Quantitative trait loci for bone mineral density and femoral morphology in an advanced intercross population of mice

Alendronate promotes osteoblast differentiation and bone formation in ovariectomy-induced osteoporosis through interferon-β/signal transducer and activator of transcription 1 pathway

Alendronate is commonly used for the treatment of postmenopausal osteoporosis; however, the underlying pathological molecular mechanisms of its action remain unclear. In the present study, the alendronate-treated signaling pathway in bone metabolism in rats with ovariectomy induced by osteoporosis was investigated. Rats with osteoporosis were orally administered alendronate or phosphate-buffered saline (control). In addition, the interferon-β (IFN-β)/signal transducer and activator of transcription 1 (STAT1) signaling pathway was investigated in osteoblasts following treatment with alendronate in vitro and in vivo. During the differentiation period, IFN-β (100 ng/ml) was used to treat the osteoblast cells, and the activity, viability and bone metabolism-associated gene expression levels (STAT1, p-STAT1, Fra1, TRAF6 and SOCS1) were analyzed in osteoblast cells. Histopathological changes were used to evaluate osteoblasts, osteoclasts, inflammatory phase of bone healing and osteonecrotic areas. The results demonstrated that alendronate significantly inhibited the activity of osteoporotic osteoclasts by stimulating expression of IFN-β, as well as markedly improved the viability and activity of osteoblasts compared with the control group. In addition, alendronate increased the expression and phosphorylation levels of STAT1 in osteoclasts, enhanced osteoblast differentiation, upregulated the expression levels of alkaline phosphatase and osteocalcin, and increased the expression of osteoblast differentiation-associated genes (osteocalcin, osterix and Runx2). Inhibition of IFN-β expression canceled the benefits of alendronate-mediated osteoblast differentiation. Notably, alendronate enhanced bone formation in rats with osteoporosis induced by ovariectomy. In conclusion, these findings suggest that alendronate can regulate osteoblast differentiation and bone formation in rats with osteoporosis induced by ovariectomy through upregulation of IFN-β/STAT1 signaling pathway.

Genetics of aging bone

With aging, the skeleton experiences a number of changes, which include reductions in mass and changes in matrix composition, leading to fragility and ultimately an increase of fracture risk. A number of aspects of bone physiology are controlled by genetic factors, including peak bone mass, bone shape, and composition; however, forward genetic studies in humans have largely concentrated on clinically available measures such as bone mineral density (BMD). Forward genetic studies in rodents have also heavily focused on BMD; however, investigations of direct measures of bone strength, size, and shape have also been conducted. Overwhelmingly, these studies of the genetics of bone strength have identified loci that modulate strength via influencing bone size, and may not impact the matrix material properties of bone. Many of the rodent forward genetic studies lacked sufficient mapping resolution for candidate gene identification; however, newer studies using genetic mapping populations such as Advanced Intercrosses and the Collaborative Cross appear to have overcome this issue and show promise for future studies. The majority of the genetic mapping studies conducted to date have focused on younger animals and thus an understanding of the genetic control of age-related bone loss represents a key gap in knowledge.

Fine-mapping QTLs in advanced intercross lines and other outbred populations

Quantitative genetic studies in model organisms, particularly in mice, have been extremely successful in identifying chromosomal regions that are associated with a wide variety of behavioral and other traits. However, it is now widely understood that identification of the underlying genes will be far more challenging. In the last few years, a variety of populations have been utilized in an effort to more finely map these chromosomal regions with the goal of identifying specific genes. The common property of these newer populations is that linkage disequilibrium spans relatively short distances, which permits fine-scale mapping resolution. This review focuses on advanced intercross lines (AILs) which are the simplest such population. As originally proposed in 1995 by Darvasi and Soller, an AIL is the product of intercrossing two inbred strains beyond the F2 generation. Unlike recombinant inbred strains, AILs are maintained as outbred populations; brother-sister matings are specifically avoided. Each generation of intercrossing beyond the F2 further degrades linkage disequilibrium between adjacent makers, which allows for fine-scale mapping of quantitative trait loci (QTLs). Advances in genotyping technology and techniques for the statistical analysis of AILs have permitted rapid advances in the application of AILs. We review some of the analytical issues and available software, including QTLRel, EMMA, EMMAX, GEMMA, TASSEL, GRAMMAR, WOMBAT, Mendel, and others.

Quantitative trait loci for energy balance traits in an advanced intercross line derived from mice divergently selected for heat loss

Obesity in human populations, currently a serious health concern, is considered to be the consequence of an energy imbalance in which more energy in calories is consumed than is expended. We used interval mapping techniques to investigate the genetic basis of a number of energy balance traits in an F₁₁ advanced intercross population of mice created from an original intercross of lines selected for increased and decreased heat loss. We uncovered a total of 137 quantitative trait loci (QTLs) for these traits at 41 unique sites on 18 of the 20 chromosomes in the mouse genome, with X-linked QTLs being most prevalent. Two QTLs were found for the selection target of heat loss, one on distal chromosome 1 and another on proximal chromosome 2. The number of QTLs affecting the various traits generally was consistent with previous estimates of heritabilities in the same population, with the most found for two bone mineral traits and the least for feed intake and several body composition traits. QTLs were generally additive in their effects, and some, especially those affecting the body weight traits, were sex-specific. Pleiotropy was extensive within trait groups (body weights, adiposity and organ weight traits, bone traits) and especially between body composition traits adjusted and not adjusted for body weight at sacrifice. Nine QTLs were found for one or more of the adiposity traits, five of which appeared to be unique. The confidence intervals among all QTLs averaged 13.3 Mb, much smaller than usually observed in an F₂ cross, and in some cases this allowed us to make reasonable inferences about candidate genes underlying these QTLs. This study combined QTL mapping with genetic parameter analysis in a large segregating population, and has advanced our understanding of the genetic architecture of complex traits related to obesity.

Impact of the environment on the skeleton: is it modulated by genetic factors?

The etiology of skeletal disease is driven by genetic and environmental factors. Genome-wide association studies (GWAS) of osteoporotic phenotypes have identified novel candidate genes, but have only uncovered a small proportion of the trait variance explained. This "missing heritability" is caused by several factors, including the failure to consider gene-by-environmental (G*E) interactions. Some G*E interactions have been investigated, but new approaches to integrate environmental data into genomic studies are needed. Advances in genotyping and meta-analysis techniques now allow combining genotype data from multiple studies, but the measurement of key environmental factors in large human cohorts still lags behind, as do the statistical tools needed to incorporate these measures in genome-wide association meta-studies. This review focuses on discussing ways to enhance G*E interaction studies in humans and how the use of rodent models can inform genetic studies. Understanding G*E interactions will provide opportunities to effectively target intervention strategies for individualized therapy.