Multiple cross and inbred strain haplotype mapping of complex-trait candidate genes

Comparative genomic analyses of multiple backcross mouse populations suggest SGCG as a novel potential obesity-modifier gene

To nominate novel disease genes for obesity and type 2 diabetes (T2D), we recently generated two mouse backcross populations of the T2D-susceptible New Zealand Obese (NZO/HI) mouse strain and two genetically different, lean and T2D-resistant strains, 129P2/OlaHsd and C3HeB/FeJ. Comparative linkage analysis of our two female backcross populations identified seven novel body fat-associated quantitative trait loci (QTL). Only the locus Nbw14 (NZO body weight on chromosome 14) showed linkage to obesity-related traits in both backcross populations, indicating that the causal gene variant is likely specific for the NZO strain as NZO allele carriers in both crosses displayed elevated body weight and fat mass. To identify candidate genes for Nbw14, we used a combined approach of gene expression and haplotype analysis to filter for NZO-specific gene variants in gonadal white adipose tissue (gWAT), defined as the main QTL-target tissue. Only two genes, Arl11 and Sgcg, fulfilled our candidate criteria. In addition, expression QTL analysis revealed cis-signals for both genes within the Nbw14 locus. Moreover, retroviral overexpression of Sgcg in 3 T3-L1 adipocytes resulted in increased insulin-stimulated glucose uptake. In humans, mRNA levels of SGCG correlated with BMI and body fat mass exclusively in diabetic subjects, suggesting that SGCG may present a novel marker for metabolically unhealthy obesity. In conclusion, our comparative-cross analysis could substantially improve the mapping resolution of the obesity locus Nbw14. Future studies will shine light on the mechanism by which Sgcg may protect from the development of obesity.

Genetic insights into the morass of metastatic heterogeneity

Tumour heterogeneity poses a substantial problem for the clinical management of cancer. Somatic evolution of the cancer genome results in genetically distinct subclones in the primary tumour with different biological properties and therapeutic sensitivities. The problem of heterogeneity is compounded in metastatic disease owing to the complexity of the metastatic process and the multiple biological hurdles that the tumour cell must overcome to establish a clinically overt metastatic lesion. New advances in sequencing technology and clinical sample acquisition are providing insights into the phylogenetic relationship of metastases and primary tumours at the level of somatic tumour genetics while also illuminating fundamental mechanisms of the metastatic process. In addition to somatically acquired genetic heterogeneity in the tumour cells, inherited population-based genetic heterogeneity can profoundly modify metastatic biology and further complicate the development of effective, broadly applicable antimetastatic therapies. Here, we examine how genetic heterogeneity impacts metastatic disease and the implications of current knowledge for future research endeavours and therapeutic interventions.

Defining the Influence of Germline Variation on Metastasis Using Systems Genetics Approaches

Cancer is estimated to be responsible for 8 million deaths worldwide and over half a million deaths every year in the United States. The majority of cancer-related deaths in solid tumors is directly associated with the effects of metastasis. While the influence of germline factors on cancer risk and development has long been recognized, the contribution of hereditary variation to tumor progression and metastasis has only gained acceptance more recently. A variety of approaches have been used to define how hereditary variation influences tumor progression and metastasis. One approach that garnered much early attention was epidemiological studies of cohorts of cancer patients, which demonstrated that specific loci within the human genome are associated with a differential propensity for aggressive tumor development. However, a powerful, and somewhat underutilized approach has been the use of systems genetics approaches in transgenic mouse models of human cancer. Such approaches are typically multifaceted, and involve integration of multiple lines of evidence derived, for example, from genetic and transcriptomic screens of genetically diverse mouse models of cancer, coupled with bioinformatics analysis of human cancer datasets, and functional analysis of candidate genes. These methodologies have allowed for the identification of multiple hereditary metastasis susceptibility genes, with wide-ranging cellular functions including regulation of gene transcription, cell proliferation, and cell-cell adhesion. In this chapter, we review how each of these approaches have facilitated the identification of these hereditary metastasis modifiers, the molecular functions of these metastasis-associated genes, and the implications of these findings upon patient survival.

The role of individual inheritance in tumor progression and metastasis

Metastasis, the dissemination and growth of tumor cells at secondary sites, is the primary cause of patient mortality from solid tumors. Metastasis is an extremely complex, inefficient process requiring contributions of not only the tumor cell but also local and distant environmental factors, at both the cellular and molecular level. Variation in the function of any of the steps in the metastatic cascade may therefore have profound implications for the ultimate course of the disease. In addition to the somatic and cellular heterogeneity that can affect cancer outcome, an individual's specific ancestry or genetic background can also significantly influence metastatic progression. These inherited variants not only encoded for metastatic susceptibility but also provided a window to study critical factors that are not easily accessible with current technologies. Furthermore, investigations into inherited metastatic susceptibility enable identification of important molecular and cellular processes that are not subject to mutation and are consequently not detectable by standard cancer genome sequencing strategies. Incorporation of inherited variation into metastasis research therefore provides methods to more comprehensively investigate the etiology of the lethal consequences of tumor progression.

Natural genetic variation of integrin alpha L (Itgal) modulates ischemic brain injury in stroke

During ischemic stroke, occlusion of the cerebrovasculature causes neuronal cell death (infarction), but naturally occurring genetic factors modulating infarction have been difficult to identify in human populations. In a surgically induced mouse model of ischemic stroke, we have previously mapped Civq1 to distal chromosome 7 as a quantitative trait locus determining infarct volume. In this study, genome-wide association mapping using 32 inbred mouse strains and an additional linkage scan for infarct volume confirmed that the size of the infarct is determined by ancestral alleles of the causative gene(s). The genetically isolated Civq1 locus in reciprocal recombinant congenic mice refined the critical interval and demonstrated that infarct size is determined by both vascular (collateral vessel anatomy) and non-vascular (neuroprotection) effects. Through the use of interval-specific SNP haplotype analysis, we further refined the Civq1 locus and identified integrin alpha L (Itgal) as one of the causative genes for Civq1. Itgal is the only gene that exhibits both strain-specific amino acid substitutions and expression differences. Coding SNPs, a 5-bp insertion in exon 30b, and increased mRNA and protein expression of a splice variant of the gene (Itgal-003, ENSMUST00000120857), all segregate with infarct volume. Mice lacking Itgal show increased neuronal cell death in both ex vivo brain slice and in vivo focal cerebral ischemia. Our data demonstrate that sequence variation in Itgal modulates ischemic brain injury, and that infarct volume is determined by both vascular and non-vascular mechanisms.

Varying coefficient models for mapping quantitative trait loci using recombinant inbred intercrosses

There has been a great deal of interest in the development of methodologies to map quantitative trait loci (QTL) using experimental crosses in the last 2 decades. Experimental crosses in animal and plant sciences provide important data sources for mapping QTL through linkage analysis. The Collaborative Cross (CC) is a renewable mouse resource that is generated from eight genetically diverse founder strains to mimic the genetic diversity in humans. The recombinant inbred intercrosses (RIX) generated from CC recombinant inbred (RI) lines share similar genetic structures of F(2) individuals but with up to eight alleles segregating at any one locus. In contrast to F(2) mice, genotypes of RIX can be inferred from the genotypes of their RI parents and can be produced repeatedly. Also, RIX mice typically do not share the same degree of relatedness. This unbalanced genetic relatedness requires careful statistical modeling to avoid false-positive findings. Many quantitative traits are inherently complex with genetic effects varying with other covariates, such as age. For such complex traits, if phenotype data can be collected over a wide range of ages across study subjects, their dynamic genetic patterns can be investigated. Parametric functions, such as sigmoidal or logistic functions, have been used for such purpose. In this article, we propose a flexible nonparametric time-varying coefficient QTL mapping method for RIX data. Our method allows the QTL effects to evolve with time and naturally extends classical parametric QTL mapping methods. We model the varying genetic effects nonparametrically with the B-spline bases. Our model investigates gene-by-time interactions for RIX data in a very flexible nonparametric fashion. Simulation results indicate that the varying coefficient QTL mapping has higher power and mapping precision compared to parametric models when the assumption of constant genetic effects fails. We also apply a modified permutation procedure to control overall significance level.

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.

Vesico-ureteric reflux: using mouse models to understand a common congenital urinary tract defect

Vesico-ureteric reflux (VUR) is a common congenital urinary tract defect in which urine flows retrogradely from the bladder to the kidneys because of an abnormally formed uretero-vesical junction. It is associated with recurrent urinary tract infections, renal hypo/dysplasia, reflux nephropathy, hypertension, and end-stage renal disease. In humans, VUR is genetically and phenotypically heterogeneous, encompassing diverse renal and urinary tract phenotypes. To understand the significance of these phenotypes, we and others have used the mouse as a model organism and this has led to the identification of new candidate genes. Through careful phenotypic analysis of these models, a new understanding of the genetics and biology of VUR is now underway.

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.

Mapping quantitative traits and strategies to find quantitative trait genes

In 1999 a meeting took place at the Jackson Laboratory, a large mouse research centre in Bar Harbor, Maine, to consider the value of systematically collecting phenotypes on inbred strains of mice (Paigen and Eppig (2000) [1]). The group concluded that cataloguing the extensive phenotypic diversity present among laboratory mice, and in particular providing the research community with data from cohorts of animals, phenotyped according to standardized protocols, was essential if we were to take advantage of the possibilities of mouse genetics. Beginning with the collection of basic physiological, biochemical and behavioral data on nine commonly used inbred strains, the project has expanded so that by the beginning of 2010 data for 178 strains had been collected, with 105 phenotype projects yielding over 2000 different measurements (Bogue et al. (2007) [2].

A novel strategy for genetic dissection of complex traits: the population of specific chromosome substitution strains from laboratory and wild mice

The mouse is an irreplaceable model for understanding the precise genetic mechanisms of mammalian physiological pathways. Thousands of quantitative trait loci (QTLs) have been mapped onto the mouse genome during the last two decades. However, only a few genes' underlying complex traits have been successfully identified, and rapid fine mapping of QTL genes still remains a challenge for mouse geneticists. Currently, the Collaborative Cross (CC) has proceeded to the goal of establishing more than 1,000 recombinant inbred strains for the sub-centimorgan mapping resolution of QTL loci. In this article, a novel complementary strategy, designated as population of specific chromosome substitution strains or PSCSS, is proposed for rapid fine mapping of QTLs on the substituted chromosome. One specific chromosome (Chr 1) of recipient mouse strain C57BL/6 J has been substituted by homologous counterparts from five different inbred strains (C3H/He, FVB/N, AKR, NOD/LtJ, NZW/LacJ), an outbred line Kunmin mouse in China, and 23 wild mice captured in different localities. The primary genetic studies on the structure of these wild donor chromosomes (Chr 1) show that these donor chromosomes harbor extensive genetic polymorphisms, with a high density of SNP distribution, abundant variations of STR alleles, and a high level of historical recombination accumulation. These specific chromosome substitution strains eventually form a special population that has the identical genetic background of the recipient strain and differs only in the donor chromosomes. With simple association studies, known QTLs on the donor chromosome can be rapidly mapped in high resolution without requirement of further crosses. This approach, taking advantage of the extensive genetic polymorphisms of wild resources and chromosome substitution strategy, brings a new outlook for genetic dissection of complex traits.

Assessing the prospects of genome-wide association studies performed in inbred mice

The remarkable success in mapping genes linked to a number of disease traits using genome-wide association studies (GWAS) in human cohorts has renewed interest in applying this same technique in model organisms such as inbred laboratory mice. Unlike humans, however, the limited genetic diversity in the ancestry of laboratory mice combined with selection pressure over the past decades have yielded an intricate population genetic structure that can complicate the results obtained from association studies. This problem is further exacerbated by the small number of strains typically used in such studies where multiple spurious associations arise as a result of random chance. We sought to empirically assess the viability of GWAS in inbred mice using hundreds of expression traits for which the true location of the expression quantitative trait locus was known a priori. We then measured transcript abundance levels for these expression traits in 16 classical and 3 wild-derived inbred strains and carried out a genome-wide association scan, demonstrating the low statistical power of such studies and empirically estimating the large extent to which allelic association of transcripts gives rise to spurious associations. We provide evidence illustrating that in a large fraction of cases, the marker with the most significant p values fails to map to the location of the true eQTL. Finally, we provide experimental support for hundreds of traits, and that combining linkage analysis with association mapping provides significant increases in statistical power over a stand-alone GWAS as well as significantly higher mapping resolution than either study alone.

Genetically engineered mouse models in cancer research

Mouse models of human cancer have played a vital role in understanding tumorigenesis and answering experimental questions that other systems cannot address. Advances continue to be made that allow better understanding of the mechanisms of tumor development, and therefore the identification of better therapeutic and diagnostic strategies. We review major advances that have been made in modeling cancer in the mouse and specific areas of research that have been explored with mouse models. For example, although there are differences between mice and humans, new models are able to more accurately model sporadic human cancers by specifically controlling timing and location of mutations, even within single cells. As hypotheses are developed in human and cell culture systems, engineered mice provide the most tractable and accurate test of their validity in vivo. For example, largely through the use of these models, the microenvironment has been established to play a critical role in tumorigenesis, since tumor development and the interaction with surrounding stroma can be studied as both evolve. These mouse models have specifically fueled our understanding of cancer initiation, immune system roles, tumor angiogenesis, invasion, and metastasis, and the relevance of molecular diversity observed among human cancers. Currently, these models are being designed to facilitate in vivo imaging to track both primary and metastatic tumor development from much earlier stages than previously possible. Finally, the approaches developed in this field to achieve basic understanding are emerging as effective tools to guide much needed development of treatment strategies, diagnostic strategies, and patient stratification strategies in clinical research.

Lessons in signaling and tumorigenesis from polyomavirus middle T antigen

The small DNA tumor viruses have provided a very long-lived source of insights into many aspects of the life cycle of eukaryotic cells. In recent years, the emphasis has been on cancer-related signaling. Here we review murine polyomavirus middle T antigen, its mechanisms, and its downstream pathways of transformation. We concentrate on the MMTV-PyMT transgenic mouse, one of the most studied models of breast cancer, which permits the examination of in situ tumor progression from hyperplasia to metastasis.

Fine mapping of an epilepsy modifier gene on mouse Chromosome 19

Mutations in voltage-gated sodium channels are associated with several types of human epilepsy. Variable expressivity and penetrance are common features of inherited epilepsy caused by sodium channel mutations, suggesting that genetic modifiers may influence clinical severity. The mouse model Scn2a(Q54) has an epilepsy phenotype due to a mutation in Scn2a that results in elevated persistent sodium current. Phenotype severity in Scn2a(Q54) mice is dependent on the genetic background. Congenic C57BL/6J.Q54 mice have delayed onset and low seizure frequency compared to (C57BL/6J x SJL/J)F1.Q54 mice. Previously, we identified two modifier loci that influence the Scn2a(Q54) epilepsy phenotype: Moe1 (modifier of epilepsy 1) on Chromosome 11 and Moe2 on Chromosome 19. We have constructed interval-specific congenic strains to further refine the position of Moe2 on Chromosome 19 to a 5-Mb region. Sequencing and expression analyses of genes in the critical interval suggested two potential modifier candidates: (1) voltage-gated potassium channel subunit subfamily V, member 2 (Kcnv2), and (2) SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily a, member 2 (Smarca2). Based on its biological role in regulating membrane excitability and the association between ion channel variants and seizures, Kcnv2 is a strong functional candidate for Moe2. Modifier genes affecting the epilepsy phenotype of Scn2a(Q54) mice may contribute to variable expressivity and penetrance in human epilepsy patients with sodium channel mutations.

In silico whole genome association scan for murine prepulse inhibition

BACKGROUND

The complex trait of prepulse inhibition (PPI) is a sensory gating measure related to schizophrenia and can be measured in mice. Large-scale public repositories of inbred mouse strain genotypes and phenotypes such as PPI can be used to detect Quantitative Trait Loci (QTLs) in silico. However, the method has been criticized for issues including insufficient number of strains, not controlling for false discoveries, the complex haplotype structure of inbred mice, and failing to account for genotypic and phenotypic subgroups.

METHODOLOGY/PRINCIPAL FINDINGS

We have implemented a method that addresses these issues by incorporating phylogenetic analyses, multilevel regression with mixed effects, and false discovery rate (FDR) control. A genome-wide scan for PPI was conducted using over 17,000 single nucleotide polymorphisms (SNPs) in 37 strains phenotyped. Eighty-nine SNPs were significant at a false discovery rate (FDR) of 5%. After accounting for long-range linkage disequilibrium, we found 3 independent QTLs located on murine chromosomes 1 and 13. One of the PPI positives corresponds to a region of human chromosome 6p which includes DTNBP1, a gene implicated in schizophrenia. Another region includes the gene Tsn which alters PPI when knocked out. These genes also appear to have correlated expression with PPI.

CONCLUSIONS/SIGNIFICANCE

These results support the usefulness of using an improved in silico mapping method to identify QTLs for complex traits such as PPI which can be then be used for to help identify loci influencing schizophrenia in humans.

Gene expression profiles and breast cancer metastasis: a genetic perspective

The majority of cancer mortality is attributed to metastasis, which is the spread of tumor cells to a secondary site. Several studies have demonstrated that the genetic background on which a tumor arises has a major effect on both metastatic efficiency and on predictive gene expression profiles. These observations suggest that there is variability in metastasis frequency between individuals and that some individuals could be more prone to secondary tumor formation and development than others. Thus, genetic background might have important clinical implications in metastasis detection, management and prevention.

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.

Mouse modifier genes in mammary tumorigenesis and metastasis

Tumorigenesis and metastasis are complex multistep processes. In addition to the numerous somatic mutations that facilitate cancer progression, there is abundant evidence that an individual's genetic background not only contributes to overall cancer risk, but also specifically influences metastatic potential. The handful of human susceptibility genes that have been identified thus far do not fully account for hereditary cancer risk, and the discovery of additional susceptibility loci using population based studies is complex, time-consuming and expensive. Therefore, we and others have used a variety of mouse models to identify novel candidate susceptibility genes. Here we review how these mouse models have contributed to our understanding of the role of genetic background in modifying tumorigenesis and metastasis susceptibility.

PPARG by dietary fat interaction influences bone mass in mice and humans

Adult BMD, an important risk factor for fracture, is the result of genetic and environmental interactions. A quantitative trait locus (QTL) for the phenotype of volumetric BMD (vBMD), named Bmd8, was found on mid-distal chromosome (Chr) 6 in mice. This region is homologous to human Chr 3p25. The B6.C3H-6T (6T) congenic mouse was previously created to study this QTL. Using block haplotyping of the 6T congenic region, expression analysis in the mouse, and examination of nonsynonymous SNPs, peroxisome proliferator activated receptor gamma (Pparg) was determined to be the most likely candidate gene for the Bmd8 QTL of the 630 genes located in the congenic region. Furthermore, in the C3H/HeJ (C3H) strain, which is the donor strain for the 6T congenic, several polymorphisms were found in the Pparg gene. On challenge with a high-fat diet, we found that the 6T mouse has a lower areal BMD (aBMD) and volume fraction of trabecular bone (BV/TV%) of the distal femur compared with B6 mice. Interactions between SNPs in the PPARG gene and dietary fat for the phenotype of BMD were examined in the Framingham Offspring Cohort. This analysis showed that there was a similar interaction of the PPARG gene and diet (fat intake) on aBMD in both men and women. These findings suggest that dietary fat has a significant influence on BMD that is dependent on the alleles present for the PPARG gene.

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

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

Single-nucleotide-polymorphism-based association mapping of dog stereotypes

Phenotypic stereotypes are traits, often polygenic, that have been stringently selected to conform to specific criteria. In dogs, Canis familiaris, stereotypes result from breed standards set for conformation, performance (behaviors), etc. As a consequence, phenotypic values measured on a few individuals are representative of the breed stereotype. We used DNA samples isolated from 148 dog breeds to associate SNP markers with breed stereotypes. Using size as a trait to test the method, we identified six significant quantitative trait loci (QTL) on five chromosomes that include candidate genes appropriate to regulation of size (e.g., IGF1, IGF2BP2 SMAD2, etc.). Analysis of other morphological stereotypes, also under extreme selection, identified many additional significant loci. Less well-documented data for behavioral stereotypes tentatively identified loci for herding, pointing, boldness, and trainability. Four significant loci were identified for longevity, a breed characteristic not under direct selection, but inversely correlated with breed size. The strengths and limitations of the approach are discussed as well as its potential to identify loci regulating the within-breed incidence of specific polygenic diseases.

Quantitative trait locus analysis: multiple cross and heterogeneous stock mapping

Until well into the 1990s, both preclinical and clinical research focused on finding "the" gene for human diseases, including alcoholism. This focus was reinforced by the emergence of technologies to either inactivate (i.e., knock out) a gene or add extra copies of an existing gene in a living organism, which clearly demonstrated that over- or underexpressing a single gene could have a profound effect on behavior. However, a small but vocal group of scientists, including many alcohol researchers, argued that behaviors, including alcohol-related behaviors, were complex traits and therefore no one gene likely would have a large effect. This view was consistent with a large body of genetic research conducted in plants and fruit flies (e.g., Paterson et al. 1988) indicating that, for example, even a presumably simple characteristic, such as the size of a tomato, was determined by several genes. However, it was difficult to convince the scientific community that, in terms of its genetic determination, behavior was similar to the size of a tomato. Only with the advent of new genetic tools did it become possible to prove that many different genes contribute to complex behavioral characteristics.

A simple method for combining genetic mapping data from multiple crosses and experimental designs

Background

Over the past decade many linkage studies have defined chromosomal intervals containing polymorphisms that modulate a variety of traits. Many phenotypes are now associated with enough mapping data that meta-analysis could help refine locations of known QTLs and detect many novel QTLs.

Methodology/Principal Findings

We describe a simple approach to combining QTL mapping results for multiple studies and demonstrate its utility using two hippocampus weight loci. Using data taken from two populations, a recombinant inbred strain set and an advanced intercross population we demonstrate considerable improvements in significance and resolution for both loci. 1-LOD support intervals were improved 51% for Hipp1a and 37% for Hipp9a. We first generate locus-wise permuted P-values for association with the phenotype from multiple maps, which can be done using a permutation method appropriate to each population. These results are then assigned to defined physical positions by interpolation between markers with known physical and genetic positions. We then use Fisher's combination test to combine position-by-position probabilities among experiments. Finally, we calculate genome-wide combined P-values by generating locus-specific P-values for each permuted map for each experiment. These permuted maps are then sampled with replacement and combined. The distribution of best locus-specific P-values for each combined map is the null distribution of genome-wide adjusted P-values.

Conclusions/Significance

Our approach is applicable to a wide variety of segregating and non-segregating mapping populations, facilitates rapid refinement of physical QTL position, is complementary to other QTL fine mapping methods, and provides an appropriate genome-wide criterion of significance for combined mapping results.

Rrp1b, a new candidate susceptibility gene for breast cancer progression and metastasis

A novel candidate metastasis modifier, ribosomal RNA processing 1 homolog B (Rrp1b), was identified through two independent approaches. First, yeast two-hybrid, immunoprecipitation, and functional assays demonstrated a physical and functional interaction between Rrp1b and the previous identified metastasis modifier Sipa1. In parallel, using mouse and human metastasis gene expression data it was observed that extracellular matrix (ECM) genes are common components of metastasis predictive signatures, suggesting that ECM genes are either important markers or causal factors in metastasis. To investigate the relationship between ECM genes and poor prognosis in breast cancer, expression quantitative trait locus analysis of polyoma middle-T transgene-induced mammary tumor was performed. ECM gene expression was found to be consistently associated with Rrp1b expression. In vitro expression of Rrp1b significantly altered ECM gene expression, tumor growth, and dissemination in metastasis assays. Furthermore, a gene signature induced by ectopic expression of Rrp1b in tumor cells predicted survival in a human breast cancer gene expression dataset. Finally, constitutional polymorphism within RRP1B was found to be significantly associated with tumor progression in two independent breast cancer cohorts. These data suggest that RRP1B may be a novel susceptibility gene for breast cancer progression and metastasis.

Metastasis, which is defined as the spread of malignant tumor cells from their original site to other parts of the body, accounts for the vast majority of solid cancer-related mortality. Our laboratory has previously shown that host germline-encoded variation modifies primary tumor metastatic capacity. Here, we detail how germline-encoded Rrp1b variation likely modulates metastasis. In mice, constitutional Rrp1b variation correlates with ECM gene expression, which are genes commonly differentially regulated in metastasis prone tumors. Furthermore, we demonstrate that Rrp1b expression levels are modulated by germline variation in mice with differing metastatic propensities, and that variation of Rrp1b expression in a highly metastatic mouse mammary tumor cell line modifies progression. Differential RRP1B functionality also appears to play an important role in human breast cancer progression. Specifically, we demonstrate that a microarray gene expression signature indicative of differential RRP1B expression predicts breast cancer-specific survival. Furthermore, we show that germline-encoded RRP1B variation is associated with markers of outcome in two breast cancer populations. In summary, these data suggest that Rrp1b may be a germline-encoded metastasis modifier in both mice and humans, which leads to the possibility that knowledge of RRP1B functionality and variation in breast cancer might facilitate improved assessment of prognosis.

Identification of Mom7, a novel modifier of Apc(Min/+) on mouse chromosome 18

The Apc(Min) mouse model of colorectal cancer provides a discrete, quantitative measurement of tumor multiplicity, allowing for robust quantitative trait locus analysis. This advantage has previously been used to uncover polymorphic modifiers of the Min phenotype: Mom1, which is partly explained by Pla2g2a; Mom2, a spontaneous mutant modifier; and Mom3, which was discovered in an outbred cross. Here, we describe the localization of a novel modifier, Mom7, to the pericentromeric region of chromosome 18. Mom7 was mapped in crosses involving four inbred strains: C57BL/6J (B6), BTBR/Pas (BTBR), AKR/J (AKR), and A/J. There are at least two distinct alleles of Mom7: the recessive, enhancing BTBR, AKR, and A/J alleles and the dominant, suppressive B6 allele. Homozygosity for the enhancing alleles increases tumor number by approximately threefold in the small intestine on both inbred and F(1) backgrounds. Congenic line analysis has narrowed the Mom7 region to within 7.4 Mb of the centromere, 28 Mb proximal to Apc. Analysis of SNP data from various genotyping projects suggests that the region could be as small as 4.4 Mb and that there may be five or more alleles of Mom7 segregating among the many strains of inbred mice. This has implications for experiments involving Apc(Min) and comparisons between different or mixed genetic backgrounds.

Prospects for association mapping in classical inbred mouse strains

The collection of classical inbred mouse strains displays heritable variation in a large number of complex traits. Many generations of historical recombination have contributed to the panel of classical strain genomes, raising the possibility that quantitative trait loci could be located with high resolution by correlating strain genotypes and phenotypes. Although this association mapping framework has been successful in several empirical applications, its expected performance remains unclear. We used computer simulations based on a publicly available, dense single-nucleotide polymorphism (SNP) map to measure the power and false-positive rate of association mapping on a genomic scale across 30 commonly used classical inbred strains. Expected power is (i) often low for phenotypic effect sizes that are realistic for complex traits, (ii) highly variable across the genome, and (iii) correlated with linkage disequilibrium, aspects of the allele frequency distribution, and haplotype characteristics, as predicted by theory. Simulations also demonstrate clear potential for spurious associations to be generated by unequal relatedness among the strains. These findings suggest that association mapping in the classical strains is best applied in combination with other procedures, such as QTL mapping.

Variation in anti-predator behavior among five strains of inbred guppies, Poecilia reticulata

Quantitative genetic studies frequently utilize inbred strains of animals as tools for partitioning the direct and indirect effects of genes from environmental effects in generating an observed phenotype, however, this approach is rarely applied to behavioral studies. Guppies, Poecilia reticulata, perform a set of anti-predator behaviors that may provide an ideal system to study how complex behavioral traits are generated. To assess the utility of ornamental guppies in quantitative genetics studies of behavior, we assayed five morphologically distinct strains of ornamental guppies for response to predator cues and for variation in response among strains. Despite individual variation, all five strains responded to predator cues and differences among strains were found for all assayed behaviors, including measures of boldness and predator avoidance.

Further studies on using multiple-cross mapping (MCM) to map quantitative trait loci

We have completed whole-genome scans for quantitative trait loci (QTLs) associated with acute ethanol-induced activation in the six F(2) intercrosses that can be formed from the C57BL/6J (B6), DBA/2J (D2) , BALB/cJ (C), and LP/J (LP) inbred strains. The goal was to test the hypothesis that given the relatively simple structure of the laboratory mouse genome, the same QTLs will be detected in multiple crosses which in turn will provide support for the strategy of multiple-cross mapping (MCM). QTLs with LOD scores greater than 4 were detected on Chrs 1, 2, 3, 8, 9, 13, 14, and 16. Only for the QTL on distal Chr 1 was there convincing evidence that the same or at least a very similar QTL was detected in multiple crosses. We also mapped the Chr 2 QTL directly in heterogeneous stock (HS) animals derived from the four inbred strains. At G(19) the QTL was mapped to an approximately 3-Mbp interval and this interval was associated with a haplotype block with a largely biallelic structure: B6-L:C-D2. We conclude that mapping in HS animals not only provides significantly greater QTL resolution, at least in some cases it provides significantly more information about the QTL haplotype structure.

Combined-cross analysis of genome-wide linkage scans for experimental autoimmune encephalomyelitis in rat

Unbiased genetic analysis of experimental autoimmune encephalomyelitis (EAE) can provide insights into the pathogenesis of multiple sclerosis. To date five genome-wide scans using F2 crosses between different inbred rats have been performed with the aim of defining EAE-regulating quantitative trait loci (QTLs) as the starting point for identification of the underlying genes. We here report the first combined-cross analysis of three F2 crosses previously performed in our group. The majority of QTLs was shared between the different strain combinations and was therefore reproduced by the combined-cross analysis. Consequently, combined-cross analysis improved the power to detect QTLs with modest effects and narrowed QTL confidence intervals. The findings also demonstrate a lack of power in previous F2 crosses and encourage future use of larger populations. Moreover, the allelic states of shared QTLs could be established, thus providing critical information for narrowing QTLs and identifying the key polymorphism by subsequent haplotype analysis.

An integrative approach for the identification of quantitative trait loci

The genetic dissection of complex traits is one of the most difficult and most important challenges facing science today. We discuss here an integrative approach to quantitative trait loci (QTL) mapping in mice. This approach makes use of the wealth of genetic tools available in mice, as well as the recent advances in genome sequence data already available for a number of inbred mouse strains. We have developed mapping strategies that allow a stepwise narrowing of a QTL mapping interval, prioritizing candidate genes for further analysis with the potential of identifying the most probable candidate gene for the given trait. This approach integrates traditional mapping tools, fine mapping tools, sequence-based analysis, bioinformatics and gene expression.

Pooled analysis of data from multiple quantitative trait locus mapping populations

Quantitative trait locus (QTL) analysis on pooled data from multiple populations (pooled analysis) provides a means for evaluating, as a whole, evidence for existence of a QTL from different studies and examining differences in gene effect of a QTL among different populations. Objectives of this study were to: (1) develop a method for pooled analysis and (2) conduct pooled analysis on data from two soybean mapping populations. Least square interval mapping was extended for pooled analysis by inclusion of populations and cofactor markers as indicator variables and covariate variables separately in the multiple linear models. The general linear test approach was applied for detecting a QTL. Single population-based and pooled analyses were conducted on data from two F(2:3) mapping populations, Hamilton (susceptible) x PI 90763 (resistant) and Magellan (susceptible) x PI 404198A (resistant), for resistance to soybean cyst nematode (SCN) in soybean. It was demonstrated that where a QTL was shared among populations, pooled analysis showed increased LOD values on the QTL candidate region over single population analyses. Where a QTL was not shared among populations, however, the pooled analysis showed decreased LOD values on the QTL candidate region over single population analyses. Pooled analysis on data from genetically similar populations may have higher power of QTL detection than single population-based analyses. QTLs were identified by pooled analysis on linkage groups (LGs) G, B1 and J for resistance to SCN race 2 whereas QTLs on LGs G, B1 and E for resistance to SCN race 5 in soybean PI 90763 and PI 404198A. QTLs on LG G and B1 were identified in both PI 90763 and PI 404198A whereas QTLs on LG E and J were identified in PI 90763 only. QTLs on LGs G and B1 for resistance to race 2 may be the same or closely linked with QTLs on LG G and B1 for resistance to race 5, respectively. It was further demonstrated that QTLs on G and B1 carried by PI 90763 were not significantly different in gene effect from QTLs on LGs G and B1 in PI 404198A, respectively.

Germline polymorphisms in SIPA1 are associated with metastasis and other indicators of poor prognosis in breast cancer

Introduction

There is growing evidence that heritable genetic variation modulates metastatic efficiency. Our previous work using a mouse mammary tumor model has shown that metastatic efficiency is modulated by the GTPase-activating protein encoded by Sipa1 ('signal-induced proliferation-associated gene 1'). The aim of this study was to determine whether single nucleotide polymorphisms (SNPs) within the human SIPA1 gene are associated with metastasis and other disease characteristics in breast cancer.

Method

The study population (n = 300) consisted of randomly selected non-Hispanic Caucasian breast cancer patients identified from a larger population-based series. Genomic DNA was extracted from peripheral leukocytes. Three previously described SNPs within SIPA1 (one within the promoter [-313G>A] and two exonic [545C>T and 2760G>A]) were characterized using SNP-specific PCR.

Results

The variant 2760G>A and the -313G>A allele were associated with lymph node involvement (P = 0.0062 and P = 0.0083, respectively), and the variant 545C>T was associated with estrogen receptor negative tumors (P = 0.0012) and with progesterone negative tumors (P = 0.0339). Associations were identified between haplotypes defined by the three SNPs and disease progression. Haplotype 3 defined by variants -313G>A and 2760G>A was associated with positive lymph node involvement (P = 0.0051), and haplotype 4 defined by variant 545C>T was associated with estrogen receptor and progesterone receptor negative status (P = 0.0053 and P = 0.0199, respectively).

Conclusion

Our findings imply that SIPA1 germline polymorphisms are associated with aggressive disease behavior in the cohort examined. If these results hold true in other populations, then knowledge of SIPA1 SNP genotypes could potentially enhance current staging protocols.

An imprinted locus epistatically influences Nstr1 and Nstr2 to control resistance to nerve sheath tumors in a neurofibromatosis type 1 mouse model

Cancer is a complex disease in which cells acquire many genetic and epigenetic alterations. We have examined how three types of alterations, mutations in tumor suppressor genes, changes in an imprinted locus, and polymorphic loci, interact to affect tumor susceptibility in a mouse model of neurofibromatosis type 1 (NF1). Mutations in tumor suppressor genes such as TP53 and in oncogenes such as KRAS have major effects on tumorigenesis due to the central roles of these genes in cell proliferation and cell survival. Imprinted genes expressed from only one parental chromosome affect tumorigenesis if their monoallelic expression is lost or duplicated. Because imprinted loci are within regions deleted or amplified in cancer, the parental origin of genomic rearrangements could affect tumorigenesis. Gene polymorphisms can vary tumor incidence by affecting rate-limiting steps in tumorigenesis within tumor cells or surrounding stroma. In our mouse model of NF1, the incidence of tumors mutant for the tumor suppressor genes Nf1 and Trp53 is strongly modified by a linked imprinted locus acting epistatically on two unlinked polymorphic loci, Nstr1 and Nstr2. This interaction of an imprinted locus and polymorphic susceptibility loci has profound implications for human mapping studies where the parental contribution of alleles is often unknown.

Quantitative trait loci that determine BMD in C57BL/6J and 129S1/SvImJ inbred mice

BMD is highly heritable; however, little is known about the genes. To identify loci controlling BMD, we conducted a QTL analysis in a (B6 x 129) F2 population of mice. We report on additional QTLs and also narrow one QTL by combining the data from multiple crosses and through haplotype analysis.

INTRODUCTION

Previous studies have identified quantitative trait loci (QTL) that determine BMD in mice; however, identification of genes underlying QTLs is impeded by the large size of QTL regions.

MATERIALS AND METHODS

To identify loci controlling BMD, we performed a QTL analysis of 291 (B6 x 129) F2 females. Total body and vertebral areal BMD (aBMD) were determined by peripheral DXA when mice were 20 weeks old and had consumed a high-fat diet for 14 weeks.

RESULTS AND CONCLUSIONS

Two QTLs were common for both total body and vertebral aBMD: Bmd20 on chromosome (Chr) 6 (total aBMD; peak cM 26, logarithm of odds [LOD] 3.8, and vertebral aBMD; cM 32, LOD 3.6) and Bmd22 on Chr 1 (total aBMD; cM 104, LOD 2.5, and vertebral aBMD; cM 98, LOD 2.6). A QTL on Chr 10 (Bmd21, cM 68, LOD 3.0) affected total body aBMD and a QTL on Chr 7 (Bmd9, cM 44, LOD 2.7) affected vertebral aBMD. A pairwise genome-wide search did not reveal significant gene-gene interactions. Collectively, the QTLs accounted for 21.6% of total aBMD and 17.3% of vertebral aBMD of the F(2) population variances. Bmd9 was previously identified in a cross between C57BL/6J and C3H/HeJ mice, and we narrowed this QTL from 34 to 22 cM by combining the data from these crosses. By examining the Bmd9 region for conservation of ancestral alleles among the low allele strains (129S1/SvImJ and C3H/HeJ) that differed from the high allele strain (C57BL/6J), we further narrowed the region to approximately 9.9 cM, where the low allele strains share a common haplotype. Identifying the genes for these QTLs will enhance our understanding of skeletal biology.

Bioinformatics toolbox for narrowing rodent quantitative trait loci

Quantitative trait locus (QTL) analysis is a powerful method for localizing disease genes, but identifying the causal gene remains difficult. Rodent models of disease facilitate QTL gene identification, and causal genes underlying rodent QTL are often associated with the corresponding human diseases. Recently developed bioinformatics methods, including comparative genomics, combined cross analysis, interval-specific and genome-wide haplotype analysis, followed by sequence and expression analysis, each facilitated by public databases, provide new tools for narrowing rodent QTLs. Here we discuss each tool, illustrate its application and generate a bioinformatics strategy for narrowing QTLs. Combining these bioinformatics tools with classical experimental methods should accelerate QTL gene identification.

Integrating QTL and high-density SNP analyses in mice to identify Insig2 as a susceptibility gene for plasma cholesterol levels

The use of inbred strains of mice to dissect the genetic complexity of common diseases offers a viable alternative to human studies, given the control over experimental parameters that can be exercised. Central to efforts to map susceptibility loci for common diseases in mice is a comprehensive map of DNA variation among the common inbred strains of mice. Here we present one of the most comprehensive high-density, single nucleotide polymorphism (SNP) maps of mice constructed to date. This map consists of 10,350 SNPs genotyped in 62 strains of inbred mice. We demonstrate the utility of these data via a novel integrative genomics approach to mapping susceptibility loci for complex traits. By integrating in silico quantitative trait locus (QTL) mapping with progressive QTL mapping strategies in segregating mouse populations that leverage large-scale mapping of the genetic determinants of gene expression traits, we not only facilitate identification of candidate quantitative trait genes, but also protect against spurious associations that can arise in genetic association studies due to allelic association among unlinked markers. Application of this approach to our high-density SNP map and two previously described F2 crosses between strains C57BL/6J (B6) and DBA/2J and between B6 ApoE(-/-) and C3H/HeJ ApoE(-/-) results in the identification of Insig2 as a strong candidate susceptibility gene for total plasma cholesterol levels.

Identification of quantitative trait loci that regulate obesity and serum lipid levels in MRL/MpJ x SJL/J inbred mice

The total body fat mass and serum concentration of total cholesterol, HDL cholesterol, and triglyceride (TG) differ between standard diet-fed female inbred mouse strains MRL/MpJ (MRL) and SJL/J (SJL) by 38-120% (P < 0.01). To investigate genetic regulation of obesity and serum lipid levels, we performed a genome-wide linkage analysis in 621 MRLx SJL F2 female mice. Fat mass was affected by two significant loci, D11Mit36 [43.7 cM, logarithm of the odds ratio (LOD) 11.2] and D16Mit51 (50.3 cM, LOD 3.9), and one suggestive locus at D7Mit44 (50 cM, LOD 2.4). TG levels were affected by two novel loci at D1Mit43 (76 cM, LOD 3.8) and D12Mit201 (26 cM, LOD 4.1), and two suggestive loci on chromosomes 5 and 17. HDL and cholesterol concentrations were influenced by significant loci on chromosomes 1, 3, 5, 7, and 17 that were in the regions identified earlier for other strains of mice, except for a suggestive locus on chromosome 14 that was specific to the MRL x SJL cross. In summary, linkage analysis in MRL x SJL F2 mice disclosed novel loci affecting TG, HDL, and fat mass, a measure of obesity. Knowledge of the genes in these quantitative trait loci will enhance our understanding of obesity and lipid metabolism.

Sipa1 is a candidate for underlying the metastasis efficiency modifier locus Mtes1

We previously identified loci in the mouse genome that substantially influence the metastatic efficiency of mammary tumors. Here, we present data supporting the idea that the signal transduction molecule, Sipa1, is a candidate for underlying the metastasis efficiency modifier locus Mtes1. Analysis of candidate genes identified a nonsynonymous amino acid polymorphism in Sipa1 that affects the Sipa1 Rap-GAP function. Spontaneous metastasis assays using cells ectopically expressing Sipa1 or cells with knocked-down Sipa1 expression showed that metastatic capacity was correlated with cellular Sipa1 levels. We examined human expression data and found that they were consistent with the idea that Sipa1 concentration has a role in metastasis. Taken together, these data suggest that the Sipa1 polymorphism is one of the genetic polymorphisms underlying the Mtes1 locus. This report is also the first demonstration, to our knowledge, of a constitutional genetic polymorphism affecting tumor metastasis.

Identifying novel genes for atherosclerosis through mouse-human comparative genetics

Susceptibility to atherosclerosis is determined by both environmental and genetic factors. Its genetic determinants have been studied by use of quantitative-trait-locus (QTL) analysis. So far, 21 atherosclerosis QTLs have been identified in the mouse: 7 in a high-fat-diet model only, 9 in a sensitized model (apolipoprotein E- or LDL [low-density lipoprotein] receptor-deficient mice) only, and 5 in both models, suggesting that different gene sets operate in each model and that a subset operates in both. Among the 27 human atherosclerosis QTLs reported, 17 (63%) are located in regions homologous (concordant) to mouse QTLs, suggesting that these mouse and human atherosclerosis QTLs have the same underlying genes. Therefore, genes regulating human atherosclerosis will be found most efficiently by first finding their orthologs in concordant mouse QTLs. Novel mouse QTL genes will be found most efficiently by using a combination of the following strategies: identifying QTLs in new crosses performed with previously unused parental strains; inducing mutations in large-scale, high-throughput mutagenesis screens; and using new genomic and bioinformatics tools. Once QTL genes are identified in mice, they can be tested in human association studies for their relevance in human atherosclerotic disease.

Association of a lithogenic Abcg5/Abcg8 allele on Chromosome 17 (Lith9) with cholesterol gallstone formation in PERA/EiJ mice

To examine further the genetic determinants of cholesterol gallstone susceptibility in inbred mice, we performed quantitative trait locus (QTL) analysis of an intercross of gallstone-susceptible PERA/EiJ and gallstone-resistant DBA/2J inbred mice. Three hundred twenty-four F2 offspring were phenotyped for cholelithiasis during consumption of a lithogenic diet and genotyped using microsatellite markers. Linkage analysis was performed by interval mapping. In addition, we analyzed the combined datasets from this cross and from an independent cross of strain PERA and gallstone-resistant I/Ln mice. QTL mapping detected one significant new gallstone susceptibility (Lith) locus on Chromosome 13 (Lith15). A second significant QTL on Chr 6 (Lith16) confirmed a previous QTL. Furthermore, suggestive QTLs confirmed Lith loci from previous crosses on Chromosomes 1, 2, 5, 16 and X. QTL analysis of the dataset derived from the combined crosses increased the detection power and narrowed confidence intervals of Lith loci on Chromosomes 2, 6, 13, and 16. Moreover, the analysis of combined datasets revealed a shared QTL between both crosses on Chromosome 17 (Lith9). Significantly higher mRNA expression of Abcg5 and Abcg8 in strain PERA compared with strains I/Ln and DBA/2 further substantiated that the PERA allele of Abcg5/Abcg8 was responsible for lithogenicity underlying Lith9.

A high-resolution multistrain haplotype analysis of laboratory mouse genome reveals three distinctive genetic variation patterns

Understanding of the structure and the origin of genetic variation patterns in the laboratory inbred mouse provides insight into the utility of the mouse model for studying human complex diseases and strategies for disease gene mapping. In order to address this issue, we have constructed a multistrain, high-resolution haplotype map for the 99-Mb mouse Chromosome 16 using approximately 70,000 single nucleotide polymorphism (SNP) markers derived from whole-genome shotgun sequencing of five laboratory inbred strains. We discovered that large polymorphic blocks (i.e., regions where only two haplotypes, thus one SNP conformation, are found in the five strains), large monomorphic blocks (i.e., regions where the five strains share the same haplotype), and fragmented blocks (i.e., regions of greater complexity not resembling at all the first two categories) span 50%, 18%, and 32% of the chromosome, respectively. The haplotype map has 98% accuracy in predicting mouse genotypes in two other studies. Its predictions are also confirmed by experimental results obtained from resequencing of 40-kb genomic sequences at 21 distinct genomic loci in 13 laboratory inbred strains and 12 wild-derived strains. We demonstrate that historic recombination, intra-subspecies variations and inter-subspecies variations have all contributed to the formation of the three distinctive genetic signatures. The results suggest that the controlled complexity of the laboratory inbred strains may provide a means for uncovering the biological factors that have shaped genetic variation patterns.

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.

A novel mouse Chromosome 2 congenic strain with obesity phenotypes

Linkage studies have identified many chromosomal regions containing obesity genes in mice. However, only a few of these quantitative trait loci (QTLs) have been used to guide the production of congenic mouse strains that retain obesity phenotypes. We seek to identify chromosomal regions containing obesity genes in the BSB model of spontaneous obesity because the BSB model is a multigenic obesity model. Previous studies identified QTLs on Chromosomes (Chrs) 2, 6, 7,12, and 15. BSB mice are made by backcross of lean C57BL/6J x Mus spretus. F(1)s were backcrossed to C57BL/6J mice to produce BSB progeny. We have constructed a new BSB cross and produced congenic mice with obesity phenotypes by marker-directed selection called B6.S- D2Mit194- D2Mit311. We found a highly significant QTL for percentage body lipid on Chr 2 just proximal to the Agouti locus. Chr 2 congenics were constructed to determine whether the main effects would be detectable. We observed highly significant linkage of the Chr 2 congenic containing Agouti and containing markers distal to D2Mit311 and proximal to D2Mit194. Thus, this congenic contains approximately 14.6 cM or 30 Mb (about 1.1% of the spretus mouse genome) and several hundred genes. The obesity phenotype of the QTL is retained in the congenic. The congenic can now be used to model the genetic and physiological basis for a relatively simple, perhaps monogenic, obesity.

Combining data from multiple inbred line crosses improves the power and resolution of quantitative trait loci mapping

Rodent inbred line crosses are widely used to map genetic loci associated with complex traits. This approach has proven to be powerful for detecting quantitative trait loci (QTL); however, the resolution of QTL locations, typically approximately 20 cM, means that hundreds of genes are implicated as potential candidates. We describe analytical methods based on linear models to combine information available in two or more inbred line crosses. Our strategy is motivated by the hypothesis that common inbred strains of the laboratory mouse are derived from a limited ancestral gene pool and thus QTL detected in multiple crosses are likely to represent shared ancestral polymorphisms. We demonstrate that the combined-cross analysis can improve the power to detect weak QTL, can narrow support intervals for QTL regions, and can be used to separate multiple QTL that colocalize by chance. Moreover, combined-cross analysis can establish the allelic states of a QTL among a set of parental lines, thus providing critical information for narrowing QTL regions by haplotype analysis.

Genes and Susceptibility to Leishmaniasis

Leishmania are digenetic protozoa which inhabit two highly specific hosts, the sandfly where they grow as motile, flagellated promastigotes in the gut, and the mammalian macrophage where they grow intracellularly as non-flagellated amastigotes. Leishmaniasis is the outcome of an evolutionary 'arms race' between the host's immune system and the parasite's evasion mechanisms which ensure survival and transmission in the population. The spectrum of disease manifestations and severity reflects the interaction between the genome of the host and that of the parasite, and the pathology is caused by a combination of host and parasite molecules. This chapter examines the genetic basis of host susceptibility to disease in humans and animal models. It describes the genetic tools used to map and identify susceptibility genes, and the lessons learned from murine and human cutaneous leishmaniasis.

Susceptibility to astrocytoma in mice mutant for Nf1 and Trp53 is linked to chromosome 11 and subject to epigenetic effects

Astrocytoma is the most common malignant brain tumor in humans. Loss of the p53 signaling pathway and up-regulation of the ras signaling pathway are common during tumor progression. We have shown previously that mice mutant for Trp53 and Nf1 develop astrocytoma, progressing to glioblastoma, on a C57BL/6J strain background. In contrast, here we present data that mice mutant for Trp53 and Nf1 on a 129S4/SvJae background are highly resistant to developing astrocytoma. Through analysis of F1 progeny, we demonstrate that susceptibility to astrocytoma is linked to chromosome 11, and that the modifier gene(s) responsible for differences in susceptibility is closely linked to Nf1 and Trp53. Furthermore, this modifier of astrocytoma susceptibility is itself epigenetically modified. These data demonstrate that epigenetic effects can have a strong effect on whether cancer develops in the context of mutant ras signaling and mutant p53, and that this mouse model of astrocytoma can be used to identify modifier phenotypes with complex inheritance patterns that would be unidentifiable in humans. Because analysis of gene function in the mouse is often performed on a mixed C57BL/6,129 strain background, these data also provide a powerful example of the potential of these strains to mask interesting gene functions.