038/s41397-018-0024-6
  • Hellwege, J. N., Stallings, S., Torstenson, E. S., Carroll, R., Borthwick, K. M., Brilliant, M. H., Crosslin, D., Gordon, A., Hripcsak, G., Jarvik, G. P., Linneman, J. G., Devi, P., Peissig, P. L., Sleiman, P. A. M., Hakonarson, H., Ritchie, M. D., Verma, S. S., Shang, N., Denny, J. C., & Roden, D. M. (2019). Heritability and genome-wide association study of benign prostatic hyperplasia (BPH) in the eMERGE network. Scientific Reports., 9(1). https://doi.org/10.1038/s41598-019-42427-z
  • Miller, J. E., Dudek, S. M., Frase, A. T., & Ritchie, M. D. (2019). P4‐486: Exploring rare variations that impact regions of proteins associated with alzheimer’s disease.. Alzheimer’s & Dementia : The Journal of the Alzheimer’s Association., 15(7S_Part_29). https://doi.org/10.1016/j.jalz.2019.08.032
  • Haggerty, C. M., Damrauer, S. M., Levin, M. G., Birtwell, D., Carey, D. J., Golden, A. M., Hartzel, D. N., Hu, Y., Judy, R., Kelly, M. A., Kember, R. L., Lester Kirchner, H., Leader, J. B., Liang, L., McDermott-Roe, C., Babu, A., Morley, M., Nealy, Z., Person, T. N., … Arany, Z. (2019). Genomics-first evaluation of heart disease associated with titin-truncating variants. Circulation, 140(1), 42–54. https://doi.org/10.1161/circulationaha.119.039573
  • Singhal, P., Verma, S. S., Dudek, S. M., & Ritchie, M. D. (2019). Neural network-based multiomics data integration in Alzheimer’s disease. Proceedings of the Genetic and Evolutionary Computation Conference Companion /, 403–404. https://doi.org/10.1145/3319619.3321920
  • Zouk, H., Venner, E., Lennon, N. J., Muzny, D. M., Abrams, D., Adunyah, S., Albertson-Junkans, L., Ames, D. C., Appelbaum, P., Aronson, S., Aufox, S., Babb, L. J., Balasubramanian, A., Bangash, H., Basford, M., Bastarache, L., Baxter, S., Behr, M., Benoit, B., … Gibbs, R. A. (2019). Harmonizing clinical sequencing and interpretation for the Emerge III Network. The American Journal of Human Genetics, 105(3), 588–605. https://doi.org/10.1016/j.ajhg.2019.07.018
  • Kember, R. L., Verma, A., Verma, S., Lucas, A., Judy, R., Chen, J., Damrauer, S., Rader, D. J., & Ritchie, M. D. (2019). Polygenic risk scores for cardio-renal-metabolic diseases in the Penn Medicine Biobank. Biorxiv. https://doi.org/10.1101/759381
  • Kember, R., Verma, S., Verma, A., Ritchie, M., Damrauer, S., Rader, D., & Merikangas, A. (2019). Using electronic health records to identify medical comorbidities of mood disorders. European Neuropsychopharmacology., 29, S13–S14. https://doi.org/10.1016/j.euroneuro.2019.07.02
  • Park, J., Katz, N., Zhang, X., Lucas, A. M., Verma, A., Judy, R. L., Kember, R. L., Chen, J., Damrauer, S. M., Ritchie, M. D., & Rader, D. J. (2019). Exome-by-phenome-wide rare variant gene burden association with electronic health record phenotypes. Biorxiv. https://doi.org/10.1101/798330
  • Zhang, X., Basile, A.O., Pendergrass, S.A. et al. Real world scenarios in rare variant association analysis: the impact of imbalance and sample size on the power in silico. BMC Bioinformatics 20, 46 (2019). https://doi.org/10.1186/s12859-018-2591-6
  • Miller, J.E., Metpally, R.P., Person, T.N. et al. Systematic characterization of germline variants from the DiscovEHR study endometrial carcinoma population. BMC Med Genomics 12, 59 (2019). https://doi.org/10.1186/s12920-019-0504-9
  • Miller, J.E., Veturi, Y. & Ritchie, M.D. Innovative strategies for annotating the “relationSNP” between variants and molecular phenotypes. BioData Mining 12, 10 (2019). https://doi.org/10.1186/s13040-019-0197-9
  • Manduchi, E., Orzechowski, P.R., Ritchie, M.D. et al. Exploration of a diversity of computational and statistical measures of association for genome-wide genetic studies. BioData Mining 12, 14 (2019). https://doi.org/10.1186/s13040-019-0201-4
  • Namjou, B., Lingren, T., Huang, Y., Parameswaran, S., Cobb, B. L., Stanaway, I. B., Connolly, J. J., Mentch, F. D., Benoit, B., Niu, X., Wei, W.-Q., Carroll, R. J., Pacheco, J. A., Harley, I. T., Divanovic, S., Carrell, D. S., Larson, E. B., Carey, D. J., Verma, S., … Harley, J. B. (2019). GWAS and enrichment analyses of non-alcoholic fatty liver disease identify new trait-associated genes and pathways across emerge network. BMC Medicine, 17(1). https://doi.org/10.1186/s12916-019-1364-z
  • Schmidt, A. F., Holmes, M. V., Preiss, D., Swerdlow, D. I., Denaxas, S., Fatemifar, G., Faraway, R., Finan, C., Valentine, D., Fairhurst-Hunter, Z., Hartwig, F. P., Horta, B. L., Hypponen, E., Power, C., Moldovan, M., van Iperen, E., Hovingh, K., Demuth, I., Norman, K., … Hingorani, A. D. (2019). Phenome-wide association analysis of LDL-cholesterol lowering genetic variants in PCSK9. BMC Cardiovascular Disorders, 19(1). https://doi.org/10.1186/s12872-019-1187-z
  • Damrauer, S. M., Chaudhary, K., Cho, J. H., Liang, L. W., Argulian, E., Chan, L., Dobbyn, A., Guerraty, M. A., Judy, R., Kay, J., Kember, R. L., Levin, M. G., Saha, A., Van Vleck, T., Verma, S. S., Weaver, J., Abul-Husn, N. S., Baras, A., Chirinos, J. A., … Do, R. (2019). Association of the V122I hereditary transthyretin amyloidosis genetic variant with heart failure among individuals of African or Hispanic/latino ancestry. JAMA, 322(22), 2191. https://doi.org/10.1001/jama.2019.17935
  • Lucas, A. M., Palmiero, N. E., McGuigan, J., Passero, K., Zhou, J., Orie, D., Ritchie, M. D., & Hall, M. A. (2019). CLARITE Facilitates the Quality Control and Analysis Process for EWAS of Metabolic-Related Traits. Frontiers in Genetics., 10. https://doi.org/10.3389/fgene.2019.01240
  • Pendergrass, S. A., Buyske, S., Jeff, J. M., Frase, A., Dudek, S., Bradford, Y., Ambite, J.-L., Avery, C. L., Buzkova, P., Deelman, E., Fesinmeyer, M. D., Haiman, C., Heiss, G., Hindorff, L. A., Hsu, C.-N., Jackson, R. D., Lin, Y., Le Marchand, L., Matise, T. C., … Crawford, D. C. (2019). A phenome-wide association study (phewas) in the population architecture using Genomics and Epidemiology (page) study reveals potential pleiotropy in African Americans. PLOS ONE, 14(12). https://doi.org/10.1371/journal.pone.0226771