The dopamine agonist apomorphine robustly disrupts prepulse inhibition of the acoustic startle response in the rat, yet published studies have not demonstrated a robust disruption of prepulse inhibition with apomorphine in the mouse. The aim of these studies was to establish the optimal prepulse conditions (using manipulations to prepulse intensity and inter-stimulus interval) and mouse strain(s) for testing apomorphine, and also the prepulse inhibition disrupting drugs amphetamine, and dizocilpine (MK-801). The effects of these drugs on startle response and prepulse inhibition were tested in outbred CD-1 and Swiss Webster (CFW) strains, and the inbred C57BL/6, 129X1/SvJ, and A/J strains. There were strain differences with baseline startle and prepulse inhibition in that the CD-1, CFW, and C57BL/6 strains exhibited high levels of startle and prepulse inhibition, the 129X1/SvJ strain exhibited low levels of startle but high levels of prepulse inhibition, while the A/J strain exhibited low startle and no prepulse inhibition. Apomorphine disrupted prepulse inhibition in the CFW and C57BL/6 strains and the effect was only evident when using a short 30 ms inter-stimulus interval. Amphetamine disrupted prepulse inhibition in the CFW, C57BL/6, and 129X1/SvJ strains, and dizocilpine disrupted prepulse inhibition in the CD-1, CFW, C57BL/6, and 129X1/SvJ strains. The effects of amphetamine and dizocilpine were independent of the inter-stimulus interval. These studies demonstrated clear strain differences in the startle response and prepulse inhibition, and the pharmacological disruptions of prepulse inhibition, and suggest that inter-stimulus intervals less than 100 ms may be optimal for detecting the effects of apomorphine in mice.

Specific fat distributions are risk factors for complex diseases, including coronary heart disease and obstructive sleep apnea. To demonstrate the utility of high-diversity mouse models for elucidating genetic associations, we describe the phenotyping and heritability of fat distributions within the five classical inbred and three wild-derived founder mouse strains of the Collaborative Cross and Diversity Outbred mice. Measurements of subcutaneous and internal fat volumes in the abdomen, thorax and neck, and fat volumes in the tongue and pericardium were obtained using magnetic resonance imaging in male mice from the A/J (n = 12), C57BL/6J (n = 17), 129S1/SvlmJ (n = 12), NOD/LtJ (n = 14), NZO/HILtJ (n = 12), CAST/EiJ (n = 14), PWK/PhJ (n = 12), and WSB/EiJ (n = 15) strains. Phenotypes were compared across strains using analysis of variance and heritability estimated as the proportion of phenotypic variability attributable to strain. Heritability ranged from 44 to 91% across traits, including >70% heritability of tongue fat. A majority of heritability estimates remained significant controlling for body weight, suggesting genetic influences independent of general obesity. Principal components analysis supports genetic influences on overall obesity and specific to increased pericardial and intra-neck fat. Thus, among the founder strains of the Collaborative Cross and Diversity Outbred mice, we observed significant heritability of subcutaneous and internal fat volumes in the neck, thorax and abdomen, pericardial fat volume and tongue fat volume, consistent with genetic architecture playing an important role in explaining trait variability. Findings pave the way for studies utilizing high-diversity mouse models to identify genes affecting fat distributions and, in turn, influencing risk for associated complex disorders.

The translation of new injectable anesthetic drugs from rodent to humans remains slow, despite the realization that reliance on the volatile agents is unsustainable from an environmental perspective. The aim of this study was to investigate the influence of rat sex and strain on the PK and PD of the anesthetic neurosteroid alfaxalone. Forty rats had cannulas inserted under isoflurane anesthesia for drug administration and sampling. Carotid artery blood samples were collected for blood gas analysis, hematology, biochemistry, and plasma concentrations of alfaxalone. Plasma samples were assayed using liquid chromatography-mass spectrometry. Compartmental non-linear mixed effects methods (NLME) models were applied to two rat populations to determine whether body weight, sex, and strain influenced PK parameters. There were significant differences between the sexes for plasma clearance, half-life and mean residence time in Lewis rats, and mean arterial blood pressure was significantly lower in the female rats at 120 min. An initial NLME PK population model was used to design an adjusted alfaxalone infusion for SD females matching plasma concentrations in males and minimizing cardiopulmonary depression but maintaining an appropriate hypnotic effect. A final NLME population model showed that alfaxalone clearance was dependent on both bodyweight and sex, whereas volume of distribution was influenced by strain. NLME PK models offer the advantage of having a single model that describes a population and therefore shares data interpretation between animals unlike the standard deterministic PK approach. This approach can be used to propose bespoke dosing regimens for optimal use of alfaxalone.

Genetic mapping studies in the mouse and other model organisms are used to search for genes underlying complex phenotypes. Traditional genetic mapping studies that employ single-generation crosses have poor mapping resolution and limit discovery to loci that are polymorphic between the two parental strains. Multiparent outbreeding populations address these shortcomings by increasing the density of recombination events and introducing allelic variants from multiple founder strains. However, multiparent crosses present new analytical challenges and require specialized software to take full advantage of these benefits. Each animal in an outbreeding population is genetically unique and must be genotyped using a high-density marker set; regression models for mapping must accommodate multiple founder alleles, and complex breeding designs give rise to polygenic covariance among related animals that must be accounted for in mapping analysis. The Diversity Outbred (DO) mice combine the genetic diversity of eight founder strains in a multigenerational breeding design that has been maintained for >16 generations. The large population size and randomized mating ensure the long-term genetic stability of this population. We present a complete analytical pipeline for genetic mapping in DO mice, including algorithms for probabilistic reconstruction of founder haplotypes from genotyping array intensity data, and mapping methods that accommodate multiple founder haplotypes and account for relatedness among animals. Power analysis suggests that studies with as few as 200 DO mice can detect loci with large effects, but loci that account for <5% of trait variance may require a sample size of up to 1000 animals. The methods described here are implemented in the freely available R package DOQTL.

Genome-wide association studies using commercially available outbred mice can detect genes involved in phenotypes of biomedical interest. Useful populations need high-frequency alleles to ensure high power to detect quantitative trait loci (QTLs), low linkage disequilibrium between markers to obtain accurate mapping resolution, and an absence of population structure to prevent false positive associations. We surveyed 66 colonies for inbreeding, genetic diversity, and linkage disequilibrium, and we demonstrate that some have haplotype blocks of less than 100 Kb, enabling gene-level mapping resolution. The same alleles contribute to variation in different colonies, so that when mapping progress stalls in one, another can be used in its stead. Colonies are genetically diverse: 45% of the total genetic variation is attributable to differences between colonies. However, quantitative differences in allele frequencies, rather than the existence of private alleles, are responsible for these population differences. The colonies derive from a limited pool of ancestral haplotypes resembling those found in inbred strains: over 95% of sequence variants segregating in outbred populations are found in inbred strains. Consequently it is possible to impute the sequence of any mouse from a dense SNP map combined with inbred strain sequence data, which opens up the possibility of cataloguing and testing all variants for association, a situation that has so far eluded studies in completely outbred populations. We demonstrate the colonies' potential by identifying a deletion in the promoter of H2-Ea as the molecular change that strongly contributes to setting the ratio of CD4+ and CD8+ lymphocytes.

Although Slc:Wistar rats are used widely in biomedical research as outbred rats, close similarities in growth curves, survival rates, and immunological and biochemical phenotypes have been reported between Slc:Wistar and F344 inbred rats. We reported previously that nine genetic variations that were fixed in Slc:Wistar rats had identical genotypes in F344 rats. Here, we examined the genetic characteristics of Slc:Wistar rats using 27 simple-sequence length polymorphism (SSLP) markers and compared them with other Wistar stocks available in Japan and with some F344 strains. Among 27 SSLP loci, 23 (85%) were fixed in the Slc:Wistar rats, which was the highest among the other Wistar stocks. The 23 fixed loci shared identical genotypes with corresponding loci in F344 rats. Further, the predominant allele types in the unfixed loci had allele frequencies as high as 80%, and these alleles were identical in the F344 rats. When the nine genetic variations reported previously are added, a total of 32 (89%) out of the 36 loci examined were fixed and identical in the Slc:Wistar and F344 rat genomes. These findings indicate the low genetic variation in Slc:Wistar rats and the high genetic similarity between the Slc:Wistar and F344 inbred rats. This study demonstrates the importance of characterizing outbred rats and the need to pay ample attention to the genetic characteristics the Slc:Wistar rats for their proper use.

Brain transcriptional variation is a heritable trait that mediates complex behaviors, including addiction. Expression quantitative trait locus (eQTL) mapping reveals genomic regions harboring genetic variants that influence transcript abundance. In this study, we profiled transcript abundance in the striatum of 386 Diversity Outbred (J:DO) mice of both sexes using RNA-Seq. All mice were characterized using a behavioral battery of widely-used exploratory and risk-taking assays prior to transcriptional profiling. We performed eQTL mapping, incorporated the results into a browser-based eQTL viewer, and deposited co-expression network members in GeneWeaver. The eQTL viewer allows researchers to query specific genes to obtain allelic effect plots, analyze SNP associations, assess gene expression correlations, and apply mediation analysis to evaluate whether the regulatory variant is acting through the expression of another gene. GeneWeaver allows multi-species comparison of gene sets using statistical and combinatorial tools. This data resource allows users to find genetic variants that regulate differentially expressed transcripts and place them in the context of other studies of striatal gene expression and function in addiction-related behavior.

Brain transcriptional variation is a heritable trait that mediates complex behaviors, including addiction. Expression quantitative trait locus (eQTL) mapping reveals genomic regions harboring genetic variants that influence transcript abundance. In this study, we profiled transcript abundance in the striatum of 386 Diversity Outbred (J:DO) mice of both sexes using RNA-Seq. All mice were characterized using a behavioral battery of widely-used exploratory and risk-taking assays prior to transcriptional profiling. We performed eQTL mapping, incorporated the results into a browser-based eQTL viewer, and deposited co-expression network members in GeneWeaver. The eQTL viewer allows researchers to query specific genes to obtain allelic effect plots, analyze SNP associations, assess gene expression correlations, and apply mediation analysis to evaluate whether the regulatory variant is acting through the expression of another gene. GeneWeaver allows multi-species comparison of gene sets using statistical and combinatorial tools. This data resource allows users to find genetic variants that regulate differentially expressed transcripts and place them in the context of other studies of striatal gene expression and function in addiction-related behavior.

The Mouse Phenome Database was originally conceived as a platform for the integration of phenotype data collected on a defined collection of 40 inbred mouse strains--the "phenome panel." This model provided an impetus for community data sharing, and integration was readily achieved through the reproducible genotypes of the phenome panel strains. Advances in the development of mouse populations lead to an expanded role of the Mouse Phenome Database to encompass new strain panels and inbred strain crosses. The recent introduction of the Collaborative Cross and Diversity Outbred mice, which share an extensive pool of genetic variation from eight founder inbred strains, presents new opportunities and challenges for community data resources. A wide variety of molecular and clinical phenotypes are being collected across genotypes, tissues, ages, environmental exposures, interventions, and treatments. The Mouse Phenome Database provides a framework for retrieval, integration, analysis, and display of these data, enabling them to be evaluated in the context of existing data from standard inbred strains. Primary data in the Mouse Phenome Database are supported by extensive metadata on protocols and procedures. These are centrally curated to ensure accuracy and reproducibility and to provide data in consistent formats. The Mouse Phenome Database represents an established and growing community data resource for mouse phenotype data and encourages submissions from new mouse resources, enabling investigators to integrate existing data into their studies of the phenotypic consequences of genetic variation.

Thousands of loci that contribute to quantitative traits in outbred crosses of mice have been reported over the last two decades. In this review we discuss how outbred mouse populations can be used to map and identify the genes and sequence variants that give rise to quantitative variation. We discuss heterogeneous stocks, the diversity outbred, and commercially available outbred populations of mice. All of these populations are descended from a small number of progenitor strains. The availability of the complete sequence of laboratory strains means that in many cases it will be possible to reconstruct the genomes of the outbred animals so that in a genetic association study we can detect the effect of all variants, a situation that has so far eluded studies in completely outbred populations. These resources constitute a major advance and make it possible to progress from a quantitative trait locus to a gene at an unprecedented speed.

Testis weight is a genetically mediated trait associated with reproductive efficiency across numerous species. We sought to evaluate the genetically diverse, highly recombinant Diversity Outbred (DO) mouse population as a tool to identify and map quantitative trait loci (QTLs) associated with testis weight. Testis weights were recorded for 502 male DO mice and the mice were genotyped on the GIGAMuga array at ~ 143,000 SNPs. We performed a genome-wide association analysis and identified one significant and two suggestive QTLs associated with testis weight. Using bioinformatic approaches, we developed a list of candidate genes and identified those with known roles in testicular size and development. Candidates of particular interest include the RNA demethylase gene Alkbh5, the cyclin-dependent kinase inhibitor gene Cdkn2c, the dynein axonemal heavy chain gene Dnah11, the phospholipase D gene Pld6, the trans-acting transcription factor gene Sp4, and the spermatogenesis-associated gene Spata6, each of which has a human ortholog. Our results demonstrate the utility of DO mice in high-resolution genetic mapping of complex traits, enabling us to identify developmentally important genes in adult mice. Understanding how genetic variation in these genes influence testis weight could aid in the understanding of mechanisms of mammalian reproductive function.

Quantitative trait locus (QTL) analysis is a powerful tool for mapping genes for complex traits in mice, but its utility is limited by poor resolution. A promising mapping approach is association analysis in outbred stocks or different inbred strains. As a proof of concept for the association approach, we applied whole-genome association analysis to hepatic gene expression traits in an outbred mouse population, the MF1 stock, and replicated expression QTL (eQTL) identified in previous studies of F2 intercross mice. We found that the mapping resolution of these eQTL was significantly greater in the outbred population. Through an example, we also showed how this precise mapping can be used to resolve previously identified loci (in intercross studies), which affect many different transcript levels (known as eQTL "hotspots"), into distinct regions. Our results also highlight the importance of correcting for population structure in whole-genome association studies in the outbred stock.

Francisella tularensis (Ft) is a biothreat agent for which there is no FDA-approved human vaccine. Currently, there are substantial efforts underway to develop both vaccines and the tools to assess these vaccines. Tularemia laboratory research has historically relied primarily upon a small number of inbred mouse strains, but the utility of such findings to outbred animals may be limited. Specifically, C57BL/6 mice are more susceptible than BALB/c mice to Ft infection and less easily protected against challenge with highly virulent type A Ft. Thus, depending on the inbred mouse strain used, one could be misled as to which immunogen(s)/vaccine will ultimately be effective in an outbred human population. Accordingly, we evaluated an outbred Swiss Webster (SW) mouse model in direct comparison to a well-established, inbred C57BL/6 mouse model. Mucosal vaccination with the live, attenuated Ft LVS superoxide dismutase (sodB) mutant demonstrated significantly higher protection in outbred SW mice compared to inbred C57BL/6 mice against Ft SchuS4 respiratory challenge. The protection observed in vaccinated outbred mice correlated with lower bacterial density, reduced tissue inflammation, and reduced levels of pro-inflammatory cytokine production. This protection was CD4+ and CD8+ T cell-dependent and characterized by lower titers of serum antibody (Ab) that qualitatively differed from vaccinated inbred mice. Enhanced protection of vaccinated outbred mice correlated with early and robust production of IFN-γ and IL-17A. Neutralizing Ab administered at the time of challenge revealed that IFN-γ was central to this protection, while IL-17A neutralization did not alter bacterial burden or survival. The present study demonstrates the utility of the outbred mouse as an alternative vaccination model for testing tularemia vaccines. Given the limited MHC repertoire in inbred mice, this outbred model is more analogous to the human in terms of immunological diversity.

Finding genetic variants that contribute to phenotypic variation is one of the main challenges of modern genetics. We used an outbred population of rats (Heterogeneous Stock, HS) in a combined sequence-based and genetic mapping analysis to identify sequence variants and genes contributing to complex traits of biomedical relevance. Here we describe the sequences of the eight inbred progenitors of the HS and the variants that segregate between them. We report the genotyping of 1,407 HS rats, and the collection from 2,006 rats of 195 phenotypic measures that are relevant to models of anxiety, type 2 diabetes, hypertension and osteoporosis. We make available haplotype dosages for the 1,407 genotyped rats, since genetic mapping in the HS is best carried out by reconstructing each HS chromosome as a mosaic of the progenitor genomes. Finally, we have deposited an R object that makes it easy to incorporate our sequence data into any genetic study of HS rats. Our genetic data are available for both Rnor3.4 and Rnor5.0 rat assemblies.

Rat and mouse strains differ in behavioral and physiological characteristics, and such differences can contribute to explain discrepant results between laboratories and better select the most appropriate strain for a particular purpose. Differences in the activity of the hypothalamic-pituitary-adrenal (HPA) axis are particularly important given the pivotal role of this system in determining consequences of exposure to stressors. In this regard, Long-Evans (LE) rats are widely used in stress research, but there is no specific study aiming at thoroughly characterizing HPA activity in LE versus other extensively used strains. In a first experiment, LE showed higher resting ACTH and corticosterone levels only at certain points of the circadian rhythm, but much greater ACTH responsiveness to stressors (novel environment and forced swim) than Sprague-Dawley (SD) rats. Accordingly, enhanced corticotropin-releasing hormone (CRH) expression in the paraventricular nucleus of the hypothalamus and reduced expression of glucocorticoid receptors were observed in the hippocampal formation. Additionally, they are hyperactive in novel environments, and prone to adopt passive-like behavior when compared to SD rats. Supporting that altered HPA function has a marked physiological impact, we observed in another set of animals much lower thymus weight in LE than SD rats. Finally, to demonstrate that LE rats are likely to have higher HPA responsiveness to stressors than most strains, we studied resting and stress levels of HPA hormones in LE versus Wistar and Fischer rats, the latter considered an example of high HPA responsiveness. Again, LE showed higher resting and stress levels of ACTH than both Wistar and Fischer rats. As ACTH responsiveness to stressors in LE rats is stronger than that previously reported when comparing other rat strains and they are commercially available, they could be an appropriate model for studying the behavioral and physiological implications of a hyper-active HPA axis under normal and pathological conditions.

The gut microbiome is thought to play a critical role in the onset and development of psychiatric disorders, including depression and substance use disorder (SUD). To test the hypothesis that the microbiome affects addiction predisposing behaviors and cocaine intravenous self-administration (IVSA) and to identify specific microbes involved in the relationship, we performed 16S rRNA gene sequencing on feces from 228 diversity outbred mice. Twelve open field measures, two light-dark assay measures, one hole board and novelty place preference measure significantly differed between mice that acquired cocaine IVSA (ACQ) and those that failed to acquire IVSA (FACQ). We found that ACQ mice are more active and exploratory and display decreased fear than FACQ mice. The microbial abundances that differentiated ACQ from FACQ mice were an increased abundance of Barnesiella, Ruminococcus, and Robinsoniella and decreased Clostridium IV in ACQ mice. There was a sex-specific correlation between ACQ and microbial abundance, a reduced Lactobacillus abundance in ACQ male mice, and a decreased Blautia abundance in female ACQ mice. The abundance of Robinsoniella was correlated, and Clostridium IV inversely correlated with the number of doses of cocaine self-administered during acquisition. Functional analysis of the microbiome composition of a subset of mice suggested that gut-brain modules encoding glutamate metabolism genes are associated with the propensity to self-administer cocaine. These findings establish associations between the microbiome composition and glutamate metabolic potential and the ability to acquire cocaine IVSA thus indicating the potential translational impact of targeting the gut microbiome or microbial metabolites for treatment of SUD. This article is part of the Special Issue on "Microbiome & the Brain: Mechanisms & Maladies".

Historically our ability to identify genetic variants underlying complex behavioral traits in mice has been limited by low mapping resolution of conventional mouse crosses. The newly developed Diversity Outbred (DO) population promises to deliver improved resolution that will circumvent costly fine-mapping studies. The DO is derived from the same founder strains as the Collaborative Cross (CC), including three wild-derived strains. Thus the DO provides more allelic diversity and greater potential for discovery compared to crosses involving standard mouse strains. We have characterized 283 male and female DO mice using open-field, light-dark box, tail-suspension and visual-cliff avoidance tests to generate 38 behavioral measures. We identified several quantitative trait loci (QTL) for these traits with support intervals ranging from 1 to 3 Mb in size. These intervals contain relatively few genes (ranging from 5 to 96). For a majority of QTL, using the founder allelic effects together with whole genome sequence data, we could further narrow the positional candidates. Several QTL replicate previously published loci. Novel loci were also identified for anxiety- and activity-related traits. Half of the QTLs are associated with wild-derived alleles, confirming the value to behavioral genetics of added genetic diversity in the DO. In the presence of wild-alleles we sometimes observe behaviors that are qualitatively different from the expected response. Our results demonstrate that high-precision mapping of behavioral traits can be achieved with moderate numbers of DO animals, representing a significant advance in our ability to leverage the mouse as a tool for behavioral genetics.

Inbred mouse strains have been used preferentially for behavioral testing over outbred counterparts, even though outbred mice reflect the genetic diversity in the human population better. Here, we compare the sociability of widely available outbred CD1 mice with the commonly used inbred C57BL/6J (C57) mice in the one-chamber social interaction test and the three-chamber sociability test. In the one-chamber task, intra-strain pairs of juvenile, non-littermate, male CD1 or C57 mice display a series of social and aggressive behaviors. While CD1 and C57 pairs spend equal amount of time socializing, CD1 pairs spend significantly more time engaged in aggressive behaviors than C57 mice. In the three-chamber task, sociability of C57 mice was less dependent on acclimation paradigms than CD1 mice. Following acclimation to all three chambers, both groups of age-matched male mice spent more time in the chamber containing a stranger mouse than in the empty chamber, suggesting that CD1 mice are sociable like C57 mice. However, the observed power suggests that it is easier to achieve statistical significance with C57 than CD1 mice. Because the stranger mouse could be considered as a novel object, we assessed for a novelty effect by adding an object. CD1 mice spend more time in the chamber with a stranger mouse than that a novel object, suggesting that their preference is social in nature. Thus, outbred CD1 mice are as appropriate as inbred C57 mice for studying social behavior using either the single or the three-chamber test using a specific acclimation paradigm.

We compared the performance of linear (GBLUP, BayesB, and elastic net) methods to a nonparametric tree-based ensemble (gradient boosting machine) method for genomic prediction of complex traits in mice. The dataset used contained genotypes for 50,112 SNP markers and phenotypes for 835 animals from 6 generations. Traits analyzed were bone mineral density, body weight at 10, 15, and 20 weeks, fat percentage, circulating cholesterol, glucose, insulin, triglycerides, and urine creatinine. The youngest generation was used as a validation subset, and predictions were based on all older generations. Model performance was evaluated by comparing predictions for animals in the validation subset against their adjusted phenotypes. Linear models outperformed gradient boosting machine for 7 out of 10 traits. For bone mineral density, cholesterol, and glucose, the gradient boosting machine model showed better prediction accuracy and lower relative root mean squared error than the linear models. Interestingly, for these 3 traits, there is evidence of a relevant portion of phenotypic variance being explained by epistatic effects. Using a subset of top markers selected from a gradient boosting machine model helped for some of the traits to improve the accuracy of prediction when these were fitted into linear and gradient boosting machine models. Our results indicate that gradient boosting machine is more strongly affected by data size and decreased connectedness between reference and validation sets than the linear models. Although the linear models outperformed gradient boosting machine for the polygenic traits, our results suggest that gradient boosting machine is a competitive method to predict complex traits with assumed epistatic effects.

Phenotypic variation of quantitative traits is orchestrated by a complex interplay between the environment (e.g. diet) and genetics. However, the impact of gene-environment interactions on phenotypic traits mostly remains elusive. To address this, we feed 1154 mice of an autoimmunity-prone intercross line (AIL) three different diets. We find that diet substantially contributes to the variability of complex traits and unmasks additional genetic susceptibility quantitative trait loci (QTL). By performing whole-genome sequencing of the AIL founder strains, we resolve these QTLs to few or single candidate genes. To address whether diet can also modulate genetic predisposition towards a given trait, we set NZM2410/J mice on similar dietary regimens as AIL mice. Our data suggest that diet modifies genetic susceptibility to lupus and shifts intestinal bacterial and fungal community composition, which precedes clinical disease manifestation. Collectively, our study underlines the importance of including environmental factors in genetic association studies.