Genomic analysis of metabolic pathway gene expression in mice

Abstract

(http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. metabolic pathway gene expression in mice <p>In order to evaluate metabolic pathways associated with obesity, global gene-expression data were integrated with phenotypic and genetic segregation analyses, identifying 13 metabolic pathways the genes of which are coordinately regulated in association with obesity. Four genomic regions were found to control the coordinated expression of these pathways and novel genes potentially associated with the identified pathways were identified.</p> Abstract Background: A segregating population of (C57BL/6J × DBA/2J)F2 intercross mice was studied for obesity-related traits and for global gene expression in liver. Quantitative trait locus analyses were applied to the subcutaneous fat-mass trait and all gene-expression data. These data were then used to identify gene sets that are differentially perturbed in lean and obese mice.

Whole Brain and Brain Regional Coexpression Network Interactions Associated with Predisposition to Alcohol Consumption

“…In most of the candidate modules, the majority of the probesets have the same eQTL, which overlaps the mQTL region. As shown in Table S3 in file S1, within many of the modules, most transcripts have cis-eQTLs (i.e., the eQTL is within 20 Kb of the physical location of the transcript) [39]. Candidate module preservation mean Z summary scores ranged from 3.05 to 16.15.…”

“…Although it was not a criterion for distinction as a candidate module, we also examined each module to determine if a common eQTL location existed for the genes within the module. Genes were considered to be cis regulated if the eQTL was within 20 Kb of the gene [39].…”

Systems-based approaches to cardiovascular disease

“…The concept of ‘systems genetics’ is simple: monitor the effects of natural variations at molecular levels (for example, mRNA, protein, or metabolite levels) and then integrate these data with clinical phenotypes (Figure 5). 19,64,65 For example, one might examine gene expression in monocytes from 100 individuals with atherosclerosis and 100 healthy controls, and then ask whether statistically significant differences exist between the two groups. With a dense single-nucleotide polymorphism (SNP) map of the DNA from both groups, one could also map eQTLs and investigate whether these loci match disease susceptibility loci identified in GWAS (Figure 2).…”

“…Transcript levels have been examined as a function of genetics in a number of clinical and animal studies and have yielded promising results. 8,31,65–75 …”

“…However, by making certain assumptions (as described below) or by introducing specific perturbations of a network component, investigators can model causal interactions and derive a ‘directed’ network. For instance, in the above networks 8,31,65–75 , siRNA knockdown or gene targeting was used to establish causality with respect to certain nodes in the network. Given such causal anchors, and knowing the nodes in a system as well as their variances, deriving a set of equations that describe the causal relationships of the various elements is theoretically possible using structural equation modeling or Bayesian analysis.…”