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PheGWAS: a new dimension to visualize GWAS across multiple phenotypes.
George, Gittu; Gan, Sushrima; Huang, Yu; Appleby, Philip; Nar, A S; Venkatesan, Radha; Mohan, Viswanathan; Palmer, Colin N A; Doney, Alex S F.
Afiliação
  • George G; NIHR Global Health Research Unit on Global Diabetes Outcomes Research, Division of Population Health and Genomics, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK.
  • Gan S; NIHR Global Health Research Unit on Global Diabetes Outcomes Research, Division of Population Health and Genomics, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK.
  • Huang Y; NIHR Global Health Research Unit on Global Diabetes Outcomes Research, Division of Population Health and Genomics, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK.
  • Appleby P; NIHR Global Health Research Unit on Global Diabetes Outcomes Research, Division of Population Health and Genomics, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK.
  • Nar AS; NIHR Global Health Research Unit on Global Diabetes Outcomes Research, Division of Population Health and Genomics, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK.
  • Venkatesan R; NIHR Global Health Research Unit on Global Diabetes Outcomes Research, Division of Population Health and Genomics, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK.
  • Mohan V; NIHR Global Health Research Unit on Global Diabetes Outcomes Research, Division of Population Health and Genomics, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK.
  • Palmer CNA; NIHR Global Health Research Unit on Global Diabetes Outcomes Research, Division of Population Health and Genomics, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK.
  • Doney ASF; NIHR Global Health Research Unit on Global Diabetes Outcomes Research, Division of Population Health and Genomics, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK.
Bioinformatics ; 36(8): 2500-2505, 2020 04 15.
Article em En | MEDLINE | ID: mdl-31860083
ABSTRACT
MOTIVATION PheGWAS was developed to enhance exploration of phenome-wide pleiotropy at the genome-wide level through the efficient generation of a dynamic visualization combining Manhattan plots from GWAS with PheWAS to create a 3D 'landscape'. Pleiotropy in sub-surface GWAS significance strata can be explored in a sectional view plotted within user defined levels. Further complexity reduction is achieved by confining to a single chromosomal section. Comprehensive genomic and phenomic coordinates can be displayed.

RESULTS:

PheGWAS is demonstrated using summary data from Global Lipids Genetics Consortium GWAS across multiple lipid traits. For single and multiple traits PheGWAS highlighted all 88 and 69 loci, respectively. Further, the genes and SNPs reported in Global Lipids Genetics Consortium were identified using additional functions implemented within PheGWAS. Not only is PheGWAS capable of identifying independent signals but also provides insights to local genetic correlation (verified using HESS) and in identifying the potential regions that share causal variants across phenotypes (verified using colocalization tests). AVAILABILITY AND IMPLEMENTATION The PheGWAS software and code are freely available at (https//github.com/georgeg0/PheGWAS). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Assuntos

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Software / Estudo de Associação Genômica Ampla Idioma: En Revista: Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Reino Unido

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Software / Estudo de Associação Genômica Ampla Idioma: En Revista: Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Reino Unido