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Qtlizer: comprehensive QTL annotation of GWAS results

View ORCID ProfileMatthias Munz, Inken Wohlers, Eric Simon, Cardiogenics Consortium, View ORCID ProfileArne S. Schaefer, View ORCID ProfileJeanette Erdmann
doi: https://doi.org/10.1101/495903
Matthias Munz
1Institute for Cardiogenetics, University of Lübeck, 23562 Lübeck, Germany
2DZHK (German Research Centre for Cardiovascular Research), partner site Hamburg/Lübeck/Kiel, 23562 Lübeck, Germany
3University Heart Center Luebeck, 23562 Lübeck, Germany
4Charité – University Medicine Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute for Dental and Craniofacial Sciences, Department of Periodontology and Synoptic Dentistry, Berlin, Germany
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Inken Wohlers
5Platform for Genome Analytics, Institute of Neurogenetics and Institute for Cardiogenetics, University of Lübeck, 23562 Lübeck, Germany
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Eric Simon
6Target Discovery Research, Boehringer Ingelheim Pharma GmbH & Co KG, Biberach 88397, Germany
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Arne S. Schaefer
4Charité – University Medicine Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute for Dental and Craniofacial Sciences, Department of Periodontology and Synoptic Dentistry, Berlin, Germany
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Jeanette Erdmann
2DZHK (German Research Centre for Cardiovascular Research), partner site Hamburg/Lübeck/Kiel, 23562 Lübeck, Germany
3University Heart Center Luebeck, 23562 Lübeck, Germany
4Charité – University Medicine Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute for Dental and Craniofacial Sciences, Department of Periodontology and Synoptic Dentistry, Berlin, Germany
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  • For correspondence: jeanette.erdmann@uni-luebeck.de
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ABSTRACT

Motivation Exploration of genetic variant-to-gene relationships by quantitative trait loci (QTLs) helps to identify candidate causal variants and genes in post genome-wide association study analyses. However, the wide range of public QTL databases and the lack of batch annotation features make it cumbersome to investigate these relationships in a comprehensive manner.

Results In this work, we introduce the tool ‘Qtlizer’ to annotate lists of common variants in human with associated changes in gene expression and protein abundance using the, to-date, most comprehensive database of published QTLs. The features include incorporation of LD variants, quality and reproducibility metrics and linking to other resources.

Availability and Implementation The web application of Qtlizer is available at http://www.genehopper.de/qtlizer, a guide on how to use the REST API is available at http://www.genehopper.de/rest.

Contact m.munz{at}uni-luebeck.de

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted December 17, 2018.
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Qtlizer: comprehensive QTL annotation of GWAS results
Matthias Munz, Inken Wohlers, Eric Simon, Cardiogenics Consortium, Arne S. Schaefer, Jeanette Erdmann
bioRxiv 495903; doi: https://doi.org/10.1101/495903
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Qtlizer: comprehensive QTL annotation of GWAS results
Matthias Munz, Inken Wohlers, Eric Simon, Cardiogenics Consortium, Arne S. Schaefer, Jeanette Erdmann
bioRxiv 495903; doi: https://doi.org/10.1101/495903

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