PGA: Post-GWAS analysis for disease gene identification

Jhih Rong Lin, Daniel Jaroslawicz, Ying Cai, Quanwei Zhang, Zhen Wang, Zhengdong D. Zhang

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Summary Although 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 implementation http://zdzlab.einstein.yu.edu/1/pga.html.

Original languageEnglish (US)
Pages (from-to)1786-1788
Number of pages3
JournalBioinformatics
Volume34
Issue number10
DOIs
StatePublished - May 15 2018

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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