Literature DB >> 25074467

Phenome-wide association studies (PheWASs) for functional variants.

Zhan Ye1, John Mayer1, Lynn Ivacic2, Zhiyi Zhou3, Min He4, Steven J Schrodi2, David Page5, Murray H Brilliant2, Scott J Hebbring6.   

Abstract

The genome-wide association study (GWAS) is a powerful approach for studying the genetic complexities of human disease. Unfortunately, GWASs often fail to identify clinically significant associations and describing function can be a challenge. GWAS is a phenotype-to-genotype approach. It is now possible to conduct a converse genotype-to-phenotype approach using extensive electronic medical records to define a phenome. This approach associates a single genetic variant with many phenotypes across the phenome and is called a phenome-wide association study (PheWAS). The majority of PheWASs conducted have focused on variants identified previously by GWASs. This approach has been efficient for rediscovering gene-disease associations while also identifying pleiotropic effects for some single-nucleotide polymorphisms (SNPs). However, the use of SNPs identified by GWAS in a PheWAS is limited by the inherent properties of the GWAS SNPs, including weak effect sizes and difficulty when translating discoveries to function. To address these challenges, we conducted a PheWAS on 105 presumed functional stop-gain and stop-loss variants genotyped on 4235 Marshfield Clinic patients. Associations were validated on an additional 10 640 Marshfield Clinic patients. PheWAS results indicate that a nonsense variant in ARMS2 (rs2736911) is associated with age-related macular degeneration (AMD). These results demonstrate that focusing on functional variants may be an effective approach when conducting a PheWAS.

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Year:  2014        PMID: 25074467      PMCID: PMC4666492          DOI: 10.1038/ejhg.2014.123

Source DB:  PubMed          Journal:  Eur J Hum Genet        ISSN: 1018-4813            Impact factor:   4.246


  25 in total

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Journal:  Ann Neurol       Date:  2008-10       Impact factor: 10.422

2.  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

3.  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

4.  A systematic survey of loss-of-function variants in human protein-coding genes.

Authors:  Daniel G MacArthur; Suganthi Balasubramanian; Adam Frankish; Ni Huang; James Morris; Klaudia Walter; Luke Jostins; Lukas Habegger; Joseph K Pickrell; Stephen B Montgomery; Cornelis A Albers; Zhengdong D Zhang; Donald F Conrad; Gerton Lunter; Hancheng Zheng; Qasim Ayub; Mark A DePristo; Eric Banks; Min Hu; Robert E Handsaker; Jeffrey A Rosenfeld; Menachem Fromer; Mike Jin; Xinmeng Jasmine Mu; Ekta Khurana; Kai Ye; Mike Kay; Gary Ian Saunders; Marie-Marthe Suner; Toby Hunt; If H A Barnes; Clara Amid; Denise R Carvalho-Silva; Alexandra H Bignell; Catherine Snow; Bryndis Yngvadottir; Suzannah Bumpstead; David N Cooper; Yali Xue; Irene Gallego Romero; Jun Wang; Yingrui Li; Richard A Gibbs; Steven A McCarroll; Emmanouil T Dermitzakis; Jonathan K Pritchard; Jeffrey C Barrett; Jennifer Harrow; Matthew E Hurles; Mark B Gerstein; Chris Tyler-Smith
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Journal:  Nat Biotechnol       Date:  2013-12       Impact factor: 54.908

6.  Knowledge-driven multi-locus analysis reveals gene-gene interactions influencing HDL cholesterol level in two independent EMR-linked biobanks.

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8.  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

9.  The Human Gene Mutation Database: 2008 update.

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Journal:  Genome Med       Date:  2009-01-22       Impact factor: 11.117

Review 10.  The challenges, advantages and future of phenome-wide association studies.

Authors:  Scott J Hebbring
Journal:  Immunology       Date:  2014-02       Impact factor: 7.397

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  29 in total

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

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3.  Fangjiomics: revealing adaptive omics pharmacological mechanisms of the myriad combination therapies to achieve personalized medicine.

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4.  Application of clinical text data for phenome-wide association studies (PheWASs).

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Journal:  Bioinformatics       Date:  2015-02-04       Impact factor: 6.937

Review 5.  Phenome-Wide Association Studies as a Tool to Advance Precision Medicine.

Authors:  Joshua C Denny; Lisa Bastarache; Dan M Roden
Journal:  Annu Rev Genomics Hum Genet       Date:  2016-05-04       Impact factor: 8.929

6.  Using phenome-wide association studies to examine the effect of environmental exposures on human health.

Authors:  Joseph M Braun; Geetika Kalloo; Samantha L Kingsley; Nan Li
Journal:  Environ Int       Date:  2019-06-11       Impact factor: 9.621

7.  RNA-Binding Protein IGF2BP1 in Cutaneous Squamous Cell Carcinoma.

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8.  Applying family analyses to electronic health records to facilitate genetic research.

Authors:  Xiayuan Huang; Robert C Elston; Guilherme J Rosa; John Mayer; Zhan Ye; Terrie Kitchner; Murray H Brilliant; David Page; Scott J Hebbring
Journal:  Bioinformatics       Date:  2018-02-15       Impact factor: 6.937

9.  PheWAS and Beyond: The Landscape of Associations with Medical Diagnoses and Clinical Measures across 38,662 Individuals from Geisinger.

Authors:  Anurag Verma; Anastasia Lucas; Shefali S Verma; Yu Zhang; Navya Josyula; Anqa Khan; Dustin N Hartzel; Daniel R Lavage; Joseph Leader; Marylyn D Ritchie; Sarah A Pendergrass
Journal:  Am J Hum Genet       Date:  2018-03-29       Impact factor: 11.025

10.  Relationship of SULT1A1 copy number variation with estrogen metabolism and human health.

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