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Quantitative traits are often complex because of the contribution of many loci, with further complexity added by environmental factors. In medical research, systems genetics is a powerful approach for the study of complex traits, as it integrates intermediate phenotypes, such as RNA, protein, and metabolite levels, to understand molecular and physiological phenotypes linking discrete DNA sequence variation to complex clinical and physiological traits. The primary purpose of this review is to describe some of the resources and tools of systems genetics in humans and rodent models, so that researchers in many areas of biology and medicine can make use of the data.

ObjectiveTo identify differences in the transcriptomic profiles during placentation from pregnancies conceived spontaneously vs. those with infertility using non-in vitro fertilization (IVF) fertility treatment (NIFT) or IVF.DesignCohort study.SettingAcademic medical center.Patient(s)Women undergoing chorionic villus sampling at gestational age 11-13 weeks (n = 141), with pregnancies that were conceived spontaneously (n = 74), with NIFT (n = 33), or with IVF (n = 34), resulting in the delivery of viable offspring.Intervention(s)Collection of chorionic villus samples from women who conceived spontaneously, with NIFT, or with IVF for gene expression analysis using RNA sequencing.Main outcome measure(s)Baseline maternal, paternal, and fetal demographics, maternal medical conditions, pregnancy complications, and outcomes. Differential gene expression of first-trimester placenta.Result(s)There were few differences in the transcriptome of first-trimester placenta from NIFT, IVF, and spontaneous pregnancies. There was one protein-coding differentially expressed gene (DEG) between the spontaneous and infertility groups, CACNA1I, one protein-coding DEG between the spontaneous and IVF groups, CACNA1I, and five protein-coding DEGs between the NIFT and IVF groups, SLC18A2, CCL21, FXYD2, PAEP, and DNER.Conclusion(s)This is the first and largest study looking at transcriptomic profiles of first-trimester placenta demonstrating similar transcriptomic profiles in pregnancies conceived using NIFT or IVF and spontaneous conceptions. Gene expression differences found to be highest in the NIFT group suggest that the underlying infertility, in addition to treatment-related factors, may contribute to the observed gene expression profiles.

BackgroundDevelopment of the placenta during the late first trimester is critical to ensure normal growth and development of the fetus. Developmental differences in this window such as sex-specific variation are implicated in later placental disease states, yet gene expression at this time is poorly understood.MethodsRNA-sequencing was performed to characterize the transcriptome of 39 first trimester human placentas using chorionic villi following genetic testing (17 females, 22 males). Gene enrichment analysis was performed to find enriched canonical pathways and gene ontologies in the first trimester. DESeq2 was used to find sexually dimorphic gene expression. Patient demographics were analyzed for sex differences in fetal weight at time of chorionic villus sampling and birth.ResultsRNA-sequencing analyses detected 14,250 expressed genes, with chromosome 19 contributing the greatest proportion (973/2852, 34.1% of chromosome 19 genes) and Y chromosome contributing the least (16/568, 2.8%). Several placenta-enriched genes as well as histone-coding genes were identified to be unique to the first trimester and common to both sexes. Further, we identified 58 genes with significantly different expression between males and females: 25 X-linked, 15 Y-linked, and 18 autosomal genes. Genes that escape X inactivation were highly represented (59.1%) among X-linked genes upregulated in females. Many genes differentially expressed by sex consisted of X/Y gene pairs, suggesting that dosage compensation plays a role in sex differences. These X/Y pairs had roles in parallel, ancient canonical pathways important for eukaryotic cell growth and survival: chromatin modification, transcription, splicing, and translation.ConclusionsThis study is the first characterization of the late first trimester placenta transcriptome, highlighting similarities and differences among the sexes in ongoing human pregnancies resulting in live births. Sexual dimorphism may contribute to pregnancy outcomes, including fetal growth and birth weight, which was seen in our cohort, with males significantly heavier than females at birth. This transcriptome provides a basis for development of early diagnostic tests of placental function that can indicate overall pregnancy heath, fetal-maternal health, and long-term adult health.

Parameters of bone geometry such as width, length, and cross-sectional area are major determinants of bone strength. Although these traits are highly heritable, few genes influencing bone geometry have been identified. Here, we dissect a major quantitative trait locus (QTL) influencing femur size. This QTL was originally identified in an F2 cross between the C57BL/6J-hg/hg (HG) and CAST/EiJ strains and was referred to as femur length in high growth mice 2 (Feml2). Feml2 was located on chromosome (Chr.) 9 at ∼20 cM. Here, we show that the HG.CAST-(D9Mit249-D9Mit133)/Ucd congenic strain captures Feml2 In an F2 congenic cross, we fine-mapped the location of Feml2 to an ∼6 Mbp region extending from 57.3 to 63.3 Mbp on Chr. 9. We have identified candidates by mining the complete genome sequence of CAST/EiJ and through allele-specific expression (ASE) analysis of growth plates in C57BL/6J × CAST/EiJ F1 hybrids. Interestingly, we also find that the refined location of Feml2 overlaps a cluster of six independent genome-wide associations for human height. This work provides the foundation for the identification of novel genes affecting bone geometry.

BackgroundMultiple testing to understand global changes in gene expression based on genetic and epigenetic modifications is evolving. Chorionic villi, obtained for prenatal testing, is limited, but can be used to understand ongoing human pregnancies. However, optimal storage, processing and utilization of CVS for multiple platform testing have not been established.ResultsLeftover CVS samples were flash-frozen or preserved in RNAlater. Modifications to standard isolation kits were performed to isolate quality DNA and RNA from samples as small as 2-5 mg. RNAlater samples had significantly higher RNA yields and quality and were successfully used in microarray and RNA-sequencing (RNA-seq). RNA-seq libraries generated using 200 versus 800-ng RNA showed similar biological coefficients of variation. RNAlater samples had lower DNA yields and quality, which improved by heating the elution buffer to 70 °C. Purification of DNA was not necessary for bisulfite-conversion and genome-wide methylation profiling. CVS cells were propagated and continue to express genes found in freshly isolated chorionic villi.ConclusionsCVS samples preserved in RNAlater are superior. Our optimized techniques provide specimens for genetic, epigenetic and gene expression studies from a single small sample which can be used to develop diagnostics and treatments using a systems biology approach in the prenatal period. © 2016 John Wiley & Sons, Ltd.

The Hybrid Mouse Diversity Panel (HMDP) is a collection of approximately 100 well-characterized inbred strains of mice that can be used to analyze the genetic and environmental factors underlying complex traits. While not nearly as powerful for mapping genetic loci contributing to the traits as human genome-wide association studies, it has some important advantages. First, environmental factors can be controlled. Second, relevant tissues are accessible for global molecular phenotyping. Finally, because inbred strains are renewable, results from separate studies can be integrated. Thus far, the HMDP has been studied for traits relevant to obesity, diabetes, atherosclerosis, osteoporosis, heart failure, immune regulation, fatty liver disease, and host-gut microbiota interactions. High-throughput technologies have been used to examine the genomes, epigenomes, transcriptomes, proteomes, metabolomes, and microbiomes of the mice under various environmental conditions. All of the published data are available and can be readily used to formulate hypotheses about genes, pathways and interactions.

BackgroundMouse chromosome 2 is linked to growth and body fat phenotypes in many mouse crosses. With the goal to identify the underlying genes regulating growth and body fat on mouse chromosome 2, we developed five overlapping subcongenic strains that contained CAST/EiJ donor regions in a C57BL/6J (hg/hg) background (hg is a spontaneous deletion of 500 Kb on mouse chromosome 10). To fine map QTL on distal mouse chromosome 2 a total of 1,712 F2 mice from the five subcongenic strains, plus 278 F2 mice from the HG2D founder congenic strain were phenotyped and analyzed. Interval mapping (IM) and composite IM (CIM) were performed on body weight and body fat traits on a combination of SNP and microsatellite markers, which generated a high-density genotyping panel.ResultsPhenotypic analysis and interval mapping of total fat mass identified two QTL on distal mouse chromosome 2. One QTL between 150 and 161 Mb, Fatq2a, and the second between 173.3 and 175.6 Mb, Fatq2b. The two QTL reside in different congenic strains with significant total fat differences between homozygous cast/cast and b6/b6 littermates. Both of these QTL were previously identified only as a single QTL affecting body fat, Fatq2. Furthermore, through a novel approach referred here as replicated CIM, Fatq2b was mapped to the Gnas imprinted locus.ConclusionsThe integration of subcongenic strains, high-density genotyping, and CIM succesfully partitioned two previously linked QTL 20 Mb apart, and the strongest QTL, Fatq2b, was fine mapped to a ~2.3 Mb region interval encompassing the Gnas imprinted locus.

Heritable epigenetic factors can contribute to complex disease etiology. Here we examine the contribution of DNA methylation to complex traits that are precursors to heart disease, diabetes, and osteoporosis. We profiled DNA methylation in the liver using bisulfite sequencing in 90 mouse inbred strains, genome-wide expression levels, proteomics, metabolomics, and 68 clinical traits and performed epigenome-wide association studies (EWAS). We found associations with numerous clinical traits including bone density, insulin resistance, expression, and protein and metabolite levels. A large proportion of associations were unique to EWAS and were not identified using GWAS. Methylation levels were regulated by genetics largely in cis, but we also found evidence of trans regulation, and we demonstrate that genetic variation in the methionine synthase reductase gene Mtrr affects methylation of hundreds of CpGs throughout the genome. Our results indicate that natural variation in methylation levels contributes to the etiology of complex clinical traits.

Patient bone mineral density (BMD) predicts the likelihood of osteoporotic fracture. While substantial progress has been made toward elucidating the genetic determinants of BMD, our understanding of the factors involved remains incomplete. Here, using a systems genetics approach in the mouse, we predicted that bicaudal C homolog 1 (Bicc1), which encodes an RNA-binding protein, is responsible for a BMD quantitative trait locus (QTL) located on murine chromosome 10. Consistent with this prediction, mice heterozygous for a null allele of Bicc1 had low BMD. We used a coexpression network-based approach to determine how Bicc1 influences BMD. Based on this analysis, we inferred that Bicc1 was involved in osteoblast differentiation and that polycystic kidney disease 2 (Pkd2) was a downstream target of Bicc1. Knock down of Bicc1 and Pkd2 impaired osteoblastogenesis, and Bicc1 deficiency-dependent osteoblast defects were rescued by Pkd2 overexpression. Last, in 2 human BMD genome-wide association (GWAS) meta-analyses, we identified SNPs in BICC1 and PKD2 that were associated with BMD. These results, in both mice and humans, identify Bicc1 as a genetic determinant of osteoblastogenesis and BMD and suggest that it does so by regulating Pkd2 transcript levels.

Many common diseases have an important inflammatory component mediated in part by macrophages. Here we used a systems genetics strategy to examine the role of common genetic variation in macrophage responses to inflammatory stimuli. We examined genome-wide transcript levels in macrophages from 92 strains of the Hybrid Mouse Diversity Panel. We exposed macrophages to control media, bacterial lipopolysaccharide (LPS), or oxidized phospholipids. We performed association mapping under each condition and identified several thousand expression quantitative trait loci (eQTL), gene-by-environment interactions, and eQTL "hot spots" that specifically control LPS responses. We used siRNA knockdown of candidate genes to validate an eQTL hot spot in chromosome 8 and identified the gene 2310061C15Rik as a regulator of inflammatory responses in macrophages. We have created a public database where the data presented here can be used as a resource for understanding many common inflammatory traits that are modeled in the mouse and for the dissection of regulatory relationships between genes.

The osteoblast-lineage consists of cells at various stages of maturation that are essential for skeletal development, growth, and maintenance. Over the past decade, many of the signaling cascades that regulate this lineage have been elucidated; however, little is known of the networks that coordinate, modulate, and transmit these signals. Here, we identify a gene network specific to the osteoblast-lineage through the reconstruction of a bone co-expression network using microarray profiles collected on 96 Hybrid Mouse Diversity Panel (HMDP) inbred strains. Of the 21 modules that comprised the bone network, module 9 (M9) contained genes that were highly correlated with prototypical osteoblast maker genes and were more highly expressed in osteoblasts relative to other bone cells. In addition, the M9 contained many of the key genes that define the osteoblast-lineage, which together suggested that it was specific to this lineage. To use the M9 to identify novel osteoblast genes and highlight its biological relevance, we knocked-down the expression of its two most connected "hub" genes, Maged1 and Pard6g. Their perturbation altered both osteoblast proliferation and differentiation. Furthermore, we demonstrated the mice deficient in Maged1 had decreased bone mineral density (BMD). It was also discovered that a local expression quantitative trait locus (eQTL) regulating the Wnt signaling antagonist Sfrp1 was a key driver of the M9. We also show that the M9 is associated with BMD in the HMDP and is enriched for genes implicated in the regulation of human BMD through genome-wide association studies. In conclusion, we have identified a physiologically relevant gene network and used it to discover novel genes and regulatory mechanisms involved in the function of osteoblast-lineage cells. Our results highlight the power of harnessing natural genetic variation to generate co-expression networks that can be used to gain insight into the function of specific cell-types.

The relationships between the levels of transcripts and the levels of the proteins they encode have not been examined comprehensively in mammals, although previous work in plants and yeast suggest a surprisingly modest correlation. We have examined this issue using a genetic approach in which natural variations were used to perturb both transcript levels and protein levels among inbred strains of mice. We quantified over 5,000 peptides and over 22,000 transcripts in livers of 97 inbred and recombinant inbred strains and focused on the 7,185 most heritable transcripts and 486 most reliable proteins. The transcript levels were quantified by microarray analysis in three replicates and the proteins were quantified by Liquid Chromatography-Mass Spectrometry using O(18)-reference-based isotope labeling approach. We show that the levels of transcripts and proteins correlate significantly for only about half of the genes tested, with an average correlation of 0.27, and the correlations of transcripts and proteins varied depending on the cellular location and biological function of the gene. We examined technical and biological factors that could contribute to the modest correlation. For example, differential splicing clearly affects the analyses for certain genes; but, based on deep sequencing, this does not substantially contribute to the overall estimate of the correlation. We also employed genome-wide association analyses to map loci controlling both transcript and protein levels. Surprisingly, little overlap was observed between the protein- and transcript-mapped loci. We have typed numerous clinically relevant traits among the strains, including adiposity, lipoprotein levels, and tissue parameters. Using correlation analysis, we found that a low number of clinical trait relationships are preserved between the protein and mRNA gene products and that the majority of such relationships are specific to either the protein levels or transcript levels. Surprisingly, transcript levels were more strongly correlated with clinical traits than protein levels. In light of the widespread use of high-throughput technologies in both clinical and basic research, the results presented have practical as well as basic implications.

Significant advances have been made in the discovery of genes affecting bone mineral density (BMD); however, our understanding of its genetic basis remains incomplete. In the current study, genome-wide association (GWA) and co-expression network analysis were used in the recently described Hybrid Mouse Diversity Panel (HMDP) to identify and functionally characterize novel BMD genes. In the HMDP, a GWA of total body, spinal, and femoral BMD revealed four significant associations (-log10P>5.39) affecting at least one BMD trait on chromosomes (Chrs.) 7, 11, 12, and 17. The associations implicated a total of 163 genes with each association harboring between 14 and 112 genes. This list was reduced to 26 functional candidates by identifying those genes that were regulated by local eQTL in bone or harbored potentially functional non-synonymous (NS) SNPs. This analysis revealed that the most significant BMD SNP on Chr. 12 was a NS SNP in the additional sex combs like-2 (Asxl2) gene that was predicted to be functional. The involvement of Asxl2 in the regulation of bone mass was confirmed by the observation that Asxl2 knockout mice had reduced BMD. To begin to unravel the mechanism through which Asxl2 influenced BMD, a gene co-expression network was created using cortical bone gene expression microarray data from the HMDP strains. Asxl2 was identified as a member of a co-expression module enriched for genes involved in the differentiation of myeloid cells. In bone, osteoclasts are bone-resorbing cells of myeloid origin, suggesting that Asxl2 may play a role in osteoclast differentiation. In agreement, the knockdown of Asxl2 in bone marrow macrophages impaired their ability to form osteoclasts. This study identifies a new regulator of BMD and osteoclastogenesis and highlights the power of GWA and systems genetics in the mouse for dissecting complex genetic traits.

One of the goals of systems genetics is the reconstruction of gene networks that underlie key processes in development and disease. To identify cartilage gene networks that play an important role in bone development, we used a systems genetics approach that integrated microarray gene expression profiles from cartilage and bone phenotypic data from two sets of recombinant inbred strains. Microarray profiles generated from isolated chondrocytes were used to generate weighted gene coexpression networks. This analysis resulted in the identification of subnetworks (modules) of coexpressed genes that then were examined for relationships with bone geometry and density. One module exhibited significant correlation with femur length (r = 0.416), anteroposterior diameter (r = 0.418), mediolateral diameter (r = 0.576), and bone mineral density (r = 0.475). Highly connected genes (n = 28) from this and other modules were tested in vitro using prechondrocyte ATDC5 cells and RNA interference. Five of the 28 genes were found to play a role in chondrocyte differentiation. Two of these, Hspd1 and Cdkn1a, were known previously to function in chondrocyte development, whereas the other three, Bhlhb9, Cugbp1, and Spcs3, are novel genes. Our integrative analysis provided a systems-level view of cartilage development and identified genes that may be involved in bone development.

Abstract Background Our understanding of the genetic basis of learning and memory remains shrouded in mystery. To explore the genetic networks governing the biology of conditional fear, we used a systems genetics approach to analyze a hybrid mouse diversity panel (HMDP) with high mapping resolution. Results A total of 27 behavioral quantitative trait loci were mapped with a false discovery rate of 5%. By integrating fear phenotypes, transcript profiling data from hippocampus and striatum and also genotype information, two gene co-expression networks correlated with context-dependent immobility were identified. We prioritized the key markers and genes in these pathways using intramodular connectivity measures and structural equation modeling. Highly connected genes in the context fear modules included Psmd6, Ube2a and Usp33, suggesting an important role for ubiquitination in learning and memory. In addition, we surveyed the architecture of brain transcript regulation and demonstrated preservation of gene co-expression modules in hippocampus and striatum, while also highlighting important differences. Rps15a, Kif3a, Stard7, 6330503K22RIK, and Plvap were among the individual genes whose transcript abundance were strongly associated with fear phenotypes. Conclusion Application of our multi-faceted mapping strategy permits an increasingly detailed characterization of the genetic networks underlying behavior.

In this study, we show that the covariance between behavior and gene expression in the brain can help further unravel the determinants of neurobehavioral traits. Previously, a QTL for novelty induced motor activity levels was identified on murine chromosome 15 using consomic strains. With the goal of narrowing down the linked region and possibly identifying the gene underlying the quantitative trait, gene expression data from this F(2)-population was collected and used for expression QTL analysis. While genetic variation in these mice was limited to chromosome 15, eQTL analysis of gene expression showed strong cis-effects as well as trans-effects elsewhere in the genome. Using weighted gene co-expression network analysis, we were able to identify modules of co-expressed genes related to novelty induced motor activity levels. In eQTL analyses, the expression of Ly6a (a.k.a. Sca-1) was found to be cis-regulated by chromosome 15. Ly6a also surfaced in a group of genes resulting from the network analysis that was correlated with behavior. Behavioral analysis of Ly6a knock-out mice revealed reduced novelty induced motor activity levels when compared to wild type controls, confirming functional importance of Ly6a in this behavior, possibly through regulating other genes in a pathway. This study shows that gene expression profiling can be used to narrow down a previously identified behavioral QTL in mice, providing support for Ly6a as a candidate gene for functional involvement in novelty responsiveness.

Abstract Background It has been proposed that the use of gene expression microarrays in nonrecombinant parental or congenic strains can accelerate the process of isolating individual genes underlying quantitative trait loci (QTL). However, the effectiveness of this approach has not been assessed. Results Thirty-seven studies that have implemented the QTL/microarray approach in rodents were reviewed. About 30% of studies showed enrichment for QTL candidates, mostly in comparisons between congenic and background strains. Three studies led to the identification of an underlying QTL gene. To complement the literature results, a microarray experiment was performed using three mouse congenic strains isolating the effects of at least 25 biometric QTL. Results show that genes in the congenic donor regions were preferentially selected. However, within donor regions, the distribution of differentially expressed genes was homogeneous once gene density was accounted for. Genes within identical-by-descent (IBD) regions were less likely to be differentially expressed in chromosome 2, but not in chromosomes 11 and 17. Furthermore, expression of QTL regulated in cis (cis eQTL) showed higher expression in the background genotype, which was partially explained by the presence of single nucleotide polymorphisms (SNP). Conclusions The literature shows limited successes from the QTL/microarray approach to identify QTL genes. Our own results from microarray profiling of three congenic strains revealed a strong tendency to select cis-eQTL over trans-eQTL. IBD regions had little effect on rate of differential expression, and we provide several reasons why IBD should not be used to discard eQTL candidates. In addition, mismatch probes produced false cis-eQTL that could not be completely removed with the current strains genotypes and low probe density microarrays. The reviewed studies did not account for lack of coverage from the platforms used and therefore removed genes that were not tested. Together, our results explain the tendency to report QTL candidates as differentially expressed and indicate that the utility of the QTL/microarray as currently implemented is limited. Alternatives are proposed that make use of microarray data from multiple experiments to overcome the outlined limitations.

Most quantitative trait loci (QTL) studies fail to account for the effect that the maternal genotype may have on an individual’s phenotypes, even though maternal effect QTL have been shown to account for considerable variation in growth and obesity traits in mouse models. Moreover, the fetal programming theory suggests that maternal effects influence an offspring’s adult fitness, although the genetic nature of fetal programming remains unclear. Within this context, our study focused on mapping genomic regions associated with maternal effect QTL by analyzing the phenotypes of chromosomes 2 and 7 subcongenic mice from genetically distinct dams. We analyzed 12 chromosome 2 subcongenic strains that spanned from 70 to 180 Mb with CAST/EiJ donor regions on the background of C57BL/6 J, and 14 chromosome 7 subcongenic strains that spanned from 81 to 111 Mb with BALB/cByJ donor regions on C57BL/6ByJ background. Maternal QTL analyses were performed on the basis of overlapping donor regions between subcongenic strains. We identified several highly significant (P

A principal task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through the integration of DNA variation, gene transcription and phenotypic information. Here we have validated our method through the characterization of transgenic and knockout mouse models of genes predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with Gas7, Me1 and Gpx3 being newly confirmed, resulted in significant changes in obesity-related traits. Liver expression signatures revealed alterations in common metabolic pathways and networks contributing to abdominal obesity and overlapped with a macrophage-enriched metabolic network module that is highly associated with metabolic traits in mice and humans. Integration of gene expression in the design and analysis of traditional F(2) intercross studies allows high-confidence prediction of causal genes and identification of pathways and networks involved.

BackgroundThe identification of novel genes is critical to understanding the molecular basis of body weight. Towards this goal, we have identified secretogranin V (Scg5; also referred to as Sgne1), as a candidate gene for growth traits.ResultsThrough a combination of DNA microarray analysis and quantitative PCR we identified a strong expression quantitative trait locus (eQTL) regulating Scg5 expression in two mouse chromosome 2 congenic strains and three additional F2 intercrosses. More importantly, the eQTL was coincident with a body weight QTL in congenic mice and Scg5 expression was negatively correlated with body weight in two of the F2 intercrosses. Analysis of haplotype blocks and genomic sequencing of Scg5 in high (C3H/HeJ, DBA/2J, BALB/cByJ, CAST/EiJ) and low (C57BL/6J) expressing strains revealed mutations unique to C57BL/6J and possibly responsible for the difference in mRNA abundance. To evaluate the functional consequence of Scg5 overexpression we measured the pituitary levels of 7B2 protein and PCSK2 activity and found both to be increased. In spite of this increase, the level of pituitary alpha-MSH, a PCSK2 processing product, was unaltered.ConclusionTogether, these data support a role for Scg5 in the modulation of body weight.

During disuse, mechanical unloading causes extensive bone loss, decreasing bone volume and strength. Variations in bone mass and risk of osteoporosis are influenced by genetics; however, it remains unclear how genetic variation affects the skeletal response to unloading. We previously found that genetic variation affects the musculoskeletal response to 3 weeks of immobilization in the 8 Jackson Laboratory J:DO founder strains: C57Bl/6J, A/J, 129S1/SvImJ, NOD/ShiLtJ, NZO/HlLtJ, CAST/EiJ, PWK/PhJ, and WSB/EiJ. Hindlimb unloading (HLU) is the best model for simulating local and systemic contributors of disuse and therefore may have a greater impact on bones than immobilization. We hypothesized that genetic variation would affect the response to HLU across the eight founder strains. Mice of each founder strain were placed in HLU for 3 weeks, and the femurs and tibias were analyzed. There were significant HLU and mouse strain interactions on body weight, femur trabecular BV/TV, and femur ultimate force. This indicates that unloading only caused significant catabolic effects in some mouse strains. C57BL/6 J mice were most affected by unloading while other strains were more protected. There were significant HLU and mouse strain interactions on gene expression of genes encoding bone metabolism genes in the tibia. This indicates that unloading only caused significant effects on bone metabolism genes in some mouse strains. Different mouse strains respond to HLU differently, and this can be explained by genetic differences. These results suggest the outbred J:DO mice will be a powerful model for examining the effects of genetics on the skeletal response to HLU. [ABSTRACT FROM AUTHOR]

BackgroundHigh growth (hg) modifier and background independent quantitative trait loci (QTL) affecting growth, adiposity and carcass composition were previously identified on mouse chromosomes (MMU) 1, 2, 5, 8, 9, 11 and 17. To confirm and further characterize each QTL, two panels of speed congenic strains were developed by introgressing CAST/EiJ (CAST) QTL alleles onto either mutant C57Bl/6J-hg/hg (HG) or wild type C57Bl/6J (B6) genetic backgrounds.ResultsThe first speed congenic panel was developed by introgressing four overlapping donor regions spanning MMU2 in its entirety onto both HG and B6 backgrounds, for a total of eight strains. Phenotypic characterization of the MMU2 panel confirmed the segregation of multiple growth and obesity QTL and strongly suggested that a subset of these loci modify the effects of the hg deletion. The second panel consisted of individual donor regions on an HG background for each QTL on MMU1, 5, 8, 9, 11 and 17. Of the six developed strains, five were successfully characterized and displayed significant differences in growth and/or obesity as compared to controls. All five displayed phenotypes similar to those originally attributed to each QTL, however, novel phenotypes were unmasked in several of the strains including sex-specific effects.ConclusionThe speed congenic strains developed herein constitute an invaluable genomic resource and provide the foundation to identify the specific nature of genetic variation influencing growth and obesity.

Bone mineral density (BMD) is a highly heritable predictor of osteoporotic fracture. GWAS have identified hundreds of loci influencing BMD, but few have been functionally analyzed. In this study, we show that SNPs within a BMD locus on chromosome 14q32.32 alter splicing and expression of PAR-1a/microtubule affinity regulating kinase 3 (MARK3), a conserved serine/threonine kinase known to regulate bioenergetics, cell division, and polarity. Mice lacking Mark3 either globally or selectively in osteoblasts have increased bone mass at maturity. RNA profiling from Mark3-deficient osteoblasts suggested changes in the expression of components of the Notch signaling pathway. Mark3-deficient osteoblasts exhibited greater matrix mineralization compared with controls that was accompanied by reduced Jag1/Hes1 expression and diminished downstream JNK signaling. Overexpression of Jag1 in Mark3-deficient osteoblasts both in vitro and in vivo normalized mineralization capacity and bone mass, respectively. Together, these findings reveal a mechanism whereby genetically regulated alterations in Mark3 expression perturb cell signaling in osteoblasts to influence bone mass., Introduction Osteoporosis, characterized by decreased bone mineral density (BMD)and increased risk of fracture, continues to affect global health with an alarming financial and social burden. BMD is a highly heritable, [...]

Genome‐wide association studies (GWASs) have advanced our understanding of the genetics of osteoporosis; however, the challenge has been converting associations to causal genes. Studies have utilized transcriptomics data to link disease‐associated variants to genes, but few population transcriptomics data sets have been generated on bone at the single‐cell level. To address this challenge, we profiled the transcriptomes of bone marrow–derived stromal cells (BMSCs) cultured under osteogenic conditions from five diversity outbred (DO) mice using single‐cell RNA‐seq (scRNA‐seq). The goal of the study was to determine if BMSCs could serve as a model to generate cell type–specific transcriptomic profiles of mesenchymal lineage cells from large populations of mice to inform genetic studies. By enriching for mesenchymal lineage cells in vitro, coupled with pooling of multiple samples and downstream genotype deconvolution, we demonstrate the scalability of this model for population‐level studies. We demonstrate that dissociation of BMSCs from a heavily mineralized matrix had little effect on viability or their transcriptomic signatures. Furthermore, we show that BMSCs cultured under osteogenic conditions are diverse and consist of cells with characteristics of mesenchymal progenitors, marrow adipogenic lineage precursors (MALPs), osteoblasts, osteocyte‐like cells, and immune cells. Importantly, all cells were similar from a transcriptomic perspective to cells isolated in vivo. We employed scRNA‐seq analytical tools to confirm the biological identity of profiled cell types. SCENIC was used to reconstruct gene regulatory networks (GRNs), and we observed that cell types show GRNs expected of osteogenic and pre‐adipogenic lineage cells. Further, CELLECT analysis showed that osteoblasts, osteocyte‐like cells, and MALPs captured a significant component of bone mineral density (BMD) heritability. Together, these data suggest that BMSCs cultured under osteogenic conditions coupled with scRNA‐seq can be used as a scalable and biologically informative model to generate cell type–specific transcriptomic profiles of mesenchymal lineage cells in large populations. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR). [ABSTRACT FROM AUTHOR]