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2014 ; 198
(4
): 1377-93
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Mapping eQTL networks with mixed graphical Markov models
#MMPMID25271303
Tur I
; Roverato A
; Castelo R
Genetics
2014[Dec]; 198
(4
): 1377-93
PMID25271303
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Expression quantitative trait loci (eQTL) mapping constitutes a challenging
problem due to, among other reasons, the high-dimensional multivariate nature of
gene-expression traits. Next to the expression heterogeneity produced by
confounding factors and other sources of unwanted variation, indirect effects
spread throughout genes as a result of genetic, molecular, and environmental
perturbations. From a multivariate perspective one would like to adjust for the
effect of all of these factors to end up with a network of direct associations
connecting the path from genotype to phenotype. In this article we approach this
challenge with mixed graphical Markov models, higher-order conditional
independences, and q-order correlation graphs. These models show that additive
genetic effects propagate through the network as function of gene-gene
correlations. Our estimation of the eQTL network underlying a well-studied yeast
data set leads to a sparse structure with more direct genetic and regulatory
associations that enable a straightforward comparison of the genetic control of
gene expression across chromosomes. Interestingly, it also reveals that eQTLs
explain most of the expression variability of network hub genes.
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