{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T10:47:18Z","timestamp":1761562038247,"version":"3.37.3"},"reference-count":32,"publisher":"Oxford University Press (OUP)","issue":"4","funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,2,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: Despite numerous successful Genome-wide Association Studies (GWAS), detecting variants that have low disease risk still poses a challenge. GWAS may miss disease genes with weak genetic effects or strong epistatic effects due to the single-marker testing approach commonly used. GWAS may thus generate false negative or inconclusive results, suggesting the need for novel methods to combine effects of single nucleotide polymorphisms within a gene to increase the likelihood of fully characterizing the susceptibility gene.<\/jats:p><jats:p>Results: We developed ancGWAS, an algebraic graph-based centrality measure that accounts for linkage disequilibrium in identifying significant disease sub-networks by integrating the association signal from GWAS data sets into the human protein\u2013protein interaction (PPI) network. We validated ancGWAS using an association study result from a breast cancer data set and the simulation of interactive disease loci in the simulation of a complex admixed population, as well as pathway-based GWAS simulation. This new approach holds promise for deconvoluting the interactions between genes underlying the pathogenesis of complex diseases. Results obtained yield a novel central breast cancer sub-network of the human interactome implicated in the proteoglycan syndecan-mediated signaling events pathway which is known to play a major role in mesenchymal tumor cell proliferation, thus providing further insights into breast cancer pathogenesis.<\/jats:p><jats:p>Availability and implementation: The ancGWAS package and documents are available at http:\/\/www.cbio.uct.ac.za\/~emile\/software.html<\/jats:p><jats:p>Contact: \u00a0emile.chimusa@uct.ac.za, Nicola.Mulder@uct.ac.za<\/jats:p><jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btv619","type":"journal-article","created":{"date-parts":[[2015,10,28]],"date-time":"2015-10-28T02:38:45Z","timestamp":1445999925000},"page":"549-556","source":"Crossref","is-referenced-by-count":23,"title":["ancGWAS: a post genome-wide association study method for interaction, pathway and ancestry analysis in homogeneous and admixed populations"],"prefix":"10.1093","volume":"32","author":[{"given":"Emile R.","family":"Chimusa","sequence":"first","affiliation":[{"name":"1 Computational Biology Group, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Medical School, 7925, Observatory, South Africa and"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mamana","family":"Mbiyavanga","sequence":"additional","affiliation":[{"name":"1 Computational Biology Group, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Medical School, 7925, Observatory, South Africa and"},{"name":"2 African Institute for Mathematical Sciences, 7945 Muizenberg, Cape Town, South Africa"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gaston K.","family":"Mazandu","sequence":"additional","affiliation":[{"name":"1 Computational Biology Group, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Medical School, 7925, Observatory, South Africa and"},{"name":"2 African Institute for Mathematical Sciences, 7945 Muizenberg, Cape Town, South Africa"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicola J.","family":"Mulder","sequence":"additional","affiliation":[{"name":"1 Computational Biology Group, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Medical School, 7925, Observatory, South Africa and"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2015,10,27]]},"reference":[{"key":"2023020110322679000_btv619-B1","doi-asserted-by":"crossref","first-page":"1359","DOI":"10.1093\/bioinformatics\/bts144","article-title":"Fast and accurate inference of local ancestry in Latino populations","volume":"28","author":"Baran","year":"2012","journal-title":"Bioinformatics"},{"key":"2023020110322679000_btv619-B2","doi-asserted-by":"crossref","DOI":"10.1515\/9781400874668","volume-title":"Adaptive Control Processes: A Guided Tour","author":"Bellman","year":"1961"},{"key":"2023020110322679000_btv619-B3","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate\u2014a practical and powerful approach to multiple testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. 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