{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T18:36:59Z","timestamp":1791398219057,"version":"4.3.3"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"24","license":[{"start":{"date-parts":[[2016,11,16]],"date-time":"2016-11-16T00:00:00Z","timestamp":1479254400000},"content-version":"vor","delay-in-days":89,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"The National Institutes of Health","doi-asserted-by":"crossref","award":["MH094267, GM102365, GM61374"],"award-info":[{"award-number":["MH094267, GM102365, GM61374"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000092","name":"The National Library of Medicine","doi-asserted-by":"crossref","award":["LM07033"],"award-info":[{"award-number":["LM07033"]}],"id":[{"id":"10.13039\/100000092","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,12,15]]},"abstract":"<jats:p>Motivation: Analyzing genome wide association data in the context of biological pathways helps us understand how genetic variation influences phenotype and increases power to find associations. However, the utility of pathway-based analysis tools is hampered by undercuration and reliance on a distribution of signal across all of the genes in a pathway. Methods that combine genome wide association results with genetic networks to infer the key phenotype-modulating subnetworks combat these issues, but have primarily been limited to network definitions with yes\/no labels for gene-gene interactions. A recent method (EW_dmGWAS) incorporates a biological network with weighted edge probability by requiring a secondary phenotype-specific expression dataset. In this article, we combine an algorithm for weighted-edge module searching and a probabilistic interaction network in order to develop a method, STAMS, for recovering modules of genes with strong associations to the phenotype and probable biologic coherence. Our method builds on EW_dmGWAS but does not require a secondary expression dataset and performs better in six test cases.<\/jats:p>\n               <jats:p>Results: We show that our algorithm improves over EW_dmGWAS and standard gene-based analysis by measuring precision and recall of each method on separately identified associations. In the Wellcome Trust Rheumatoid Arthritis study, STAMS-identified modules were more enriched for separately identified associations than EW_dmGWAS (STAMS P-value 3.0 \u00d7 10\u22124; EW_dmGWAS- P-value = 0.8). We demonstrate that the area under the Precision-Recall curve is 5.9 times higher with STAMS than EW_dmGWAS run on the Wellcome Trust Type 1 Diabetes data.<\/jats:p>\n               <jats:p>Availability and Implementation: STAMS is implemented as an R package and is freely available at https:\/\/simtk.org\/projects\/stams.<\/jats:p>\n               <jats:p>Contact: \u00a0rbaltman@stanford.edu<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btw530","type":"journal-article","created":{"date-parts":[[2016,8,20]],"date-time":"2016-08-20T01:49:34Z","timestamp":1471657774000},"page":"3815-3822","source":"Crossref","is-referenced-by-count":21,"title":["STAMS: STRING-assisted module search for genome wide association studies and application to autism"],"prefix":"10.1093","volume":"32","author":[{"given":"Sara","family":"Hillenmeyer","sequence":"first","affiliation":[{"name":"1Biomedical Informatics Training Program, Stanford University, Stanford, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lea K.","family":"Davis","sequence":"additional","affiliation":[{"name":"2Vanderbilt Genetics Institute"},{"name":"3Division of Genetic Medicine, Department of Medicine, Vanderbilt University, Nashville, TN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric R.","family":"Gamazon","sequence":"additional","affiliation":[{"name":"3Division of Genetic Medicine, Department of Medicine, Vanderbilt University, Nashville, TN, USA"},{"name":"4Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Edwin H.","family":"Cook","sequence":"additional","affiliation":[{"name":"5Institute for Juvenile Research, Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nancy J.","family":"Cox","sequence":"additional","affiliation":[{"name":"2Vanderbilt Genetics Institute"},{"name":"3Division of Genetic Medicine, Department of Medicine, Vanderbilt University, Nashville, TN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Russ B.","family":"Altman","sequence":"additional","affiliation":[{"name":"6Departments of Bioengineering and Genetics, Stanford University, Stanford, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2016,8,19]]},"reference":[{"key":"2023020114104737700_btw530-B1","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1038\/75556","article-title":"Gene Ontology: tool for the unification of biology","volume":"25","author":"Ashburner","year":"2000","journal-title":"Nat. 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