Mouse Genetic Reference Populations: Cellular Platforms for Integrative Systems Genetics.

Interrogation of disease-relevant cellular and molecular traits exhibited by genetically diverse cell populations enables in vitro systems genetics approaches for uncovering the basic properties of cellular function and identity. Primary cells, stem cells, and organoids derived from genetically diverse mouse strains, such as Collaborative Cross and Diversity Outbred populations, offer the opportunity for parallel in vitro/in vivo screening. These panels provide genetic resolution for variant discovery and functional characterization, as well as disease modeling and in vivo validation capabilities. Here we review mouse cellular systems genetics approaches for characterizing the influence of genetic variation on signaling networks and phenotypic diversity, and we discuss approaches for data integration and cross-species validation.

Mouse Systems Genetics as a Prelude to Precision Medicine.

Mouse models have been instrumental in understanding human disease biology and proposing possible new treatments. The precise control of the environment and genetic composition of mice allows more rigorous observations, but limits the generalizability and translatability of the results into human applications. In the era of precision medicine, strategies using mouse models have to be revisited to effectively emulate human populations. Systems genetics is one promising paradigm that may promote the transition to novel precision medicine strategies. Here, we review the state-of-the-art resources and discuss how mouse systems genetics helps to understand human diseases and to advance the development of precision medicine, with an emphasis on the existing resources and strategies.

Identifying modifier genes for hypertrophic cardiomyopathy.

BACKGROUND: Hypertrophic cardiomyopathy (HCM) severity greatly varies among patients even with the same HCM gene mutations. This variation is largely regulated by modifier gene(s), which, however, remain largely unknown. The current study is aimed to identify modifier genes using BXD strains, a large murine genetic reference population (GRP) derived from crosses between C57BL/6 J (B6) and D2 DBA/2 J (D2) mice. D2 mice natualy carrythe genetic basis and phenotypes of HCM. METHODS: Myocardial hypertrophy, the major phenotype of HCM, was determined by cardiomyocyte size on cardiac sections in 30 BXD strains, and their parental B6 and D2 strains and morphometric analysis was performed. Quantitative Trait Locus (QTL) mapping for cardiomyocyte sizes was conducted with WebQTL in GeneNetwork. Correlation of cardiomyocyte size and cardiac gene expression in BXDs accessed from GeneNetwork were evaluated. QTL candidate genes associated with cardiomyocyte sizes were prioritized based on the score system. RESULTS: Cardiomyocyte size varied significantly among BXD strains. Interval mapping on cardiomyocyte size data showed a significant QTL on chromosome (Chr) 2 at 66- 73.5 Mb and a suggestive QTL on Chr 5 at 20.9-39.7 Mb. Further score system revealed a high QTL score for Xirp2 in Chr 2. Xirp2 encodes xin actin-binding repeat containing 2, which is highly expressed in cardiac tissue and associate with cardiomyopathy and heart failure. In Chr5 QTL, Nos3, encoding nitric oxide synthase 3, received the highest score, which is significantly correlated with cardiomyocyte size. CONCLUSION: These results indicate that Xirp2 and Nos3 serve as novel candidate modifier genes for myocardial hypertrophy in HCM. These candidate genes will be validated in our future studies.

Systems genetics of metabolic health in the BXD mouse genetic reference population.

Susceptibility to metabolic syndrome (MetS) is dependent on genetics, environment, and gene-by-environment interactions, rendering the study of underlying mechanisms challenging. The majority of experiments in model organisms do not incorporate genetic variation and lack specific evaluation criteria for MetS. Here, we derived a continuous metric, the metabolic health score (MHS), based on standard clinical parameters and defined its molecular signatures in the liver and circulation. In human UK Biobank, the MHS associated with MetS status and was predictive of future disease incidence, even in individuals without MetS. Using quantitative trait locus analyses in mice, we found two MHS-associated genetic loci and replicated them in unrelated mouse populations. Through a prioritization scheme in mice and human genetic data, we identified TNKS and MCPH1 as candidates mediating differences in the MHS. Our findings provide insights into the molecular mechanisms sustaining metabolic health across species and uncover likely regulators. A record of this paper's transparent peer review process is included in the supplemental information.

Integrative systems analysis identifies genetic and dietary modulators of bile acid homeostasis.

Bile acids (BAs) are complex and incompletely understood enterohepatic-derived hormones that control whole-body metabolism. Here, we profiled postprandial BAs in the liver, feces, and plasma of 360 chow- or high-fat-diet-fed BXD male mice and demonstrated that both genetics and diet strongly influence BA abundance, composition, and correlation with metabolic traits. Through an integrated systems approach, we mapped hundreds of quantitative trait loci that modulate BAs and identified both known and unknown regulators of BA homeostasis. In particular, we discovered carboxylesterase 1c (Ces1c) as a genetic determinant of plasma tauroursodeoxycholic acid (TUDCA), a BA species with established disease-preventing actions. The association between Ces1c and plasma TUDCA was validated using data from independent mouse cohorts and a Ces1c knockout mouse model. Collectively, our data are a unique resource to dissect the physiological importance of BAs as determinants of metabolic traits, as underscored by the identification of CES1C as a master regulator of plasma TUDCA levels.

Multiomic profiling of the liver across diets and age in a diverse mouse population.

We profiled the liver transcriptome, proteome, and metabolome in 347 individuals from 58 isogenic strains of the BXD mouse population across age (7 to 24 months) and diet (low or high fat) to link molecular variations to metabolic traits. Several hundred genes are affected by diet and/or age at the transcript and protein levels. Orthologs of two aging-associated genes, St7 and Ctsd, were knocked down in C. elegans, reducing longevity in wild-type and mutant long-lived strains. The multiomics data were analyzed as segregating gene networks according to each independent variable, providing causal insight into dietary and aging effects. Candidates were cross-examined in an independent diversity outbred mouse liver dataset segregating for similar diets, with ∼80%-90% of diet-related candidate genes found in common across datasets. Together, we have developed a large multiomics resource for multivariate analysis of complex traits and demonstrate a methodology for moving from observational associations to causal connections.

Characterizing modifier genes of cardiac fibrosis phenotype in hypertrophic cardiomyopathy.

BACKGROUND: Clinical phenotypes of hypertrophic cardiomyopathy (HCM) vary greatly even among patients with the same gene mutations. This variability is largely regulated by unidentified modifier loci. The purpose of the study is to identify modifier genes for cardiac fibrosis-a major phenotype of HCM-using the BXD family, a murine cohort. METHODS: The relative severity of cardiac fibrosis was estimated by quantitation of cardiac collagen volume fraction (CCVF) across 66 members of the BXD family. Quantitative trait locus (QTL) mapping for cardiac fibrosis was done using GeneNetwork. Candidate modifier loci and genes associated with fibrosis were prioritized based on an explicit scoring system. Networks of correlation between fibrosis and cardiac transcriptomes were evaluated to generate causal models of disease susceptibility. RESULTS: CCVF levels varied greatly within this family. Interval mapping identified a significant CCVF-related QTL on chromosome (Chr) 2 in males, and a significant QTL on Chr 4 Mb in females. The scoring system highlighted two strong candidate genes in the Chr 2 locus-Nek6 and Nr6a1. Both genes are highly expressed in the heart. Cardiac Nek6 mRNA levels are significantly correlated with CCVF. Nipsnap3b and Fktn are lead candidate genes for the Chr 4 locus, and both are also highly expressed in heart. Cardiac Nipsnap3b gene expression correlates well with CCVF. CONCLUSION: Our study demonstrated that candidate modifier genes of cardiac fibrosis phenotype in HCM are different in males and females. Nek6 and Nr6a1 are strong candidates in males, while Nipsnap3b and Fktn are top candidates in females.

Complex Genetic Architecture Underlies Regulation of Influenza-A-Virus-Specific Antibody Responses in the Collaborative Cross.

Host genetic factors play a fundamental role in regulating humoral immunity to viral infection, including influenza A virus (IAV). Here, we utilize the Collaborative Cross (CC), a mouse genetic reference population, to study genetic regulation of variation in antibody response following IAV infection. CC mice show significant heritable variation in the magnitude, kinetics, and composition of IAV-specific antibody response. We map 23 genetic loci associated with this variation. Analysis of a subset of these loci finds that they broadly affect the antibody response to IAV as well as other viruses. Candidate genes are identified based on predicted variant consequences and haplotype-specific expression patterns, and several show overlap with genes identified in human mapping studies. These findings demonstrate that the host antibody response to IAV infection is under complex genetic control and highlight the utility of the CC in modeling and identifying genetic factors with translational relevance to human health and disease.

Designing a QTL Mapping Study for Implementation in the Realized Collaborative Cross Genetic Reference Population.

The Collaborative Cross (CC) mouse resource is a next-generation mouse genetic reference population (GRP) designed for high-resolution mapping of quantitative trait loci (QTL) of large effect affecting complex traits during health and disease. The CC resource consists of a set of 72 recombinant inbred lines (RILs) generated by reciprocal crossing of five classical and three wild-derived mouse founder strains. Complex traits are controlled by variations within multiple genes and environmental factors, and their mutual interactions. These traits are observed at multiple levels of the animals' systems, including metabolism, body weight, immune profile, and susceptibility or resistance to the development and progress of infectious or chronic diseases. Herein, we present general guidelines for design of QTL mapping experiments using the CC resource-along with full step-by-step protocols and methods that were implemented in our lab for the phenotypic and genotypic characterization of the different CC lines-for mapping the genes underlying host response to infectious and chronic diseases. © 2019 by John Wiley & Sons, Inc. Basic Protocol 1: CC lines for whole body mass index (BMI) Basic Protocol 2: A detailed assessment of the power to detect effect sizes based on the number of lines used, and the number of replicates per line Basic Protocol 3: Obtaining power for QTL with given target effect by interpolating in Table 1 of Keele et al. (2019).

Efficient protocols and methods for high-throughput utilization of the Collaborative Cross mouse model for dissecting the genetic basis of complex traits.

The Collaborative Cross (CC) mouse model is a next-generation mouse genetic reference population (GRP) designated for a high-resolution quantitative trait loci (QTL) mapping of complex traits during health and disease. The CC lines were generated from reciprocal crosses of eight divergent mouse founder strains composed of five classical and three wild-derived strains. Complex traits are defined to be controlled by variations within multiple genes and the gene/environment interactions. In this article, we introduce and present variety of protocols and results of studying the host response to infectious and chronic diseases, including type 2 diabetes and metabolic diseases, body composition, immune response, colorectal cancer, susceptibility to Aspergillus fumigatus, Klebsiella pneumoniae, Pseudomonas aeruginosa, sepsis, and mixed infections of Porphyromonas gingivalis and Fusobacterium nucleatum, which were conducted at our laboratory using the CC mouse population. These traits are observed at multiple levels of the body systems, including metabolism, body weight, immune profile, susceptibility or resistance to the development and progress of infectious or chronic diseases. Herein, we present full protocols and step-by-step methods, implemented in our laboratory for the phenotypic and genotypic characterization of the different CC lines, mapping the gene underlying the host response to these infections and chronic diseases. The CC mouse model is a unique and powerful GRP for dissecting the host genetic architectures underlying complex traits, including chronic and infectious diseases.

An Integrated Systems Genetics and Omics Toolkit to Probe Gene Function.

Identifying genetic and environmental factors that impact complex traits and common diseases is a high biomedical priority. Here, we developed, validated, and implemented a series of multi-layered systems approaches, including (expression-based) phenome-wide association, transcriptome-/proteome-wide association, and (reverse-) mediation analysis, in an open-access web server (systems-genetics.org) to expedite the systems dissection of gene function. We applied these approaches to multi-omics datasets from the BXD mouse genetic reference population, and identified and validated associations between genes and clinical and molecular phenotypes, including previously unreported links between Rpl26 and body weight, and Cpt1a and lipid metabolism. Furthermore, through mediation and reverse-mediation analysis we established regulatory relations between genes, such as the co-regulation of BCKDHA and BCKDHB protein levels, and identified targets of transcription factors E2F6, ZFP277, and ZKSCAN1. Our multifaceted toolkit enabled the identification of gene-gene and gene-phenotype links that are robust and that translate well across populations and species, and can be universally applied to any populations with multi-omics datasets.

Modeling the quantitative nature of neurodevelopmental disorders using Collaborative Cross mice.

Background: Animal models for neurodevelopmental disorders (NDD) generally rely on a single genetic mutation on a fixed genetic background. Recent human genetic studies however indicate that a clinical diagnosis with ASDAutism Spectrum Disorder (ASD) is almost always associated with multiple genetic fore- and background changes. The translational value of animal model studies would be greatly enhanced if genetic insults could be studied in a more quantitative framework across genetic backgrounds. Methods: We used the Collaborative Cross (CC), a novel mouse genetic reference population, to investigate the quantitative genetic architecture of mouse behavioral phenotypes commonly used in animal models for NDD. Results: Classical tests of social recognition and grooming phenotypes appeared insufficient for quantitative studies due to genetic dilution and limited heritability. In contrast, digging, locomotor activity, and stereotyped exploratory patterns were characterized by continuous distribution across our CC sample and also mapped to quantitative trait loci containing genes associated with corresponding phenotypes in human populations. Conclusions: These findings show that the CC can move animal model studies beyond comparative single gene-single background designs, and point out which type of behavioral phenotypes are most suitable to quantify the effect of developmental etiologies across multiple genetic backgrounds.

Genetic Regulation of Plasma Lipid Species and Their Association with Metabolic Phenotypes.

The genetic regulation and physiological impact of most lipid species are unexplored. Here, we profiled 129 plasma lipid species across 49 strains of the BXD mouse genetic reference population fed either chow or a high-fat diet. By integrating these data with genomics and phenomics datasets, we elucidated genes by environment (diet) interactions that regulate systemic metabolism. We found quantitative trait loci (QTLs) for ∼94% of the lipids measured. Several QTLs harbored genes associated with blood lipid levels and abnormal lipid metabolism in human genome-wide association studies. Lipid species from different classes provided signatures of metabolic health, including seven plasma triglyceride species that associated with either healthy or fatty liver. This observation was further validated in an independent mouse model of non-alcoholic fatty liver disease (NAFLD) and in plasma from NAFLD patients. This work provides a resource to identify plausible genes regulating the measured lipid species and their association with metabolic traits.

Systems Analyses Reveal Physiological Roles and Genetic Regulators of Liver Lipid Species.

The genetics of individual lipid species and their relevance in disease is largely unresolved. We profiled a subset of storage, signaling, membrane, and mitochondrial liver lipids across 385 mice from 47 strains of the BXD mouse population fed chow or high-fat diet and integrated these data with complementary multi-omics datasets. We identified several lipid species and lipid clusters with specific phenotypic and molecular signatures and, in particular, cardiolipin species with signatures of healthy and fatty liver. Genetic analyses revealed quantitative trait loci for 68% of the lipids (lQTL). By multi-layered omics analyses, we show the reliability of lQTLs to uncover candidate genes that can regulate the levels of lipid species. Additionally, we identified lQTLs that mapped to genes associated with abnormal lipid metabolism in human GWASs. This work provides a foundation and resource for understanding the genetic regulation and physiological significance of lipid species.

Systems Genetics of Liver Fibrosis.

This systems genetics analysis comprises quantitative measurements of hepatic fibrogenesis in mouse models and mapping of quantitative traits in mouse genetic reference populations. It is part of a large mapping project of fibrogenic genes including the analyses of experimental crosses from different inbred mouse strains. Extensive quantitative trait loci (QTL) mapping of fibrosis phenotypes and liver expression profiling in combination with in silico mapping facilitated the identification of QTL regions and underlying candidate genes that confer fibrosis susceptibility also in humans. Moreover, the approach led to the identification of interacting QTLs and gene networks in liver fibrosis, providing a key experimental platform for the development of novel, more precise therapeutic interventions. Here, we provide a use case for the application of different analysis tools and the integration of multiple datasets determined in F2 intercrosses and BXD recombinant inbred lines to identify, finemap and affirm fibrosis susceptibility loci.

Resources for Systems Genetics.

A key characteristic of systems genetics is its reliance on populations that vary to a greater or lesser degree in genetic complexity-from highly admixed populations such as the Collaborative Cross and Diversity Outcross to relatively simple crosses such as sets of consomic strains and reduced complexity crosses. This protocol is intended to help investigators make more informed decisions about choices of resources given different types of questions. We consider factors such as costs, availability, and ease of breeding for common scenarios. In general, we recommend using complementary resources and minimizing depth of resampling of any given genome or strain.

Genomic Prediction for Quantitative Traits Is Improved by Mapping Variants to Gene Ontology Categories in Drosophila melanogaster.

Predicting individual quantitative trait phenotypes from high-resolution genomic polymorphism data is important for personalized medicine in humans, plant and animal breeding, and adaptive evolution. However, this is difficult for populations of unrelated individuals when the number of causal variants is low relative to the total number of polymorphisms and causal variants individually have small effects on the traits. We hypothesized that mapping molecular polymorphisms to genomic features such as genes and their gene ontology categories could increase the accuracy of genomic prediction models. We developed a genomic feature best linear unbiased prediction (GFBLUP) model that implements this strategy and applied it to three quantitative traits (startle response, starvation resistance, and chill coma recovery) in the unrelated, sequenced inbred lines of the Drosophila melanogaster Genetic Reference Panel. Our results indicate that subsetting markers based on genomic features increases the predictive ability relative to the standard genomic best linear unbiased prediction (GBLUP) model. Both models use all markers, but GFBLUP allows differential weighting of the individual genetic marker relationships, whereas GBLUP weighs the genetic marker relationships equally. Simulation studies show that it is possible to further increase the accuracy of genomic prediction for complex traits using this model, provided the genomic features are enriched for causal variants. Our GFBLUP model using prior information on genomic features enriched for causal variants can increase the accuracy of genomic predictions in populations of unrelated individuals and provides a formal statistical framework for leveraging and evaluating information across multiple experimental studies to provide novel insights into the genetic architecture of complex traits.

Quantitative genetics in the study of virus-induced disease.

While the role of viral variants has long been known to play a key role in causing variation in disease severity, it is also clear that host genetic variation plays a critical role in determining virus-induced disease responses. However, a variety of factors, including confounding environmental variables, rare genetic variants requiring extremely large cohorts, the temporal dynamics of infections, and ethical limitation on human studies, have made the identification and dissection of variant host genes and pathways difficult within human populations. This difficulty has led to the development of a variety of experimental approaches used to identify host genetic contributions to disease responses. In this chapter, we describe the history of genetic associations within the human population, the development of experimentally tractable systems, and the insights these specific approaches provide. We conclude with a discussion of recent advances that allow for the investigation of the role of complex genetic networks that underlie host responses to infection, with the goal of drawing connections to human infections. In particular, we highlight the need for robust animal models with which to directly control and assess the role of host genetics on viral infection outcomes.