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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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.
GWAS does not require the identification of the target gene involved in...

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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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SNPEVG: a graphical tool for GWAS graphing with mouse clicks.

Shengwen Wang1, Daniel Dvorkin, Yang Da

  • 1Department of Animal Science, University of Minnesota, St. Paul, Minnesota, USA.

BMC Bioinformatics
|December 4, 2012
PubMed
Summary

The SNPEVG package offers graphical tools for rapid analysis of genome-wide association study (GWAS) results. It provides instant global and local viewing of single nucleotide polymorphism (SNP) data across multiple traits and chromosomes.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Genome-wide association studies (GWAS) generate extensive single nucleotide polymorphism (SNP) data.
  • Effective visualization is crucial for interpreting large-scale GWAS results.

Purpose of the Study:

  • To introduce SNPEVG, a software package designed for efficient graphical analysis of GWAS data.
  • To provide tools for both global and local viewing of SNP effects across multiple traits and chromosomes.

Main Methods:

  • The SNPEVG package comprises three programs: SNPEVG1, SNPEVG2, and SNPEVG3.
  • SNPEVG1 facilitates SNP effect P-value visualization for multiple traits, generating Manhattan and Q-Q plots.
  • SNPEVG2 enables multi-trait graphing with customizable options, while SNPEVG3 processes epiSNP output for detailed genetic effect visualization.

Main Results:

  • SNPEVG provides a user-friendly graphical user interface (GUI) for immediate data interpretation.
  • The package can generate publication-quality graphs for all chromosomes and selected traits.
  • SNPEVG efficiently handles large datasets, displaying various genetic effects (genotypic, additive, dominance).

Conclusions:

  • SNPEVG is a versatile and efficient tool for rapid analysis of substantial GWAS datasets.
  • The package simplifies the digestion of complex genetic association data through intuitive graphical representations.
  • Mouse-click functionality allows for swift and flexible exploration of GWAS findings.