Complex genetic interactions lie at the foundation of many diseases. Understanding the nature of these interactions is critical to developing rational intervention strategies. In mammalian systems hypothesis testing in vivo is expensive, time consuming, and often restricted to a few physiological endpoints. Thus, computational methods that generate causal hypotheses can help to prioritize targets for experimental intervention. We propose a Bayesian statistical method to infer networks of causal relationships among genotypes and phenotypes using expression quantitative trait loci (eQTL) data from genetically randomized populations. Causal relationships between network variables are described with hierarchical regression models. Prior distributions on the network structure enforce graph sparsity and have the potential to encode prior biological knowledge about the network. An efficient Monte Carlo method is used to search across the model space and sample highly probable networks. The result is an ensemble of networks that provide a measure of confidence in the estimated network topology. These networks can be used to make predictions of system-wide response to perturbations. We applied our method to kidney gene expression data from an MRL/MpJ 3 SM/J intercross population and predicted a previously uncharacterized feedback loop in the local renin-angiotensin system.

SummaryThe IGF-1 signaling pathway plays an important role in regulating longevity. To identify the genetic loci and genes that regulate plasma IGF-1 levels, we intercrossed MRL ⁄ MpJ and SM ⁄ J, inbred mouse strains that differ in IGF-1 levels. Quantitative trait loci (QTL) analysis of IGF-1 levels of these F2 mice detected four QTL on chromosomes (Chrs) 9 (48 Mb), 10 (86 Mb), 15 (18 Mb), and 17 (85 Mb). Haplotype association mapping of IGF-1 levels in 28 domesticated inbred strains identified three suggestive loci in females on Chrs 2 (13 Mb), 10 (88 Mb), and 17 (28 Mb) and in four males on Chrs 1 (159 Mb), 3 (52 and 58 Mb), and 16 (74 Mb). Except for the QTL on Chr 9 and 16, all loci co-localized with IGF-1 QTL previously identified in other mouse crosses. The most significant locus was the QTL on Chr 10, which contains the Igf1 gene and which had a LOD score of 31.8. Haplotype analysis among 28 domesticated inbred strains revealed a major QTL on Chr 10 overlapping with the QTL identified in the F2 mice. This locus showed three major haplotypes; strains with haplotype 1 had significantly lower plasma IGF-1 and extended longevity (P < 0.05) than strains with haplotype 2 or 3. Bioinformatic analysis, combined with sequencing and expression studies, showed that Igf1 is the most likely QTL gene, but that other genes may also play a role in this strong QTL.

The aim of this study was to characterize the responses of individual tissues to high-fat feeding as a function of mass, fat composition, and transcript abundance. We examined a panel of eight tissues [5 white adipose tissues (WAT), brown adipose tissue (BAT), liver, muscle] obtained from DBA/2J mice on either a standard breeding diet (SBD) or a high-fat diet (HFD). HFD led to weight gain, decreased insulin sensitivity, and tissue-specific responses, including inflammation, in these mice. The dietary fatty acids were partially metabolized and converted in both liver and fat tissues. Saturated fatty acids (SFA) were converted in the liver to monounsaturated fatty acids (MUFA) and polyunsaturated fatty acids (PUFA), and oleic acid (C18:1) was the preferred MUFA for storage of excess energy in all tissues of HFD-fed mice. Transcriptional changes largely reflected the tissue-specific fat deposition. SFA were negatively correlated with genes in the collagen family and processes involving the extracellular matrix. We propose a novel role of the tryptophan hydroxylase 2 (Tph2) gene in adipose tissues of diet-induced obesity. Tissue-specific responses to HFD were identified. Liver steatosis was evident in HFD-fed mice. Gonadal, retroperitoneal and subcutaneous adipose tissue and BAT exhibited severe inflammatory and immune responses. Mesenteric adipose tissue was the most metabolically active adipose tissue. Gluteal adipose tissue had the highest mass gain but was sluggish in its metabolism. In HFD conditions, BAT functioned largely like WAT in its role as a depot for excess energy, whereas WAT played a role in thermogenesis.

This article is available online at http://www.jlr.org considerable interest has been given to the results of genome-wide association studies of triglycerides and lipids in humans ( 2-5 ). However, these studies often do not control for environment and only explain about 10% of the overall lipid variation, indicating that additional genes involved in lipid metabolism are yet to be discovered ( 5 ).Studies in inbred mice successfully accommodate both genetic and environmental issues. Because we have such a tightly controlled environment in our mouse rooms, any phenotypic variation in triglyceride levels among inbred strains must be attributed primarily to genetic variation. This makes quantitative trait loci (QTL) analysis in inbred mouse strains a powerful approach for identifying loci and genes regulating lipid levels. To date, our laboratory and others have identifi ed more than 30 mouse triglyceride QTL ( 6, 7 ). We continue to improve the ways in which we convert these QTL into the identifi cation of QTL genes (QTG). For example, since 2003 we have used a list of QTG criteria published by the Complex Trait Consortium (CTC) to indentify causal QTL genes ( 8 ). These criteria are based on the premise that a QTG must carry a polymorphism between the parental strains of the mouse cross that affects either the structure/function of the gene (a nonsynonymous coding polymorphism) or the expression of the gene. Still, most of the CTC criteria involve in vitro and in vivo experiments and are not practical strategies when more than 100 genes are located under the QTL, a common characteristic of most QTL.To improve and accelerate the process of gene identification, our laboratory and others developed a set of bioinformatic tools to help narrow QTL in the mouse and Abstract To identify genetic loci infl uencing lipid levels, we performed quantitative trait loci (QTL) analysis between inbred mouse strains MRL/MpJ and SM/J, measuring triglyceride levels at 8 weeks of age in F2 mice fed a chow diet. We identifi ed one signifi cant QTL on chromosome (Chr) 15 and three suggestive QTL on Chrs 2, 7, and 17. We also carried out microarray analysis on the livers of parental strains of 282 F2 mice and used these data to fi nd cis -regulated expression QTL. We then narrowed the list of candidate genes under signifi cant QTL using a "toolbox" of bioinformatic resources, including haplotype analysis; parental strain comparison for gene expression differences and nonsynonymous coding single nucleotide polymorphisms (SNP); cis -regulated eQTL in livers of F2 mice; correlation between gene expression and phenotype; and conditioning of expression on the phenotype. We suggest Slc25a7 as a candidate gene for the Chr 7 QTL and, based on expression differences, fi ve genes ( Polr3 h, Cyp2d22, Cyp2d26, Tspo, and One of the major predictors of the development of coronary artery disease (CAD) is lipid levels, which are determined by a complex interaction of genetic and environmental factors. High levels of low density lipoprotein (LDL) cholest...

Identifying the genes underlying quantitative trait loci (QTL) for disease is difficult, mainly because of the low resolution of the approach and the complex genetics involved. However, recent advances in bioinformatics and the availability of genetic resources now make it possible to narrow the genetic intervals, test candidate genes, and define pathways affected by these QTL. In this study, we mapped three significant QTL and one suggestive QTL for an increased albumin-to-creatinine ratio on chromosomes (Chrs) 1, 4, 15, and 17, respectively, in a cross between the inbred MRL/MpJ and SM/J strains of mice. By combining data from several sources and by utilizing gene expression data, we identified Tlr12 as a likely candidate for the Chr 4 QTL. Through the mapping of 33,881 transcripts measured by microarray on kidney RNA from each of the 173 male F2 animals, we identified several downstream pathways associated with these QTL, including the glycan degradation, leukocyte migration, and antigen-presenting pathways. We demonstrate that by combining data from multiple sources, we can identify not only genes that are likely to be causal candidates for QTL but also the pathways through which these genes act to alter phenotypes. This combined approach provides valuable insights into the causes and consequences of renal disease. Chronic kidney disease is a growing medical problem caused by various environmental and genetic factors. Identifying the genes underlying common forms of kidney disease in humans has proven difficult, expensive, and time consuming. However, quantitative trait loci (QTL) for several complex traits, including renal phenotypes, 1 are concordant among mice, rats, and humans, suggesting that genetic findings from animal models are relevant to human disease. With respect to chronic kidney disease, QTL analysis using mice is likely to contribute new findings in the near future.In addition to mapping the causative loci, it is of equal importance to identify the pathways regulated by the loci so that we gain a better understanding of the processes that drive renal damage. One approach to identify the genes that are driven by certain loci is a method known as genetical genomics, in which gene transcripts are treated as quantitative traits and mapped in a cross in the same way as any other phenotype. 2 In this study we generated an F2 intercross between the kidney damage-susceptible SM/J (SM) and the nonsusceptible MRL/MpJ (MRL) mouse inbred strains. In addition to measuring the urinary albumin-to-creatinine ratio (ACR), we obtained a kidney expression profile from each mouse using Affymetrix arrays. This allowed us to identify the loci responsible for the difference in ACR between