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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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PGA: post-GWAS analysis for disease gene identification.

Jhih-Rong Lin1, Daniel Jaroslawicz1, Ying Cai1

  • 1Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA.

Bioinformatics (Oxford, England)
|January 5, 2018
PubMed
Summary

This study introduces PGA, a tool for post-genome-wide association study (GWAS) analysis. PGA predicts disease genes by integrating genomic, eQTL, and network data to uncover biological mechanisms.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Genome-wide association studies (GWAS) identify genetic variants linked to diseases but do not elucidate underlying biological mechanisms.
  • Understanding the functional impact of GWAS findings is crucial for disease gene discovery.

Purpose of the Study:

  • To present PGA, a novel computational tool for post-GWAS analysis.
  • To predict candidate disease genes by integrating diverse biological datasets.

Main Methods:

  • PGA is a Perl- and Java-based program with a command-line interface.
  • It incorporates genomic data, expression quantitative trait loci (eQTL) data, gene networks, and ontology data.
  • Candidate genes are scored based on their relationship strength to the disease.

Main Results:

  • PGA facilitates the identification of likely disease genes from GWAS-reported variants.
  • The tool aids in prioritizing genes for further functional investigation.

Conclusions:

  • PGA enhances the interpretation of GWAS results by predicting disease-associated genes.
  • This approach aids in uncovering the biological mechanisms of genetic associations.