biostudies-literatureChimusa ERNHLBI NIH HHSNHGRI NIH HHSWellcome Trust690-700https://www.ebi.ac.uk/biostudies/studies/S-EPMC6556901biostudies-literatureUnknown20(2)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.Briefings in bioinformaticsPost genome-wide association analysis: dissecting computational pathway/network-based approaches.PMC6556901H3A/18/001U24 HL135600U54 HG009790U01 HG009716U24 HG006941Dalvie SWonkam AChimusa ERMazandu GKDandara CfalsePost genome-wide association analysis: dissecting computational pathway/network-based approaches.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.2019-01-01T00:00:00Z2019 Mar2025-04-04T22:08:33.585Z2019-07-24T07:13:28ZS-EPMC65569012970176210.1093/bib/bby035