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1.  Unraveling inflammatory responses using systems genetics and gene-environment interactions in macrophages 
Cell  2012;151(3):658-670.
Many common diseases have an important inflammatory component mediated in part by macrophages. Here we used a systems genetics strategy to examine the role of common genetic variation in macrophage responses to inflammatory stimuli.
We examined genome-wide transcript levels in macrophages from 92 strains of the Hybrid Mouse Diversity Panel. We exposed macrophages to control media, bacterial lipopolysaccharide, or oxidized phospholipids. We performed association mapping under each condition and identified several thousand expression quantitative trait loci (eQTL), gene-by-environment interactions and several eQTL “hotspots” that specifically control LPS responses. We validated an eQTL hotspot in chromosome 8 using siRNA knock-down of candidate genes and identified the gene 2310061C15Rik, as a novel regulator of inflammatory responses in macrophages.
We have created a public database where the data presented here can be used as a resource for understanding many common inflammatory traits which are modeled in the mouse, and for the dissection of regulatory relationships between genes.
PMCID: PMC3513387  PMID: 23101632
2.  Hybrid mouse diversity panel: a panel of inbred mouse strains suitable for analysis of complex genetic traits 
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.
PMCID: PMC3586763  PMID: 22892838
3.  Intergenerational genomic DNA methylation patterns in mouse hybrid strains 
Genome Biology  2014;15(5):R68.
DNA methylation is a contributing factor to both rare and common human diseases, and plays a major role in development and gene silencing. While the variation of DNA methylation among individuals has been partially characterized, the degree to which methylation patterns are preserved across generations is still poorly understood. To determine the extent of methylation differences between two generations of mice we examined DNA methylation patterns in the livers of eight parental and F1 mice from C57BL/6J and DBA/2J mouse strains using bisulfite sequencing.
We find a large proportion of reproducible methylation differences between C57BL/6J and DBA/2J chromosomes in CpGs, which are highly heritable between parent and F1 mice. We also find sex differences in methylation levels in 396 genes, and 11% of these are differentially expressed between females and males. Using a recently developed approach to identify allelically methylated regions independently of genotypic differences, we identify 112 novel putative imprinted genes and microRNAs, and validate imprinting at the RNA level in 10 of these genes.
The majority of DNA methylation differences among individuals are associated with genetic differences, and a much smaller proportion of these epigenetic differences are due to sex, imprinting or stochastic intergenerational effects. Epigenetic differences can be a determining factor in heritable traits and should be considered in association studies for molecular and clinical traits, as we observed that methylation differences in the mouse model are highly heritable and can have functional consequences on molecular traits such as gene expression.
PMCID: PMC4076608  PMID: 24887417
4.  Maximal information component analysis: a novel non-linear network analysis method 
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.
PMCID: PMC3594742  PMID: 23487572
gene expression; ICMg; scale-free topology; MINE; GxE interactions
5.  NF-E2–Related Factor 2 Promotes Atherosclerosis by Effects on Plasma Lipoproteins and Cholesterol Transport That Overshadow Antioxidant Protection 
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.
Nrf2 expression unexpectedly promotes atherosclerotic lesion formation in a sex-dependent manner, most likely by a combination of systemic metabolic and local vascular effects.
PMCID: PMC3037185  PMID: 20947826
atherosclerosis; cytokines; lipoproteins; reactive oxygen species; foam cell formation; lipogenesis; Nrf2
6.  Copy number variation influences gene expression and metabolic traits in mice 
Human Molecular Genetics  2009;18(21):4118-4129.
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.
PMCID: PMC2758141  PMID: 19648292

Results 1-6 (6)