Gene Network Inference via Structural Equation Modeling in Genetical Genomics Experiments.

@article{citeulike:2340727, abstract = {Our goal is gene network inference in Genetical Genomics or Systems Genetics experiments. For species where sequence information is available, we first perform expression QTL mapping by jointly utilizing cis, cistrans 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 eQTLs, and directed edges from eQTLs to cis-regulated target genes, from cis-regulated genes to cistrans regulated target genes, from trans-regulator genes to target genes and from trans-eQTLs 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. Based on 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.}, address = {The Monsanto Company.}, author = {Liu, Bing and de la Fuente, Alberto and Hoeschele, Ina }, citeulike-article-id = {2340727}, doi = {10.1534/genetics.107.080069}, issn = {0016-6731}, journal = {Genetics}, keywords = {eqtl, method, network, system}, month = {February}, posted-at = {2008-02-12 23:12:26}, priority = {3}, title = {Gene Network Inference via Structural Equation Modeling in Genetical Genomics Experiments.}, url = {http://dx.doi.org/10.1534/genetics.107.080069}, year = {2008} }

TY - JOUR ID - citeulike:2340727 TI - Gene Network Inference via Structural Equation Modeling in Genetical Genomics Experiments. JF - Genetics AD - The Monsanto Company. SN - 0016-6731 N2 - Our goal is gene network inference in Genetical Genomics or Systems Genetics experiments. For species where sequence information is available, we first perform expression QTL mapping by jointly utilizing cis, cistrans 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 eQTLs, and directed edges from eQTLs to cis-regulated target genes, from cis-regulated genes to cistrans regulated target genes, from trans-regulator genes to target genes and from trans-eQTLs 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. Based on 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. KW - eqtl KW - method KW - network KW - system AU - Liu, Bing AU - de la Fuente, Alberto AU - Hoeschele, Ina PY - 2008/02/03/ UR - http://dx.doi.org/10.1534/genetics.107.080069 ER -