Literature DB >> 24733291

R PheWAS: data analysis and plotting tools for phenome-wide association studies in the R environment.

Robert J Carroll1, Lisa Bastarache1, Joshua C Denny2.   

Abstract

UNLABELLED: Phenome-wide association studies (PheWAS) have been used to replicate known genetic associations and discover new phenotype associations for genetic variants. This PheWAS implementation allows users to translate ICD-9 codes to PheWAS case and control groups, perform analyses using these and/or other phenotypes with covariate adjustments and plot the results. We demonstrate the methods by replicating a PheWAS on rs3135388 (near HLA-DRB, associated with multiple sclerosis) and performing a novel PheWAS using an individual's maximum white blood cell count (WBC) as a continuous measure. Our results for rs3135388 replicate known associations with more significant results than the original study on the same dataset. Our PheWAS of WBC found expected results, including associations with infections, myeloproliferative diseases and associated conditions, such as anemia. These results demonstrate the performance of the improved classification scheme and the flexibility of PheWAS encapsulated in this package.
AVAILABILITY AND IMPLEMENTATION: This R package is freely available under the Gnu Public License (GPL-3) from http://phewascatalog.org. It is implemented in native R and is platform independent.
© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

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Year:  2014        PMID: 24733291      PMCID: PMC4133579          DOI: 10.1093/bioinformatics/btu197

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  8 in total

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Journal:  Am J Hum Genet       Date:  2011-10-07       Impact factor: 11.025

4.  PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations.

Authors:  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
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Authors:  Philip L De Jager; Xiaoming Jia; Joanne Wang; Paul I W de Bakker; Linda Ottoboni; Neelum T Aggarwal; Laura Piccio; Soumya Raychaudhuri; Dong Tran; Cristin Aubin; Rebeccah Briskin; Susan Romano; Sergio E Baranzini; Jacob L McCauley; Margaret A Pericak-Vance; Jonathan L Haines; Rachel A Gibson; Yvonne Naeglin; Bernard Uitdehaag; Paul M Matthews; Ludwig Kappos; Chris Polman; Wendy L McArdle; David P Strachan; Denis Evans; Anne H Cross; Mark J Daly; Alastair Compston; Stephen J Sawcer; Howard L Weiner; Stephen L Hauser; David A Hafler; Jorge R Oksenberg
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6.  Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data.

Authors:  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
Journal:  Nat Biotechnol       Date:  2013-12       Impact factor: 54.908

7.  Visually integrating and exploring high throughput Phenome-Wide Association Study (PheWAS) results using PheWAS-View.

Authors:  Sarah A Pendergrass; Scott M Dudek; Dana C Crawford; Marylyn D Ritchie
Journal:  BioData Min       Date:  2012-06-08       Impact factor: 2.522

8.  Phenome-wide association study (PheWAS) for detection of pleiotropy within the Population Architecture using Genomics and Epidemiology (PAGE) Network.

Authors:  Sarah A Pendergrass; Kristin Brown-Gentry; Scott Dudek; Alex Frase; Eric S Torstenson; Robert Goodloe; Jose Luis Ambite; Christy L Avery; Steve Buyske; Petra Bůžková; 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
Journal:  PLoS Genet       Date:  2013-01-31       Impact factor: 5.917

  8 in total
  124 in total

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Review 2.  Unravelling the human genome-phenome relationship using phenome-wide association studies.

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Journal:  Science       Date:  2016-02-12       Impact factor: 47.728

Review 4.  Recent Genetics and Epigenetics Approaches to PTSD.

Authors:  Nikolaos P Daskalakis; Chuda M Rijal; Christopher King; Laura M Huckins; Kerry J Ressler
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5.  Seeing the forest through the trees: uncovering phenomic complexity through interactive network visualization.

Authors:  Jeremy L Warner; Joshua C Denny; David A Kreda; Gil Alterovitz
Journal:  J Am Med Inform Assoc       Date:  2014-10-21       Impact factor: 4.497

6.  Application of clinical text data for phenome-wide association studies (PheWASs).

Authors:  Scott J Hebbring; Majid Rastegar-Mojarad; Zhan Ye; John Mayer; Crystal Jacobson; Simon Lin
Journal:  Bioinformatics       Date:  2015-02-04       Impact factor: 6.937

7.  A Fast and Accurate Algorithm to Test for Binary Phenotypes and Its Application to PheWAS.

Authors:  Rounak Dey; Ellen M Schmidt; Goncalo R Abecasis; Seunggeun Lee
Journal:  Am J Hum Genet       Date:  2017-06-08       Impact factor: 11.025

8.  Hospitalizations for mitochondrial disease across the lifespan in the U.S.

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9.  An Atlas of Genetic Variation Linking Pathogen-Induced Cellular Traits to Human Disease.

Authors:  Liuyang Wang; Kelly J Pittman; Jeffrey R Barker; Raul E Salinas; Ian B Stanaway; Graham D Williams; Robert J Carroll; Tom Balmat; Andy Ingham; Anusha M Gopalakrishnan; Kyle D Gibbs; Alejandro L Antonia; Joseph Heitman; Soo Chan Lee; Gail P Jarvik; Joshua C Denny; Stacy M Horner; Mark R DeLong; Raphael H Valdivia; David R Crosslin; Dennis C Ko
Journal:  Cell Host Microbe       Date:  2018-08-08       Impact factor: 21.023

Review 10.  Phenome-Wide Association Studies as a Tool to Advance Precision Medicine.

Authors:  Joshua C Denny; Lisa Bastarache; Dan M Roden
Journal:  Annu Rev Genomics Hum Genet       Date:  2016-05-04       Impact factor: 8.929

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