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PGA: post-GWAS analysis for disease gene identification

PGA: post-GWAS analysis for disease gene identification SummaryAlthough the genome-wide association study (GWAS) is a powerful method to identify disease-associated variants, it does not directly address the biological mechanisms underlying such genetic association signals. Here, we present PGA, a Perl- and Java-based program for post-GWAS analysis that predicts likely disease genes given a list of GWAS-reported variants. Designed with a command line interface, PGA incorporates genomic and eQTL data in identifying disease gene candidates and uses gene network and ontology data to score them based upon the strength of their relationship to the disease in question.Availability and implementationhttp://zdzlab.einstein.yu.edu/1/pga.htmlSupplementary informationSupplementary data are available at Bioinformatics online. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Bioinformatics Oxford University Press

PGA: post-GWAS analysis for disease gene identification

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Publisher
Oxford University Press
Copyright
© The Author(s) 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com
ISSN
1367-4803
eISSN
1460-2059
DOI
10.1093/bioinformatics/btx845
Publisher site
See Article on Publisher Site

Abstract

SummaryAlthough the genome-wide association study (GWAS) is a powerful method to identify disease-associated variants, it does not directly address the biological mechanisms underlying such genetic association signals. Here, we present PGA, a Perl- and Java-based program for post-GWAS analysis that predicts likely disease genes given a list of GWAS-reported variants. Designed with a command line interface, PGA incorporates genomic and eQTL data in identifying disease gene candidates and uses gene network and ontology data to score them based upon the strength of their relationship to the disease in question.Availability and implementationhttp://zdzlab.einstein.yu.edu/1/pga.htmlSupplementary informationSupplementary data are available at Bioinformatics online.

Journal

BioinformaticsOxford University Press

Published: May 15, 2018

References