Mapping EQTL Networks with Mixed Graphical Markov Models

Overview

Journal Genetics

Publisher Oxford University Press

Specialty Genetics

Date 2014 Oct 2

PMID 25271303

Citations 6

Authors

Inma Tur

Alberto Roverato

Robert Castelo

Affiliations

Soon will be listed here.

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.

Citing Articles

A deep auto-encoder model for gene expression prediction.

Xie R, Wen J, Quitadamo A, Cheng J, Shi X BMC Genomics. 2017; 18(Suppl 9):845.

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Networks Underpinning Symbiosis Revealed Through Cross-Species eQTL Mapping.

Guo Y, Fudali S, Gimeno J, DiGennaro P, Chang S, Williamson V Genetics. 2017; 206(4):2175-2184.

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Metabox: A Toolbox for Metabolomic Data Analysis, Interpretation and Integrative Exploration.

Wanichthanarak K, Fan S, Grapov D, Barupal D, Fiehn O PLoS One. 2017; 12(1):e0171046.

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Modelling local gene networks increases power to detect trans-acting genetic effects on gene expression.

Rakitsch B, Stegle O Genome Biol. 2016; 17:33.

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The propagation of perturbations in rewired bacterial gene networks.

Baumstark R, Hanzelmann S, Tsuru S, Schaerli Y, Francesconi M, Mancuso F Nat Commun. 2015; 6:10105.

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