Mapping major quantitative trait loci for postnatal growth in an intersubspecific backcross between C57BL/6J and Philippine wild mice by using principal component analysis

GWAS with principal component analysis identify QTLs associated with main peanut flavor-related traits

Peanut flavor is a complex and important trait affected by raw material and processing technology owing to its significant impact on consumer preference. In this research, principal component analysis (PCA) on 33 representative traits associated with flavor revealed that total sugars, sucrose, and total tocopherols provided more information related to peanut flavor. Genome-wide association studies (GWAS) using 102 U.S. peanut mini-core accessions were performed to study associations between 12,526 single nucleotide polymorphic (SNP) markers and the three traits. A total of 7 and 22 significant quantitative trait loci (QTLs) were identified to be significantly associated with total sugars and sucrose, respectively. Among these QTLs, four and eight candidate genes for the two traits were mined. In addition, two and five stable QTLs were identified for total sugars and sucrose in both years separately. No significant QTLs were detected for total tocopherols. The results from this research provide useful knowledge about the genetic control of peanut flavor, which will aid in clarifying the molecular mechanisms of flavor research in peanuts.

GWAS with a PCA uncovers candidate genes for accumulations of microelements in maize seedlings

Microelements are necessary for plant growth and development, they control key processes of physiological metabolism. Herein, we evaluated three accumulation-related performances for each of the four microelements (Fe, Zn, Cu, and Mn) among 305 inbred maize lines. Quantification of these microelements in maize roots and shoots revealed abundant phenotypic variations in the association panel, with the variation coefficients ranging from 0.31 to 0.76. Principal component analysis (PCA) of the three related traits (concentration in root, concentration in shoot, and transport coefficient) showed that PC1 and PC2 explained >95% of phenotypic variations for each element. The scores of PC1 and PC2 were thereby used for a genome-wide association study by combining 44,134 SNPs of this panel. A total of 27, 1, 5, and 3 SNPs were significantly (P < .05) associated with Zn-PC1, Zn-PC2, Cu-PC1, and Mn-PC2, respectively, with 11 genes closely linked (r²  > 0.8) to these SNPs. Of them, GRMZM2G142870, GRMZM2G045531, and GRMZM2G143512 were individually annotated as ABC transporter C family member 14, zinc transporter 3, and heavy metal ATPase10. A candidate gene association analysis further verified that GRMZM2G142870 and GRMZM2G045531 affect Zn and Mn accumulations, respectively. Evaluation of contrasting allele ratios in elite lines indicated that the majority of the alleles correlating with higher Zn or Cu had not been utilized in maize breeding. Integration of more "higher-accumulation" alleles in the elite lines will be practical for improving Zn and Cu accumulations in maize. Our findings contribute to genetic revelation and molecular marker-assisted selection of microelement accumulations in maize.

Univariate and Multivariate QTL Analyses Reveal Covariance Among Mineral Elements in the Rice Ionome

Rice provides more than one fifth of daily calories for half of the world’s human population, and is a major dietary source of both essential mineral nutrients and toxic elements. Rice grains are generally poor in some essential nutrients but may contain unsafe levels of some toxic elements under certain conditions. Identification of quantitative trait loci (QTLs) controlling the concentrations of mineral nutrients and toxic trace metals (the ionome) in rice will facilitate development of nutritionally improved rice varieties. However, QTL analyses have traditionally considered each element separately without considering their interrelatedness. In this study, we performed principal component analysis (PCA) and multivariate QTL analyses to identify the genetic loci controlling the covariance among mineral elements in the rice ionome. We resequenced the whole genomes of a rice recombinant inbred line (RIL) population, and performed univariate and multivariate QTL analyses for the concentrations of 16 elements in grains, shoots and roots of the RIL population grown in different conditions. We identified a total of 167 unique elemental QTLs based on analyses of individual elemental concentrations as separate traits, 53 QTLs controlling covariance among elemental concentrations within a single environment/tissue (PC-QTLs), and 152 QTLs which determined covariation among elements across environments/tissues (aPC-QTLs). The candidate genes underlying the QTL clusters with elemental QTLs, PC-QTLs and aPC-QTLs co-localized were identified, including OsHMA4 and OsNRAMP5. The identification of both elemental QTLs and PC QTLs will facilitate the cloning of underlying causal genes and the dissection of the complex regulation of the ionome in rice.

Extensive haplotypes are associated with population differentiation and environmental adaptability in Upland cotton (Gossypium hirsutum)

Three extensive eco-haplotypes associated with population differentiation and environmental adaptability in Upland cotton were identified, with A06_85658585, A08_43734499 and A06_113104285 considered the eco-loci for environmental adaptability. Population divergence is suggested to be the primary force driving the evolution of environmental adaptability in various species. Chromosome inversion increases reproductive isolation between subspecies and accelerates population divergence to adapt to new environments. Although modern cultivated Upland cotton (Gossypium hirsutum L.) has spread worldwide, the noticeable phenotypic differences still existed among cultivars grown in different areas. In recent years, the long-distance migration of cotton cultivation areas throughout China has demanded that breeders better understand the genetic basis of environmental adaptability in Upland cotton. Here, we integrated the genotypes of 419 diverse accessions, long-term environment-associated variables (EAVs) and environment-associated traits (EATs) to evaluate subgroup differentiation and identify adaptive loci in Upland cotton. Two highly divergent genomic regions were found on chromosomes A06 and A08, which likely caused by extensive chromosome inversions. The subgroups could be geographically classified based on distinct haplotypes in the divergent regions. A genome-wide association study (GWAS) also confirmed that loci located in these regions were significantly associated with environmental adaptability in Upland cotton. Our study first revealed the cause of population divergence in Upland cotton, as well as the consequences of variation in its environmental adaptability. These findings provide new insights into the genetic basis of environmental adaptability in Upland cotton, which could accelerate the development of molecular markers for adaptation to climate change in future cotton breeding.

GWAS with principal component analysis identifies a gene comprehensively controlling rice architecture

Rice architecture is an important agronomic trait for determining yield; however, the complexity of this trait makes it difficult to elucidate the molecular mechanisms. This study applied a strategy of using principal components (PCs) as dependent variables for a genome-wide association study (GWAS). SPINDLY was identified to regulate rice architecture by suppressing gibberellin (GA) signaling. Further study using GA-signaling mutants confirmed that levels of GA responsiveness regulate rice architecture, suggesting that the utilization of a favorable SPINDLY allele will improve crop productivity. The strategy presented in this study of performing GWAS using PC scores will provide valuable information for plant genetics and will improve our understanding of complex traits at the molecular level.

Elucidation of the genetic control of rice architecture is crucial due to the global demand for high crop yields. Rice architecture is a complex trait affected by plant height, tillering, and panicle morphology. In this study, principal component analysis (PCA) on 8 typical traits related to plant architecture revealed that the first principal component (PC), PC1, provided the most information on traits that determine rice architecture. A genome-wide association study (GWAS) using PC1 as a dependent variable was used to isolate a gene encoding rice, SPINDLY (OsSPY), that activates the gibberellin (GA) signal suppression protein SLR1. The effect of GA signaling on the regulation of rice architecture was confirmed in 9 types of isogenic plant having different levels of GA responsiveness. Further population genetics analysis demonstrated that the functional allele of OsSPY associated with semidwarfism and small panicles was selected in the process of rice breeding. In summary, the use of PCA in GWAS will aid in uncovering genes involved in traits with complex characteristics.

A Strategy for Identifying Quantitative Trait Genes Using Gene Expression Analysis and Causal Analysis

Large numbers of quantitative trait loci (QTL) affecting complex diseases and other quantitative traits have been reported in humans and model animals. However, the genetic architecture of these traits remains elusive due to the difficulty in identifying causal quantitative trait genes (QTGs) for common QTL with relatively small phenotypic effects. A traditional strategy based on techniques such as positional cloning does not always enable identification of a single candidate gene for a QTL of interest because it is difficult to narrow down a target genomic interval of the QTL to a very small interval harboring only one gene. A combination of gene expression analysis and statistical causal analysis can greatly reduce the number of candidate genes. This integrated approach provides causal evidence that one of the candidate genes is a putative QTG for the QTL. Using this approach, I have recently succeeded in identifying a single putative QTG for resistance to obesity in mice. Here, I outline the integration approach and discuss its usefulness using my studies as an example.

Identification of a Putative Quantitative Trait Gene for Resistance to Obesity in Mice Using Transcriptome Analysis and Causal Inference Tests

It is still challenging to identify causal genes governing obesity. Pbwg1.5, a quantitative trait locus (QTL) for resistance to obesity, was previously discovered from wild Mus musculus castaneus mice and was fine-mapped to a 2.1-Mb genomic region of mouse chromosome 2, where no known gene with an effect on white adipose tissue (WAT) has been reported. The aim of this study was to identify a strong candidate gene for Pbwg1.5 by an integration approach of transcriptome analysis (RNA-sequencing followed by real-time PCR analysis) and the causal inference test (CIT), a statistical method to infer causal relationships between diplotypes, gene expression and trait values. Body weight, body composition and biochemical traits were measured in F2 mice obtained from an intercross between the C57BL/6JJcl strain and a congenic strain carrying Pbwg1.5 on the C57BL/6JJcl background. The F2 mice showed significant diplotype differences in 12 traits including body weight, WAT weight and serum cholesterol/triglyceride levels. The transcriptome analysis revealed that Ly75, Pla2r1, Fap and Gca genes were differentially expressed in the liver and that Fap, Ifih1 and Grb14 were differentially expressed in WAT. However, CITs indicated statistical evidence that only the liver Ly75 gene mediated between genotype and WAT. Ly75 expression was negatively associated with WAT weight. The results suggested that Ly75 is a putative quantitative trait gene for the obesity-resistant Pbwg1.5 QTL discovered from the wild M. m. castaneus mouse. The finding provides a novel insight into a better understanding of the genetic basis for prevention of obesity.

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.

Genetics of Rapid and Extreme Size Evolution in Island Mice

Organisms on islands provide a revealing window into the process of adaptation. Populations that colonize islands often evolve substantial differences in body size from their mainland relatives. Although the ecological drivers of this phenomenon have received considerable attention, its genetic basis remains poorly understood. We use house mice (subspecies: Mus musculus domesticus) from remote Gough Island to provide a genetic portrait of rapid and extreme size evolution. In just a few hundred generations, Gough Island mice evolved the largest body size among wild house mice from around the world. Through comparisons with a smaller-bodied wild-derived strain from the same subspecies (WSB/EiJ), we demonstrate that Gough Island mice achieve their exceptional body weight primarily by growing faster during the 6 weeks after birth. We use genetic mapping in large F(2) intercrosses between Gough Island mice and WSB/EiJ to identify 19 quantitative trait loci (QTL) responsible for the evolution of 16-week weight trajectories: 8 QTL for body weight and 11 QTL for growth rate. QTL exhibit modest effects that are mostly additive. We conclude that body size evolution on islands can be genetically complex, even when substantial size changes occur rapidly. In comparisons to published studies of laboratory strains of mice that were artificially selected for divergent body sizes, we discover that the overall genetic profile of size evolution in nature and in the laboratory is similar, but many contributing loci are distinct. Our results underscore the power of genetically characterizing the entire growth trajectory in wild populations and lay the foundation necessary for identifying the mutations responsible for extreme body size evolution in nature.

Fine mapping and candidate gene search of quantitative trait loci for growth and obesity using mouse intersubspecific subcongenic intercrosses and exome sequencing

Although growth and body composition traits are quantitative traits of medical and agricultural importance, the genetic and molecular basis of those traits remains elusive. Our previous genome-wide quantitative trait locus (QTL) analyses in an intersubspecific backcross population between C57BL/6JJcl (B6) and wild Mus musculus castaneus mice revealed a major growth QTL (named Pbwg1) on a proximal region of mouse chromosome 2. Using the B6.Cg-Pbwg1 intersubspecific congenic strain created, we revealed 12 closely linked QTLs for body weight and body composition traits on an approximately 44.1-Mb wild-derived congenic region. In this study, we narrowed down genomic regions harboring three (Pbwg1.12, Pbwg1.3 and Pbwg1.5) of the 12 linked QTLs and searched for possible candidate genes for the QTLs. By phenotypic analyses of F2 intercross populations between B6 and each of four B6.Cg-Pbwg1 subcongenic strains with overlapping and non-overlapping introgressed regions, we physically defined Pbwg1.12 affecting body weight to a 3.8-Mb interval (61.5-65.3 Mb) on chromosome 2. We fine-mapped Pbwg1.3 for body length to an 8.0-Mb interval (57.3-65.3) and Pbwg1.5 for abdominal white fat weight to a 2.1-Mb interval (59.4-61.5). The wild-derived allele at Pbwg1.12 and Pbwg1.3 uniquely increased body weight and length despite the fact that the wild mouse has a smaller body size than that of B6, whereas it decreased fat weight at Pbwg1.5. Exome sequencing and candidate gene prioritization suggested that Gcg and Grb14 are putative candidate genes for Pbwg1.12 and that Ly75 and Itgb6 are putative candidate genes for Pbwg1.5. These genes had nonsynonymous SNPs, but the SNPs were predicted to be not harmful to protein functions. These results provide information helpful to identify wild-derived quantitative trait genes causing enhanced growth and resistance to obesity.

Wild mice as bountiful resources of novel genetic variants for quantitative traits

Most traits of biological importance, including traits for human complex diseases (e.g., obesity and diabetes), are continuously distributed. These complex or quantitative traits are controlled by multiple genetic loci called QTLs (quantitative trait loci), environments and their interactions. The laboratory mouse has long been used as a pilot animal model for understanding the genetic architecture of quantitative traits. Next-generation sequencing analyses and genome-wide SNP (single nucleotide polymorphism) analyses of mouse genomes have revealed that classical inbred strains commonly used throughout the world are derived from a few fancy mice with limited and non-randomly distributed genetic diversity that occurs in nature and also indicated that their genomes are predominantly Mus musculus domesticus in origin. Many QTLs for a huge variety of traits have so far been discovered from a very limited gene pool of classical inbred strains. However, wild M. musculus mice consisting of five subspecies widely inhabit areas all over the world, and hence a number of novel QTLs may still lie undiscovered in gene pools of the wild mice. Some of the QTLs are expected to improve our understanding of human complex diseases. Using wild M. musculus subspecies in Asia as examples, this review illustrates that wild mice are untapped natural resources for valuable QTL discovery.

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.

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 alleles for body weight in LG/J × SM/J F₂ and F(34) advanced intercross lines

The present study measured variation in body weight using a combined analysis in an F(2) intercross and an F(34) advanced intercross line (AIL). Both crosses were derived from inbred LG/J and SM/J mice, which were selected for large and small body size prior to inbreeding. Body weight was measured at 62 (± 5) days of age. Using an integrated GWAS and forward model selection approach, we identified 11 significant QTLs that affected body weight on ten different chromosomes. With these results we developed a full model that explained over 18% of the phenotypic variance. The median 1.5-LOD support interval was 5.55 Mb, which is a significant improvement over most prior body weight QTLs. We identified nonsynonymous coding SNPs between LG/J and SM/J mice in order to further narrow the list of candidate genes. Three of the genes with nonsynonymous coding SNPs (Rad23b, Stk33, and Anks1b) have been associated with adiposity, waist circumference, and body mass index in human GWAS, thus providing evidence that these genes may underlie our QTLs. Our results demonstrate that a relatively small number of loci contribute significantly to the phenotypic variance in body weight, which is in marked contrast to the situation in humans. This difference is likely to be the result of strong selective pressure and the simplified genetic architecture, both of which are important advantages of our system.

Intersubspecific subcongenic mouse strain analysis reveals closely linked QTLs with opposite effects on body weight

A previous genome-wide QTL study revealed many QTLs affecting postnatal body weight and growth in an intersubspecific backcross mouse population between the C57BL/6J (B6) strain and wild Mus musculus castaneus mice captured in the Philippines. Subsequently, several closely linked QTLs for body composition traits were revealed in an F(2) intercross population between B6 and B6.Cg-Pbwg1, a congenic strain on the B6 genetic background carrying the growth QTL Pbwg1 on proximal chromosome 2. However, no QTL affecting body weight has been duplicated in the F(2) population, except for mapping an overdominant QTL that causes heterosis of body weight. In this study, we developed 17 intersubspecific subcongenic strains with overlapping and nonoverlapping castaneus regions from the B6.Cg-Pbwg1 congenic strain in order to search for and genetically dissect QTLs affecting body weight into distinct closely linked loci. Phenotypic comparisons of several developed subcongenic strains with the B6 strain revealed that two closely linked but distinct QTLs that regulate body weight, named Pbwg1.11 and Pbwg1.12, are located on an 8.9-Mb region between D2Mit270 and D2Mit472 and on the next 3.6-Mb region between D2Mit205 and D2Mit182, respectively. Further analyses using F(2) segregating populations obtained from intercrosses between B6 and each of the two selected subcongenic strains confirmed the presence of these two body weight QTLs. Pbwg1.11 had an additive effect on body weight at 6, 10, and 13 weeks of age, and its castaneus allele decreased it. In contrast, the castaneus allele at Pbwg1.12 acted in a dominant fashion and surprisingly increased body weight at 6, 10, and 13 weeks of age despite the body weight of wild castaneus mice being 60% of that of B6 mice. These findings illustrate the complex genetic nature of body weight regulation and support the importance of subcongenic mouse analysis to dissect closely linked loci.

A proximal genomic region of mouse chromosome 10 contains quantitative trait loci affecting fatness

Body weight and fatness are quantitative traits of agricultural and medical importance. In previous genome-wide quantitative trait locus (QTL) analyses, two QTLs for body weight and weight gain at an early postnatal growth period were discovered on mouse chromosome 10 from a gene pool of wild subspecies mice, Mus musculus castaneus. In this study, we developed a congenic strain with an approximately 63-Mb wild-derived genomic region on which the two growth QTLs could be located, by recurrent backcrossing to the common inbred strain C57BL/6J. We compared body weights at 1-10 weeks of age, body weight gains at 1-3, 3-6 and 6-10 weeks, internal organ weights and body lengths between the congenic strain developed and C57BL/6J. Unfortunately, no effects of the two growth QTLs on body weights and weight gains were confirmed. However, at least two new QTLs affecting fatness traits were discovered within the introgressed congenic region. The wild-derived allele at one QTL increased body mass index, whereas at another one it decreased white fat pad weight and adiposity index. Thus, the congenic mouse strain developed here is a useful model animal for understanding the genetic and molecular basis of fat deposition in livestock as well as humans.

Development and characterization of a congenic strain carrying Pbwg12, a growth QTL on mouse chromosome 12

In previous genome-wide QTL studies, Pbwg12 on mouse chromosome 12 was discovered in a gene pool of wild Mus musculus castaneus mice. Pbwg12 does not have a main effect but has an epistatic interaction effect on body weight after birth. In this study, we developed a congenic strain, named B6.Cg-Pbwg12, with an approximately 59-Mb wild-derived genomic region harboring Pbwg12, by recurrent backcrossing to C57BL/6J. A phenotypic comparison between B6.Cg-Pbwg12 and C57BL/6J revealed that Pbwg12 does not have any main effects on body weight at 1-10 weeks of age but has a main effect on body weight gain at 6-10 weeks. A new QTL with a male-specific effect on kidney weight was discovered within the introgressed region.

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.

A dominant X-linked QTL regulating pubertal timing in mice found by whole genome scanning and modified interval-specific congenic strain analysis

BACKGROUND

Pubertal timing in mammals is triggered by reactivation of the hypothalamic-pituitary-gonadal (HPG) axis and modulated by both genetic and environmental factors. Strain-dependent differences in vaginal opening among inbred mouse strains suggest that genetic background contribute significantly to the puberty timing, although the exact mechanism remains unknown.

METHODOLOGY/PRINCIPAL FINDINGS

We performed a genome-wide scanning for linkage in reciprocal crosses between two strains, C3H/HeJ (C3H) and C57BL6/J (B6), which differed significantly in the pubertal timing. Vaginal opening (VO) was used to characterize pubertal timing in female mice, and the age at VO of all female mice (two parental strains, F1 and F2 progeny) was recorded. A genome-wide search was performed in 260 phenotypically extreme F2 mice out of 464 female progeny of the F1 intercrosses to identify quantitative trait loci (QTLs) controlling this trait. A QTL significantly associated was mapped to the DXMit166 marker (15.5 cM, LOD = 3.86, p<0.01) in the reciprocal cross population (C3HB6F2). This QTL contributed 2.1 days to the timing of VO, which accounted for 32.31% of the difference between the original strains. Further study showed that the QTL was B6-dominant and explained 10.5% of variation to this trait with a power of 99.4% at an alpha level of 0.05.The location of the significant ChrX QTL found by genome scanning was then fine-mapped to a region of approximately 2.5 cM between marker DXMit68 and rs29053133 by generating and phenotyping a panel of 10 modified interval-specific congenic strains (mISCSs).

CONCLUSIONS/SIGNIFICANCE

Such findings in our study lay a foundation for positional cloning of genes regulating the timing of puberty, and also reveal the fact that chromosome X (the sex chromosome) does carry gene(s) which take part in the regulative pathway of the pubertal timing in mice.

Studies on the Small Body Size Mouse Developed by Mutagen N-Ethyl-N-nitrosourea

Mutant mouse which show dwarfism has been developed by N-ethyl-N-nitrosourea (ENU) mutagenesis using BALB/c mice. The mutant mouse was inherited as autosomal recessive trait and named Small Body Size (SBS) mouse. The phenotype of SBS mouse was not apparent at birth, but it was possible to distinguish mutant phenotype from normal mice 1 week after birth. In this study, we examined body weight changes and bone mineral density (BMD), and we also carried out genetic linkage analysis to map the causative gene(s) of SBS mouse. Body weight changes were observed from birth to 14 weeks of age in both affected (n = 30) and normal mice (n = 24). BMD was examined in each five SBS and normal mice between 3 and 6 weeks of age, respectively. For the linkage analysis, we produced backcross progeny [(SBS × C57BL/6J) F₁ × SBS] N₂ mice (n = 142), and seventy-four microsatellite markers were used for primary linkage analysis. Body weight of affected mice was consistently lower than that of the normal mice, and was 43.7% less than that of normal mice at 3 weeks of age (P < 0.001). As compared with normal mice at 3 and 6 weeks of age, BMD of the SBS mice was significantly low. The results showed 15.5% and 14.1% lower in total body BMD, 15.3% and 8.7% lower in forearm BMD, and 29.7% and 20.1% lower in femur BMD, respectively. The causative gene was mapped on chromosome 10. The map order and the distance between markers were D10Mit248 - 2.1 cM - D10Mit51 - 4.2 cM - sbs - 0.7 cM - D10Mit283 - 1.4 cM - D10Mit106 - 11.2 cM - D10Mit170.

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.

A growth QTL (Pbwg1) region of mouse chromosome 2 contains closely linked loci affecting growth and body composition

Previous QTL studies have identified 24 QTLs for body weight and growth from 3 to 10 weeks after birth in an intersubspecific backcross mouse population between C57BL/6J and wild Mus musculus castaneus that has 60% of the body size of C57BL/6J. The castaneus allele at the most potent QTL (Pbwg1) on proximal chromosome 2 retards growth. In this study we have developed a congenic strain with a 44.1-Mb interval containing the castaneus allele at Pbwg1 by recurrent backcrossing to C57BL/6J. The congenic mouse developed was characterized by significantly higher body weight gain between 1 and 3 weeks of age and lower weight of white fat pads at 10 weeks of age than C57BL/6J. However, no clear difference in body weight at 1-10 weeks of age was observed between congenic and C57BL/6J strains. QTL analysis with 269 F(2) mice between the two strains did not identify any QTLs for body weight at 1, 3, 6, and 10 weeks of age, but it discovered eight closely linked QTLs affecting body weight gain from 1 to 3 weeks of age, lean body weight, weight of white fat pads, and body length within the Pbwg1 region. The castaneus alleles at all fat pad QTLs reduced the phenotypes, whereas at the remaining growth and body composition QTLs, they increased the trait values. These results illustrate that Pbwg1, which initially appeared to be a single locus, was resolved into several loci with opposite effects on the composition traits of overall body weight. This gives a reason for the loss of the Pbwg1 effect found in the original backcross population.

Principal component analysis of quantitative trait loci for immune response to adenovirus in mice

Data on the duration of transgene expression in the liver, the presence of cytotoxic T lymphocytes (CTLs) against adenovirus, and serum cytokines from 18 strains of C57BL/6 x DBA/2 (B x D) recombinant inbred mice were analyzed. Our aim was to detect quantitative trait loci (QTLs) that may have causal relationship with the duration of adenovirus-mediated transgene expression in the liver. Information from beta-galactosidase (LacZ) expression; CTL production; and serum levels of gamma interferon, tumor necrosis factor-alpha, and interleukin-6 30 days after intravenous injection of liver LacZ were summarized by principal component analysis and analyzed using maximum likelihood interval mapping implemented in the QTL cartographer software. Two principal component (PC) scores explained 82.5% of the phenotypic variance in the original variables and identified QTLs not identified by analysis of individual traits. The distribution of original variables among PCs was such that variables in PC1 were predominantly cytokines with little CTL response whereas LacZ and CTL were the predominant contributors to PC2 with practically no contribution from cytokines. PC1 was significantly associated with two QTLs on chromosomes 7 and 9 located at 57.5 cM and 41.01 cM, respectively. Five QTLs were significantly associated with PC2 on chromosomes 12 (23.01 and 31.01 cM) and 15 (29.21, 36.01, and 56.31 cM). These results illustrate the use of principal component analysis in mapping QTLs using multiple correlated traits.

A meta-analysis of quantitative trait loci associated with body weight and adiposity in mice

OBJECTIVE

Cross-breeding experiments with different mouse strains have successfully been used by many groups to identify genetic loci that predispose for obesity. In order to provide a statistical assessment of these quantitative trait loci (QTL) as a basis for a systematic investigation of candidate genes, we have performed a meta-analysis of genome-wide linkage scans for body weight and body fat.

DATA

From a total of 34 published mouse cross-breeding experiments, we compiled a list of 162 non-redundant QTL for body weight and 117 QTL for fat weight and body fat percentage. Collectively, these studies include data from 42 different parental mouse strains and >14,500 individual mice.

METHODS

The results of the studies were analyzed using the truncated product method (TPM).

RESULTS

The analysis revealed significant evidence (logarithm of odds (LOD) score >4.3) for linkage of body weight and adiposity to 49 different segments of the mouse genome. The most prominent regions with linkage for body weight and body fat (LOD scores 14.8-21.8) on chromosomes 1, 2, 7, 11, 15, and 17 contain a total of 58 QTL for body weight and body fat. At least 34 candidate genes and genetic loci, which have been implicated in regulation of body weight and body composition in rodents and/or humans, are found in these regions, including CCAAT/enhancer-binding protein alpha (C/EBPA), sterol regulatory element-binding transcription factor 1 (SREBP-1), peroxisome proliferator activator receptor delta (PPARD), and hydroxysteroid 11-beta dehydrogenase 1 (HSD11B1). Our results demonstrate the presence of numerous distinct consensus QTL regions with highly significant LOD scores that control body weight and body composition. An interactive physical map of the QTL is available online at (http://www.obesitygenes.org).

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.

Quantitative trait Loci for glomerulosclerosis, kidney weight and body weight in the focal glomerulosclerosis mouse model

In 183 male progeny derived from a backcross between the FGS/Kist strain, a new mouse model for focal glomerulosclerosis (FGS) in humans, and the standard normal strain, C57BL/6J, we performed a genome-wide scan for quantitative trait loci (QTLs) affecting the glomerulosclerosis index (GSI) based on histological observation as well as kidney and body weights. Two QTLs for GSI (Gsi1-2) located on chromosomes (Chrs) 8 and 10, a kidney weight QTL (Kdw1) on Chr 19, and a body weight QTL (Bdw1) on Chr 13 were detected at the genome-wide 5% or less level. The allele derived from FGS/Kist increased GSI at Gsi1, but decreased it at Gsi2. The mice homozygous for the FGS/Kist allele decreased body and kidney weights. The identified QTLs accounted for 5-8% of the phenotypic variance.

A paternally imprinted QTL for mature body mass on mouse Chromosome 8

Body mass (BM) is a classic polygenic trait that has been extensively investigated to determine the underlying genetic architecture. Many previous studies looking at the genetic basis of variation in BM in murine animal models by quantitative trait loci (QTL) mapping have used crosses between two inbred lines. As a consequence it has not been possible to explore imprinting effects which have been shown to play an important role in the genetic basis of early growth with persistent effects throughout the growth curve. Here we use partially inbred mouse lines to identify QTL for mature BM by applying both Mendelian and Imprinting models. The analysis of an F2 population (n approximately 500) identified a number of QTL at 14, 16, and 18 weeks explaining in total 31.5%, 34.4%, and 30.5% of total phenotypic variation, respectively. On Chromosome 8 a QTL of large effect (14% of the total phenotypic variance at 14 weeks) was found to be explained by paternal imprinting. Although Chromosome 8 has not been previously associated with imprinting effects, features of candidate genes within the QTL confidence interval (CpG islands and direct clustered repeats) support the hypothesis that Insulin receptor substrate 2 may be associated with imprinting, but as yet is unidentified as being so.

Quantitative trait loci for proteinuria in the focal glomerulosclerosis mouse model

The FGS/Kist strain of mice, a new animal model for focal glomerulosclerosis (FGS) in humans, was previously established by recurrent selection for high proteinuria, which is a principal marker of FGS, from descendants of CBA/Nga and RFM/Nga strains. We performed a genome-wide scan for quantitative trait loci (QTLs) affecting proteinuria in a population of 356 backcross progeny derived from a cross between FGS/Kist and the standard normal strain, C57BL/6J. Five proteinuria QTLs (Ptnu1-5) were detected at the genome-wide 5% or less level. Ptnu1 and Ptnu2, located on Chromosomes (Chrs) 8 and 17, respectively, had main effects on proteinuria and also interacted epistatically with each other. However, Ptnu3 on Chr 9 and Ptnu4 and Ptnu5 both on Chr 15 had epistatic interaction effects only. Except for the epistatic interaction effect of Ptnu4 and Ptnu5, all alleles derived from FGS/Kist were responsible for the high proteinuria. These results indicated that the genetic control of proteinuria is complex and the identified QTLs may provide new insights into the pathogenesis of FGS in mice as well as in humans.