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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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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. |
format | Online Article Text |
id | pubmed-7178436 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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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