Data-driven assessment of eQTL mapping methods

Jacob J Michaelson,Klaus Schughart,Rudi Alberts,Andreas Beyer

doi:10.1186/1471-2164-11-502

Jacob J Michaelson, Klaus Schughart + Show 2 more

Open Access

https://doi.org/10.1186/1471-2164-11-502

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Abstract

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.

Highlights

The analysis of expression quantitative trait loci is a potentially powerful way to detect transcriptional regulatory relationships at the genomic scale
In order to evaluate the performance of the expression quantitative trait loci (eQTL) mapping methods in a comprehensive way, we used both simulated data and a variety of published and previously unpublished experimental data from mouse and yeast
These applications demonstrate a certain degree of correspondence between eQTL data and external data sources, and imply that such correspondence is desirable in an eQTL mapping method, no benchmarks based on the systematic recovery of biological information have been proposed and applied to a wide variety of mapping methods and data sets

Summary

Introduction

The analysis of expression quantitative trait loci (eQTL) is a potentially powerful way to detect transcriptional regulatory relationships at the genomic scale. 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, in the case of epistasis. For decades scientists have used a variety of analytical techniques to relate allelic inheritance patterns in the genome to variation in continuous physical traits of interest. The goal of such analyses is often to locate quantitative trait loci (QTL), or genomic locations that exert an influence on the manifested trait. By using DNA microarrays eQTL can be measured for basically all genes in the genome, rendering eQTL data information rich and potentially very powerful. eQTL have been

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