{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T07:09:44Z","timestamp":1761808184809,"version":"3.37.3"},"reference-count":38,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2018,6,19]],"date-time":"2018-06-19T00:00:00Z","timestamp":1529366400000},"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\/100000025","name":"National Institute of Mental Health","doi-asserted-by":"publisher","award":["R01MH100351"],"award-info":[{"award-number":["R01MH100351"]}],"id":[{"id":"10.13039\/100000025","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["R01CA158113"],"award-info":[{"award-number":["R01CA158113"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"KG Jebsen Stiftelsen"},{"DOI":"10.13039\/501100005416","name":"Research Council of Norway","doi-asserted-by":"publisher","award":["223273","229129"],"award-info":[{"award-number":["223273","229129"]}],"id":[{"id":"10.13039\/501100005416","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>Multiple marker analysis of the genome-wide association study (GWAS) data has gained ample attention in recent years. However, because of the ultra high-dimensionality of GWAS data, such analysis is challenging. Frequently used penalized regression methods often lead to large number of false positives, whereas Bayesian methods are computationally very expensive. Motivated to ameliorate these issues simultaneously, we consider the novel approach of using non-local priors in an iterative variable selection framework.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We develop a variable selection method, named, iterative non-local prior based selection for GWAS, or GWASinlps, that combines, in an iterative variable selection framework, the computational efficiency of the screen-and-select approach based on some association learning and the parsimonious uncertainty quantification provided by the use of non-local priors. The hallmark of our method is the introduction of \u2018structured screen-and-select\u2019 strategy, that considers hierarchical screening, which is not only based on response-predictor associations, but also based on response-response associations and concatenates variable selection within that hierarchy. Extensive simulation studies with single nucleotide polymorphisms having realistic linkage disequilibrium structures demonstrate the advantages of our computationally efficient method compared to several frequentist and Bayesian variable selection methods, in terms of true positive rate, false discovery rate, mean squared error and effect size estimation error. Further, we provide empirical power analysis useful for study design. Finally, a real GWAS data application was considered with human height as phenotype.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>An R-package for implementing the GWASinlps method is available at https:\/\/cran.r-project.org\/web\/packages\/GWASinlps\/index.html.<\/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\/bty472","type":"journal-article","created":{"date-parts":[[2018,6,12]],"date-time":"2018-06-12T19:10:33Z","timestamp":1528830633000},"page":"1-11","source":"Crossref","is-referenced-by-count":13,"title":["GWASinlps: non-local prior based iterative SNP selection tool for genome-wide association studies"],"prefix":"10.1093","volume":"35","author":[{"given":"Nilotpal","family":"Sanyal","sequence":"first","affiliation":[{"name":"Department of Radiology, University of California, San Diego, La Jolla, CA, USA"}]},{"given":"Min-Tzu","family":"Lo","sequence":"additional","affiliation":[{"name":"Department of Radiology, University of California, San Diego, La Jolla, CA, USA"}]},{"given":"Karolina","family":"Kauppi","sequence":"additional","affiliation":[{"name":"Department of Radiation Sciences, Ume\u00e5 University, Ume\u00e5, Sweden"}]},{"given":"Srdjan","family":"Djurovic","sequence":"additional","affiliation":[{"name":"Department of Medical Genetics, NORMENT, KG Jebsen Centre, University of Bergen, Bergen, Oslo University Hospital, Oslo, Norway"}]},{"given":"Ole A","family":"Andreassen","sequence":"additional","affiliation":[{"name":"Division of Mental Health and Addiction, NORMENT, KG Jebsen Centre, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway"}]},{"given":"Valen E","family":"Johnson","sequence":"additional","affiliation":[{"name":"Department of Statistics, Texas A&M University, College Station, TX, USA"}]},{"given":"Chi-Hua","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Radiology, University of California, San Diego, La Jolla, CA, USA"}]}],"member":"286","published-online":{"date-parts":[[2018,6,19]]},"reference":[{"key":"2023013107223317700_bty472-B1","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1214\/10-BA523","article-title":"Evolutionary stochastic search for Bayesian model exploration","volume":"5","author":"Bottolo","year":"2010","journal-title":"Bayesian Anal"},{"key":"2023013107223317700_bty472-B2","doi-asserted-by":"crossref","first-page":"e1003657.","DOI":"10.1371\/journal.pgen.1003657","article-title":"Guess-ing polygenic associations with multiple phenotypes using a gpu-based evolutionary stochastic search algorithm","volume":"9","author":"Bottolo","year":"2013","journal-title":"PLoS Genet"},{"key":"2023013107223317700_bty472-B3","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1214\/12-BA703","article-title":"Scalable variational inference for Bayesian variable selection in regression, and its accuracy in genetic association studies","volume":"7","author":"Carbonetto","year":"2012","journal-title":"Bayesian Anal"},{"key":"2023013107223317700_bty472-B4","doi-asserted-by":"crossref","first-page":"1547","DOI":"10.1214\/16-AOAS948","article-title":"A Bayesian predictive model for imaging genetics with application to schizophrenia","volume":"10","author":"Chekouo","year":"2016","journal-title":"Ann. 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