biostudies-literatureIancu ODNCATS NIH HHSNIMH NIH HHS29-37https://www.ebi.ac.uk/biostudies/studies/S-EPMC4460837biostudies-literatureUnknown11(1)The current study examined the changes in striatal gene network structure induced by short-term selective breeding from a heterogeneous stock for haloperidol response. Brain (striatum) gene expression data were obtained using the Illumina WG 8.2 array, and the datasets from responding and non-responding selected lines were independently interrogated using a weighted gene coexpression network analysis (WGCNA). We detected several gene modules (groups of coexpressed genes) in each dataset; the membership of the modules was found to be largely concordant, and a consensus network was constructed. Further validation of the network topology showed that using approximately 35 samples is sufficient to reliably infer the transcriptome network. An in-depth analysis showed significant changes in network structure and gene connectivity associated with the selected lines; these changes were validated using a bootstrapping procedure. The most dramatic changes were associated with a gene module richly annotated with neurobehavioral traits. The changes in network connectivity were concentrated in the links between this module and the rest of the network, in addition to changes within the module; this observation is consistent with recent results in protein and metabolic networks. These results suggest that a network-based strategy will help identify the genetic factors associated with haloperidol response.Genes, brain, and behaviorGene networks and haloperidol-induced catalepsy.PMC4460837R01 MH051372MH 51372UL1 TR000128McWeeney SHitzemann RDarakjian PIancu ODMalmanger BWalter NAfalseGene networks and haloperidol-induced catalepsy.The current study examined the changes in striatal gene network structure induced by short-term selective breeding from a heterogeneous stock for haloperidol response. Brain (striatum) gene expression data were obtained using the Illumina WG 8.2 array, and the datasets from responding and non-responding selected lines were independently interrogated using a weighted gene coexpression network analysis (WGCNA). We detected several gene modules (groups of coexpressed genes) in each dataset; the membership of the modules was found to be largely concordant, and a consensus network was constructed. Further validation of the network topology showed that using approximately 35 samples is sufficient to reliably infer the transcriptome network. An in-depth analysis showed significant changes in network structure and gene connectivity associated with the selected lines; these changes were validated using a bootstrapping procedure. The most dramatic changes were associated with a gene module richly annotated with neurobehavioral traits. The changes in network connectivity were concentrated in the links between this module and the rest of the network, in addition to changes within the module; this observation is consistent with recent results in protein and metabolic networks. These results suggest that a network-based strategy will help identify the genetic factors associated with haloperidol response.2012-01-01T00:00:00Z2012 Feb2020-10-29T13:45:06Z2019-03-27T01:53:08ZS-EPMC44608372196716410.1111/j.1601-183X.2011.00736.x10.1111/j.1601-183x.2011.00736.x