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

Brief Bioinform. 2019 Mar 25;20(2):690-700. doi: 10.1093/bib/bby035.


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.

Keywords: biological network; genome-wide association; pathways; post-GWAS; protein–protein interaction; subnetwork.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Computational Biology / methods*
  • Epistasis, Genetic
  • Genome-Wide Association Study*
  • Humans
  • Protein Interaction Maps