{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Chimusa ER"],"funding":["NHLBI NIH HHS","NHGRI NIH HHS","Wellcome Trust"],"pagination":["690-700"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC6556901"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["20(2)"],"pubmed_abstract":["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."],"journal":["Briefings in bioinformatics"],"pubmed_title":["Post genome-wide association analysis: dissecting computational pathway/network-based approaches."],"pmcid":["PMC6556901"],"funding_grant_id":["H3A/18/001","U24 HL135600","U54 HG009790","U01 HG009716","U24 HG006941"],"pubmed_authors":["Dalvie S","Wonkam A","Chimusa ER","Mazandu GK","Dandara C"],"additional_accession":[]},"is_claimable":false,"name":"Post genome-wide association analysis: dissecting computational pathway/network-based approaches.","description":"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.","dates":{"release":"2019-01-01T00:00:00Z","publication":"2019 Mar","modification":"2025-04-04T22:08:33.585Z","creation":"2019-07-24T07:13:28Z"},"accession":"S-EPMC6556901","cross_references":{"pubmed":["29701762"],"doi":["10.1093/bib/bby035"]}}