Mapping eQTL networks with mixed graphical Markov models

Tur Mongé, Imma, 1985-; Roverato, Alberto; Castelo Valdueza, Robert

doi:http://dx.doi.org/10.1534/genetics.114.169573

Mapping eQTL networks with mixed graphical Markov models

Citation

Tur I, Roverato A, Castelo R. Mapping eQTL networks with mixed graphical Markov models. Genetics. 2014 Dec 1;198(4):1377-93. DOI: 10.1534/genetics.114.169573

Abstract

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.