A generic hidden Markov model for multi-parent populations

A common step in the analysis of multi-parent populations is genotype reconstruction: identifying the founder origin of haplotypes from dense marker data. This process often makes use of a probability model for the pattern of founder alleles along chromosomes, including the relative frequency of founder alleles and the probability of exchanges among them, which depend on a model for meiotic recombination and on the mating design for the population. While the precise experimental design used to generate the population may be used to derive a precise characterization of the model for exchanges among founder alleles, this can be tedious, particularly given the great variety of experimental designs that have been proposed. We describe an approximate model that can be applied for a variety of multi-parent populations. We have implemented the approach in the R/qtl2 software, and we illustrate its use in applications to publicly-available data on Diversity Outbred and Collaborative Cross mice.

Relating multivariate shapes to genescapes using phenotype-biological process associations for craniofacial shape

Realistic mappings of genes to morphology are inherently multivariate on both sides of the equation. The importance of coordinated gene effects on morphological phenotypes is clear from the intertwining of gene actions in signaling pathways, gene regulatory networks, and developmental processes underlying the development of shape and size. Yet, current approaches tend to focus on identifying and localizing the effects of individual genes and rarely leverage the information content of high dimensional phenotypes. Here, we explicitly model the joint effects of biologically coherent collections of genes on a multivariate trait-craniofacial shape - in a sample of n = 1,145 mice from the Diversity Outbred (DO) experimental line. We use biological process gene ontology (GO) annotations to select skeletal and facial development gene sets and solve for the axis of shape variation that maximally covaries with gene set marker variation. We use our process-centered, multivariate genotype-phenotype (process MGP) approach to determine the overall contributions to craniofacial variation of genes involved in relevant processes and how variation in different processes corresponds to multivariate axes of shape variation. Further, we compare the directions of effect in phenotype space of mutations to the primary axis of shape variation associated with broader pathways within which they are thought to function. Finally, we leverage the relationship between mutational and pathway-level effects to predict phenotypic effects beyond craniofacial shape in specific mutants. We also introduce an online application which provides users the means to customize their own process-centered craniofacial shape analyses in the DO. The process-centered approach is generally applicable to any continuously varying phenotype and thus has wide-reaching implications for complex-trait genetics.

A Diversity Outbred F1 mouse model identifies host-intrinsic genetic regulators of response to immune checkpoint inhibitors

: Immune checkpoint inhibitors (ICI) have improved outcomes for a variety of malignancies; however, many patients fail to benefit. While tumor-intrinsic mechanisms are likely involved in therapy resistance, it is unclear to what extent host genetic background influences response. To investigate this, we utilized the Diversity Outbred (DO) and Collaborative Cross (CC) mouse models. DO mice are an outbred stock generated by crossbreeding 8 inbred founder strains, and CC mice are recombinant inbred mice generated from the same 8 founders. We generated 207 DOB6F1 mice representing 48 DO Dams and demonstrated that these mice reliably accept the C57BL/6 syngeneic B16F0 tumor and that host genetic background influences response to ICI. Genetic linkage analysis from 142 mice identified multiple regions including one within chromosome 13 that associated with therapeutic response. We utilized 6 CC strains bearing the positive (NZO) or negative (C57BL/6) driver genotype in this locus. We found that 2/3 of predicted responder CCB6F1 crosses show reproducible ICI response. The chromosome 13 locus contains the murine prolactin family, which is a known immunomodulating cytokine associated with various autoimmune disorders. To directly test whether prolactin influences ICI response rates, we implanted inbred C57BL/6 mice with subcutaneous slow-release prolactin pellets to induce mild hyperprolactinemia. Prolactin augmented ICI response against B16F0, with 5/8 mice exhibiting slowed tumor growth relative to controls. This study highlights the role of host genetics in ICI response and supports the use of F1 crosses in the DO and CC mouse populations as powerful cancer immunotherapy models.

Variation in the response to malaria in Diversity Outbred mice

We infected Diversity Outbred mice with Plasmodium chabaudi to better understand how the host response to infection can vary and to try to identify genetic loci responsible for this variation. We identified two loci correlating with binary traits: one on chromosome two was linked to undetectable parasite loads and another on chromosome ten which was linked to death. Though we tested many variable traits, none of those reached statistical significance using the 489 mice we tested.

Intermittent fasting and caloric restriction interact with genetics to shape physiological health in mice

Dietary interventions can dramatically affect physiological health and organismal lifespan. The degree to which organismal health is improved depends upon genotype and the severity of dietary intervention, but neither the effects of these factors, nor their interaction, have been quantified in an outbred population. Moreover, it is not well understood what physiological changes occur shortly after dietary change and how these may affect the health of an adult population. In this article, we investigated the effect of six month exposure of either caloric restriction or intermittent fasting on a broad range of physiological traits in 960 one year old Diversity Outbred mice. We found caloric restriction and intermittent fasting affected distinct aspects of physiology and neither the magnitude nor the direction (beneficial or detrimental) of effects were concordant with the severity of the intervention. In addition to the effects of diet, genetic variation significantly affected 31 of 36 traits (heritabilties ranged from 0.04-0.65). We observed significant covariation between many traits that was due to both diet and genetics and quantified these effects with phenotypic and genetic correlations. We genetically mapped 16 diet-independent and 2 diet-dependent significant quantitative trait loci, both of which were associated with cardiac physiology. Collectively, these results demonstrate the degree to which diet and genetics interact to shape the physiological health of adult mice following six months of dietary intervention.

Genome-wide association mapping of ethanol sensitivity in the Diversity Outbred mouse population

BackgroundA strong predictor for the development of alcohol use disorders (AUDs) is altered sensitivity to the intoxicating effects of alcohol. Individual differences in the initial sensitivity to alcohol are controlled in part by genetic factors. Mice offer a powerful tool for elucidating the genetic basis of behavioral and physiological traits relevant to AUDs; but conventional experimental crosses have only been able to identify large chromosomal regions rather than specific genes. Genetically diverse, highly recombinant mouse populations allow for the opportunity to observe a wider range of phenotypic variation, offer greater mapping precision, and thus increase the potential for efficient gene identification.MethodsWe have taken advantage of the Diversity Outbred (DO) mouse population to identify and precisely map quantitative trait loci (QTL) associated with ethanol sensitivity. We phenotyped 798 male J:DO mice for three measures of ethanol sensitivity: ataxia, hypothermia, and loss of the righting response. We used high density MEGAMuga and GIGAMuga arrays to obtain genotypes ranging from 77,808 – 143,259 SNPs. In addition, we performed RNA sequencing in striatum to map expression QTLs and to identify gene expression-trait correlations.ResultsWe then applied a systems genetic strategy to identify narrow QTLs and construct the network of correlations that exist between DNA sequence, gene expression values and ethanol-related phenotypes to prioritize our list of positional candidate genes.ConclusionsOur results can be used to identify alleles that contribute to AUDs in humans, elucidate causative biological mechanisms, or assist in the development of novel therapeutic interventions.

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. The present 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.

Identification of sample mix-ups and mixtures in microbiome data in Diversity Outbred mice

In a Diversity Outbred mouse project with genotype data on 500 mice, including 297 with microbiome data, we identified three sets of sample mix-ups (two pairs and one trio) as well as at least 15 microbiome samples that appear to be mixtures of pairs of mice. The microbiome data consisted of shotgun sequencing reads from fecal DNA, used to characterize the gut microbial communities present in these mice. These sequence reads included sufficient reads derived from the host mouse to identify the individual. A number of microbiome samples appeared to contain a mixture of DNA from two mice. We describe a method for identifying sample mix-ups in such microbiome data, as well as a method for evaluating sample mixtures in this context.