Originally published as Genetics Published Articles Ahead of Print on February 3, 2008.

Genetics, Vol. 178, 1763-1776, March 2008, Copyright © 2008
doi:10.1534/genetics.107.080069

Gene Network Inference via Structural Equation Modeling in Genetical Genomics Experiments

Bing Liu*,{dagger},1,2, Alberto de la Fuente{dagger},{ddagger},1 and Ina Hoeschele*,{dagger},3

* Department of Statistics, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, {dagger} Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061-0477 and {ddagger} CRS4 Bioinformatica, Parco Scientifico e Tecnologico, POLARIS, 09010 Pula (CA), Italy

3 Corresponding author: Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061-0477.
E-mail: inah{at}vbi.vt.edu

Our goal is gene network inference in genetical genomics or systems genetics experiments. For species where sequence information is available, we first perform expression quantitative trait locus (eQTL) mapping by jointly utilizing cis-, cis–trans-, and trans-regulation. After using local structural models to identify regulator–target pairs for each eQTL, we construct an encompassing directed network (EDN) by assembling all retained regulator–target relationships. The EDN has nodes corresponding to expressed genes and eQTL and directed edges from eQTL to cis-regulated target genes, from cis-regulated genes to cis–trans-regulated target genes, from trans-regulator genes to target genes, and from trans-eQTL to target genes. For network inference within the strongly constrained search space defined by the EDN, we propose structural equation modeling (SEM), because it can model cyclic networks and the EDN indeed contains feedback relationships. On the basis of a factorization of the likelihood and the constrained search space, our SEM algorithm infers networks involving several hundred genes and eQTL. Structure inference is based on a penalized likelihood ratio and an adaptation of Occam's window model selection. The SEM algorithm was evaluated using data simulated with nonlinear ordinary differential equations and known cyclic network topologies and was applied to a real yeast data set.


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Genetics 2008 178: NP. [Full Text]