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BMC Bioinformatics. 2014 Jan 14;15:11. doi: 10.1186/1471-2105-15-11.

kruX: matrix-based non-parametric eQTL discovery.

Author information

1
School of Life Sciences - LifeNet, Freiburg Institute for Advanced Studies (FRIAS), University of Freiburg, Freiburg, Germany. tom.michoel@roslin.ed.ac.uk.

Abstract

BACKGROUND:

The Kruskal-Wallis test is a popular non-parametric statistical test for identifying expression quantitative trait loci (eQTLs) from genome-wide data due to its robustness against variations in the underlying genetic model and expression trait distribution, but testing billions of marker-trait combinations one-by-one can become computationally prohibitive.

RESULTS:

We developed kruX, an algorithm implemented in Matlab, Python and R that uses matrix multiplications to simultaneously calculate the Kruskal-Wallis test statistic for several millions of marker-trait combinations at once. KruX is more than ten thousand times faster than computing associations one-by-one on a typical human dataset. We used kruX and a dataset of more than 500k SNPs and 20k expression traits measured in 102 human blood samples to compare eQTLs detected by the Kruskal-Wallis test to eQTLs detected by the parametric ANOVA and linear model methods. We found that the Kruskal-Wallis test is more robust against data outliers and heterogeneous genotype group sizes and detects a higher proportion of non-linear associations, but is more conservative for calling additive linear associations.

CONCLUSION:

kruX enables the use of robust non-parametric methods for massive eQTL mapping without the need for a high-performance computing infrastructure and is freely available from http://krux.googlecode.com.

PMID:
24423115
PMCID:
PMC3897912
DOI:
10.1186/1471-2105-15-11
[Indexed for MEDLINE]
Free PMC Article

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