Literature DB >> 21594894

The use of phenome-wide association studies (PheWAS) for exploration of novel genotype-phenotype relationships and pleiotropy discovery.

S A Pendergrass1, 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.   

Abstract

The field of phenomics has been investigating network structure among large arrays of phenotypes, and genome-wide association studies (GWAS) have been used to investigate the relationship between genetic variation and single diseases/outcomes. A novel approach has emerged combining both the exploration of phenotypic structure and genotypic variation, known as the phenome-wide association study (PheWAS). The Population Architecture using Genomics and Epidemiology (PAGE) network is a National Human Genome Research Institute (NHGRI)-supported collaboration of four groups accessing eight extensively characterized epidemiologic studies. The primary focus of PAGE is deep characterization of well-replicated GWAS variants and their relationships to various phenotypes and traits in diverse epidemiologic studies that include European Americans, African Americans, Mexican Americans/Hispanics, Asians/Pacific Islanders, and Native Americans. The rich phenotypic resources of PAGE studies provide a unique opportunity for PheWAS as each genotyped variant can be tested for an association with the wide array of phenotypic measurements available within the studies of PAGE, including prevalent and incident status for multiple common clinical conditions and risk factors, as well as clinical parameters and intermediate biomarkers. The results of PheWAS can be used to discover novel relationships between SNPs, phenotypes, and networks of interrelated phenotypes; identify pleiotropy; provide novel mechanistic insights; and foster hypothesis generation. The PAGE network has developed infrastructure to support and perform PheWAS in a high-throughput manner. As implementing the PheWAS approach has presented several challenges, the infrastructure and methodology, as well as insights gained in this project, are presented herein to benefit the larger scientific community.
© 2011 Wiley-Liss, Inc.

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Year:  2011        PMID: 21594894      PMCID: PMC3116446          DOI: 10.1002/gepi.20589

Source DB:  PubMed          Journal:  Genet Epidemiol        ISSN: 0741-0395            Impact factor:   2.135


  22 in total

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4.  Recruitment in the Coronary Artery Disease Risk Development in Young Adults (Cardia) Study.

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Journal:  Control Clin Trials       Date:  1998-02

6.  A multiethnic cohort in Hawaii and Los Angeles: baseline characteristics.

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Journal:  Am J Epidemiol       Date:  2000-02-15       Impact factor: 4.897

7.  The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives. The ARIC investigators.

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Journal:  Am J Epidemiol       Date:  1989-04       Impact factor: 4.897

8.  Probing genetic overlap among complex human phenotypes.

Authors:  Andrey Rzhetsky; David Wajngurt; Naeun Park; Tian Zheng
Journal:  Proc Natl Acad Sci U S A       Date:  2007-07-03       Impact factor: 11.205

9.  Consent for genetic research in a general population: the NHANES experience.

Authors:  Geraldine M McQuillan; Kathryn S Porter; Maria Agelli; Raynard Kington
Journal:  Genet Med       Date:  2003 Jan-Feb       Impact factor: 8.822

10.  Genetic variants regulating ORMDL3 expression contribute to the risk of childhood asthma.

Authors:  Miriam F Moffatt; Michael Kabesch; Liming Liang; Anna L Dixon; David Strachan; Simon Heath; Martin Depner; Andrea von Berg; Albrecht Bufe; Ernst Rietschel; Andrea Heinzmann; Burkard Simma; Thomas Frischer; Saffron A G Willis-Owen; Kenny C C Wong; Thomas Illig; Christian Vogelberg; Stephan K Weiland; Erika von Mutius; Gonçalo R Abecasis; Martin Farrall; Ivo G Gut; G Mark Lathrop; William O C Cookson
Journal:  Nature       Date:  2007-07-04       Impact factor: 49.962

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  81 in total

Review 1.  The role of phenotype in gene discovery in the whole genome sequencing era.

Authors:  Laura Almasy
Journal:  Hum Genet       Date:  2012-06-22       Impact factor: 4.132

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

Authors:  William S Bush; Matthew T Oetjens; Dana C Crawford
Journal:  Nat Rev Genet       Date:  2016-02-15       Impact factor: 53.242

3.  Replication and characterisation of genetic variants in the fibrinogen gene cluster with plasma fibrinogen levels and haematological traits in the Third National Health and Nutrition Examination Survey.

Authors:  Janina M Jeff; Kristin Brown-Gentry; Dana C Crawford
Journal:  Thromb Haemost       Date:  2012-01-25       Impact factor: 5.249

4.  Temporal phenome analysis of a large electronic health record cohort enables identification of hospital-acquired complications.

Authors:  Jeremy L Warner; Amin Zollanvari; Quan Ding; Peijin Zhang; Graham M Snyder; Gil Alterovitz
Journal:  J Am Med Inform Assoc       Date:  2013-08-01       Impact factor: 4.497

5.  A genome- and phenome-wide association study to identify genetic variants influencing platelet count and volume and their pleiotropic effects.

Authors:  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
Journal:  Hum Genet       Date:  2013-09-12       Impact factor: 4.132

Review 6.  Clinical neurogenetics: stroke.

Authors:  Natalia S Rost
Journal:  Neurol Clin       Date:  2013-07-17       Impact factor: 3.806

Review 7.  Genetic architecture of cancer and other complex diseases: lessons learned and future directions.

Authors:  Lucia A Hindorff; Elizabeth M Gillanders; Teri A Manolio
Journal:  Carcinogenesis       Date:  2011-03-31       Impact factor: 4.944

8.  [Pharmacogenomic Biomarkers for the Prediction of Statin Efficacy and Safety].

Authors:  Damiano Baldassarre; Mauro Amato; Beatrice Frigerio; Gualtiero Columbo; Philip F Binkley; Saurabh R Pandey; Adam M Suhy; Katherine Hartmann; Joseph P Kitzmiller
Journal:  G Ital Arterioscler       Date:  2013-11

9.  Enabling high-throughput genotype-phenotype associations in the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) project as part of the Population Architecture using Genomics and Epidemiology (PAGE) study.

Authors:  William S Bush; Jonathan Boston; Sarah A Pendergrass; Logan Dumitrescu; Robert Goodloe; Kristin Brown-Gentry; Sarah Wilson; Bob McClellan; Eric Torstenson; Melissa A Basford; Kylee L Spencer; Marylyn D Ritchie; Dana C Crawford
Journal:  Pac Symp Biocomput       Date:  2013

10.  Clinically relevant pharmacogenomic testing in pediatric practice.

Authors:  Lindsey Korbel; Mathew George; Joseph Kitzmiller
Journal:  Clin Pediatr (Phila)       Date:  2014-05-06       Impact factor: 1.168

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