{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T18:11:46Z","timestamp":1775931106866,"version":"3.50.1"},"reference-count":32,"publisher":"Oxford University Press (OUP)","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2009,3,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: In ordinary regression, imposition of a lasso penalty makes continuous model selection straightforward. Lasso penalized regression is particularly advantageous when the number of predictors far exceeds the number of observations.<\/jats:p><jats:p>Method: The present article evaluates the performance of lasso penalized logistic regression in case\u2013control disease gene mapping with a large number of SNPs (single nucleotide polymorphisms) predictors. The strength of the lasso penalty can be tuned to select a predetermined number of the most relevant SNPs and other predictors. For a given value of the tuning constant, the penalized likelihood is quickly maximized by cyclic coordinate ascent. Once the most potent marginal predictors are identified, their two-way and higher order interactions can also be examined by lasso penalized logistic regression.<\/jats:p><jats:p>Results: This strategy is tested on both simulated and real data. Our findings on coeliac disease replicate the previous SNP results and shed light on possible interactions among the SNPs.<\/jats:p><jats:p>Availability: The software discussed is available in Mendel 9.0 at the UCLA Human Genetics web site.<\/jats:p><jats:p>Contact: \u00a0klange@ucla.edu<\/jats:p><jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btp041","type":"journal-article","created":{"date-parts":[[2009,1,29]],"date-time":"2009-01-29T01:31:13Z","timestamp":1233192673000},"page":"714-721","source":"Crossref","is-referenced-by-count":590,"title":["Genome-wide association analysis by lasso penalized logistic regression"],"prefix":"10.1093","volume":"25","author":[{"given":"Tong Tong","family":"Wu","sequence":"first","affiliation":[{"name":"1 Department of Epidemiology and Biostatistics, University of Maryland, College Park, MD 20742, 2Department of Statistics, 3Department of Biostatistics, Stanford University, Stanford, CA 94305, 4Department of Human Genetics and 5Department of Biomathematics, University of California, Los Angeles, CA 90095"}]},{"given":"Yi Fang","family":"Chen","sequence":"additional","affiliation":[{"name":"1 Department of Epidemiology and Biostatistics, University of Maryland, College Park, MD 20742, 2Department of Statistics, 3Department of Biostatistics, Stanford University, Stanford, CA 94305, 4Department of Human Genetics and 5Department of Biomathematics, University of California, Los Angeles, CA 90095"}]},{"given":"Trevor","family":"Hastie","sequence":"additional","affiliation":[{"name":"1 Department of Epidemiology and Biostatistics, University of Maryland, College Park, MD 20742, 2Department of Statistics, 3Department of Biostatistics, Stanford University, Stanford, CA 94305, 4Department of Human Genetics and 5Department of Biomathematics, University of California, Los Angeles, CA 90095"},{"name":"1 Department of Epidemiology and Biostatistics, University of Maryland, College Park, MD 20742, 2Department of Statistics, 3Department of Biostatistics, Stanford University, Stanford, CA 94305, 4Department of Human Genetics and 5Department of Biomathematics, University of California, Los Angeles, CA 90095"}]},{"given":"Eric","family":"Sobel","sequence":"additional","affiliation":[{"name":"1 Department of Epidemiology and Biostatistics, University of Maryland, College Park, MD 20742, 2Department of Statistics, 3Department of Biostatistics, Stanford University, Stanford, CA 94305, 4Department of Human Genetics and 5Department of Biomathematics, University of California, Los Angeles, CA 90095"}]},{"given":"Kenneth","family":"Lange","sequence":"additional","affiliation":[{"name":"1 Department of Epidemiology and Biostatistics, University of Maryland, College Park, MD 20742, 2Department of Statistics, 3Department of Biostatistics, Stanford University, Stanford, CA 94305, 4Department of Human Genetics and 5Department of Biomathematics, University of California, Los Angeles, CA 90095"},{"name":"1 Department of Epidemiology and Biostatistics, University of Maryland, College Park, MD 20742, 2Department of Statistics, 3Department of Biostatistics, Stanford University, Stanford, CA 94305, 4Department of Human Genetics and 5Department of Biomathematics, University of California, Los Angeles, CA 90095"}]}],"member":"286","published-online":{"date-parts":[[2009,1,28]]},"reference":[{"key":"2023051209025003300_B1","doi-asserted-by":"crossref","first-page":"1596","DOI":"10.1093\/bioinformatics\/btn236","article-title":"Penalized estimation of haplotype frequencies","volume":"24","author":"Ayers","year":"2008","journal-title":"Bioinformatics"},{"key":"2023051209025003300_B2","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1038\/nrg1916","article-title":"A tutorial on statistical methods for population association studies","volume":"7","author":"Balding","year":"2006","journal-title":"Nat. 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