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PheGWAS: a new dimension to visualize GWAS across multiple phenotypes

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...

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Autores principales: George, Gittu, Gan, Sushrima, Huang, Yu, Appleby, Philip, Nar, A S, Venkatesan, Radha, Mohan, Viswanathan, Palmer, Colin N A, Doney, Alex S F
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7178436/
https://www.ncbi.nlm.nih.gov/pubmed/31860083
http://dx.doi.org/10.1093/bioinformatics/btz944
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author George, Gittu
Gan, Sushrima
Huang, Yu
Appleby, Philip
Nar, A S
Venkatesan, Radha
Mohan, Viswanathan
Palmer, Colin N A
Doney, Alex S F
author_facet George, Gittu
Gan, Sushrima
Huang, Yu
Appleby, Philip
Nar, A S
Venkatesan, Radha
Mohan, Viswanathan
Palmer, Colin N A
Doney, Alex S F
author_sort George, Gittu
collection PubMed
description 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.
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spelling pubmed-71784362020-04-28 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 Bioinformatics Original Papers 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. Oxford University Press 2020-04-15 2019-12-20 /pmc/articles/PMC7178436/ /pubmed/31860083 http://dx.doi.org/10.1093/bioinformatics/btz944 Text en © The Author(s) 2019. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Papers
George, Gittu
Gan, Sushrima
Huang, Yu
Appleby, Philip
Nar, A S
Venkatesan, Radha
Mohan, Viswanathan
Palmer, Colin N A
Doney, Alex S F
PheGWAS: a new dimension to visualize GWAS across multiple phenotypes
title PheGWAS: a new dimension to visualize GWAS across multiple phenotypes
title_full PheGWAS: a new dimension to visualize GWAS across multiple phenotypes
title_fullStr PheGWAS: a new dimension to visualize GWAS across multiple phenotypes
title_full_unstemmed PheGWAS: a new dimension to visualize GWAS across multiple phenotypes
title_short PheGWAS: a new dimension to visualize GWAS across multiple phenotypes
title_sort phegwas: a new dimension to visualize gwas across multiple phenotypes
topic Original Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7178436/
https://www.ncbi.nlm.nih.gov/pubmed/31860083
http://dx.doi.org/10.1093/bioinformatics/btz944
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