Literature DB >> 26958218

Contrasting Association Results between Existing PheWAS Phenotype Definition Methods and Five Validated Electronic Phenotypes.

Joseph B Leader1, Sarah A Pendergrass2, Anurag Verma3, David J Carey4, Dustin N Hartzel1, Marylyn D Ritchie3, H Lester Kirchner1.   

Abstract

Phenome-Wide Association Studies (PheWAS) comprehensively investigate the association between genetic variation and a wide array of outcome traits. Electronic health record (EHR) based PheWAS uses various abstractions of International Classification of Diseases, Ninth Revision (ICD-9) codes to identify case/control status for diagnoses that are used as the phenotypic variables. However, there have not been comparisons within a PheWAS between results from high quality derived phenotypes and high-throughput but potentially inaccurate use of ICD-9 codes for case/control definition. For this study we first developed a group of high quality algorithms for five phenotypes. Next we evaluated the association of these "gold standard" phenotypes and 4,636,178 genetic variants with minor allele frequency > 0.01 and compared the results from high-throughput associations at the 3 digit, 5 digit, and PheWAS codes for defining case/control status. We found that certain diseases contained similar patient populations across phenotyping methods but had differences in PheWAS.

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Year:  2015        PMID: 26958218      PMCID: PMC4765620     

Source DB:  PubMed          Journal:  AMIA Annu Symp Proc        ISSN: 1559-4076


  13 in total

1.  Potential etiologic and functional implications of genome-wide association loci for human diseases and traits.

Authors:  Lucia A Hindorff; Praveen Sethupathy; Heather A Junkins; Erin M Ramos; Jayashri P Mehta; Francis S Collins; Teri A Manolio
Journal:  Proc Natl Acad Sci U S A       Date:  2009-05-27       Impact factor: 11.205

2.  Imputation and quality control steps for combining multiple genome-wide datasets.

Authors:  Shefali S Verma; Mariza de Andrade; Gerard Tromp; Helena Kuivaniemi; Elizabeth Pugh; Bahram Namjou-Khales; Shubhabrata Mukherjee; Gail P Jarvik; Leah C Kottyan; Amber Burt; Yuki Bradford; Gretta D Armstrong; Kimberly Derr; Dana C Crawford; Jonathan L Haines; Rongling Li; David Crosslin; Marylyn D Ritchie
Journal:  Front Genet       Date:  2014-12-11       Impact factor: 4.599

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

Authors:  Robert J Carroll; Lisa Bastarache; Joshua C Denny
Journal:  Bioinformatics       Date:  2014-04-14       Impact factor: 6.937

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
Journal:  Bioinformatics       Date:  2010-03-24       Impact factor: 6.937

5.  A PheWAS approach in studying HLA-DRB1*1501.

Authors:  S J Hebbring; S J Schrodi; Z Ye; Z Zhou; D Page; M H Brilliant
Journal:  Genes Immun       Date:  2013-02-07       Impact factor: 2.676

6.  Validation of ICD-9 codes with a high positive predictive value for incident strokes resulting in hospitalization using Medicaid health data.

Authors:  Christianne L Roumie; Edward Mitchel; Patricia S Gideon; Cristina Varas-Lorenzo; Jordi Castellsague; Marie R Griffin
Journal:  Pharmacoepidemiol Drug Saf       Date:  2008-01       Impact factor: 2.890

7.  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

8.  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

Review 9.  The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future.

Authors:  Omri Gottesman; Helena Kuivaniemi; Gerard Tromp; W Andrew Faucett; Rongling Li; Teri A Manolio; Saskia C Sanderson; Joseph Kannry; Randi Zinberg; Melissa A Basford; Murray Brilliant; David J Carey; Rex L Chisholm; Christopher G Chute; John J Connolly; David Crosslin; Joshua C Denny; Carlos J Gallego; Jonathan L Haines; Hakon Hakonarson; John Harley; Gail P Jarvik; Isaac Kohane; Iftikhar J Kullo; Eric B Larson; Catherine McCarty; Marylyn D Ritchie; Dan M Roden; Maureen E Smith; Erwin P Böttinger; Marc S Williams
Journal:  Genet Med       Date:  2013-06-06       Impact factor: 8.822

Review 10.  eMERGEing progress in genomics-the first seven years.

Authors:  Dana C Crawford; David R Crosslin; Gerard Tromp; Iftikhar J Kullo; Helena Kuivaniemi; M Geoffrey Hayes; Joshua C Denny; William S Bush; Jonathan L Haines; Dan M Roden; Catherine A McCarty; Gail P Jarvik; Marylyn D Ritchie
Journal:  Front Genet       Date:  2014-06-17       Impact factor: 4.599

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  8 in total

1.  Using Electronic Health Records To Generate Phenotypes For Research.

Authors:  Sarah A Pendergrass; Dana C Crawford
Journal:  Curr Protoc Hum Genet       Date:  2018-12-05

2.  PheProb: probabilistic phenotyping using diagnosis codes to improve power for genetic association studies.

Authors:  Jennifer A Sinnott; Fiona Cai; Sheng Yu; Boris P Hejblum; Chuan Hong; Isaac S Kohane; Katherine P Liao
Journal:  J Am Med Inform Assoc       Date:  2018-10-01       Impact factor: 4.497

3.  AUDIT-C and ICD codes as phenotypes for harmful alcohol use: association with ADH1B polymorphisms in two US populations.

Authors:  Amy C Justice; Rachel V Smith; Janet P Tate; Kathleen McGinnis; Ke Xu; William C Becker; Kuang-Yao Lee; Kevin Lynch; Ning Sun; John Concato; David A Fiellin; Hongyu Zhao; Joel Gelernter; Henry R Kranzler
Journal:  Addiction       Date:  2018-08-01       Impact factor: 6.526

4.  Defining Phenotypes from Clinical Data to Drive Genomic Research.

Authors:  Jamie R Robinson; Wei-Qi Wei; Dan M Roden; Joshua C Denny
Journal:  Annu Rev Biomed Data Sci       Date:  2018-04-25

Review 5.  Using Phecodes for Research with the Electronic Health Record: From PheWAS to PheRS.

Authors:  Lisa Bastarache
Journal:  Annu Rev Biomed Data Sci       Date:  2021-07-20

6.  The Impact of Diagnostic Code Misclassification on Optimizing the Experimental Design of Genetic Association Studies.

Authors:  Steven J Schrodi
Journal:  J Healthc Eng       Date:  2017-10-18       Impact factor: 2.682

7.  Evaluating phecodes, clinical classification software, and ICD-9-CM codes for phenome-wide association studies in the electronic health record.

Authors:  Wei-Qi Wei; Lisa A Bastarache; Robert J Carroll; Joy E Marlo; Travis J Osterman; Eric R Gamazon; Nancy J Cox; Dan M Roden; Joshua C Denny
Journal:  PLoS One       Date:  2017-07-07       Impact factor: 3.240

Review 8.  Genome-wide and Phenome-wide Approaches to Understand Variable Drug Actions in Electronic Health Records.

Authors:  Jamie R Robinson; Joshua C Denny; Dan M Roden; Sara L Van Driest
Journal:  Clin Transl Sci       Date:  2017-11-17       Impact factor: 4.689

  8 in total

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