{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,8]],"date-time":"2025-02-08T20:10:28Z","timestamp":1739045428172,"version":"3.37.0"},"reference-count":49,"publisher":"Oxford University Press (OUP)","issue":"11","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2009,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: We consider the problem of multiple locus linkage analysis for expression traits of genes in a pathway or a network. To capitalize on co-expression of functionally related genes, we propose a penalized regression method that maps multiple expression quantitative trait loci (eQTLs) for all related genes simultaneously while accounting for their shared functions as specified a priori by a gene pathway or network.<\/jats:p><jats:p>Results: An analysis of a mouse dataset and simulation studies clearly demonstrate the advantage of the proposed method over a standard approach that ignores biological knowledge of gene networks.<\/jats:p><jats:p>Contact: \u00a0weip@biostat.umn.edu<\/jats:p><jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btp177","type":"journal-article","created":{"date-parts":[[2009,4,1]],"date-time":"2009-04-01T00:33:57Z","timestamp":1238546037000},"page":"1390-1396","source":"Crossref","is-referenced-by-count":9,"title":["Network-based multiple locus linkage analysis of expression traits"],"prefix":"10.1093","volume":"25","author":[{"given":"Wei","family":"Pan","sequence":"first","affiliation":[{"name":"Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building (MMC 303), Minneapolis, MN 55455-0378, USA"}]}],"member":"286","published-online":{"date-parts":[[2009,3,31]]},"reference":[{"key":"2023013111551915300_B1","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1038\/75556","article-title":"Gene ontology: tool for the unification of biology. 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