Qtlizer: comprehensive QTL annotation of GWAS results

Abstract Exploration of genetic variant-to-gene relationships by quantitative trait loci such as expression QTLs is a frequently used tool in genome-wide association studies. However, the wide range of public QTL databases and the lack of batch annotation features complicate a comprehensive annotati...

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Autores principales:	Matthias Munz, Inken Wohlers, Eric Simon, Tobias Reinberger, Hauke Busch, Arne S. Schaefer, Jeanette Erdmann
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c15af683c6d44f738b2725a1b8a69822
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spelling	oai:doaj.org-article:c15af683c6d44f738b2725a1b8a698222021-12-02T16:08:58ZQtlizer: comprehensive QTL annotation of GWAS results10.1038/s41598-020-75770-72045-2322https://doaj.org/article/c15af683c6d44f738b2725a1b8a698222020-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-75770-7https://doaj.org/toc/2045-2322Abstract Exploration of genetic variant-to-gene relationships by quantitative trait loci such as expression QTLs is a frequently used tool in genome-wide association studies. However, the wide range of public QTL databases and the lack of batch annotation features complicate a comprehensive annotation of GWAS results. In this work, we introduce the tool “Qtlizer” for annotating lists of variants in human with associated changes in gene expression and protein abundance using an integrated database of published QTLs. Features include incorporation of variants in linkage disequilibrium and reverse search by gene names. Analyzing the database for base pair distances between best significant eQTLs and their affected genes suggests that the commonly used cis-distance limit of 1,000,000 base pairs might be too restrictive, implicating a substantial amount of wrongly and yet undetected eQTLs. We also ranked genes with respect to the maximum number of tissue-specific eQTL studies in which a most significant eQTL signal was consistent. For the top 100 genes we observed the strongest enrichment with housekeeping genes (P = 2 × 10–6) and with the 10% highest expressed genes (P = 0.005) after grouping eQTLs by r2 > 0.95, underlining the relevance of LD information in eQTL analyses. Qtlizer can be accessed via https://genehopper.de/qtlizer or by using the respective Bioconductor R-package ( https://doi.org/10.18129/B9.bioc.Qtlizer ).Matthias MunzInken WohlersEric SimonTobias ReinbergerHauke BuschArne S. SchaeferJeanette ErdmannNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-8 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Matthias Munz Inken Wohlers Eric Simon Tobias Reinberger Hauke Busch Arne S. Schaefer Jeanette Erdmann Qtlizer: comprehensive QTL annotation of GWAS results
description	Abstract Exploration of genetic variant-to-gene relationships by quantitative trait loci such as expression QTLs is a frequently used tool in genome-wide association studies. However, the wide range of public QTL databases and the lack of batch annotation features complicate a comprehensive annotation of GWAS results. In this work, we introduce the tool “Qtlizer” for annotating lists of variants in human with associated changes in gene expression and protein abundance using an integrated database of published QTLs. Features include incorporation of variants in linkage disequilibrium and reverse search by gene names. Analyzing the database for base pair distances between best significant eQTLs and their affected genes suggests that the commonly used cis-distance limit of 1,000,000 base pairs might be too restrictive, implicating a substantial amount of wrongly and yet undetected eQTLs. We also ranked genes with respect to the maximum number of tissue-specific eQTL studies in which a most significant eQTL signal was consistent. For the top 100 genes we observed the strongest enrichment with housekeeping genes (P = 2 × 10–6) and with the 10% highest expressed genes (P = 0.005) after grouping eQTLs by r2 > 0.95, underlining the relevance of LD information in eQTL analyses. Qtlizer can be accessed via https://genehopper.de/qtlizer or by using the respective Bioconductor R-package ( https://doi.org/10.18129/B9.bioc.Qtlizer ).
format	article
author	Matthias Munz Inken Wohlers Eric Simon Tobias Reinberger Hauke Busch Arne S. Schaefer Jeanette Erdmann
author_facet	Matthias Munz Inken Wohlers Eric Simon Tobias Reinberger Hauke Busch Arne S. Schaefer Jeanette Erdmann
author_sort	Matthias Munz
title	Qtlizer: comprehensive QTL annotation of GWAS results
title_short	Qtlizer: comprehensive QTL annotation of GWAS results
title_full	Qtlizer: comprehensive QTL annotation of GWAS results
title_fullStr	Qtlizer: comprehensive QTL annotation of GWAS results
title_full_unstemmed	Qtlizer: comprehensive QTL annotation of GWAS results
title_sort	qtlizer: comprehensive qtl annotation of gwas results
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/c15af683c6d44f738b2725a1b8a69822
work_keys_str_mv	AT matthiasmunz qtlizercomprehensiveqtlannotationofgwasresults AT inkenwohlers qtlizercomprehensiveqtlannotationofgwasresults AT ericsimon qtlizercomprehensiveqtlannotationofgwasresults AT tobiasreinberger qtlizercomprehensiveqtlannotationofgwasresults AT haukebusch qtlizercomprehensiveqtlannotationofgwasresults AT arnesschaefer qtlizercomprehensiveqtlannotationofgwasresults AT jeanetteerdmann qtlizercomprehensiveqtlannotationofgwasresults
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Qtlizer: comprehensive QTL annotation of GWAS results

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