Qtlizer: comprehensive QTL annotation of GWAS results

Matthias Munz; Inken Wohlers; Eric Simon; Tobias Reinberger; Hauke Busch; Arne S. Schaefer; Jeanette Erdmann

doi:10.1038/s41598-020-75770-7

Qtlizer: comprehensive QTL annotation of GWAS results

Matthias Munz^*, Inken Wohlers, Eric Simon, Tobias Reinberger, Hauke Busch, Arne S. Schaefer, Jeanette Erdmann

^*Corresponding author for this work

18 Citations (Scopus)

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 ).

Original language	English
Article number	20417
Journal	Scientific Reports
Volume	10
Issue number	1
ISSN	2045-2322
DOIs	https://doi.org/10.1038/s41598-020-75770-7 https://doi.org/10.1038/s41598-020-75770-7
Publication status	Published - 24.11.2020

Funding

We acknowledge computational support from the OMICS compute cluster at the University of Lübeck. Special thanks to Dr. Carsten Kemena for extensive testing of the web application, Dr. Loreto Munoz Venegas for revising the manuscript, Julia Remes who contributed to the R package of Qtlizer and to Lena Friedrichsen for her support in the variant-gene distance analyses. We cordially thank the Cardiogenics Consortium for data related to the macrophage and monocyte eQTLs. Members of the Cardiogenics Consortium are listed in Supplementary Table 4. Open Access funding enabled and organized by Projekt DEAL. This work was supported by a research grant of the German Research Foundation DFG (Deutsche Forschungsgemeinschaft; GZ: SCHA 1582/3-1) as well as the Land Schleswig-Holstein within the funding program “Open Access Publikationsfonds” and the clusters of excellence “Inflammation at Interfaces” (IaI) and “Precision Medicine in Chronic Inflammation” (PMI). I.W. was supported by the Peter und Traudl Engelhorn Foundation. H.B. and J.E. are supported by the German Research Foundation DFG (Deutsche Forschungsgemeinschaft) under Germany’s Excellence Strategy–EXC 22167-390884018.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 5 Gender Equality
SDG 9 Industry, Innovation, and Infrastructure
SDG 10 Reduced Inequalities

Research Areas and Centers

Research Area: Medical Genetics

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@article{b29f3bd60a3c4f05b6291eb217841b27,

title = "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 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 ).",

author = "Matthias Munz and Inken Wohlers and Eric Simon and Tobias Reinberger and Hauke Busch and Schaefer, \{Arne S.\} and Jeanette Erdmann",

note = "Funding Information: We acknowledge computational support from the OMICS compute cluster at the University of L{\"u}beck. Special thanks to Dr. Carsten Kemena for extensive testing of the web application, Dr. Loreto Munoz Venegas for revising the manuscript, Julia Remes who contributed to the R package of Qtlizer and to Lena Friedrichsen for her support in the variant-gene distance analyses. We cordially thank the Cardiogenics Consortium for data related to the macrophage and monocyte eQTLs. Members of the Cardiogenics Consortium are listed in Supplementary Table 4. Funding Information: Open Access funding enabled and organized by Projekt DEAL. This work was supported by a research grant of the German Research Foundation DFG (Deutsche Forschungsgemeinschaft; GZ: SCHA 1582/3-1) as well as the Land Schleswig-Holstein within the funding program “Open Access Publikationsfonds” and the clusters of excellence “Inflammation at Interfaces” (IaI) and “Precision Medicine in Chronic Inflammation” (PMI). I.W. was supported by the Peter und Traudl Engelhorn Foundation. H.B. and J.E. are supported by the German Research Foundation DFG (Deutsche Forschungsgemeinschaft) under Germany{\textquoteright}s Excellence Strategy–EXC 22167-390884018. Publisher Copyright: {\textcopyright} 2020, The Author(s). Copyright: Copyright 2020 Elsevier B.V., All rights reserved.",

year = "2020",

month = nov,

day = "24",

doi = "10.1038/s41598-020-75770-7",

language = "English",

volume = "10",

journal = "Scientific Reports",

issn = "2045-2322",

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T2 - comprehensive QTL annotation of GWAS results

AU - Munz, Matthias

AU - Wohlers, Inken

AU - Simon, Eric

AU - Reinberger, Tobias

AU - Busch, Hauke

AU - Schaefer, Arne S.

AU - Erdmann, Jeanette

N1 - Funding Information: We acknowledge computational support from the OMICS compute cluster at the University of Lübeck. Special thanks to Dr. Carsten Kemena for extensive testing of the web application, Dr. Loreto Munoz Venegas for revising the manuscript, Julia Remes who contributed to the R package of Qtlizer and to Lena Friedrichsen for her support in the variant-gene distance analyses. We cordially thank the Cardiogenics Consortium for data related to the macrophage and monocyte eQTLs. Members of the Cardiogenics Consortium are listed in Supplementary Table 4. Funding Information: Open Access funding enabled and organized by Projekt DEAL. This work was supported by a research grant of the German Research Foundation DFG (Deutsche Forschungsgemeinschaft; GZ: SCHA 1582/3-1) as well as the Land Schleswig-Holstein within the funding program “Open Access Publikationsfonds” and the clusters of excellence “Inflammation at Interfaces” (IaI) and “Precision Medicine in Chronic Inflammation” (PMI). I.W. was supported by the Peter und Traudl Engelhorn Foundation. H.B. and J.E. are supported by the German Research Foundation DFG (Deutsche Forschungsgemeinschaft) under Germany’s Excellence Strategy–EXC 22167-390884018. Publisher Copyright: © 2020, The Author(s). Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020/11/24

Y1 - 2020/11/24

N2 - 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 ).

AB - 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 ).

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DO - 10.1038/s41598-020-75770-7

M3 - Journal articles

C2 - 33235230

AN - SCOPUS:85096541491

SN - 2045-2322

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JO - Scientific Reports

JF - Scientific Reports

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ER -

Qtlizer: comprehensive QTL annotation of GWAS results

Abstract

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