Quantitative trait loci for obesity- and diabetes-related traits and their dietary responses to high-fat feeding in LGXSM recombinant inbred mouse strains

The common genetic variant upstream of INSIG2 gene is not associated with obesity in Indian population

The high incidence of obesity has resulted in increased morbidity and mortality worldwide. Obesity, a common lifestyle disorder, is caused by multiple factors with heredity playing a strong causal role. Recently, a genetic variation upstream of insulin-induced gene 2 (INSIG2) (rs7566605) has been reported to be associated with obesity in four separate cohorts. Because the lifestyle and food preferences of a large proportion of Indian population differ from the rest of the world, we studied the impact of this polymorphism with body mass index (BMI). The study consisted of two cohorts--1577 healthy individuals from three major linguistic lineages in India and 610 coronary artery disease cases and controls. In the two cohorts studied, no significant association was observed between the polymorphism and BMI. However, frequency of homozygous variant genotype was higher in non-obese individuals as compared with obese individuals in both cohorts although the difference was marginally significant only in the case-control cohort under the assumption of a recessive model. Furthermore, regardless of age and sex, mean BMI did not vary with genotype under the assumptions of recessive model. Thus, in contrast to earlier reports, the variant upstream of INSIG2 is not a determinant of BMI in Indian population.

Quantitative trait loci linked to thalamus and cortex gray matter volumes in BXD recombinant inbred mice

To investigate whether there are separate or shared genetic influences on the development of the thalamus and cerebral cortex, we identified quantitative trait loci (QTLs) for relevant structural volumes in BXD recombinant inbred (RI) strains of mice. In 34 BXD RI strains and two parental strains (C57BL/6J and DBA/2J), we measured the volumes of the entire thalamus and cortex gray matter using point counting and Cavalieri's rule. Heritability was calculated using analysis of variance (ANOVA), and QTL analysis was carried out using WebQTL (http://www.genenetwork.org). The heritability of thalamus volume was 36%, and three suggestive QTLs for thalamus volume were identified on chromosomes 10, 11 and 16. The heritability of cortical gray matter was 43%, and four suggestive QTLs for cortex gray matter volume were identified on chromosomes 2, 8, 16 and 19. The genetic correlation between thalamus and cortex gray matter volumes was 0.64. Also, a single QTL on chromosome 16 (D16Mit100) was identified for thalamus volume, cortex gray matter volume and Morris water maze search-time preference (r=0.71). These results suggest that there are separate and shared genetic influences on the development of the thalamus and cerebral cortex.

The genetic landscape of type 2 diabetes in mice

Inbred mouse strains provide genetic diversity comparable to that of the human population. Like humans, mice have a wide range of diabetes-related phenotypes. The inbred mouse strains differ in the response of their critical physiological functions, such as insulin sensitivity, insulin secretion, beta-cell proliferation and survival, and fuel partitioning, to diet and obesity. Most of the critical genes underlying these differences have not been identified, although many loci have been mapped. The dramatic improvements in genomic and bioinformatics resources are accelerating the pace of gene discovery. This review describes how mouse genetics can be used to discover diabetes-related genes, summarizes how the mouse strains differ in their diabetes-related phenotypes, and describes several examples of how loci identified in the mouse may directly relate to human diabetes.

Independent quantitative trait loci influence ventral and dorsal hippocampal volume in recombinant inbred strains of mice

Anatomical and functional studies support segregation of the hippocampus into ventral and dorsal components along its septotemporal axis. However, it is unknown whether the development of these two components of the hippocampus is influenced by common or separate genetic factors. In this study, we used recombinant inbred strains of mice to determine whether the same or different quantitative trait loci (QTL) influence ventral and dorsal hippocampal volume. Using two sets of strains of recombinant inbred mice (BXD and AXB/BXA), we identified separate QTLs for ventral and dorsal hippocampal volume. In BXD mice, suggestive QTLs for ventral hippocampus were identified on chromosomes 2, 8 and 13, and a significant QTL for dorsal hippocampal volume was identified on chromosome 15. There was also a suggestive QTL for dorsal hippocampal volume on chromosome 13. In AXB/BXA mice, there were no significant or suggestive QTLs for ventral hippocampal volume, but a significant QTL for dorsal hippocampus was identified on chromosome 5. These findings suggest that the development of the ventral and dorsal components of the hippocampus is influenced by separate genetic loci.

A locus on mouse Chromosome 9 (Adip5) affects the relative weight of the gonadal but not retroperitoneal adipose depot

To identify the gene or genes on mouse Chromosome 9 that contribute to strain differences in fatness, we conducted an expanded mapping analysis to better define the region where suggestive linkage was found, using the F(2 )generation of an intercross between the C57BL/6ByJ and 129P3/J mouse strains. Six traits were studied: the summed weight of two adipose depots, the weight of each depot, analyzed individually (the gonadal and retroperitoneal depot), and the weight of each depot (summed and individual) relative to body size. We found significant linkage (LOD = 4.6) that accounted for the relative weight of the summed adipose depots, and another for the relative weight of the gonadal (LOD = 5.3) but not retroperitoneal (LOD = 0.9) adipose depot. This linkage is near marker rs30280752 (61.1 Mb, Build 34) and probably is equivalent to the quantitative trait locus (QTL) Adip5. Because the causal gene is unknown, we identified and evaluated several candidates within the confidence interval with functional significance to the body fatness phenotype (Il18, Acat1, Cyp19a1, Crabp1, Man2c1, Neil1, Mpi1, Csk, Lsm16, Adpgk, Bbs4, Hexa, Thsd4, Dpp8, Anxa2, and Lipc). We conclude that the Adip5 locus is specific to the gonadal adipose depot and that a gene or genes near the linkage peak may account for this QTL.

Is a gene important for bone resorption a candidate for obesity? An association and linkage study on the RANK (receptor activator of nuclear factor-kappaB) gene in a large Caucasian sample

In light of findings that osteoporosis and obesity may share some common genetic determination and previous reports that RANK (receptor activator of nuclear factor-kappaB) is expressed in skeletal muscles which are important for energy metabolism, we hypothesize that RANK, a gene essential for osteoclastogenesis, is also important for obesity. In order to test the hypothesis with solid data we first performed a linkage analysis around the RANK gene in 4,102 Caucasian subjects from 434 pedigrees, then we genotyped 19 SNPs in or around the RANK gene. A family-based association test (FBAT) was performed with both a quantitative measure of obesity [fat mass, lean mass, body mass index (BMI), and percentage fat mass (PFM)] and a dichotomously defined obesity phenotype-OB (OB if BMI > or = 30 kg/m(2)). In the linkage analysis, an empirical P = 0.004 was achieved at the location of the RANK gene for BMI. Family-based association analysis revealed significant associations of eight SNPs with at least one obesity-related phenotype (P < 0.05). Evidence of association was obtained at SNP10 (P = 0.002) and SNP16 (P = 0.001) with OB; SNP1 with fat mass (P = 0.003); SNP1 (P = 0.003) and SNP7 (P = 0.003) with lean mass; SNP1 (P = 0.002) and SNP7 (P = 0.002) with BMI; SNP1 (P = 0.003), SNP4 (P = 0.007), and SNP7 (P = 0.002) with PFM. In order to deal with the complex multiple testing issues, we performed FBAT multi-marker test (FBAT-MM) to evaluate the association between all the 18 SNPs and each obesity phenotype. The P value is 0.126 for OB, 0.033 for fat mass, 0.021 for lean mass, 0.016 for BMI, and 0.006 for PFM. The haplotype data analyses provide further association evidence. In conclusion, for the first time, our results suggest that RANK is a novel candidate for determination of obesity.

Insulin secretory responses and phospholipid composition of pancreatic islets from mice that do not express Group VIA phospholipase A2 and effects of metabolic stress on glucose homeostasis

Studies involving pharmacologic or molecular biologic manipulation of Group VIA phospholipase A(2) (iPLA(2)beta) activity in pancreatic islets and insulinoma cells suggest that iPLA(2)beta participates in insulin secretion. It has also been suggested that iPLA(2)beta is a housekeeping enzyme that regulates cell 2-lysophosphatidylcholine (LPC) levels and arachidonate incorporation into phosphatidylcholine (PC). We have generated iPLA(2)beta-null mice by homologous recombination and have reported that they exhibit reduced male fertility and defective motility of spermatozoa. Here we report that pancreatic islets from iPLA(2)beta-null mice have impaired insulin secretory responses to D-glucose and forskolin. Electrospray ionization mass spectrometric analyses indicate that the abundance of arachidonate-containing PC species of islets, brain, and other tissues from iPLA(2)beta-null mice is virtually identical to that of wild-type mice, and no iPLA(2)beta mRNA was observed in any tissue from iPLA(2)beta-null mice at any age. Despite the insulin secretory abnormalities of isolated islets, fasting and fed blood glucose concentrations of iPLA(2)beta-null and wild-type mice are essentially identical under normal circumstances, but iPLA(2)beta-null mice develop more severe hyperglycemia than wild-type mice after administration of multiple low doses of the beta-cell toxin streptozotocin, suggesting an impaired islet secretory reserve. A high fat diet also induces more severe glucose intolerance in iPLA(2)beta-null mice than in wild-type mice, but PLA(2)beta-null mice have greater responsiveness to exogenous insulin than do wild-type mice fed a high fat diet. These and previous findings thus indicate that iPLA(2)beta-null mice exhibit phenotypic abnormalities in pancreatic islets in addition to testes and macrophages.

Identification of candidate alkylator-induced cancer susceptibility genes by whole genome scanning in mice

Secondary malignancies are a serious adverse consequence of alkylator chemotherapy. The risk of developing an alkylator-associated malignancy is influenced by genetic background, although the relevant genetic factors are poorly understood. To screen for novel susceptibility factors, we established a mouse model of alkylator-induced malignancy. We exposed mice from 20 inbred strains to the prototypical alkylating agent, N-nitroso-N-ethylurea (ENU). ENU was a potent carcinogen in many of the strains tested, inducing 140 tumors in 240 ENU-treated mice (66% incidence of at least one tumor in evaluable mice), compared with a background incidence of 8% spontaneous tumors in 240 strain-, age-, and sex-matched control mice (relative risk, 8.4; P < 0.0001). A wide variety of tumor histologies were noted, including epithelial carcinomas, soft tissue sarcomas, and hematopoietic tumors. Cancer susceptibility was a heritable trait for the most common tumor types, lung adenocarcinoma (H(2) = 0.25), T cell lymphoma (H(2) = 0.19), and myeloid malignancies (H(2) = 0.10). Quantitative trait locus mapping identified regions on chromosomes 3, 6, 9, and 15 containing candidate genes associated with lung adenoma, lung carcinoma, and lymphoma susceptibility. This novel mouse model recapitulates many features of human alkylator-associated cancer and supports the hypothesis that susceptibility to this syndrome is influenced by inherited polymorphisms that could be used to make informed clinical treatment decisions.

Genetic characterization of a new set of recombinant inbred lines (LGXSM) formed from the inter-cross of SM/J and LG/J inbred mouse strains

A new set of LGXSM recombinant inbred (RI) strains is presented. The RI strain panel consists of 18 remaining strains of the original 55 founding strains. Strain characterization is based on 506 polymorphic microsatellites and 4,289 single nucleotide polymorphisms (SNPs) distributed across the genome. Average microsatellite inter-marker distance is 4.80+/-4.84 Mb or 2.91+/-3.21 F(2) cM. SNPs are more densely spaced at 0.57+/-1.27 Mb. Ninety-five percent of all microsatellite inter-marker intervals are separated by less than 15.00 Mb or 8.50 F(2) cM, while 95% of the SNPs are less than 0.95 Mb apart. Strains show expected low levels of nonsyntenic association among loci and complete genomic independence. During inbreeding, the RI strains went through strong natural selection on the agouti locus on Chromosome 2, especially when the epistatically interacting tyrosinase locus on Chromosome 7 carried the wild-type allele. The LG/J and SM/J strains differ in a large number of biomedically important traits, and they and their inter-cross progeny have been used in multiple mapping studies. The LGxSM RI strain panel provides a powerful new resource for mapping the genetic bases of complex traits and should prove to be of great biomedical utility in modeling complex human diseases such as obesity and diabetes.

Quantitative trait loci for body size components in mice

Do body size components, such as weights of internal organs and long bone lengths, with different functions and different developmental histories also have different genetic architectures and pleiotropic patterns? We examine murine quantitative trait loci (QTL) for necropsy weight, four long bone lengths, and four organ weights in the LG/J x SM/J intercross. Differences between trait categories were found in number of QTL, dominance, and pleiotropic patterns. Ninety-seven QTLs for individual traits were identified: 52 for long bone lengths, 30 for organ weights, and 15 for necropsy weight. Results for long bones are typically more highly significant than for organs. Organ weights were more frequently over- or underdominant than long bone lengths or necropsy weight. The single-trait QTLs map to 35 pleiotropic loci. Long bones are much more frequently affected in groups while organs tend to be affected singly or in pairs. Organs and long bones are found at the same locus in only 11 cases, 8 of which also include necropsy weight. Our results suggest mainly separate genetic modules for organ weights and long bone lengths, with a few loci that affect overall body size. Antagonistic pleiotropy, in which a locus has opposite effects on different characteristics, is uncommon.

Structural model analysis of multiple quantitative traits

We introduce a method for the analysis of multilocus, multitrait genetic data that provides an intuitive and precise characterization of genetic architecture. We show that it is possible to infer the magnitude and direction of causal relationships among multiple correlated phenotypes and illustrate the technique using body composition and bone density data from mouse intercross populations. Using these techniques we are able to distinguish genetic loci that affect adiposity from those that affect overall body size and thus reveal a shortcoming of standardized measures such as body mass index that are widely used in obesity research. The identification of causal networks sheds light on the nature of genetic heterogeneity and pleiotropy in complex genetic systems.

Disease states are often associated with multiple, correlated traits that may result from shared genetic and nongenetic factors. Genetic analysis of multiple traits can reveal a network of effects in which each trait is influenced by more than one genetic locus (heterogeneity) and different traits share one or more loci in common (pleiotropy). Physiological interactions independent of genetic factors may also contribute to the observed correlations. Structural equation modeling is proposed as a statistical method to characterize the architecture of multiple trait genetic systems. Application of structural equation modeling to body size, adiposity, and bone geometry traits illustrates how the effects of a genetic locus can be decomposed along direct and indirect paths that may be mediated through interactions with other traits. Using this technique the authors identify adiposity loci that act independently of loci affecting overall body size.

A common genetic variant is associated with adult and childhood obesity

Obesity is a heritable trait and a risk factor for many common diseases such as type 2 diabetes, heart disease, and hypertension. We used a dense whole-genome scan of DNA samples from the Framingham Heart Study participants to identify a common genetic variant near the INSIG2 gene associated with obesity. We have replicated the finding in four separate samples composed of individuals of Western European ancestry, African Americans, and children. The obesity-predisposing genotype is present in 10% of individuals. Our study suggests that common genetic polymorphisms are important determinants of 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.

The case for strategic international alliances to harness nutritional genomics for public and personal health

Nutrigenomics is the study of how constituents of the diet interact with genes, and their products, to alter phenotype and, conversely, how genes and their products metabolise these constituents into nutrients, antinutrients, and bioactive compounds. Results from molecular and genetic epidemiological studies indicate that dietary unbalance can alter gene-nutrient interactions in ways that increase the risk of developing chronic disease. The interplay of human genetic variation and environmental factors will make identifying causative genes and nutrients a formidable, but not intractable, challenge. We provide specific recommendations for how to best meet this challenge and discuss the need for new methodologies and the use of comprehensive analyses of nutrient-genotype interactions involving large and diverse populations. The objective of the present paper is to stimulate discourse and collaboration among nutrigenomic researchers and stakeholders, a process that will lead to an increase in global health and wellness by reducing health disparities in developed and developing countries.

Maternal genotype affects adult offspring lipid, obesity, and diabetes phenotypes in LGXSM recombinant inbred strains

Maternal effects on offspring phenotypes occur because mothers in many species provide an environment for their developing young. Although these factors are correctly "environmental" with respect to the offspring genome, their variance may have both a genetic and an environmental basis in the maternal generation. Here, reciprocal crosses between C57BL/6J and 10 LGXSM recombinant inbred (RI) strains were performed, and litters were divided at weaning into high-fat and low-fat dietary treatments. Differences between reciprocal litters were used to measure genetic maternal effects on offspring phenotypes. Nearly all traits, including weekly body weights and adult blood serum traits, show effects indicative of genetic variation in maternal effects across RI strains, allowing the quantitative trait loci involved to be mapped. Although much of the literature on maternal effects relates to early life traits, we detect strong and significant maternal effects on traits measured at adulthood (as much as 10% of the trait variance at 17 or more weeks after weaning). We also found an interaction affecting adult phenotype between the effects of maternal care between RI strain mothers and C57BL/6J mothers and a later environmental factor (dietary fat intake) for some age-specific weights.