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

Emile R. Chimusa, Shareefa Dalvie, Collet Dandara, Ambroise Wonkam, Gaston K. Mazandu

Research output: Contribution to journalArticlepeer-review


Over thousands of genetic associations to diseases have been identified by genome-wide association studies (GWASs), which conceptually is a single-marker-based approach. There are potentially many uses of these identified variants, including a better understanding of the pathogenesis of diseases, new leads for studying underlying risk prediction and clinical prediction of treatment. However, because of inadequate power, GWAS might miss disease genes and/or pathways with weak genetic or strong epistatic effects. Driven by the need to extract useful information from GWAS summary statistics, post-GWAS approaches (PGAs) were introduced. Here, we dissect and discuss advances made in pathway/network-based PGAs, with a particular focus on protein-protein interaction networks that leverage GWAS summary statistics by combining effects of multiple loci, subnetworks or pathways to detect genetic signals associated with complex diseases. We conclude with a discussion of research areas where further work on summary statistic-based methods is needed.

Original languageEnglish (US)
Pages (from-to)690-700
Number of pages11
JournalBriefings in bioinformatics
Issue number2
StatePublished - Mar 22 2019
Externally publishedYes


  • Genome-Wide association
  • biological network
  • pathways
  • post-GWAS
  • protein-protein interaction
  • subnetwork

ASJC Scopus subject areas

  • Information Systems
  • Molecular Biology


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