Gene networks associated with conditional fear in mice identified using a systems genetics approach

Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA.
BMC Systems Biology (Impact Factor: 2.44). 03/2011; 5(1):43. DOI: 10.1186/1752-0509-5-43
Source: PubMed


Our understanding of the genetic basis of learning and memory remains shrouded in mystery. To explore the genetic networks governing the biology of conditional fear, we used a systems genetics approach to analyze a hybrid mouse diversity panel (HMDP) with high mapping resolution.
A total of 27 behavioral quantitative trait loci were mapped with a false discovery rate of 5%. By integrating fear phenotypes, transcript profiling data from hippocampus and striatum and also genotype information, two gene co-expression networks correlated with context-dependent immobility were identified. We prioritized the key markers and genes in these pathways using intramodular connectivity measures and structural equation modeling. Highly connected genes in the context fear modules included Psmd6, Ube2a and Usp33, suggesting an important role for ubiquitination in learning and memory. In addition, we surveyed the architecture of brain transcript regulation and demonstrated preservation of gene co-expression modules in hippocampus and striatum, while also highlighting important differences. Rps15a, Kif3a, Stard7, 6330503K22RIK, and Plvap were among the individual genes whose transcript abundance were strongly associated with fear phenotypes.
Application of our multi-faceted mapping strategy permits an increasingly detailed characterization of the genetic networks underlying behavior.

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    • "An extensive overview of WGCNA, including numerous tutorials, can be found at and this method has been extensively used to create co-expression networks [23-28]. To begin, we filtered the array data to include 8173 probes expressed in the liver as previously described [29]. "
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    • "Finally candidate genes associated with the trait are prioritised based on network statistics like module membership and gene significance. WGCNA has been used to identify genes and gene networks associated with specific tissues, distinct biological states or diseases, and qualitative as well as quantitative phenotypes [22–24]. "
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