Biomedical and Translational Informatics Laboratory


Method Description:

We have been using Phenome-Wide Association Studies (PheWAS) to explore the association between genetic variation and comprehensive and diverse phenotypic measurement data [1]. These PheWAS studies have been pursued with data from a series of epidemiological studies across ancestry [2] as well as with data from the National Health and Nutrition Surveys of the Centers for Disease Control (CDC). These PheWAS analyses have included survey, laboratory, and medical data coupled with genetic data for exploring associations. We have also used de-identified electronic-health record (EHR) data coupled with genetic data for PheWAS, using data from Geisinger MyCode as well as from the Vanderbilt Medical Center biobank BioVU. We also have been performing PheWAS using clinical trial based data, including laboratory measurements.

Dynamic networks exist between genetic architecture, signaling pathways, intermediate phenotypes, and outcome traits. Pleiotropy contributes additional complexity to these networks, with genetic variation affecting two or more outcome traits. Thus far, much of the exploration of the association between genetic variation and complex traits and outcome has been through the use of genome-wide association studies (GWAS), using single-nucleotide polymorphisms (SNPs) and a specific outcome or a very related series of measurements. The focus on a limited phenotypic domain neglects the potential power gained by using intermediate phenotypes, sub-phenotypes, biomarkers, and endophenotypes that may more closely reflect a gene’s mechanism. Further, the GWAS approach also misses information that can be gained through exploring the networks of connections between multiple phenotypes and genotypes, combined with additional biological information, that may better explain the relationship between genetic architecture and outcome.

Related Publications:

  • Phenome-wide association study (PheWAS) for detection of pleiotropy within the Population Architecture using Genomics and Epidemiology (PAGE) Network. Sarah A. Pendergrass, Kristin Brown-Gentry, Scott Dudek, Alex Frase, Eric S. Torstenson, Robert Goodloe, Jose Luis Ambite, Christy L. Avery, Steve Buyske, Petra Buzkova, Ewa Deelman, Megan D. Fesinmeyer, Christopher A. Haiman, Gerardo Heiss, Lucia A. Hindorff, Chu-Nan Hsu, Rebecca D. Jackson, Charles Kooperberg, Loic Le Marchand, Yi Lin, Tara C. Matise, Kristine R. Monroe, Larry Moreland, Sungshim L. Park, Alex Reiner, Robert Wallace, Lynn R. Wilkens, Dana C. Crawford, Marylyn D. Ritchie, 9, 1, PLoS genetics, 2013, PMID: 23382687 PMCID: PMC3561060
  • Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Joshua C. Denny, Lisa Bastarache, Marylyn D. Ritchie, Robert J. Carroll, Raquel Zink, Jonathan D. Mosley, Julie R. Field, Jill M. Pulley, Andrea H. Ramirez, Erica Bowton, Melissa A. Basford, David S. Carrell, Peggy L. Peissig, Abel N. Kho, Jennifer A. Pacheco, Luke V. Rasmussen, David R. Crosslin, Paul K. Crane, Jyotishman Pathak, Suzette J. Bielinski, Sarah A. Pendergrass, Hua Xu, Lucia A. Hindorff, Rongling Li, Teri A. Manolio, Christopher G. Chute, Rex L. Chisholm, Eric B. Larson, Gail P. Jarvik, Murray H. Brilliant, Catherine A. McCarty, Iftikhar J. Kullo, Jonathan L. Haines, Dana C. Crawford, Daniel R. Masys, Dan M. Roden, 31, 12, 1102-1110, Nature biotechnology, 2013 Dec, PMID: 24270849
  • Genome- and phenome-wide analyses of cardiac conduction identifies markers of arrhythmia risk. Marylyn D. Ritchie, Joshua C. Denny, Rebecca L. Zuvich, Dana C. Crawford, Jonathan S. Schildcrout, Lisa Bastarache, Andrea H. Ramirez, Jonathan D. Mosley, Jill M. Pulley, Melissa A. Basford, Yuki Bradford, Luke V. Rasmussen, Jyotishman Pathak, Christopher G. Chute, Iftikhar J. Kullo, Catherine A. McCarty, Rex L. Chisholm, Abel N. Kho, Christopher S. Carlson, Eric B. Larson, Gail P. Jarvik, Nona Sotoodehnia, Teri A. Manolio, Rongling Li, Daniel R. Masys, Jonathan L. Haines, Dan M. Roden, 127, 13, 1377-1385, Circulation, 2013 Apr 2, PMID: 23463857 PMCID: PMC3713791
  • A genome- and phenome-wide association study to identify genetic variants influencing platelet count and volume and their pleiotropic effects. Khader Shameer, Joshua C. Denny, Keyue Ding, Hayan Jouni, David R. Crosslin, Mariza de Andrade, Christopher G. Chute, Peggy Peissig, Jennifer A. Pacheco, Rongling Li, Lisa Bastarache, Abel N. Kho, Marylyn D. Ritchie, Daniel R. Masys, Rex L. Chisholm, Eric B. Larson, Catherine A. McCarty, Dan M. Roden, Gail P. Jarvik, Iftikhar J. Kullo, 133, 1, Human genetics, 2014 Jan, PMID: 24026423 PMCID: PMC3880605
  • PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations. Joshua C. Denny, Marylyn D. Ritchie, Melissa A. Basford, Jill M. Pulley, Lisa Bastarache, Kristin Brown-Gentry, Deede Wang, Dan R. Masys, Dan M. Roden, Dana C. Crawford, 26, 9, 1205-1210, Bioinformatics (Oxford, England), 2010 May 1, PMID: 20335276 PMCID: PMC2859132
  • The use of phenome-wide association studies (PheWAS) for exploration of novel genotype-phenotype relationships and pleiotropy discovery. S. A. Pendergrass, K. Brown-Gentry, S. M. Dudek, E. S. Torstenson, J. L. Ambite, C. L. Avery, S. Buyske, C. Cai, M. D. Fesinmeyer, C. Haiman, G. Heiss, L. A. Hindorff, C.-N. Hsu, R. D. Jackson, C. Kooperberg, L. Le Marchand, Y. Lin, T. C. Matise, L. Moreland, K. Monroe, A. P. Reiner, R. Wallace, L. R. Wilkens, D. C. Crawford, M. D. Ritchie, (c) 2011 Wiley-Liss, Inc., 35, 5, 410-422, Genetic epidemiology, 2011 Jul, PMID: 21594894 PMCID: PMC3116446
  • Visually integrating and exploring high throughput Phenome-Wide Association Study (PheWAS) results using PheWAS-View. Sarah A. Pendergrass, Scott M. Dudek, Dana C. Crawford, Marylyn D. Ritchie, 5, 1, BioData mining, 2012, PMID: 22682510 PMCID: PMC3476448


  1. Pendergrass SA, Brown-Gentry K, Dudek SM, Torstenson ES, Ambite JL, Avery CL, Buyske S, Cai C, Fesinmeyer MD, Haiman C, Heiss G, Hindorff LA, Hsu C-N, Jackson RD, Kooperberg C, Le Marchand L, Lin Y, Matise TC, Moreland L, Monroe K, Reiner AP, Wallace R, Wilkens LR, Crawford DC, Ritchie MD. The Use of Phenome-Wide Association Studies (PheWAS) for Exploration of Novel Genotype-Phenotype Relationships and Pleiotropy Discovery. Genet Epidemiol. 2011 Jul;35(5):410–422. PMID: 21594894
  2. Pendergrass SA, Brown-Gentry K, Dudek S, Frase A, Torstenson ES, Goodloe R, Ambite JL, Avery CL, Buyske S, Bůžková P, Deelman E, Fesinmeyer MD, Haiman CA, Heiss G, Hindorff LA, Hsu C-N, Jackson RD, Kooperberg C, Le Marchand L, Lin Y, Matise TC, Monroe KR, Moreland L, Park SL, Reiner A, Wallace R, Wilkens LR, Crawford DC, Ritchie MD. Phenome-Wide Association Study (PheWAS) for Detection of Pleiotropy within the Population Architecture using Genomics and Epidemiology (PAGE) Network. PLoS Genet. 2013 Jan 31;9(1):e1003087.