microRNA-146a-5p association with the cardiometabolic disease risk factor TMAO.

Trimethylamine-N-oxide (TMAO), a microbial choline metabolism byproduct that is processed in the liver and excreted into circulation, is associated with increased atherosclerotic lesion formation and cardiovascular disease risk. Genetic regulators of TMAO levels are largely unknown. In the present study, we used 288 mice from a genetically heterogeneous mouse population [Diversity Outbred (DO)] to determine hepatic microRNA associations with TMAO in the context of an atherogenic diet. We also validated findings in two additional animal models of atherosclerosis: liver-specific insulin receptor knockout mice fed a chow diet (LIRKO) and African green monkeys fed high-fat/high-cholesterol diet. Small RNA-sequencing analysis in DO mice, LIRKO mice, and African green monkeys identified only one hepatic microRNA (miR-146a-5p) that is aberrantly expressed across all three models. Moreover, miR-146a-5p levels are associated with circulating TMAO after atherogenic diet in each of these models. We also performed high-resolution genetic mapping and identified a novel quantitative trait locus on Chromosome 12 for TMAO levels. This interval includes two genes, Numb and Dlst, which are inversely correlated with both miR-146a and TMAO and are predicted targets of miR-146a. Both of these genes have been validated as direct targets of miR-146a, though in other cellular contexts. This is the first report to our knowledge of a link between miR-146 and TMAO. Our findings suggest that miR-146-5p, as well as one or more genes at the Chromosome 12 QTL (possibly Numb or Dlst), is strongly linked to TMAO levels and likely involved in the control of atherosclerosis.

Correction to: Quantitative trait mapping in Diversity Outbred mice identifies two genomic regions associated with heart size.

The original article has been published with an incorrect text in Materials and Methods section. The corrected text should read as.

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

Heart size is an important factor in cardiac health and disease. In particular, increased heart weight is predictive of adverse cardiovascular outcomes in multiple large community-based studies. We use two cohorts of Diversity Outbred (DO) mice to investigate the role of genetics, sex, age, and diet on heart size. DO mice (n = 289) of both sexes from generation 10 were fed a standard chow diet, and analyzed at 12-15 weeks of age. Another cohort of female DO mice (n = 258) from generation 11 were fed either a high-fat, cholesterol-containing (HFC) diet or a low-fat, high-protein diet, and analyzed at 24-25 weeks. We did not observe an effect of diet on body or heart weight in generation 11 mice, although we previously reported an effect on other cardiovascular risk factors, including cholesterol, triglycerides, and insulin. We do observe a significant genetic effect on heart weight in this population. We identified two quantitative trait loci for heart weight, one (Hwtf1) at a genome-wide significance level of p ≤ 0.05 on MMU15 and one (Hwtf2) at a genome-wide suggestive level of p ≤ 0.1 on MMU10, that together explain 13.3% of the phenotypic variance. Hwtf1 contained collagen type XXII alpha 1 chain (Col22a1), and the NZO/HlLtJ and WSB/EiJ haplotypes were associated with larger hearts. This is consistent with heart tissue Col22a1 expression in DO founders and SNP patterns within Hwtf1 for Col22a1. Col22a1 has been previously associated with cardiac fibrosis in mice, suggesting that Col22a1 may be involved in pathological cardiac hypertrophy.

A multi-megabase copy number gain causes maternal transmission ratio distortion on mouse chromosome 2.

Significant departures from expected Mendelian inheritance ratios (transmission ratio distortion, TRD) are frequently observed in both experimental crosses and natural populations. TRD on mouse Chromosome (Chr) 2 has been reported in multiple experimental crosses, including the Collaborative Cross (CC). Among the eight CC founder inbred strains, we found that Chr 2 TRD was exclusive to females that were heterozygous for the WSB/EiJ allele within a 9.3 Mb region (Chr 2 76.9 - 86.2 Mb). A copy number gain of a 127 kb-long DNA segment (designated as responder to drive, R2d) emerged as the strongest candidate for the causative allele. We mapped R2d sequences to two loci within the candidate interval. R2d1 is located near the proximal boundary, and contains a single copy of R2d in all strains tested. R2d2 maps to a 900 kb interval, and the number of R2d copies varies from zero in classical strains (including the mouse reference genome) to more than 30 in wild-derived strains. Using real-time PCR assays for the copy number, we identified a mutation (R2d2WSBdel1) that eliminates the majority of the R2d2WSB copies without apparent alterations of the surrounding WSB/EiJ haplotype. In a three-generation pedigree segregating for R2d2WSBdel1, the mutation is transmitted to the progeny and Mendelian segregation is restored in females heterozygous for R2d2WSBdel1, thus providing direct evidence that the copy number gain is causal for maternal TRD. We found that transmission ratios in R2d2WSB heterozygous females vary between Mendelian segregation and complete distortion depending on the genetic background, and that TRD is under genetic control of unlinked distorter loci. Although the R2d2WSB transmission ratio was inversely correlated with average litter size, several independent lines of evidence support the contention that female meiotic drive is the cause of the distortion. We discuss the implications and potential applications of this novel meiotic drive system.

Systems genetics identifies a co-regulated module of liver microRNAs associated with plasma LDL cholesterol in murine diet-induced dyslipidemia.

Chronically altered levels of circulating lipids, termed dyslipidemia, is a significant risk factor for a number of metabolic and cardiovascular morbidities. MicroRNAs (miRNAs) have emerged as important regulators of lipid balance, have been implicated in dyslipidemia, and have been proposed as candidate therapeutic targets in lipid-related disorders including atherosclerosis. A major limitation of most murine studies of miRNAs in lipid metabolic disorders is that they have been performed in just one (or very few) inbred strains, such as C57BL/6. Moreover, although individual miRNAs have been associated with lipid phenotypes, it is well understood that miRNAs likely work together in functional modules. To address these limitations, we implemented a systems genetics strategy using the Diversity Outbred (DO) mouse population. Specifically, we performed gene and miRNA expression profiling in the livers from ~300 genetically distinct DO mice after 18 wk on either a high-fat/high-cholesterol diet or a high-protein diet. Large-scale correlative analysis of these data with a wide range of cardio-metabolic end points revealed a co-regulated module of miRNAs significantly associated with circulating low-density lipoprotein cholesterol (LDL-C) levels. The hubs of this module were identified as miR-199a, miR-181b, miR-27a, miR-21_-_1, and miR-24. In sum, we demonstrate that a high-fat/high-cholesterol diet robustly rewires the miRNA regulatory network, and we identify a small group of co-regulated miRNAs that may exert coordinated effects to control circulating LDL-C.

R2d2 Drives Selfish Sweeps in the House Mouse.

A selective sweep is the result of strong positive selection driving newly occurring or standing genetic variants to fixation, and can dramatically alter the pattern and distribution of allelic diversity in a population. Population-level sequencing data have enabled discoveries of selective sweeps associated with genes involved in recent adaptations in many species. In contrast, much debate but little evidence addresses whether "selfish" genes are capable of fixation-thereby leaving signatures identical to classical selective sweeps-despite being neutral or deleterious to organismal fitness. We previously described R2d2, a large copy-number variant that causes nonrandom segregation of mouse Chromosome 2 in females due to meiotic drive. Here we show population-genetic data consistent with a selfish sweep driven by alleles of R2d2 with high copy number (R2d2(HC)) in natural populations. We replicate this finding in multiple closed breeding populations from six outbred backgrounds segregating for R2d2 alleles. We find that R2d2(HC) rapidly increases in frequency, and in most cases becomes fixed in significantly fewer generations than can be explained by genetic drift. R2d2(HC) is also associated with significantly reduced litter sizes in heterozygous mothers, making it a true selfish allele. Our data provide direct evidence of populations actively undergoing selfish sweeps, and demonstrate that meiotic drive can rapidly alter the genomic landscape in favor of mutations with neutral or even negative effects on overall Darwinian fitness. Further study will reveal the incidence of selfish sweeps, and will elucidate the relative contributions of selfish genes, adaptation and genetic drift to evolution.

Genetic determinants of voluntary exercise.

Variation in voluntary exercise behavior is an important determinant of long-term human health. Increased physical activity is used as a preventative measure or therapeutic intervention for disease, and a sedentary lifestyle has generally been viewed as unhealthy. Predisposition to engage in voluntary activity is heritable and induces protective metabolic changes, but its complex genetic/genomic architecture has only recently begun to emerge. We first present a brief historical perspective and summary of the known benefits of voluntary exercise. Second, we describe human and mouse model studies using genomic and transcriptomic approaches to reveal the genetic architecture of exercise. Third, we discuss the merging of genomic information and physiological observations, revealing systems and networks that lead to a more complete mechanistic understanding of how exercise protects against disease pathogenesis. Finally, we explore potential regulation of physical activity through epigenetic mechanisms, including those that persist across multiple generations.

Maternal exercise before and during pregnancy does not impact offspring exercise or body composition in mice.

BACKGROUND: The genome, the environment, and their interactions simultaneously regulate complex traits such as body composition and voluntary exercise levels. One such environmental influence is the maternal milieu (i.e., in utero environment or maternal care). Variability in the maternal environment may directly impact the mother, and simultaneously has the potential to influence the physiology and/or behavior of offspring in utero, post birth, and into adulthood. Here, we utilized a murine model to examine the effects of the maternal environment in regard to voluntary exercise (absence of wheel running, wheel running prior to gestation, and wheel running prior to and throughout gestation) on offspring weight and body composition (% fat tissue and % lean tissue) throughout development (~3 to ~9 weeks of age). Additionally, we examined the effects of ~6 weeks of maternal exercise (prior to and during gestation) on offspring exercise levels at ~9 weeks of age. RESULTS: We observed no substantial effects of maternal exercise on subsequent male or female offspring body composition throughout development, or on the propensity of offspring to engage in voluntary wheel running. At the level of the individual, correlational analyses revealed some statistically significant relationships between maternal and offspring exercise levels, likely reflecting previously known heritability estimates for such traits. CONCLUSIONS: The current results conflict with previous findings in human and mouse models demonstrating that maternal exercise has the potential to alter offspring phenotypes. We discuss our negative findings in the context of the timing of the maternal exercise and the level of biological organization of the examined phenotypes within the offspring.

Quantitative genomics of voluntary exercise in mice: transcriptional analysis and mapping of expression QTL in muscle.

Motivation and ability both underlie voluntary exercise, each with a potentially unique genetic architecture. Muscle structure and function are one of many morphological and physiological systems acting to simultaneously determine exercise ability. We generated a large (n = 815) advanced intercross line of mice (G4) derived from a line selectively bred for increased wheel running (high runner) and the C57BL/6J inbred strain. We previously mapped quantitative trait loci (QTL) contributing to voluntary exercise, body composition, and changes in body composition as a result of exercise. Using brain tissue in a subset of the G4 (n = 244), we have also previously reported expression QTL (eQTL) colocalizing with the QTL for the higher-level phenotypes. Here, we examined the transcriptional landscape of hind limb muscle tissue via global mRNA expression profiles. Correlations revealed an ∼1,168% increase in significant relationships between muscle transcript expression levels and the same exercise and body composition phenotypes examined previously in the brain. The exercise trait most often significantly correlated with gene expression in the brain was running duration while in the muscle it was maximum running speed. This difference may indicate that time spent engaging in exercise behavior may be more influenced by central (neurobiological) mechanisms, while intensity of exercise may be largely controlled by peripheral mechanisms. Additionally, we used subsets of cis-acting eQTL, colocalizing with QTL, to identify candidate genes based on both positional and functional evidence. We discuss three plausible candidate genes (Insig2, Prcp, Sparc) and their potential regulatory role.

Genetic analysis of complex traits in the emerging Collaborative Cross.

The Collaborative Cross (CC) is a mouse recombinant inbred strain panel that is being developed as a resource for mammalian systems genetics. Here we describe an experiment that uses partially inbred CC lines to evaluate the genetic properties and utility of this emerging resource. Genome-wide analysis of the incipient strains reveals high genetic diversity, balanced allele frequencies, and dense, evenly distributed recombination sites-all ideal qualities for a systems genetics resource. We map discrete, complex, and biomolecular traits and contrast two quantitative trait locus (QTL) mapping approaches. Analysis based on inferred haplotypes improves power, reduces false discovery, and provides information to identify and prioritize candidate genes that is unique to multifounder crosses like the CC. The number of expression QTLs discovered here exceeds all previous efforts at eQTL mapping in mice, and we map local eQTL at 1-Mb resolution. We demonstrate that the genetic diversity of the CC, which derives from random mixing of eight founder strains, results in high phenotypic diversity and enhances our ability to map causative loci underlying complex disease-related traits.

Individuality in gut microbiota composition is a complex polygenic trait shaped by multiple environmental and host genetic factors.

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 genome-wide 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.

Exercise and diet affect quantitative trait loci for body weight and composition traits in an advanced intercross population of mice.

Driven by the recent obesity epidemic, interest in understanding the complex genetic and environmental basis of body weight and composition is great. We investigated this by searching for quantitative trait loci (QTLs) affecting a number of weight and adiposity traits in a G(10) advanced intercross population produced from crosses of mice in inbred strain C57BL/6J with those in a strain selected for high voluntary wheel running. The mice in this population were fed either a high-fat or a control diet throughout the study and also measured for four exercise traits prior to death, allowing us to test for pre- and postexercise QTLs as well as QTL-by-diet and QTL-by-exercise interactions. Our genome scan uncovered a number of QTLs, of which 40% replicated QTLs previously found for similar traits in an earlier (G(4)) generation. For those replicated QTLs, the confidence intervals were reduced from an average of 19 Mb in the G(4) to 8 Mb in the G(10). Four QTLs on chromosomes 3, 8, 13, and 18 were especially prominent in affecting the percentage of fat in the mice. About of all QTLs showed interactions with diet, exercise, or both, their genotypic effects on the traits showing a variety of patterns depending on the diet or level of exercise. It was concluded that the indirect effects of these QTLs provide an underlying genetic basis for the considerable variability in weight or fat loss typically found among individuals on the same diet and/or exercise regimen.

Inferring genetic architecture of complex traits using Bayesian integrative analysis of genome and transcriptome data.

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 F2 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.

Exercise, weight loss, and changes in body composition in mice: phenotypic relationships and genetic architecture.

The regulation of body weight and composition is complex, simultaneously affected by genetic architecture, the environment, and their interactions. We sought to analyze the complex phenotypic relationships between voluntary exercise, food consumption, and changes in body weight and composition and simultaneously localize quantitative trait loci (QTL) controlling these traits. A large (n = 815) murine advanced intercross line (G(4)) was created from a reciprocal cross between a high-running line and the inbred strain C57BL/6J. Body weight and composition (% fat, % lean) were measured at 4, 6, and 8 wk of age. After measurements at 8 wk of age, mice were given access to running wheels, during which food consumption was quantified and after which body weight and composition were assessed to evaluate exercise-induced changes. Phenotypic correlations indicated that the relationship between exercise and overall change in weight and adiposity depended on body composition before the initiation of exercise. Interval mapping revealed QTL for body weight, % fat, and % lean at 4, 6, and 8 wk of age. Furthermore, QTL were observed for food consumption and changes in weight, % fat, and % lean in response to short-term exercise. Here we provide some clarity for the relationship between weight loss, reduction in adiposity, food consumption, and exercise. Simultaneously, we reinforce the genetic basis for body weight and composition with some independent loci controlling growth at different ages. Finally, we present unique QTL providing insight regarding variation in weight loss and reduction in adiposity in response to exercise.

Architecture of energy balance traits in emerging lines of the Collaborative Cross.

The potential utility of the Collaborative Cross (CC) mouse resource was evaluated to better understand complex traits related to energy balance. A primary focus was to examine if genetic diversity in emerging CC lines (pre-CC) would translate into equivalent phenotypic diversity. Second, we mapped quantitative trait loci (QTL) for 15 metabolism- and exercise-related phenotypes in this population. We evaluated metabolic and voluntary exercise traits in 176 pre-CC lines, revealing phenotypic variation often exceeding that seen across the eight founder strains from which the pre-CC was derived. Many phenotypic correlations existing within the founder strains were no longer significant in the pre-CC population, potentially representing reduced linkage disequilibrium (LD) of regions harboring multiple genes with effects on energy balance or disruption of genetic structure of extant inbred strains with substantial shared ancestry. QTL mapping revealed five significant and eight suggestive QTL for body weight (Chr 4, 7.54 Mb; CI 3.32-10.34 Mb; Bwq14), body composition, wheel running (Chr 16, 33.2 Mb; CI 32.5-38.3 Mb), body weight change in response to exercise (1: Chr 6, 77.7Mb; CI 72.2-83.4 Mb and 2: Chr 6, 42.8 Mb; CI 39.4-48.1 Mb), and food intake during exercise (Chr 12, 85.1 Mb; CI 82.9-89.0 Mb). Some QTL overlapped with previously mapped QTL for similar traits, whereas other QTL appear to represent novel loci. These results suggest that the CC will be a powerful, high-precision tool for examining the genetic architecture of complex traits such as those involved in regulation of energy balance.

Epistatic interactions of genes influence within-individual variation of physical activity traits in mice.

A number of quantitative trait loci (QTLs) recently have been discovered that affect various activity traits in mice, but their collective impact does not appear to explain the consistently moderate to high heritabilities for these traits. We previously suggested interactions of genes, or epistasis, might account for additional genetic variability of activity, and tested this for the average distance, duration and speed run by mice during a 3 week period. We found abundant evidence for epistasis affecting these traits, although, recognized that epistatic effects may well vary within individuals over time. We therefore conducted a full genome scan for epistatic interactions affecting these traits in each of seven three-day intervals. Our intent was to assess the extent and trends in epistasis affecting these traits in each of the intervals. We discovered a number of epistatic interactions of QTLs that influenced the activity traits in the mice, the majority of which were not previously found and appeared to affect the activity traits (especially distance and speed) primarily in the early or in the late age intervals. The overall impact of epistasis was considerable, its contribution to the total phenotypic variance varying from an average of 22-35% in the three traits across all age intervals. It was concluded that epistasis is more important than single-locus effects of genes on activity traits at specific ages and it is therefore an essential component of the genetic architecture of physical activity.

Advances in comparative genetics: influence of genetics on obesity.

Obesity has reached epidemic proportions and is recognised as a significant global health problem. Increased food intake and decreased physical activity are traditionally to blame for the development of obesity; however, many variables such as behaviour, diet, environment, social structures and genetics also contribute to this multifactorial disease. Complex interactions among these variables (for example, gene-environment, gene-diet and gene-gene) contribute not only to individual differences in the development of obesity, but also in treatment response. Mouse models have historically played valuable roles in understanding the genetics of traits related to energy balance and obesity. In the present review, we survey past use and examine new advances in mouse models designed to uncover the genetic architecture of obesity and its component traits. We discuss traditional models such as inbred strains and selectively bred lines and their contributions and shortcomings. We consider the evolution of mouse models into more informative resources such as outbred crosses and the Hybrid Mouse Diversity Panel, as well as novel next-generation approaches such as the Collaborative Cross. Moreover, the genetic architecture of voluntary exercise and the interactive relationship between host genetics and the gut microbiome are presented as novel phenotypes that augment studies using body weight and body fat percentage as endpoints. Understanding the intricate network of phenotypic, genotypic and environmental variables that predispose individuals to obesity will elucidate biological networks involved in the development of obesity. Knowledge obtained from advances in mouse models will inform human health and provide insight into inter-individual variability in the aetiology of obesity-related diseases.

Importance of randomization in microarray experimental designs with Illumina platforms.

Measurements of gene expression from microarray experiments are highly dependent on experimental design. Systematic noise can be introduced into the data at numerous steps. On Illumina BeadChips, multiple samples are assayed in an ordered series of arrays. Two experiments were performed using the same samples but different hybridization designs. An experiment confounding genotype with BeadChip and treatment with array position was compared to another experiment in which these factors were randomized to BeadChip and array position. An ordinal effect of array position on intensity values was observed in both experiments. We demonstrate that there is increased rate of false-positive results in the confounded design and that attempts to correct for confounded effects by statistical modeling reduce power of detection for true differential expression. Simple analysis models without post hoc corrections provide the best results possible for a given experimental design. Normalization improved differential expression testing in both experiments but randomization was the most important factor for establishing accurate results. We conclude that lack of randomization cannot be corrected by normalization or by analytical methods. Proper randomization is essential for successful microarray experiments.

Genetic architecture modulates diet-induced hepatic mRNA and miRNA expression profiles in Diversity Outbred mice.

Genetic approaches in model organisms have consistently demonstrated that molecular traits such as gene expression are under genetic regulation, similar to clinical traits. The resulting expression quantitative trait loci (eQTL) have revolutionized our understanding of genetic regulation and identified numerous candidate genes for clinically relevant traits. More recently, these analyses have been extended to other molecular traits such as protein abundance, metabolite levels, and miRNA expression. Here, we performed global hepatic eQTL and microRNA expression quantitative trait loci (mirQTL) analysis in a population of Diversity Outbred mice fed two different diets. We identified several key features of eQTL and mirQTL, namely differences in the mode of genetic regulation (cis or trans) between mRNA and miRNA. Approximately 50% of mirQTL are regulated by a trans-acting factor, compared to ∼25% of eQTL. We note differences in the heritability of mRNA and miRNA expression and variance explained by each eQTL or mirQTL. In general, cis-acting variants affecting mRNA or miRNA expression explain more phenotypic variance than trans-acting variants. Finally, we investigated the effect of diet on the genetic architecture of eQTL and mirQTL, highlighting the critical effects of environment on both eQTL and mirQTL. Overall, these data underscore the complex genetic regulation of two well-characterized RNA classes (mRNA and miRNA) that have critical roles in the regulation of clinical traits and disease susceptibility.

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.