A large QTL for fear and anxiety mapped using an F2 cross can be dissected into multiple smaller QTLs

Evolution of Boldness and Exploratory Behavior in Giant Mice from Gough Island

Island populations are hallmarks of extreme phenotypic evolution. Radical changes in resource availability and predation risk accompanying island colonization drive changes in behavior, which Darwin likened to tameness in domesticated animals. Although many examples of animal boldness are found on islands, the heritability of observed behaviors, a requirement for evolution, remains largely unknown. To fill this gap, we profiled anxiety and exploration in island and mainland inbred strains of house mice raised in a common laboratory environment. The island strain was descended from mice on Gough Island, the largest wild house mice on record. Experiments utilizing open environments across two ages showed that Gough Island mice are bolder and more exploratory, even when a shelter is provided. Concurrently, Gough Island mice retain an avoidance response to predator urine. F1 offspring from crosses between these two strains behave more similarly to the mainland strain for most traits, suggesting recessive mutations contributed to behavioral evolution on the island. Our results provide a rare example of novel, inherited behaviors in an island population and demonstrate that behavioral evolution can be specific to different forms of perceived danger. Our discoveries pave the way for a genetic understanding of how island populations evolve unusual behaviors.

Network-Based Functional Prediction Augments Genetic Association To Predict Candidate Genes for Histamine Hypersensitivity in Mice

Genetic mapping is a primary tool of genetics in model organisms; however, many quantitative trait loci (QTL) contain tens or hundreds of positional candidate genes. Prioritizing these genes for validation is often ad hoc and biased by previous findings. Here we present a technique for prioritizing positional candidates based on computationally inferred gene function. Our method uses machine learning with functional genomic networks, whose links encode functional associations among genes, to identify network-based signatures of functional association to a trait of interest. We demonstrate the method by functionally ranking positional candidates in a large locus on mouse Chr 6 (45.9 Mb to 127.8 Mb) associated with histamine hypersensitivity (Histh). Histh is characterized by systemic vascular leakage and edema in response to histamine challenge, which can lead to multiple organ failure and death. Although Histh risk is strongly influenced by genetics, little is known about its underlying molecular or genetic causes, due to genetic and physiological complexity of the trait. To dissect this complexity, we ranked genes in the Histh locus by predicting functional association with multiple Histh-related processes. We integrated these predictions with new single nucleotide polymorphism (SNP) association data derived from a survey of 23 inbred mouse strains and congenic mapping data. The top-ranked genes included Cxcl12, Ret, Cacna1c, and Cntn3, all of which had strong functional associations and were proximal to SNPs segregating with Histh. These results demonstrate the power of network-based computational methods to nominate highly plausible quantitative trait genes even in challenging cases involving large QTL and extreme trait complexity.

Identification of a novel gene regulating amygdala-mediated fear extinction

Recent years have seen advances in our understanding of the neural circuits associated with trauma-related disorders, and the development of relevant assays for these behaviors in rodents. Although inherited factors are known to influence individual differences in risk for these disorders, it has been difficult to identify specific genes that moderate circuit functions to affect trauma-related behaviors. Here, we exploited robust inbred mouse strain differences in Pavlovian fear extinction to uncover quantitative trait loci (QTL) associated with this trait. We found these strain differences to be resistant to developmental cross-fostering and associated with anatomical variation in basolateral amygdala (BLA) perineuronal nets, which are developmentally implicated in extinction. Next, by profiling extinction-driven BLA expression of QTL-linked genes, we nominated Ppid (peptidylprolyl isomerase D, a member of the tetratricopeptide repeat (TPR) protein family) as an extinction-related candidate gene. We then showed that Ppid was enriched in excitatory and inhibitory BLA neuronal populations, but at lower levels in the extinction-impaired mouse strain. Using a virus-based approach to directly regulate Ppid function, we demonstrated that downregulating BLA-Ppid impaired extinction, while upregulating BLA-Ppid facilitated extinction and altered in vivo neuronal extinction encoding. Next, we showed that Ppid colocalized with the glucocorticoid receptor (GR) in BLA neurons and found that the extinction-facilitating effects of Ppid upregulation were blocked by a GR antagonist. Collectively, our results identify Ppid as a novel gene involved in regulating extinction via functional actions in the BLA, with possible implications for understanding genetic and pathophysiological mechanisms underlying risk for trauma-related disorders.

Dissection of Host Susceptibility to Bacterial Infections and Its Toxins

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

Mapping of Genetic Factors That Elicit Intermale Aggressive Behavior on Mouse Chromosome 15: Intruder Effects and the Complex Genetic Basis

Despite high estimates of the heritability of aggressiveness, the genetic basis for individual differences in aggression remains unclear. Previously, we showed that the wild-derived mouse strain MSM/Ms (MSM) exhibits highly aggressive behaviors, and identified chromosome 15 (Chr 15) as the location of one of the genetic factors behind this escalated aggression by using a panel of consomic strains of MSM in a C57BL/6J (B6) background. To understand the genetic effect of Chr 15 derived from MSM in detail, this study examined the aggressive behavior of a Chr 15 consomic strain towards different types of opponent. Our results showed that both resident and intruder animals had to have the same MSM Chr 15 genotype in order for attack bites to increase and attack latency to be reduced, whereas there was an intruder effect of MSM Chr 15 on tail rattle behavior. To narrow down the region that contains the genetic loci involved in the aggression-eliciting effects on Chr 15, we established a panel of subconsomic strains of MSM Chr 15. Analysis of these strains suggested the existence of multiple genes that enhance and suppress aggressive behavior on Chr 15, and these loci interact in a complex way. Regression analysis successfully identified four genetic loci on Chr 15 that influence attack latency, and one genetic locus that partially elicits aggressive behaviors was narrowed down to a 4.1-Mbp region (from 68.40 Mb to 72.50 Mb) on Chr 15.

High-resolution genetic mapping of complex traits from a combined analysis of F2 and advanced intercross mice

Genetic influences on anxiety disorders are well documented; however, the specific genes underlying these disorders remain largely unknown. To identify quantitative trait loci (QTL) for conditioned fear and open field behavior, we used an F2 intercross (n = 490) and a 34th-generation advanced intercross line (AIL) (n = 687) from the LG/J and SM/J inbred mouse strains. The F2 provided strong support for several QTL, but within wide chromosomal regions. The AIL yielded much narrower QTL, but the results were less statistically significant, despite the larger number of mice. Simultaneous analysis of the F2 and AIL provided strong support for QTL and within much narrower regions. We used a linear mixed-model approach, implemented in the program QTLRel, to correct for possible confounding due to familial relatedness. Because we recorded the full pedigree, we were able to empirically compare two ways of accounting for relatedness: using the pedigree to estimate kinship coefficients and using genetic marker estimates of "realized relatedness." QTL mapping using the marker-based estimates yielded more support for QTL, but only when we excluded the chromosome being scanned from the marker-based relatedness estimates. We used a forward model selection procedure to assess evidence for multiple QTL on the same chromosome. Overall, we identified 12 significant loci for behaviors in the open field and 12 significant loci for conditioned fear behaviors. Our approach implements multiple advances to integrated analysis of F2 and AILs that provide both power and precision, while maintaining the advantages of using only two inbred strains to map QTL.

Fine-mapping QTLs in advanced intercross lines and other outbred populations

Quantitative genetic studies in model organisms, particularly in mice, have been extremely successful in identifying chromosomal regions that are associated with a wide variety of behavioral and other traits. However, it is now widely understood that identification of the underlying genes will be far more challenging. In the last few years, a variety of populations have been utilized in an effort to more finely map these chromosomal regions with the goal of identifying specific genes. The common property of these newer populations is that linkage disequilibrium spans relatively short distances, which permits fine-scale mapping resolution. This review focuses on advanced intercross lines (AILs) which are the simplest such population. As originally proposed in 1995 by Darvasi and Soller, an AIL is the product of intercrossing two inbred strains beyond the F2 generation. Unlike recombinant inbred strains, AILs are maintained as outbred populations; brother-sister matings are specifically avoided. Each generation of intercrossing beyond the F2 further degrades linkage disequilibrium between adjacent makers, which allows for fine-scale mapping of quantitative trait loci (QTLs). Advances in genotyping technology and techniques for the statistical analysis of AILs have permitted rapid advances in the application of AILs. We review some of the analytical issues and available software, including QTLRel, EMMA, EMMAX, GEMMA, TASSEL, GRAMMAR, WOMBAT, Mendel, and others.