Literature DB >> 26146598

Phenome-Wide Association Studies: Leveraging Comprehensive Phenotypic and Genotypic Data for Discovery.

S A Pendergrass, M D Ritchie.   

Abstract

With the large volume of clinical and epidemiological data being collected, increasingly linked to extensive genotypic data, coupled with expanding high-performance computational resources, there are considerable opportunities for comprehensively exploring the networks of connections that exist between the phenome and the genome. These networks can be identified through Phenome-Wide Association Studies (PheWAS) where the association between a collection of genetic variants, or in some cases a particular clinical lab variable, and a wide and diverse range of phenotypes, diagnoses, traits, and/or outcomes are evaluated. This is a departure from the more familiar genome-wide association study (GWAS) approach, which has been used to identify single nucleotide polymorphisms (SNPs) associated with one outcome or a very limited phenotypic domain. In addition to highlighting novel connections between multiple phenotypes and elucidating more of the phenotype-genotype landscape, PheWAS can generate new hypotheses for further exploration, and can also be used to narrow the search space for research using comprehensive data collections. The complex results of PheWAS also have the potential for uncovering new mechanistic insights. We review here how the PheWAS approach has been used with data from epidemiological studies, clinical trials, and de-identified electronic health record data. We also review methodologies for the analyses underlying PheWAS, and emerging methods developed for evaluating the comprehensive results of PheWAS including genotype-phenotype networks. This review also highlights PheWAS as an important tool for identifying new biomarkers, elucidating the genetic architecture of complex traits, and uncovering pleiotropy. There are many directions and new methodologies for the future of PheWAS analyses, from the phenotypic data to the genetic data, and herein we also discuss some of these important future PheWAS developments.

Entities:  

Keywords:  Electronic Health Record (EHR); International Classification of Disease Codes (ICD); genotype-phenotype networks; phenomics; pleiotropy

Year:  2015        PMID: 26146598      PMCID: PMC4489156          DOI: 10.1007/s40142-015-0067-9

Source DB:  PubMed          Journal:  Curr Genet Med Rep        ISSN: 2167-4876


  46 in total

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10.  Pleiotropic genes for metabolic syndrome and inflammation.

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Journal:  Mol Genet Metab       Date:  2014-05-09       Impact factor: 4.797

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  17 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

Review 2.  Informatics and machine learning to define the phenotype.

Authors:  Anna Okula Basile; Marylyn DeRiggi Ritchie
Journal:  Expert Rev Mol Diagn       Date:  2018-02-16       Impact factor: 5.225

3.  A regression framework to uncover pleiotropy in large-scale electronic health record data.

Authors:  Ruowang Li; Rui Duan; Rachel L Kember; Daniel J Rader; Scott M Damrauer; Jason H Moore; Yong Chen
Journal:  J Am Med Inform Assoc       Date:  2019-10-01       Impact factor: 4.497

4.  The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities.

Authors:  Lauren J Beesley; Maxwell Salvatore; Lars G Fritsche; Anita Pandit; Arvind Rao; Chad Brummett; Cristen J Willer; Lynda D Lisabeth; Bhramar Mukherjee
Journal:  Stat Med       Date:  2019-12-20       Impact factor: 2.373

5.  Biological findings from the PheWAS catalog: focus on connective tissue-related disorders (pelvic floor dysfunction, abdominal hernia, varicose veins and hemorrhoids).

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Authors:  Scott Hebbring
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Review 7.  Implementing genome-driven personalized cardiology in clinical practice.

Authors:  Ares Pasipoularides
Journal:  J Mol Cell Cardiol       Date:  2018-01-16       Impact factor: 5.000

Review 8.  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

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

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

10.  MR-PheWAS: hypothesis prioritization among potential causal effects of body mass index on many outcomes, using Mendelian randomization.

Authors:  Louise A C Millard; Neil M Davies; Nic J Timpson; Kate Tilling; Peter A Flach; George Davey Smith
Journal:  Sci Rep       Date:  2015-11-16       Impact factor: 4.379

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