Epistatic interaction between two nonstructural loci on chromosomes 7 and 3 influences hepatic lipase activity in BSB mice

ZBTB32 performs crosstalk with the glucocorticoid receptor and is crucial in glucocorticoid responses to starvation

The hypothalamic-pituitary-adrenal (HPA) axis forms a complex neuroendocrine system that regulates the body’s response to stress such as starvation. In contrast with the glucocorticoid receptor (GR), Zinc finger and BTB domain containing 32 (ZBTB32) is a transcription factor with poorly described functional relevance in physiology. This study shows that ZBTB32 is essential for the production of glucocorticoids (GCs) in response to starvation, since ZBTB32^−/− mice fail to increase their GC production in the absence of nutrients. In terms of mechanism, GR-mediated upregulation of adrenal Scarb1 gene expression was absent in ZBTB32^−/− mice, implicating defective cholesterol import as the cause of the poor GC synthesis. These lower GC levels are further associated with aberrations in the metabolic adaptation to starvation, which could explain the progressive weight gain of ZBTB32^−/− mice. In conclusion, ZBTB32 performs a crosstalk with the GR in the metabolic adaptation to starvation via regulation of adrenal GC production.

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ZBTB32 is involved in the glucocorticoid production in response to starvation

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GR-mediated upregulation of adrenal Scarb1 regulates cholesterol import

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The weight gain of ZBTB32^−/− mice is associated with aberrant metabolic adaptations

The Human Obesity Gene Map: The 2004 Update

This paper presents the eleventh update of the human obesity gene map, which incorporates published results up to the end of October 2004. Evidence from single-gene mutation obesity cases, Mendelian disorders exhibiting obesity as a clinical feature, transgenic and knockout murine models relevant to obesity, quantitative trait loci (QTLs) from animal cross-breeding experiments, association studies with candidate genes, and linkages from genome scans is reviewed. As of October 2004, 173 human obesity cases due to single-gene mutations in 10 different genes have been reported, and 49 loci related to Mendelian syndromes relevant to human obesity have been mapped to a genomic region, and causal genes or strong candidates have been identified for most of these syndromes. There are 166 genes which, when mutated or expressed as transgenes in the mouse, result in phenotypes that affect body weight and adiposity. The number of QTLs reported from animal models currently reaches 221. The number of human obesity QTLs derived from genome scans continues to grow, and we have now 204 QTLs for obesity-related phenotypes from 50 genome-wide scans. A total of 38 genomic regions harbor QTLs replicated among two to four studies. The number of studies reporting associations between DNA sequence variation in specific genes and obesity phenotypes has also increased considerably with 358 findings of positive associations with 113 candidate genes. Among them, 18 genes are supported by at least five positive studies. The obesity gene map shows putative loci on all chromosomes except Y. Overall, >600 genes, markers, and chromosomal regions have been associated or linked with human obesity phenotypes. The electronic version of the map with links to useful publications and genomic and other relevant sites can be found at http://obesitygene.pbrc.edu.

Fine-mapping of obesity-related quantitative trait loci in an F9/10 advanced intercross line

Obesity develops in response to a combination of environmental effects and multiple genes of small effect. Although there has been significant progress in characterizing genes in many pathways contributing to metabolic disease, knowledge about the relationships of these genes to each other and their joint effects upon obesity lags behind. The LG,SM advanced intercross line (AIL) model of obesity has been used to characterize over 70 loci involved in fatpad weight, body weight, and organ weights. Each of these quantitative trait loci (QTLs) encompasses large regions of the genome and require fine-mapping to isolate causative sequence changes and possible mechanisms of action as indicated by the genetic architecture. In this study we fine-map QTLs first identified in the F(2) and F(2/3) populations in the combined F(9/10) advanced intercross generations. We observed significantly narrowed QTL confidence regions, identified many single QTL that resolve into multiple QTL peaks, and identified new QTLs that may have been previously masked due to opposite gene effects at closely linked loci. We also present further characterization of the pleiotropic and epistatic interactions underlying these obesity-related traits.

Epistatic analysis of carcass characteristics in pigs reveals genomic interactions between quantitative trait loci attributable to additive and dominance genetic effects

The present study focused on the identification of epistatic QTL pairs for body composition traits (carcass cut, lean tissue, and fat tissue weights) measured at slaughter weight (140 kg of BW) in a 3-generation full-sib population developed by crossing Pietrain sires with a crossbred dam line. Depending on the trait, phenotypic observations were available for 306 to 315 F(2) animals. For the QTL analysis, 386 animals were genotyped for 88 molecular markers covering chromosomes SSC1, SSC2, SSC4, SSC6, SSC7, SSC8, SSC9, SSC10, SSC13, and SSC14. In total, 23 significant epistatic QTL pairs were identified, with the additive x additive genetic interaction being the most prevalent. Epistatic QTL were identified across all chromosomes except for SSC13, and epistatic QTL pairs accounted for between 5.8 and 10.2% of the phenotypic variance. Seven epistatic QTL pairs were between QTL that resided on the same chromosome, and 16 were between QTL that resided on different chromosomes. Sus scrofa chromosome 1, SSC2, SSC4, SSC6, SSC8, and SSC9 harbored the greatest number of epistatic QTL. The epistatic QTL pair with the greatest effect was for the entire loin weight between 2 locations on SSC7, explaining 10.2% of the phenotypic variance. Epistatic associations were identified between regions of the genome that contain the IGF-2 or melanocortin-4 receptor genes, with QTL residing in other genomic locations. Quantitative trait loci in the region of the melanocortin-4 receptor gene and on SSC7 showed significant positive dominance effects for entire belly weight, which were offset by negative dominance x dominance interactions between these QTL. In contrast, the QTL in the region of the IGF-2 gene showed significant negative dominance effects for entire ham weight, which were largely overcompensated for by positive additive x dominance genetic effects with a QTL on SSC9. The study shows that epistasis is of great importance for the genomic regulation of body composition in pigs and contributes substantially to the variation in complex traits.

Mapping interacting QTL for count phenotypes using hierarchical Poisson and binomial models: an application to reproductive traits in mice

We proposed hierarchical Poisson and binomial models for mapping multiple interacting quantitative trait loci (QTLs) for count traits in experimental crosses. We applied our methods to two counted reproductive traits, live fetuses (LF) and dead fetuses (DF) at 17 days gestation, in an F2 female mouse population. We treated observed number of corpora lutea (ovulation rate) as the baseline and the total trials in our Poisson and binomial models, respectively. We detected more than 10 QTLs for LF and DF, most having epistatic and pleiotropic effects. The epistatic effects were larger, involved more QTLs, and explained a larger proportion of phenotypic variance than the main effects. Our analyses revealed a complex network of multiple interacting QTLs for the reproductive traits, and increase our understanding of the genetic architecture of reproductive characters. The proposed statistical models and methods provide valuable tools for detecting multiple interacting QTLs for complex count phenotypes.

Evidence for three novel QTLs for adiposity on chromosome 2 with epistatic interactions: the NHLBI Family Heart Study

We sought to identify quantitative trait loci (QTLs) by genome-wide linkage analysis for BMI and waist circumference (WC) exploring various strategies to address heterogeneity including covariate adjustments and complex models based on epistatic components of variance. Because cholesterol-lowering drugs and diabetes medications may affect adiposity and risk of coronary heart disease, we excluded subjects medicated for hypercholesterolemia and hyperglycemia. The evidence of linkage increased on 2p25 (BMI: lod = 1.59 vs. 2.43, WC: lod = 1.32 vs. 2.26). Because environmental and/or genetic components could mask the effect of a specific locus, we investigated further whether a QTL could influence adiposity independently of lipid pathway and dietary habits. Strong evidence of linkage on 2p25 (BMI: lod = 4.31; WC: lod = 4.23) was found using Willet's dietary factors and lipid profile together with age and sex in adjustment. It suggests that lipid profile and dietary habits are confounding factors for detecting a 2p25 QTL for adiposity. Because evidence of linkage has been previously detected for BMI on 7q34 and 13q14 in National Heart, Lung, and Blood Institute Family Heart Study (NHLBI FHS), and for diabetes on 15q13, we investigated epistasis between chromosome 2 and these loci. Significant epistatic interactions were found between QTLs 2p25 and 7q34, 2q37 and 7q34, 2q31 and 13q14, and 2q31-q36 and 15q13. These results suggest multiple pathways and factors involving genetic and environmental effects influencing adiposity. By taking some of these known factors into account, we clarified our linkage evidence of a QTL on 2p25 influencing BMI and WC. The 2p25, 2q24-q31, and 2q36-q37 showed evidence of epistatic interaction with 7q34, 13q14, and 15q13.

The Thrsp null mouse (Thrsp(tm1cnm)) and diet-induced obesity

We created a Thrsp (Spot 14 or S14) null mouse (Thrsp(tm1cnm)) to study the role of Thrsp in de novo lipid synthesis. The Thrsp null mouse exhibits marked deficiencies in de novo lipogenesis in the lactating mammary gland. We now report the Thrsp gene deletion affects body weight and glucose tolerance associated with increased insulin sensitivity. By post-natal day 150 the rate of first generation C57BL/6J backcross Thrsp null mouse weight gain slowed compared to wild type animals. This was due to changes in body fat mass. We studied mice backcrossed for 5 and 11 generations. The weight difference between the null and wild type adult mice diminished with progressive backcross generations. In conclusion the Thrsp gene is involved in the regulation of diet-induced obesity and deletion of Thrsp leads to an improvement in age associated glucose tolerance.

Bayesian analyses of multiple epistatic QTL models for body weight and body composition in mice

To comprehensively investigate the genetic architecture of growth and obesity, we performed Bayesian analyses of multiple epistatic quantitative trait locus (QTL) models for body weights at five ages (12 days, 3, 6, 9 and 12 weeks) and body composition traits (weights of two fat pads and five organs) in mice produced from a cross of the F1 between M16i (selected for rapid growth rate) and CAST/Ei (wild-derived strain of small and lean mice) back to M16i. Bayesian model selection revealed a temporally regulated network of multiple QTL for body weight, involving both strong main effects and epistatic effects. No QTL had strong support for both early and late growth, although overlapping combinations of main and epistatic effects were observed at adjacent ages. Most main effects and epistatic interactions had an opposite effect on early and late growth. The contribution of epistasis was more pronounced for body weights at older ages. Body composition traits were also influenced by an interacting network of multiple QTLs. Several main and epistatic effects were shared by the body composition and body weight traits, suggesting that pleiotropy plays an important role in growth and obesity.

The human obesity gene map: the 2005 update

This paper presents the 12th update of the human obesity gene map, which incorporates published results up to the end of October 2005. Evidence from single-gene mutation obesity cases, Mendelian disorders exhibiting obesity as a clinical feature, transgenic and knockout murine models relevant to obesity, quantitative trait loci (QTL) from animal cross-breeding experiments, association studies with candidate genes, and linkages from genome scans is reviewed. As of October 2005, 176 human obesity cases due to single-gene mutations in 11 different genes have been reported, 50 loci related to Mendelian syndromes relevant to human obesity have been mapped to a genomic region, and causal genes or strong candidates have been identified for most of these syndromes. There are 244 genes that, when mutated or expressed as transgenes in the mouse, result in phenotypes that affect body weight and adiposity. The number of QTLs reported from animal models currently reaches 408. The number of human obesity QTLs derived from genome scans continues to grow, and we now have 253 QTLs for obesity-related phenotypes from 61 genome-wide scans. A total of 52 genomic regions harbor QTLs supported by two or more studies. The number of studies reporting associations between DNA sequence variation in specific genes and obesity phenotypes has also increased considerably, with 426 findings of positive associations with 127 candidate genes. A promising observation is that 22 genes are each supported by at least five positive studies. The obesity gene map shows putative loci on all chromosomes except Y. The electronic version of the map with links to useful publications and relevant sites can be found at http://obesitygene.pbrc.edu.

Mapping quantitative trait loci affecting variation in Drosophila triacylglycerol storage

OBJECTIVE

Recent genetic studies indicate that Drosophila melanogaster could be a powerful model to identify genes involved in mammalian adipocyte differentiation and fat storage. The objective of our study was to identify quantitative trait loci (QTLs) that contribute to variation in triacylglycerol (TAG) storage in two D. melanogaster laboratory strains.

RESEARCH METHODS AND PROCEDURES

We used two genetic mapping procedures to identify loci with main and epistatic effects on TAG storage. First, using 68 recombinant inbred lines derived from the unrelated Oregon R and Russian 2b strains, we mapped the location of QTLs affecting TAG storage using both composite interval mapping and Bayesian epistatic methods. Second, we used the quantitative deficiency mapping procedure to identify candidate genes affecting this trait within one of the QTLs identified on the second chromosome. For both mapping experiments, flies were cultured in standard conditions. TAG content of 4- to 5-day-old flies, adjusted for live body mass and total proteins, was used as the phenotypic measure.

RESULTS

Multiple QTLs associated with variation in TAG storage were identified by the genome-wide recombination mapping method, and some of them were sex-specific. The QTLs had main effects, but a male-specific epistatic interaction between two QTLs was also found. Finally, two closely linked QTLs were detected by deficiency mapping at 57E1-57E3 and 57E4-57F1 on chromosome 2, the first of which causes female-specific variation in TAG between the Oregon R and 2b strains.

DISCUSSION

Our results suggest that variation in TAG storage in D. melanogaster is controlled by different genetic mechanisms and different sets of QTLs in male and female flies.

Bayesian model selection for genome-wide epistatic quantitative trait loci analysis

The problem of identifying complex epistatic quantitative trait loci (QTL) across the entire genome continues to be a formidable challenge for geneticists. The complexity of genome-wide epistatic analysis results mainly from the number of QTL being unknown and the number of possible epistatic effects being huge. In this article, we use a composite model space approach to develop a Bayesian model selection framework for identifying epistatic QTL for complex traits in experimental crosses from two inbred lines. By placing a liberal constraint on the upper bound of the number of detectable QTL we restrict attention to models of fixed dimension, greatly simplifying calculations. Indicators specify which main and epistatic effects of putative QTL are included. We detail how to use prior knowledge to bound the number of detectable QTL and to specify prior distributions for indicators of genetic effects. We develop a computationally efficient Markov chain Monte Carlo (MCMC) algorithm using the Gibbs sampler and Metropolis-Hastings algorithm to explore the posterior distribution. We illustrate the proposed method by detecting new epistatic QTL for obesity in a backcross of CAST/Ei mice onto M16i.