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Data-driven assessment of eQTL mapping methods

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 express...

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Autores principales: Michaelson, Jacob J, Alberts, Rudi, Schughart, Klaus, Beyer, Andreas
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2996998/
https://www.ncbi.nlm.nih.gov/pubmed/20849587
http://dx.doi.org/10.1186/1471-2164-11-502
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author Michaelson, Jacob J
Alberts, Rudi
Schughart, Klaus
Beyer, Andreas
author_facet Michaelson, Jacob J
Alberts, Rudi
Schughart, Klaus
Beyer, Andreas
author_sort Michaelson, Jacob J
collection PubMed
description 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.
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spelling pubmed-29969982010-12-07 Data-driven assessment of eQTL mapping methods Michaelson, Jacob J Alberts, Rudi Schughart, Klaus Beyer, Andreas BMC Genomics Methodology Article 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. BioMed Central 2010-09-17 /pmc/articles/PMC2996998/ /pubmed/20849587 http://dx.doi.org/10.1186/1471-2164-11-502 Text en Copyright ©2010 Michaelson et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methodology Article
Michaelson, Jacob J
Alberts, Rudi
Schughart, Klaus
Beyer, Andreas
Data-driven assessment of eQTL mapping methods
title Data-driven assessment of eQTL mapping methods
title_full Data-driven assessment of eQTL mapping methods
title_fullStr Data-driven assessment of eQTL mapping methods
title_full_unstemmed Data-driven assessment of eQTL mapping methods
title_short Data-driven assessment of eQTL mapping methods
title_sort data-driven assessment of eqtl mapping methods
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2996998/
https://www.ncbi.nlm.nih.gov/pubmed/20849587
http://dx.doi.org/10.1186/1471-2164-11-502
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