{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T00:45:35Z","timestamp":1740185135586,"version":"3.37.3"},"reference-count":12,"publisher":"Oxford University Press (OUP)","issue":"20","license":[{"start":{"date-parts":[[2021,5,7]],"date-time":"2021-05-07T00:00:00Z","timestamp":1620345600000},"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\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["CA204120"],"award-info":[{"award-number":["CA204120"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12071273"],"award-info":[{"award-number":["12071273"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,10,25]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Summary<\/jats:title>\n                  <jats:p>For understanding complex diseases, gene\u2013environment (G\u2013E) interactions have important implications beyond main G and E effects. Most of the existing analysis approaches and software packages cannot accommodate data contamination\/long-tailed distribution. We develop GEInter, a comprehensive R package tailored to robust G\u2013E interaction analysis. For both marginal and joint analysis, for data without and with missingness, for continuous and censored survival responses, it comprehensively conducts identification, estimation, visualization and prediction. It can fill an important gap in the existing literature and enjoy broad applicability.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>TCGA data is analyzed as demonstrating examples. It is well known that such data is publicly available https:\/\/cran.r-project.org\/web\/packages\/GEInter\/.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab318","type":"journal-article","created":{"date-parts":[[2021,4,26]],"date-time":"2021-04-26T20:46:37Z","timestamp":1619469997000},"page":"3691-3692","source":"Crossref","is-referenced-by-count":5,"title":["GEInter: an R package for robust gene\u2013environment interaction analysis"],"prefix":"10.1093","volume":"37","author":[{"given":"Mengyun","family":"Wu","sequence":"first","affiliation":[{"name":"School of Statistics and Management, Shanghai University of Finance and Economics , Shanghai 200433, China"}]},{"given":"Xing","family":"Qin","sequence":"additional","affiliation":[{"name":"School of Statistics and Management, Shanghai University of Finance and Economics , Shanghai 200433, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9001-4999","authenticated-orcid":false,"given":"Shuangge","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Yale University , New Haven, CT 06520, USA"}]}],"member":"286","published-online":{"date-parts":[[2021,5,7]]},"reference":[{"key":"2023051609032840000_btab318-B1","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1159\/000363347","article-title":"Incorporating gene-environment interaction in testing for association with rare genetic variants","volume":"78","author":"Chen","year":"2014","journal-title":"Hum. 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