{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T15:54:28Z","timestamp":1775058868068,"version":"3.50.1"},"reference-count":45,"publisher":"Oxford University Press (OUP)","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2013,1,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: Exploring the genetic basis of heritable traits remains one of the central challenges in biomedical research. In traits with simple Mendelian architectures, single polymorphic loci explain a significant fraction of the phenotypic variability. However, many traits of interest seem to be subject to multifactorial control by groups of genetic loci. Accurate detection of such multivariate associations is non-trivial and often compromised by limited statistical power. At the same time, confounding influences, such as population structure, cause spurious association signals that result in false-positive findings.<\/jats:p><jats:p>Results: We propose linear mixed models LMM-Lasso, a mixed model that allows for both multi-locus mapping and correction for confounding effects. Our approach is simple and free of tuning parameters; it effectively controls for population structure and scales to genome-wide datasets. LMM-Lasso simultaneously discovers likely causal variants and allows for multi-marker\u2013based phenotype prediction from genotype. We demonstrate the practical use of LMM-Lasso in genome-wide association studies in Arabidopsis thaliana and linkage mapping in mouse, where our method achieves significantly more accurate phenotype prediction for 91% of the considered phenotypes. At the same time, our model dissects the phenotypic variability into components that result from individual single nucleotide polymorphism effects and population structure. Enrichment of known candidate genes suggests that the individual associations retrieved by LMM-Lasso are likely to be genuine.<\/jats:p><jats:p>Availability: Code available under http:\/\/webdav.tuebingen.<\/jats:p><jats:p>mpg.de\/u\/karsten\/Forschung\/research.html.<\/jats:p><jats:p>Contact: \u00a0rakitsch@tuebingen.mpg.de, ippert@microsoft.com or stegle@ebi.ac.uk<\/jats:p><jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/bts669","type":"journal-article","created":{"date-parts":[[2012,11,23]],"date-time":"2012-11-23T03:52:09Z","timestamp":1353642729000},"page":"206-214","source":"Crossref","is-referenced-by-count":112,"title":["A Lasso multi-marker mixed model for association mapping with population structure correction"],"prefix":"10.1093","volume":"29","author":[{"given":"Barbara","family":"Rakitsch","sequence":"first","affiliation":[{"name":"1 Machine Learning and Computational Biology Research Group, Max Planck Institute for Intelligent Systems and Max Planck Institute for Developmental Biology and 2Zentrum f\u00fcr Bioinformatik, Eberhard Karls Universit\u00e4t, 72076 T\u00fcbingen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christoph","family":"Lippert","sequence":"additional","affiliation":[{"name":"1 Machine Learning and Computational Biology Research Group, Max Planck Institute for Intelligent Systems and Max Planck Institute for Developmental Biology and 2Zentrum f\u00fcr Bioinformatik, Eberhard Karls Universit\u00e4t, 72076 T\u00fcbingen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oliver","family":"Stegle","sequence":"additional","affiliation":[{"name":"1 Machine Learning and Computational Biology Research Group, Max Planck Institute for Intelligent Systems and Max Planck Institute for Developmental Biology and 2Zentrum f\u00fcr Bioinformatik, Eberhard Karls Universit\u00e4t, 72076 T\u00fcbingen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karsten","family":"Borgwardt","sequence":"additional","affiliation":[{"name":"1 Machine Learning and Computational Biology Research Group, Max Planck Institute for Intelligent Systems and Max Planck Institute for Developmental Biology and 2Zentrum f\u00fcr Bioinformatik, Eberhard Karls Universit\u00e4t, 72076 T\u00fcbingen, Germany"},{"name":"1 Machine Learning and Computational Biology Research Group, Max Planck Institute for Intelligent Systems and Max Planck Institute for Developmental Biology and 2Zentrum f\u00fcr Bioinformatik, Eberhard Karls Universit\u00e4t, 72076 T\u00fcbingen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2012,11,22]]},"reference":[{"key":"2023012810164103100_bts669-B1","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1038\/nature08800","article-title":"Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines","volume":"465","author":"Atwell","year":"2010","journal-title":"Nature"},{"key":"2023012810164103100_bts669-B2","first-page":"321","article-title":"Parallel coordinate descent for l1-regularized loss minimization","author":"Bradley","year":"2011","journal-title":"ICML"},{"key":"2023012810164103100_bts669-B3","article-title":"Statistical significance in high-dimensional linear models","author":"B\u00fchlmann","year":"2012"},{"key":"2023012810164103100_bts669-B4","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1038\/nature08979","article-title":"Genome-wide association study of cnvs in 16,000 cases of eight common diseases and 3,000 shared controls","volume":"464","author":"Craddock","year":"2010","journal-title":"Nature"},{"key":"2023012810164103100_bts669-B5","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1101\/gr.086660.108","article-title":"Genetic architecture of quantitative traits in mice, flies, and humans","volume":"19","author":"Flint","year":"2009","journal-title":"Genome Res."},{"key":"2023012810164103100_bts669-B6","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1198\/108571107X200396","article-title":"Incorporating lasso effects into a mixed model for quantitative trait loci detection","volume":"12","author":"Foster","year":"2007","journal-title":"J. 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