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.


  1. Mapping eQTL networks with mixed graphical Markov models.
    Cite this
    Tur I, Roverato A, Castelo R, 2014-12-01 - Genetics
  2. Reverse engineering molecular regulatory networks from microarray data with qp-graphs.
    Cite this
    Castelo R, Roverato A, 2009-02-01 - Journal of computational biology : a journal of computational molecular cell biology


  1. Inma Tur

    Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Spain

  2. Alberto Roverato

    Department of Statistical Sciences, Università di Bologna, Italy

  3. Robert Castelo

    Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Spain

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Submitted ByRobert Castelo