{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T07:02:57Z","timestamp":1767855777628,"version":"3.49.0"},"reference-count":34,"publisher":"Oxford University Press (OUP)","issue":"8","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2013,4,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: Expression quantitative trait loci (eQTL) studies investigate how gene expression levels are affected by DNA variants. A major challenge in inferring eQTL is that a number of factors, such as unobserved covariates, experimental artifacts and unknown environmental perturbations, may confound the observed expression levels. This may both mask real associations and lead to spurious association findings.<\/jats:p><jats:p>Results: In this article, we introduce a LOw-Rank representation to account for confounding factors and make use of Sparse regression for eQTL mapping (LORS). We integrate the low-rank representation and sparse regression into a unified framework, in which single-nucleotide polymorphisms and gene probes can be jointly analyzed. Given the two model parameters, our formulation is a convex optimization problem. We have developed an efficient algorithm to solve this problem and its convergence is guaranteed. We demonstrate its ability to account for non-genetic effects using simulation, and then apply it to two independent real datasets. Our results indicate that LORS is an effective tool to account for non-genetic effects. First, our detected associations show higher consistency between studies than recently proposed methods. Second, we have identified some new hotspots that can not be identified without accounting for non-genetic effects.<\/jats:p><jats:p>Availability: The software is available at: http:\/\/bioinformatics.med.yale.edu\/software.aspx.<\/jats:p><jats:p>Contact: \u00a0hongyu.zhao@yale.edu<\/jats:p><jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btt075","type":"journal-article","created":{"date-parts":[[2013,2,19]],"date-time":"2013-02-19T12:32:56Z","timestamp":1361277176000},"page":"1026-1034","source":"Crossref","is-referenced-by-count":33,"title":["Accounting for non-genetic factors by low-rank representation and sparse regression for eQTL mapping"],"prefix":"10.1093","volume":"29","author":[{"given":"Can","family":"Yang","sequence":"first","affiliation":[{"name":"1 Department of Biostatistics, Yale School of Public Health, New Haven, CT 06520, USA, 2Center for Theoretical Biology, Peking University, Beijing 100871 and 3Center for Computational Systems Biology, School of Mathematical Sciences, Fudan University, Shanghai 200433, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Wang","sequence":"additional","affiliation":[{"name":"1 Department of Biostatistics, Yale School of Public Health, New Haven, CT 06520, USA, 2Center for Theoretical Biology, Peking University, Beijing 100871 and 3Center for Computational Systems Biology, School of Mathematical Sciences, Fudan University, Shanghai 200433, China"},{"name":"1 Department of Biostatistics, Yale School of Public Health, New Haven, CT 06520, USA, 2Center for Theoretical Biology, Peking University, Beijing 100871 and 3Center for Computational Systems Biology, School of Mathematical Sciences, Fudan University, Shanghai 200433, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuqin","family":"Zhang","sequence":"additional","affiliation":[{"name":"1 Department of Biostatistics, Yale School of Public Health, New Haven, CT 06520, USA, 2Center for Theoretical Biology, Peking University, Beijing 100871 and 3Center for Computational Systems Biology, School of Mathematical Sciences, Fudan University, Shanghai 200433, China"},{"name":"1 Department of Biostatistics, Yale School of Public Health, New Haven, CT 06520, USA, 2Center for Theoretical Biology, Peking University, Beijing 100871 and 3Center for Computational Systems Biology, School of Mathematical Sciences, Fudan University, Shanghai 200433, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongyu","family":"Zhao","sequence":"additional","affiliation":[{"name":"1 Department of Biostatistics, Yale School of Public Health, New Haven, CT 06520, USA, 2Center for Theoretical Biology, Peking University, Beijing 100871 and 3Center for Computational Systems Biology, School of Mathematical Sciences, Fudan University, Shanghai 200433, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2013,2,17]]},"reference":[{"key":"2023012810330738000_btt075-B1","doi-asserted-by":"crossref","first-page":"10101","DOI":"10.1073\/pnas.97.18.10101","article-title":"Singular value decomposition for genome-wide expression data processing and modeling","volume":"97","author":"Alter","year":"2000","journal-title":"Proc. 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