2.50
Hdl Handle:
http://hdl.handle.net/10033/620717
Title:
Data-driven assessment of eQTL mapping methods
Authors:
Michaelson, Jacob J; Alberts, Rudi; Schughart, Klaus; Beyer, Andreas
Abstract:
Abstract Background The 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. Results Here we compare legacy QTL mapping methods with several modern multi-locus methods and evaluate their ability to produce eQTL that agree with independent external data in a systematic way. We found that the modern multi-locus methods (Random Forests, sparse partial least squares, lasso, and elastic net) clearly outperformed the legacy QTL methods (Haley-Knott regression and composite interval mapping) in terms of biological relevance of the mapped eQTL. In particular, we found that our new approach, based on Random Forests, showed superior performance among the multi-locus methods. Conclusions Benchmarks based on the recapitulation of experimental findings provide valuable insight when selecting the appropriate eQTL mapping method. Our battery of tests suggests that Random Forests map eQTL that are more likely to be validated by independent data, when compared to competing multi-locus and legacy eQTL mapping methods.
Citation:
BMC Genomics. 2010 Sep 17;11(1):502
Issue Date:
17-Sep-2010
URI:
http://dx.doi.org/10.1186/1471-2164-11-502; http://hdl.handle.net/10033/620717
Type:
Journal Article
Appears in Collections:
publications of the department infection genetics (INFG)

Full metadata record

DC FieldValue Language
dc.contributor.authorMichaelson, Jacob Jen
dc.contributor.authorAlberts, Rudien
dc.contributor.authorSchughart, Klausen
dc.contributor.authorBeyer, Andreasen
dc.date.accessioned2017-01-17T09:56:24Z-
dc.date.available2017-01-17T09:56:24Z-
dc.date.issued2010-09-17en
dc.identifier.citationBMC Genomics. 2010 Sep 17;11(1):502en
dc.identifier.urihttp://dx.doi.org/10.1186/1471-2164-11-502en
dc.identifier.urihttp://hdl.handle.net/10033/620717-
dc.description.abstractAbstract Background The 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. Results Here we compare legacy QTL mapping methods with several modern multi-locus methods and evaluate their ability to produce eQTL that agree with independent external data in a systematic way. We found that the modern multi-locus methods (Random Forests, sparse partial least squares, lasso, and elastic net) clearly outperformed the legacy QTL methods (Haley-Knott regression and composite interval mapping) in terms of biological relevance of the mapped eQTL. In particular, we found that our new approach, based on Random Forests, showed superior performance among the multi-locus methods. Conclusions Benchmarks based on the recapitulation of experimental findings provide valuable insight when selecting the appropriate eQTL mapping method. Our battery of tests suggests that Random Forests map eQTL that are more likely to be validated by independent data, when compared to competing multi-locus and legacy eQTL mapping methods.en
dc.titleData-driven assessment of eQTL mapping methodsen
dc.typeJournal Articleen
dc.language.rfc3066enen
dc.rights.holderMichaelson et al.en
dc.date.updated2015-09-04T08:29:51Zen
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