Background: Network construction and analysis algorithms provide scientists with the ability to sift through high-throughput biological outputs, such as transcription microarrays, for small groups of genes (modules) that are relevant for further research. Most of these algorithms ignore the important role of non-linear interactions in the data, and the ability for genes to operate in multiple functional groups at once, despite clear evidence for both of these phenomena in observed biological systems.

Results: We have created a novel co-expression network analysis algorithm that incorporates both of these principles by combining the information-theoretic association measure of the maximal information coefficient (MIC) with an Interaction Component Model. We evaluate the performance of this approach on two datasets collected from a large panel of mice, one from macrophages and the other from liver by comparing the two measures based on a measure of module entropy, Gene Ontology (GO) enrichment, and scale-free topology (SFT) fit. Our algorithm outperforms a widely used co-expression analysis method, weighted gene co-expression network analysis (WGCNA), in the macrophage data, while returning comparable results in the liver dataset when using these criteria. We demonstrate that the macrophage data has more non-linear interactions than the liver dataset, which may explain the increased performance of our method, termed Maximal Information Component Analysis (MICA) in that case.

Conclusions: In making our network algorithm more accurately reflect known biological principles, we are able to generate modules with improved relevance, particularly in networks with confounding factors such as gene by environment interactions.

Objective

To test the hypothesis that NF-E2–related factor 2 (Nrf2) expression plays an antiatherogenic role by its vascular antioxidant and anti-inflammatory properties.

Methods and Results

Nrf2 is an important transcription factor that regulates the expression of phase 2 detoxifying enzymes and antioxidant genes. Its expression in vascular cells appears to be an important factor in the protection against vascular oxidative stress and inflammation. We developed Nrf2 heterozygous (HET) and homozygous knockout (KO) mice on an apolipoprotein (apo) E–null background by sequential breeding, resulting in Nrf2−/−, apoE−/− (KO), Nrf2−/+, apoE−/− (HET) and Nrf2+/+, and apoE−/− wild-type littermates. KO mice exhibited decreased levels of antioxidant genes with evidence of increased reactive oxygen species generation compared with wild-type controls. Surprisingly, KO males exhibited 47% and 53% reductions in the degree of aortic atherosclerosis compared with HET or wild-type littermates, respectively. Decreased atherosclerosis in KO mice correlated with lower plasma total cholesterol in a sex-dependent manner. KO mice also had a decreased hepatic cholesterol content and a lower expression of lipogenic genes, suggesting that hepatic lipogenesis could be reduced. In addition, KO mice exhibited atherosclerotic plaques characterized by a lesser macrophage component and decreased foam cell formation in an in vitro lipid-loading assay. This was associated with a lower rate of cholesterol influx, mediated in part by decreased expression of the scavenger receptor CD36.

Conclusion

Nrf2 expression unexpectedly promotes atherosclerotic lesion formation in a sex-dependent manner, most likely by a combination of systemic metabolic and local vascular effects.

Copy number variants (CNVs) are genomic segments which are duplicated or deleted among different individuals. CNVs have been implicated in both Mendelian and complex traits, including immune and behavioral disorders, but the study of the mechanisms by which CNVs influence gene expression and clinical phenotypes in humans is complicated by the limited access to tissues and by population heterogeneity. We now report studies of the effect of 19 CNVs on gene expression and metabolic traits in a mouse intercross between strains C57BL/6J and C3H/HeJ. We found that 83% of genes predicted to occur within CNVs were differentially expressed. The expression of most CNV genes was correlated with copy number, but we also observed evidence that gene expression was altered in genes flanking CNVs, suggesting that CNVs may contain regulatory elements for these genes. Several CNVs mapped to hotspots, genomic regions influencing expression of tens or hundreds of genes. Several metabolic traits including cholesterol, triglycerides, glucose and body weight mapped to three CNVs in the genome, in mouse chromosomes 1, 4 and 17. Predicted CNV genes, such as Itlna, Defcr-1, Trim12 and Trim34 were highly correlated with these traits. Our results suggest that CNVs have a significant impact on gene expression and that CNVs may be playing a role in the mechanisms underlying metabolic traits in mice.

We have developed an association-based approach using classical inbred strains of mice in which we correct for population structure, which is very extensive in mice, using an efficient mixed-model algorithm. Our approach includes inbred parental strains as well as recombinant inbred strains in order to capture loci with effect sizes typical of complex traits in mice (in the range of 5 % of total trait variance). Over the last few years, we have typed the hybrid mouse diversity panel (HMDP) strains for a variety of clinical traits as well as intermediate phenotypes and have shown that the HMDP has sufficient power to map genes for highly complex traits with resolution that is in most cases less than a megabase. In this essay, we review our experience with the HMDP, describe various ongoing projects, and discuss how the HMDP may fit into the larger picture of common diseases and different approaches.