Simultaneous detection and fine mapping of quantitative trait loci in mice using heterogeneous stocks

Integration of evidence across human and model organism studies: A meeting report

The National Institute on Drug Abuse and Joint Institute for Biological Sciences at the Oak Ridge National Laboratory hosted a meeting attended by a diverse group of scientists with expertise in substance use disorders (SUDs), computational biology, and FAIR (Findability, Accessibility, Interoperability, and Reusability) data sharing. The meeting's objective was to discuss and evaluate better strategies to integrate genetic, epigenetic, and 'omics data across human and model organisms to achieve deeper mechanistic insight into SUDs. Specific topics were to (a) evaluate the current state of substance use genetics and genomics research and fundamental gaps, (b) identify opportunities and challenges of integration and sharing across species and data types, (c) identify current tools and resources for integration of genetic, epigenetic, and phenotypic data, (d) discuss steps and impediment related to data integration, and (e) outline future steps to support more effective collaboration-particularly between animal model research communities and human genetics and clinical research teams. This review summarizes key facets of this catalytic discussion with a focus on new opportunities and gaps in resources and knowledge on SUDs.

Alcohol Sensitivity as an Endophenotype of Alcohol Use Disorder: Exploring Its Translational Utility between Rodents and Humans

Alcohol use disorder (AUD) is a complex, chronic, relapsing disorder with multiple interacting genetic and environmental influences. Numerous studies have verified the influence of genetics on AUD, yet the underlying biological pathways remain unknown. One strategy to interrogate complex diseases is the use of endophenotypes, which deconstruct current diagnostic categories into component traits that may be more amenable to genetic research. In this review, we explore how an endophenotype such as sensitivity to alcohol can be used in conjunction with rodent models to provide mechanistic insights into AUD. We evaluate three alcohol sensitivity endophenotypes (stimulation, intoxication, and aversion) for their translatability across human and rodent research by examining the underlying neurobiology and its relationship to consumption and AUD. We show examples in which results gleaned from rodents are successfully integrated with information from human studies to gain insight in the genetic underpinnings of AUD and AUD-related endophenotypes. Finally, we identify areas for future translational research that could greatly expand our knowledge of the biological and molecular aspects of the transition to AUD with the broad hope of finding better ways to treat this devastating disorder.

Facilitating Complex Trait Analysis via Reduced Complexity Crosses

Genetically diverse inbred strains are frequently used in quantitative trait mapping to identify sequence variants underlying trait variation. Poor locus resolution and high genetic complexity impede variant discovery. As a solution, we explore reduced complexity crosses (RCCs) between phenotypically divergent, yet genetically similar, rodent substrains. RCCs accelerate functional variant discovery via decreasing the number of segregating variants by orders of magnitude. The simplified genetic architecture of RCCs often permit immediate identification of causal variants or rapid fine-mapping of broad loci to smaller intervals. Whole-genome sequences of substrains make RCCs possible by supporting the development of array- and targeted sequencing-based genotyping platforms, coupled with rapid genome editing for variant validation. In summary, RCCs enhance discovery-based genetics of complex traits.

Using Heterogeneous Stocks for Fine-Mapping Genetically Complex Traits

In this chapter we will review both the rationale and experimental design for using Heterogeneous Stock (HS) populations for fine-mapping of complex traits in mice and rats. We define an HS as an outbred population derived from an intercross between two or more inbred strains. HS have been used to perform genome-wide association studies (GWAS) for multiple behavioral, physiological, and gene expression traits. GWAS using HS require four key steps, which we review: selection of an appropriate HS population, phenotyping, genotyping, and statistical analysis. We provide advice on the selection of an HS, comment on key issues related to phenotyping, discuss genotyping methods relevant to these populations, and describe statistical genetic analyses that are applicable to genetic analyses that use HS.

Development of a MEL Cell-Derived Allograft Mouse Model for Cancer Research

Murine erythroleukemia (MEL) cells are often employed as a model to dissect mechanisms of erythropoiesis and erythroleukemia in vitro. Here, an allograft model using MEL cells resulting in splenomegaly was established to develop a diagnostic model for isolation/quantification of metastatic cells, anti-cancer drug screening, and evaluation of the tumorigenic or metastatic potentials of molecules in vivo. In this animal model, circulating MEL cells from the blood stream were successfully isolated and quantified with an additional in vitro cultivation step. In terms of the molecular-pathological analysis, we were able to successfully evaluate the functional discrimination between methyl-CpG-binding domain 2 (Mbd2) and p66α in erythroid differentiation, and tumorigenic potential in spleen and blood stream of allograft model mice. In addition, we found that the number of circulating MEL cells in anti-cancer drug-treated mice was dose-dependently decreased. Our data demonstrate that the newly established allograft model is useful to dissect erythroleukemia pathologies and non-invasively provides valuable means for isolation of metastatic cells, screening of anti-cancer drugs, and evaluation of the tumorigenic potentials.

Experimental Models to Define the Genetic Predisposition to Liver Cancer

Hepatocellular carcinoma (HCC) is a frequent human cancer and the most frequent liver tumor. The study of genetic mechanisms of the inherited predisposition to HCC, implicating gene-gene and gene-environment interaction, led to the discovery of multiple gene loci regulating the growth and multiplicity of liver preneoplastic and neoplastic lesions, thus uncovering the action of multiple genes and epistatic interactions in the regulation of the individual susceptibility to HCC. The comparative evaluation of the molecular pathways involved in HCC development in mouse and rat strains differently predisposed to HCC indicates that the genes responsible for HCC susceptibility control the amplification and/or overexpression of c-Myc, the expression of cell cycle regulatory genes, and the activity of Ras/Erk, AKT/mTOR, and of the pro-apoptotic Rassf1A/Nore1A and Dab2IP/Ask1 pathways, the methionine cycle, and DNA repair pathways in mice and rats. Comparative functional genetic studies, in rats and mice differently susceptible to HCC, showed that preneoplastic and neoplastic lesions of resistant mouse and rat strains cluster with human HCC with better prognosis, while the lesions of susceptible mouse and rats cluster with HCC with poorer prognosis, confirming the validity of the studies on the influence of the genetic predisposition to hepatocarinogenesis on HCC prognosis in mouse and rat models. Recently, the hydrodynamic gene transfection in mice provided new opportunities for the recognition of genes implicated in the molecular mechanisms involved in HCC pathogenesis and prognosis. This method appears to be highly promising to further study the genetic background of the predisposition to this cancer.

Cleaning Genotype Data from Diversity Outbred Mice

Data cleaning is an important first step in most statistical analyses, including efforts to map the genetic loci that contribute to variation in quantitative traits. Here we illustrate approaches to quality control and cleaning of array-based genotyping data for multiparent populations (experimental crosses derived from more than two founder strains), using MegaMUGA array data from a set of 291 Diversity Outbred (DO) mice. Our approach employs data visualizations that can reveal problems at the level of individual mice or with individual SNP markers. We find that the proportion of missing genotypes for each mouse is an effective indicator of sample quality. We use microarray probe intensities for SNPs on the X and Y chromosomes to confirm the sex of each mouse, and we use the proportion of matching SNP genotypes between pairs of mice to detect sample duplicates. We use a hidden Markov model (HMM) reconstruction of the founder haplotype mosaic across each mouse genome to estimate the number of crossovers and to identify potential genotyping errors. To evaluate marker quality, we find that missing data and genotyping error rates are the most effective diagnostics. We also examine the SNP genotype frequencies with markers grouped according to their minor allele frequency in the founder strains. For markers with high apparent error rates, a scatterplot of the allele-specific probe intensities can reveal the underlying cause of incorrect genotype calls. The decision to include or exclude low-quality samples can have a significant impact on the mapping results for a given study. We find that the impact of low-quality markers on a given study is often minimal, but reporting problematic markers can improve the utility of the genotyping array across many studies.

R/qtl2: Software for Mapping Quantitative Trait Loci with High-Dimensional Data and Multiparent Populations

R/qtl2 is an interactive software environment for mapping quantitative trait loci (QTL) in experimental populations. The R/qtl2 software expands the scope of the widely-used R/qtl software package to include multiparental populations, better handles modern high-dimensional data....

R/qtl2 is an interactive software environment for mapping quantitative trait loci (QTL) in experimental populations. The R/qtl2 software expands the scope of the widely used R/qtl software package to include multiparent populations derived from more than two founder strains, such as the Collaborative Cross and Diversity Outbred mice, heterogeneous stocks, and MAGIC plant populations. R/qtl2 is designed to handle modern high-density genotyping data and high-dimensional molecular phenotypes, including gene expression and proteomics. R/qtl2 includes the ability to perform genome scans using a linear mixed model to account for population structure, and also includes features to impute SNPs based on founder strain genomes and to carry out association mapping. The R/qtl2 software provides all of the basic features needed for QTL mapping, including graphical displays and summary reports, and it can be extended through the creation of add-on packages. R/qtl2, which is free and open source software written in the R and C++ programming languages, comes with a test framework.

Heterogeneous Stock Populations for Analysis of Complex Traits

Heterogeneous Stock (HS) populations allow for fine-resolution genetic mapping of a variety of complex traits. HS mice and rats were created from breeding together eight inbred strains, followed by maintaining the colony in a manner that minimizes inbreeding. After 50 or more generations of breeding, the resulting animals' chromosomes represent a genetic mosaic of the founders' haplotypes, with the average distance between recombination events in the centiMorgan range. This allows for genetic mapping to only a few Mb, a much smaller region than what can be identified using traditional F2 intercross or backcross mapping strategies. HS animals have been used to fine-map a variety of complex traits including anxiety and fear behaviors, diabetes, asthma, and heart disease, among others. Once a quantitative trait locus (QTL) has been identified, founder sequence and expression analysis can be used to identify underlying causal genes. In the following review, we provide an overview of how HS rats and mice have been used to identify genetic loci, and in some cases the causal genes, underlying complex traits. We discuss the creation and breeding strategies for both HS rats and mice. We then discuss the statistical analyses used to identify genetic loci, as well as strategies to identify causal genes underlying these loci. We end the chapter by discussing limitations faced when using HS populations, including several statistical challenges that have not been fully resolved.

Prion Protein and Genetic Susceptibility to Diseases Caused by Its Misfolding

Early genetic studies on scrapie, an infectious neurodegenerative disease of sheep that was adapted to mice, provided evidence in support of the hypothesis that the agent was a slow virus with a nucleic acid genome independent of the host. Particularly compelling support for an independent genome came from the existence of strains of scrapie agent, some of which were true breeding, while others appeared to mutate under selective pressure. Kuru, a neurodegenerative disease in the remote highlands of Papua New Guinea, had pathological changes similar to those in scrapie and also proved to be transmissible. Genetic studies with the tools of molecular biology and transgenic mice forced a reevaluation of earlier work and supported the prion hypothesis of a novel pathogen devoid of nucleic acid. In this chapter, I discuss the contributions of classical and molecular genetics to understanding PrP prion diseases and to determining that heritable information is enciphered in protein conformation.

QTL and systems genetics analysis of mouse grooming and behavioral responses to novelty in an open field

The open field is a classic test used to assess exploratory behavior, anxiety and locomotor activity in rodents. Here, we mapped quantitative trait loci (QTLs) underlying behaviors displayed in an open field, using a panel of 53 BXD recombinant inbred mouse strains with deep replication (10 per strain and sex). The use of these strains permits the integration and comparison of data obtained in different laboratories, and also offers the possibility to study trait covariance by exploiting powerful bioinformatics tools and resources. We quantified behavioral traits during 20-min test sessions including (1) percent time spent and distance traveled near the wall (thigmotaxis), (2) leaning against the wall, (3) rearing, (4) jumping, (5) grooming duration, (6) grooming frequency, (7) locomotion and (8) defecation. All traits exhibit moderate heritability making them amenable to genetic analysis. We identified a significant QTL on chromosome M.m. 4 at approximately 104 Mb that modulates grooming duration in both males and females (likelihood ratio statistic values of approximately 18, explaining 25% and 14% of the variance, respectively) and a suggestive QTL modulating locomotion that maps to the same locus. Bioinformatic analysis indicates Disabled 1 (Dab1, a key protein in the reelin signaling pathway) as a particularly strong candidate gene modulating these behaviors. We also found 2 highly suggestive QTLs for a sex by strain interaction for grooming duration on chromosomes 13 and 17. In addition, we identified a pairwise epistatic interaction between loci on chromosomes 12 at 36-37 Mb and 14 at 34-36 Mb that influences rearing frequency in males.

Improved detection of gene-microbe interactions in the mouse skin microbiota using high-resolution QTL mapping of 16S rRNA transcripts

BACKGROUND

Recent studies highlight the utility of quantitative trait locus (QTL) mapping for determining the contribution of host genetics to interindividual variation in the microbiota. We previously demonstrated that similar to the gut microbiota, abundances of bacterial taxa in the skin are significantly influenced by host genetic variation. In this study, we analyzed the skin microbiota of mice from the 15th generation of an advanced intercross line using a novel approach of extending bacterial trait mapping to both the 16S rRNA gene copy (DNA) and transcript (RNA) levels, which reflect relative bacterial cell number and activity, respectively.

RESULTS

Remarkably, the combination of highly recombined individuals and 53,203 informative SNPs allowed the identification of genomic intervals as small as <0.1 megabases containing single genes. Furthermore, the inclusion of 16S rRNA transcript-level mapping dramatically increased the number of significant associations detected, with five versus 21 significant SNP-bacterial trait associations based on DNA- compared to RNA-level profiling, respectively. Importantly, the genomic intervals identified contain many genes involved in skin inflammation and cancer and are further supported by the bacterial traits they influence, which in some cases have known genotoxic or probiotic capabilities.

CONCLUSIONS

These results indicate that profiling based on the relative activity levels of bacterial community members greatly enhances the capability of detecting interactions between the host and its associated microbes. Finally, the identification of several genes involved in skin cancer suggests that similar to colon carcinogenesis, the resident microbiota may play a role in skin cancer susceptibility and its potential prevention and/or treatment.

Collaborative Cross Mouse Population for Studying Alveolar Bone Changes and Impaired Glucose Tolerance Comorbidity After High-Fat Diet Consumption

BACKGROUND

High-fat diet (HFD), body weight (BW) gain, and impaired glucose tolerance development are associated with alveolar bone loss (ABL) in susceptible individuals. This report explores the Collaborative Cross (CC) mouse population for studying the impact of genetic background on comorbidity of alveolar bone change and glucose tolerance after HFD consumption.

METHODS

Seventy-eight mice from 19 different CC lines were maintained on rodent chow diet for 8 weeks and were subsequently transferred to an HFD (42% fat) for an additional 12 weeks. BW changes were assessed, and glucose tolerance was measured using an intraperitoneal glucose tolerance test (IPGTT). Six cytokines/chemokines were quantified by multiplex immunoassay, alveolar bone volume was quantified by microcomputed tomography, and the ABL phenotype was calculated relative to a control group (143 mice maintained on standard chow diet for 20 weeks).

RESULTS

The glucose tolerance response after HFD significantly varied among CC lines (P <0.01), with a significant effect of sex (P <0.01). Alveolar bone changes significantly varied among CC lines (P <0.01). Overall, there was no significant correlation between alveolar bone volume changes and increased BW or glucose tolerance response. However, individual CC lines were identified that showed type 2 diabetes mellitus (t2DM) development and significant alveolar bone volume change (P <0.05), whereas others showed t2DM development, regardless of periodontitis. Interleukin-6 significantly correlated with alveolar bone changes (P <0.05), whereas adipsin showed a negative correlation with IPGTT area under the curve values (P <0.05).

CONCLUSION

The present results demonstrate the power of CC mice for studying the genetic background impact between comorbidity of t2DM and bone loss.

Dissection of Host Susceptibility to Bacterial Infections and Its Toxins

Infection is one of the leading causes of human mortality and morbidity. Exposure to microbial agents is obviously required. However, also non-microbial environmental and host factors play a key role in the onset, development and outcome of infectious disease, resulting in large of clinical variability between individuals in a population infected with the same microbe. Controlled and standardized investigations of the genetics of susceptibility to infectious disease are almost impossible to perform in humans whereas mouse models allow application of powerful genomic techniques to identify and validate causative genes underlying human diseases with complex etiologies. Most of current animal models used in complex traits diseases genetic mapping have limited genetic diversity. This limitation impedes the ability to create incorporated network using genetic interactions, epigenetics, environmental factors, microbiota, and other phenotypes. A novel mouse genetic reference population for high-resolution mapping and subsequently identifying genes underlying the QTL, namely the Collaborative Cross (CC) mouse genetic reference population (GRP) was recently developed. In this chapter, we discuss a variety of approaches using CC mice for mapping genes underlying quantitative trait loci (QTL) to dissect the host response to polygenic traits, including infectious disease caused by bacterial agents and its toxins.

Positional Gene Cloning in Experimental Populations

Positional cloning is a technique that identifies a trait-associated gene based on its location in the genome and involves methods such as linkage analysis, association mapping, and bioinformatics. This approach can be used for gene identification even when little is known about the molecular basis of the trait. Vast majority of traits are regulated by multiple genomic loci called quantitative trait loci (QTL). We describe experimental populations and designs that can be used for positional cloning, including backcrosses, intercrosses, and heterogeneous stocks, and advantages and disadvantages of different approaches. Once the phenotype and genotype of each individual in an experimental population have been determined, QTL identification can be accomplished. We describe the statistical tools used to identify the existence, location, and significance of QTLs. These different methods have advantages and disadvantages to consider when selecting the appropriate model to be used, which is briefly discussed.Although the objective of QTL mapping is to identify genomic regions associated with a trait, the ultimate goal is to identify the gene and the genetic variation (which is often quantitative trait nucleotide, QTN) or haplotype that is responsible for the phenotype. By discovering the function of causative variants or haplotypes we can understand the molecular changes that lead to the phenotype. We briefly describe how the genomic sequences can be exploited to identify QTNs and how these can be validated in congenic strains and functionally tested to understand their influence on phenotype expression.

Multi-environment multi-QTL association mapping identifies disease resistance QTL in barley germplasm from Latin America

Multi-environment multi-QTL mixed models were used in a GWAS context to identify QTL for disease resistance. The use of mega-environments aided the interpretation of environment-specific and general QTL. Diseases represent a major constraint for barley (Hordeum vulgare L.) production in Latin America. Spot blotch (caused by Cochliobolus sativus), stripe rust (caused by Puccinia striiformis f.sp. hordei) and leaf rust (caused by Puccinia hordei) are three of the most important diseases that affect the crop in the region. Since fungicide application is not an economically or environmentally sound solution, the development of durably resistant varieties is a priority for breeding programs. Therefore, new resistance sources are needed. The objective of this work was to detect genomic regions associated with field level plant resistance to spot blotch, stripe rust, and leaf rust in Latin American germplasm. Disease severities measured in multi-environment trials across the Americas and 1,096 SNPs in a population of 360 genotypes were used to identify genomic regions associated with disease resistance. Optimized experimental design and spatial modeling were used in each trial to estimate genotypic means. Genome-Wide Association Mapping (GWAS) in each environment was used to detect Quantitative Trait Loci (QTL). All significant environment-specific QTL were subsequently included in a multi-environment-multi-QTL (MEMQ) model. Geographical origin and inflorescence type were the main determinants of population structure. Spot blotch severity was low to intermediate while leaf and stripe rust severity was high in all environments. Mega-environments were defined by locations for spot blotch and leaf rust. Significant marker-trait associations for spot blotch (9 QTL), leaf (6 QTL) and stripe rust (7 QTL) and both global and environment-specific QTL were detected that will be useful for future breeding efforts.

Association mapping in crop plants: opportunities and challenges

The research area of association mapping (AM) is currently receiving major attention for genetic studies of quantitative traits in all major crops. However, the level of success and utility of AM achieved for crop improvement is not comparable to that in the area of human health care for diagnosis of complex human diseases. These AM studies in plants, as in humans, became possible due to the availability of DNA-based molecular markers and a variety of sophisticated statistical tools that are evolving on a regular basis. In this chapter, we first briefly review the significance of a variety of populations that are used in AM studies, then briefly describe the molecular markers and high-throughput genotyping strategies, and finally describe the approaches used for AM studies. The major part of the chapter is, however, devoted to analysis of reasons why the results of AM have been underutilized in plant breeding. We also examine the opportunities available and challenges faced while using AM for crop improvement programs. This includes a detailed discussion of the issues that have plagued AM studies, and the solutions that have become available to deal with these issues, so that in future, the results of AM studies may prove increasingly fruitful for crop improvement programs.

QTL mapping in outbred populations: successes and challenges

Quantitative trait locus (QTL) mapping in animal populations has been a successful strategy for identifying genomic regions that play a role in complex diseases and traits. When conducted in an F2 intercross or backcross population, the resulting QTL is frequently large, often encompassing 30 Mb or more and containing hundreds of genes. To narrow the locus and identify candidate genes, additional strategies are needed. Congenic strains have proven useful but work less well when there are multiple tightly linked loci, frequently resulting in loss of phenotype. As an alternative, we discuss the use of highly recombinant outbred models for directly fine-mapping QTL to only a few megabases. We discuss the use of several currently available models such as the advanced intercross (AI), heterogeneous stocks (HS), the diversity outbred (DO), and commercially available outbred stocks (CO). Once a QTL has been fine-mapped, founder sequence and expression QTL mapping can be used to identify candidate genes. In this regard, the large number of alleles found in outbred stocks can be leveraged to identify causative genes and variants. We end this review by discussing some important statistical considerations when analyzing outbred populations. Fine-resolution mapping in outbred models, coupled with full genome sequence, has already led to the identification of several underlying causative genes for many complex traits and diseases. These resources will likely lead to additional successes in the coming years.

Rats are the smart choice: Rationale for a renewed focus on rats in behavioral genetics

Due in part to their rich behavioral repertoire rats have been widely used in behavioral studies of drug abuse-related traits for decades. However, the mouse became the model of choice for researchers exploring the genetic underpinnings of addiction after the first mouse study was published demonstrating the capability of engineering the mouse genome through embryonic stem cell technology. The sequencing of the mouse genome and more recent re-sequencing of numerous inbred mouse strains have further cemented the status of mice as the premier mammalian organism for genetic studies. As a result, many of the behavioral paradigms initially developed and optimized for rats have been adapted to mice. However, numerous complex and interesting drug abuse-related behaviors that can be studied in rats are very difficult or impossible to adapt for use in mice, impeding the genetic dissection of those traits. Now, technological advances have removed many of the historical limitations of genetic studies in rats. For instance, the rat genome has been sequenced and many inbred rat strains are now being re-sequenced and outbred rat stocks are being used to fine-map QTLs. In addition, it is now possible to create "knockout" rats using zinc finger nucleases (ZFN), transcription activator-like effector nucleases (TALENs) and related techniques. Thus, rats can now be used to perform quantitative genetic studies of sophisticated behaviors that have been difficult or impossible to study in mice. This article is part of a Special Issue entitled 'NIDA 40th Anniversary Issue'.

Anxiety genetics - findings from cross-species genome-wide approaches

Anxiety disorders are complex diseases, which often occur in combination with major depression, alcohol use disorder, or general medical conditions. Anxiety disorders were the most common mental disorders within the EU states in 2010 with 14% prevalence. Anxiety disorders are triggered by environmental factors in genetically susceptible individuals, and therefore genetic research offers a great route to unravel molecular basis of these diseases. As anxiety is an evolutionarily conserved response, mouse models can be used to carry out genome-wide searches for specific genes in a setting that controls for the environmental factors. In this review, we discuss translational approaches that aim to bridge results from unbiased genome-wide screens using mouse models to anxiety disorders in humans. Several methods, such as quantitative trait locus mapping, gene expression profiling, and proteomics, have been used in various mouse models of anxiety to identify genes that regulate anxiety or play a role in maintaining pathological anxiety. We first discuss briefly the evolutionary background of anxiety, which justifies cross-species approaches. We then describe how several genes have been identified through genome-wide methods in mouse models and subsequently investigated in human anxiety disorder samples as candidate genes. These studies have led to the identification of completely novel biological pathways that regulate anxiety in mice and humans, and that can be further investigated as targets for therapy.

Genome-wide association studies for agronomical traits in a world wide spring barley collection

BACKGROUND

Genome-wide association studies (GWAS) based on linkage disequilibrium (LD) provide a promising tool for the detection and fine mapping of quantitative trait loci (QTL) underlying complex agronomic traits. In this study we explored the genetic basis of variation for the traits heading date, plant height, thousand grain weight, starch content and crude protein content in a diverse collection of 224 spring barleys of worldwide origin. The whole panel was genotyped with a customized oligonucleotide pool assay containing 1536 SNPs using Illumina's GoldenGate technology resulting in 957 successful SNPs covering all chromosomes. The morphological trait "row type" (two-rowed spike vs. six-rowed spike) was used to confirm the high level of selectivity and sensitivity of the approach. This study describes the detection of QTL for the above mentioned agronomic traits by GWAS.

RESULTS

Population structure in the panel was investigated by various methods and six subgroups that are mainly based on their spike morphology and region of origin. We explored the patterns of linkage disequilibrium (LD) among the whole panel for all seven barley chromosomes. Average LD was observed to decay below a critical level (r2-value 0.2) within a map distance of 5-10 cM. Phenotypic variation within the panel was reasonably large for all the traits. The heritabilities calculated for each trait over multi-environment experiments ranged between 0.90-0.95. Different statistical models were tested to control spurious LD caused by population structure and to calculate the P-value of marker-trait associations. Using a mixed linear model with kinship for controlling spurious LD effects, we found a total of 171 significant marker trait associations, which delineate into 107 QTL regions. Across all traits these can be grouped into 57 novel QTL and 50 QTL that are congruent with previously mapped QTL positions.

CONCLUSIONS

Our results demonstrate that the described diverse barley panel can be efficiently used for GWAS of various quantitative traits, provided that population structure is appropriately taken into account. The observed significant marker trait associations provide a refined insight into the genetic architecture of important agronomic traits in barley. However, individual QTL account only for a small portion of phenotypic variation, which may be due to insufficient marker coverage and/or the elimination of rare alleles prior to analysis. The fact that the combined SNP effects fall short of explaining the complete phenotypic variance may support the hypothesis that the expression of a quantitative trait is caused by a large number of very small effects that escape detection. Notwithstanding these limitations, the integration of GWAS with biparental linkage mapping and an ever increasing body of genomic sequence information will facilitate the systematic isolation of agronomically important genes and subsequent analysis of their allelic diversity.

Forward genetic approaches to understanding complex behaviors

Assigning function to genes has long been a focus of biomedical research.Even with complete knowledge of the genomic sequences of humans, mice and other experimental organisms, there is still much to be learned about gene function and control. Ablation or overexpression of single genes using knockout or transgenic technologies has provided functional annotation for many genes, but these technologies do not capture the extensive genetic variation present in existing experimental mouse populations. Researchers have only recently begun to truly appreciate naturally occurring genetic variation resulting from single nucleotide substitutions,insertions, deletions, copy number variation, epigenetic changes (DNA methylation,histone modifications, etc.) and gene expression differences and how this variation contributes to complex phenotypes. In this chapter, we will discuss the benefits and limitations of different forward genetic approaches that capture the genetic variation present in inbred mouse strains and present the utility of these approaches for mapping QTL that influence complex behavioral phenotypes.

Effects of environmental and physiological covariates on sex differences in unconditioned and conditioned anxiety and fear in a large sample of genetically heterogeneous (N/Nih-HS) rats

Physiological and environmental variables, or covariates, can account for an important portion of the variability observed in behavioural/physiological results from different laboratories even when using the same type of animals and phenotyping procedures. We present the results of a behavioural study with a sample of 1456 genetically heterogeneous N/Nih-HS rats, including males and females, which are part of a larger genome-wide fine-mapping QTL (Quantitative Trait Loci) study. N/Nih-HS rats have been derived from 8 inbred strains and provide very small distance between genetic recombinations, which makes them a unique tool for fine-mapping QTL studies. The behavioural test battery comprised the elevated zero-maze test for anxiety, novel-cage (open-field like) activity, two-way active avoidance acquisition (related to conditioned anxiety) and context-conditioned freezing (i.e. classically conditioned fear). Using factorial analyses of variance (ANOVAs) we aimed to analyse sex differences in anxiety and fear in this N/Nih-HS rat sample, as well as to assess the effects of (and interactions with) other independent factors, such as batch, season, coat colour and experimenter. Body weight was taken as a quantitative covariate and analysed by covariance analysis (ANCOVA). Obliquely-rotated factor analyses were also performed separately for each sex, in order to evaluate associations among the most relevant variables from each behavioural test and the common dimensions (i.e. factors) underlying the different behavioural responses. ANOVA analyses showed a consistent pattern of sex effects, with females showing less signs of anxiety and fear than males across all tests. There were also significant main effects of batch, season, colour and experimenter on almost all behavioural variables, as well as "sex × batch", "sex × season" and "sex × experimenter" interactions. Body weight showed significant effects in the ANCOVAs of most behavioural measures, but sex effects were still present in spite of (and after controlling for) these "body weight" effects. Factor analyses of relevant variables from each test showed a two-fold factor structure in both sexes, with the first factor mainly representing anxiety and conditioned fear in males, while in females the first factor was dominated by loadings of activity measures. Thus, besides showing consistent sex differences in anxiety-, fear- and activity-related responses in N/Nih-HS rats, the present study shows that females' behaviour is predominantly influenced by activity while males are more influenced by anxiety. Moreover, the results point out that, besides "sex" effects, physiological variables such as colour and body weight, and environmental factors as batch/season or "experimenter", have to be taken into account in both behavioural and quantitative genetic studies because of their demonstrated influences on phenotypic outcomes.

Dark matter: are mice the solution to missing heritability?

Genome-wide association studies (GWAS) in humans have identified hundreds of single nucleotide polymorphisms associated with complex traits, yet for most traits studied, the sum total of all these identified variants fail to explain a significant portion of the heritable variation. Reasons for this “missing heritability” are thought to include the existence of rare causative variants not captured by current genotyping arrays, structural variants that go undetected by existing technology, insufficient power to identify multi-gene interactions, small sample sizes, and the influence of environmental and epigenetic effects. As genotyping technologies have evolved it has become inexpensive and relatively straightforward to perform GWAS in mice. Mice offer a powerful tool for elucidating the genetic architecture of behavioral and physiological traits, and are complementary to human studies. Unlike F₂ crosses of inbred strains, advanced intercross lines, heterogeneous stocks, outbred, and wild-caught mice have more rapid breakdown of linkage disequilibrium which allow for increasingly high resolution mapping. Because some of these populations are created using a small number of founder chromosomes they are not expected to harbor rare alleles. We discuss the differences between these mouse populations and examine their potential to overcome some of the pitfalls that have plagued human GWAS studies.

High-resolution mapping of a complex disease, a model for rheumatoid arthritis, using heterogeneous stock mice

Resolving the genetic basis of complex diseases like rheumatoid arthritis will require knowledge of the corresponding diseases in experimental animals to enable translational functional studies. Mapping of quantitative trait loci in mouse models of arthritis, such as collagen-induced arthritis (CIA), using F(2) crosses has been successful, but can resolve loci only to large chromosomal regions. Using an inbred-outbred cross design, we identified and fine-mapped CIA loci on a genome-wide scale. Heterogeneous stock mice were first intercrossed with an inbred strain, B10.Q, to introduce an arthritis permitting MHCII haplotype. Homozygous H2(q) mice were then selected to set up an F(3) generation with fixed major histocompatibility complex that was used for arthritis experiments. We identified 26 loci, 18 of which are novel, controlling arthritis traits such as incidence of disease, severity and time of onset and fine-mapped a number of previously mapped loci.

Cancer evolution and individual susceptibility

Cancer susceptibility is due to interactions between inherited genetic factors and exposure to environmental carcinogens. The genetic component is constituted mainly by weakly acting low-penetrance genetic variants that interact among themselves, as well as with the environment. These low susceptibility genes can be categorized into two main groups: one includes those that control intrinsic tumor cell activities (i.e. apoptosis, proliferation or DNA repair), and the other contains those that modulate the function of extrinsic tumor cell compartments (i.e. stroma, angiogenesis, or endocrine and immune systems). Genome-Wide Association Studies (GWAS) of human populations have identified numerous genetic loci linked with cancer risk and behavior, but nevertheless the major component of cancer heritability remains to be explained. One reason may be that GWAS cannot readily capture gene-gene or gene-environment interactions. Mouse model approaches offer an alternative or complementary strategy, because of our ability to control both the genetic and environmental components of risk. Recently developed genetic tools, including high-throughput technologies such as SNP, CGH and gene expression microarrays, have led to more powerful strategies for refining quantitative trait loci (QTL) and identifying the critical genes. In particular, the cross-species approaches will help to refine locations of QTLs, and reveal their genetic and environmental interactions. The identification of human tumor susceptibility genes and discovery of their roles in carcinogenesis will ultimately be important for the development of methods for prediction of risk, diagnosis, prevention and therapy for human cancers.

Identifying quantitative trait loci (QTLs) and genes (QTGs) for alcohol-related phenotypes in mice

Alcoholism is a complex clinical disorder with genetic and environmental contributions. Although no animal model duplicates alcoholism, models for specific factors, such as the withdrawal syndrome, are useful to identify potential genetic determinants of liability in humans. Murine models have been invaluable to identify quantitative trait loci (QTLs) that influence a variety of alcohol responses. However, the QTL regions are typically large, at least initially, and contain numerous genes, making identification of the causal quantitative trait gene(s) (QTGs) challenging. Here, we present QTG identification strategies currently used in the field of alcohol genetics and discuss relevance to alcoholic human populations.

Genome-wide compatible SNP intervals and their properties

Intraspecific genomes can be subdivided into blocks with limited diversity. Understanding the distribution and structure of these blocks will help to unravel many biological problems including the identification of genes associated with complex diseases, finding the ancestral origins of a given population, and localizing regions of historical recombination, gene conversion, and homoplasy. We present methods for partitioning a genome into blocks for which there are no apparent recombinations, thus providing parsimonious sets of compatible genome intervals based on the four-gamete test. Our contribution is a thorough analysis of the problem of dividing a genome into compatible intervals, in terms of its computational complexity, and by providing an achievable lower-bound on the minimal number of intervals required to cover an entire data set. In general, such minimal interval partitions are not unique. However, we identify properties that are common to every possible solution. We also define the notion of an interval set that achieves the interval lower-bound, yet maximizes interval overlap. We demonstrate algorithms for partitioning both haplotype data from inbred mice as well as outbred heterozygous genotype data using extensions of the standard four-gamete test. These methods allow our algorithms to be applied to a wide range of genomic data sets.

Gene expression profiling as a tool for positional cloning of genes-shortcut or the longest way round

The identification of quantitative trait loci, QTL, in arthritis animal models is a straight forward process. However, to identify the underlying genes is a great challenge. One strategy frequently used, is to combine QTL analysis with genomic/proteomic screens. This has resulted in a number of publications where carefully performed genomic analyses present likely candidate genes for their respective QTL s. However, seldom the findings are reconnected to the QTL controlled phenotypes. In this review, we use our own data as an illustrative example that "very likely candidate genes" identified by genomic/proteomics is not necessarily the same as true QTL underlying genes.

Genetic analysis of skull shape variation and morphological integration in the mouse using interspecific recombinant congenic strains between C57BL/6 and mice of the mus spretus species

To assess the genetic basis of the skull shape variation and morphological integration in mice, we have used a tool based on the cross between the distantly related mouse species Mus spretus (SEG/Pas strain) and the laboratory strain C57BL/6 called interspecific recombinant congenic strains (IRCSs). The genome of each IRCS consists on average of 1.3% of SEG/Pas derived sequences, located on multiple chromosomes as small-sized, DNA segments. Quantitative trait loci (QTL) on the skull shape, separated into dorsal and ventral sides, were analyzed in 17 IRCSs by a Procrustes superimposition method using 3D landmarks. The shapes of 16 strains differed significantly from C57BL/6. Discrepancy in the QTLs effects was found between the dorsal side and the anterior region of the ventral side due to a differential effect of the SEG/Pas alleles on the skull shape. A comprehensive analysis of all allelic combinations of the BCG-66H strain showed strong epistatic interactions between SEG/Pas segment acting on both skull sides. Epistatic pleiotropy and covariation between sides were dependent in SEG/Pas alleles direction and contributed to the strong morphological integration between sides. Introduction of Mus spretus alleles in a C57BL/6 background induced strong morphological changes mostly in SEG/Pas alleles direction and provided evidence for high level of morphological integration.

The future of mouse QTL mapping to diagnose disease in mice in the age of whole-genome association studies

Genome-wide association analysis is emerging as a powerful tool to define novel genes and molecular pathways involved in susceptibility to human complex disorders. However, in spite of recent successes, this approach is not without its limitations, the most notable of which is inconsistent phenotype penetrance due to varied environmental exposures. Mouse models do, however, circumvent some of these drawbacks by allowing for a much higher degree of control over genetic variation and environmental exposure, and although their application to human complex genetics is not always straightforward, they do serve as a powerful means of complementing observations in human populations. Mouse quantitative trait locus mapping has proven a successful, yet technically demanding method for defining trait susceptibility. In this review, we focus upon recent advances that are both reducing the technical burden traditionally associated with quantitative trait locus mapping, and enhancing the applicability of these approaches to human disease.

Connecting QTLS to the g-matrix of evolutionary quantitative genetics

Evolutionary quantitative genetics has recently advanced in two distinct streams. Many biologists address evolutionary questions by estimating phenotypic selection and genetic (co)variances (G matrices). Simultaneously, an increasing number of studies have applied quantitative trait locus (QTL) mapping methods to dissect variation. Both conceptual and practical difficulties have isolated these two foci of quantitative genetics. A conceptual integration follows from the recognition that QTL allele frequencies are the essential variables relating the G-matrix to marker-based mapping experiments. Breeding designs initiated from randomly selected parental genotypes can be used to estimate QTL-specific genetic (co)variances. These statistics appropriately distill allelic variation and provide an explicit population context for QTL mapping estimates. Within this framework, one can parse the G-matrix into a set of mutually exclusive genomic components and ask whether these parts are similar or dissimilar in their respective features, for example the magnitude of phenotypic effects and the extent and nature of pleiotropy. As these features are critical determinants of sustained response to selection, the integration of QTL mapping methods into G-matrix estimation can provide a concrete, genetically based experimental program to investigate the evolutionary potential of natural populations.

Genetic design and statistical power of nested association mapping in maize

We investigated the genetic and statistical properties of the nested association mapping (NAM) design currently being implemented in maize (26 diverse founders and 5000 distinct immortal genotypes) to dissect the genetic basis of complex quantitative traits. The NAM design simultaneously exploits the advantages of both linkage analysis and association mapping. We demonstrated the power of NAM for high-power cost-effective genome scans through computer simulations based on empirical marker data and simulated traits with different complexities. With common-parent-specific (CPS) markers genotyped for the founders and the progenies, the inheritance of chromosome segments nested within two adjacent CPS markers was inferred through linkage. Genotyping the founders with additional high-density markers enabled the projection of genetic information, capturing linkage disequilibrium information, from founders to progenies. With 5000 genotypes, 30-79% of the simulated quantitative trait loci (QTL) were precisely identified. By integrating genetic design, natural diversity, and genomics technologies, this new complex trait dissection strategy should greatly facilitate endeavors to link molecular variation with phenotypic variation for various complex traits.

Fearfulness in a large N/Nih genetically heterogeneous rat stock: differential profiles of timidity and defensive flight in males and females

Anxiety-related behaviors were evaluated across various tests in a large sample (n=787, both sexes) of genetically heterogeneous (N/Nih-HS) rats, derived from an eight-way cross of inbred strains. These tests either evoke unlearned (black-white box, BWB-; novel-cage activity, NACT-; elevated "zero" maze, ZM-; baseline acoustic startle response, BAS-) or learned (fear-potentiated startle, FPS-; two-way active-shuttle box-avoidance acquisition, SHAV-) anxious/fearful responses. The results showed that, with the exception of fear-potentiated startle, almost all (unlearned and learned) behaviors assessed fit with a pattern of sex effects characterized by male rats as being more fearful than females. We applied factor analyses (oblique rotation) to each sex, with the final two-factor solution showing: (1) a first factor (labelled as "Timidity") comprising BWB, NACT and ZM variables in both sexes, plus SHAV responding in the case of males, and (2) a second factor (called "Defensive Flight") which grouped BAS, FPS, and SHAV responding in both sexes. An additional regression analysis showed significant influences of (unlearned) risk assessment (i.e. stretch-attendance) behavior on SHAV in males, while FPS was the main variable positively influencing SHAV (in the intermediate and advanced phases of acquisition) in females. This indicates, for the first time, that fear-potentiated startle may have a facilitating role in the rat's active responses (at least in females) to the cue in the intermediate to advanced phases (i.e. when the initial "passive avoidance/active avoidance" begins to fade) of shuttle box avoidance acquisition. The results of this first extensive behavioral evaluation of N/Nih-HS rats are discussed in terms of their potential usefulness for present and future neurobehavioral and genetic studies of fearfulness/anxiety.

Interspecific recombinant congenic strains between C57BL/6 and mice of the Mus spretus species: a powerful tool to dissect genetic control of complex traits

Complex traits are under the genetic control of multiple genes, often with weak effects and strong epistatic interactions. We developed two new collections of mouse strains to improve genetic dissection of complex traits. They are derived from several backcrosses of the Mus spretus SEG/Pas or STF/Pas strains on the C57BL/6J background. Each of the 55 interspecific recombinant congenic strains (IRCSs) carries up to eight SEG/Pas chromosomal segments with an average size of 11.7 Mb, totalizing 1.37% of the genome. The complete series covers 39.7% of the SEG/Pas genome. As a complementary resource, six partial or complete interspecific consomic strains were developed and increased genome coverage to 45.6%. To evaluate the usefulness of these strains for QTL mapping, 16 IRCSs were compared with C57BL/6J for seven hematological parameters. Strain 66H, which carries three SEG/Pas chromosomal segments, had lower red blood cell volume and higher platelet count than C57BL/6J. Each chromosomal segment was isolated in a congenic strain to evaluate individual effects. Congenic strains were combined to assess epistasis. Our data show that both traits were controlled by several genes with complex epistatic interactions. IRCSs are therefore useful to unravel QTL with small effects and gene-by-gene interactions.

The genetic landscape of type 2 diabetes in mice

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

Methods for linkage disequilibrium mapping in crops

Linkage disequilibrium (LD) mapping in plants detects and locates quantitative trait loci (QTL) by the strength of the correlation between a trait and a marker. It offers greater precision in QTL location than family-based linkage analysis and should therefore lead to more efficient marker-assisted selection, facilitate gene discovery and help to meet the challenge of connecting sequence diversity with heritable phenotypic differences. Unlike family-based linkage analysis, LD mapping does not require family or pedigree information and can be applied to a range of experimental and non-experimental populations. However, care must be taken during analysis to control for the increased rate of false positive results arising from population structure and variety interrelationships. In this review, we discuss how suitable the recently developed alternative methods of LD mapping are for crops.

Genome-wide genetic association of complex traits in heterogeneous stock mice

Difficulties in fine-mapping quantitative trait loci (QTLs) are a major impediment to progress in the molecular dissection of complex traits in mice. Here we show that genome-wide high-resolution mapping of multiple phenotypes can be achieved using a stock of genetically heterogeneous mice. We developed a conservative and robust bootstrap analysis to map 843 QTLs with an average 95% confidence interval of 2.8 Mb. The QTLs contribute to variation in 97 traits, including models of human disease (asthma, type 2 diabetes mellitus, obesity and anxiety) as well as immunological, biochemical and hematological phenotypes. The genetic architecture of almost all phenotypes was complex, with many loci each contributing a small proportion to the total variance. Our data set, freely available at http://gscan.well.ox.ac.uk, provides an entry point to the functional characterization of genes involved in many complex traits.

Identification of informative strains and provisional QTL mapping of amphetamine (AMPH)-induced locomotion in recombinant congenic strains (RCS) of mice

Amphetamine (AMPH)-induced locomotor activity is a rodent behavioral trait that reflects mesolimbic dopaminergic activity. To identify potential quantitative trait loci (QTL) associated with this behavior, we used 34 recombinant congenic strains (RCSs) of mice derived from A/J (A strains) and C57BL/6J (B strains) and measured AMPH-induced total distance traveled (AMPH-TDIST). Two strains in the A panel (A52 and A63) showed significantly elevated AMPH-TDIST compared to the parental A/J strain and behaved similarly to C57BL/6J. Simple sequence length polymorphism (SSLP) markers on chromosomes 1, 2, 3, 5, 6, 8, 9, 10 and 20 were significantly associated with AMPH-TDIST in the A strains. Within the B panel, two strains (B81 and B74) had significantly higher and two strains (B69 and B75) had significantly lower AMPH-TDIST than C57BL/6J. Markers associated with AMPH-TDIST in the B strains appeared on chromosomes 5, 17 and 20. Combining data from this approach and other genetic (mapping data in humans) and functional (cDNA expression) sources may help to identify suitable candidate genes relevant to human disorders where mesolimbic dopamine dysregulation has been postulated.

Two quantitative trait loci for prepulse inhibition of startle identified on mouse chromosome 16 using chromosome substitution strains

Prepulse inhibition (PPI) of acoustic startle is a genetically complex quantitative phenotype of considerable medical interest due to its impairment in psychiatric disorders such as schizophrenia. To identify quantitative trait loci (QTL) involved in mouse PPI, we studied mouse chromosome substitution strains (CSS) that each carry a homologous chromosome pair from the A/J inbred strain on a host C57BL/6J inbred strain background. We determined that the chromosome 16 substitution strain has elevated PPI compared to C57BL/6J (P = 1.6 x 10(-11)), indicating that chromosome 16 carries one or more PPI genes. QTL mapping using 87 F(2) intercross progeny identified two significant chromosome 16 loci with LODs of 3.9 and 4.7 (significance threshold LOD is 2.3). The QTL were each highly significant independently and do not appear to interact. Sequence variation between B6 and A/J was used to identify strong candidate genes in the QTL regions, some of which have known neuronal functions. In conclusion, we used mouse CSS to rapidly and efficiently identify two significant QTL for PPI on mouse chromosome 16. The regions contain a limited number of strong biological candidate genes that are potential risk genes for psychiatric disorders in which patients have PPI impairments.

The genetic factors in cancer development and their implications for cancer prevention and detection

Experimental data from laboratory animals indicate that the same extent of DNA damage or the same mutations in oncogenes and tumor suppressor genes in different hosts result in widely differing cancer development because of numerous polymorphic tumor susceptibility genes. Similarly, recent epidemiological data indicate that susceptibility to common, "sporadic" cancers in humans is influenced considerably by multiple polymorphic host genes with relatively weak effects. This indicates that in addition to hereditary familial cancer syndromes, the sporadic cancer is also under strong genetic control. The multiplicity of genes involved, variation in exposure to environmental carcinogens, and small sizes of cancer families prevent efficient searches for the responsible genes in humans. Therefore, an alternative strategy based on the definition of susceptibility genes in experimental animals and the subsequent study of their human homologues has been successfully employed by several groups. This strategy also helped reveal several important features of cancer susceptibility, including mutual interactions of cancer susceptibility genes, their functional heterogeneity, and the existence of stage-specific control of cancer development. This latter phenomenon is especially important, because the susceptibility to early stages of cancer development may be quite different from that of late stages of cancer development. This needs to be taken into account when introducing preventive testing of biomarkers of early preneoplastic lesions or early cancers, because their predictive value is greatly influenced by the genetically determined individual tendency to proceed toward a more advanced form of neoplasia. Therefore, genetic testing of persons in danger of being exposed to carcinogenic factors should be an important part of the personnel selection.

Integrating Genetic and Gene Expression Data to Study the Metabolic Syndrome and Diabetes in Mice

Increasingly, the mouse is becoming the standard model for mammalian physiology and disease. It can be genetically analyzed and manipulated with relative ease. Moreover, the endogenous genetic variation that exists among inbred mouse strains can be exploited to identify genetic control of complex physiologic processes involved in diabetes and the metabolic syndrome, among other conditions relevant to human disease. Recent advances in genetics and gene expression technology have greatly increased the knowledge to be derived from this approach when applied to traditional genetic studies.

Hepatocellular carcinoma as a complex polygenic disease. Interpretive analysis of recent developments on genetic predisposition

The different frequency of hepatocellular carcinoma (HCC) in humans at risk suggests a polygenic predisposition. However, detection of genetic variants is difficult in genetically heterogeneous human population. Studies on mouse and rat models identified 7 hepatocarcinogenesis susceptibility (Hcs) and 2 resistance (Hcr) loci in mice, and 7 Hcs and 9 Hcr loci in rats, controlling multiplicity and size of neoplastic liver lesions. Six liver neoplastic nodule remodeling (Lnnr) loci control number and volume of re-differentiating lesions in rat. A Hcs locus, with high phenotypic effects, and various epistatic gene-gene interactions were identified in rats, suggesting a genetic model of predisposition to hepatocarcinogenesis with different subset of low-penetrance genes, at play in different subsets of population, and a major locus. This model is in keeping with human HCC epidemiology. Several putative modifier genes in rodents, deregulated in HCC, are located in chromosomal segments syntenic to sites of chromosomal aberrations in humans, suggesting possible location of predisposing loci. Resistance to HCC is associated with lower genomic instability and downregulation of cell cycle key genes in preneoplastic and neoplastic lesions. p16(INK4A) upregulation occurs in susceptible and resistant rat lesions. p16(INK4A)-induced growth restraint was circumvented by Hsp90/Cdc37 chaperons and E2f4 nuclear export by Crm1 in susceptible, but not in resistant rats and human HCCs with better prognosis. Thus, protective mechanisms seem to be modulated by HCC modifiers, and differences in their efficiency influence the susceptibility to hepatocarcinogenesis and probably the prognosis of human HCC.

Strategies for mapping and cloning quantitative trait genes in rodents

Over the past 15 years, more than 2,000 quantitative trait loci (QTLs) have been identified in crosses between inbred strains of mice and rats, but less than 1% have been characterized at a molecular level. However, new resources, such as chromosome substitution strains and the proposed Collaborative Cross, together with new analytical tools, including probabilistic ancestral haplotype reconstruction in outbred mice, Yin-Yang crosses and in silico analysis of sequence variants in many inbred strains, could make QTL cloning tractable. We review the potential of these strategies to identify genes that underlie QTLs in rodents.

Genetic background has a major effect on the penetrance and severity of craniofacial defects in mice heterozygous for the gene encoding the nucleolar protein Treacle

Treacher Collins syndrome (TCS) is a craniofacial disorder that results from mutations in TCOF1, which encodes the nucleolar protein Treacle. The severity of the clinical features exhibits wide variation and includes hypoplasia of the mandible and maxilla, abnormalities of the external ears and middle ear ossicles, and cleft palate. To determine the in vivo function of Treacle, we previously generated Tcof1 heterozygous mice on a mixed C57BL/6 and 129 background. These mice exhibited a lethal phenotype, which included abnormal development of the maxilla, absence of the eyes and nasal passages, and neural tube defects. Here, we show that placing the mutation onto different genetic backgrounds has a major effect on the penetrance and severity of the craniofacial and other defects. The offspring exhibit markedly variable strain-dependent phenotypes that range from extremely severe and lethal in a mixed CBA/Ca and 129 background, to apparently normal and viable in a mixed BALB/c and 129 background. In the former case, in addition to a profoundly severe craniofacial phenotype, CBA-derived heterozygous mice also exhibited delayed ossification of the long bones, rib fusions, and digit anomalies. The results of our studies indicate that factors in the different genetic backgrounds contribute extensively to the Tcof1 phenotype.

Forty years of cancer modelling in the mouse

Mouse models of human cancer have played an important role in formulating modern concepts of multistage carcinogenesis, and are providing us with a new armoury of tools for the testing of novel therapeutic approaches to cancer treatment. The development of inducible and conditional technologies provide us with greater opportunity to generate mouse models which faithfully recapitulate human tumorigenesis, in terms of both the biology and the genetics of this disease. It is now feasible to control, in time and space, the development of tumours in almost any mouse tissue, such that we now have available mouse models of all major human cancers. Moreover, novel non-invasive approaches to tumour imaging will enable us to follow tumour development and metastasis in vivo, as well as the effects of candidate therapeutic drugs. Such new generation tumour models, which accurately emulate the disease state in situ, should provide a useful platform with which to experimentally test drugs targeted to specific gene products, or combinations of genes that control rate-limiting steps of tumour development.