{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T08:45:26Z","timestamp":1765356326668,"version":"3.37.3"},"reference-count":40,"publisher":"Oxford University Press (OUP)","issue":"16","license":[{"start":{"date-parts":[[2018,3,14]],"date-time":"2018-03-14T00:00:00Z","timestamp":1520985600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/about_us\/legal\/notices"}],"funder":[{"name":"Regional Bioinformatics Research"},{"DOI":"10.13039\/100017228","name":"European Genomic Institute for Diabetes","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100017228","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100017228","name":"EGID","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100017228","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Chair in Biostatistics"},{"name":"Centre National de la Recherche Scientifique and Lille 2 University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,8,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>Large scale genome-wide association studies (GWAS) are tools of choice for discovering associations between genotypes and phenotypes. To date, many studies rely on univariate statistical tests for association between the phenotype and each assayed single nucleotide polymorphism (SNP). However, interaction between SNPs, namely epistasis, must be considered when tackling the complexity of underlying biological mechanisms. Epistasis analysis at large scale entails a prohibitive computational burden when addressing the detection of more than two interacting SNPs. In this paper, we introduce a stochastic causal graph-based method, SMMB, to analyze epistatic patterns in GWAS data.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We present Stochastic Multiple Markov Blanket algorithm (SMMB), which combines both ensemble stochastic strategy inspired from random forests and Bayesian Markov blanket-based methods. We compared SMMB with three other recent algorithms using both simulated and real datasets. Our method outperforms the other compared methods for a majority of simulated cases of 2-way and 3-way epistasis patterns (especially in scenarii where minor allele frequencies of causal SNPs are low). Our approach performs similarly as two other compared methods for large real datasets, in terms of power, and runs faster.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>Parallel version available on https:\/\/ls2n.fr\/listelogicielsequipe\/DUKe\/128\/.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/bty154","type":"journal-article","created":{"date-parts":[[2018,3,9]],"date-time":"2018-03-09T20:15:43Z","timestamp":1520626543000},"page":"2773-2780","source":"Crossref","is-referenced-by-count":19,"title":["SMMB: a stochastic Markov blanket framework strategy for epistasis detection in GWAS"],"prefix":"10.1093","volume":"34","author":[{"given":"Cl\u00e9ment","family":"Niel","sequence":"first","affiliation":[{"name":"Laboratoire des Sciences du Num\u00e9rique de Nantes (LS2N), Centre National de la recherche Scientifique UMR6004, University of Nantes, Nantes, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christine","family":"Sinoquet","sequence":"additional","affiliation":[{"name":"Laboratoire des Sciences du Num\u00e9rique de Nantes (LS2N), Centre National de la recherche Scientifique UMR6004, University of Nantes, Nantes, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christian","family":"Dina","sequence":"additional","affiliation":[{"name":"Institut du Thorax, Institut National de la Sant\u00e9 et de la Recherche M\u00e9dicale UMR 1087, Centre National de la Recherche Scientifique UMR 6291, University of Nantes, Nantes, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ghislain","family":"Rocheleau","sequence":"additional","affiliation":[{"name":"European Genomic Institute for Diabetes FR3508, Centre National de la Recherche Scientifique UMR 8199, Lille 2 University, Lille, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2018,3,14]]},"reference":[{"key":"2023012712583546300_bty154-B1","doi-asserted-by":"crossref","first-page":"184.","DOI":"10.1186\/s12859-017-1599-7","article-title":"KNN-MDR: a learning approach for improving interactions mapping performances in genome wide association studies","volume":"18","author":"Abo Alchamlat","year":"2017","journal-title":"BMC Bioinformatics"},{"year":"2003","author":"Aliferis","key":"2023012712583546300_bty154-B2"},{"key":"2023012712583546300_bty154-B3","doi-asserted-by":"crossref","DOI":"10.5962\/bhl.title.44575","volume-title":"Mendel\u2019s Principles of Heredity","author":"Bateson","year":"1909"},{"key":"2023012712583546300_bty154-B4","doi-asserted-by":"crossref","first-page":"2633","DOI":"10.1104\/pp.16.00399","article-title":"nana plant2 encodes a maize ortholog of the Arabidopsis brassinosteroid biosynthesis gene DWARF1, identifying developmental interactions between brassinosteroids and gibberellins","volume":"171","author":"Best","year":"2016","journal-title":"Plant. 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