Literature DB >> 28229460

Phenome-wide association studies: a new method for functional genomics in humans.

Dan M Roden1.   

Abstract

In experimental physiological research, a common study design for examining the functional role of a gene or a genetic variant is to introduce that genetic variant into a model organism (such as yeast or mouse) and then to search for phenotypic consequences. The development of DNA biobanks linked to dense phenotypic information enables such an experiment to be applied to human subjects in the form of a phenome-wide association study (PheWAS). The PheWAS paradigm takes advantage of a curated medical phenome, often derived from electronic health records, to search for associations between 'input functions' and phenotypes in an unbiased fashion. The most commonly studied input function to date has been single nucleotide polymorphisms (SNPs), but other inputs, such as sets of SNPs or a disease or drug exposure, are now being explored to probe the genetic and phenotypic architecture of human traits. Potential outcomes of these approaches include defining subsets of complex diseases (that can then be targeted by specific therapies) and drug repurposing.
© 2017 The Authors. The Journal of Physiology © 2017 The Physiological Society.

Entities:  

Keywords:  GWAS; PheWAS; biobanks; electronic health records; genomics; phenome; polygenic risk score

Mesh:

Year:  2017        PMID: 28229460      PMCID: PMC5471509          DOI: 10.1113/JP273122

Source DB:  PubMed          Journal:  J Physiol        ISSN: 0022-3751            Impact factor:   5.182


  32 in total

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Authors:  Richard Jones; Marcus Pembrey; Jean Golding; David Herrick
Journal:  Paediatr Perinat Epidemiol       Date:  2005-07       Impact factor: 3.980

2.  Polymorphisms associated with cholesterol and risk of cardiovascular events.

Authors:  Sekar Kathiresan; Olle Melander; Dragi Anevski; Candace Guiducci; Noël P Burtt; Charlotta Roos; Joel N Hirschhorn; Göran Berglund; Bo Hedblad; Leif Groop; David M Altshuler; Christopher Newton-Cheh; Marju Orho-Melander
Journal:  N Engl J Med       Date:  2008-03-20       Impact factor: 91.245

3.  Development of a large-scale de-identified DNA biobank to enable personalized medicine.

Authors:  D M Roden; J M Pulley; M A Basford; G R Bernard; E W Clayton; J R Balser; D R Masys
Journal:  Clin Pharmacol Ther       Date:  2008-05-21       Impact factor: 6.875

4.  Associations of autoantibodies, autoimmune risk alleles, and clinical diagnoses from the electronic medical records in rheumatoid arthritis cases and non-rheumatoid arthritis controls.

Authors:  Katherine P Liao; Fina Kurreeman; Gang Li; Grant Duclos; Shawn Murphy; Raul Guzman; Tianxi Cai; Namrata Gupta; Vivian Gainer; Peter Schur; Jing Cui; Joshua C Denny; Peter Szolovits; Susanne Churchill; Isaac Kohane; Elizabeth W Karlson; Robert M Plenge
Journal:  Arthritis Rheum       Date:  2013-03

5.  Variants near FOXE1 are associated with hypothyroidism and other thyroid conditions: using electronic medical records for genome- and phenome-wide studies.

Authors:  Joshua C Denny; Dana C Crawford; Marylyn D Ritchie; Suzette J Bielinski; Melissa A Basford; Yuki Bradford; High Seng Chai; Lisa Bastarache; Rebecca Zuvich; Peggy Peissig; David Carrell; Andrea H Ramirez; Jyotishman Pathak; Russell A Wilke; Luke Rasmussen; Xiaoming Wang; Jennifer A Pacheco; Abel N Kho; M Geoffrey Hayes; Noah Weston; Martha Matsumoto; Peter A Kopp; Katherine M Newton; Gail P Jarvik; Rongling Li; Teri A Manolio; Iftikhar J Kullo; Christopher G Chute; Rex L Chisholm; Eric B Larson; Catherine A McCarty; Daniel R Masys; Dan M Roden; Mariza de Andrade
Journal:  Am J Hum Genet       Date:  2011-10-07       Impact factor: 11.025

6.  China Kadoorie Biobank of 0.5 million people: survey methods, baseline characteristics and long-term follow-up.

Authors:  Zhengming Chen; Junshi Chen; Rory Collins; Yu Guo; Richard Peto; Fan Wu; Liming Li
Journal:  Int J Epidemiol       Date:  2011-09-21       Impact factor: 7.196

7.  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

8.  Phenome based analysis as a means for discovering context dependent clinical reference ranges.

Authors:  Jeremy L Warner; Gil Alterovitz
Journal:  AMIA Annu Symp Proc       Date:  2012-11-03

9.  Common variants in 22 loci are associated with QRS duration and cardiac ventricular conduction.

Authors:  Nona Sotoodehnia; Aaron Isaacs; Paul I W de Bakker; Marcus Dörr; Christopher Newton-Cheh; Ilja M Nolte; Pim van der Harst; Martina Müller; Mark Eijgelsheim; Alvaro Alonso; Andrew A Hicks; Sandosh Padmanabhan; Caroline Hayward; Albert Vernon Smith; Ozren Polasek; Steven Giovannone; Jingyuan Fu; Jared W Magnani; Kristin D Marciante; Arne Pfeufer; Sina A Gharib; Alexander Teumer; Man Li; Joshua C Bis; Fernando Rivadeneira; Thor Aspelund; Anna Köttgen; Toby Johnson; Kenneth Rice; Mark P S Sie; Ying A Wang; Norman Klopp; Christian Fuchsberger; Sarah H Wild; Irene Mateo Leach; Karol Estrada; Uwe Völker; Alan F Wright; Folkert W Asselbergs; Jiaxiang Qu; Aravinda Chakravarti; Moritz F Sinner; Jan A Kors; Astrid Petersmann; Tamara B Harris; Elsayed Z Soliman; Patricia B Munroe; Bruce M Psaty; Ben A Oostra; L Adrienne Cupples; Siegfried Perz; Rudolf A de Boer; André G Uitterlinden; Henry Völzke; Timothy D Spector; Fang-Yu Liu; Eric Boerwinkle; Anna F Dominiczak; Jerome I Rotter; Gé van Herpen; Daniel Levy; H-Erich Wichmann; Wiek H van Gilst; Jacqueline C M Witteman; Heyo K Kroemer; W H Linda Kao; Susan R Heckbert; Thomas Meitinger; Albert Hofman; Harry Campbell; Aaron R Folsom; Dirk J van Veldhuisen; Christine Schwienbacher; Christopher J O'Donnell; Claudia Beu Volpato; Mark J Caulfield; John M Connell; Lenore Launer; Xiaowen Lu; Lude Franke; Rudolf S N Fehrmann; Gerard te Meerman; Harry J M Groen; Rinse K Weersma; Leonard H van den Berg; Cisca Wijmenga; Roel A Ophoff; Gerjan Navis; Igor Rudan; Harold Snieder; James F Wilson; Peter P Pramstaller; David S Siscovick; Thomas J Wang; Vilmundur Gudnason; Cornelia M van Duijn; Stephan B Felix; Glenn I Fishman; Yalda Jamshidi; Bruno H Ch Stricker; Nilesh J Samani; Stefan Kääb; Dan E Arking
Journal:  Nat Genet       Date:  2010-11-14       Impact factor: 38.330

10.  Genome- and phenome-wide analyses of cardiac conduction identifies markers of arrhythmia risk.

Authors:  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
Journal:  Circulation       Date:  2013-03-05       Impact factor: 29.690

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  18 in total

1.  Cardiac regulatory mechanisms: new concepts and challenges.

Authors:  Björn C Knollmann
Journal:  J Physiol       Date:  2017-06-15       Impact factor: 5.182

Review 2.  The genetic epidemiology of substance use disorder: A review.

Authors:  Elizabeth C Prom-Wormley; Jane Ebejer; Danielle M Dick; M Scott Bowers
Journal:  Drug Alcohol Depend       Date:  2017-08-01       Impact factor: 4.492

Review 3.  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

4.  Bronchopulmonary dysplasia is associated with polyhydramnios in a scan for novel perinatal risk factors.

Authors:  Meredith S Campbell; Lisa A Bastarache; Sara L Van Driest; Margaret A Adgent; Jeffery A Goldstein; Joern-Hendrik Weitkamp; Meaghan A Ransom; Rolanda L Lister; Elaine L Shelton; Jennifer M S Sucre
Journal:  Pediatr Res       Date:  2022-04-07       Impact factor: 3.953

Review 5.  Genomic and Phenomic Research in the 21st Century.

Authors:  Scott Hebbring
Journal:  Trends Genet       Date:  2018-10-17       Impact factor: 11.639

Review 6.  Hypertension genomics and cardiovascular prevention.

Authors:  Fu Liang Ng; Helen R Warren; Mark J Caulfield
Journal:  Ann Transl Med       Date:  2018-08

7.  Fate or coincidence: do COPD and major depression share genetic risk factors?

Authors:  Victoria L Martucci; Bradley Richmond; Lea K Davis; Timothy S Blackwell; Nancy J Cox; David Samuels; Digna Velez Edwards; Melinda C Aldrich
Journal:  Hum Mol Genet       Date:  2021-05-12       Impact factor: 6.150

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

9.  Phenome-wide association studies across large population cohorts support drug target validation.

Authors:  Dorothée Diogo; Chao Tian; Christopher S Franklin; Mervi Alanne-Kinnunen; Michael March; Chris C A Spencer; Ciara Vangjeli; Michael E Weale; Hannele Mattsson; Elina Kilpeläinen; Patrick M A Sleiman; Dermot F Reilly; Joshua McElwee; Joseph C Maranville; Arnaub K Chatterjee; Aman Bhandari; Khanh-Dung H Nguyen; Karol Estrada; Mary-Pat Reeve; Janna Hutz; Nan Bing; Sally John; Daniel G MacArthur; Veikko Salomaa; Samuli Ripatti; Hakon Hakonarson; Mark J Daly; Aarno Palotie; David A Hinds; Peter Donnelly; Caroline S Fox; Aaron G Day-Williams; Robert M Plenge; Heiko Runz
Journal:  Nat Commun       Date:  2018-10-16       Impact factor: 14.919

10.  Current Scope and Challenges in Phenome-Wide Association Studies.

Authors:  Anurag Verma; Marylyn D Ritchie
Journal:  Curr Epidemiol Rep       Date:  2017-11-02
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