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How genomics has informed our understanding of the pathogenesis of osteoporosis


Osteoporosis is a skeletal disorder characterized by compromised bone strength that predisposes a person to an increased risk of fracture. Osteoporosis is a complex trait that involves multiple genes, environmental factors, and gene-gene and gene-environment interactions. Twin and family studies have indicated that between 25% and 85% of the variation in bone mass and other skeletal phenotypes is heritable, but our knowledge of the underlying genes is limited. Bone mineral density is the most common assessment for diagnosing osteoporosis and is the most often used quantitative value in the design of genetic studies. In recent years, our understanding of the pathophysiology of osteoporosis has been greatly facilitated by advances brought about by the Human Genome Project. Genetic approaches ranging from family studies of monogenic traits to association studies with candidate genes, to whole-genome scans in both humans and animals have identified a small number of genes that contribute to the heritability of bone mass. Studies with transgenic and knockout mouse models have revealed major new insights into the biology of many of these identified genes, but much more needs to be learned. Ultimately, we hope that by revealing the underlying genetics and biology driving the pathophysiology of osteoporosis, new and effective treatment can be developed to combat and possibly cure this devastating disease. Here we review the rapidly evolving field of the genomics of osteoporosis with a focus on important gene discoveries, new biological/physiological paradigms that are emerging, and many of the unanswered questions and hurdles yet to be overcome in the field.


The past decade has been witness to one of the greatest scientific efforts and achievements in recorded history, the Human Genome Project (HGP). The impact of this effort in terms of our understanding of the genetic basis of disease will be manifest in multiple ways for many decades, even centuries, to come. In the field of osteoporosis, as in all diseases that have a genetic component, the sequencing and cataloguing of the repertoire of genes in the human genome and other species, the development of high-density single nucleotide polymorphism (SNP) maps, high-throughput sequencing along with other genomic technologies, advances in statistical approaches for the analysis of the derived genetic information, and the ability to generate transgenic and knockout mouse models to assess the contribution of individual genes to bone mass regulation have propelled research in the field at an incredible pace.

As in all genetic studies, the choice of phenotype ultimately determines the end-product of the genetic analysis, that is, the genes that may eventually be identified. In the simplest terms, osteoporosis has been defined by The National Institutes of Health (NIH) Consensus Development Panel on Osteoporosis Prevention, Diagnosis, and Therapy as a '...skeletal disorder characterized by compromised bone strength predisposing a person to an increased risk of fracture' [1]. However, this definition offers little phenotypic guidance for the design of studies to dissect out the underlying genetics or for identifying the causal genes of the disease. The World Health Organization Study Group suggested an operational definition for osteoporosis of a bone mineral density (BMD) of 2.5 standard deviations below the mean for young healthy adult women [2]. This definition is widely used today, but does not take into account bone microarchitecture and/or bone quality, which are much more difficult to define. BMD does provide a quantitative variable that can serve as a surrogate for 'osteoporosis' and thus is useful for genetic studies. As such, BMD has been one of the primary phenotypes used in genetic studies of osteoporosis, although as will be briefly referenced in this review there are several other surrogate phenotypes that have also been used in this regard.

Lessons from 'the early days'

In order to fully appreciate how genomics has informed our understanding of the pathogenesis of osteoporosis, a short historical review to provide perspective seems warranted. Osteoporosis is a complex trait involving multiple genes, strong environmental influences and, between individuals, a variable mix of gene-gene and gene-environment interactions. Evidence from twin and family studies has suggested that the heritability of BMD and other skeletal phenotypes ranges between 25% and 85% [3, 4]. Given the high heritability of some of these bone traits (as surrogates for osteoporosis), there is reason to be optimistic that the underlying disease-causing genes can be identified.

Candidate genes

The entrée of the bone field into the identification of genes involved in 'osteoporosis' was launched by Eisman and colleagues in 1992, with their seminal work on vitamin D receptor gene (VDR) polymorphisms [5]. This report was quickly followed by a number of studies (now totaling over 400) on VDR polymorphisms in relation to BMD and several other phenotypes. Not surprisingly, some studies showed positive associations [68], negative correlations [911] and some positive or negative correlations depending on phenotype [12, 13]. While on the surface this might seem paradoxical, what these studies demonstrated with this single candidate gene approach is that differences in skeletal site, ethnicity, sex, age, menopausal status and diet all have effects that may account for why a candidate gene could have a positive association in one study and not in another. Even more important, these studies illustrated the need for careful study design, including the choice of phenotype, consideration of sample size, choice of study population and statistical methods of analysis, in order to be able to tease out the underlying genetic basis of osteoporosis.

Following the first report on VDR gene polymorphisms, several other favorite candidate gene studies (estrogen receptor (ESR1, ESR2), collagen type 1 (COL1A1), parathyroid hormone (PTH), calcitonin (CALCA), transforming growth factor-β (TGFB1), and so on) began to appear, again with variable positive and negative correlations [14]. These studies all began in the pre-HGP later half of the decade of the 1990s and mainly used polymerase chain reaction (PCR) to amplify gene fragments with known restriction enzyme-based polymorphisms in candidate genes that were chosen based on their known role in bone biology. As the field evolved from a candidate gene focus towards more genome-wide approaches, the hope remained that the number of genes would be limited to a few with a 'large effect size', as the technology and statistical methods had not yet reached a capability of dealing with a large number of genes each accounting for less than 5% of the phenotypic variation. As we have moved into the post-HGP era, both technology and statistical methods of analysis have reached a level of sophistication that allows us to begin to identify these small-effect-size genes.

Monogenic traits

In terms of understanding the pathophysiology of osteoporosis, there were major advances during this pre-HGP period that came out of studies of human families with monogenic traits. Advances in our understanding of osteoclast biology were a direct consequence of the identification of genes causing osteopetrosis [15, 16], and osteoblast biology was greatly advanced from studies of both human kindreds and mouse model systems [1719]. Another major advance in our understanding of bone mass regulation resulted from studies of decreased and increased bone mass conditions as in the case of osteoporosis pseudoglioma syndrome (OPPG) [20] and high bone mass (HBM) [21], respectively. Both were initially mapped to human chromosome 11q12-13 [22, 23] using standard linkage mapping approaches. In the case of the HBM family, the genome linkage study after the initial definition of the linkage interval was accomplished in one month because of newly available (at that time) dinucleotide repeat-based human linkage mapping panels that used fluorescence-based detection methods on a DNA sequencing platform [23]. The causal mutations for these two conditions were subsequently shown to lie in the low-density lipoprotein receptor-related protein 5 (LRP 5) gene [24, 25]. Subsequently, several other groups have found mutations in LRP5 that give rise to conditions of low and high bone mass [2628]. LRP5 is a co-receptor along with the frizzled family of proteins for Wnt proteins, and this discovery introduced the importance of Wnt/β-catenin signaling [29] in the regulation of bone mass and, since those initial published reports, it has become one of the most heavily studied areas in bone biology. The discovery of LRP5 also illustrates a major limitation of candidate gene studies; they are based on some knowledge for the selection of genes tested, and the linkage interval containing LRP5 had no previous identified genes with a known role in bone biology. Similarly, whole-genome scans have produced a number of potential regions or quantitative trait loci (QTLs); many do not harbor any gene with a known role in bone biology. While this makes actual gene identification potentially more difficult, it also increases the likelihood of revealing unsuspected and dramatic new insights [3034].

LRP5 polymorphisms have subsequently been investigated for a role in bone mass variation, and the majority of studies report positive associations, but on average only approximately 2 to 3% of the variance can be explained, depending upon the SNPs and phenotypes examined [3538]. Thus, while specific mutations in LRP5 can have a dramatic effect on bone mass, more common variants of the gene appear to only contribute modestly to changes in BMD and consequently the pathophysiology of osteoporosis. LRP5 and the Wnt/β-catenin signaling pathway have been suggested as major targets for the development of new therapies to treat osteoporosis, although this has been the subject of some debate in the literature [3941].

Another important family-based gene discovery that also relates to the Wnt/β-catenin signaling pathway was the identification of the SOST gene and its role in the pathogenesis of sclerosteosis [4244] and van Buchem's disease [45, 46]. The protein product of the Sost gene, sclerostin, inhibits Wnt/β-catenin signaling by binding to LRP5 and preventing Wnt from binding [47, 48]. Sclerostin is thought to be exclusively produced by osteocytes [49, 50] and functions on osteoblasts to regulate the pathway. Currently, an antibody to sclerostin is under development as a bone anabolic therapeutic for diseases of low bone mass such as osteoporosis [51].

Animal studies

Additional advances have came from mouse mapping studies that employed a combination of genetic and genomic approaches to identify the Alox15 gene as an important negative regulator of bone mass in mice [52]. Subsequent studies in humans suggested that variants in ALOX12 but not ALOX15 accounted for approximately 3% of the variation in human bone mass [53, 54]. The 12/15-lipoxygenase pathway might therefore be another therapeutic target for osteoporosis [52]. Other genes that have appeared recently from various mouse mapping studies include the Duffy antigen receptor for chemokines (Darc) [55], endothelin-converting enzyme 1 (Ece1) [56] and secreted frizzled related-protein 4 (sFRP4) [57]. Whether these genes will contribute to bone mass variation in humans has not yet been fully investigated.

Several QTLs in the mouse have been identified [5860] and it appears that the locations of these loci vary between inbred lines of mice. This can partially be explained by the known differences in geometry and BMD that is known to exist between strains [58]. One advantage of the mouse models besides the ability to generate specific breeding crosses is the ability to perform functional testing that cannot be carried out in humans, and thereby identify a different set of genes underlying these phenotypes [56, 61, 62]. Completion of the mouse genome DNA sequence and development of dense marker maps will greatly facilitate the identification of the genes underlying these mouse QTLs, in a similar way to the advances made in human studies.

Mapping studies in other animal model systems such as the baboon [6365], chicken [66] and cow [67] have also been reported in the literature. Because of its close genetic relatedness to humans, the baboon offers an excellent model system and has been used in an integrative approach using chromosomal syntenic overlaps to define QTLs that are shared between species [63, 68].

Recent advances

In recent years, there has been a literal explosion in the number of published reports based on SNP-based candidate gene studies and whole-genome scans employing different phenotypes and study designs [30, 31, 6971]. This can largely be attributed to the development of physical maps, cataloguing of genes and development of high-density SNP maps along with high-throughput technology platforms and statistical advances that came out of the HGP. A comprehensive description of these published reports is not within the scope of this review and so we will highlight just a few of the studies. What is perhaps the most exciting aspect of the more recent studies is that the first genes associated with these various traits are beginning to be identified, and this holds great promise for advancing our understanding of the pathophysiology of osteoporosis.

Genome scans identify numerous QTLs

A recent review by Ferrari [4] summarizes the location of BMD-related QTLs that have been reported for the human genome and those that have been confirmed by meta-analysis. Interestingly, QTLs have been found on every human autosome, but only a subset has been confirmed by meta-analysis. The first of the genes to be identified from a whole-genome scan was BMP2, which came out of an analysis of extended osteoporotic families in Iceland and a subsequent follow-up association analysis [33]. Other genes that have been associated with BMD and/or fractures include VDR, estrogen receptor alpha (ESR1) and beta (ESR2), collagen type 1α (COL1A1), LRP5, TGF-β (TGFB1), interleukin-6 (IL6), osteoprotegerin or OPG (TNFRSF11b) and receptor activator of nuclear factor κB ligand or RANKL (TNFSF11). Recently LTBP2 [72] and signal transducer and activator of transcription (STAT1) [73] have also been proposed to be genes involved in osteoporotic phenotypes. STAT1 is an interesting candidate, as the Stat1-/- mouse has increased bone mass and has been shown to function as an attenuator of Runx2-mediated osteoblast differentiation [74]. Given the large number of candidate intervals identified from the whole-genome scan studies, and the potential large number of genes within those intervals, the next step towards underlying gene identification will be refinement of the mapped intervals. Based on the Icelandic population study [75], refinement of established genome-wide significant loci resulted in the identification of prime candidate genes MAP/microtubule affinity-regulating kinase 3 (MARK3) and SOST. Another approach is to combine data from multiple genome-wide studies and perform meta-analysis [4] to focus initial efforts for fine mapping of intervals. Interestingly, new intervals have appeared from this type of analysis [76], likely due to the sample size and power to detect small effect loci that is often the case with any one study. Added into this mix is a consideration of the relative contribution that one particular gene may have in one ethnic group versus another population. Also, in addition to choice of phenotype, which can yield different QTLs and genes, overlapping and different QTLs in males versus females have been identified in several of the published studies [30, 34, 69, 71, 76, 77]. Clearly the gene discovery process is far from over at this point.

Mouse genomic/genetic approaches

While the identification of an underlying trait gene within one of the QTLs is an important first step, understanding how this gene relates to pathophysiology of osteoporosis is even more important. In this regard, advances in genomics have played another critical role. Bacterial artificial chromosome (BAC) recombineering [78, 79] has become a mainline approach in the bone field, and the pace with which these genetic mouse models can be generated is no longer a rate-limiting step for understanding gene function. The ability to create transgenic and knockout mice to explore the gene function has become a main investigative tool for bone biologists. The development of various Cre recombinase lines of mice in which Cre expression is driven by bone cell-specific promoter elements has propelled the field forward rapidly in the past few years. Crossing these mice with a mouse line containing floxed alleles of the particular gene of interest has provided us with the means to examine the role of any gene in just about any tissue or cell type for a role in bone biology. These conditional knockout mice represent an alternative approach to overcome the lethality of gene deletion that can be encountered in a traditional global knockout approach. In the bone field, mice such as the human osteocalcin-Cre [80], rat 3.6 kb Col1-Cre and 2.3 kb Col1-Cre [81] and the mouse α1(I)-Col-Cre [82] are commonly used for selective deletion of genes in osteoblastic cells; the DMP-1 Cre mouse is used for deletion of genes in osteocytes [83] and the Trap-Cre and Cathepsin-Cre [84] mice can be used for deletion of genes in osteoclasts. A common refinement of these Cre approaches is to include an inducible control element in the promoter construct [85] to overcome lethality problems that can sometimes be encountered even with conditional knockout crosses. However, all of these Cre approaches are not without limitations. As different promoters are used to drive Cre, the cell/tissue specificity and extent of gene deletion in each model system/cross needs to be carefully determined, especially with respect to the developmental pattern and timing of expression as well as the extent to which potential cells downstream in a differentiation pathway are affected, in order to fully interpret the results.

Nonetheless, considerable advances in our understanding of bone biology have resulted from the use of these mouse genetic approaches. For example, we now appreciate that bone plays a key role in several endocrine loops. Recent studies have suggested that bone mass is partially regulated by a fat-brain-bone axis involving leptin and the sympathetic nervous system [86, 87]. Also, a key role for the skeleton in the regulation of energy metabolism involving osteoblast-produced osteocalcin has been shown [88]. Finally, bone mass regulation by a gut-brain-bone axis involving serotonin [89] has been proposed. The serotonin story is also interesting from the perspective that Lrp5 expression in the gut rather than the osteoblast has been proposed to be the critical site for Lrp5-mediated bone mass regulation. While this finding will require independent confirmation, it potentially adds another level of complexity to bone mass regulation and raises an important question of what tissue key genes need to be expressed in. Obviously these proposed endocrine loop systems are complicated and there are published data to suggest that there may be different effects on trabecular versus cortical bone [90, 91], which adds an additional level of complexity. Regardless of how these evolving stories ultimately turn out, what they clearly illustrate is that bone mass regulation is also dependent on other organ systems besides the classical parathyroid-kidney-gut level of regulation that controls calcium and phosphate homeostasis. These new endocrine loop systems point out the complex nature of the regulation of bone mass and the need to not restrict genomic and functional study efforts solely to genes expressed in bone.

Other approaches and advances

Gene-expression studies in the bone field have yet to be fully integrated into the genomic effort. There are many reasons for this lack of integration, mainly relating to the difficulty of obtaining appropriate human bone or other tissue samples (besides blood) for conducting expression profiling. One success in this regard was the combination of mapping and gene expression microarray analysis to identify Alox15 as an important regulator of bone mass in mice [52]. A recent report combined gene expression profiling in isolated human peripheral monocytes with a genome-wide association study to identify the STAT1 gene [73], but human expression profiling relating to osteoporosis has lagged far behind other diseases.

Another important recent development is related to our understanding of the multifaceted roles played by the osteocyte. This cell is the most abundant cell type in bone. It is embedded within the mineralized matrix and through the lacunar canalicular system forms a communication network via its dendritic processes with other osteocytes and cells on the bone surfaces and within the vascular beds of bone. The osteocyte has long been thought to be the mechanosensory cell within bone, and as such plays a central role in controlling bone mass in response to changes in load environment. Recent studies have now shown that the osteocyte also plays a key role in regulating phosphate metabolism and mineral homeostasis, and orchestrating the activity of the bone-forming osteoblasts and bone-resorbing osteoclasts through autocrine, paracrine and endocrine mechanisms [92]. Osteocytes also appear to be the only cells producing sclerostin, the product of the SOST gene, which modulates the activity of Lrp5/6 and the Wnt signaling pathway. What all of these recent findings illustrate is that bone and bone cells do not exist in their own sequestered environment. Bone mass regulation over an individual's lifetime is finely tuned by a number of factors produced from both outside and within bone. As such, there is still a great amount of bone biology that we do not yet know, and a combination of genomic, proteomic and other approaches will be needed to fully understand the pathophysiology of bone diseases like osteoporosis.


Advances in genomics have propelled discovery in the bone field forward at an ever-accelerating pace, but there is still much to be learned. How many genes will ultimately be involved in explaining variation in BMD or other bone phenotypes is clearly an open question at this time. Given the new insights recently achieved regarding the complexities of bone mass regulation, it seems likely that many of the genes that will be discovered in the near future will be expressed in tissues other than bone, and understanding this will be an important aspect of future genomic efforts.

Given the pace of research, it seems almost certain that we will eventually catalogue the entire repertoire of genes that contribute to the pathophysiology of osteoporosis. It is not unreasonable to believe that in the near future, technology platforms will evolve to the point where sequencing one's entire genome will become feasible. New approaches to high-throughput sequencing are currently being developed, with real potential to make this a reality [9395]. This implies that individual whole-genome analysis is not that far away. However, one major lesson from previous research is that within individuals, or groups of individuals, only a subset of genes may play a predominant role. Furthermore, in the case of osteoporosis, which is mainly a disease of older age and involves not only genetic, but also environmental, gene-gene and gene-environment interactions, determining lifetime risk will be much more complicated. One of the great challenges going forward will be not only identifying the genes, but also understanding how these genes are influenced by other factors.

Ultimately we hope to fully understand the pathophysiology of osteoporosis so that effective treatment can be achieved. As shown in Table 1, several genes have now been identified that contribute to the variation in bone density or other phenotypes. Three of the strongest candidate genes that have currently been identified are components of the Wnt/β-catenin signaling pathway. An antibody to sclerostin is already being developed; it is an anabolic agent and initial studies in rats have demonstrated a significant increase in BMD and show great promise [51]. Also, a series of sulfonamide derivatives have been shown to modulate Wnt signaling through inhibition of sFRP-1 [96], suggesting another class of compounds that might be developed into a therapeutic agent to increase bone mass. Other targets such as GSK-3β [97, 98] and Dickkopf-1 [99, 100] have been described in the literature. A major concern with targeting the Wnt pathway is its known role in tumor formation and whether manipulating the pathway in bone will increase the risk of tumor formation in other tissues [39, 41, 101106]. Manipulating sclerostin appears to circumvent this concern because it is produced by osteocytes and therefore potentially restricts the target to bone, although the extent to which sclerostin circulates could be a potential unknown issue that will need to be resolved. While it appears that a new generation of anabolic agents is now on the horizon, clearly our understanding of the underlying genetics of osteoporosis has yet to reveal all there is to be discovered. Looking further ahead as new agents for treating osteoporosis are developed, studies will be needed to understand how any given individual's response to different therapeutic approaches may vary as a function of their specific genetic background. Clearly, advances in genomics will also play a major role in addressing and solving these and many other future questions.

Table 1 List of the most prominent genes associated with osteoporosis phenotypes confirmed by multiple study designs



bacterial artificial chromosome


bone mineral density


high bone mass


Human Genome Project




low density lipoprotein-related protein 5


National Institutes of Health




osteoporosis pseudoglia syndrome


quantitative trait locus


parathyroid hormone


receptor activator of nuclear factor κB ligand


single nucleotide polymorphism


transforming growth factor


vitamin D receptor.


  1. 1.

    , : Osteoporosis prevention, diagnosis and therapy. JAMA. 2001, 285: 785-795. 10.1001/jama.285.6.785

    Google Scholar 

  2. 2.

    Kanis JA, Melton LJ, Christiansen C, Johnston CC, Khaltaev N: The diagnosis of osteoporosis. J Bone Miner Res. 1994, 9: 1137-1141.

    PubMed  Google Scholar 

  3. 3.

    Ralston SH: Genetic control of susceptibility to osteoporosis. J Clin Endocrinol Metab. 2002, 87: 2460-2466. 10.1210/jc.87.6.2460

    PubMed  Google Scholar 

  4. 4.

    Ferrari S: Human genetics of osteoporosis. Best Pract Res Clin Endocrinol Metab. 2008, 22: 723-735. 10.1016/j.beem.2008.08.007

    PubMed  Google Scholar 

  5. 5.

    Morrison NA, Yeoman R, Kelly PJ, Eisman JA: Contribution of trans-acting factor alleles to normal physiological variability: vitamin D receptor gene polymorhpisms and circulating osteocalcin. Proc Natl Acad Sci USA. 1992, 89: 6665-6669. 10.1073/pnas.89.15.6665

    PubMed  PubMed Central  Google Scholar 

  6. 6.

    Houston LA, Grant SF, Reid DM, Ralston SH: Vitamin D receptor polymorphism, bone mineral density, and osteoporotic vertebral fracture: studies in a UK population. Bone. 1996, 18: 249-252. 10.1016/8756-3282(95)00483-1

    PubMed  Google Scholar 

  7. 7.

    Dawson-Hughes B, Harris SS, Finneran S: Calcium absorption on high and low calcium intakes in relation to vitamin D receptor genotype. J Clin Endocrinol Metab. 1995, 80: 3657-3661. 10.1210/jc.80.12.3657

    PubMed  Google Scholar 

  8. 8.

    Riggs BL, Nguyen TV, Melton LJ, Morrison NA, O'Fallon WM, Kelly PJ, Egan KS, Sambrook PN, Muhs JM, Eisman JA: The contribution of vitamin D receptor gene alleles to the determination of bone mineral density in normal and osteoporotic women. J Bone Miner Res. 1995, 10: 991-996.

    PubMed  Google Scholar 

  9. 9.

    Garnero P, Borel O, Sornay-Rendu E, Arlot ME, Delmas PD: Vitamin D receptor gene polymorphisms are not related to bone turnover, rate of bone loss, and bone mass in post-menopausal women: the OFELY study. J Bone Miner Res. 1996, 11: 827-834.

    PubMed  Google Scholar 

  10. 10.

    Boschitsch E, Suk EK, Mayr WR, Lang T, Schwartz WM, Panzer S: Genotypes of the vitamin-D-receptor gene and bone mineral density in Caucasoid postmenopausal females. Maturitas. 1996, 24: 91-96. 10.1016/0378-5122(95)01023-8

    PubMed  Google Scholar 

  11. 11.

    Arden NK, Keen RW, Lanchbury JS, Spector TD: Polymorphisms of the vitamin D receptor gene do not predict quantitative ultrasound of the calcaneus or hip axis length. Osteoporos Int. 1996, 6: 334-337. 10.1007/BF01623395

    PubMed  Google Scholar 

  12. 12.

    Salamone LM, Ferrell R, Black DM, Palermo L, Epstein RS, Petro N, Steadman N, Kuller LH, Cauley JA: The association between vitamin D receptor gene polymorphisms and bone mineral density at the spine, hip and whole-body in premenopausal women. Osteoporos Int. 1996, 6: 63-68. 10.1007/BF01626540

    PubMed  Google Scholar 

  13. 13.

    Barger-Lux MJ, Heaney RP, Hayes J, DeLuca HF, Johnson ML, Gong G: Vitamin D receptor gene polymorphism, bone mass, body size, and vitamin D receptor density. Calcif Tissue Int. 1995, 57: 161-162. 10.1007/BF00298438

    PubMed  Google Scholar 

  14. 14.

    Liu YZ, Liu YJ, Recker RR, Deng HW: Molecular studies of identification of genes for osteoporosis: the 2002 update. J Endocrinol. 2003, 177: 147-196. 10.1677/joe.0.1770147

    PubMed  Google Scholar 

  15. 15.

    Frattini A, Orchard PJ, Sobacchi C, Giliani S, Abinun M, Mattsson JP, Keeling DJ, Andersson AK, Wallbrandt P, Zecca L, Notarangelo LD, Vezzoni P, Villa A: Defects in TCIRG1 subunit of the vacuolar proton pump are responsible for a subset of human autosomal recessive osteopetrosis. Nat Genet. 2000, 25: 343-346. 10.1038/77131

    PubMed  Google Scholar 

  16. 16.

    Kornak U, Kasper D, Bosl MR, Kaiser E, Schweizer M, Schulz A, Friedrich W, Delling G, Jentsch TJ: Loss of the ClC-7 chloride channel leads to osteopetrosis in mice and man. Cell. 2001, 26: 205-215. 10.1016/S0092-8674(01)00206-9.

    Google Scholar 

  17. 17.

    Komori T, Yagi H, Nomura S, Yamaguchi A, Sasaki K, Deguchi K, Shimizu Y, Bronson RT, Gao YH, Inada M, Sato M, Okamoto R, Kitamura Y, Yoshiki S, Kishimoto T: Targeted disruption of Cbfa1 results in a complete lack of bone formation owing to maturational arrest of osteoblasts. Cell. 1997, 89: 755-764. 10.1016/S0092-8674(00)80258-5

    PubMed  Google Scholar 

  18. 18.

    Otto F, Thornell AP, Crompton T, Denzel A, Gilmour KC, Rosewell IR, Stamp GW, Beddington RS, Mundlos S, Olsen BR, Selby PB, Owen MJ: Cbfa1, a candidate gene for cleidocranial dysplasia syndrome, is essential for osteoblast differentiation and bone development. Cell. 1997, 89: 765-771. 10.1016/S0092-8674(00)80259-7

    PubMed  Google Scholar 

  19. 19.

    Mundlos S, Otto F, Mundlos C, Mulliken JB, Aylsworth AS, Albright S, Lindhout D, Cole WG, Henn W, Knoll JH, Owen MJ, Mertelsmann R, Zabel BU, Olsen BR: Mutations involving the transcription factor CBFA1 cause cleidocranial dysplasia. Cell. 1997, 89: 773-779. 10.1016/S0092-8674(00)80260-3

    PubMed  Google Scholar 

  20. 20.

    Osteoporosis-Pseudoglioma Syndrome; OPPG. (accessed 25 August 2009).

  21. 21.

    Bone Mineral Density Quantitative Trait Locus 1; BMND1. (accessed 25 August 2009).

  22. 22.

    Gong Y, Vikkula M, Boon L, Liu J, Beighton P, Ramesar R, Peltonen L, Somer H, Hirose T, Dallapiccola B, De Paepe A, Swoboda W, Zabel B, Superti-Furga A, Steinmann B, Brunner HG, Jans A, Boles RG, Adkins W, Boogaard van den MJ, Olsen BR, Warman ML: Osteoporosis-pseudoglioma syndrome, a disorder affecting skeletal strength and vision, is assigned to chromosome region 11q12-13. Am J Hum Genet. 1996, 59: 146-151.

    PubMed  PubMed Central  Google Scholar 

  23. 23.

    Johnson ML, Gong G, Kimberling WJ, Recker SM, Kimmel DK, Recker RR: Linkage of a gene causing high bone mass to human chromosome 11 (11q12-13). Am J Hum Genet. 1997, 60: 1326-1332. 10.1086/515470

    PubMed  PubMed Central  Google Scholar 

  24. 24.

    Gong Y, Slee RB, Fukai N, Rawadi G, Roman-Roman S, Reginato AM, Wang H, Cundy T, Glorieux FH, Lev D, Zacharin M, Oexle K, Marcelino J, Suwairi W, Heeger S, Sabatakos G, Apte S, Adkins WN, Allgrove J, Arslan-Kirchner M, Batch JA, Beighton P, Black GC, Boles RG, Boon LM, Borrone C, Brunner HG, Carle GF, Dallapiccola B, De Paepe A: LDL receptor-related protein 5 (LRP5) affects bone accrual and eye development. Cell. 2001, 107: 513-523. 10.1016/S0092-8674(01)00571-2

    PubMed  Google Scholar 

  25. 25.

    Little RD, Carulli JP, Del Mastro RG, Dupuis J, Osborne M, Folz C, Manning SP, Swain PM, Zhao SC, Eustace B, Lappe MM, Spitzer L, Zweier S, Braunschweiger K, Benchekroun Y, Hu X, Adair R, Chee L, FitzGerald MG, Tulig C, Caruso A, Tzellas N, Bawa A, Franklin B, McGuire S, Nogues X, Gong G, Allen KM, Anisowicz A, Morales AJ: A mutation in the LDL receptor-related protein 5 gene results in the autosomal dominant high-bone-mass trait. Am J Hum Genet. 2002, 70: 11-19. 10.1086/338450

    PubMed  PubMed Central  Google Scholar 

  26. 26.

    Boyden LM, Mao J, Belsky J, Mitzner L, Farhi A, Mitnick MA, Wu D, Insogna K, Lifton RP: High bone density due to a mutation in LDL-receptor-related protein 5. N Engl J Med. 2002, 346: 1513-1521. 10.1056/NEJMoa013444

    PubMed  Google Scholar 

  27. 27.

    Van Wesenbeeck L, Cleiren E, Gram J, Beals RK, Bénichou O, Scopelliti D, Key L, Renton T, Bartels C, Gong Y, Warman ML, De Vernejoul MC, Bollerslev J, Van Hul W: Six novel mis-sense mutations in the LDL receptor-related protein5 (LRP5) gene in different conditions with an increased bone density. Am J Hum Genet. 2003, 72: 763-771. 10.1086/368277

    PubMed  PubMed Central  Google Scholar 

  28. 28.

    Streeten EA, Morton H, McBride DJ: Osteoporosis pseudoglioma syndrome: 3 siblings with a novel LRP5 mutation. J Bone Miner Res. 2003, 18 (Suppl 2): S35-

    Google Scholar 

  29. 29.

    Johnson ML, Harnish K, Nusse R, Van Hul W: LRP5 and Wnt signaling: a union made for bone. J Bone Miner Res. 2004, 19: 1749-1757. 10.1359/JBMR.040816

    PubMed  Google Scholar 

  30. 30.

    Ralston SH, Galwey N, MacKay I, Albagha OM, Cardon L, Compston JE, Cooper C, Duncan E, Keen R, Langdahl B, McLellan A, O'Riordan J, Pols HA, Reid DM, Uitterlinden AG, Wass J, Bennett ST: Loci for regulation of bone mineral density in men and women identified by genome wide linkage scan: the FAMOS study. Hum Mol Genet. 2005, 14: 943-951. 10.1093/hmg/ddi088

    PubMed  Google Scholar 

  31. 31.

    Chinappen-Horsley U, Blake GM, Fogelman I, Kato B, Ahmadi KR, Spector TD: Quantitative trait loci for bone lengths on chromosome 5 using dual energy X-ray absorptiometry imaging in the Twins UK cohort. PLoS One. 2008, 3: e1752- 10.1371/journal.pone.0001752

    PubMed  PubMed Central  Google Scholar 

  32. 32.

    Zmuda JM, Sheu YT, Moffett SP: The search for human osteoporosis genes. J Musculoskelet Neuronal Interact. 2006, 6: 3-15.

    PubMed  Google Scholar 

  33. 33.

    Styrkarsdottir U, Cazier JB, Kong A, Rolfsson O, Larsen H, Bjarnadottir E, Johannsdottir VD, Sigurdardottir MS, Bagger Y, Christiansen C, Reynisdottir I, Grant SF, Jonasson K, Frigge ML, Gulcher JR, Sigurdsson G, Stefansson K: Linkage of osteoporosis to chromosome 20p12 and association to BMP2. PLoS Biol. 2003, 1: E69- 10.1371/journal.pbio.0000069

    PubMed  PubMed Central  Google Scholar 

  34. 34.

    Xiong DH, Wang JT, Wang W, Guo YF, Xiao P, Shen H, Jiang H, Chen Y, Deng H, Drees B, Recker RR, Deng HW: Genetic determination of osteoporosis: lessons learned from a large genome-wide linkage study. Hum Biol. 2007, 79: 593-608.

    PubMed  Google Scholar 

  35. 35.

    Mizuguchi T, Furuta I, Watanabe Y, Tsukamoto K, Tomita H, Tsujihata M, Ohta T, Kishino T, Matsumoto N, Minakami H, Niikawa N, Yoshiura K: LRP5, low-density-lipoprotein-receptor-related protein 5, is a determinant for bone mineral density. J Hum Genet. 2004, 49: 80-86. 10.1007/s10038-003-0111-6

    PubMed  Google Scholar 

  36. 36.

    Bollerslev J, Wilson SG, Dick IM, Islam FM, Ueland T, Palmer L, Devine A, Prince RL: LRP5 gene polymorphisms predict bone mass and incident fractures in elderly Australian women. Bone. 2005, 36: 599-606. 10.1016/j.bone.2005.01.006

    PubMed  Google Scholar 

  37. 37.

    Ferrari SL, Deutsch S, Baudoin C, Cohen-Solal M, Ostertag A, Antonarakis SE, Rizzoli R, de Vernejoul MC: LRP5 gene polymorphisms and idiopathic osteoporosis in men. Bone. 2005, 37: 770-775. 10.1016/j.bone.2005.06.017

    PubMed  Google Scholar 

  38. 38.

    Giroux S, Elfassihi L, Cardinal G, Laflamme N, Rousseau F: LRP5 coding polymorphisms influence the variation of peak bone mass in a normal population of French-Canadian women. Bone. 2007, 40: 1299-1307. 10.1016/j.bone.2007.01.004

    PubMed  Google Scholar 

  39. 39.

    Baron R, Rawadi G: Targeting the Wnt/β-catenin pathway to regulate bone formation in the adult skeleton. Endocrinology. 2007, 148: 2635-2643. 10.1210/en.2007-0270

    PubMed  Google Scholar 

  40. 40.

    Shahnazari M, Yao W, Corr M, Lane NE: Targeting the Wnt signaling pathway to augment bone formation. Curr Osteoporos Rep. 2008, 6: 142-148. 10.1007/s11914-008-0025-5

    PubMed  PubMed Central  Google Scholar 

  41. 41.

    Hoeppner LH, Secreto FJ, Westendorf JJ: Wnt signaling as a therapeutic target for bone diseases. Expert Opin Ther Targets. 2009, 13: 485-496. 10.1517/14728220902841961

    PubMed  PubMed Central  Google Scholar 

  42. 42.

    Sclerosteosis; SOST. (accessed 24 August 2009).

  43. 43.

    Brunkow ME, Gardner JC, Van Ness J, Paeper BW, Kovacevich BR, Proll S, Skonier JE, Zhao L, Sabo PJ, Fu Y, Alisch RS, Gillett L, Colbert T, Tacconi P, Galas D, Hamersma H, Beighton P, Mulligan J: Bone dysplasia sclerosteosis results from loss of the SOST gene product, a novel cystine knot-containing protein. Am J Hum Genet. 2001, 68: 577-589. 10.1086/318811

    PubMed  PubMed Central  Google Scholar 

  44. 44.

    Balemans W, Ebeling M, Patel N, Van Hul E, Olson P, Dioszegi M, Lacza C, Wuyts W, Ende Van Den J, Willems P, Paes-Alves AF, Hill S, Bueno M, Ramos FJ, Tacconi P, Dikkers FG, Stratakis C, Lindpaintner K, Vickery B, Foernzler D, Van Hul W: Increased bone density in sclerosteosis is due to the deficiency of a novel secreted protein (SOST). Hum Mol Genet. 2001, 10: 537-543. 10.1093/hmg/10.5.537

    PubMed  Google Scholar 

  45. 45.

    Hyperostosis Corticalis Generalisata. (accessed 24 August 2009).

  46. 46.

    Balemans W, Patel N, Ebeling M, Van Hul E, Wuyts W, Lacza C, Dioszegi M, Dikkers FG, Hildering P, Willems PJ, Verheij JB, Lindpaintner K, Vickery B, Foernzler D, Van Hul W: Identification of a 52 kb deletion downstream of the SOST gene in pateints with van Buchem disease. J Med Genet. 2002, 39: 91-97. 10.1136/jmg.39.2.91

    PubMed  PubMed Central  Google Scholar 

  47. 47.

    Semenov M, Tamai K, He X: SOST is a ligand for LRP5/LRP6 and a Wnt signaling inhibitor. J Biol Chem. 2005, 280: 26770-26775. 10.1074/jbc.M504308200

    PubMed  Google Scholar 

  48. 48.

    Ellies DL, Viviano B, McCarthy J, Rey JP, Itasaki N, Saunders S, Krumlauf R: Bone density ligand, sclerostin, directly interacts with LRP5 but not LRP5G171V to modulate Wnt activity. J Bone Miner Res. 2006, 21: 1738-1749. 10.1359/jbmr.060810

    PubMed  Google Scholar 

  49. 49.

    van Bezooijen RL, Roelen BA, Visser A, Wee-Pals van der L, de Wilt E, Karperien M, Hamersma H, Papapoulos SE, ten Dijke P, Lowik CW: Sclerostin is an osteocyte-expressed negative regulator of bone formation, but not a classical BMP antagonist. J Exp Med. 2004, 199: 805-814. 10.1084/jem.20031454

    PubMed  PubMed Central  Google Scholar 

  50. 50.

    Winkler DG, Sutherland MK, Geoghegan JC, Yu C, Hayes T, Skonier JE, Shpektor D, Jonas M, Kovacevich BR, Staehling-Hampton K, Appleby M, Brunkow ME, Latham JA: Osteocyte control of bone formation via sclerostin, a novel BMP antagonist. EMBO J. 2003, 22: 6267-6276. 10.1093/emboj/cdg599

    PubMed  PubMed Central  Google Scholar 

  51. 51.

    Li X, Ominsky MS, Warmington KS, Morony S, Gong J, Cao J, Gao Y, Shalhoub V, Tipton B, Haldankar R, Chen Q, Winters A, Boone T, Geng Z, Niu QT, Ke HZ, Kostenuik PJ, Simonet WS, Lacey DL, Paszty C: Sclerostin antibody treatment increases bone formation, bone mass, and bone strength in a rat model of postmenopausal osteoporosis. J Bone Miner Res. 2009, 24: 578-588. 10.1359/jbmr.081206

    PubMed  Google Scholar 

  52. 52.

    Klein RF, Allard J, Anur Z, Nikolcheva T, Rotstein D, Carlos AS, Shea M, Waters R, Belknap J, Peltz G, Orwoll ES: Regulation of bone mass in mice by the lipoxygenase gene Alox15. Science. 2004, 303: 229-232. 10.1126/science.1090985

    PubMed  Google Scholar 

  53. 53.

    Ichikawa S, Koller DL, Johnson ML, Lai D, Xuei X, Edenberg HJ, Klein RF, Orwoll ES, Hui SL, Foroud TM, Peacock M, Econs MJ: Humna ALOX12, but not ALOX15, is Associated with BMD in white men and women. J Bone Miner Res. 2006, 21: 556-564. 10.1359/jbmr.051212

    PubMed  Google Scholar 

  54. 54.

    Mullin BH, Spector TD, Curtis CC, Ong GN, Hart DJ, Hakim AJ, Worthy T, Wison SG: Polymorphisms in ALOX12, but not ALOX15, are significantly associated with BMD in post-menopausal women. Calcif Tissue Int. 2007, 81: 10-17. 10.1007/s00223-007-9023-3

    PubMed  Google Scholar 

  55. 55.

    Edderkaoui B, Baylink DJ, Beamer WG, Wergedal JE, Porte R, Chaudhuri A, Mohan S: Identification of mouse Duffy antigen receptor for chemokines (Darc) as a BMD QTL gene. Genome Res. 2007, 17: 577-585. 10.1101/gr.6009507

    PubMed  PubMed Central  Google Scholar 

  56. 56.

    Saless N, Litscher SJ, Lopez Franco GE, Houlihan MJ, Sudhakaran S, Raheem KA, O'Neil TK, Vanderby R, Demant P, Blank RD: Quantitative trait loci for biomechanical performance and femoral geometry in an intercross of recombinant congenic mice: restriction of the Bmd7 candidate interval. FASEB J. 2009, 23: 2142-2154. 10.1096/fj.08-118679

    PubMed  Google Scholar 

  57. 57.

    Nakanishi R, Shimizu M, Mori M, Akiyama H, Okudaira S, Otsuki B, Hashimoto M, Higuchi K, Hosokawa M, Tsuboyama T, Nakamura T: Secreted frizzled-related protein 4 is a negative regulator of peak BMD in SAMP6 mice. J Bone Miner Res. 2006, 21: 1713-1721. 10.1359/jbmr.060719

    PubMed  Google Scholar 

  58. 58.

    Wergedal JE, Sheng MH-C, Ackert-Bicknell CL, Beamer WG, Baylink DJ: Genetic variation in femur extrinsic strength in 29 different inbred strains of mice is dependent on variations in femur cross-sectional geometry and bone density. Bone. 2005, 36: 111-122. 10.1016/j.bone.2004.09.012

    PubMed  Google Scholar 

  59. 59.

    Xiong Q, Han C, Beamer WG, Gu W: A close examination of genes within quantitative trait loci of bone mineral density in whole mouse genome. Crit Rev Eukaryot Gene Expr. 2008, 18: 323-343.

    PubMed  Google Scholar 

  60. 60.

    Xiong Q, Jiao Y, Hasty KA, Canale ST, Stuart JM, Beamer WG, Deng HW, Baylink D, Gu W: Quantitative trait loci, genes, and polymorphisms that regulate bone mineral density in mouse. Genomics. 2009, 93: 401-414. 10.1016/j.ygeno.2008.12.008

    PubMed  PubMed Central  Google Scholar 

  61. 61.

    Yershov Y, Baldini TH, Villagomez S, Young T, Martin M, Bockman RS, Peterson MG, Blank RD: Bone strength and related traits in HcB/Dem recombinant congenic mice. J Bone Miner Res. 2001, 16: 992-1003. 10.1359/jbmr.2001.16.6.992

    PubMed  Google Scholar 

  62. 62.

    Kodama Y, Dimai HP, Wergedal J, Sheng M, Malpe R, Kutilek S, Beamer W, Donahue LR, Rosen C, Baylink DJ, Farley J: Cortical tibial bone volume in two strains of mice: effects of sciatic neurectomy and genetic regulation of bone response to mechanical loading. Bone. 1999, 25: 183-190. 10.1016/S8756-3282(99)00155-6

    PubMed  Google Scholar 

  63. 63.

    Havill LM, Mahaney MC, Cox LA, Morin PA, Joslyn G, Rogers J: A quantitative trait locus for normal variation in forearm bone mineral density in pedigreed baboons maps to the ortholog of human chromosome 11q. J Clin Endocrinol Metab. 2005, 90: 3638-3645. 10.1210/jc.2004-1618

    PubMed  Google Scholar 

  64. 64.

    Havill LM, Rogers J, Cox LA, Mahaney MC: QTL with pleiotropic effects on serum levels of bone-specific alkaline phosphatase and osteocalcin maps to the baboon ortholog of human chromosome 6p23-21.3. J Bone Miner Res. 2006, 21: 1888-1896. 10.1359/jbmr.060812

    PubMed  Google Scholar 

  65. 65.

    Sherwood RJ, Duren DL, Havill LM, Rogers J, Cox LA, Towne B, Mahaney MC: A genomewide linkage scan for quantitative trait loci influencing the craniofacial complex in baboons (Papio hamadryas spp.). Genetics. 2008, 180: 619-628. 10.1534/genetics.108.090407

    PubMed  PubMed Central  Google Scholar 

  66. 66.

    Dunn IC, Fleming RH, McCormack HA, Morrice D, Burt DW, Preisinger R, Whitehead CC: A QTL for osteoporosis detected in an F2 population derived from White Leghorn chicken lines divergently selected for bone index. Anim Genet. 2007, 38: 45-49. 10.1111/j.1365-2052.2006.01547.x

    PubMed  Google Scholar 

  67. 67.

    Buitenhuis AJ, Lund MS, Thomasen JR, Thomsen B, Nielsen VH, Bendixen C, Guldbrandtsen B: Detection of quantitative trait loci affecting lameness and leg conformation traits in Danish Holstein cattle. J Dairy Sci. 2007, 90: 472-481.

    PubMed  Google Scholar 

  68. 68.

    Havill LM, Cox LA, Rogers J, Mahaney MC: Cross-species replication of a serum osteocalcin quantitative trait locus on human chromosome 16q in pedigreed baboons. Calcif Tissue Int. 2005, 77: 205-211. 10.1007/s00223-005-0056-1

    PubMed  Google Scholar 

  69. 69.

    Peacock M, Koller DL, Lai D, Hui S, Foroud T, Econs MJ: Bone mineral density variation in men is influenced by sex-specific and non sex-specific quantitative trait loci. Bone. 2009, 45: 443-448. 10.1016/j.bone.2009.05.002

    PubMed  PubMed Central  Google Scholar 

  70. 70.

    Richards JB, Rivadeneira F, Inouye M, Pastinen TM, Soranzo N, Wilson SG, Andrew T, Falchi M, Gwilliam R, Ahmadi KR, Valdes AM, Arp P, Whittaker P, Verlaan DJ, Jhamai M, Kumanduri V, Moorhouse M, van Meurs JB, Hofman A, Pols HA, Hart D, Zhai G, Kato BS, Mullin BH, Zhang F, Deloukas P, Uitterlinden AG, Spector TD: Bone mineral density, osteoporosis, and osteoporotic fractures: a genome-wide association study. Lancet. 2008, 371: 1505-1512. 10.1016/S0140-6736(08)60599-1

    PubMed  PubMed Central  Google Scholar 

  71. 71.

    Zhang F, Xiao P, Yang F, Shen H, Xiong DH, Deng HY, Papasian CJ, Drees BM, Hamilton JJ, Recker RR, Deng HW: A whole genome linkage scan for QTLs underlying peak bone mineral density. Osteoporos Int. 2008, 19: 303-310. 10.1007/s00198-007-0468-z

    PubMed  Google Scholar 

  72. 72.

    Cheung CL, Shan PC, Chan V, Paterson AD, Luk KDK, Kung AWC: Identification of LTBP2 on chromosome 14q as a novel candidate gene for bone mineral density variation and fracture risk association. J Clin Endocrinol Metab. 2008, 93: 4448-4455. 10.1210/jc.2007-2836

    PubMed  Google Scholar 

  73. 73.

    Chen XD, Xiao P, Lei SF, Liu YZ, Guo YF, Deng FY, Tan LJ, Zhu XZ, Chen FR, Recker RR, Deng HW: Gene expression profiling in monocytes and SNP association suggest the importance of STAT1 Gene for osteoporosis in both Chinese and Caucasians. J Bone Miner Res. 2009

    Google Scholar 

  74. 74.

    Kim S, Koga T, Isobe M, Kern BE, Yokochi T, Chin YE, Karsenty G, Taniguchi T, Takayanagi H: Stat1 functions as a cytoplasmic attenuator of Runx2 in the transcriptional program of osteoblast differentiation. Genes Dev. 2003, 17: 1979-1991. 10.1101/gad.1119303

    PubMed  PubMed Central  Google Scholar 

  75. 75.

    Styrkarsdottir U, Halldorsson BV, Gretarsdottir S, Gudbjartsson DF, Walters GB, Ingvarsson T, Jonsdottir T, Saemundsdottir J, Snorradóttir S, Center JR, Nguyen TV, Alexandersen P, Gulcher JR, Eisman JA, Christiansen C, Sigurdsson G, Kong A, Thorsteinsdottir U, Stefansson K: New sequence variants associated with bone mineral density. Nat Genet. 2009, 41: 15-17. 10.1038/ng.284

    PubMed  Google Scholar 

  76. 76.

    Ioannidis JP, Ng MY, Sham PC, Zintzaras E, Lewis CM, Deng HW, Econs MJ, Karasik D, Devoto M, Kammerer CM, Spector T, Andrew T, Cupples LA, Duncan EL, Foroud T, Kiel DP, Koller D, Langdahl B, Mitchell BD, Peacock M, Recker R, Shen H, Sol-Church K, Spotila LD, Uitterlinden AG, Wilson SG, Kung AW, Ralston SH: Meta-analysis of genome-wide scans provides evidence for sex- and site-specific regulation of bone mass. J Bone Miner Res. 2007, 22: 173-183. 10.1359/jbmr.060806

    PubMed  PubMed Central  Google Scholar 

  77. 77.

    van Meurs JB, Trikalinos TA, Ralston SH, Balcells S, Brandi ML, Brixen K, Kiel DP, Langdahl BL, Lips P, Ljunggren O, Lorenc R, Obermayer-Pietsch B, Ohlsson C, Pettersson U, Reid DM, Rousseau F, Scollen S, Van Hul W, Agueda L, Akesson K, Benevolenskaya LI, Ferrari SL, Hallmans G, Hofman A, Husted LB, Kruk M, Kaptoge S, Karasik D, Karlsson MK, Lorentzon M, et al.: Large-scale analysis of association between LRP5 and LRP6 variants and osteoporosis. JAMA. 2008, 299: 1277-1290. 10.1001/jama.299.11.1277

    PubMed  PubMed Central  Google Scholar 

  78. 78.

    Parkitna JR, Engblom D, Schutz G: Generation of Cre recombinase-expressing transgenic mice using bacterial artificial chromosomes. Methods Mol Biol. 2009, 530: 325-342. full_text

    PubMed  Google Scholar 

  79. 79.

    Ohtsuka M, Kimura M, Tanaka M, Inoko H: Recombinant DNA technologies for construction of precisely designed trans-gene constructs. Curr Pharm Biotechnol. 2009, 10: 244-251. 10.2174/138920109787315033

    PubMed  Google Scholar 

  80. 80.

    Zhang M, Xuan S, Bouxsein ML, von Stechow D, Akeno N, Faugere MC, Malluche H, Zhao G, Rosen CJ, Efstratiadis A, Clemens TL: Osteoblast-specific knockout of the insulin-like growth factor (IGF) receptor gene reveals an essential role of IGF signaling in bone matrix mineralization. J Biol Chem. 2002, 277: 44005-44012. 10.1074/jbc.M208265200

    PubMed  Google Scholar 

  81. 81.

    Liu F, Woitge HW, Braut A, Kronenberg MS, Lichtler AC, Mina M, Kream BE: Expression and activity of osteoblast-targeted Cre recombinase transgenes in murine skeletal tissues. Int J Dev Biol. 2004, 48: 645-653. 10.1387/ijdb.041816fl

    PubMed  Google Scholar 

  82. 82.

    Dacquin R, Starbuck M, Schinke T, Karsenty G: Mouse alpha1(I)-collagen promoter is the best known promoter to drive efficient Cre recombinase expression in osteoblast. Dev Dyn. 2002, 224: 245-251. 10.1002/dvdy.10100

    PubMed  Google Scholar 

  83. 83.

    Lu Y, Xie Y, Zhang S, Dusevich V, Bonewald LF, Feng JQ: DMP1-targeted Cre expression in odontoblasts and osteocytes. J Dent Res. 2007, 86: 320-325. 10.1177/154405910708600404

    PubMed  Google Scholar 

  84. 84.

    Chiu WS, McManus JF, Notini AJ, Cassady AI, Zajac JD, Davey RA: Transgenic mice that express Cre recombinase in osteoclasts. Genesis. 2004, 39: 178-185. 10.1002/gene.20041

    PubMed  Google Scholar 

  85. 85.

    Garcia-Otin AL, Guillou F: Mammalian genome targeting using site-specific recombinases. Front Biosci. 2006, 11: 1108-1136. 10.2741/1867

    PubMed  Google Scholar 

  86. 86.

    Takeda S, Elefteriou F, Levasseur R, Liu X, Zhao L, Parker KL, Armstrong D, Ducy P, Karsenty G: Leptin regulates bone formation via the sympathetic nervous system. Cell. 2002, 111: 305-317. 10.1016/S0092-8674(02)01049-8

    PubMed  Google Scholar 

  87. 87.

    Takeda S, Karsenty G: Molecular bases of the sympathetic regulation of bone mass. Bone. 2008, 42: 837-840. 10.1016/j.bone.2008.01.005

    PubMed  Google Scholar 

  88. 88.

    Lee NK, Sowa H, Hinoi E, Ferron M, Ahn JD, Confavreux C, Dacquin R, Mee PJ, McKee MD, Jung DY, Zhang Z, Kim JK, Mauvais-Jarvis F, Ducy P, Karsenty G: Endocrine regulation of energy metabolism by the skeleton. Cell. 2007, 130: 456-469. 10.1016/j.cell.2007.05.047

    PubMed  PubMed Central  Google Scholar 

  89. 89.

    Yadav VK, Ryu JH, Suda N, Tanaka KF, Gingrich JA, Schütz G, Glorieux FH, Chiang CY, Zajac JD, Insogna KL, Mann JJ, Hen R, Ducy P, Karsenty G: Lrp5 controls bone formation by inhibiting serotonin synthesis in the duodenum. Cell. 2008, 135: 825-837. 10.1016/j.cell.2008.09.059

    PubMed  PubMed Central  Google Scholar 

  90. 90.

    Hamrick MW, Ferrari SL: Leptin and the sympathetic connection of fat to bone. Osteoporos Int. 2008, 19: 905-912. 10.1007/s00198-007-0487-9

    PubMed  Google Scholar 

  91. 91.

    Gustafsson BI, Westbroek I, Waarsing JH, Waldum H, Solligard E, Brunsvik A, Dimmen S, van Leeuwen JP, Weinans H, Syversen U: Long-term serotonin administration leads to higher bone mineral density, affects bone architecture, and leads to higher femoral bone stiffness in rats. J Cell Biochem. 2006, 97: 1283-1291. 10.1002/jcb.20733

    PubMed  Google Scholar 

  92. 92.

    Bonewald LF, Johnson ML: Osteocytes, mechanosensing and Wnt signaling. Bone. 2008, 42: 606-615. 10.1016/j.bone.2007.12.224

    PubMed  PubMed Central  Google Scholar 

  93. 93.

    Koboldt DC, Chen K, Wylie T, Larson DE, McLellan MD, Mardis ER, Weinstock GM, Wilson RK, Ding L: VarScan: variant detection in massively parallel sequencing of individual and pooled samples. Bioinformatics. 2009, 25: 2283-2285. 10.1093/bioinformatics/btp373

    PubMed  PubMed Central  Google Scholar 

  94. 94.

    Biesecker LG, Mullikin JC, Facio FM, Turner C, Cherukuri PF, Blakesley RW, Bouffard GG, Chines PS, Cruz P, Hansen NF, Teer JK, Maskeri B, Young AC, , Manolio TA, Wilson AF, Finkel T, Hwang P, Arai A, Remaley AT, Sachdev V, Shamburek R, Cannon RO, Green ED: The ClinSeq Project: piloting large-scale genome sequencing for research in genomic medicine. Genome Res. 2009, 19: 1665-1674. 10.1101/gr.092841.109

    PubMed  PubMed Central  Google Scholar 

  95. 95.

    Tucker T, Marra M, Friedman JM: Massively parallel sequencing: the next big thing in genetic medicine. Am J Hum Genet. 2009, 85: 142-154. 10.1016/j.ajhg.2009.06.022

    PubMed  PubMed Central  Google Scholar 

  96. 96.

    Moore WJ, Kern JC, Bhat R, Commons TJ, Fukayama S, Goljer I, Krishnamurthy G, Magolda RL, Nogle L, Pitts K, Stauffer B, Trybulski EJ, Welmaker GS, Wilson M, Bodine PV: Modulation of Wnt signaling through inhibition of secreted frizzled-related protein I (sFRP-1) with N-substituted piperidinyl diphenylsulfonyl sulfonamides. J Med Chem. 2009, 52: 105-116. 10.1021/jm801144h

    PubMed  Google Scholar 

  97. 97.

    Clement-Lacroix P, Ai M, Morvan F, Roman-Roman S, Vayssiere B, Belleville C, Estrera K, Warman ML, Baron R, Rawadi G: Lrp5-independent activation of wnt signaling by lithium chloride increases bone formation and bone mass in mice. Proc Natl Acad Sci USA. 2005, 102: 17406-17411. 10.1073/pnas.0505259102

    PubMed  PubMed Central  Google Scholar 

  98. 98.

    Kulkarni NH, Onyia JE, Zeng Q, Tian X, Liu M, Halladay DL, Frolik CA, Engler T, Wei T, Kriauciunas A, Martin TJ, Sato M, Bryant HU, Ma YL: Orally bioavailable GSK-3α/β dual inhibitor increases markers of cellular differentiation in vitro and bone mass in vivo. J Bone Miner Res. 2006, 21: 910-920. 10.1359/jbmr.060316

    PubMed  Google Scholar 

  99. 99.

    Morvan F, Boulukos K, Clément-Lacroix P, Roman Roman S, Suc-Royer I, Vayssière B, Ammann P, Martin P, Pinho S, Pognonec P, Mollat P, Niehrs C, Baron R, Rawadi G: Deletion of a single allele of the Dkk1 gene leads to an increase in bone formation and bone mass. J Bone Miner Res. 2006, 21: 934-945. 10.1359/jbmr.060311

    PubMed  Google Scholar 

  100. 100.

    Yacoby S, Ling W, Zhan F, Walker R, Barlogie B, Shaughnessy JDJ: Anitbody-based inhibition of DKK1 suppresses tumor-induced bone resorption and multiple myeloma growth in vivo. Blood. 2007, 109: 2106-2111. 10.1182/blood-2006-09-047712

    Google Scholar 

  101. 101.

    Kikuchi A: Tumor formation by genetic mutations in the components of the wnt signaling pathway. Cancer Sci. 2003, 94: 225-229. 10.1111/j.1349-7006.2003.tb01424.x

    PubMed  Google Scholar 

  102. 102.

    Logan CY, Nusse R: The Wnt signaling pathway in development and disease. Annu Rev Cell Dev Biol. 2004, 20: 781-810. 10.1146/annurev.cellbio.20.010403.113126

    PubMed  Google Scholar 

  103. 103.

    Moon RT, Bowerman B, Boutros M, Perrimon N: The promise and perils of wnt signaling through β-catenin. Science. 2002, 296: 1644-1646. 10.1126/science.1071549

    PubMed  Google Scholar 

  104. 104.

    Nusse R: Wnt signaling in disease and development. Cell Res. 2005, 15: 28-32. 10.1038/

    PubMed  Google Scholar 

  105. 105.

    Rawadi G, Roman-Roman S: Wnt signaling pathway: a new target for the treatment of osteoporosis. Expert Opin Ther targets. 2005, 9: 1063-1077. 10.1517/14728222.9.5.1063

    PubMed  Google Scholar 

  106. 106.

    Piters E, Bouding E, Van Hul W: Wnt signaling: A win for bone. Arch Biochem Biophys. 2008, 473: 112-116. 10.1016/

    PubMed  Google Scholar 

  107. 107.

    Kiel DP, Demissie S, Dupuis J, Lunetta KL, Murabito JM, Karasik D: Genome-wide association with bone mass and geometry in the Framingham Heart Study. BMC Med Genet. 2007, 8 (Suppl 1): S14- 10.1186/1471-2350-8-S1-S14

    PubMed  PubMed Central  Google Scholar 

  108. 108.

    Babij P, Zhao W, Small C, Kharode Y, Yaworsky PJ, Bouxsein ML, Reddy PS, Bodine PV, Robinson JA, Bhat B, Marzolf J, Moran RA, Bex F: High bone mass in mice expressing a mutant LRP5 gene. J Bone Miner Res. 2003, 18: 960-974. 10.1359/jbmr.2003.18.6.960

    PubMed  Google Scholar 

  109. 109.

    Kato M, Patel MS, Levasseur R, Lobov I, Chang BH, Glass DA, Hartmann C, Li L, Hwang TH, Brayton CF, Lang RA, Karsenty G, Chan L: Cbfa 1-independent decrease in osteoblast proliferation, osteopenia, and persistent embryonic eye vascularization in mice deficient in Lrp5, a Wnt coreceptor. J Cell Biol. 2002, 157: 303-314. 10.1083/jcb.200201089

    PubMed  PubMed Central  Google Scholar 

  110. 110.

    Cho YS, Go MJ, Kim YJ, Heo JY, Oh JH, Ban HJ, Yoon D, Lee MH, Kim DJ, Park M, Cha SH, Kim JW, Han BG, Min H, Ahn Y, Park MS, Han HR, Jang HY, Cho EY, Lee JE, Cho NH, Shin C, Park T, Park JW, Lee JK, Cardon L, Clarke G, McCarthy MI, Lee JY, Lee JK, et al.: A large-scale genome-wide association study of Asian populations uncovers genetic factors influencing eight quantitative traits. Nat Genet. 2009, 41: 527-534. 10.1038/ng.357

    PubMed  Google Scholar 

  111. 111.

    Nakanishi R, Akiyama H, Kimura H, Otsuki B, Shimizu M, Tsuboyama T, Nakamura T: Osteoblast-targeted expression of Sfrp4 in mice results in low bone mass. J Bone Miner Res. 2008, 23: 271-277. 10.1359/jbmr.071007

    PubMed  Google Scholar 

  112. 112.

    McGuigan FE, Larzenius E, Callreus M, Gerdhem P, Luthman H, Akesson K: Variation in the BMP2 gene: bone mineral density and ultrasound in young adult and elderly women. Calcif Tissue Int. 2007, 81: 254-262. 10.1007/s00223-007-9054-9

    PubMed  Google Scholar 

  113. 113.

    Kamiya N, Ye L, Kobayashi T, Lucas DJ, Mochida Y, Yamauchi M, Kronenberg HM, Feng JQ, Mishina Y: Disruption of BMP signaling in osteoblasts through type IA receptor (BMPRIA) increases bone mass. J Bone Miner Res. 2008, 23: 2007-2017. 10.1359/jbmr.080809

    PubMed  PubMed Central  Google Scholar 

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MLJ is supported by grants from NIH/NIAMS grants RO1 AR053949 and PO1 AR046798 and NIH/NIA RO1 AG030637.

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Correspondence to Mark L Johnson.

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The authors declare that they have no competing interests.

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The authors shared equally in the writing of this review and agree to its content.

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Johnson, M.L., Lara, N. & Kamel, M.A. How genomics has informed our understanding of the pathogenesis of osteoporosis. Genome Med 1, 84 (2009).

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  • Bone Mineral Density
  • Osteoporosis
  • Bone Mass
  • High Bone Mass
  • Bone Biology