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In vertebrates, including humans, individuals harbor gut microbial communities whose species composition and relative proportions of dominant microbial groups are tremendously varied. Although external and stochastic factors clearly contribute to the individuality of the microbiota, the fundamental principles dictating how environmental factors and host genetic factors combine to shape this complex ecosystem are largely unknown and require systematic study. Here we examined factors that affect microbiota composition in a large (n = 645) mouse advanced intercross line originating from a cross between C57BL/6J and an ICR-derived outbred line (HR). Quantitative pyrosequencing of the microbiota defined a core measurable microbiota (CMM) of 64 conserved taxonomic groups that varied quantitatively across most animals in the population. Although some of this variation can be explained by litter and cohort effects, individual host genotype had a measurable contribution. Testing of the CMM abundances for cosegregation with 530 fully informative SNP markers identified 18 host quantitative trait loci (QTL) that show significant or suggestive genomewide linkage with relative abundances of specific microbial taxa. These QTL affect microbiota composition in three ways; some loci control individual microbial species, some control groups of related taxa, and some have putative pleiotropic effects on groups of distantly related organisms. These data provide clear evidence for the importance of host genetic control in shaping individual microbiome diversity in mammals, a key step toward understanding the factors that govern the assemblages of gut microbiota associated with complex diseases.

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.

In various organisms, sequencing of selectively bred lines at apparent selection limits has demonstrated that genetic variation can remain at many loci, implying that evolution at the genetic level may continue even if the population mean phenotype remains constant. We compared selection signatures at generations 22 and 61 of the “High Runner” mouse experiment, which includes 4 replicate lines bred for voluntary wheel-running behavior (HR) and 4 non-selected control (C) lines. Previously, we reported multiple regions of differentiation between the HR and C lines, based on whole-genome sequence data for 10 mice from each line at generation 61, which was >31 generations after selection limits had been reached in all HR lines. Here, we analyzed pooled sequencing data from ~20 mice for each of the 8 lines at generation 22, around when HR lines were reaching limits. Differentiation analyses of allele frequencies at ~4.4 million SNP loci used the regularized T-test and detected 258 differentiated regions with FDR = 0.01. Comparable analyses involving pooling generation 61 individual mouse genotypes into allele frequencies by line produced only 11 such regions, with almost no overlap among the largest and most statistically significant peaks between the two generations. These results implicate a sort of “genetic churn” that continues at loci relevant for running. Simulations indicate that loss of statistical power due to random genetic drift and sampling error are insufficient to explain the differences in selection signatures. The 13 differentiated regions at generation 22 with strict culling measures include 79 genes related to a wide variety of functions. Gene ontology identified pathways related to olfaction and vomeronasal pathways as being overrepresented, consistent with generation 61 analyses, despite those specific regions differing between generations. Genes Dspp and Rbm24 are also identified as potentially explaining known bone and skeletal muscle differences, respectively, between the linetypes.

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.

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.

Background To understand the genetic architecture of complex traits and bridge the genotype-phenotype gap, it is useful to study intermediate -omics data, e.g. the transcriptome. The present study introduces a method for simultaneous quantification of the contributions from single nucleotide polymorphisms (SNPs) and transcript abundances in explaining phenotypic variance, using Bayesian whole-omics models. Bayesian mixed models and variable selection models were used and, based on parameter samples from the model posterior distributions, explained variances were further partitioned at the level of chromosomes and genome segments. Results We analyzed three growth-related traits: Body Weight (BW), Feed Intake (FI), and Feed Efficiency (FE), in an F 2 population of 440 mice. The genomic variation was covered by 1806 tag SNPs, and transcript abundances were available from 23,698 probes measured in the liver. Explained variances were computed for models using pedigree, SNPs, transcripts, and combinations of these. Comparison of these models showed that for BW, a large part of the variation explained by SNPs could be covered by the liver transcript abundances; this was less true for FI and FE. For BW, the main quantitative trait loci (QTLs) are found on chromosomes 1, 2, 9, 10, and 11, and the QTLs on 1, 9, and 10 appear to be expression Quantitative Trait Locus (eQTLs) affecting gene expression in the liver. Chromosome 9 is the case of an apparent eQTL, showing that genomic variance disappears, and that a tri-modal distribution of genomic values collapses, when gene expressions are added to the model. Conclusions With increased availability of various -omics data, integrative approaches are promising tools for understanding the genetic architecture of complex traits. Partitioning of explained variances at the chromosome and genome-segment level clearly separated regulatory and structural genomic variation as the areas where SNP effects disappeared/remained after adding transcripts to the model. The models that include transcripts explained more phenotypic variance and were better at predicting phenotypes than a model using SNPs alone. The predictions from these Bayesian models are generally unbiased, validating the estimates of explained variances.

Aging is associated with declining exercise and unhealthy changes in body composition. Exercise ameliorates certain adverse age‐related physiological changes and protects against many chronic diseases. Despite these benefits, willingness to exercise and physiological responses to exercise vary widely, and long‐term exercise and its benefits are difficult and costly to measure in humans. Furthermore, physiological effects of aging in humans are confounded with changes in lifestyle and environment. We used C57BL/6J mice to examine long‐term patterns of exercise during aging and its physiological effects in a well‐controlled environment. One‐year‐old male (n = 30) and female (n = 30) mice were divided into equal size cohorts and aged for an additional year. One cohort was given access to voluntary running wheels while another was denied exercise other than home cage movement. Body mass, composition, and metabolic traits were measured before, throughout, and after 1 year of treatment. Long‐term exercise significantly prevented gains in body mass and body fat, while preventing loss of lean mass. We observed sex‐dependent differences in body mass and composition trajectories during aging. Wheel running (distance, speed, duration) was greater in females than males and declined with age. We conclude that long‐term exercise may serve as a preventive measure against age‐related weight gain and body composition changes, and that mouse inbred strains can be used to characterize effects of long‐term exercise and factors (e.g. sex, age) modulating these effects. These findings will facilitate studies on relationships between exercise and health in aging populations, including genetic predisposition and genotype‐by‐environment interactions. This study demonstrates that exercise prevents doubling of body fat, significant gains in body mass, and loss of lean mass during aging. It also provides evidence of the effect of sex on exercise, body mass, and body composition trajectories during aging. Thus, this study demonstrates that long‐term exercise (starting in midlife) may be used as a preventive measure against age‐related weight gain and changes in body composition.

The problem of identifying complex epistatic quantitative trait loci (QTL) across the entire genome continues to be a formidable challenge for geneticists. The complexity of genome-wide epistatic analysis results mainly from the number of QTL being unknown and the number of possible epistatic effects being huge. In this article, we use a composite model space approach to develop a Bayesian model selection framework for identifying epistatic QTL for complex traits in experimental crosses from two inbred lines. By placing a liberal constraint on the upper bound of the number of detectable QTL we restrict attention to models of fixed dimension, greatly simplifying calculations. Indicators specify which main and epistatic effects of putative QTL are included. We detail how to use prior knowledge to bound the number of detectable QTL and to specify prior distributions for indicators of genetic effects. We develop a computationally efficient Markov chain Monte Carlo (MCMC) algorithm using the Gibbs sampler and Metropolis-Hastings algorithm to explore the posterior distribution. We illustrate the proposed method by detecting new epistatic QTL for obesity in a backcross of CAST/Ei mice onto M16i.

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.

Understanding the genetic architecture of traits such as growth, body composition, and energy balance has become a primary focus for biomedical and agricultural research. The objective of this study was to map QTL in a large F2 (n = 1181) population resulting from an intercross between the M16 and ICR lines of mice. The M16 line, developed by long-term selection for 3- to 6-week weight gain, is larger, heavier, fatter, hyperphagic, and diabetic relative to its randomly selected control line of ICR origin. The F2 population was phenotyped for growth and energy intake at weekly intervals from 4 to 8 weeks of age and for body composition and plasma levels of insulin, leptin, TNFα, IL6, and glucose at 8 weeks and was genotyped for 80 microsatellite markers. Since the F2 was a cross between a selection line and its unselected control, the QTL identified likely represent genes that contributed to direct and correlated responses to long-term selection for rapid growth rate. Across all traits measured, 95 QTL were identified, likely representing 19 unique regions on 13 chromosomes. Four chromosomes (2, 6, 11, and 17) harbored loci contributing disproportionately to selection response. Several QTL demonstrating differential regulation of regional adipose deposition and age-dependent regulation of growth and energy consumption were identified.