Data-driven assessment of eQTL mapping methods

BMC Genomics

Methodology Article

Jacob J Michaelson¹  Andreas Beyer¹  Rudi Alberts²  Klaus Schughart² 

[1] Cellular Networks and Systems Biology, Biotechnology Center - TU Dresden, Dresden, Germany;Helmholtz Center for Infection Research, Braunschweig, Germany;

关键词: Quantitative Trait Locus;    Random Forest;    Lasso;    Composite Interval Mapping;    Importance Measure;   

DOI  :  10.1186/1471-2164-11-502

 received in 2010-04-13, accepted in 2010-09-17,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundThe 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.ResultsHere 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.ConclusionsBenchmarks 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.

【 授权许可】

Unknown   
© Michaelson et al; licensee BioMed Central Ltd. 2010. This article is published under license to BioMed Central Ltd. 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.