{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T10:29:00Z","timestamp":1759400940115,"version":"3.41.2"},"reference-count":51,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2021,7,29]],"date-time":"2021-07-29T00:00:00Z","timestamp":1627516800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11901387"],"award-info":[{"award-number":["11901387"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010240","name":"National Planning Office of Philosophy and Social Science","doi-asserted-by":"publisher","award":["2018EJB006"],"award-info":[{"award-number":["2018EJB006"]}],"id":[{"id":"10.13039\/501100010240","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,11,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>With the development of genome-wide association studies, how to gain information from a large scale of data has become an issue of common concern, since traditional methods are not fully developed to solve problems such as identifying loci-to-loci interactions (also known as epistasis). Previous epistatic studies mainly focused on local information with a single outcome (phenotype), while in this paper, we developed a two-stage global search algorithm, Greedy Equivalence Search with Local Modification (GESLM), to implement a global search of directed acyclic graph in order to identify genome-wide epistatic interactions with multiple outcome variables (phenotypes) in a case\u2013control design. GESLM integrates the advantages of score-based methods and constraint-based methods to learn the phenotype-related Bayesian network and is powerful and robust to find the interaction structures that display both genetic associations with phenotypes and gene interactions. We compared GESLM with some common phenotype-related loci detecting methods in simulation studies. The results showed that our method improved the accuracy and efficiency compared with others, especially in an unbalanced case\u2013control study. Besides, its application on the UK Biobank dataset suggested that our algorithm has great performance when handling genome-wide association data with more than one phenotype.<\/jats:p>","DOI":"10.1093\/bib\/bbab276","type":"journal-article","created":{"date-parts":[[2021,7,22]],"date-time":"2021-07-22T19:09:54Z","timestamp":1626980994000},"source":"Crossref","is-referenced-by-count":6,"title":["GESLM algorithm for detecting causal SNPs in GWAS with multiple phenotypes"],"prefix":"10.1093","volume":"22","author":[{"given":"Ruiqi","family":"Lyu","sequence":"first","affiliation":[{"name":"Shanghai Jiao Tong University, Department of Bioinformatics and Biostatistics, Shanghai, 200240, China"}]},{"given":"Jianle","family":"Sun","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Department of Bioinformatics and Biostatistics, Shanghai, 200240, China"}]},{"given":"Dong","family":"Xu","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Department of Bioinformatics and Biostatistics, Shanghai, 200240, China"}]},{"given":"Qianxue","family":"Jiang","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Department of Bioinformatics and Biostatistics, Shanghai, 200240, China"}]},{"given":"Chaochun","family":"Wei","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Department of Bioinformatics and Biostatistics, Shanghai, 200240, China"}]},{"given":"Yue","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Department of Bioinformatics and Biostatistics, Shanghai, 200240, China"}]}],"member":"286","published-online":{"date-parts":[[2021,7,29]]},"reference":[{"key":"2021110815073023400_ref1","article-title":"Recent advances in network-based methods for disease gene prediction","author":"Ata","year":"2020","journal-title":"Brief Bioinform"},{"key":"2021110815073023400_ref2","article-title":"Genome-wide association study of flowering 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