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dc.contributor.authorMichaelson, Jacob J
dc.contributor.authorAlberts, Rudi
dc.contributor.authorSchughart, Klaus
dc.contributor.authorBeyer, Andreas
dc.date.accessioned2011-03-02T11:51:15Zen
dc.date.available2011-03-02T11:51:15Zen
dc.date.issued2010en
dc.identifier.citationData-driven assessment of eQTL mapping methods. 2010, 11:502 BMC Genomicsen
dc.identifier.issn1471-2164en
dc.identifier.pmid20849587en
dc.identifier.doi10.1186/1471-2164-11-502en
dc.identifier.urihttp://hdl.handle.net/10033/123291en
dc.description.abstractThe analysis of expression quantitative trait loci (eQTL) is a potentially powerful way to detect transcriptional regulatory relationships at the genomic scale. However, eQTL data sets often go underexploited because legacy QTL methods are used to map the relationship between the expression trait and genotype. Often these methods are inappropriate for complex traits such as gene expression, particularly in the case of epistasis.
dc.language.isoenen
dc.subject.meshAnimalsen
dc.subject.meshBias (Epidemiology)en
dc.subject.meshChromosome Mappingen
dc.subject.meshComputer Simulationen
dc.subject.meshDatabases, Geneticen
dc.subject.meshGene Expression Regulationen
dc.subject.meshHematopoietic Stem Cellsen
dc.subject.meshHippocampusen
dc.subject.meshLungen
dc.subject.meshMiceen
dc.subject.meshModels, Geneticen
dc.subject.meshMutationen
dc.subject.meshPolymorphism, Single Nucleotideen
dc.subject.meshQuantitative Trait Locien
dc.subject.meshQuantitative Trait, Heritableen
dc.subject.meshSample Sizeen
dc.subject.meshT-Lymphocytes, Regulatoryen
dc.titleData-driven assessment of eQTL mapping methods.en
dc.typeArticleen
dc.contributor.departmentCellular Networks and Systems Biology, Biotechnology Center - TU Dresden, Dresden, Germany.en
dc.identifier.journalBMC genomicsen
refterms.dateFOA2018-06-13T02:30:37Z
html.description.abstractThe analysis of expression quantitative trait loci (eQTL) is a potentially powerful way to detect transcriptional regulatory relationships at the genomic scale. However, eQTL data sets often go underexploited because legacy QTL methods are used to map the relationship between the expression trait and genotype. Often these methods are inappropriate for complex traits such as gene expression, particularly in the case of epistasis.


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