Mapping eQTL Networks with Mixed Graphical Markov Models. Issue 4 (29th September 2014)

• Record Type: Journal Article
• Authors: Tur, Inma; Roverato, Alberto; Castelo, Robert
• Is Part Of: Genetics. Volume 198:Issue 4(2014)
• Page Start: 1377
• Page End: 1393
• Publication Date: 2014-09-29
• Subjects: eQTL -- gene network -- exact-likelihood-ratio test -- conditional Gaussian distribution -- mixed graphical Markov model; Genetics -- Periodicals; 576.5
• DOI: 10.1534/genetics.114.169573
• Languages: English
• ISSNs: 0016-6731

Abstract

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.