Quantitative trait locus analysis for obesity reveals multiple networks of interacting loci

Genetic connection of carotid atherosclerosis with coat color and body weight in an intercross between hyperlipidemic mouse strains

Atherosclerosis in the carotid artery is a major cause of ischemic stroke and has a strong genetic component. The aim of this study was to identify genetic factors contributing to carotid atherosclerosis. 154 female F2mice were generated from an intercross between LP/J and BALB/cJ Apoe-null (Apoe^-/-) mice and fed 12 weeks of Western diet. Atherosclerotic lesions, body weight, and coat color were measured and genotyping was performed using miniMUGA arrays. A significant QTL on chromosome (Chr) 7, named Cath20, and 5 suggestive QTL on Chr 6, 12, 13, 15, and X were identified for carotid lesions. Three significant QTL, Bwfq2, Bw1n, Bwtq6, on Chr 2, 7, and 15 were identified for body weight. Two significant QTL, Chop2 and Albc2, on Chr 4 and 7 were identified for coat color, with Tyr, encoding tyrosinase, being the causal gene of Albc2. Cath20 coincided precisely with QTL Bw1n and Albc2 on Chr7. Carotid lesion sizes were significantly correlated with body weight and graded coat color in F2 mice. Cath20 on Chr7 disappeared after adjustment for coat color but remained after adjustment for body weight. These results demonstrate genetic connections of carotid atherosclerosis with body weight and coat color in hyperlipidemic mice and suggest a potential role for Tyr in carotid atherosclerosis.

Independent evolution toward larger body size in the distinctive Faroe Island mice

Most phenotypic traits in nature involve the collective action of many genes. Traits that evolve repeatedly are particularly useful for understanding how selection may act on changing trait values. In mice, large body size has evolved repeatedly on islands and under artificial selection in the laboratory. Identifying the loci and genes involved in this process may shed light on the evolution of complex, polygenic traits. Here, we have mapped the genetic basis of body size variation by making a genetic cross between mice from the Faroe Islands, which are among the largest and most distinctive natural populations of mice in the world, and a laboratory mouse strain selected for small body size, SM/J. Using this F2 intercross of 841 animals, we have identified 111 loci controlling various aspects of body size, weight and growth hormone levels. By comparing against other studies, including the use of a joint meta-analysis, we found that the loci involved in the evolution of large size in the Faroese mice were largely independent from those of a different island population or other laboratory strains. We hypothesize that colonization bottleneck, historical hybridization, or the redundancy between multiple loci have resulted in the Faroese mice achieving an outwardly similar phenotype through a distinct evolutionary path.

Thirty Mouse Strain Survey of Voluntary Physical Activity and Energy Expenditure: Influence of Strain, Sex and Day-Night Variation

We measured indirect calorimetry and activity parameters, VO₂ and VCO₂ to extract respiratory exchange ratio (RER) and energy expenditure in both sexes of 30 inbred mouse strains of 6 genetic families at 9–13 weeks during one photophase and the subsequent scotophase. We observed a continuous distribution of all traits. While males had higher body weights than females, we observed no sex difference for food and water intake. All strains drank and fed more during the night even if they displayed no day–night difference in activity traits. Several strains showed absent or weak day–night variation in one or more activity traits and these included FVB and 129X1, males of 129S1, SWR, NZW, and SM, and females of SJL. In general females showed higher rearing and ambulatory activity with 6 and 9 strains, respectively, showing a sex difference. Fine motor movements, like grooming, showed less sex differences. RER underlied a strong day–night difference and no sex effect. Only FVB females and males of the RIIIS and SM strain had no day–night variation. Energy expenditure underlies a large day–night variation which was absent in SWR and in FVB females and RIIIS males. In general, female bodies had a tendency to higher energy expenditure values, which became a significant difference in C3H, MAMy, SM, DBA1, and BUB. Our data illustrate the diversity of these traits in male and female inbred mice and provide a resource in the selection of strains for future studies.

Panx3 links body mass index and tumorigenesis in a genetically heterogeneous mouse model of carcinogen-induced cancer

Background

Body mass index (BMI) has been implicated as a primary factor influencing cancer development. However, understanding the relationship between these two complex traits has been confounded by both environmental and genetic heterogeneity.

Methods

In order to gain insight into the genetic factors linking BMI and cancer, we performed chemical carcinogenesis on a genetically heterogeneous cohort of interspecific backcross mice ((Mus Spretus × FVB/N) F1 × FVB/N). Using this cohort, we performed quantitative trait loci (QTL) analysis to identify regions linked to BMI. We then performed an integrated analysis incorporating gene expression, sequence comparison between strains, and gene expression network analysis to identify candidate genes influencing both tumor development and BMI.

Results

Analysis of QTL linked to tumorigenesis and BMI identified several loci associated with both phenotypes. Exploring these loci in greater detail revealed a novel relationship between the Pannexin 3 gene (Panx3) and both BMI and tumorigenesis. Panx3 is positively associated with BMI and is strongly tied to a lipid metabolism gene expression network. Pre-treatment Panx3 gene expression levels in normal skin are associated with tumor susceptibility and inhibition of Panx function strongly influences inflammation.

Conclusions

These studies have identified several genetic loci that influence both BMI and carcinogenesis and implicate Panx3 as a candidate gene that links these phenotypes through its effects on inflammation and lipid metabolism.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-016-0334-8) contains supplementary material, which is available to authorized users.

Armour GE, Mackay TFC, Anholt RRH. Epistatic partners of neurogenic genes modulate Drosophila olfactory behavior

The extent to which epistasis affects the genetic architecture of complex traits is difficult to quantify, and identifying variants in natural populations with epistatic interactions is challenging. Previous studies in Drosophila implicated extensive epistasis between variants in genes that affect neural connectivity and contribute to natural variation in olfactory response to benzaldehyde. In this study, we implemented a powerful screen to quantify the extent of epistasis as well as identify candidate interacting variants using 203 inbred wild-derived lines with sequenced genomes of the Drosophila melanogaster Genetic Reference Panel (DGRP). We crossed the DGRP lines to P[GT1]-element insertion mutants in Sema-5c and neuralized (neur), two neurodevelopmental loci which affect olfactory behavior, and to their coisogenic wild-type control. We observed significant variation in olfactory responses to benzaldehyde among F1 genotypes and for the DGRP line by mutant genotype interactions for both loci, showing extensive nonadditive genetic variation. We performed genome-wide association analyses to identify the candidate modifier loci. None of these polymorphisms were in or near the focal genes; therefore, epistasis is the cause of the nonadditive genetic variance. Candidate genes could be placed in interaction networks. Several candidate modifiers are associated with neural development. Analyses of mutants of candidate epistatic partners with neur (merry-go-round (mgr), prospero (pros), CG10098, Alhambra (Alh) and CG12535) and Sema-5c (CG42540 and bruchpilot (brp)) showed aberrant olfactory responses compared with coisogenic controls. Thus, integrating genome-wide analyses of natural variants with mutations at defined genomic locations in a common coisogenic background can unmask specific epistatic modifiers of behavioral phenotypes.

High-resolution mapping of a genetic locus regulating preferential carbohydrate intake, total kilocalories, and food volume on mouse chromosome 17

The specific genes regulating the quantitative variation in macronutrient preference and food intake are virtually unknown. We fine mapped a previously identified mouse chromosome 17 region harboring quantitative trait loci (QTL) with large effects on preferential macronutrient intake-carbohydrate (Mnic1), total kilcalories (Kcal2), and total food volume (Tfv1) using interval-specific strains. These loci were isolated in the [C57BL/6J.CAST/EiJ-17.1-(D17Mit19-D17Mit50); B6.CAST-17.1] strain, possessing a ∼ 40.1 Mb region of CAST DNA on the B6 genome. In a macronutrient selection paradigm, the B6.CAST-17.1 subcongenic mice eat 30% more calories from the carbohydrate-rich diet, ∼ 10% more total calories, and ∼ 9% more total food volume per body weight. In the current study, a cross between carbohydrate-preferring B6.CAST-17.1 and fat-preferring, inbred B6 mice was used to generate a subcongenic-derived F2 mapping population; genotypes were determined using a high-density, custom SNP panel. Genetic linkage analysis substantially reduced the 95% confidence interval for Mnic1 (encompassing Kcal2 and Tfv1) from 40.1 to 29.5 Mb and more precisely established its boundaries. Notably, no genetic linkage for self-selected fat intake was detected, underscoring the carbohydrate-specific effect of this locus. A second key finding was the separation of two energy balance QTLs: Mnic1/Kcal2/Tfv1 for food intake and a newly discovered locus regulating short term body weight gain. The Mnic1/Kcal2/Tfv1 QTL was further de-limited to 19.0 Mb, based on the absence of nutrient intake phenotypes in subcongenic HQ17IIa mice. Analyses of available sequence data and gene ontologies, along with comprehensive expression profiling in the hypothalamus of non-recombinant, cast/cast and b6/b6 F2 controls, focused our attention on candidates within the QTL interval. Zfp811, Zfp870, and Btnl6 showed differential expression and also contain stop codons, but have no known biology related to food intake regulation. The genes Decr2, Ppard and Agapt1 are more appealing candidates because of their involvement in lipid metabolism and down-regulation in carbohydrate-preferring animals.

Epistasis and quantitative traits: using model organisms to study gene-gene interactions

The role of epistasis in the genetic architecture of quantitative traits is controversial, despite the biological plausibility that nonlinear molecular interactions underpin the genotype-phenotype map. This controversy arises because most genetic variation for quantitative traits is additive. However, additive variance is consistent with pervasive epistasis. In this Review, I discuss experimental designs to detect the contribution of epistasis to quantitative trait phenotypes in model organisms. These studies indicate that epistasis is common, and that additivity can be an emergent property of underlying genetic interaction networks. Epistasis causes hidden quantitative genetic variation in natural populations and could be responsible for the small additive effects, missing heritability and the lack of replication that are typically observed for human complex traits.

QTL analysis of dietary obesity in C57BL/6byj X 129P3/J F2 mice: diet- and sex-dependent effects

Obesity is a heritable trait caused by complex interactions between genes and environment, including diet. Gene-by-diet interactions are difficult to study in humans because the human diet is hard to control. Here, we used mice to study dietary obesity genes, by four methods. First, we bred 213 F₂ mice from strains that are susceptible [C57BL/6ByJ (B6)] or resistant [129P3/J (129)] to dietary obesity. Percent body fat was assessed after mice ate low-energy diet and again after the same mice ate high-energy diet for 8 weeks. Linkage analyses identified QTLs associated with dietary obesity. Three methods were used to filter candidate genes within the QTL regions: (a) association mapping was conducted using >40 strains; (b) differential gene expression and (c) comparison of genomic DNA sequence, using two strains closely related to the progenitor strains from Experiment 1. The QTL effects depended on whether the mice were male or female or which diet they were recently fed. After feeding a low-energy diet, percent body fat was linked to chr 7 (LOD = 3.42). After feeding a high-energy diet, percent body fat was linked to chr 9 (Obq5; LOD = 3.88), chr 12 (Obq34; LOD = 3.88), and chr 17 (LOD = 4.56). The Chr 7 and 12 QTLs were sex dependent and all QTL were diet-dependent. The combination of filtering methods highlighted seven candidate genes within the QTL locus boundaries: Crx, Dmpk, Ahr, Mrpl28, Glo1, Tubb5, and Mut. However, these filtering methods have limitations so gene identification will require alternative strategies, such as the construction of congenics with very small donor regions.

Positional cloning of diabetes genes

Several mouse strains are diabetic already at the juvenile age or develop diabetes mellitus during their life. Before these strains become diabetic, they often show several or all features of the metabolic syndrome, which is very similar to the etiology of diabetes in humans. Under the assumption that natural mutations are responsible for the development of diabetes in those mouse strains, they are valuable resources for the identification of diabetes genes and modifiers. Usually, several steps are necessary to detect the causative genes in the genome. These include the initial identification of the genomic regions contributing to the disease which is typically done by linkage mapping in an F(2) intercross or backcross population, fine mapping of the identified chromosomal interval to narrow down the target region carrying the causative genetic variation and subsequent functional and genetic characterization of the target gene or a small subset of genes. Here, we give a general overview on genetic models and the strategy for identifying diabetes genes and provide a specific protocol for the mapping and fine mapping of chromosomal regions carrying diabetes genes.

Subcongenic analysis of tabw2 obesity QTL on mouse chromosome 6

Background

We previously established a congenic mouse strain with TALLYHO/Jng (TH) donor segment on chromosome 6 in a C57BL/6 (B6) background that harbors an obesity quantitative trait locus, tabw2. The B6.TH-tabw2 congenic mice developed increased adiposity that became exacerbated upon feeding a high fat-high sucrose (HFS) diet. To fine map the tabw2, in this study we generated and characterized subcongenic lines with smaller TH donor segments.

Results

We fixed four subcongenic lines, with maximum size of donor segment retained in the lines ranging from 10.8 – 92.5 Mb. For mapping, all the subcongenic mice, along with B6.TH-tabw2 congenic and B6-homozygous control mice were fed either chow or HFS diets, and their post-mortem fat pads were weighed. Mice were also characterized for energy expenditure, respiratory exchange ratio, locomotor activity, and food intake. As previously reported, B6.TH-tabw2 congenic mice showed a significantly larger fat mass than controls on both diets. On chow, a subcongenic line retaining the distal region of the TH donor congenic interval exhibited significantly larger fat mass than B6-homozygous controls, and comparable that to B6.TH-tabw2 congenic mice. Two nested subcongenic lines within that region suggested that the effect of tabw2 on obesity could be attributed to at least two subloci. On HFS diets, on the other hand, all the subcongenic mice had significantly larger fat mass than controls without genotype differences, but none of them had fat mass as large as the original congenic mice. This possibly implicates that further genetic complexity involves in the effect of tabw2 on diet-induced obesity. Significantly reduced locomotor activity was exhibited in B6.TH-tabw2 congenic and subcongenic mice compared to controls when animals were fed HFS diets. B6.TH-tabw2 congenic mice, but not subcongenic mice, also had significantly increased food intake on HFS diets.

Conclusions

It appears that at least two subloci explaining the tabw2 effect under chow feeding map to the distal region of the congenic interval, whereas the diet-induced obesity mediated by tabw2 is attributed to more complex genetic mechanism.

Identification of exercise capacity QTL using association mapping in inbred mice

There are large interindividual differences in exercise capacity. It is well established that there is a genetic basis for these differences. However, the genetic factors underlying this variation are undefined. Therefore, the purpose of this study was to identify novel putative quantitative trait loci (QTL) for exercise capacity by measuring exercise capacity in inbred mice and performing genome-wide association mapping. Exercise capacity, defined as run time and work, was assessed in male mice (n = 6) from 34 strains of classical and wild-derived inbred mice performing a graded treadmill test. Genome-wide association mapping was performed with an efficient mixed-model association (EMMA) algorithm to identify QTL. Exercise capacity was significantly different across strains. Run time varied by 2.7-fold between the highest running strain (C58/J) and the lowest running strain (A/J). These same strains showed a 16.5-fold difference in work. Significant associations were identified for exercise time on chromosomes 1, 2, 7, 11, and 13. The QTL interval on chromosome 2 (~168 Mb) contains one gene, Nfatc2, and overlaps with a suggestive QTL for training responsiveness in humans. These results provide phenotype data on the widest range of inbred strains tested thus far and indicate that genetic background significantly influences exercise capacity. Furthermore, the novel QTLs identified in the current study provide new targets for investigating the underlying mechanisms for variation in exercise capacity.

Obesity-insulin targeted genes in the 3p26-25 region in human studies and LG/J and SM/J mice

Identifying metabolic syndrome (MetS) genes is important for novel drug development and health care. This study extends the findings on human chromosome 3p26-25 for an identified obesity-insulin factor QTL, with an LOD score above 3. A focused association analysis comprising up to 9578 African American and Caucasian subjects from the HyperGEN Network (908 African Americans and 1025 whites), the Family Heart Study (3035 whites in time 1 and 1943 in time 2), and the Framingham Heart Study (1317 in Offspring and 1320 in Generation 3) was performed. The homologous mouse region was explored in an F(16) generation of an advanced intercross between the LG/J and SM/J inbred strains, in an experiment where 1002 animals were fed low-fat (247 males; 254 females) or high-fat (253 males; 248 females) diets. Association results in humans indicate pleiotropic effects for SNPs within or surrounding CNTN4 on obesity, lipids and blood pressure traits and for SNPs near IL5RA, TRNT1, CRBN, and LRRN1 on central obesity and blood pressure. Linkage analyses of this region in LG/J×SM/J mice identify a highly significant pleiotropic QTL peak for insulin and glucose levels, as well as response to glucose challenge. The mouse results show that insulin and glucose levels interact with high and low fat diets and differential gene expression was identified for Crbn and Arl8b. In humans, ARL8B resides ~137kbps away from BHLHE40, expression of which shows up-regulation in response to insulin treatment. This focused human genetic analysis, incorporating mouse research evidenced that 3p26-25 has important genetic contributions to MetS components. Several of the candidate genes have functions in the brain. Their interaction with MetS and the brain warrants further investigation.

Murine gut microbiota is defined by host genetics and modulates variation of metabolic traits

The gastrointestinal tract harbors a complex and diverse microbiota that has an important role in host metabolism. Microbial diversity is influenced by a combination of environmental and host genetic factors and is associated with several polygenic diseases. In this study we combined next-generation sequencing, genetic mapping, and a set of physiological traits of the BXD mouse population to explore genetic factors that explain differences in gut microbiota and its impact on metabolic traits. Molecular profiling of the gut microbiota revealed important quantitative differences in microbial composition among BXD strains. These differences in gut microbial composition are influenced by host-genetics, which is complex and involves many loci. Linkage analysis defined Quantitative Trait Loci (QTLs) restricted to a particular taxon, branch or that influenced the variation of taxa across phyla. Gene expression within the gastrointestinal tract and sequence analysis of the parental genomes in the QTL regions uncovered candidate genes with potential to alter gut immunological profiles and impact the balance between gut microbial communities. A QTL region on Chr 4 that overlaps several interferon genes modulates the population of Bacteroides, and potentially Bacteroidetes and Firmicutes–the predominant BXD gut phyla. Irak4, a signaling molecule in the Toll-like receptor pathways is a candidate for the QTL on Chr15 that modulates Rikenellaceae, whereas Tgfb3, a cytokine modulating the barrier function of the intestine and tolerance to commensal bacteria, overlaps a QTL on Chr 12 that influence Prevotellaceae. Relationships between gut microflora, morphological and metabolic traits were uncovered, some potentially a result of common genetic sources of variation.

Understanding the sexome: measuring and reporting sex differences in gene systems

The current male bias in biomedical research should be eliminated. The large sex differences in incidence and progression of diseases mean that sex-biased factors are an untapped source of factors that protect from disease. Greater understanding will come from intensified study of the "sexome," which is the sum of sex-biased effects on gene networks and cell systems. The global search for sites and mechanisms of sex-specific regulation in diverse tissues will provide unanticipated insights into physiological regulation and targets for novel therapies.

Genetic regulation of life span, metabolism, and body weight in Pohn, a new wild-derived mouse strain

Quantitative trait loci (QTL) of longevity identified in human and mouse are significantly colocalized, suggesting that common mechanisms are involved. However, the limited number of strains that have been used in mouse longevity studies undermines the ability to identify longevity genes. We crossed C57BL/6J mice with a new wild-derived strain, Pohn, and identified two life span QTL-Ls1 and Ls2. Interestingly, homologous human longevity QTL colocalize with Ls1. We also defined new QTL for metabolic heat production and body weight. Both phenotypes are significantly correlated with life span. We found that large clone ratio, an in vitro indicator for cellular senescence, is not correlated with life span, suggesting that cell senescence and intrinsic aging are not always associated. Overall, by using Pohn mice, we identified new QTL for longevity-related traits, thus facilitating the exploration of the genetic regulation of aging.

Genetic basis of atherosclerosis: insights from mice and humans

Atherosclerosis is a complex and heritable disease involving multiple cell types and the interactions of many different molecular pathways. The genetic and molecular mechanisms of atherosclerosis have, in part, been elucidated by mouse models; at least 100 different genes have been shown to influence atherosclerosis in mice. Importantly, unbiased genome-wide association studies have recently identified a number of novel loci robustly associated with atherosclerotic coronary artery disease. Here, we review the genetic data elucidated from mouse models of atherosclerosis, as well as significant associations for human coronary artery disease. Furthermore, we discuss in greater detail some of these novel human coronary artery disease loci. The combination of mouse and human genetics has the potential to identify and validate novel genes that influence atherosclerosis, some of which may be candidates for new therapeutic approaches.

QTL Analysis of Type I and Type IIA Fibers in Soleus Muscle in a Cross between LG/J and SM/J Mouse Strains

Properties of muscle fibers, i.e., their type, number and size, are important determinants of functional characteristics of skeletal muscle, and of the quality of meat in livestock. Genetic factors play an important role in determining variation in fiber properties, however, specific genes remain largely elusive. We examined histological properties of soleus muscle fibers in two strains of mice exhibiting a twofold difference in muscle mass, LG/J and SM/J, and their F2 intercross. The total number of muscle fibers (555 ± 106; mean ± SD) did not differ between the strains or between males and females. A higher percentage of type I fibers was observed in the LG/J compared to the SM/J strain (P < 0.001) in both males (45 ± 3 vs. 37 ± 4%) and females (58 ± 4 vs. 41 ± 3%). Across strains, females had a higher percentage of type I fibers than males (P < 0.001), and the sex effect was greater in the LG/J strain (strain-by-sex interaction, P < 0.001). The cross-sectional area (CSA) did not differ between type I and type IIA fibers, but was greater in the LG/J than the SM/J strain (1365 ± 268 vs. 825 ± 229 μm(2), P < 0.001). Three significant quantitative trait locus (QTL) affecting CSA for type I and type IIA fibers mapped to chromosomes (Chr) 1, 6, and 11 and three suggestive QTL for percentage of type I fibers mapped to Chr 2, 3, and 4. Within each significant QTL, regions of conserved synteny were also implicated in variation of similar traits in an analogous study in pigs. Our results provide the evidence that the intercross between the SM/J and LG/J strains is a promising model to search for genes affecting muscle fiber properties.

Advances in comparative genetics: influence of genetics on obesity

Obesity has reached epidemic proportions and is recognised as a significant global health problem. Increased food intake and decreased physical activity are traditionally to blame for the development of obesity; however, many variables such as behaviour, diet, environment, social structures and genetics also contribute to this multifactorial disease. Complex interactions among these variables (for example, gene-environment, gene-diet and gene-gene) contribute not only to individual differences in the development of obesity, but also in treatment response. Mouse models have historically played valuable roles in understanding the genetics of traits related to energy balance and obesity. In the present review, we survey past use and examine new advances in mouse models designed to uncover the genetic architecture of obesity and its component traits. We discuss traditional models such as inbred strains and selectively bred lines and their contributions and shortcomings. We consider the evolution of mouse models into more informative resources such as outbred crosses and the Hybrid Mouse Diversity Panel, as well as novel next-generation approaches such as the Collaborative Cross. Moreover, the genetic architecture of voluntary exercise and the interactive relationship between host genetics and the gut microbiome are presented as novel phenotypes that augment studies using body weight and body fat percentage as endpoints. Understanding the intricate network of phenotypic, genotypic and environmental variables that predispose individuals to obesity will elucidate biological networks involved in the development of obesity. Knowledge obtained from advances in mouse models will inform human health and provide insight into inter-individual variability in the aetiology of obesity-related diseases.

Sex-, diet-, and cancer-dependent epistatic effects on complex traits in mice

The genetic basis of quantitative traits such as body weight and obesity is complex, with several hundred quantitative trait loci (QTLs) known to affect these and related traits in humans and mice. It also has become increasingly evident that the single-locus effects of these QTLs vary considerably depending on factors such as the sex of the individuals and their dietary environment, and we were interested to know whether this context-dependency also applies to two-locus epistatic effects of QTLs as well. We therefore conducted a genome scan to search for epistatic effects on 13 different weight and adiposity traits in an F₂ population of mice (created from an original intercross of the FVB strain with M16i, a polygenic obesity model) that were fed either a control or a high-fat diet and half of which harbored a transgene (PyMT) that caused the development of metastatic mammary cancer. We used a conventional interval mapping approach with SNPs to scan all 19 autosomes, and found extensive epistasis affecting all of these traits. More importantly, we also discovered that the majority of these epistatic effects exhibited significant interactions with sex, diet, and/or presence of PyMT. Analysis of these interactions showed that many of them appeared to involve QTLs previously identified as affecting these traits, but whose single-locus effects were variously modified by two-locus epistatic effects of other QTLs depending on the sex, diet, or PyMT environment. It was concluded that this context-dependency of epistatic effects is an important component of the genetic architecture of complex traits such as those contributing to weight and obesity.

Secretory pathway genes assessed by high-throughput microscopy and synthetic genetic array analysis

We developed a procedure for automated confocal microscopy to image the effect of the non-essential yeast gene deletion set on the localisation of the plasma membrane GFP-labelled protein Mrh1p-GFP. To achieve this it was necessary to devise an expression system expressing Redstar2 RFP-fluorescence specifically in the nucleus, mCherry RFP at a lower intensity in the cytoplasm and Mrh1p-GFP in the plasma membrane. This fluorescence labelling scheme utilising specifically designed image analysis scripts allowed automated segmentation of the cells into sub-regions comprising nuclei, cytoplasm and cell-surface. From this high-throughput high content screening approach we were able to determine that gene deletions including emc1Δ, emc2Δ, emc3Δ, emc4Δ, emc5Δ and emc6Δ, caused intracellular mislocalisation at the ER of a plasma membrane protein Mrh1p-GFP. CPY processing patterns were unaffected in these mutants and collectively our data suggest a transport role for the EMC genes within the early secretory pathway. HAC1 is central to the unfolded protein response (UPR) and in its absence, i.e. the absence of UPR, emc1Δ-, emc3Δ-, emc4Δ-, emc5Δ-hac1Δ double mutants were specifically hypersensitive to ER-stress (tunicamycin) lending credence to the usefulness of the high content microscope screening for discovery of functional effects of single mutants.

Molecular mechanisms of epistasis within and between genes

'Disease-causing' mutations do not cause disease in all individuals. One possible important reason for this is that the outcome of a mutation can depend upon other genetic variants in a genome. These epistatic interactions between mutations occur both within and between molecules, and studies in model organisms show that they are extremely prevalent. However, epistatic interactions are still poorly understood at the molecular level, and consequently difficult to predict de novo. Here I provide an overview of our current understanding of the molecular mechanisms that can cause epistasis, and areas where more research is needed. A more complete understanding of epistasis will be vital for making accurate predictions about the phenotypes of individuals.

Genetics of Type 2 Diabetes

BACKGROUND

Type 2 diabetes (T2D) is a complex disorder that is affected by multiple genetic and environmental factors. Extensive efforts have been made to identify the disease-affecting genes to better understand the disease pathogenesis, find new targets for clinical therapy, and allow prediction of disease.

CONTENT

Our knowledge about the genes involved in disease pathogenesis has increased substantially in recent years, thanks to genomewide association studies and international collaborations joining efforts to collect the huge numbers of individuals needed to study complex diseases on a population level. We have summarized what we have learned so far about the genes that affect T2D risk and their functions. Although more than 40 loci associated with T2D or glycemic traits have been reported and reproduced, only a minor part of the genetic component of the disease has been explained, and the causative variants and affected genes are unknown for many of the loci.

SUMMARY

Great advances have recently occurred in our understanding of the genetics of T2D, but much remains to be learned about the disease etiology. The genetics of T2D has so far been driven by technology, and we now hope that next-generation sequencing will provide important information on rare variants with stronger effects. Even when variants are known, however, great effort will be required to discover how they affect disease risk.

Mapping QTL main and interaction influences on milling quality in elite US rice germplasm

Rice (Oryza sativa L.) head-rice yield (HR) is a key export and domestic quality trait whose genetic control is poorly understood. With the goal of identifying genomic regions influencing HR, quantitative-trait-locus (QTL) mapping was carried out for quality-related traits in recombinant inbred lines (RILs) derived from crosses of common parent Cypress, a high-HR US japonica cultivar, with RT0034, a low-HR indica line (129 RILs) and LaGrue, a low-HR japonica cultivar (298 RILs), grown in two US locations in 2005-2007. Early heading increased HR in the Louisiana (LA) but not the Arkansas (AR) location. Fitting QTL-mapping models to separate QTL main and QTL × environment interaction (QEI) effects and identify epistatic interactions revealed six main-effect HR QTLs in the two crosses, at four of which Cypress contributed the increasing allele. Multi-QTL models accounted for 0.36 of genetic and 0.21 of genetic × environment interaction of HR in MY1, and corresponding proportions of 0.25 and 0.37 in MY2. The greater HR advantage of Cypress in LA than in AR corresponded to a genomewide pattern of opposition of HR-increasing QTL effects by AR-specific effects, suggesting a selection strategy for improving this cultivar for AR. Treating year-location combinations as independent environments resulted in underestimation of QEI effects, evidently owing to lower variation among years within location than between location. Identification of robust HR QTLs in elite long-grain germplasm is suggested to require more detailed attention to the interaction of plant and grain development parameters with environmental conditions than has been given to date.

Mapping the epistatic network underlying murine reproductive fatpad variation

Genome-wide mapping analyses are now commonplace in many species and several networks of interacting loci have been reported. However, relatively few details regarding epistatic interactions and their contribution to complex trait variation in multicellular organisms are available and the identification of positional candidate loci for epistatic QTL (epiQTL) is hampered, especially in mammals, by the limited genetic resolution inherent in most study designs. Here we further investigate the genetic architecture of reproductive fatpad weight in mice using the F(10) generation of the LG,SM advanced intercross (AI) line. We apply multiple mapping techniques including a single-locus model, locus-specific composite interval mapping (CIM), and tests for multiple QTL per chromosome to the 12 chromosomes known to harbor single-locus QTL (slQTL) affecting obesity in this cross. We also perform a genome-wide scan for pairwise epistasis. Using this combination of approaches we detect 199 peaks spread over all 19 autosomes, which potentially contribute to trait variation including all eight original F(2) loci (Adip1-8), novel slQTL peaks on chromosomes 7 and 9, and several novel epistatic loci. Extensive epistasis is confirmed involving both slQTL confidence intervals (C.I.) as well as regions that show no significant additive or dominance effects. These results provide important new insights into mapping complex genetic architectures and the role of epistasis in complex trait variation.

Genetic analysis of abdominal fat distribution in SM/J and A/J mice

Each abdominal fat depot, such as mesenteric or epididymal, differently contributes to the development of insulin resistance. The aim of this study was to identify the genetic regions that contribute to fat accumulation in epididymal/mesenteric fat and to examine whether or not the genetic regions that affect glucose metabolism and body fat distribution are coincident. We previously mapped a major quantitative trait locus (QTL) (T2dm2sa) for impaired glucose tolerance on chromosome 2 and revealed that SM.A-T2dm2sa congenic mice showed not only glucose tolerance but also fat accumulation. In the present study, to identify the loci/genes that control the accumulation of abdominal fat, we performed QTL analyses of epididymal/mesenteric fat weight by using (A/J x SM.A-T2dm2sa)F2 mice in which the effect of T2dm2sa was excluded. As a result, two highly significant QTLs for mesenteric fat, as well as three significant QTLs for epididymal/mesenteric fat, were mapped on the different chromosomal regions. This suggests that the fat accumulations in individual fat depots are controlled by distinct genomic regions. Our comparison of these QTLs for abdominal fat distribution with those for glucose metabolism revealed that the major genetic factors affecting body fat distribution do not coincide with genetic factors affecting glucose metabolism in (A/J x SM.A-T2dm2sa)F2.

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.

Nonparametric Bayesian variable selection with applications to multiple quantitative trait loci mapping with epistasis and gene-environment interaction

The joint action of multiple genes is an important source of variation for complex traits and human diseases. However, mapping genes with epistatic effects and gene-environment interactions is a difficult problem because of relatively small sample sizes and very large parameter spaces for quantitative trait locus models that include such interactions. Here we present a nonparametric Bayesian method to map multiple quantitative trait loci (QTL) by considering epistatic and gene-environment interactions. The proposed method is not restricted to pairwise interactions among genes, as is typically done in parametric QTL analysis. Rather than modeling each main and interaction term explicitly, our nonparametric Bayesian method measures the importance of each QTL, irrespective of whether it is mostly due to a main effect or due to some interaction effect(s), via an unspecified function of the genotypes at all candidate QTL. A Gaussian process prior is assigned to this unknown function. In addition to the candidate QTL, nongenetic factors and covariates, such as age, gender, and environmental conditions, can also be included in the unspecified function. The importance of each genetic factor (QTL) and each nongenetic factor/covariate included in the function is estimated by a single hyperparameter, which enters the covariance function and captures any main or interaction effect associated with a given factor/covariate. An initial evaluation of the performance of the proposed method is obtained via analysis of simulated and real data.

Chromosomal mapping of excessive physical activity in mice in response to a restricted feeding schedule

Excessive physical activity plays an important role in the progression of anorexia nervosa (AN) by accelerating weight loss during dietary restriction. To search for mechanisms underlying this trait, a panel of mouse chromosome substitution strains derived from C57BL/6J and A/J strains was exposed to a scheduled feeding paradigm and to voluntary running wheel (RW) access. Here, we showed that A/J chromosomes 4, 12 and 13 contribute to the development of a disrupted RW activity in response to daily restricted feeding. This pattern is characterized by intense RW activity during the habitual rest phase and leads to accelerated body weight loss. Regions on mouse chromosomes 4, 12 and 13 display homology with regions on human chromosomes linked with anxiety and obsessionality in AN cohorts. Therefore, our data open new roads for interspecies genetic studies of AN and for unraveling novel mechanisms and potential effective treatment strategies for these neurobehavioral traits.

Genotype-phenotype associations in understanding the role of corticosteroid-binding globulin in health and disease animal models

Corticosteroid-binding globulin (CBG) is a plasma glycoprotein discovered more than 60 years ago for its high-affinity for glucocorticoids. Although its molecular structure and its biochemical properties have been described, its various biological roles and its importance are not yet fully understood. This review focuses first on studies that have used no-hypothesis-driven genetic approaches in animal models to reveal the higher than expected importance of CBG in particular in glucocorticoid stress responses. Then the dissection of some CBG physiological roles in an animal model of genetic CBG deficiency is reported. Finally, studies on the role of CBG genetic variability in human obesity traits are reviewed and discussed.

Metabolic syndrome components in murine models

Animal models have enriched understanding of the physiological basis of metabolic disorders and advanced identification of genetic risk factors underlying the metabolic syndrome (MetS). Murine models are especially appropriate for this type of research, and are an excellent resource not only for identifying candidate genomic regions, but also for illuminating the possible molecular mechanisms or pathways affected in individual components of MetS. In this review, we briefly discuss findings from mouse models of metabolic disorders, particularly in light of issues raised by the recent flood of human genome-wide association studies (GWAS) results. We describe how mouse models are revealing that genotype interacts with environment in important ways, indicating that the underlying genetics of MetS is highly context dependant. Further we show that epistasis, imprinting and maternal effects each contribute to the genetic architecture underlying variation in metabolic traits, and mouse models provide an opportunity to dissect these aspects of the genetic architecture that are difficult if not impossible to ascertain in humans. Finally we discuss how knowledge gained from mouse models can be used in conjunction with comparative genomic methods and bioinformatic resources to inform human MetS research.

Mapping main, epistatic and sex-specific QTL for body composition in a chicken population divergently selected for low or high growth rate

Background

Delineating the genetic basis of body composition is important to agriculture and medicine. In addition, the incorporation of gene-gene interactions in the statistical model provides further insight into the genetic factors that underlie body composition traits. We used Bayesian model selection to comprehensively map main, epistatic and sex-specific QTL in an F₂ reciprocal intercross between two chicken lines divergently selected for high or low growth rate.

Results

We identified 17 QTL with main effects across 13 chromosomes and several sex-specific and sex-antagonistic QTL for breast meat yield, thigh + drumstick yield and abdominal fatness. Different sets of QTL were found for both breast muscles [Pectoralis (P) major and P. minor], which suggests that they could be controlled by different regulatory mechanisms. Significant interactions of QTL by sex allowed detection of sex-specific and sex-antagonistic QTL for body composition and abdominal fat. We found several female-specific P. major QTL and sex-antagonistic P. minor and abdominal fatness QTL. Also, several QTL on different chromosomes interact with each other to affect body composition and abdominal fatness.

Conclusions

The detection of main effects, epistasis and sex-dimorphic QTL suggest complex genetic regulation of somatic growth. An understanding of such regulatory mechanisms is key to mapping specific genes that underlie QTL controlling somatic growth in an avian model.

Increased physical activity cosegregates with higher intake of carbohydrate and total calories in a subcongenic mouse strain

C57BL/6 J (B6) and CAST/EiJ (CAST), the inbred strain derived from M. musculus castaneus, differ in nutrient intake behaviors, including dietary fat and carbohydrate consumption in a two-diet-choice paradigm. Significant quantitative trait loci (QTLs) for carbohydrate (Mnic1) and total energy intake (Kcal2) are present between these strains on chromosome (Chr) 17. Here we report the refinement of the Chr 17 QTL in a subcongenic strain of the B6.CAST-( D17Mit19-D17Mit91 ) congenic mice described previously. This new subcongenic strain possesses CAST Chr 17 donor alleles from 4.8 to 45.4 Mb on a B6 background. Similar to CAST, the subcongenic mice exhibit increased carbohydrate and total calorie intake per body weight, while fat intake remains equivalent. Unexpectedly, this CAST genomic segment also confers two new physical activity phenotypes: 22% higher spontaneous physical activity levels and significantly increased voluntary wheel-running activity compared with the parental B6 strain. Overall, these data suggest that gene(s) involved in carbohydrate preference and increased physical activity are contained within the proximal region of Chr 17. Interval-specific microarray analysis in hypothalamus and skeletal muscle revealed differentially expressed genes within the subcongenic region, including neuropeptide W (Npw); glyoxalase I (Glo1); cytochrome P450, family 4, subfamily f, polypeptide 1 (Cyp4f15); phospholipase A2, group VII (Pla2g7); and phosphodiesterase 9a (Pde9a). This subcongenic strain offers a unique model for dissecting the contributions and possible interactions among genes controlling food intake and physical activity, key components of energy balance.

Controlling false positives in the mapping of epistatic QTL

This study addresses the poorly explored issue of the control of false positive rate (FPR) in the mapping of pair-wise epistatic quantitative trait loci (QTL). A nested test framework was developed to (1) allow pre-identified QTL to be used directly to detect epistasis in one-dimensional genome scans, (2) to detect novel epistatic QTL pairs in two-dimensional genome scans and (3) to derive genome-wide thresholds through permutation and handle multiple testing. We used large-scale simulations to evaluate the performance of both the one- and two-dimensional approaches in mapping different forms and levels of epistasis and to generate profiles of FPR, power and accuracy to inform epistasis mapping studies. We showed that the nested test framework and genome-wide thresholds were essential to control FPR at the 5% level. The one-dimensional approach was generally more powerful than the two-dimensional approach in detecting QTL-associated epistasis and identified nearly all epistatic pairs detected from the two-dimensional approach. However, only the two-dimensional approach could detect epistatic QTL with weak main effects. Combining the two approaches allowed effective mapping of different forms of epistasis, whereas using the nested test framework kept the FPR under control. This approach provides a good search engine for high-throughput epistasis analyses.

A Cmv2 QTL on chromosome X affects MCMV resistance in New Zealand male mice

NK cell-mediated resistance to viruses is subject to genetic control in humans and mice. Here we used classical and quantitative genetic strategies to examine NK-mediated murine cytomegalovirus (MCMV) control in genealogically related New Zealand white (NZW) and black (NZB) mice. NZW mice display NK cell-dependent MCMV resistance while NZB NK cells fail to limit viral replication after infection. Unlike Ly49H(+) NK resistance in C57BL/6 mice, NZW NK-mediated MCMV control was Ly49H-independent. Instead, MCMV resistance in NZW (Cmv2) involves multiple genetic factors. To establish the genetic basis of Cmv2 resistance, we further characterized a major chromosome X-linked resistance locus (DXMit216) responsible for innate MCMV control in NZW x NZB crosses. We found that the DXMit216 locus affects early MCMV control in New Zealand F(2) crosses and demonstrate that the NZB-derived DXMit216 allele enhances viral resistance in F(2) males. The evolutionary conservation of the DXMit216 region in mice and humans suggests that a Cmv2-related mechanism may affect human antiviral responses.

Characterization of Nob3, a major quantitative trait locus for obesity and hyperglycemia on mouse chromosome 1

New Zealand obese (NZO) mice present a metabolic syndrome of obesity, insulin resistance, and diabetes. To identify chromosomal segments associated with these traits, we intercrossed NZO mice with the lean and diabetes-resistant C57BL/6J (B6) strain. Obesity and hyperglycemia in the (NZO x B6)F2 intercross population were predominantly due to a broad quantitative trait locus (QTL) on chromosome 1 (Nob3; logarithm of the odds score 16.1, 16.0, 4.0 for body weight, body fat, and blood glucose, respectively), producing a difference between genotypes of 12.7 or 5.2 g of body weight and 12.0 or 4.0 g of body fat in females or males, respectively. In addition, significant QTL on chromosomes 3 and 13 and suggestive QTL on chromosomes 4, 6, 9, 12, 14, and 19 contributed to the obese phenotype. Distal chromosome 5 was significantly linked with plasma cholesterol (LOD score 10.7). Introgression of two segments of Nob3 into B6 confirmed the adipogenic effect of the QTL and suggested the presence of at least one causal gene. Haplotype mapping reduced the critical region of the distal part of the QTL to 31 Mbp containing the potential candidates Nr1i3, Apoa2, Atp1a2, Prox1, and Hsd11b1. We conclude that obesity and hyperglycemia of NZO is to a large part caused by variant genes located in Nob3 on chromosome 1. Since these exert robust effects on a B6 background, the QTL Nob3 is a prime target for identification of a novel diabesity gene.

Gaussian process based bayesian semiparametric quantitative trait Loci interval mapping

In linkage analysis, it is often necessary to include covariates such as age or weight to increase power or avoid spurious false positive findings. However, if a covariate term in the model is specified incorrectly (e.g., a quadratic term misspecified as a linear term), then the inclusion of the covariate may adversely affect power and accuracy of the identification of quantitative trait loci (QTL). Furthermore, some covariates may interact with each other in a complicated fashion. We implement semiparametric models for single and multiple QTL mapping. Both mapping methods include an unspecified function of any covariate found or suspected to have a more complex than linear but unknown relationship with the response variable. They also allow for interactions among different covariates. This analysis is performed in a Bayesian inference framework using Markov chain Monte Carlo. The advantages of our methods are demonstrated via extensive simulations and real data analysis.

Mapping an overdominant quantitative trait locus for heterosis of body weight in mice

The genetic basis of heterosis has not been elucidated. Previously, a congenic mouse strain with a 44-Mb genomic region of proximal chromosome 2 containing the allele derived from wild Mus musculus castaneus at Pbwg1, a quantitative trait locus (QTL) for body weight and growth, has been developed. In this study, to fine-map and characterize body weight QTLs on the congenic region, QTL analysis of body weight at 1, 3, 6, and 10 weeks after birth was performed on a population of 265 F(2) intercross mice between the developed congenic strain and its background strain C57BL/6J. A significant QTL (named Pbwg1.10) affecting body weight at 6 and 10 weeks of age was identified within an approximately 21-Mb support interval. Surprisingly, Pbwg1.10 had an overdominance effect and caused heterosis for body weight. This result supported the overdominance hypothesis explaining heterosis.

Genetic loci that control the size of laser-induced choroidal neovascularization

Angiogenesis is controlled by a balance between stimulators and inhibitors. We propose that the balance, as well as the general sensitivity of the endothelium to these factors, varies from individual to individual. Indeed, we have found that individual mouse strains have dramatically different responses to growth factor-induced neovascularization. Quantitative trait loci (QTLs), which influence the extent of corneal angiogenesis induced by vascular endothelial growth factor (VEGF) and basic fibroblast growth factor (FGF2), were previously identified by our laboratory. To investigate the genetic contribution to choroidal neovascularization (CNV), a leading cause of blindness, we have undertaken a similar mapping approach to identify QTLs that influence laser-induced CNV in the BXD series of recombinant inbred mouse strains. Composite interval mapping identified new angiogenic QTLs on chromosomes 2 and 19, in addition to confirming our previous corneal neovascularization QTLs of AngVq1 and AngFq2. The new QTLs are named AngCNVq1 and AngCNVq2. The newly mapped regions contain several candidate genes involved in the angiogenic process, including thrombospondin 1, delta-like 4, BclII modifying factor, phospholipase C, beta 2, adrenergic receptor, beta 1, actin-binding LIM protein 1 and colony stimulating factor 2 receptor, alpha. Differences in these regions may control individual susceptibility to CNV.

Altered upper airway and soft tissue structures in the New Zealand Obese mouse

RATIONALE

The effect of obesity on upper airway soft tissue structure and size was examined in the New Zealand Obese (NZO) mouse and in a control lean mouse, the New Zealand White (NZW).

OBJECTIVES

We hypothesized that the NZO mouse has increased volume of neck fat and upper airway soft tissues and decreased pharyngeal airway caliber.

METHODS

Pharyngeal airway size, volume of the upper airway soft tissue structures, and distribution of fat in the neck and body were measured using magnetic resonance imaging (MRI). Dynamic MRI was used to examine the differences in upper airway caliber between inspiration and expiration in NZO versus NZW mice.

MEASUREMENTS AND MAIN RESULTS

The data support the hypothesis that, in obese NZO versus lean NZW mice, airway caliber was significantly smaller (P < 0.03), with greater parapharyngeal fat pad volumes (P < 0.0001) and a greater volume of other upper airway soft tissue structures (tongue, P = 0.003; lateral pharyngeal walls, P = 0.01; soft palate, P = 0.02). Dynamic MRI showed that the airway of the obese NZO mouse dilated during inspiration, whereas in the lean NZW mouse, the upper airway was reduced in size during inspiration.

CONCLUSIONS

In addition to the increased volume of pharyngeal soft tissue structures, direct fat deposits within the tongue may contribute to airway compromise in obesity. Pharyngeal airway dilation during inspiration in NZO mice compared with narrowing in NZW mice suggests that airway compromise in obese mice may lead to muscle activation to defend upper airway patency during inspiration.

Epistasis--the essential role of gene interactions in the structure and evolution of genetic systems

Epistasis, or interactions between genes, has long been recognized as fundamentally important to understanding the structure and function of genetic pathways and the evolutionary dynamics of complex genetic systems. With the advent of high-throughput functional genomics and the emergence of systems approaches to biology, as well as a new-found ability to pursue the genetic basis of evolution down to specific molecular changes, there is a renewed appreciation both for the importance of studying gene interactions and for addressing these questions in a unified, quantitative manner.

Quantitative trait loci for BMD in an SM/J by NZB/BlNJ intercross population and identification of Trps1 as a probable candidate gene

Identification of genes that regulate BMD will enhance our understanding of osteoporosis and could provide novel molecular targets for treatment or prevention. We generated a mouse intercross population and carried out a quantitative trait locus (QTL) analysis of 143 female and 124 male F(2) progeny from progenitor strains SM/J and NZB/BlNJ using whole body and vertebral areal BMD (aBMD) as measured by DXA. We found that both whole body and vertebral aBMD was affected by two loci on chromosome 9: one with a significant epistatic interaction on distal chromosome 8 and the other with a sex-specific effect. Two additional significant QTLs were identified on chromosome 12, and several suggestive ones were identified on chromosomes 5, 8, 15, and 19. The chromosome 9, 12, and 15 loci have been previously identified in other crosses. SNP-based haplotype analysis of the progenitor strains identified blocks within the QTL region that distinguish the low allele strains from the high allele strains, significantly narrowing the QTL region and reducing the possible candidate genes to 98 for chromosome 9, 31 for chromosome 12, and only 2 for chromosome 15. Trps1 is the most probable candidate gene for the chromosome 15 QTL. The sex-specific effects may help to elucidate the BMD differences between males and females. This study shows the power of statistical modeling to resolve linked QTLs and the use of haplotype analysis in narrowing the list of candidates.

Advances in Bayesian multiple quantitative trait loci mapping in experimental crosses

Many complex human diseases and traits of biological and/or economic importance are determined by interacting networks of multiple quantitative trait loci (QTL) and environmental factors. Mapping QTL is critical for understanding the genetic basis of complex traits, and for ultimate identification of genes responsible. A variety of sophisticated statistical methods for QTL mapping have been developed. Among these developments, the evolution of Bayesian approaches for multiple QTL mapping over the past decade has been remarkable. Bayesian methods can jointly infer the number of QTL, their genomic positions and their genetic effects. Here, we review recently developed and still developing Bayesian methods and associated computer software for mapping multiple QTL in experimental crosses. We compare and contrast these methods to clearly describe the relationships among different Bayesian methods. We conclude this review by highlighting some areas of future research.

Applying gene expression, proteomics and single-nucleotide polymorphism analysis for complex trait gene identification

Previous quantitative trait locus (QTL) analysis of an intercross involving the inbred mouse strains NZB/BlNJ and SM/J revealed QTL for a variety of complex traits. Many QTL have large intervals containing hundreds of genes, and methods are needed to rapidly sort through these genes for probable candidates. We chose nine QTL: the three most significant for high-density lipoprotein (HDL) cholesterol, gallstone formation, and obesity. We searched for candidate genes using three different approaches: mRNA microarray gene expression technology to assess >45,000 transcripts, publicly available SNPs to locate genes that are not identical by descent and that contain nonsynonymous coding differences, and a mass-spectrometry-based proteomics technology to interrogate nearly 1000 proteins for differential expression in the liver of the two parental inbred strains. This systematic approach reduced the number of candidate genes within each QTL from hundreds to a manageable list. Each of the three approaches selected candidates that the other two approaches missed. For example, candidate genes such as Apoa2 and Acads had differential protein levels although the mRNA levels were similar. We conclude that all three approaches are important and that focusing on a single approach such as mRNA expression may fail to identify a QTL gene.

Quantitative trait Loci for regional adiposity in mouse lines divergently selected for food intake

OBJECTIVE

Obesity is thought to result from an interaction between genotype and environment. Excessive adiposity is associated with a number of important comorbidities; however, the risk of obesity-related disease varies with the distribution of fat throughout the body. The aim of this study was to map quantitative trait loci (QTLs) associated with regional fat depots in mouse lines divergently selected for food intake corrected for body mass.

RESEARCH METHODS AND PROCEDURES

Using an F2 intercross design (n = 457), the dry mass of regional white (subcutaneous, gonadal, retroperitoneal, and mesenteric) adipose tissue (WAT) and brown adipose tissue (BAT) depots were analyzed to map QTLs.

RESULTS

The total variance explained by the mapped QTL varied between 12% and 39% for BAT and gonadal fat depots, respectively. Using the genome-wide significance threshold, nine QTLs were associated with multiple fat depots. Chromosomes 4 and 19 were associated with WAT and BAT and chromosome 9 with WAT depots. Significant sex x QTL interactions were identified for gonadal fat on chromosomes 9, 16, and 19. The pattern of QTLs identified for the regional deposits showed the most similarity between retroperitoneal and gonadal fat, whereas BAT showed the least similarity to the WAT depots. Analysis of total fat mass explained in excess of 40% of total variance.

DISCUSSION

There was limited concordance between the QTLs mapped in our study and those reported previously. This is likely to reflect the unique nature of the mouse lines used. Results provide an insight into the genetic basis of regional fat distribution.

Genetic loci affecting body weight and fatness in a C57BL/6J x PWK/PhJ mouse intercross

To determine the genetic variation that contributes to body composition in the mouse, we interbred a wild-derived strain (PWK/PhJ; PWK) with a common laboratory strain (C57BL/6J; B6). The parental, F(1), and F(2) mice were phenotyped at 18 weeks old for body weight and composition using dual-energy X-ray absorptiometry (DEXA). A total of 479 (244 male and 235 female) F(2) mice were genotyped for 117 polymorphic markers spanning the autosomes. Twenty-eight suggestive or significant linkages for four traits (body weight, adjusted lean and fat weight, and percent fat) were detected. Of these, three QTLs were novel: one on the proximal portion of Chr 5 for body weight (Bwq8; LOD = 4.7), one on Chr 3 for lean weight (Bwtq13; LOD = 3.6), and one on Chr 11 for percent fat (Adip19; LOD = 5.8). The remaining QTLs overlapped previously identified linkages, e.g., Adip5 on Chr 9. One QTL was sex-specific (present in males only) and seven were sex-biased (more prominent in one sex than the other). Most alleles that increased body weight were contributed by the B6 strain, and most alleles that increased percent fat were contributed by the PWK strain. Eight pairs of interacting loci were identified, none of which exactly overlapped the main-effect QTLs. Many of the QTLs found in the B6 x PWK cross map to the location of previously reported linkages, suggesting that some QTLs are common to many strains (consensus QTLs), but three new QTLs appear to be particular to the PWK strain. The location and type of QTLs detected in this new cross will assist in future efforts to identify the genetic variation that determines the ratio of lean to fat weight as well as body size in mice.