Literature DB >> 26223525

The detection and characterization of pleiotropy: discovery, progress, and promise.

Anna L Tyler, Dana C Crawford, Sarah A Pendergrass.   

Abstract

The impact of a single genetic locus on multiple phenotypes, or pleiotropy, is an important area of research. Biological systems are dynamic complex networks, and these networks exist within and between cells. In humans, the consideration of multiple phenotypes such as physiological traits, clinical outcomes and drug response, in the context of genetic variation, can provide ways of developing a more complete understanding of the complex relationships between genetic architecture and how biological systems function in health and disease. In this article, we describe recent studies exploring the relationships between genetic loci and more than one phenotype. We also cover methodological developments incorporating pleiotropy applied to model organisms as well as humans, and discuss how stepping beyond the analysis of a single phenotype leads to a deeper understanding of complex genetic architecture.
© The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  PheWAS; complex traits; phenomics; pleiotropy

Mesh:

Year:  2015        PMID: 26223525     DOI: 10.1093/bib/bbv050

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  15 in total

Review 1.  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

2.  Evidence for extensive pleiotropy among pharmacogenes.

Authors:  Matthew T Oetjens; William S Bush; Joshua C Denny; Kelly Birdwell; Nuri Kodaman; Anurag Verma; Holli H Dilks; Sarah A Pendergrass; Marylyn D Ritchie; Dana C Crawford
Journal:  Pharmacogenomics       Date:  2016-06-01       Impact factor: 2.533

3.  Extracting Disease-Relevant Features with Adversarial Regularization.

Authors:  Junxiang Chen; Li Sun; Ke Yu; Kayhan Batmanghelich
Journal:  Proceedings (IEEE Int Conf Bioinformatics Biomed)       Date:  2021-12

Review 4.  Genomic and Phenomic Research in the 21st Century.

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

5.  Neuroimaging PheWAS (Phenome-Wide Association Study): A Free Cloud-Computing Platform for Big-Data, Brain-Wide Imaging Association Studies.

Authors:  Lu Zhao; Ishaan Batta; William Matloff; Caroline O'Driscoll; Samuel Hobel; Arthur W Toga
Journal:  Neuroinformatics       Date:  2021-04

Review 6.  New insights into pathogenesis of IgA nephropathy.

Authors:  Jinjin Xia; Ming Wang; Weiqiang Jiang
Journal:  Int Urol Nephrol       Date:  2022-01-20       Impact factor: 2.266

7.  Multiphenotype association study of patients randomized to initiate antiretroviral regimens in AIDS Clinical Trials Group protocol A5202.

Authors:  Anurag Verma; Yuki Bradford; Shefali S Verma; Sarah A Pendergrass; Eric S Daar; Charles Venuto; Gene D Morse; Marylyn D Ritchie; David W Haas
Journal:  Pharmacogenet Genomics       Date:  2017-03       Impact factor: 2.089

8.  The Gene scb-1 Underlies Variation in Caenorhabditis elegans Chemotherapeutic Responses.

Authors:  Kathryn S Evans; Erik C Andersen
Journal:  G3 (Bethesda)       Date:  2020-07-07       Impact factor: 3.154

9.  Ontology-based validation and identification of regulatory phenotypes.

Authors:  Maxat Kulmanov; Paul N Schofield; Georgios V Gkoutos; Robert Hoehndorf
Journal:  Bioinformatics       Date:  2018-09-01       Impact factor: 6.937

10.  Human-Disease Phenotype Map Derived from PheWAS across 38,682 Individuals.

Authors:  Anurag Verma; Lisa Bang; Jason E Miller; Yanfei Zhang; Ming Ta Michael Lee; Yu Zhang; Marta Byrska-Bishop; David J Carey; Marylyn D Ritchie; Sarah A Pendergrass; Dokyoon Kim
Journal:  Am J Hum Genet       Date:  2018-12-29       Impact factor: 11.025

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