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Qtlizer: comprehensive QTL annotation of GWAS results

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 GWA...

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Autores principales: Munz, Matthias, Wohlers, Inken, Simon, Eric, Reinberger, Tobias, Busch, Hauke, Schaefer, Arne S., Erdmann, Jeanette
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7687904/
https://www.ncbi.nlm.nih.gov/pubmed/33235230
http://dx.doi.org/10.1038/s41598-020-75770-7
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author Munz, Matthias
Wohlers, Inken
Simon, Eric
Reinberger, Tobias
Busch, Hauke
Schaefer, Arne S.
Erdmann, Jeanette
author_facet Munz, Matthias
Wohlers, Inken
Simon, Eric
Reinberger, Tobias
Busch, Hauke
Schaefer, Arne S.
Erdmann, Jeanette
author_sort Munz, Matthias
collection PubMed
description 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 r(2) > 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).
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spelling pubmed-76879042020-11-27 Qtlizer: comprehensive QTL annotation of GWAS results Munz, Matthias Wohlers, Inken Simon, Eric Reinberger, Tobias Busch, Hauke Schaefer, Arne S. Erdmann, Jeanette Sci Rep Article 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 r(2) > 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). Nature Publishing Group UK 2020-11-24 /pmc/articles/PMC7687904/ /pubmed/33235230 http://dx.doi.org/10.1038/s41598-020-75770-7 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Munz, Matthias
Wohlers, Inken
Simon, Eric
Reinberger, Tobias
Busch, Hauke
Schaefer, Arne S.
Erdmann, Jeanette
Qtlizer: comprehensive QTL annotation of GWAS results
title 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_short Qtlizer: comprehensive QTL annotation of GWAS results
title_sort qtlizer: comprehensive qtl annotation of gwas results
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7687904/
https://www.ncbi.nlm.nih.gov/pubmed/33235230
http://dx.doi.org/10.1038/s41598-020-75770-7
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