Quantitative trait mapping in Diversity Outbred mice identifies two genomic regions associated with heart size

Identification of endocrine-disrupting chemicals targeting key DCM-associated genes via bioinformatics and machine learning

Dilated cardiomyopathy (DCM) is a primary cause of heart failure (HF), with the incidence of HF increasing consistently in recent years. DCM pathogenesis involves a combination of inherited predisposition and environmental factors. Endocrine-disrupting chemicals (EDCs) are exogenous chemicals that interfere with endogenous hormone action and are capable of targeting various organs, including the heart. However, the impact of these disruptors on heart disease through their effects on genes remains underexplored. In this study, we aimed to explore key DCM-related genes using machine learning (ML) and the construction of a predictive model. Using the Gene Expression Omnibus (GEO) database, we screened differentially expressed genes (DEGs) and performed enrichment analyses of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to DCM. Through ML techniques combining maximum relevance minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) logistic regression, we identified key genes for predicting DCM (IL1RL1, SEZ6L, SFRP4, COL22A1, RNASE2, HB). Based on these key genes, 79 EDCs with the potential to affect DCM were identified, among which 4 (3,4-dichloroaniline, fenitrothion, pyrene, and isoproturon) have not been previously associated with DCM. These findings establish a novel relationship between the EDCs mediated by key genes and the development of DCM.

Genome-wide transcript and protein analysis highlights the role of protein homeostasis in the aging mouse heart

Investigation of the molecular mechanisms of aging in the human heart is challenging because of confounding factors, such as diet and medications, as well as limited access to tissues from healthy aging individuals. The laboratory mouse provides an ideal model to study aging in healthy individuals in a controlled environment. However, previous mouse studies have examined only a narrow range of the genetic variation that shapes individual differences during aging. Here, we analyze transcriptome and proteome data from 185 genetically diverse male and female mice at ages 6, 12, and 18 mo to characterize molecular changes that occur in the aging heart. Transcripts and proteins reveal activation of pathways related to exocytosis and cellular transport with age, whereas processes involved in protein folding decrease with age. Additional changes are apparent only in the protein data including reduced fatty acid oxidation and increased autophagy. For proteins that form complexes, we see a decline in correlation between their component subunits with age, suggesting age-related loss of stoichiometry. The most affected complexes are themselves involved in protein homeostasis, which potentially contributes to a cycle of progressive breakdown in protein quality control with age. Our findings highlight the important role of post-transcriptional regulation in aging. In addition, we identify genetic loci that modulate age-related changes in protein homeostasis, suggesting that genetic variation can alter the molecular aging process.

The genomic basis of high-elevation adaptation in wild house mice (Mus musculus domesticus) from South America

Understanding the genetic basis of environmental adaptation in natural populations is a central goal in evolutionary biology. The conditions at high elevation, particularly the low oxygen available in the ambient air, impose a significant and chronic environmental challenge to metabolically active animals with lowland ancestry. To understand the process of adaptation to these novel conditions and to assess the repeatability of evolution over short timescales, we examined the signature of selection from complete exome sequences of house mice (Mus musculus domesticus) sampled across two elevational transects in the Andes of South America. Using phylogenetic analysis, we show that house mice colonized high elevations independently in Ecuador and Bolivia. Overall, we found distinct responses to selection in each transect and largely nonoverlapping sets of candidate genes, consistent with the complex nature of traits that underlie adaptation to low oxygen availability (hypoxia) in other species. Nonetheless, we also identified a small subset of the genome that appears to be under parallel selection at the gene and SNP levels. In particular, three genes (Col22a1, Fgf14, and srGAP1) bore strong signatures of selection in both transects. Finally, we observed several patterns that were common to both transects, including an excess of derived alleles at high elevation, and a number of hypoxia-associated genes exhibiting a threshold effect, with a large allele frequency change only at the highest elevations. This threshold effect suggests that selection pressures may increase disproportionately at high elevations in mammals, consistent with observations of some high-elevation diseases in humans.

A systems approach using Diversity Outbred mice distinguishes the cardiovascular effects and genetics of circulating GDF11 from those of its homolog, myostatin

Growth differentiation factor 11 (GDF11) is a member of the TGF-β protein family that has been implicated in the development of cardiac hypertrophy. While some studies have suggested that systemic GDF11 protects against cardiomyocyte enlargement and left ventricular wall thickening, there remains uncertainty about the true impact of GDF11 and whether its purported effects are actually attributable to its homolog myostatin. This study was conducted to resolve the statistical and genetic relationships among GDF11, myostatin, and cardiac hypertrophy in a mouse model of human genetics, the Diversity Outbred (DO) stock. In the DO population, serum GDF11 concentrations positively correlated with cardiomyocyte cross-sectional area, while circulating myostatin levels were negatively correlated with body weight, heart weight, and left ventricular wall thickness and mass. Genetic analyses revealed that serum GDF11 concentrations are modestly heritable (0.23) and identified a suggestive peak on murine chromosome 3 in close proximity to the gene Hey1, a transcriptional repressor. Bioinformatic analyses located putative binding sites for the HEY1 protein upstream of the Gdf11 gene in the mouse and human genomes. In contrast, serum myostatin concentrations were more heritable (0.57) than GDF11 concentrations, and mapping identified a significant locus near the gene FoxO1, which has binding motifs within the promoter regions of human and mouse myostatin genes. Together, these findings more precisely define the independent cardiovascular effects of GDF11 and myostatin, as well as their distinct regulatory pathways. Hey1 is a compelling candidate for the regulation of GDF11 and will be further evaluated in future studies.

INFIMA leverages multi-omics model organism data to identify effector genes of human GWAS variants

Genome-wide association studies reveal many non-coding variants associated with complex traits. However, model organism studies largely remain as an untapped resource for unveiling the effector genes of non-coding variants. We develop INFIMA, Integrative Fine-Mapping, to pinpoint causal SNPs for diversity outbred (DO) mice eQTL by integrating founder mice multi-omics data including ATAC-seq, RNA-seq, footprinting, and in silico mutation analysis. We demonstrate INFIMA's superior performance compared to alternatives with human and mouse chromatin conformation capture datasets. We apply INFIMA to identify novel effector genes for GWAS variants associated with diabetes. The results of the application are available at http://www.statlab.wisc.edu/shiny/INFIMA/ .

Heritability of fat distributions in male mice from the founder strains of the Diversity Outbred mouse population

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.

Systems genetics in diversity outbred mice inform BMD GWAS and identify determinants of bone strength

Genome-wide association studies (GWASs) for osteoporotic traits have identified over 1000 associations; however, their impact has been limited by the difficulties of causal gene identification and a strict focus on bone mineral density (BMD). Here, we use Diversity Outbred (DO) mice to directly address these limitations by performing a systems genetics analysis of 55 complex skeletal phenotypes. We apply a network approach to cortical bone RNA-seq data to discover 66 genes likely to be causal for human BMD GWAS associations, including the genes SERTAD4 and GLT8D2. We also perform GWAS in the DO for a wide-range of bone traits and identify Qsox1 as a gene influencing cortical bone accrual and bone strength. In this work, we advance our understanding of the genetics of osteoporosis and highlight the ability of the mouse to inform human genetics.

Osteoporosis GWAS faces two challenges, causal gene discovery and a lack of phenotypic diversity. Here, the authors use the Diversity Outbred mouse population to inform human GWAS using networks and map genetic loci for 55 bone traits, identifying new potential bone strength genes.

High-throughput sleep phenotyping produces robust and heritable traits in Diversity Outbred mice and their founder strains

STUDY OBJECTIVES

This study describes high-throughput phenotyping strategies for sleep and circadian behavior in mice, including examinations of robustness, reliability, and heritability among Diversity Outbred (DO) mice and their eight founder strains.

METHODS

We performed high-throughput sleep and circadian phenotyping in male mice from the DO population (n = 338) and their eight founder strains: A/J (n = 6), C57BL/6J (n = 14), 129S1/SvlmJ (n = 6), NOD/LtJ (n = 6), NZO/H1LtJ (n = 6), CAST/EiJ (n = 8), PWK/PhJ (n = 8), and WSB/EiJ (n = 6). Using infrared beam break systems, we defined sleep as at least 40 s of continuous inactivity and quantified sleep-wake amounts and bout characteristics. We developed assays to measure sleep latency in a new environment and during a modified Murine Multiple Sleep Latency Test, and estimated circadian period from wheel-running experiments. For each trait, broad-sense heritability (proportion of variability explained by all genetic factors) was derived in founder strains, while narrow-sense heritability (proportion of variability explained by additive genetic effects) was calculated in DO mice.

RESULTS

Phenotypes were robust to different inactivity durations to define sleep. Differences across founder strains and moderate/high broad-sense heritability were observed for most traits. There was large phenotypic variability among DO mice, and phenotypes were reliable, although estimates of heritability were lower than in founder mice. This likely reflects important nonadditive genetic effects.

CONCLUSIONS

A high-throughput phenotyping strategy in mice, based primarily on monitoring of activity patterns, provides reliable and heritable estimates of sleep and circadian traits. This approach is suitable for discovery analyses in DO mice, where genetic factors explain some proportion of phenotypic variation.

Using genomic resources for linkage analysis in Peromyscus with an application for characterizing Dominant Spot

Background

Peromyscus are the most common mammalian species in North America and are widely used in both laboratory and field studies. The deer mouse, P. maniculatus and the old-field mouse, P. polionotus, are closely related and can generate viable and fertile hybrid offspring. The ability to generate hybrid offspring, coupled with developing genomic resources, enables researchers to conduct linkage analysis studies to identify genomic loci associated with specific traits.

Results

We used available genomic data to identify DNA polymorphisms between P. maniculatus and P. polionotus and used the polymorphic data to identify the range of genetic complexity that underlies physiological and behavioral differences between the species, including cholesterol metabolism and genes associated with autism. In addition, we used the polymorphic data to conduct a candidate gene linkage analysis for the Dominant spot trait and determined that Dominant spot is linked to a region of chromosome 20 that contains a strong candidate gene, Sox10. During the linkage analysis, we found that the spot size varied quantitively in affected Peromyscus based on genetic background.

Conclusions

The expanding genomic resources for Peromyscus facilitate their use in linkage analysis studies, enabling the identification of loci associated with specific traits. More specifically, we have linked a coat color spotting phenotype, Dominant spot, with Sox10, a member the neural crest gene regulatory network, and that there are likely two genetic modifiers that interact with Dominant spot. These results establish Peromyscus as a model system for identifying new alleles of the neural crest gene regulatory network.

High-fat diet negatively impacts both metabolic and behavioral health in outbred heterogeneous stock rats

Obesity is influenced by genetics and diet and has wide ranging comorbidities, including anxiety and depressive disorders. Outbred heterogeneous stock (HS) rats are used for fine-genetic mapping of complex traits and may be useful for understanding gene by diet interactions. In this study, HS rats were fed diets containing 60% kcal from fat (high-fat diet, HFD) or 10% kcal from fat (low-fat diet, LFD) and tested for metabolic (study 1) and behavioral (study 2) outcomes. In study 1, we measured glucose tolerance, fasting glucose and insulin, fat pad weights and despair-like behavior in the forced swim test (FST). In study 2, we assessed anxiety-like (elevated plus maze, EPM; open field test, OFT) and despair-like/coping (splash test, SpT; and FST) behaviors. Body weight and food intake were measured weekly in both studies. We found negative effects of HFD on metabolic outcomes, including increased body weight and fat pad weights, decreased glucose tolerance, and increased fasting insulin. We also found negative effects of HFD on despair-like/coping and anxiety-like behaviors. These include increased immobility in the FST, decreased open arm time in the EPM, and increased movement and rest episodes and decreased rearing in the OFT. The diet-induced changes in EPM and OFT were independent of overall locomotion. Additionally, diet-induced changes in OFT behaviors were independent of adiposity, while adiposity was a confounding factor for EPM and FST behavior. This work establishes the HS as a model to study gene by diet interactions affecting metabolic and behavioral health.

Facial shape and allometry quantitative trait locus intervals in the Diversity Outbred mouse are enriched for known skeletal and facial development genes

The biology of how faces are built and come to differ from one another is complex. Discovering normal variants that contribute to differences in facial morphology is one key to untangling this complexity, with important implications for medicine and evolutionary biology. This study maps quantitative trait loci (QTL) for skeletal facial shape using Diversity Outbred (DO) mice. The DO is a randomly outcrossed population with high heterozygosity that captures the allelic diversity of eight inbred mouse lines from three subspecies. The study uses a sample of 1147 DO animals (the largest sample yet employed for a shape QTL study in mouse), each characterized by 22 three-dimensional landmarks, 56,885 autosomal and X-chromosome markers, and sex and age classifiers. We identified 37 facial shape QTL across 20 shape principal components (PCs) using a mixed effects regression that accounts for kinship among observations. The QTL include some previously identified intervals as well as new regions that expand the list of potential targets for future experimental study. Three QTL characterized shape associations with size (allometry). Median support interval size was 3.5 Mb. Narrowing additional analysis to QTL for the five largest magnitude shape PCs, we found significant overrepresentation of genes with known roles in growth, skeletal and facial development, and sensory organ development. For most intervals, one or more of these genes lies within 0.25 Mb of the QTL's peak. QTL effect sizes were small, with none explaining more than 0.5% of facial shape variation. Thus, our results are consistent with a model of facial diversity that is influenced by key genes in skeletal and facial development and, simultaneously, is highly polygenic.

Using Heterogeneous Stocks for Fine-Mapping Genetically Complex Traits

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

High-Diversity Mouse Populations for Complex Traits

Contemporary mouse genetic reference populations are a powerful platform to discover complex disease mechanisms. Advanced high-diversity mouse populations include the Collaborative Cross (CC) strains, Diversity Outbred (DO) stock, and their isogenic founder strains. When used in systems genetics and integrative genomics analyses, these populations efficiently harnesses known genetic variation for precise and contextualized identification of complex disease mechanisms. Extensive genetic, genomic, and phenotypic data are already available for these high-diversity mouse populations and a growing suite of data analysis tools have been developed to support research on diverse mice. This integrated resource can be used to discover and evaluate disease mechanisms relevant across species.

The Collaborative Cross: A Systems Genetics Resource for Studying Host-Pathogen Interactions

Host genetic variation has a major impact on infectious disease susceptibility. The study of pathogen resistance genes, largely aided by mouse models, has significantly advanced our understanding of infectious disease pathogenesis. The Collaborative Cross (CC), a newly developed multi-parental mouse genetic reference population, serves as a tractable model system to study how pathogens interact with genetically diverse populations. In this review, we summarize progress utilizing the CC as a platform to develop improved models of pathogen-induced disease and to map polymorphic host response loci associated with variation in susceptibility to pathogens.

Genome-wide association for testis weight in the diversity outbred mouse population

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.

Developmental constraint through negative pleiotropy in the zygomatic arch

BACKGROUND

Previous analysis suggested that the relative contribution of individual bones to regional skull lengths differ between inbred mouse strains. If the negative correlation of adjacent bone lengths is associated with genetic variation in a heterogeneous population, it would be an example of negative pleiotropy, which occurs when a genetic factor leads to opposite effects in two phenotypes. Confirming negative pleiotropy and determining its basis may reveal important information about the maintenance of overall skull integration and developmental constraint on skull morphology.

RESULTS

We identified negative correlations between the lengths of the frontal and parietal bones in the midline cranial vault as well as the zygomatic bone and zygomatic process of the maxilla, which contribute to the zygomatic arch. Through gene association mapping of a large heterogeneous population of Diversity Outbred (DO) mice, we identified a quantitative trait locus on chromosome 17 driving the antagonistic contribution of these two zygomatic arch bones to total zygomatic arch length. Candidate genes in this region were identified and real-time PCR of the maxillary processes of DO founder strain embryos indicated differences in the RNA expression levels for two of the candidate genes, Camkmt and Six2.

CONCLUSIONS

A genomic region underlying negative pleiotropy of two zygomatic arch bones was identified, which provides a mechanism for antagonism in component bone lengths while constraining overall zygomatic arch length. This type of mechanism may have led to variation in the contribution of individual bones to the zygomatic arch noted across mammals. Given that similar genetic and developmental mechanisms may underlie negative correlations in other parts of the skull, these results provide an important step toward understanding the developmental basis of evolutionary variation and constraint in skull morphology.