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Post genome-wide association analysis: dissecting computational pathway/network-based approaches.

Emile R Chimusa1, Shareefa Dalvie2, Collet Dandara3

  • 1Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Level 3, Wernher and Beit North, Private Bag, Rondebosch, 7700, Anzio road, Observatory Cape Town, South Africa.

Briefings in Bioinformatics
|April 28, 2018
PubMed
Summary

Genome-wide association studies (GWASs) identify genetic links to diseases. Post-GWAS approaches (PGAs) using protein-protein interaction networks improve detection of complex genetic signals missed by traditional methods.

Keywords:
biological networkgenome-wide associationpathwayspost-GWASprotein–protein interactionsubnetwork

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Area of Science:

  • Genetics
  • Bioinformatics
  • Systems Biology

Background:

  • Genome-wide association studies (GWASs) have identified thousands of genetic associations with diseases.
  • Traditional GWASs, a single-marker approach, may lack the power to detect complex genetic effects like epistasis or weak signals.
  • This limitation necessitates the development of advanced methods to fully leverage GWAS data.

Purpose of the Study:

  • To review and discuss advancements in pathway/network-based post-GWAS approaches (PGAs).
  • To highlight the utility of protein-protein interaction networks in analyzing GWAS summary statistics.
  • To explore methods for detecting genetic signals of complex diseases by integrating multiple genetic loci or pathways.

Main Methods:

  • Focus on pathway/network-based PGAs utilizing GWAS summary statistics.
  • Integration of protein-protein interaction networks with genetic data.
  • Methods combining effects of multiple genetic loci, subnetworks, or pathways.

Main Results:

  • Pathway/network-based PGAs enhance the detection of genetic signals for complex diseases.
  • These approaches can identify genetic associations missed by single-marker GWAS.
  • Leveraging protein-protein interaction networks improves the interpretation of GWAS findings.

Conclusions:

  • Pathway/network-based PGAs are crucial for extracting comprehensive genetic insights from GWAS data.
  • Further research is needed to refine summary statistic-based methods for complex disease genetics.
  • Integrating multi-locus and network information offers a powerful strategy for genetic discovery.