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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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PheGWAS: a new dimension to visualize GWAS across multiple phenotypes.

Gittu George1, Sushrima Gan1, Yu Huang1

  • 1NIHR Global Health Research Unit on Global Diabetes Outcomes Research, Division of Population Health and Genomics, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK.

Bioinformatics (Oxford, England)
|December 21, 2019
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Summary

PheGWAS software visualizes genome-wide association studies (GWAS) and phenome-wide association studies (PheWAS) to explore pleiotropy. This tool aids in identifying genetic loci associated with multiple traits, enhancing genetic correlation insights.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Exploring phenome-wide pleiotropy at the genome-wide level is crucial for understanding complex genetic architectures.
  • Existing methods may lack efficient visualization and exploration capabilities for large-scale genetic and phenomic data.

Purpose of the Study:

  • To develop and present PheGWAS, a novel software tool for enhanced exploration of phenome-wide pleiotropy.
  • To create a dynamic 3D visualization integrating Manhattan plots from GWAS with PheWAS data.

Main Methods:

  • PheGWAS generates a 3D 'landscape' by combining GWAS Manhattan plots with PheWAS data.
  • The tool allows exploration of pleiotropy within specific significance strata and chromosomal sections.
  • It visualizes comprehensive genomic and phenomic coordinates.

Main Results:

  • PheGWAS successfully identified 88 and 69 loci for single and multiple traits, respectively, using Global Lipids Genetics Consortium data.
  • The software identified known genes and SNPs from the consortium.
  • PheGWAS provided insights into local genetic correlation and identified regions sharing causal variants across phenotypes.

Conclusions:

  • PheGWAS is an effective tool for exploring phenome-wide pleiotropy and identifying genetic associations across multiple traits.
  • The software enhances the understanding of genetic correlations and shared causal variants.
  • PheGWAS is freely available, facilitating broader research applications.