Abstract
Osteoporosis is a complex human disease that results in increased susceptibility to fragility fractures. It can be phenotypically characterized using several traits, including bone mineral density, bone size, bone strength, and bone turnover markers. The identification of gene variants that contribute to osteoporosis phenotypes, or responses to therapy, can eventually help individualize the prognosis, treatment, and prevention of fractures and their adverse outcomes. Our previously published reviews have comprehensively summarized the progress of molecular genetic studies of gene identification for osteoporosis and have covered the data available to the end of September 2007. This review represents our continuing efforts to summarize the important and representative findings published between October 2007 and November 2009. The topics covered include genetic association and linkage studies in humans, transgenic and knockout mouse models, as well as gene-expression microarray and proteomics studies. Major results are tabulated for comparison and ease of reference. Comments are made on the notable findings and representative studies for their potential influence and implications on our present understanding of the genetics of osteoporosis.
This review aims to summarize the progress of molecular genetic studies of gene identification for osteoporosis published between October 2007 and November 2009. It is intended to constitute a sequential update of our previously published review covering the available data up to September 2007. The topics covered include genetic association and linkage studies in humans, transgenic and knockout mouse models, as well as gene-expression microarray and proteomics studies.
I. Introduction
- II. Candidate Gene Association Studies
- A. Receptors for calciotropic hormones
- B. Cytokines and receptors
- C. Bone matrix proteins
- D. Limitations and improvements
- III. Genome-Wide Linkage (GWL) Studies
- A. Univariate linkage analyses
- B. Bivariate linkage analyses
- IV. Genome-Wide Association (GWA) Studies
- A. Single nucleotide polymorphism (SNP) analyses
- B. Copy number variation (CNV) analyses
V. Transgenic/Knockout Mouse Models
- VI. Gene-Expression Microarray Studies
- A. Regulation of osteoblast and osteoclast activity
- B. Proliferation and differentiation of mesenchymal stem cells (MSCs)
- C. Gene expression in healthy vs. diseased tissues
- D. Effects of therapeutic agents on the healing of fractures
- E. Endocrine regulation of bone remodeling
- VII. Proteomics Studies
- A. Studying cultured cells using in vitro systems
- B. Studying fresh cells, serum, or tissues
- VIII. Future Directions
- A. Functional studies
- B. Epigenetic variation
- C. New phenotypes
IX. Future Prospects for the Application of Genetic Risk Assessment in Osteoporosis Prediction and Treatment
X. Summary
I. Introduction
Osteoporosis is a systemic skeletal disease characterized by low bone mineral density (BMD) and microarchitectural deterioration of bone tissue, with a consequent increase in susceptibility to fracture. Molecular genetic studies have been extensively performed to search for genes underlying osteoporosis. Since 2002, we have regularly published reviews that summarize the progress in molecular genetic studies of gene identification for osteoporosis (1,2,3). In this review, we continue these reviews with an update that captures the important and representative findings published from October 2007 to November 2009.
Similar to our previous updates (1,2,3), this article systematically reviews publications relevant to osteogenesis and osteoporosis that involve genetic association and linkage studies or functional genomics (including gene-expression microarray and proteomics) in human populations and transgenic and knockout animal models. The data presented in this review were collected from PubMed using the searching key words “BMD,” “osteoporosis,” or “bone” in combination with “association,” “polymorphisms,” “linkage,” “knock out,” “transgenic,” “microarray,” or “proteomics.” The results of important studies are incorporated in tables for clear comparison and ease of reference. Table 1 summarizes the major candidate genes subjected to association studies, classified by their functional relevance to bone and mineral metabolism. Table 2 reviews major results from approximately 120 reported candidate gene association studies. Genome-wide linkage (GWL) and genome-wide association (GWA) studies for osteoporosis-related phenotypes in humans are summarized in Tables 3 and 4, respectively. Table 5 highlights studies using transgenic and knockout mouse models relevant to osteoporosis, and Table 6 reviews approximately 110 gene-expression microarray studies on the pathogenesis of osteoporosis and other bone-related diseases. In this review, due to space limitations, we only comment on the most representative results that have had an immediate influence on our understanding and research of genetic mechanisms underlying osteoporosis.
Table 1.
Candidate genes | Protein | Chromosome location |
---|---|---|
Calciotropic hormones and receptors | ||
AR | Androgen receptor | Xq11.2-q12 |
CASR | Calcium-sensing receptor | 3q13 |
CRHR1 | Corticotropin-releasing hormone receptor 1 | 17q12-q22 |
CTR | Calcitonin receptor | 7q21.3 |
CYP17A1 | Steroid 17-α-hydroxylase | 10q24.3 |
CYP19 | Cytochrome P450, family XIX | 10q26 |
CYP19A1 | Aromatase | 15q21.1 |
CYP1B1 | Aryl hydrocarbon hydroxylase | 2p21 |
DBP | Vitamin D-binding protein | 4q12-q13 |
ER-a | Estrogen receptor-a | 6q25.1 |
ER-b | Estrogen receptor-b | 14q23.2 |
ESRRA | Estrogen-related receptor α | 11q13 |
GR | Glucocorticoid receptor | 5q31.3 |
LHB | Luteinizing hormone β polypeptide | 19q13.32 |
LHCGR | Luteinizing hormone-choriogonadotropin receptor | 2p21 |
PRL | Prolactin | 6p22.2-p21.3 |
PTH | Parathyroid hormone | 11p15.3-p15.1 |
PTHLH | Parathyroid hormone-like hormone | 12p12.1-p11.2 |
PTHR1 | Parathyroid hormone receptor 1 | 3p22-p21.1 |
PTHR2 | Parathyroid hormone receptor 2 | 2q33 |
SHBG | Sex hormone-binding globulin | 17p13-p12 |
SRD5A2 | Steroid-5-α-reductase, α polypeptide 2 | 2p23 |
TSHR | Thyroid-stimulating hormone receptor | 14q31 |
VDR | Vitamin-D receptor | 12q13.11 |
Cytokines, growth factors, and receptors | ||
BMP2 | Bone morphogenetic protein 2 | 20p12 |
BMP7 | Bone morphogenetic protein 7 | 20q13 |
CD40 | Tumor necrosis factor receptor superfamily member 5 | 20q12-q13.2 |
CNR2 | Cannabinoid receptor 2 | 1p36.11 |
DKK2 | Dickkopf homolog 2 | 4q25 |
FGFR1 | Fibroblast growth factor receptor 1 | 8p11.2-p11.1 |
FGFR2 | Fibroblast growth factor receptor 2 | 10q26 |
GDF5 | Growth differentiation factor 5 | 20q11.2 |
GHRH | Growth hormone-releasing hormone | 20q11.2 |
IGFBP2 | Insulin-like growth factor binding protein 2 | 2q33-q34 |
IL-23 | Interleukin 23 | 12q13.2 |
IL-23R | Interleukin 23 receptor | 1p31.3 |
IL-6 | Interleukin 6 | 7p21 |
IL-6R | Interleukin 6 receptor | 1q21 |
IL-15 | Interleukin 15 | 4q31 |
LEPR | Leptin receptor | 1p31 |
LRP1 | Low-density lipoprotein receptor-related protein 1 | 12q13-q14 |
LRP5 | Low-density lipoprotein receptor-related protein 5 | 11q13.4 |
LRP6 | Low-density lipoprotein receptor-related protein 6 | 12p11-p13 |
LTBP2 | Latent transforming growth factor β binding protein 2 | 14q24 |
MSTN | Myostatin | 2q32.2 |
NPY | Neuropeptide Y | 7p15.1 |
OPG | Osteoprotegerin | 8q24 |
RANK | Receptor activator of nuclear factor κ-β | 18q22.1 |
RANKL | Receptor activator of nuclear factor κ-β ligand | 13q14 |
TGF-b1 | Transforming growth factor b-1 | 19q13.1 |
TNF-a | Tumor necrosis factor a | 6p21.3 |
TNFRSF1B | Tumor necrosis factor receptor superfamily, member 1B | 1p36.3-p36.2 |
TNF-β | Lymphotoxin α | 6p21.3 |
VEGF | Vascular endothelial growth factor A | 6p12 |
Bone matrix proteins | ||
COL1A1 | Collagen type I α-1 | 17q21.33 |
COL1A2 | Collagen type I α-2 | 7q22.1 |
ITGA1 | Integrin, α 1 | 5q11.2 |
MMP2 | Matrix metallopeptidase 2 | 16q13-q21 |
OCIL | Osteoclast inhibitory lectin | 12p13 |
SPARC | Osteonectin | 5q31.3-q32 |
(Continued) | ||
Candidate genes |
Protein |
Chromosome location |
Miscellaneous | ||
ADCY10 | Adenylate cyclase 10 | 1q24 |
ALOX15 | Arachidonate 15-lipoxygenase | 17p13.3 |
ALOX5 | Arachidonate 5-lipoxygenase | 10q11.2 |
ALPL | Alkaline phosphatase | 1p36.12 |
ANXA6 | Annexin A6 | 5q32-q34 |
APC | Adenomatous polyposis coli | 5q21-q22 |
ARHGEF3 | Rho guanine nucleotide exchange factor 3 | 3p21-p13 |
BMPR1B | Bone morphogenetic protein receptor, type IB | 4q22-q24 |
CA10 | Carbonic anhydrase X | 17q21.33 |
CA8 | Carbonic anhydrase VIII | 8q11-q12 |
CALM1 | Calmodulin 1 | 14q24-q31 |
CLCN7 | Chloride channel 7 | 16p13 |
COMT | Catechol-O-methyltransferase | 22q11.21 |
CRTAP | Cartilage-associated protein | 3p22.3 |
DMP1 | Dentin matrix acidic phosphoprotein 1 | 4q21 |
ENPP1 | Ectonucleotide pyrophosphatase/phosphodiesterase 1 | 6q22-q23 |
FABP3 | Fatty acid-binding protein 3 | 1p33-p32 |
FLNB | Filamin β | 3p14.3 |
FLT1 | Fms-related tyrosine kinase 1 | 13q12 |
FOXC2 | Forkhead box C2 | 16q22-q24 |
FZD1 | Frizzled homolog 1 | 7q21 |
FZD6 | Frizzled homolog 6 | 8q22.3-q23.1 |
HMGA2 | High mobility group AT-hook 2 | 12q15 |
HOXA | Homeobox A cluster | 7p15-p14 |
HSD11B1 | Hydroxysteroid (11-β) dehydrogenase 1 | 1q32-q41 |
MDR1 | Multidrug resistance 1 | 7q21.1 |
MTHFR | Methylenetetrahydrofolate reductase | 1p36.3 |
NFATC1 | Nuclear factor of activated T-cells, cytoplasmic, calcineurin-dependent 1 | 18q23 |
NOG | Noggin | 17q21-q22 |
NOS3 | Endothelial nitric oxidase synthase | 7q36 |
NR1I3 | Constitutive androstane receptor | 1q23.3 |
P2X 7 | Purinergic receptor P2X, ligand-gated ion channel, 7 | 12q24 |
PBX1 | Pre-B-cell leukemia homeobox 1 | 1q23 |
PIR | Pirin | Xp22.2 |
PLOD | Procollagen-lysine 1, 2-oxoglutarate 5-dioxygenase 1 | 1p36.22 |
PPAR-g | Peroxisome proliferator-activated receptor γ | 3p25 |
PTN | Pleiotrophin | 7q33-q34 |
RIZ1 | Retinoblastoma protein-interacting zinc finger protein | 1p36.21 |
ROR2 | Receptor tyrosine kinase-like orphan receptor 2 | 9q22 |
RUNX2 | Runt-related transcription factor 2 | 6p21 |
SFRP1 | Secreted frizzled-related protein 1 | 8p12-p11.1 |
SFRP2 | Secreted frizzled-related protein 2 | 4q31.3 |
SOST | Sclerostin | 17q11.2 |
SREBF1 | Sterol regulatory element binding transcription factor 1 | 17p11.2 |
THSD4 | Thrombospondin, type I, domain containing 4 | 15q23 |
THSD7 | Thrombospondin, type I, domain containing 7A | 7p21.3 |
TIMP2 | Tissue inhibitor of metalloproteinase 2 | 17q25 |
TWIST1 | Twist homolog 1 | 7p21.2 |
WISP3 | WNT1 inducible signaling pathway protein 3 | 6q21 |
WNT10B | Wingless-type MMTV integration site family, member 10B | 12q13 |
WNT3A | Wingless-type MMTV integration site family, member 3A | 1q42 |
WNT7B | Wingless-type MMTV integration site family, member 7B | 22q13 |
Adapted from Y. Guo et al.: Expert Rev Endocrinol Metab 3:223–267, 2008 (3), with permission from Expert Reviews Ltd. MMTV, Mouse mammary tumor virus.
Table 2.
Gene | Marker locus | Population characteristics | Phenotype | P value | Ref. |
---|---|---|---|---|---|
ADCY10 | 16 SNPs | 1,692 premenopausal Caucasian women and 715 Caucasian men | BMD at LS | <0.01 | 36 |
ALPL | 3 SNPs | 360 postmenopausal Hungarian women | BMD at LS, total hip and distal radius, non-vertebral fracture | NS | 137 |
ALOX5 | rs7084793, rs378090, rs3802548 | 1,688 premenopausal European-American sisters, 512 premenopausal African-American sisters, and 715 European-American brothers | BMD at LS and FN | ≤0.05 | 138 |
ALOX15 | rs2619112, rs916055 | 942 southern Chinese women, with either low or high BMD | High BMD in premenopausal women and low BMD in postmenopausal women | <0.05 | 139 |
rs7220870 (G48924T) | 6,752 Caucasian women recruited at four U.S. clinical centers and enrolled into the Study of Osteoporotic Fractures | Hip fracture | <0.05 | 12 | |
ANXA6 | rs9324679, rs9324677, rs10037814, rs11960458, and haplotypes | 443 Korean patients with osteonecrosis of the femoral head and 273 Korean control | Osteonecrosis of the femoral head | <0.05 | 140 |
APC | rs459552, rs4705573, rs6594646 | 862 Caucasian men aged ≥65 yr | BMD at LS and FN | <0.05 | 141 |
AR | CAG repeat | 299 Swedish men aged 41–76 yr, from the population register in the county of Uppsala | BMD at LS, FN, and total body modified by androgen levels | <0.05 | 142 |
2,693 European men aged 40–79 yr | Ultrasound BMD | 0.021 | 143 | ||
ARHGEF3 | rs7646054 | 769 Caucasian women from 335 families, recruited in Australia and the UK | BMD at LS, FN, and total hip | 0.0007–0.041 | 144 |
BMP2 | rs2273073 (Ser37Ala) | 192 Chinese including 57 OPLL patients and 135 controls | Occurrence of OPLL in the cervical spine | <0.05 | 145 |
rs235764 (A125611G) | 6,752 Caucasian women recruited at four U.S. clinical centers and enrolled into the Study of Osteoporotic Fractures | Vertebral fracture | <0.05 | 12 | |
BMP7 | rs17404303 | 920 European-Americans from 374 Diabetes Heart Study families | Quantitative CT at thoracic spine and LS | 0.03, 0.02 | 146 |
BMPR1B | rs1434536, rs3796443 | 862 Caucasian men aged ≥65 yr | BMD at LS | <0.05 | 141 |
CA8 | rs6984526 | 337 Japanese women with osteoporosis | BMD at LS and FN | 0.00017, 0.00029 | 147 |
CA10 | rs2106329 | 337 Japanese women with osteoporosis | BMD at FN | 0.00002 | 147 |
CALM1 | rs12885713 | 158 patients with idiopathic knee osteoarthritis and 193 controls in a Greek Caucasian population | Knee osteoarthritis | NS | 148 |
CAR | rs2502815 | 548 healthy Japanese postmenopausal women | BMD at LS and total body | 0.0185, 0.0416 | 149 |
CASR | rs1801725 (A986S) | 176 premenarche Chinese girls aged 9–11.5 yr | Increased BMD at LS and BMD at Ward’s triangle | 0.022, 0.049 | 150 |
CD40 | rs1883832 | 602 postmenopausal Spanish women | BMD at LS and FN, osteopenia or osteoporosis at LS and FN | <0.05 | 151 |
CLCN7 | 6 SNPs and 1 VNTR | 1,692 healthy premenopausal white sisters aged 33.1 ± 7.2 yr and 715 healthy white brothers aged 33.6 ± 10.9 yr | BMD at LS and FN | NS | 152 |
CNR2 | 16 SNPs | 574 Caucasians from 126 two- to four-generation pedigrees | BMD at radiographic hand breaking bending resistance index | 0.007–0.008, 0.001–0.003 | 153 |
rs2501431 | 1,243 Chinese subjects | BMD at hip and trochanter | 0.02, 0.04 | 154 | |
COL1A1 | rs1800012 (Sp1, +1245G /T) | 21 osteoporotic patients with proximal femur fracture and 21 controls | BMD at FN, Ward’s triangle, trochanter and total body | NS | 155 |
rs2412298 (−1663indelT), rs1800012 (Sp1, +1245G /T) | 124 females aged 50 to 70 yr cases and 150 controls from Volga-Ural region | Traumatic fracture | <0.05 | 156 | |
(Continued) | |||||
Gene |
Marker locus |
Population characteristics |
Phenotype |
P value |
Ref. |
rs1107946, rs2412298 (−1663indelT), rs1800012 (Sp1, +1245G /T) | 98 consecutive patients including 75 women and 23 men who were admitted to Aberdeen Royal Infirmary with low-trauma hip fractures; 3275 perimenopausal women from the Aberdeen Prospective Osteoporosis Screening Study and 143 postmenopausal women from the same region | Material density of the bone core, yield strength, and toughness | <0.0001 | 23 | |
COL1A2 | rs42524 | 2,004 elderly Swedish men | BMD at several skeletal sites | <0.05 | 24 |
COMT | rs4680 (Val158met) | 2,822 Swedish men aged 75.4 ± 3.2 yr | Early fractures (≤50 yr of age) | <0.05 | 157 |
CRHR1 | rs1876828 | 157 men long-term survived of ALL at St. Jude Children’s Research Hospital | BMD at LS | 0.02 | 158 |
CRTAP | rs7623768, rs4076086-rs7623768 haplotype | 1080 Chinese females | BMD at FN | 0.009, 0.003 | 159 |
CTR | AluI | 201 Korean men aged 51.6 ± 11.7 yr | QUS | <0.05 | 160 |
AluI | 301 healthy young Caucasian men aged 18–25 yr | BMD, BMC, and geometrical areas at specific skeletal sites of the forearm | NS | 161 | |
CYP1B1 | rs1056836 (Val432Leu) | 468 Caucasian postmenopausal women (220 from St. Louis, MO, mean age 63.5 ± 0.53 yr; and 248 from Palermo, Italy, mean age 72.9 ± 0.44 yr) | BMD at LS and FN | 0.03, 0.03 | 162 |
rs1056836 (L432V) | 124 Japanese women, diagnosed with osteopenia or osteoporosis and taking hormone therapy for 12 months | Lumbar BMD and low-density lipoprotein cholesterol before and after hormone therapy | <0.05 | 163 | |
CYP17A1 | rs743572, rs743575 | 2,693 European men aged 40–79 yr | Ultrasound BMD | 0.018, 0.025 | 143 |
CYP19 | (TTTA)7-3/ER-α (TA)19 combination, | 92 Croatian males aged 21–35 yr | BMD at LS | 0.02 | 11 |
(TTTA)7-3/ER-α (TA)21 combination | BMD at LS, FN, and total hip | 0.02, 0.02, 0.008 | |||
CYP19A1 | rs11575899 | 2,693 European men aged 40–79 yr | Ultrasound BMD | <0.01 | 143 |
rs1062033 | 1,163 postmenopausal Spanish women | BMD at hip | <0.05 | 164 | |
DBP | rs222029, rs222020 | 1,873 Caucasians from 405 nuclear families | CSI at FN | 0.0019, 0.0042 | 131 |
DKK2 | rs17037102, rs6827902, rs17037297 | 344 Australian women with extreme BMD, aged 55–80 and >5 yr postmenopausal | BMD, BMC at total hip | <0.05 | 18 |
DMP1 | rs1381632 | 862 Caucasian men aged ≥65 yr | BMD at FN | <0.05 | 141 |
ENPP1 | rs1974201 | 1,513 unrelated subjects from the Framingham Offspring cohort | FNW | 3.8 × 10−7 | 165 |
ER-α | rs2228480 (G2014A) | 640 Mexican natives including 70 osteoporotic women, 70 nonosteoporotic women, and 500 subjects from the Mexican population | Osteoporosis | <0.05 | 13 |
rs2234693 (PvuII) | 228 premenarche Chinese girls aged 9–11.5 yr | BMD at total body and femoral intertrochanter | 0.010, 0.038 | 9 | |
rs2234693 (PvuII), rs9340799 (XbaI) | 158 healthy Chinese adolescent girls aged 12–14 yr | BMD and bone turnover markers | NS | 166 | |
rs2234693 (PvuII), rs9340799 (XbaI) | 146 Chinese boys aged 13–17 yr | BMD at total body, forearm and LS influenced by the first spermorrhea | <0.05 | 10 | |
rs2234693 (PvuII), rs9340799 (XbaI) | 42 Turkish including 21 osteoporotic patients with proximal femur fracture and 21 controls | BMD at FN, Ward’s triangle, and trochanteric | NS | 155 | |
rs2234693 (PvuII)–rs9340799 (XbaI) haplotype | 691 postmenopausal Chinese women aged 45–65 yr | Decreased BMD at whole body, LS, and hip | <0.05 | 8 | |
rs2234693 (PvuII)- IL-6 rs2010963 (634C/G) interaction | 228 premenarche Chinese girls aged 9–11.5 yr | BMD at total left hip and femoral intertrochanter | 0.009, 0.007 | 9 | |
rs1801132, rs726282 | 2,693 European men aged 40–79 yr | Ultrasound BMD | 0.002, 0.019 | 143 | |
rs3020314, rs1884051 | 350 Chinese with osteoporotic hip fractures and 350 Chinese controls | Hip fracture | 0.0004, 0.0004 | 14 | |
(Continued) | |||||
Gene |
Marker locus |
Population characteristics |
Phenotype |
P value |
Ref. |
rs1801132 | 6,752 Caucasian women recruited at four U.S. clinical centers and enrolled into the Study of Osteoporotic Fractures | Vertebral fracture | <0.05 | 12 | |
(TA)19 repeat | 92 unrelated healthy males aged 21–35 yr | BMD at LS, trochanter | 0.006, 0.02 | 11 | |
(TA)21 repeat | BMD at LS, trochanter, and total hip | 0.04, 0.02, 0.03 | |||
rs2234693, rs1884052, rs3778099 | 425 Chinese adults | BMD | NS | 71 | |
ER-β | CA repeat | 400 Chinese women including 78 FN PMO cases and 122 controls | Postmenopausal osteoporosis | 0.001 | 167 |
108 LS PMO cases and 92 controls | 0.023 | ||||
rs1256063, rs1256049, rs1256031 | 2,693 European men aged 40–79 yr | Ultrasound BMD | NS | 143 | |
rs960070 | 350 Chinese with osteoporotic hip fractures and 350 Chinese controls | Hip fracture | 0.0070 | 14 | |
ESRRA | 23 bp repeat polymorphism in promoter | 673 premenopausal women from the Toronto metropolitan area | BMD at LS | NS | 168 |
FABP3 | rs10914367 | 360 postmenopausal Hungarian women | BMD at total hip | 0.028 | 137 |
FGFR1 | rs6996321 | 360 postmenopausal Hungarian women | BMD at LS | 0.002 | 137 |
FGFR2 | rs7916940 | 862 Caucasian men aged ≥65 yr | BMD at FN | <0.05 | 141 |
FLNB | rs7637505, rs9822918, rs2177153, rs2001972 | 1,085 UK female twins and 1,315 Australian women | BMD at LS and FN | <0.05 | 169 |
rs9828717 | 1,080 Chinese females | BMD at LS | <0.05 | 159 | |
FLT1 | rs1408245 | 862 Caucasian men aged ≥65 yr | BMD at FN | <0.05 | 141 |
FOXC2 | rs3751797 | 862 Caucasian men aged ≥65 yr | BMD at LS | <0.05 | 141 |
FZD1 | rs2232157, rs2232158 | 1,084 African men | BMD at FN, bone size at the radius, strength-strain index | <0.05 | 170 |
FZD6 | rs3808553 (L345 M), rs12549394 (E664A) | 371 postmenopausal Korean women | BMD at LS and FN | NS | 171 |
GDF5 | rs143383 | 2,487 European cases and 2,018 age-matched controls from the UK and Spain | Osteoarthritis | <0.05 | 172 |
rs143383 | 6,365 Caucasian elderly subjects | Hip axis length and nonvertebral fractures | 0.0004, 0.02 | 173 | |
GHRH | rs4988492 | 498 men and 468 women aged 59–71 yr from the Hertfordshire Cohort Study | BMC and BMD at proximal femur and LS | <0.05 | 174 |
GR | rs1866388 | 400 Chinese women and 400 Chinese men | Extreme age-adjusted BMD at hip | 0.028 | 175 |
HMGA2 | rs1042725 | 1,680 Afro-Caribbean men aged ≥40 yr and 1,548 Caucasian American men aged ≥69 yr | Trabecular volumetric BMD | 0.007 | 176 |
0.0007 | |||||
HOXA | rs6951180, rs6964896 | 862 Caucasian men aged ≥65 yr | BMD at FN and LS | <0.05 | 141 |
HSD11B1 | 6 SNPs | 1,392 postmenopausal Korean women | BMD at FN and vertebral fracture | <0.05 | 177 |
IGFBP2 | rs10932669 | 862 Caucasian men aged ≥65 yr | BMD at LS | <0.05 | 141 |
IL-6 | rs603573 (−174G/C), CA repeat | 640 Mexican natives including 70 osteoporotic women, 70 nonosteoporotic women, and 500 subjects from the Mexican population | BMD at LS | < 0.0001 | 178 |
rs603573 (−174G/C) | 267 postmenopausal women of Wielkopolska region aged 58.5 ± 5.9 yr | BMD at LS | NS | 179 | |
rs603573 (−174G/C) | 42 Turkish including 21 osteoporotic patients with proximal femur fracture and 21 controls | BMD at FN, Ward’s triangle, and trochanteric | NS | 155 | |
rs2010963 (−634C/G) | 228 premenarche Chinese girls aged 9–11.5 yr | Percentage accrual in BMD of total body and femoral trochanter | 0.032, 0.048 | 9 | |
rs2010963 (−634C/G) | 176 premenarche Chinese girls aged 9–11.5 yr | Increased BMD at total body and femoral trochanter | 0.027, 0.028 | 150 | |
rs2010963 (−634C/G) interaction with ER-α PvuII | 228 premenarche Chinese girls aged 9–11.5 yr | BMD at total left hip and femoral intertrochanter | 0.009, 0.007 | 9 | |
(Continued) | |||||
Gene |
Marker locus |
Population characteristics |
Phenotype |
P value |
Ref. |
IL6R | rs3887104, rs4845617, rs8192284 | 559 postmenopausal Spanish women | BMD at FN BMD at LS | 0.011, 0.025, 0.038 | 180 |
IL-15 | rs2857261, rs10519613, rs56245420, rs1057972 | 1,921 postmenopausal Korean women | BMD at LS and FN | <0.05 | 181 |
IL-23R | rs4655686, rs1569922, rs7539625 | 443 patients with osteonecrosis of the femoral head and 273 control subjects enrolled in Korea | Osteonecrosis of the femoral head | 0.0198–0.0447 | 182 |
ITGA1 | 8 SNPs | 946 postmenopausal Korean women | BMD at proximal femur and LS | 0.009–0.05, 0.002–0.005 | 183 |
LEPR | rs1137100 (Lys109Arg) | 145 premenopausal and 118 postmenopausal Korean women | BMD at total hip | 0.044 | 184 |
LHB | rs2013040 | 2,693 European men aged 40–79 yr | Ultrasound BMD | NS | 143 |
LHCGR | rs6545061 | 2,693 European men aged 40–79 yr | Ultrasound BMD | 0.023 | 143 |
LRP1 | rs4759044 and rs4759044-rs11172113 haplotype | 344 Australian women with extreme BMD, aged 55–80 and >5 yr postmenopausal | BMD, BMC at total hip and FN area | <0.05 | 18 |
LRP5 | rs3781590 | 344 Australian women with extreme BMD, aged 55–80 and >5 yr postmenopausal | BMD at total hip | 0.0006 | 18 |
rs3736228 (Ala1330Val) | 6,082 men from three cohorts (MrOS Sweden: n = 3,014, aged 69–81 yr; MrOs Hong Kong: n = 2,000, aged >65 yr; Swedish GOOD study: n = 1,068, aged 18–20 yr) | BMD at LS and FN | NS | 21 | |
rs3736228 (Ala1330Val) | 739 postmenopausal Japanese women | BMD at total body | 0.0026 | 185 | |
rs3736228 (Ala1330Val) | Meta-analysis | BMD at LS and FN | 0.55, 0.05 | 186 | |
rs3736228 (Ala1330Val) | Meta-analysis | BMD at LS, FN, and trochanter | <0.001, <0.001, 0.053 | 187 | |
rs4988321 (Val667Met) | 3,800 men from two cohorts (MrOS Sweden: n = 3,014, aged 69–81 yr; Swedish GOOD study: n = 1,068, aged 18–20 yr) | BMD at LS | <0.05 | 21 | |
rs4988321 (Val667Met) | 673 premenopausal women from the Toronto metropolitan area | BMD at LS | 0.015 | 168 | |
rs3736228 (Ala1330Val), rs4988321 (Val667Met) | 589 physically active Caucasian men aged 20–30 yr | BMD at LS and whole body BMD at FN | <0.02, 0.04 | 188 | |
rs3736228 (Ala1330Val), rs4988321 (Val667Met) | 37,534 subjects from 18 participating teams in Europe and North America | BMD at LS and FN, prevalence of all fractures and vertebral fractures | <10−7 | 20 | |
rs312009, rs2508836, rs729635, rs643892 | 964 postmenopausal Spanish women | BMD at LS and FN, fracture | <0.05 | 189 | |
rs682429, rs686921 | 286 young southern Chinese females, aged 22–44 yr with low BMD | BMD at LS and hip | <0.05 | 190 | |
rs4988330, rs4988331, rs312786 | 608 with OA (348 undergoing hip replacement surgery and 260 knee surgery) and 520 with hip fractures | Osteoporosis and osteoarthritis | 0.008–0.03 | 191 | |
rs41494349 (266A>G) | 800 healthy recently perimenopausal Danish women | Parameters of hip geometry | NS | 192 | |
rs545382, rs2277268 | 126 prepubertal children (64 with premature adrenarche and 62 controls) | BMD at LS and FN | <0.05 | 193 | |
rs3736228 | 652 Slovenian subjects | BMD at LS and FN | NS | 194 | |
LRP6 | rs2075241, rs11054704 | 344 Australian women with extreme BMD, aged 55–80 and >5 yr postmenopausal | BMD and BMC at total hip | <0.05 | 18 |
rs2302685 (Ile1062Val) | 37,534 subjects from 18 participating teams in Europe and North America | BMD at LS and FN, prevalence of all fractures and vertebral fractures | NS | 20 | |
(Continued) | |||||
Gene |
Marker locus |
Population characteristics |
Phenotype |
P value |
Ref. |
rs1181334 | 608 with OA (348 undergoing hip replacement surgery and 260 knee surgery) and 520 with hip fractures | Osteoporosis and osteoarthritis | <0.05 | 191 | |
rs2302685 | 652 Slovenian subjects | BMD at LS and FN | NS | 194 | |
LTBP2 | 16 SNPs | 1,459 subjects in 306 southern Chinese pedigrees, 706 and 760 case-control subject pairs with extremely high and low trochanter and total hip BMD | BMD at total hip and fracture | 0.0004, 0.01 | 195 |
MDR1 | rs1045642 (C3435T) | 127 Chinese systemic lupus erythematosus patients | Development of osteonecrosis of femoral head observed by magnetic resonance imaging | 0.038 | 196 |
rs2032582 (G2677T) | 0.05 | ||||
MMP2 | 5 SNPs | 360 postmenopausal Hungarian women | BMD at LS, total hip and distal radius, nonvertebral fracture | NS | 137 |
rs243865 (C595T) | 6,752 Caucasian women recruited at four U.S. clinical centers and enrolled into the Study of Osteoporotic Fractures | Vertebral fracture | <0.05 | 12 | |
MSTN | 3 SNPs | 1,260 Chinese from 401 nuclear families | BMD at proximal femur and LS | <0.05 | 197 |
MTHFR | rs1801133 (677C>T) | 3,196 children aged 9.9 yr from the Avon Longitudinal Study of Parents and Children | BMD at LS | <0.001 | 198 |
rs1801133 (677C>T) | 1,243 Chinese subjects | BMD at FN, hip, and trochanter | 0.02–0.05 | 154 | |
rs1801133 (677C>T) | 800 healthy recently perimenopausal Danish women | Parameters of hip geometry | NS | 192 | |
NFATC1 | rs177820 | 862 Caucasian men aged ≥65 yr | BMD at LS | <0.05 | 141 |
NOG | 7 SNPs | 2,060 Afro-Caribbean men age 40 and older | BMD at the proximal femur and LS | NS | 199 |
NOS3 | 18 SNPs and haplotypes | 1,451 subjects from Framingham Offspring Cohort | Bone density/ultrasound and geometry | NS | 200 |
rs2070744 [T(−786)C] | 167 Americans including 95 cases with femoral head necrosis and 72 controls | Osteonecrosis of femoral head | <0.05 | 201 | |
rs2070744 [T(−786)C] | 305 postmenopausal Turkish females | BMD at femoral trochanter and LS | 0.046, 0.005 | 202 | |
NPY | rs16135, rs16123 | 1,113 randomly selected men of African ancestry | BMD at proximal femur | <0.05 | 203 |
OCIL | rs16914640 (Asn19Lys) | 500 postmenopausal Spanish women | BMD at LS and FN | <0.05 | 204 |
OPG | rs2073618 (Lys3Asn) | 69 premenopausal and 263 postmenopausal Spanish women | BMD at LS | <0.05 | 205 |
rs2073618 (Lys3Asn) | 6,695 Caucasian women aged 65 yr and older in the United States | BMD at calcaneus and LS, FN fracture | <0.05 | 16 | |
rs237025 (A163G) | 87 postmenopausal Caucasian women | Calcaneal velocity of sound | 0.0102 | 206 | |
G209A, T245G | 42 Turkish including 21 osteoporotic patients with proximal femur fracture and 21 controls | BMD at FN, Ward’s triangle, and trochanter | NS | 155 | |
rs2073618 (K3N) | 239 osteoporotic and 228 nonosteoporotic postmenopausal, 57 premenopausal women, and 117 elderly men | BMD at LS, FN, and total hip | 0.021, 0.041, 0.032 | 5 | |
rs1032129 | 964 postmenopausal Spanish women | BMD at FN | 0.001 | 207 | |
PBX1 | rs2800791, rs9661977 | 720 southern Chinese subjects from 231 families; another independent unrelated southern Chinese cohort with a total of 835 extremely high and extremely low BMD subjects; 1,268 case-control Japanese subjects with 703 osteoporotic subjects and 565 normal controls | BMD at LS | 0.004, 0.050 | 208 |
PIR | rs5935970 | 4,000 Chinese subjects | BMD at LS | <0.05 | 209 |
PLOD | rs7529452, rs2273291, rs7514577 | 1,243 Chinese subjects | BMD | <0.05 | 154 |
PPAR-γ | rs1801282 | 239 Korean women with mean age 51 yr | Serum OPG level | 0.035 | 210 |
rs1801282-rs3856806 haplotype | 0.010 | ||||
(Continued) | |||||
Gene |
Marker locus |
Population characteristics |
Phenotype |
P value |
Ref. |
rs12486170 (−796A>G), rs1801282 (+34C>G), rs3856806 (+82466C>T) | 448 patients with osteonecrosis of the femoral head and 336 control subjects | Osteonecrosis of the femoral head | NS | 211 | |
P2X7 | rs3751143 (Glu496Ala), rs1653624 (Ile568Asn) | 800 healthy recently perimenopausal Danish women | Parameters of hip geometry | NS | 192 |
PRL | rs7739889 (T228C) | 6,752 Caucasian women recruited at four U.S. clinical centers and enrolled into the Study of Osteoporotic Fractures | Vertebral and hip fracture | <0.05 | 12 |
PTH | 3 haplotypes | 1,044 women aged 75 yr from the Malmo Osteoporosis Prospective Risk Assessment study | Fractures | 0.038 | 212 |
PTHRLH | rs805512, rs10492364, rs1268693 | 1,044 women aged 75 yr from the Malmo Osteoporosis Prospective Risk Assessment study | BMD at LS and FN, fracture | NS | 212 |
VNTR | 234 young Finnish males | BMD, BUA, and SOS at LS | <0.0034 | 213 | |
PTHR1 | rs6442037, rs724449, rs7652849 | 1,044 women aged 75 yr from the Malmo Osteoporosis Prospective Risk Assessment study | BMD at LS and FN, fracture | NS | 212 |
rs724448-rs2242116 haplotype | 1,080 Chinese females | BMD at LS and FN | 0.02, 0.044 | 159 | |
PTHR2 | rs9288393, rs10497900, rs897083 | 1,044 women aged 75 yr from the Malmo Osteoporosis Prospective Risk Assessment study | BMD at LS and FN, fracture | NS | 212 |
PTN | rs322297 | 862 Caucasian men aged ≥65 yr | BMD at FN | <0.05 | 141 |
RANK | +35966insdelC | 467 postmenopausal Slovenian women | BMD at LS, FN, and hip | 0.020, 0.034, 0.024 | 214 |
RANKL | −290C>T, −643C>T, −693G>C | 404 postmenopausal Slovenian women aged 42–91 yr | BMD at LS | 0.001, 0.041, 0.013 | 17 |
CCG, TTC haplotypes | 0.005, 0.007 | ||||
−290C>T, | 239 osteoporotic and 228 nonosteoporotic postmenopausal, 57 premenopausal women, and 117 elderly men | BMD at LS | 0.017 | 5 | |
−290C>T-K3N combination | BMD at FN and total hip | 0.029, 0.041 | |||
rs12585014, rs7988338, rs2148073 | 1,873 Caucasians from 405 nuclear families | CSI at FN | 0.0007, 0.0007, 0.0005 | 130 | |
RIZ1 | Pro704 ins/del | 2,424 men and 3,517 women from the Rotterdam study | BMD at LS and FN, fracture | NS | 215 |
ROR2 | 19 SNPs | 705 Caucasians from 212 nuclear families | Bone length and BMD for hand bones, proximal phalanges and metacarpal bones | <0.05 | 216 |
Runx2 | 4 SNPs and haplotypes | 743 Caucasians from 212 nuclear families | Hand bone length and BMD | <0.05 | 217 |
rs7771980 | 729 postmenopausal Korean women | BMD at LS, trochanter and total femur | 0.02, 0.05, 0.04 | 218 | |
SFRP1 | rs921142, rs4736965, and their haplotype | 344 Australian women with extreme BMD, aged 55–80 and >5 yr postmenopausal | BMD and BMC at total hip | <0.005 | 18 |
rs16890444, rs3242 | 931 Japanese women | BMD at LS, FN, and hip | 0.037, 0.025, 0.027 | 219 | |
SFRP2 | 4 SNPs | 344 Australian women with extreme BMD, aged 55–80 and >5 yr postmenopausal | BMD, BMC at total hip and FN | NS | 18 |
SHBG | rs1799941, rs6259, TAAAA repeat in promoter | 2,693 European men aged 40–79 yr | Ultrasound BMD | NS | 143 |
rs1799941, rs6259 | 213 healthy postmenopausal Caucasian women | BMD at the proximal femur sites | <0.05 | 220 | |
(Continued) | |||||
Gene |
Marker locus |
Population characteristics |
Phenotype |
P value |
Ref. |
Smad6 | rs755451 | 721 postmenopausal Japanese women | BMD at total body and LS | 0.0004, 0.005 | 221 |
SOST | rs851054, rs851056 | 344 Australian women with extreme BMD, aged 55–80 and >5 yr postmenopausal | BMD and BMC at total hip | 0.004–0.008 | 18 |
rs1230399 | 1,243 Chinese subjects with low BMD and high BMD | BMD at LS, FN, trochanter and, total hip, osteoporosis | 0.004–0.03 0.005 | 222 | |
rs1534401, rs1877632 | 862 Caucasian men aged ≥65 yr | BMD at LS | <0.05 | 141 | |
rs851054, rs851056 | |||||
rs10534024 | 652 Slovenian subjects | BMD at LS and FN | NS | 194 | |
SPARC | 1046C-1599C-1970T, 1046C-1599G-1970T haplotypes | 115 middle-aged Caucasian men with idiopathic, low turnover osteoporosis (n = 56) and matched controls (n = 59) | BMD at LS, hip, and radius | <0.05 | 223 |
SRD5A2 | rs632148, rs523349, rs765138, rs7571644, TA repeat in exon 5 | 2,693 European men aged 40–79 yr | Ultrasound BMD | NS | 143 |
SREBF1 | rs12601420 (IVS6-48 C>T), rs9925115 (IVS7+117A>G) | 423 patients with osteonecrosis of the femoral head and 348 controls | Osteonecrosis of the femoral head | <0.0001 | 224 |
TGFB1 | rs1800469 (1348 C>T), rs1800472 (788 C>T) | 28,924 subjects from 10 European research studies | BMD at LS, vertebral fracture | <0.05 | 225 |
THSD4 | rs10851839 | Japanese women with osteoporosis aged 72.7 ± 7.3 yr | BMD at LS and FN | 0.0092, 0.0046 | 226 |
THSD7A | rs12673692 | 337 Japanese women with osteoporosis aged 72.7 ± 7.3 yr | BMD at LS and FN | 0.00017, 0.036 | 226 |
TIMP2 | 7 SNPs | 360 postmenopausal Hungarian women | Nonvertebral fracture | 0.0187 | 137 |
TNF-α | rs1800629 (−308G/A) | 267 postmenopausal women of Wielkopolska region aged 58.5 ± 5.9 yr | BMD at LS | NS | 179 |
rs1800629 (−308 G/A) | 159 Portuguese | BMD at LS and hip | NS | 227 | |
rs1799964 (−1031 T>C) | 377 postmenopausal Korean women | Osteoporosis | <0.05 | 228 | |
TNF-β | rs909253 (A252G) | 377 postmenopausal Korean women | Osteoporosis | <0.05 | 228 |
TNFRII | rs1061622 (T676G), rs3397 (1690T>C), A1663G | 377 postmenopausal Korean women | Osteoporosis, bone turnover markers | <0.05 | 228 |
TNFRSF1B | rs976881 | 1,243 Chinese subjects | BMD | 0.04–0.08 | 154 |
TSHR | rs1991517 (Asp727Glu) | 4,934 elderly Caucasian men and women of the Rotterdam Study | BMD at FN | <0.05 | 229 |
TWIST1 | BD0027 (+1871A>G) | 729 postmenopausal Korean women | Osteoporosis, BMD at FN | 0.02, 0.039 | 230 |
VEGF | rs2010963 (−634G>C) | 814 Korean including 317 osteonecrosis of the femoral head cases and 497 controls | Osteonecrosis of the femoral head cases | 0.015 | 231 |
rs2010963 (−634 G>C), rs3025039 (+936 C>T) | 252 postmenopausal Caucasian women aged 46–80 yr | BMD at LS | 0.017 | 232 | |
0.05 | |||||
VDR | rs11568820 (Cdx-2) | 190 postmenopausal Brazilian women | BMD at LS, FN, great trochanter, and Ward’s triangle with different physical activity level | <0.05 | 6 |
rs10735810 (Fokl) | 192 postmenopausal active women | BMD at FN and Ward’s triangle | <0.05 | 233 | |
rs1544410, rs7975232, rs731236 (Bsml, ApaI, TaqI) | 246 postmenopausal Turkey women including 100 osteoporosis and 146 controls | Osteoporosis | NS | 234 | |
rs1544410, rs7975232, rs731236 (Bsml, ApaI, TaqI) | 47 Caucasian patients with ulcerative colitis and 47 control subjects matched for age and gender | BMD at LS | <0.05 | 235 | |
rs1544410, rs7975232, rs731236 (haplotype BBAAtt) | 147 postmenopausic Venezuelan women including 71 cases with osteoporosis and 76 controls | Osteoporosis | <0.05 | 236 | |
(Continued) | |||||
Gene |
Marker locus |
Population characteristics |
Phenotype |
P value |
Ref. |
rs1544410, rs7975232, rs731236, rs10735810 (BsmI, ApaI, TaqI, FokI) | 238 Polish women including 75 premenopausal cases with Graves’ disease and 163 controls | BMD at LS and FN | NS | 237 | |
rs1544410, rs7975232, rs731236, rs10735810 (BsmI, ApaI, TaqI, FokI) | 126 Chilean elderly women aged 65–94 yr including 67 hip fractures and 59 controls | Hip fracture | NS | 238 | |
rs1544410 (BsmI) | 301 healthy young Caucasian men aged 18–25 yr | BMD, BMC and geometrical areas at specific skeletal sites of the forearm | NS | 161 | |
rs1544410 (BsmI) | 335 Korean women older than 65 yr with low calcium intake | BUA of calcaneus | 0.013 | 239 | |
rs1544410 (BsmI) | 42 Turkish including 21 osteoporotic patients with proximal femur fracture and 21 controls | BMD at FN, Ward’s triangle, and trochanteric | NS | 155 | |
rs1544410 (BsmI) | 68 healthy, 54 osteopenic, and 64 osteoporotic Argentine postmenopausal women | BMD at LS and FN | <0.05 | 240 | |
rs1544410 (BsmI) interaction with ER-α PvuII and XbaI | 68 healthy, 54 osteopenic, and 64 osteoporotic Argentine postmenopausal women | BMD at FN | <0.05 | 240 | |
rs7975232, rs731236 (ApaI, TaqI ) | 40 Lactating Brazilian adolescents aged 15–18 yr | BMD at LS and BMC at total body | <0.05 | 241 | |
BsmI, FokI, Cdx2 and combination of K3N(OPG)-Cdx2 | 239 osteoporotic and 228 nonosteoporotic postmenopausal, 57 premenopausal women, and 117 elderly men | BMD at FN, LS, and total hip | <0.05 | 5 | |
rs7975232 (ApaI) | 136 postmenopausal women | BMD at LS and hip | <0.05 | 242 | |
WISP3 | 4 SNPs | 344 Australian women with extreme BMD, aged 55–80 and >5 yr postmenopausal | BMD, BMC at total hip and FN area | NS | 18 |
WNT3a | rs708114, rs4653533, rs752107 | 344 Australian women with extreme BMD, aged 55–80 and >5 yr postmenopausal | BMD at total hip | <0.05 | 18 |
WNT7b | 8 SNPs | 344 Australian women with extreme BMD, aged 55–80 and >5 yr postmenopausal | BMD at total hip | <0.05 | 18 |
WNT10b | rs1051886, rs3741627 | 1,035 Afro-Caribbean men aged ≥40 yr and 980 Afro-Caribbean men | Cross-sectional area, periosteal circumference, BMD at hip and BMC at the radius and cortical | <0.05 | 243 |
7 SNPs | 1,438 Caucasian postmenopausal women | BMD at LS and hip | NS | 244 |
Adapted from Y. Guo et al.: Expert Rev Endocrinol Metab 3:223–267, 2008 (3), with permission from Expert Reviews Ltd. ALL, Acute lymphoblastic leukemia; BMC, bone mineral content; BUA, broadband ultrasound attenuation; CSI, compression strength index; FNW, FN width; GOOD study, Gothenburg Osteoporosis and Obesity Determinants study; NS, not significant; PMO, postmenopausal osteoporosis; QUS, quantitative ultrasound; SOS, speed of sound; VNTR, variable number tandem repeat.
Table 3.
Study subjects | No. of markers | Phenotype | Results | Candidate gene | Ref. |
---|---|---|---|---|---|
300 Mexican-Americans (>45 yr of age) from the San Antonio Family Osteoporosis Study | 460 | ΔBMD at 33% ulna | 3p (81 cM), LOD = 1.9 | 30 | |
ΔBMD at hip | 6q (103 cM), LOD = 1.75 | ||||
103 Caucasian pedigrees (Network in Europe on Male Osteoporosis Family Study) ascertained through a male relative with low (Z-score ≤ −2) BMD values at either LS or FN | 441 | BMD at LS | 1q42-43, LOD = 1.75 | 26 | |
11q12-13, LOD = 2.64 | LRP5, TCIRG1 | ||||
12q23-24, LOD = 1.65 | |||||
17q21-23, LOD = 3.63 | COL1A1, SOST | ||||
21q22, LOD = 2.05 | COL6, COL6A2 | ||||
22q11, LOD = 2.74 | |||||
BMD at FN | 5q31-33, LOD = 1.53 | RIL | |||
3q12-14, LOD = 2.71 | RANKL | ||||
879 Caucasians of AFOS (mean age ± sd = 49.8± 16.1 yr, range 18–91 yr) from large multigenerational families | 731 | NN_ID | 1p35.5, LOD = 2.57; 2p25, LOD = 2.37 | ALKPL (1p35.5) | 245 |
NN_Z | 1p36, LOD = 2.36; 14q23, LOD = 2.5 | BMND3, MTHFR (1p36) | |||
S_BR | 1q23-24, LOD = 2.65; 12q24.2, LOD = 2.63 | SAC, Hypercalciuria (1q23-24) | |||
S_Z | 4p11, LOD = 1.94 | ||||
S_ID | 5q24-30, LOD = 2.59 | LOX | |||
S_CSA | 11q22.1, LOD = 2.3 | ||||
NN_CSA | 2p11.2, LOD = 1.92; 6q22-23, LOD = 2.46 | COL10A1, ENPP-1, WISP3 (6q22-23) | |||
17q11.2-12, LOD = 2.16 | SOST | ||||
2522 Caucasian females from 414 pedigrees | 410 | BMD at FN/AAM | 22q13, LOD = 3.33; 3q13, LOD = 2.31 | EP300 (22q13) | 35 |
BMD at LS/AAM | 22q13, LOD = 3.30; 15q13, LOD = 2.97 | ||||
3p25, LOD = 2.36; Xp22, LOD = 2.20 | PPARG (3p25) | ||||
Xq13, LOD = 2.18; Xq23, LOD = 2.53 | |||||
Xq27, LOD = 2.34 | |||||
BMD at UD/AAM | 22q13, LOD = 3.12; 7p15, LOD = 2.44 | ||||
2200 Caucasians aged 20–50 yr from 207 pedigrees | 410 | PBMD at wrist in total sample | 2p13, LOD = 2.04 | 29 | |
10p14, LOD = 2.31 | |||||
14q23, LOD = 2.07 | ER-β, BMP-4 | ||||
Xq27, LOD = 2.64 | |||||
PBMD at hip in total sample | 2p12, LOD = 2.79 | MGP | |||
22q13, LOD = 2.16 | MCHR1 | ||||
PBMD at wrist in female | 2p13, LOD = 2.64 | ||||
6q24, LOD = 1.91 | ER-α | ||||
11q13, LOD = 1.97 | FRA-1, LRP5, TCIRG1 | ||||
18q21, LOD = 2.29 | |||||
PBMD at hip in male | 15q26, LOD = 2.93 | ||||
7p21, LOD = 2.10 | IL-6 | ||||
4126 Caucasians for a composite osteoporosis phenotype that combines OF and low BMD | 393 | A composite osteoporosis phenotype that combines OF and low BMD in total sample | 14q32, LOD = 2.61; 7p14, LOD = 2.42; 11q25, LOD = 2.09; 9p21, LOD = 1.26 | BMP4 | 246 |
A composite osteoporosis phenotype that combines OF and low BMD in female | 14q22, LOD = 3.53; 9p21, LOD = 2.29; 7p14, LOD = 3.07 | GLI3 | |||
Imprinting analyses by assigning weights to allele sharing specific to parental origins among affected sibling pairs | 1q42, LODFA = 2.12; 9q34, LODFA = 1.88; 7q22, LODMO = 1.67 | COL1A2 | |||
(Continued) | |||||
Study subjects |
No. of markers |
Phenotype |
Results |
Candidate gene |
Ref. |
1473 Caucasians aged 31–96 yr in 323 pedigrees from the Framingham Osteoporosis Study | 636 | S_CSA/height | Chr. 2, LOD = 1.56; Chr. 4, LOD < 1.0 Chr. 6, LOD = 2.34; Chr. 9, LOD = 1.73 | CALM2, FSHR, TGF α (Chr. 2); IL2 (Chr. 4) | 247 |
NN_CSA/height | Chr. 9, LOD = 1.04 | ||||
FNL/height | Chr. 15, LOD = 1.57 | ||||
NN_Z/height | Chr. 4, LOD = 2.05 | ||||
IT_OD/height | Chr. 6, LOD = 2.13 | ESR1, IGF2R | |||
S_Z/height | Chr. 6, LOD = 2.08; Chr. 21, LOD = 2.45 | ||||
S_OD/height | Chr. X, LOD = 3.28 | ||||
3899 Caucasians from 451 pedigrees | 410 | PC phenotype of BS at LS, hip, and wrist in total sample | 7q34, LOD = 2.85 | IRF 5, Leptin | 248 |
PC phenotype of BS at LS, hip, and wrist in male | |||||
PC phenotype of BS at LS, hip, and wrist in total female | 5q23, LOD = 2.39 | ||||
11p11, LOD = 2.82 | EXT2 | ||||
7q34, LOD = 1.79; 8q24, LOD = 2.30 | |||||
21q21, LOD = 1.85 | |||||
34 Caucasians in a single extended family | 380 | BMD at LS | 1q36.3, LOD = 3.07 | WDR8, EGFL3 | 27 |
4126 Caucasians from 451 pedigrees | 410 | aBMD/ABS at FN | 20q11, LOD = 3.11; Xp11, LOD = 2.46 | GDF5 (20q11); BMP15 (Xp11) | 32 |
aBMD/ABS at LS | Xq27, LOD = 4.30; 12p11, LOD = 2.86 | BGN, IRAK1 (Xq27); LRP6 (12p11) | |||
17q21, LOD = 2.44 | COL1A1, CHAD, HOXB, SOST | ||||
aBMD/ABS at UD forearm | 5q23, LOD = 3.01; Xq27, LOD = 2.43 | IL4, LOX (5q23) | |||
7p15, LOD = 2.92; 12p11, LOD = 2.42 | IL6, NPY, HOXA@, HOXB@ (7p15) | ||||
2582 Caucasian females from 451 pedigrees including 1486 premenopausal and 1096 postmenopausal women | 410 | BMD at LS in total sample | 15q13, LOD = 3.67; 3p25, LOD = 3.06 | 28 | |
16p13, LOD = 1.94; Xq25, LOD = 2.95 | |||||
BMD at LS in postmenopausal women | 15q13, LOD = 2.49 | HERC2 | |||
BMD at LS in premenopausal women | 15q13, LOD = 1.52 | ||||
BMD at hip in total sample | Xp11.4, LOD = 1.95 | ||||
4126 Caucasians from 451 pedigrees | 410 | BMD at LS/TBLM in total sample | 7p22, LOD = 2.53; Xq25, LOD = 5.26 | TWIST, IL6 | 33 |
Xp22.3, LOD = 5.20; Xq13.3, LOD = 4.31 | |||||
Xp11.4-Xq11.1, LOD = 4.54 | |||||
BMD at hip/TBLM in female | Xq23-Xq24, LOD = 3.78; Xq27.1, LOD = 3.68 | ||||
BMD at LS/TBLM in female | 7q32, LOD = 2.67 | LEP | |||
15q13, LOD = 4.86; 7q32, LOD = 2.44 | GREM1 (15q13) | ||||
Xq13.3, LOD = 3.94; Xp22.3, LOD = 3.92 | |||||
BMD at hip/TBLM in male | Xq22.33, LOD = 3.92 | ||||
BMD at LS/TBLM in male | 7q21, LOD = 2.52 | ||||
13p11, LOD = 3.25; Xq11.1, LOD = 3.78 | |||||
(Continued) | |||||
Study subjects |
No. of markers |
Phenotype |
Results |
Candidate gene |
Ref. |
4126 Caucasians from 451 pedigrees | 410 | BMD at LS/BFM in total sample | 7p22-p21, LOD = 2.69 | IL6, RAC1 | 34 |
6q27, LOD = 2.42 | ESR1 | ||||
BMD at hip/BFM in total sample | 6q27, LOD = 2.30 | ||||
2q32, LOD = 2.29 | GDF8, STAT1 | ||||
BMD at wrist/BFM in total sample | 11q13, LOD = 2.64 | GAB2, FRA-1, LRP5 | |||
BMD at hip, LS/BFM in total sample | 6p21, LOD = 2.32 | TNF α, RUNX2, HLA-A | |||
BMD at LS/BFM in female | 6q27, LOD = 2.34 | ||||
BMD at wrist/BFM in female | 15q13, LOD = 3.32 | ||||
BMD at LS/BFM in male | 6p25-24, LOD = 3.15 | BMP6 | |||
BMD at hip/BFM in male | 13q12, LOD = 3.23 | KL | |||
7q21, LOD = 2.59 | CTR, SERPINE1, PON1 | ||||
2584 Caucasian females from 414 pedigrees | 410 | BMD at LS/AAM | 1q44, LOD = 4.28; 2q37, LOD = 3.39 | 36 | |
8q24, LOD = 4.59; 13q33, LOD = 3.31 | EXT1 (8q24) | ||||
15q13, LOD = 4.80; 22q13, LOD = 4.60 | |||||
BMD at FN/AAM | 8q11, LOD = 3.36; 22q13, LOD = 4.88 | ||||
BMD at UD/AAM | 15q24, LOD = 5.25; 22q13, LOD = 4.79 | ||||
4498 Caucasians from 451 pedigrees | 410 | BR/TBFM | 20q12, LOD = 3.23 | 249 | |
CSA/TBFM | 20p11, LOD = 2.47 | ||||
CT/TBFM | 6q27, LOD = 3.19 | ||||
W/TBFM | 20p12, LOD = 1.68 | ||||
Z/TBFM | 7q11, LOD = 2.47 | ||||
327 Mexican-Americans (aged 25–45 yr) from 32 extended pedigrees | 460 | ΔBMD at FNK BMD in total sample | 1q23, LOD = 3.6 | BGLAP, osteocalcin, IL6R, SLC39A1 | 250 |
ΔBMD at 33% ulna | 11p14-15, LOD = 2.5 | CALCA, CALCB, CALCP | |||
1346 Caucasians from 327 extended families of the Framingham Study | 636 | S_CSA / LLM | 12p12.3-12p13.2, LOD = 3.49 | 251 | |
NSA/LLM | 14q21.3-22.1, LOD = 3.77 | ||||
3782 Caucasian females aged 18–80 yr | 400 | Length of spine | 5q15-5q23.1, LOD = 3.00 | IRX | 31 |
Length of femur | 5q15-5q23.1, LOD = 2.19 | ||||
348 Caucasian dizygotic healthy female twin volunteers | 737 | Lumbar degenerative disc disease | Chr. 1 (285 cM), LOD = 6.05 | 252 | |
Chr. 5 (175 cM), LOD = 6.05 | |||||
Chr. 19 (80 cM), LOD = 4.06 | |||||
210 individuals from 39 families of predominantly British descent without mutations of SQSTM1 | 382 | Paget’s disease of bone | 10p13, LOD = 4.08 | 253 | |
405 Caucasian pairs and 110 American black pairs of brothers aged 18–61 yr | 402 | aBMD at LS | 7q34, LOD = 5.26 | 254 | |
aBMD at hip | 4q21, LOD = 4.14; 14q32, LOD = 4.19 | ESRβ (14q32) | |||
19p13, LOD = 5.77; 21q21, LOD = 4.63 | |||||
22q13, LOD = 4.71 |
Adapted from Y. Guo et al.: Expert Rev Endocrinol Metab 3:223–267, 2008 (3), with permission from Expert Reviews Ltd. Z, Section modulus; NN, narrowest section of the femoral neck; AFOS, Amish Family Osteoporosis Study; TR, trochanter; OF, osteoporotic fracture; IT, intertrochanteric; S, femoral shaft; NSA, neck-shaft angle; CSA, cross-sectional area; FNL, femoral neck length; PC, Principal component; aBMD, areal BMD; ABS, areal bone size; TBFM, total body fat mass; LLM, leg lean mass; ID, inner diameter; UD, ultradistal; W, sub-periosteal diameter; LOD, likelihood; Chr., chromosome; OD, outer diameter; BR, buckling ratio. The two traits for bivariate analysis are separated by a slash (/). Bivariate LOD scores listed here were converted into one degree of freedom, equivalent to univariate LOD score level.
Table 4.
Genotyping methods | Total markers | Significant markers | Discovery subjects | Replication subjects | Phenotype | P value | Candidate gene | Ref. |
---|---|---|---|---|---|---|---|---|
Infinium HumanHap300; HumanCNV370 SNP chip; Centaurus platform (Nanogen) | 301,019 SNPs with genotypic call rates ≥ 97%, HWE P ≥ 10−7 | rs9594759, rs9594738 | 5,861 Icelandic persons, 87% of whom are women | 4,165 other Icelandic persons, 74% of whom are women; 2,269 postmenopausal Danish women; 1,491 persons from the Australian Dubbo Osteoporosis Epidemiology Study cohort, 61% of whom are women | BMD at LS and hip, osteoporotic fracture | 1.2 × 10−7 to 2.0 × 10−21 | RANKL, OPG, ZBTB4, MHC, RANK, ESR1 | 39 |
rs6469804, rs6993813 | ||||||||
rs7524102, rs6696981 | ||||||||
rs3130340, rs3018362 | ||||||||
rs9479055, rs4870044 | ||||||||
rs1038304, rs6929137 | ||||||||
rs1999805 | ||||||||
rs2306033, rs7935346 | LRP4 | |||||||
rs11898505 | SPTBN1 | |||||||
Infinium assay; HumanHap 550 v3.0 array; Taqman system | 314,075 SNPs with genotypic call rates ≥ 90%, HWE P ≥ 0.0001, and MAF ≥ 1% | rs4355801 | 2,094 women from the Twins UK cohort | 6,463 people from three other cohorts in Western Europe (4081 from Rotterdam cohort, 1,692 from Twins UK replication cohort, and 690 from Chingford cohort) | BMD at LS and FN, osteoporosis | <5 × 10−8 | TNFRSF11B | 40 |
rs3736228 | LRP5 | |||||||
IlluminaHap300K | 186 SNPs | rs1513670, rs7220711 | 6,865 Icelanders, 5,934 women, and 931 men | 3,015 Icelanders, 3,884 Danish postmenopausal women, and 1,491 from the Australian cohort | BMD at hip or spine | 1.8 × 10−9 to 1.3 × 10−7 | SOST | 42 |
rs1107748 | ||||||||
rs2010281, rs10876432 | MARK3, SP7 | |||||||
rs3018362 | TNFRSF11A | |||||||
Affymetrix Human Mapping 500K Array Set | 727 CNV regions | CNV 4q13.2 | 700 elderly Chinese individuals including 350 cases with homogeneous hip osteoporotic fractures and 350 controls | Chinese, 399 cases with hip osteoporotic fracture and 400 controls; 689 Chinese unrelated subjects, and 1,000 white unrelated subjects; 236 young Chinese males | Osteoporotic fracture, hip BMD and FN bone geometry, concentrations of serum testosterone and estradiol | 2.0 × 10−4 | UGT2B17 | 47 |
Affymetrix Mapping 250 k Nsp and Affymetrix Mapping 250 k Sty arrays; Illumina Infinium assay; Affymetrix Human Mapping 500 K array set | 379,319 SNPs with genotypic call rates ≥ 95%, HWE P ≥ 0.001 and MAF ≥ 1% | rs7595412 | 1,000 homogeneous unrelated Caucasian subjects, including 501 females and 499 males | 1,216 women of Caucasian European ancestry from the UK; a Chinese sample containing 403 female subjects including 226 with low trauma osteoporotic hip fracture, and 177 controls | Hip bone size | 3.72 × 10−7 | PLCL1 | 51 |
rs892515 | 8.62 × 10−3 | |||||||
rs9789480 | 2.44 × 10−3 | |||||||
rs3771362 | 7.66 × 10−3 | |||||||
(Continued) | ||||||||
Genotyping methods |
Total markers |
Significant markers |
Discovery subjects |
Replication subjects |
Phenotype |
P value |
Candidate gene |
Ref. |
Affymetrix Gene Chip Human Mapping 500K Array Set; Affymetrix 50K supplemental array | 379,319 SNPs with genotypic call rates ≥ 95%, HWE P ≥ 0.001 and MAF ≥ 1% | rs11864477, rs11860781, rs16945612, rs11859065 | 1,000 homogeneous unrelated Caucasian subjects, including 501 females and 499 males | 593 white U.S. families (1,972 subjects), a Chinese hip fracture sample (350 cases, 350 controls), a Chinese BMD sample (2,955 subjects), and a Tobago cohort of African ancestry (908 males) | BMD and hip fracture | 2.56 × 10−5 to 2.13 × 10−8 (meta-analyses) | ADAMTS18 | 46 |
rs17131547 | TGFBR3 | |||||||
Illumina HumanHap317K SNP chip | 317,504 SNPs with missing genotypes ≤5%, HWE P ≥ 10−7 and MAF ≥ 1% | rs4759021 | 1,518 children from the Avon Longitudinal Study of Parents and Children | 3,692 children from the Avon Longitudinal Study of Parents and Children | BMD | 5.8 × 10−4 | Osterix | 255 |
BMC | 1.7 × 10−3 | |||||||
Bone area | 3.8 × 10−3 | |||||||
Affymetrix Genome-Wide Human SNP array 5.0 | 352,228 SNPs with missing genotypes ≤5%, HWE P ≥ 10−6, and MAF ≥ 1% | rs7776725 | 8,842 Korea samples from population-based cohorts | 7,861 independent Korean samples | BMD at radius, tibia, and heel | 1.0 × 10−11 to 1.6 × 10−6 | FAM3C | 41 |
rs1721400 | 1.4 × 10−7 to 2.2 × 10−3 | SFRP4 | ||||||
Illumina HumanHap300; HumanHapCNV370 | 303,120 SNPs with missing genotypes ≤5%, HWE P ≥ 0.001, and MAF ≥ 1% | rs219780 | 3,773 kidney stone cases and 42,510 controls from Iceland and The Netherlands | 1,520 Icelandic kidney stone cases and 4,726 controls, 746 kidney stone cases and 3,751 controls from The Netherlands | BMD at hip and spine | 0.00039; 0.0077 | CLDN14 | 43 |
Affymetrix Gene Chip Human Mapping 500K Array Set | 342,854 SNPs with HWE P ≥ 0.0001 and MAF ≥ 5% | rs9630182, rs2036417, rs7125774; rs8057551, rs8061992, rs7199138 | 983 unrelated Caucasian subjects | A family-based sample of 2,557 Caucasian subjects | BMD at FN | 3.98 × 10−7 to 6.74 × 10−3 | PTH, IL21R | 256 |
Meta-analysis | 36,016 SNPs in 150 candidate genes | 241 SNPs | BMD data were obtained from 19,195 participants from 5 populations of European origin; data on fractures were obtained from a prospective cohort (n = 5,974) from The Netherlands | BMD at LS and FN fracture risk | P < 2.39 × 10−6 | ESR1, LRP4, ITGA1, LRP5, SOST, SPP1, TNFRSF11A, TNFRSF11B, TNFSF11 | 45 | |
(Continued) | ||||||||
Genotyping methods |
Total markers |
Significant markers |
Discovery subjects |
Replication subjects |
Phenotype |
P value |
Candidate gene |
Ref. |
Meta-analysis | 2,543,686 SNPs | 467 SNPs | 19,195 participants from five populations of European origin, e.g., the Rotterdam Study (n = 4,987), Erasmus Rucphen Family Study (n = 1,228), Twins UK Study (n = 2,734), deCODE Genetics Study (n = 6,743), and Framingham Osteoporosis Study (n = 3,503) | BMD at LS and FN | P < 5 × 10−8 | GPR177, MEPE, SPTBN1, CTNNB1, MEF2C, ZBTB40, STARD3NL, FLJ42280, LRP4, ARHGAP1, F2, DCDC5, SOX6, ESR1, FOXL1, HDAC5, CRHR1, TNFRSF11B LRP5, SP7, TNFSF11, TNFRSF11A | 44 |
BMC, Bone mineral content; HWE, Hardy-Weinberg equilibrium; MAF, minor allele frequency.
Table 5.
Candidate gene | Protein | Manipulation | Phenotype | Ref. |
---|---|---|---|---|
Adipoq | Adiponectin | Overexpression | Decreased BMC at femur and lumbar vertebra | 257 |
AR | Androgen receptor | Knockout | Decreased trabecular bone mass in male mice | 258 |
AR and ER-α double-knockout | Additionally reduced cortical bone and muscle mass | 258 | ||
Agtr2 | Angiotensin II receptor, type 2 | Treatment with AT2 receptor blocker | Enhanced levels of bone mass | 259 |
BSP | Bone sialoprotein | Overexpression | Osteopenia and mild dwarfism | 260 |
CB1 | Type 1 cannabinoid receptor | CB1−/− mice | Increased peak bone mass due to reduced bone resorption, age-related osteoporosis with reduced bone formation, and accumulation of adipocytes in the bone marrow space | 261 |
CCR2 | C-C chemokine receptor-2 | Knockout | High bone mass | 262 |
CD200 | CD 200 molecule | CD200−/− mice, produced by homologous recombination | Fewer osteoclasts and more bone | 263 |
Cftr | Cystic fibrosis transmembrane conductance regulator | Knockout | Increased trabecular bone volume | 264 |
COL1A2 | Collagen, type I, α 2 | Knock-in | Reduced body mass, areal BMD, and bone strength | 265 |
Col6a1 | Collagen, type VI, α 1 | Knockout | Accelerated development of OA joint degeneration; delayed secondary ossification and reduced BMD | 266 |
Col9a1 | Short collagen IX | Col9a1−/− and col9a1+/− mice | Trabecular bone loss in young adult female col9a1 (−/−) and col9a1 (+/−) mice; trabecular bone architecture deterioration in both male and female heterozygous col9a1 (+/−) mice while aging | 267 |
Cthrc1 | Collagen triple helix repeat containing-1 | Knockout and overexpression | Low bone mass in Cthrc1-null mice and high bone mass in Cthrc1 transgenic mice | 55 |
Cxcr4 | Chemokine (C-X-C motif) receptor 4 | Overexpression | Increased cell trafficking to bone in ovariectomy mice | 268 |
CYLD | Deubiquitinating enzyme | Knockout | Osteoporosis | 269 |
CYP191A1 | Aromatase | Osteoblast-specific overexpress aromatase | Increased total body BMD, trabecular BMD, cortical BMD, and cortical thickness associated with elevated osteoprotegerin mRNA levels and reduced number of osteoclasts | 270 |
DKK1 | Dickkopf-1 | Mice with hypomorphic Dkk1d (doubleridge) alleles, express low amounts of Dkk1 | Increased trabecular and cortical bone mass | 271 |
Dlx5 | Distal-less homeobox 5 | Dlx5+/− mice | Lower BMD and reduction in cortical thickness of femoral midshafts | 272 |
ER-α/ER-β | Estrogen receptor | Knockout/knockdown | ERKs are not activated by stretching in osteocytic and osteoblastic cells | 273 |
Frzb | Frizzled-related protein | Knockout | Increased cartilage proteoglycan loss | 274 |
GIP | Glucose-dependent insulinotropic peptide | Overexpression | Increased osteoblastic activity | 56 |
Gpnmb | Osteoactivin | Mutation leading to the generation of a truncated osteoactivin protein | Reduced alkaline phosphatase activity and calcium deposition | 275 |
GPR30 | G protein-coupled receptor 30 | GPR30−/− mice | Reduced bone growth | 276 |
Gpr48 | G-protein-coupled receptor 48 | Gpr48−/− mice | Dramatic delay in osteoblast differentiation and mineralization | 277 |
HIF-1α | Hypoxia inducible factor-1 α | Knockout | Reduced bone formation ability of osteoblasts | 278 |
Knockout | Reduced values of bone histomorphometry and BMD | 279 | ||
HIP/RPL29 | Ribosomal modulator of protein synthesis rate | HIP/RPL29-deficient mice | Increased bone fragility | 280 |
HSD17B2TG | Hydroxysteroid (17β ) dehydrogenase 2 | Express human hydroxysteroid (17β) dehydrogenase 2 | Decreased bone formation rate at prepubertal age in male mice | 281 |
(Continued) | ||||
Candidate gene |
Protein |
Manipulation |
Phenotype |
Ref. |
IGF-I | Insulin-like growth factor 1 | Knockout | Lower femur BMD | 282 |
Knockout | Reduced body length, areal BMD, and BMC | 283 | ||
Crossbred IGF-I transgenic mice with homozygous weaver mice | Improved BMD and BMC | 284 | ||
IL-7 | Interleukin 7 | Overexpression | Age-related loss of trabecular bone in both axial and long bones | 285 |
Irf8 | Interferon regulatory factor-8 | Deficient in Irf8 | Osteoporosis | 286 |
JunD | An activator protein-1 component | Knockout | Increased bone volume and sustained high bone mass even after estrogen depletion | 287 |
Mecp2 | Methyl CpG binding protein 2 | Knockout | Decreased osteoblast activity | 288 |
Nfatc1 | Nuclear factor of activated T cells c1 | Knockout | Osteopetrosis and inhibition of osteoclastogenesis | 289 |
Nmp4 | Nuclear matrix protein 4 | Knockout | Greater PTH-induced acquisition of femoral trabecular bone | 290 |
NMU | Neuromedin U | Knockout | Increased bone mass | 291 |
OT | Oxytocin | Deletion of OT or the OT receptor (Oxtr) in mice | Osteoporosis | 292 |
PAI-1 | Plasminogen activator inhibitor-1 | Overexpression | Increased mineralization and biomechanical properties in 32-wk females | 293 |
PAPP-A | Pregnancy-associated plasma protein-A | Knockout | Insufficiency in mass, density, architecture, and strength | 294 |
PECAM-1 | Platelet/endothelial cell adhesion molecule 1 | Knockout | Reduced trabecular bone volume and number of trabeculae in femoral and tibial long bones | 295 |
PPAR-γ | Peroxisome proliferator-activated receptor-γ | PPAR-γ is deleted in osteoclasts but not in osteoblasts | Increased bone mass, reduced medullary cavity space, and extramedullary hematopoiesis in the spleen | 296 |
PTHr1 | Parathyroid hormone receptor 1 | Expressing a constitutively active PTH receptor | Increased bone mass and elevated bone remodeling | 297 |
Rac1 | Rho-GTPase | Nf1+/−, Rac1−/− mice | Normalized Erk activation compared with Nf1+/− osteoclasts | 298 |
Rac2 | A member of the Rho family of small GTPases | Knockout | Increased bone mass and anabolic response to PTH | 299 |
RANKL | Receptor activator of nuclear factor-κ B ligand | Deletion of the distal control region, located 76 kb upstream from the transcription start site | Increased bone mass and strength | 300 |
Runx2 | Runt related transcription factor 2 | Runx2+/− mice | Decreased BMD and trabecular bone volume and delayed bone formation | 301 |
Sca-1 | Stem cell antigen 1 | Knockout | Decreased BMD at femoral and whole body | 302 |
Sfrp1 | Secreted frizzled-related protein 1 | Knockout | Reduction of the cartilage callous, increased intramembranous bone formation | 303 |
Sfrp4 | Secreted frizzled-related protein 4 | Overexpressing in osteoblasts | Reduction of trabecular bone mass | 304 |
SOST | Sclerostin | Knockout | High bone mass phenotype characterized by marked increases in BMD, bone volume, bone formation, and bone strength | 59 |
Short treatment with an antibody to sclerostin | Halted bone loss | 305 | ||
Sp7 | Osterix | Inactivated in all osteoblasts | Decreased BMD and bone-forming rate in lumbar vertebra, thinner cortical bone, and more porous with reduced bone length | 306 |
Sparc | Osteonectin | Knockout | Low-turnover osteopenia | 307 |
Spp1 | Osteopontin | Lacking expression of the osteopontin | Lower resorptive capacity of osteoclast | 308 |
SRC-1 | Steroid receptor coactivator 1 | Knockout | Decrease in trabecular volumetric BMD and impaired skeletal response to estrogen in female mice | 57 |
SRC-2 | Steroid receptor coactivator 2 | Knockout | Increased bone mass, decrease in bone marrow adipocytes | 309 |
(Continued) | ||||
Candidate gene |
Protein |
Manipulation |
Phenotype |
Ref. |
Spp24 | Secreted phosphoprotein 24 kDa | Constitutive expression of fl bovine spp24 (1-203) | Inhibition of ectopic bone formation in male mice and adverse affects on BMD and histological parameters related to bone mass and formation in female mice | 310 |
Terc | Telomerase | Knockout | Osteoporosis | 311 |
TIMP-1 | Matrix metalloproteinases | Overexpression | Decrease in bone loss induced by estrogen deficiency | 312 |
TNFr | Tumor necrosis factor receptor | Knockout | Reduced adverse effects of magnesium deficiency on bone | 313 |
TNFR2 | Tumor necrosis factor type 2 receptor | Knockout | Decrease by lipopolysaccharide on the BMD of tibiae, femurs, and lumber vertebrae | 314 |
TRα1 | Thyroid hormone receptor α1 | TRα 1(+/m)β(+/−) mice, express a mutant thyroid hormone receptor α1 with dominant-negative properties due to reduced ligand-binding affinity | Osteosclerosis in adults and delayed ossification in juveniles | 315 |
TRPV4 | Transient receptor potential vanilloid 4 | Knockout | Suppressed unloading-induced bone loss | 316 |
uPAR | Urokinase receptor | Knockout | Increased BMD and osteogenic potential of osteoblasts; decreased osteoclast formation; altered cytoskeletal reorganization in mature osteoclasts | 317 |
VDR | Vitamin D receptor | VDR−/− mice | Shorter latency to fall from the rotarod, smaller fall angle in the tilting box test, and aberrant poor swimming | 318 |
Wnt10b | Wnt10b protein | Transgenic mice, in which mouse Wnt10b is expressed from the human osteocalcin promoter | Increased mandibular bone and impaired eruption of incisors during postnatal development, higher BMD, bone volume fraction, and trabecular number at femoral distal metaphyses | 319 |
Zmpste24 | Zinc metallopeptidase, STE24 homolog | Zmpste24-null progeroid mice | Age-related bone loss including lower osteoblast and osteocyte numbers and higher levels of marrow adipogenesis | 320 |
5-HT(2B)R | Monoamine serotonin (2B) receptor | Knockout | Osteopenia that worsens with age in female mice | 321 |
Adapted from Y. Guo et al.: Expert Rev Endocrinol Metab 3:223–267, 2008 (3), with permission from Expert Reviews Ltd. BMC, Bone mineral content.
Table 6.
Subject or sample categorization | Cell type or tissues | Treatments | Differentially expressed genes (n) | Important differentially expressed genes | Up-regulated genes (n) | Important up-regulated genes | Down-regulated genes (n) | Important down-regulated genes | Ref. |
---|---|---|---|---|---|---|---|---|---|
Human trabecular bone | Trabecular bone from the IT region of the proximal femur from individuals with OP, OA, and control | Comparing the gene expression profiles of OA, control, and OP bone samples to identify altered gene expression in OP fracture bone | 150 | TREM2, ANXA2, CD14, CCR1, ADAM9, ST14, CTSB, FST, PRRX1, IL10, CTGF, KLF10, COL4A1, CCL2, SPP1, ANGPTL4, PEA15, MARCO, AEBP1 | SPARC, DOK4, SLC14A1, ADM, TAZ | 82 | |||
Trabecular bone from the IT region of the proximal femur from individuals with OP, OA, and control | Comparing the gene expression profiles of OA, control and OP bone samples to identify altered gene expression in OA bone | 150 | Targets component or modulating genes of either the WNT signaling pathway or the TGF-β/BMP signaling pathway | MMP25, S100A4, SMAD3, WNT5B | IBSP, TWIST1, TIMP4, ADAMTS4, ADM, GADD45B, MEPE, COL4A1 | 81 | |||
Human MG63 cells | MG63 cell lines stably expressing pcDNA3-Flag-VP16C-SXR or the empty vector Flag-tagged pcDNA3 | Vehicle (0.1% ethanol) or MK-4 (10 μm) treatment | 85 | 85 | GDF15, STC2, TRIB3, GDF8,MGC4504, ASNS, SLC7A11, COL15A1, TNFAIP6, DDIT4, PCK2, FGF2, PSAT1, HMOX1 | 322 | |||
Human MG63 cells | Bio-Oss and PerioGlas | NOG, CALCR, COL11A2, COMP, TNFRSF11B, CASR, HOXA13, DLX5 | 59 | ||||||
Human MG63 cells | Titanium | FGFR3, IGF1, MSX1, GDF10, CALCA, BMP1, BMP7, COL1A1, PHEX, CASR, COL11A1 | 60 | ||||||
MG63 osteoblast cells | 1.035 MHz pulsed ultrasound with three different acoustic pressures | 377 | Genes involved with cellular membranes, regulation of transcription | LRP5, BMP1, COL1A1, CD151, PTRF, HOXB8 | EZH2, BMP2K | 323 | |||
Human MG63 cells | PerioGlas and P-15 | NOG, COL11A2, IGF1, COL11A1, CLO2A1, CHRD, HOXD13, ALPL, BMP1, COMP, CALCR | GDF10, ANXA2, | 324 | |||||
(Continued) | |||||||||
Subject or sample categorization |
Cell type or tissues |
Treatments |
Differentially expressed genes (n) |
Important differentially expressed genes |
Up-regulated genes (n) |
Important up-regulated genes |
Down-regulated genes (n) |
Important down-regulated genes |
Ref. |
Human MG63 cells | Zirconium oxide | EGR2, FKHL18, SEP1, PRDM2, DLX2, FGF5, SP110, TOP3A, PKD2, PTPRG, PTPN6, KCNK4, LEPR, NR4A1, PDCD8, DUSP16, CD79B | GABRA6, RORB, GRCA, CLTCL1, PCBP4, WDR33, CDKN3, F11R, AAMP, VPS18, SEC23B, ILF2 | 325 | |||||
Human MG63 cells | Mixed a type I collagen solution with human osteoblast-like MG-63 cells and intermittent compression, gels loaded in wells without mechanical stimulation were used as controls | 43 | 30 | COX-2, MMP-3, ODC | 13 | MIL-1RAcP | 326 | ||
Human MG63 cells | PerioGlas | Genes involved in bone formation and skeletal and cartilage development | ZNF547 | NOG, EN1, CHRD, GHRHR, COMP | 58 | ||||
Human MG63 cells | High magneto-gravitational environment | Adducing 3, WIPF1, coactosin-like 1, filamin A, SORBS3, CDC42BPB, talin 1, paxillin, WASF2, tropomodulin 3, SPTBN1, supervillin, plectin 1 | 327 | ||||||
Human MG63 cells | Cultured with porous polyethylene | CHRD, EN1, NOG, ADAMTS4, GHRHR, OSTF1, MGP, PTH, LECT1 | 63 | ||||||
Human MG63 cells | Anatase coating | PRDX1, COL9A2, ADAMTS4, SHOX ALPL, AMBN, TUFT1 | PHEX, FBN1, IGFBP4, CALCA PTH, TFIP11 | 61 | |||||
(Continued) | |||||||||
Subject or sample categorization |
Cell type or tissues |
Treatments |
Differentially expressed genes (n) |
Important differentially expressed genes |
Up-regulated genes (n) |
Important up-regulated genes |
Down-regulated genes (n) |
Important down-regulated genes |
Ref. |
Human MG63 cells | Peptide-15 | TFIP11 | NOG, HOXD13, AEBP1, SHOX, EN1, COMP, IGF1, MATN1, SUFU | 328 | |||||
Human MG63 cells | Zirconium oxide | NOG, SHOX, IGF1, BMP1, FGFR1 | TRAPPC2 | COMP, CHRD, BMP1, SHOX, IGF1, FGFR1, GHRHR, PMF1, AHSG, AMBN | 65 | ||||
Human MG63 cells | Titanium and ZDCs | BMP7 | NOG, FGFR1, COMP, MSX1 | 62 | |||||
Human MG63 cells | Demineralized freeze-dried bone allograft | Genes involved in cell cycle regulation, immunity, vesicular transport, bone remodeling, production of cytoskeletal elements | 67 | ||||||
Human MG63 cells | Calcium sulfate | BMP1, BMP7, PTH, FGFR1, CALCA, SHOX, EXT2 | MSX1, EBP, EN1, INHBA, CMKLR1, COMP, NOG | 64 | |||||
Human MG63 cells | Anatase coating | ERBB3, TSPAN-2, CRK, HYAL3, WDR10, MYOC, ANK3, LILRB4 | CCR2, MYH11, CLTB, RTN4, MECP2, GLI2 | 329 | |||||
Human MG63 cells | Danggui Buxue Tang | 883 | CCL-2, CCL-7, CCL-8, galectin-9 | 330 | |||||
Human MG63 cell and sub-line model | Cisplatin, doxorubicin, and etoposide | ABCG2, ADD3, NMT2, WNTSa, PTN | 331 | ||||||
Human osteoblast | Human osteoblast | Osteoblasts cocultured with U-266 myeloma | 38 | Genes involved in bone metabolism and other cell functions | 7 | E4BP4, UNC5C, VCAM1, UGP2, PRKAR2A, Follistatin precursor, enzyme similar to tissue plasminogen activator | 31 | BMPs, Runx2, COX-2, Coronin, Osterix, osteocalcin, MTRR, PMS2L9, NRIF3, CYP3A43, LIC2, Type XVIII collagen | 70 |
(Continued) | |||||||||
Subject or sample categorization |
Cell type or tissues |
Treatments |
Differentially expressed genes (n) |
Important differentially expressed genes |
Up-regulated genes (n) |
Important up-regulated genes |
Down-regulated genes (n) |
Important down-regulated genes |
Ref. |
Human osteoblasts | Cyclosporin A | Wnt signaling molecules and the cytoskeletal and focal adhesion cascade | 332 | ||||||
Human osteoblasts isolated from trabecular bone tissue of FN and proximal femur | Comparing the gene expression profiles of osteoblasts from osteoporotic vs. nonosteoporotic bone tissues | 1606 | 144 | IBSP, CXCL2 | 208 | PTN, COL15A1 | 333 | ||
Human osteoblasts isolated from cancellous bone fragments | Shock wave | 94 | PTHrP, prostaglandin E2-receptor EP3, chordin, BMP-2 inducible kinase | Matrilin 3, cartilage oligomeric matrix protein | 334 | ||||
Human osteoblasts derived from alveolar bone | TAK-778 | ALP, osteocalcin, Msh homeobox 2, RANKL adhesion molecule 1 | 335 | ||||||
ODHPSCs and normal osteoblasts derived from human dental pulpar and alveolar bone fragments | Identify genes that are differently regulated in ODHPSC in comparison to normal osteoblasts | Genes involved in cell differentiation, developmental maturation, cell adhesion, and production of cytoskeleton elements | SYNE1, LAMA1, MMP2, PPP1R9B NEO1, CDH1, AGT, MAP4K4, CDC2L5, GPC3, FYN, GNE, ATP2A2, TTN | MARK2, BAIAP2, EPB42, SDK1, PGM5, LRRN5, CSF3R, CNTN2, CXCL12, MSN, DNM1, ACTA2, SNTA1, MMP24, COL6A1, PLOD1, ARMCX3, PRDM2 | 336 | ||||
Human MC3T3-E1 preosteoblastic cells | Hydroxyapatite | 11 | SOX9, GUGA1B, HAPLN 2, RRAGA | 6 | Calpain 9, PI3KS, AGTR 2, OGFR, TSLRP | 337 | |||
Human nonunion osteoblasts | Evaluated global gene expression in human osteoblasts and human nonunion osteoblasts | 281 | 200 | HBEGF, VEGFB | 81 | IGF-2, TGF-β2, FGF-1, FGF-R2, BMP-4, PDGF, WISP2, WISP3 | 338 | ||
Primary cultures of osteoblastic cells differentiated from the human bone marrow | Polymethylmethacrylate, hydroxyapatite, bioactive glass 45S5, titanium and stainless steel | IGF binding protein 4 | BMP5, osteocalcin | 66 | |||||
(Continued) | |||||||||
Subject or sample categorization |
Cell type or tissues |
Treatments |
Differentially expressed genes (n) |
Important differentially expressed genes |
Up-regulated genes (n) |
Important up-regulated genes |
Down-regulated genes (n) |
Important down-regulated genes |
Ref. |
Osteoblasts differentiated from human MSCs | siRNAs generated from PDE7 and PDE8 PCR products | 473 | β-catenin, osteocalcin, caspase-8, CREB-5, BMP-5 | 156 | BMP-1, osteoflycin, 1,25-dihydroxyvitamin D3 receptor | 80 | |||
Tumor chemonaive osteoblastic populations | Comparing the expression profiles of osteoblasts from osteosarcoma patients vs. controls | EBF2 | 339 | ||||||
Human MSCs | Human MSCs | Treated with lactate for different time periods | Genes involved in wound healing | Interleukin-6, heat shock protein, hypoxia-inducible factor-1 α | Superoxide dismutase 2, BCL2- associated X protein | 340 | |||
Human MSCs | Two osteogenic nanoscale topographies (pitted surface vs. raised islands) are compared with cells treated with dexamethasone | Integrin αM, TGFR1, MMP8, collagens, ALPL, integrinα1 | 341 | ||||||
Human MSCs | Cell culture | Osteogenic and adipogenic differentiation capacity of hMSCs in late stage of the culture | 75 | ||||||
Human MSCs | Growth medium and osteoblastogenesis induction medium | Interferon γ-inducible genes | ALP, OCN, TGFBRII, PDGFR, BSPII, OPN, LRP5, Runx2 | 342 | |||||
Human MSCs | Extracellular matrix substrates | MCAM, angiopoietin-1 | 343 | ||||||
Human MSCs | Dexamethasone and 1,25-(OH)2D3 | 2095 | 961 | c-Myc | 344 | ||||
Human MSCs and ASCs | Osteogenic differentiation medium | 41 | Extracellular matrix-related genes | COL1A2, COL3A1, COL4A1, COL5A2, COL15A1, PHR, COL2A1, COL6A1, COL9A1, INT-β 3, INT-β 1, osteopontin, osteonectin | 345 | ||||
(Continued) | |||||||||
Subject or sample categorization |
Cell type or tissues |
Treatments |
Differentially expressed genes (n) |
Important differentially expressed genes |
Up-regulated genes (n) |
Important up-regulated genes |
Down-regulated genes (n) |
Important down-regulated genes |
Ref. |
Human MSCs and osteoblasts | Comparing gene expression in osteoblasts vs. MSCs from the same donor | 170 | 120 | COL15A1, CDKNIA, DST, GADD45A, MACF1, COMP, COL8A2, STAT1, TNFRSF10B | MK167, PARP3, RAD54L, ECM1, NRP1, GSTP1 | 346 | |||
Human unrestricted somatic stem cells | Human unrestricted somatic stem cells | Has-mir-135b | IBSP, osterix | 347 | |||||
Human monocytes | Monocytes from premenopausal women with extremely low vs. high PBMD | 49 | GBP1, STAT1, CXCL10 | 71 | |||||
Monocytes from premenopausal women with extremely low vs. high BMD | 13 | 6 | STAT1, IFI44L, CXCL10, IFI44, GPB1, GPB2 | 348 | |||||
Human hFOB1.19 cells | Immortalized human osteoblast cells hFOB1.19 | Two-stage cell transformation using MNNG and TPA treatments | 10 | H19, MKRN3, NDN, CDKN1C, PHLDA2, MEST, CD81, GRB10, SLC22A18, SLC22A3 | 6 | H19, PHLDA2, SLC22A18 | 4 | CD18 | 349 |
Human osteoblastic hFOB1.19 cells | Oral administration of collagen tripeptide | 169 | Osterix | 68 | |||||
Human STRO-1+ skeletal stem cell | STRO-1+ cell from bone marrow of hip | Cells cultured in basal, osteogenic, chondrogenic and adipogenic lineage-specifying culture conditions | 38 | 16 | COL111A1, FKBP5, LOC388610, MT1E, MT1F, MT1X, LBP, MT1H/MT1P2, XTLT1, C1Oorf10, CXCL6, NPY2R, C22orf16, PTGFR, FAM89A, STC2 | 22 | GATA6, LDB2, GREM1,QSOX1, CLDN11, KCTD12, RBMS3, ZFP36L1, RBM39, ROCK1 | 350 | |
Human HeLa and NIH3T3 cells | Human HeLa and NIH3T3 cells | Transfected with 5–10 μg siRNA vector | 2 | OPG, VEGF-A | 351 | ||||
Human myeloma cell lines | KMS-26 cell line | Enzastaurin | 94 | 62 | CXCL12, CXCR4, CTSB, TRAF5, BCL2L1, IGF1, GADD45A, CDC20 | 32 | MYC, IRF4, MX11 | 352 | |
(Continued) | |||||||||
Subject or sample categorization |
Cell type or tissues |
Treatments |
Differentially expressed genes (n) |
Important differentially expressed genes |
Up-regulated genes (n) |
Important up-regulated genes |
Down-regulated genes (n) |
Important down-regulated genes |
Ref. |
Human MNCs | MNCs from bone marrow and umbilical cord blood | Chondrogenic differentiation | Receptors for fibronectin, vitronectin, osteopontin, and collagens | Ilk, CD47, Integrin a5b1, components of the vitronectin/osteopontin-receptors (avb5), collagens (a1b1, a2b1, a3b1) | 353 | ||||
Human chondrogenic cell line OUMS27 | Human OUMS27 cells | Compared mRNA levels in OUMS27 cells adenovirally transfected with KLF5 and the control empty vector | MMMp9 | 354 | |||||
Human aortic valve interstitial cells | Human aortic valve interstitial cells | LPS stimulation or BGP treatment; media alone was used as positive control | BMP2, PDGFA, FGF2, MMP1 | 355 | |||||
Human OA cases and controls | Serum samples | Human OA cases compared to controls | 16 | MMP-7, IL-15, sVAP-1 | PAI-1, ICAM-1 | 121 | |||
Bone samples | Patients suffering from hip OA compared to younger donors undergoing spinal arthrodesis | 83 | CCL2, FOS, OMD, PRSS11, DVL2, AKT1, CA2, BMP6, MMP2, TGFBR3, FLT1, TNFRS11B, BMP1 | 96 | |||||
Human cartilage | Chondrocytes from human knee cartilage | IL-1β | CXCL1, CXCL2, CXCL3, CXCL5, CXCL6, CXCL8, CCL2, CCL3, CCL4, CCL5, CCL8, CCL20, CCL3L1, CX3CL1, LIF, IL-6, GCSF3, ELAM1, MMP-13, BMP-2 | COL2A1, aggrecan | 356 | ||||
Human OA cartilage | Notch signaling was blocked by DAPT treatment | IGFiBP7, TNFSF15, IGF2, TNFRSF10c, FGFR2, CALCR1 | BMP2, IL8 | 95 | |||||
Human bone tissue | Human femoral bone tissue from non-osteoporosis controls and cases with osteoporosis | IGSF4, FABP3, FABP4, FKBP2, TIMP2, TRIB, TMSB4X | 357 | ||||||
(Continued) | |||||||||
Subject or sample categorization |
Cell type or tissues |
Treatments |
Differentially expressed genes (n) |
Important differentially expressed genes |
Up-regulated genes (n) |
Important up-regulated genes |
Down-regulated genes (n) |
Important down-regulated genes |
Ref. |
Human OPLL cells and non-OPLL cells | Human OPLL cells and non-OPLL cells | Osteogenic induction and siRNA-mediated knockdown of Runx2 | 47 | 24 | Stathmin-like 3, Sestrin 2 | 23 | COMP, CTGF, angiopoietin-1 | 93 | |
Human RBM cell line | Human RBM cell line | Compared gene expression between two human RBM tissues and patient-matched primary RCC tissues | MIP-1δ | 74 | |||||
Human dental pulp cells and human MSCs | Human dental pulp cells and human MSCs | Inorganic polyphosphate | MMP1, OPN, OC, osteoprotegerin | 79 | |||||
Human periodontal ligament cells | Human periodontal ligament cells | Static tensional forces for 2 h | 2856 | IL-8, MMP2, PDGF-A | 2574 | ALP, COL1A2 | 358 | ||
Human osteosarcoma cell line Saos-2 | Human RANK-positive Saos-2 osteosarcoma cells | RANKL | 69 | 21 | ROCK1, EMA3A | 48 | GDF15 | 89 | |
Human sarcoma cells | Human sarcoma cells from osteosarcoma patients | Comparing gene expression profiles of sarcoma cells vs. human MSCs | ROR2 | 359 | |||||
Human MDNSCs | Human adult MDNSCs | Investigated the expression profile of cancer-related genes in MDNSCs by comparing with that in fresh normal human adult bone marrow depleted of red blood cells | 63 | MYC, MMP2, Notch2, STC1, ITGA3, Wnt1, STAT5b, RhoC | 360 | ||||
Human chondrocytes | Human chondrocytes in osteoarthritic cartilage | Compared gene expression profiles of the divided 3 zones of the cartilage | 198 | 94 | |||||
Human cultured discs and cells | Human discs and anulus cells cultured in 3-dimensional | IGFBP-2, IGFBP-4, IGFBP-5 | 361 | ||||||
Mouse RAW 264.7 cell line | Mouse RAW264.7 cell line | RANKL | MMP-9, TRAP, cathepsin K, MST1R, integrin b3, NFATc1, calcitonin receptor | CD14 antigen, toll-like receptor-6, TNF, STAT-1, Fc receptor 11b, TNF receptor 1B | 362 | ||||
(Continued) | |||||||||
Subject or sample categorization |
Cell type or tissues |
Treatments |
Differentially expressed genes (n) |
Important differentially expressed genes |
Up-regulated genes (n) |
Important up-regulated genes |
Down-regulated genes (n) |
Important down-regulated genes |
Ref. |
Mouse RAW 264.7 cells | Mouse RAW 264.7 mature osteoclasts | SPHK1, Wnt10b, BMP6 | Sclerostin | 363 | |||||
Mouse RAW 264.7 cell and BMC | Mouse RAW 264.7 cell and BMC | RANKL | NHA-oc/NHA2 | 73 | |||||
Mouse MC3T3 E1 cells | Mouse osteoblasts MC3T3 E1 cells, clone 4 | Three dishes for 1-h thapsigargin treatment at 10 nm and the other three dishes as control | ATF4, eIF2α-p, FOS, FGF-9, BMP-2 | 364 | |||||
Mouse MC3T3-E1 preosteoblasts | Histone deacetylase inhibitors, such as trichostatin A, MS-275, or valproic acid | Slc9a3r1, sorbitol dehydrogenase 1, glutathione S-transferase α 4 | Proteasome subunit, β type 10, adaptor-related protein complex AP-4 σ 1 | 365 | |||||
Mouse osteoblastic cell line MC3T3-E1 subclone 4 | Mechanical straining | 674 | Fos, Ptgs2, Rgs2, Pthlp | 366 | |||||
Mouse bone marrow cells | Mouse osteoclast progenitors | IL-6 and RANKL | Genes related to MAPK phosphatases and the ubiquitin pathway | MKP 1, MKP 7 | Senp 2, Cul4A | 367 | |||
Primary bone marrow cells | IGF2 | CXCL7, SDF1 | 72 | ||||||
Mouse C3H10T1/2 cells | Mouse C3H10T1/2 cells | Runx2 or osterix adenovirus | Wnt4, Bglap1, BMP7 | 69 | |||||
Mouse C3H/HeJ and C57BL/6J bone | Mouse C3H/HeJ and C57BL/6J femur | Rosiglitazone | Genes associated with PPARG signaling pathway and fatty acid metabolism | Genes associated with cell cycle | 368 | ||||
Mouse C57BL/6 tissues | Tibiae from wild-type mice | Mechanical loading for 3 h | 642 | 324 | Osteopontin, Postn, Ostn, Dlx5, Bmp4, Bmp10, Sost, Timp1, Timp2, Ctgf, Esr1 | 318 | 369 | ||
Bone samples from the tibiae | Ankle load | 242 | C-fos, Egr1, Atf3, MMP3 | 199 | TNF, CRLF1, BARX2 | 370 | |||
Mouse breast adenocarcinoma cell lines | Tumor-bone interface and the tumor alone area | Genetic expression at the tumor-bone interface was compared with the tumor alone area | Cathepsin G, cathepsin K, MMP9, MMP13 | 92 | |||||
(Continued) | |||||||||
Subject or sample categorization |
Cell type or tissues |
Treatments |
Differentially expressed genes (n) |
Important differentially expressed genes |
Up-regulated genes (n) |
Important up-regulated genes |
Down-regulated genes (n) |
Important down-regulated genes |
Ref. |
Mouse MSC line, Kusa 4b10 cells | Differentiating osteoblasts | PTH or PTHrP | 4675 | 1444 genes were significantly changed by both treatments, 1384genes changed specifically by PTH, and 403 genes specifically by PTHrP at all time points | Genes belonging to the family of ephrins and their receptors, such as ephrinB2 | PDZ-RGS3 | 87 | ||
Mouse ES cells | Mouse ES cells | Investigate the differentiation of mouse ES cells cultured under three differentiation conditions, embryoid body, gelatin, matrigel | Spp1, Csf1, Gsn, Bmp8b, Crlf1 | 371 | |||||
Mouse chondrogenic cell line ATDC5 | Mouse chondrogenic cell line ATDC5 | Dexamethasone | 96 | CTGF, integrinα10, SGCK, DMP1, lipocalin 2 | SFRP, IGF-1, Lumican | 372 | |||
Mouse chondrogenic cell line ATDC5 | Leucine and rapamycin | 1,571 genes affected by leucine restriction and 535 genes affected by rapamycin | 373 | ||||||
Mouse VSMCs | Mouse VSMCs | MK 4 | DT-diaphorase | Osteoprotegrin | 374 | ||||
Mouse 129SvEv | 129SvEv Sparctm1cam null, male vs. 129Sv/Ev wild-type, male, femoral midshaft | Sparc, Zfp162, Bysl, E2F4 | 307 | ||||||
Swiss Webster mouse BVECs and MVECs | VECs from trabecular bone regions and central marrow cavity regions of mouse long bones | Compared the gene expression in BVECs and MVECs | 5 | ALDH3A1, SMOC-2, MMP-13, C/EBP-b, ANX8 | 2 | Spa, MGP | 375 | ||
(Continued) | |||||||||
Subject or sample categorization |
Cell type or tissues |
Treatments |
Differentially expressed genes (n) |
Important differentially expressed genes |
Up-regulated genes (n) |
Important up-regulated genes |
Down-regulated genes (n) |
Important down-regulated genes |
Ref. |
Mouse TM40D and TM40D-MB cells | Mouse TM40D and TM40D-MB cells | Compared the gene expression profile of low (TM40D) vs. high metastatic (TM40D-MB) tumor cells | 20 | COX2, Fgf7, Ptx 3 | CD1d1 antigen, Cxcl1 | 376 | |||
Mouse osteoblasts | Osteoblasts | Wnt3a and FGF alone or in combination | 70% of the genes induced by Wnt3a were down-regulated by combined FGF treatment | 377 | |||||
Calvarial osteoblasts from 7-d-old CD1 mice | Unit gravity (1 g) vs. modeled microgravity (0.0008 g) | Genes involved in osteoblast differentiation, function, and osteoblast-osteoclast cross-talk | 45 | IL-6, Lcn2, Nqo1 | 88 | Penk1, Tnmd, Aspn, Cdh11, Ogn, Wisp2, Sfrp2 | 378 | ||
Primary osteoblast cultures isolated from wild-type and Arrb2 −/− mouse calvaria | Intermittent PTH | 215 | Slc11a1, Unc93b1, Pla2g7, Lgmn, Cebpd | 200 | Sept7, Ttc3, Steap4, Bmpr1a, Sh3bgrl, Hltf | 379 | |||
Osteoblasts and osteocytes from mouse neonatal calvaria | Comparing genes expression between osteocytes and osteoblasts | 385 | 249 | Col15a1, Ank, Gnas, ENpp1, Enpp6, Notch1, Dlk1, Ptprz1, Bmp4, Gdf10, Tgfβ3, Fgf1 | 136 | Col16a1, Mmp9, Mmp23, Adamts18, Fzd1 | 380 | ||
Mouse osteocyte Y4 Cells | Osteocyte Y4 cells from the long bone | 10−7m risedronate and alendronate for 48 h | Genes encoding zinc ion binding proteins | 381 | |||||
Mouse osteocyte | Mouse osteocyte | Comparing the gene expression profiles of GFP-positive vs. GFP-negative cells | 269 | DMP1, Sost | 382 | ||||
Mouse ST2 cells | Mouse ST2 osteoblastic/stromal cells | P. gingivalis ATCC33277, gingipain-mutants | Cyclin D, Cyclin E | 383 | |||||
Other parts or tissues of mouse | Calvarial cells of Wnt5a−/− and wild-type mice | Gene expression profiles of the Wnt5a−/− calvarial cells as compared to wild-type cells were evaluated | 3528 | 2002 | Fb1, Fst, Cccng1, Wisp1 | 1526 | Runx2, Osterix, ALP, Sfrp2, Igf2, Dhh, Col2a1 | 384 | |
(Continued) | |||||||||
Subject or sample categorization |
Cell type or tissues |
Treatments |
Differentially expressed genes (n) |
Important differentially expressed genes |
Up-regulated genes (n) |
Important up-regulated genes |
Down-regulated genes (n) |
Important down-regulated genes |
Ref. |
Suture-associated dura mater | Compare genomic changes in the dura mater underlying the posterior frontal and sagittal sutures of mice | 6057 | 2529 genes at postnatal d 5, increasing to 3439 genes at postnatal d 10 and 6057 genes at postnatal d 20 | Igf2, Tgfb2, Vegf-C, cadherin 1, Collagen 1 | Mmp13 | 385 | |||
Cartilage of the distal femur of neonatal mice | Dicer-deficient | Hmga 2 | 386 | ||||||
p85α−/− and wild-type OCs | M-CSF and RANKL | 94 | MITF, JDP2, cathepsin K, TRAP, MMP-9, integrin β3 | 387 | |||||
Diabetic mice and control mice | Insulin | FUNX2, RUNX2-related osteogenic genes | 388 | ||||||
Mouse femurs and tibiae excluding joints and primary metaphyses | Glucocorticoid treatment | Csf1, c-fms, Ibsp, Itgb3, disintegrin, Adam8, Trem2, Oscar, PlCγ, c-Fos, Nfatcl, c-Src, Syk, Vav3, ATPase, Ctsk | TGFβ1, BMP-2, LEF1, Akp2, MAPK | 88 | |||||
Region of regeneration with the underlying dura mater in skeletally immature and mature mice with injured calvaria | Parietal bone defects were created by a 4-mm trephine bit | 25 | BMP-2, BMP-4, BMP-7, IGF-2, FGFR-1, Ptn, Acp5, Ctsk, Mmp2, Mnp14, Ttr, Ptgds | 389 | |||||
Mouse calvarial bone | Examined the effect of the col9al-null mutation on the expression of osteoblastic genes | 1 | Col9a1 | 1 | Col9a1 | 267 | |||
Femur of C3H and B6 strains mice | Compared the effect of rosiglitazone in the C3H and B6 strains | 7 gene sets | PPARG signaling pathway and fatty acid metabolism in both C3H and B6 strains with no significant difference between the two strains | Genes associated with cell cycle in the C3H strain | 368 | ||||
(Continued) | |||||||||
Subject or sample categorization |
Cell type or tissues |
Treatments |
Differentially expressed genes (n) |
Important differentially expressed genes |
Up-regulated genes (n) |
Important up-regulated genes |
Down-regulated genes (n) |
Important down-regulated genes |
Ref. |
Mouse wounds | HOXA3 | 837 | 332 | Lrp1, MCP-1/Ccl2 | 505 | Tnf-α, Myd88, Tollip | 390 | ||
Rats of Sprague-Dawley | Kidney of female Sprague-Dawley rats | Ovariectomy and herbal formula | 64 | 22 | HSD17B2, rGK-4, PDK4, HMGCS2 | 42 | PTGER3, NQO1, YC2, UGT1A6, GSTP2, AKR7A3, SREBP-1 | 391 | |
Both pairs of femora and tibiae of Sprague-Dawley female rats | Three different PTH peptides, PTH (1-34), (1-31), and (3-34) | Slpi, tfpi2, socs3, gro1 | RANKL, c-fos, phex, Gprc5c | CXCR4 | 86 | ||||
Fractured femur of male Sprague-Dawley laboratory rats | TP580 | PI3KR1,CAMKK1, Fibronectin, NFATC1, NFATC2, VHL, COX2 | Dynamin 2, caveolin 1 | 84 | |||||
BMSCs of Sprague-Dawley rats | Exposed to simulated microgravity and static controls | 413 | 206 | ABI1, PTTG1, PMP22, ETS1 | 207 | CCND1,CDK5, CDC5L, CATNB, FZD1, GPNMB, WNT5A, WISP1 | 78 | ||
Growth plate chondrocytes from the PC and RZ of Sprague-Dawley rats | Captured the unique features of the PC and RZ | 8 transcripts showing high expression unique to the PC and RZ, respectively | Tpt1, Ctgf, Morf4l1, Gas6, Col5a2, Frzb, Gdi2, Jund; Col9a1, Hapln1, Col1a2, LOC689064, Smoc2, Cast, Rp137, LOC497729 | 392 | |||||
BMSCs of Sprague-Dawley | α-Minimal essential medium containing β-glyceraldehyde-3-phosphate, l-ascorbic acid, dexamethasone and 1,25-(OH)2D3 | 12 | Pla2g2a, Expi, Pcp4, Cx3cl1, GluAP, Fcna, Mmp9, Lcn2, Il10, Fmo1 | 1 | 76 | ||||
Cultured bone marrow MSCs of Sprague-Dawley rats | Electromagnetic field | 19 | 6 | Bmp1, Bmp7 | 13 | Egf, Egfr | 77 | ||
Rats of Wistar strain | Femurs | Titanium implants | 86 | Collagenous and noncollagenous extracellular matrix-related genes, proteoglycans and bone resorption-related genes | 85 | ||||
Abdominal aorta tissues | High phosphorus diet | 53 | 53 | SFRP1, SFRP2, SFRP4, cathepsin K | 393 | ||||
(Continued) | |||||||||
Subject or sample categorization |
Cell type or tissues |
Treatments |
Differentially expressed genes (n) |
Important differentially expressed genes |
Up-regulated genes (n) |
Important up-regulated genes |
Down-regulated genes (n) |
Important down-regulated genes |
Ref. |
Rats of Fisher 344 | Irradiated and nonirradiated primary osteoblast cultures | HBO treatments | 26 | Integrin β1, β-tubulin, ADP-ribosylation factor 1, stearoyl-coenzyme A desaturase 2, aldolase A, HSP90 β, peroxiredoxin | 7 | 394 | |||
Rats of Fisher 344 and LEW strains | Proximal femora | Copenhagen 2331 and Dark Agouti were used as a negative control | 99 | VEGF, FGF2, IGF2, IGF2BP3, TNF | 395 | ||||
Rats of Fischer 344, LEW, COP, and DA strains | Femoral bone tissues | Comparing the mRNA sequence of QTL regions contributing to the variation in lumbar vBMD with the NCBI database | 285 | Akap1, Asgr2, Esd, Fam101b, Irf1, Lcp1, Ltc4s, Mdp-1, Pdhb, Plxdc1, Rabep1, Rhot1, Slc2a4, Xpo4 | 396 |
Adapted from Y. Guo et al.: Expert Rev Endocrinol Metab 3:223–267, 2008 (3), with permission from Expert Reviews Ltd. IT, Intertrochanteric; OP, osteoporosis; ZDCs, zirconium dioxide ceramics; ODHPSC, osteoblasts derived from human pulpar stem cell; PDE, phosphodiesterases; TPA, 12-O-tetradecanoyl phorbol-13-acetate; MNNG, N-methyl-N′-nitro-N-nitrosoguanidine; MNC, mononuclear cell; KLF, Kruppel-like factor; LPS, lipopolysaccharide; BGP, β-glycerol phosphate; DAPT, N-[N-(3,5-diflurophenylacetate)-L-alanyl]-(S)-phenylglycine t-butyl; RCC, renal cell carcinoma; RBM, RCC bone metastasis; MDNSCs, marrow-derived neural stemlike cell; ES, embryonic stem; VSMC, vascular smooth muscle cell; MK-4, menaquinone-4; BVEC, bone-derived vascular endothelial cell; VECs, vascular endothelial cells; MVEC, marrow-derived vascular endothelial cell; M-CSF, macrophage colony stimulating factor; OC, osteoclast; PC, perichondral zone; RZ, reserve zone; HBO, hyperbaric oxygen: ASCs, adipose tissue-derived stromal cells; GFP, green fluorescent protein; siRNA, small interfering RNA; vBMD, volume BMD.
II. Candidate Gene Association Studies
Candidate gene association analysis has the advantages of higher statistical power and easier sample recruitment compared with the linkage approach. During the period from October 2007 to November 2009, a series of genes catalogued on the basis of biological functions in Table 1 have been recognized and tested as candidates to osteoporosis-related traits by association studies. Several classical candidate genes, such as vitamin D receptor (VDR), estrogen receptor (ER), and low-density lipoprotein receptor-related protein 5 (LRP5), have been most widely studied (Table 2).
A. Receptors for calciotropic hormones
1. VDR
1,25-Dihydroxyvitamin D3 [1,25-(OH)2D3], the biologically active metabolite of vitamin D, is required for calcium and phosphorus homeostasis, for normal skeletal development, and for maintenance of skeletal architecture. The action of 1,25-(OH)2D3 is mediated by VDR, which is a member of the superfamily of steroid/thyroid hormone/retinoid receptors. VDR was the first candidate gene to be studied in relation to osteoporosis (4). Frequently studied markers of VDR include BsmI, ApaI, TaqI, Cdx2, and FokI. For example, Cdx2 polymorphism was associated with femoral neck (FN) BMD in a study of 239 osteoporotic postmenopausal women carried out by Mencej-Bedrac et al. (5). In another study performed by Gentil et al. (6), Cdx2 polymorphism did not influence BMD in postmenopausal women by itself, but actually affected the BMD response to physical activity. The interpretation of VDR polymorphisms is currently hindered by the fact that most studies were performed with relatively small sample sizes and investigated only limited polymorphisms (e.g., BsmI, ApaI, TaqI, Cdx2, and FokI), which largely have unknown effects. Whole-gene analyses that exhaustively explore all potential sequence variations within/around the VDR gene in samples of larger size are critical for identifying potential functional variants.
2. ER-α and ER-β
Estrogens play an important role in regulating bone homeostasis, bone turnover, and maintenance of bone mass. The effects of estrogens on skeletal structure are mediated through binding to two different ERs, which are encoded by the ER-α and ER-β genes. Both receptors are highly expressed in bone (7). Although some studies showed significant associations between the three major polymorphisms [e.g., PvuII (T>C) and XbaI (A>G) in intron 1, and the TA repeat in the promoter region] of ER-α and osteoporosis-related phenotypes, the sample sizes for most of these studies were relatively small (8,9,10,11). Rs1801132, another marker for ER-α, was found to be associated with osteoporotic fractures in 6752 Caucasian women (12). Other positive results included associations between G2014A polymorphisms and osteoporosis (13) and between rs3020314 and rs1884051 and hip fractures (14). For ER-β, polymorphisms of rs960070 were associated with susceptibility to fractures (14). The effects of individual polymorphisms of ER-α and ER-β on osteoporosis warrant further confirmatory studies. These results are important and have the potential to impact therapy of osteoporosis through the development of estrogen replacement therapy and selective ER modulators that may be based on individual genetic makeup.
B. Cytokines and receptors
1. OPG-RANKL system
Two cytokines, osteoprotegerin (OPG) and receptor activator of nuclear factor κ-β ligand (RANKL), have been identified as important mediators in the pathogenesis of osteoporosis. OPG is a decoy receptor for RANKL. Its binding to RANKL blocks interaction of the latter with the receptor activator of nuclear factor κ-β (RANK) on the osteoclast surface, thereby inhibiting bone resorption (15). Moffett et al. (16) investigated the association between the Lys3Asn polymorphism (G-to-C polymorphism at codon 3 in exon 1) in the OPG gene and both BMD and the risk of fractures in 6695 white women. Women homozygous for the G (Lys) allele had significantly lower BMD at the intertrochanter, distal radius, and lumbar spine (LS) than those with the C (Asn) allele. Additionally, compared with women with the G/G (Lys-Lys) genotype, those with the C/C (Asn-Asn) genotype had a 26% increased risk of hip fractures (95% confidence interval, 1.02–1.54) and 51% increased risk of FN fractures (95% confidence interval, 1.13–2.02) (16). In postmenopausal women, the promoter polymorphisms of RANKL, including −290C>T, −643C>T, −693G>C, and two common haplotypes, CCG and TTC, showed association with LS BMD (17). Interestingly, the −290C>T (RANKL)-K3N (OPG) combination was associated with total hip BMD and FN BMD in 239 osteoporotic and 228 nonosteoporotic postmenopausal women, respectively (5), suggesting the presence of gene-gene interactions between RANKL and OPG on BMD. Further confirmation of their combined influence in larger cohorts is needed.
2. LRP5
LRP5 functions as a cell-membrane coreceptor for Wnt proteins in the canonical Wnt signaling pathway (18). Several lines of evidence suggest that LRP5 may be a key determinant of bone mass (19). Val667Met and Ala1330Val polymorphisms of the LRP5 gene have been widely studied, and both of these polymorphisms were consistently associated with LS BMD, FN BMD, and fracture risk across different Caucasian populations (20). Grundberg et al. (21) also showed an association between the Val667Met polymorphism and LS BMD by a meta-analysis in 3800 young and elderly men from Swedish cohorts. The approach of meta-analyses, by combining results across studies, is helpful in resolving problems of underpowered studies, revealing unexpected sources of heterogeneity, and resolving discrepancies in genetic studies (22).
C. Bone matrix proteins
1. COL1A1 and COL1A2
Collagen type I is the most abundant protein in connective tissue and is essential for normal bone function. The collagen I triple helix consists of two α1(I) chains and one α2(I) chain encoded by the collagen α1(I) (COL1A1) and collagen α2(I) (COL1A2) genes, respectively. Genetic variants of these two genes may cause normal variation in BMD and bone strength. A common noncoding polymorphism lying in the transcription factor Specificity protein 1 (Sp1) binding site, named Sp1 polymorphism, is the most widely studied polymorphism in the COL1A1 gene. Jin et al. (23) found that Sp1 was associated with biomechanical properties of bone and reduced bone quality. This polymorphism is one of the few polymorphisms that has been repeatedly associated with fractures and BMD (24). Genotypes of a common coding polymorphism in the COL1A2 gene, rs42524, were tested for associations with bone phenotypes in 2004 elderly Swedish men (24). Associations were observed between rs42524 genotype and BMD at several skeletal sites, and elderly Swedish men heterozygous for rs42524 were found to have lower BMD than homozygous subjects (24).
D. Limitations and improvements
Candidate gene association studies have substantial limitations for detecting the genetic basis of osteoporosis because this approach relies on selection of the correct genes for association studies based on either a biological hypothesis or the location of a particular gene in implicated linkage regions. In addition, most current candidate gene association studies in the bone field have generated inconsistent/inconclusive results. This limitation has been well addressed in our previous review (1). Several steps can be taken to increase the probability of discovering genes important to the pathogenesis of osteoporosis using candidate gene association studies, including: 1) controlling population stratification and enlarging sample size to increase statistical power; 2) using more efficient and reasonable methods, such as the single nucleotide polymorphism (SNP) spectral decomposition method, to correct for multiple testing by estimating the effective test number (25); 3) performing whole-gene analyses, rather than analyses for limited polymorphisms in the target genes, to explore all potential sequence variations within and around the gene of interest; and 4) taking into account the influence of gene-gene epistasis and gene-environment interactions on osteoporosis.
III. GWL Studies
Generally, family-based GWL studies are conducted to examine whether any markers from panels of microsatellite markers spaced about uniformly throughout the entire human genome cosegregate with phenotypic traits. In contrast to candidate gene association studies, the GWL approach is robust with respect to population admixture/stratification. Because GWL studies do not rely on linkage disequilibrium among genes or markers in adjacent genomic regions, it is a promising approach for identifying genomic regions contributing to relatively large variations in complex traits, without any prior knowledge about the potentially important function of specific genomic regions. Both univariate linkage analyses and bivariate linkage analyses were used extensively in GWL studies between October 2007 and November 2009. Specific details regarding these two types of GWL studies are outlined in Table 3.
A. Univariate linkage analyses
Most of the univariate GWL studies focused on BMD variations. Quantitative trait loci (QTLs) were identified on chromosomes 1q42-43, 11q12-13, 12q23-24, 17q21-23, 21q22, 22q11 (26), 1q36 (27) and 15q13 (28) for LS BMD; on chromosomes 5q31-33, 13q12-14 (26) for FN BMD; on chromosomes 12p12 and 15q26 for hip peak BMD (PBMD); and on chromosomes 2p13 and Xq27 for wrist PBMD (29). To identify genetic factors influencing bone loss, Shaffer et al. (30) measured the 5-yr change in BMD in 300 Mexican-Americans and found that chromosomes 6q and 3p were linked to BMD changes at the hip and the distal third of the ulna, respectively. Another frequently studied phenotype is bone size (BS). Preliminary evidence shows that spine length is linked to chromosome 5 (31). It is important to recognize that the univariate linkage analyses discussed above have limited statistical power and the linkage regions identified are relatively broad.
B. Bivariate linkage analyses
Bivariate linkage analyses may improve statistical power considerably and facilitate the identification of QTLs whose effects are too small to be detected by univariate linkage analyses for genetically correlated traits. Efforts have been made to explore the correlations between BMD and BS, total body lean mass (TBLM), body fat mass (BFM), and age at menarche (AAM). Significant linkage was observed on chromosome Xq27 for BMD and BS (32) and on chromosomes Xq25 and 15q13 for spine BMD and TBLM in 4126 Caucasian individuals from 451 pedigrees (33,34). A strong linkage signal influencing both BMD and AAM was found on chromosome 22q13 in 2522 females from 414 Caucasian pedigrees (35).
In total, more than 60 QTLs have been identified, and they have been found on all chromosomes, with the exception of chromosome Y. A number of QTLs have been replicated by at least two studies, such as 7p21-22 (29,33,34), 11q12-13 (26,29,34), 15q13 (28,33,34,35,36), and Xq27 (29,32,35). 7p21-22 and 11q13 are of particular interest because these regions harbor two strong candidate genes, IL6 in 7p21-22 and LRP5 in 11q13. However, many of the QTLs have not been replicated in independent studies. A significant limiting factor in replicating these linkages is genetic heterogeneity, especially when the sample size is relatively small or participants from various ethnic origins are included. In addition, other factors such as magnitude of the genetic effect, density of markers, definition and assessment of phenotypes, and statistical approaches might contribute to difficulties in replicating the findings of GWL scans. Despite these concerns and limitations, significant progress has been made in the last 2 yr. As the number of GWL studies continues to grow and more new GWA studies start to emerge, it is anticipated that some previously identified genomic regions will be replicated for linkages and that some genomic regions will eventually be proven to be falsely positive.
IV. GWA Studies
In contrast to candidate gene studies that select genes for study based on known or suspected disease mechanisms, GWA study is a non-hypothesis-driven approach in which a large number of subjects are genotyped for dense genetic markers covering the genome, usually in the form of SNPs and copy number variations (CNVs), in an unbiased fashion. Consequently, GWA studies have the potential to identify totally novel genes/genomic loci with modest effects on human complex diseases/traits (37). However, GWA studies have their own issues that involve multiple considerations in study design and data analyses, such as sample size, level of statistical significance, correction for multiple testing, population stratification, marker density, and replication of results by independent studies. Shortly after the initial GWA study on osteoporosis (38), results of 12 more GWA studies were published (Table 4). Nine of these were published in the New England Journal of Medicine (NEJM) (39), The Lancet (40), Nature Genetics (41,42,43,44), Annals of Internal Medicine (45), and The American Journal of Human Genetics (AJHG) (46,47).
A. Single nucleotide polymorphism (SNP) analyses
In the NEJM paper, sequence variants in five genomic regions were significantly associated with spine BMD in the discovery set (5861 Icelandic subjects) and were confirmed in the replication sets (3750 Icelandic, Danish, and Australia subjects) (39). Three of these five regions are close to, or within, candidate genes (RANKL, OPG, ESR1) for osteoporosis that were previously implicated by candidate gene association studies. The other two regions are close to the zinc finger and BTB (Bric-a-brac, Tramtrack, Broad-complex) domain containing 40 (ZBTB40) gene (1p36) and the major histocompatibility complex region (6p21). The ZBTB40 gene, of unknown function, is expressed in bone (UniGene accession no., Hs.418966), indicating a potentially unidentified role in bone biology. By enlarging the discovery sample size from 5861 to 6865 Icelanders and the follow-up replication sample size from 3750 to 5375 subjects of European descent, two new genome-wide significant loci for hip BMD were found near the SOST and MAPK3 genes (42). The SOST gene encodes sclerostin, which interferes with the Wnt pathway by disrupting Wnt-induced frizzled-Lrp complex formation. The MAPK3 gene encodes a protein kinase that phosphorylates microtubule-associated proteins and plays a role in determining cell polarity. However, 20p12 and BMP2, which the authors found to be associated with osteoporosis in their previous study of the same Icelandic population, were not confirmed in their current GWA papers. In the Lancet paper, GWAs with hip BMD and LS BMD were found for two SNPs: rs4355801 on chromosome 8, close to the OPG gene, and rs3736228 on chromosome 11 in the LRP5 gene (40). Further analyses in the replication cohort (6463 people from three other cohorts in Western Europe) corroborated the findings in discovery subjects (2094 women from the Twins UK cohort). The relationships of these two genes with bone metabolism have been well established in previous candidate gene association studies. In the Nature Genetics paper (41), rs7776725 within the FAM3C gene and rs1721400 mapping close to the SFRP4 gene showed association with BMD at the radius, tibia, and heel in a sample of 8842 Korean subjects from population-based cohorts and were replicated in an independent sample of 7861 Korean subjects. Previous evidence that the FAM3C gene is related to bone is rare, suggesting that this gene might be a novel candidate gene for osteoporosis. SFRP4 belongs to the SFRP family, members of which are well known for their involvement in bone formation and resorption and act as soluble modulators of the Wnt pathway (48).
In each of the GWA studies mentioned above, the tested subjects had similar ancestry, e.g., in the NEJM paper, all the participants were whites of European descent. Thus, the effects of the identified candidate genes are of interest to be investigated in individuals from other ancestries. In the AJHG paper (46), two genes, ADAMTS18 and TGFBR3, were associated with BMD in Caucasian U.S. subjects. Importantly, this association was replicated in Chinese samples and a Tabogo cohort of African ancestry (46). Furthermore, National Center for Biotechnology Information (NCBI) Gene Expression Omnibus expression profiles showed that decreased expression levels of ADAMTS18 and increased expression levels of TGFBR3 in vivo may potentially contribute to the nonhealing of skeletal fractures (46). Results of previous studies have suggested that the genetic basis for osteoporosis may be gender specific (49,50), so testing sex differences in GWA studies is encouraged. In the first GWA study of hip BS, Liu et al. (51) identified a novel gene, PLCL1, that had four SNPs associated with hip BS in female subjects. The importance of this gene to hip BS was replicated in an independent UK cohort, and the relevance to hip fractures was also observed in a Chinese sample (51). In male subjects from the Caucasian sample, two SNPs in SOX6 gene were identified to be associated with both hip BMD and body mass index by a bivariate GWA study (52), the approach of which has the potential to find pleiotropic genes underlying two genetically correlated diseases, like osteoporosis and obesity.
Due to the relatively small sample size in each dataset alone, the power to identify variants with small effects on osteoporosis is insufficient. Meta-analysis involves multidatasets, enlarges the sample size, and is designed to improve the power to detect more associations and investigate the consistency of associations across diverse study populations (53). Rivadeneira et al. (44) conducted a large-scale collaborative meta-analysis involving 19,195 participants from five study populations of European origin. A total of 20 loci showed associations with BMD at the genome-wide significance level, of which 13 were new for BMD and the remaining seven have been reported previously. For osteoporosis, it is important to further investigate whether the candidate genes associated with BMD are also associated with fractures. In a meta-analysis involving 19,195 participants with BMD data and 5,974 subjects with fracture data (45), of the nine candidate genes associated with BMD, four (LRP5, SOST, SPP1, and TNFRSF11A) were also significantly associated with risk for fractures. Potential noise may be introduced by including multiple populations that may be heterogeneous in environments and genetics factors and their interaction in influencing the study traits. The usefulness of meta-analyses may partially depend on the noise introduced relative to the genetic signal enhanced in larger samples. Therefore, findings from even meta-analyses should be replicated in various individual populations for their robustness.
Although the GWA studies reviewed above have brought us new insights into the genetic mechanisms of osteoporosis, the clinical value of these GWA studies is not immediately obvious due to the small variation (namely, ∼1–4%) in BMD that is accounted for by each of the implicated SNPs. It is likely that many other sequence variants imparting smaller effects on osteoporosis will be uncovered, but the primary value of these studies is to identify targets for future functional studies that enhance our knowledge of the molecular pathogenesis of osteoporosis, which will, in turn, facilitate the development of novel therapeutic modalities targeting these molecular pathways.
Approximately 30,000 genes are coded by the human genome, and currently, GenBank contains only about 16,000 known genes. Thus, SNPs associated with osteoporosis in future GWA studies may fall into genomic regions with known and predicted genes or into genomic regions that are more poorly characterized. For SNPs that are located within genes or within regulatory regions close to known genes, public databases (e.g., HapMap, NCBI, University of California Santa Cruz Genome Browser, and Ensembl) or private databases (e.g., Applied Biosystems SNPbrowser, Celera, and Perlegen) can be used to characterize the linkage disequilibrium block structure around those positive SNPs and then to identify the genes annotated on or near the linkage disequilibrium blocks. The strategy for identifying unknown genes in these genomic regions is to blast the Expression Sequence Tag databases and then perform computational gene prediction using gene prediction software (Genscan, GrailEXP, Michael Zhang’s Exon Finder, GeneMark) and Vista analysis in combination with experimental confirmation by real-time RT-PCR in bone-related cells and tissues. Due to false-positives and false-negatives in gene prediction using current software, new and better gene prediction tools are being developed and are likely to become available. In addition, databases containing information on known and predicted genes will be continuously updated. The new tools for gene prediction and enhanced information about existing genes will enhance our capacity to identify genes that reside around positive association signals, and RT-PCR can be used to confirm any unknown candidates in major bone cells and bone-related tissues.
B. Copy number variation (CNV) analyses
CNVs are newly appreciated structural genomic variations that include duplication or deletion of genomic segments. In contrast to SNPs, which only represent a single nucleotide, CNVs represent a relatively large segment of DNA, with variations in size ranging from 1 kilobase to several megabases. Using the Affymetrix 500K Array Set, Yang et al. (47) conducted case-controlled genome-wide CNV analyses in elderly Chinese individuals. A variant of UGT2B17 in CNV 4q13.2 showed association with osteoporotic fractures that was confirmed in replication cohorts. Because the UGT2B17 gene encodes a key enzyme catabolizing steroid hormones, the relationship between serum levels of sex hormones and UGT2B17 gene copy number was further assessed. Subjects with homozygous deletions of UGT2B17 were found to have significantly higher concentrations of total testosterone and estradiol than subjects with one or two copies of this gene. Compared with the well-developed resources available for SNP association studies, we are still in the early phases of incorporating structural genetic variation in GWA studies. GWA CNV analysis generates a distinct set of statistical and technical challenges, and the list of CNVs available for detection and analysis is undoubtedly incomplete (54). Over the next few years, however, a much more global understanding of the extent and precise location of CNVs will likely be achieved as new platforms and novel technical, statistical methods become available to accurately capture CNV information. These advances will undoubtedly benefit the complex phenotype studies, providing a more comprehensive understanding of the role of CNVs in the pathogenesis of osteoporosis. Despite the many obstacles yet to be overcome, we expect analyses of CNVs to continue to gain importance in osteoporosis studies.
The variants themselves found in candidate gene association and GWA studies may not directly cause higher risk to osteoporosis. They may just be closely linked to the actual causal variants. Therefore, further analyses, such as sequencing DNA base pairs in the implicated genome, are needed to identify the exact genetic change responsible for the development of osteoporosis.
V. Transgenic/Knockout Mouse Models
Transgenic mice contain artificially introduced inducible or tissue-specific transgenes that have been added to the genome to study the function and regulation of those genes. These transgenes often confer a gain of function by producing a new protein, increasing expression levels of an existing protein, or presenting the protein in a different type of cells. However, if the transgene disturbs the expression of an existing gene, it will cause loss of function, which is a strategy that has been used to generate knockout mice. Knocking out the activity of a specific gene can offer valuable clues about the function of that gene. Because humans share many genes with mice, detailed study of transgenic/knockout mice has been used to provide a better understanding of how similar genes may contribute to similar complex traits in humans. Knockout and transgenic mouse models have been widely used to confirm the contribution of many well-known or novel genes to abnormal bone phenotypes. From October 2007 to November 2009, approximately 70 new studies (summarized in Table 5) using transgenic and knockout mouse models were reported in the osteoporosis field. For instance, in a study using knockout and overexpression manipulation, bone mass in Cthrc1-null mice was significantly lower than in Cthrc1 transgenic mice (55). Using overexpression manipulation, Ding et al. (56) observed increased osteoblastic activity in glucose-dependent insulinotropic peptide transgenic mice. Transgenic animal models also help isolate and highlight the functions of a particular gene, providing further evidence to support the gene’s relevance to osteoporosis. For example, to determine the role of steroid receptor coactivator (SRC)-1 in mediating the maintenance effects of estrogen on bone, Mödder et al. (57) used SRC-1 knockout mice with gonadectomy and estrogen replacement (10 μg/kg/d) to show that the skeletal response to estrogen was impaired in female, but not in male, SRC-1 knockout mice. It is important to note that there are also some important limitations to the use of knockout mouse technology, as reviewed by the National Institutes of Health (http://www.genome.gov/12514551). Generally, approximately 15% of the genes that have been knocked out are developmentally lethal, so these genes cannot be studied using a knockout model. Additionally, despite the genetic similarities of mice and humans, there are still important differences, so knocking out a gene in mice may have effects that are not indicative that genes function in humans. Despite these limitations, knockout mice give us one of the most powerful methods now available for studying gene functions in vivo.
VI. Gene-Expression Microarray Studies
Because osteoporosis is largely determined genetically, the etiology of osteoporosis cannot be explicitly and comprehensively revealed unless the mechanisms of gene action underlying osteoporosis are established. This problem has been partially addressed by gene expression studies of osteoporosis. Gene expression studies explore relationships between diseases and certain genes at the mRNA level rather than at the DNA level. This approach can shed light on gene function and can also be used to elucidate some of the intermediate biochemical processes leading to a disease. In contrast, genetic epidemiology studies at the DNA level obviate the study of biological processes. Gene-expression microarray has proven to be a powerful tool for assessing gene expression for tens of thousands of genes simultaneously. Approximately 110 gene-expression microarray studies of osteoporosis and other related traits have been published since our 2007 review. These studies have focused primarily on a few key topics such as regulation of osteoblast and osteoclast activity, differentiation of mesenchymal stem cells (MSCs), gene expression comparing healthy and diseased tissues, effects of therapeutic agents on the healing of fractures, and endocrine regulation of bone remodeling. The results from these studies are summarized in Table 6.
A. Regulation of osteoblast and osteoclast activity
Regulation of osteoblast and osteoclast activity has been a major focus of microarray experiments in the past 2 yr and has provided novel insights into the mechanisms of bone formation and resorption. The activity of osteoblasts and osteoclasts is regulated by a variety of specific biomaterials, transcription factors, growth differentiation factors, and cytokines.
Several important biomaterials have been identified that promote bone formation by regulating the activity of osteoblasts. For example, Palmieri et al. (58) used microRNA (miRNA) microarray techniques to investigate translation regulation in human MG63 cells cultured with PerioGlas, an alloplastic material that is effective as an adjunct to conventional surgery for the treatment of intrabony defects. There were 10 up-regulated and two down-regulated miRNAs that in turn regulate the expression of several genes by either enhancing catabolism or repressing translation of their mRNA targets. The vast majority of these genes are down-regulated by miRNA, some of which are homeobox genes like NOG, EN1, and CHRD. Other down-regulated genes include receptors like GHRHR and extracellular matrix proteins like COMP. Among the genes that were down-regulated by PerioGlas, NOG, and COMP were up-regulated by Bio-Oss, a deproteinized sterilized bovine bone material containing calcium-deficient carbonate apatite (59). Palmieri et al. (60,61,62,63,64,65) also explored osteoblast regulation by several other biomaterials including zirconium oxide, calcium sulfate, porous polyethylene, titanium, and anatase. These materials were found to alter expression of several genes related to osteogenesis and bone remodeling, such as SHOX, IGF-I, GHRHR, BMP1, and FGFR1. Additional biomaterials like hydroxyapatite (HA), bioactive glass, stainless steel, polymethylmethacrylate (66), and demineralized freeze-dried bone allografts (67) were also used to study early osteogenesis. Results of the gene expression studies reviewed above have enhanced our understanding of the molecular mechanism underlying osteoblast function in bone regenerative procedures. This new knowledge can aid in the development of surgical techniques and biomaterials that are more suitable for use in the treatment of fractures in osteoporotic patients.
The recent discoveries of transcription factors and signal transduction pathways critical for osteoblast differentiation have opened up new approaches to understanding the pathogenesis of osteoporosis. The identification of the critical role for Osterix/Sp7, a member of the Sp1 transcription factor family, in bone formation and osteoblastogenesis is of particular interest. Using Affymetrix GeneChip arrays with collagen tripeptide-treated human osteoblastic hFOB1.19 cells, Tsuruoka et al. (68) identified 169 genes that were up-regulated more than 2.12-fold. Among them, Sp7 transcription factor showed the greatest change in expression. Sp7 was found to be regulated by BMP2 signaling through Msx2 and Runx2 during osteoblast differentiation (69). A novel inhibitory pathway of osteoblast function, namely the cyclooxygenase-2 (COX-2) pathway, was described by Silvestris et al. (70). Their study supports the role of E4BP4 as a negative osteoblast transcriptional regulator of the COX-2 pathway, by negatively regulating the suppressive COX-2 pathway, thus up-regulating the expression of both Runx2 and Sp7. In view of these findings, it will be quite interesting to determine whether polymorphisms of these transcription factors are associated with osteoporosis.
Gene expression studies of osteoclastogenesis have also provided important new insights into the maintenance of bone homeostasis. Circulating monocytes, which serve as early progenitors of osteoclasts, were collected from human premenopausal subjects with extremely low vs. high peak bone mass, and the expression of three genes (GBP1, CXCL10, and STAT1) was significantly different in these two populations (71). SDF1 and CXCL7 were identified as osteoclast enhancers in primary mouse bone marrow cells treated with IGF-II (72). Battaglino et al. (73) identified a novel gene, NHA-oc/NHA2, that was strongly up-regulated during RANKL-induced osteoclast differentiation in vitro and in vivo. NHA-oc/NHA2 is a new member of the cation-proton antiporter and is the first mitochondrial NHA characterized to date. In addition, Kominsky et al. (74) discovered a novel osteoclast stimulating factor, macrophage inflammatory protein-1 δ, which is secreted by renal cell carcinomas that metastasize to bone. Collectively, these studies have helped fill important gaps in our knowledge regarding regulation of osteoclastogenesis, thereby enhancing our fundamental understanding of the pathogenesis of osteoporosis.
B. Proliferation and differentiation of mesenchymal stem cells (MSCs)
MSCs, which can differentiate into osteoblasts, chondrocytes, adipocytes, and myoblasts in vivo and in vitro, have been regarded as the most hopeful cell sources for bone tissue engineering, regenerative medicine, and gene therapy. Osteoporosis might be due to defects in MSCs that lead to reduced proliferation and osteoblast differentiation. However, the use of MSCs as seeding cells in bone tissue engineering is hampered by a lack of fundamental knowledge about the molecular mechanisms underlying osteogenic differentiation of MSCs. During the past 2 yr, several studies have explored the molecular mechanisms underlying osteogenic differentiation of MSCs (75). Evidence suggests that the capacity of MSCs for osteogenic differentiation was highly suppressed during late culture stages. The expression of four genes, EPHA5, NOV, NDN, and RUNX2, varied depending on culture stage, suggesting that these genes have the potential to act as stage-specific markers during osteogenic differentiation of MSCs (75). Purkinje cell protein 4 (Pcp4), a gene involved in the deposition of calcium and the modulation of calmodulin-dependent protein kinase, was found to be increased during osteoblastic differentiation of MSCs in vitro (76). Bmp1 and Bmp7 were up-regulated, whereas Egf and Egfr were down-regulated during osteogenesis of MSCs subjected to electromagnetic field treatment (77). In addition, simulated microgravity, inorganic polyphosphate, and phosphodiesterases all play important roles in the regulation of osteoblastic differentiation from MSCs by inhibiting population growth of MSCs or by activating fibroblast growth factor (FGF) signaling pathways to induce both proliferation and mineralization of MSCs (78,79,80). However, the specific signaling pathways controlling gene expression in the osteogenic differentiation process are still not clear. Further study of the molecular mechanisms underlying osteogenic differentiation of bone marrow cells will have important theoretical and practical significance in bone regeneration and repair.
C. Gene expression in healthy vs. diseased tissues
Comparative gene expression studies of healthy vs. diseased tissues can be quite informative because they provide immediate information about the differential regulation of genes that may contribute to the disease process. Two studies by Hopwood et al. (81,82) used microarray analysis to compare expression levels of thousands of genes from osteoporotic individuals to age-matched osteoarthritic or control individuals; for these studies, they investigated trabecular bone from the intertrochanteric region of the proximal femur. A substantial number of the top-ranking differentially expressed genes identified in osteoarthritic bone are known to play roles in osteoblasts, osteocytes, and osteoclasts. Many of these genes are targets of either the Wnt (TWIST1, IBSP, S100A4, MMP25, RUNX2, and CD14) or TGF-β/BMP signaling pathways (ADAMTS4, ADM, MEPE, GADD45B, COL4A1, and FST) (81).
D. Effects of therapeutic agents on the healing of fractures
An enhanced understanding of the molecular mechanisms by which therapeutic agents affect the healing of fractures has provided substantial motivation for performing expression profiling studies of bone repair. TP508, a 23-amino acid peptide that represents the thrombin-binding domain for a specific class of thrombin receptors, has a wide range of therapeutic effects on tissue repair (83). By comparing the translational profiles of fractured femurs treated with TP508 vs. saline controls, Li et al. (84) demonstrated that TP508 accelerated fracture healing by modulating expression levels of proteins primarily involved in the functional categories of cell cycle, cell growth, proliferation, and cell death. In another study of healing in rats treated by titanium implant vs. osteotomy, Kojima et al. (85) determined that 86 gene transcripts, including extracellular matrix-related, bone resorption-related, and proteoglycan gene transcripts, were up-regulated in at least one time point. Further studies are needed to identify the biological roles of the transcripts in osteointegration. These data not only provide new insights into fracture healing physiology, but also provide the rationale of potential new treatment strategies for fractures.
E. Endocrine regulation of bone remodeling
Systemic effects of hormones and growth factors play an important role in physiological and pathological mechanisms of bone remodeling. PTH, which is currently used in the therapy of osteoporosis, has been studied intensively. One important study of PTH and bone investigated the anabolic and catabolic effects of intermittent and continuous treatments with three different PTH peptides, PTH (1-34), (1-31), and (3-34) (86). This study identified and validated a large number of genes such as slpi, tfpi2, socs3, and gro1, which were previously not considered to be expressed in bone or to be regulated by PTH treatment. The precise function of these newly identified genes in the anabolic and catabolic bone state, if any, deserve further investigation. In using mouse whole-genome cDNA microarrays to assess the responses to PTH (1-34) and PTHrP (1-141) in the Kusa 4b10 mouse marrow stromal cell line, members of the ephrin and Eph family were identified as targets of PTH (1-34) and PTHrP (1-141) (87). Among the regulated genes, ephrinB2 mRNA was up-regulated in response to both PTH and PTHrP. EphrinB2 protein might act in a paracrine or autocrine manner on the osteoblast itself to stimulate osteoblast maturation and/or bone formation under the influence of local PTHrP or administered PTH (87). Glucocorticoid excess can induce alterations in bone metabolism that weaken bone structure and increase fracture risk. Using microarrays in glucocorticoid excess-treated mice, Yao et al. (88) revealed that glucocorticoid excess was associated with early activation of genes associated with osteoclastogenesis (csf1, c-fms, lbsp, and ltgb3) and adipogenesis (c/EBPα and PPARγ) and a later suppression of genes associated with osteogenesis and mineralization (TGFβ1, BMP2, LEF1, Akp2, and MAPK). These gene expression changes may correspond to alterations in bone metabolism with glucocorticoid exposure that result in rapid bone loss. These results enhance our understanding of glucocorticoid-induced bone loss by providing in vivo evidence supporting the concept that glucocorticoid excess directly or indirectly regulates the transcription of specific genes associated with bone physiology.
Besides the aforementioned studies, during the past 2 yr, fruitful results were also generated by microarray studies in several additional bone biology research fields, including osteosarcoma biology (89,90), spine fusion (91), regulation of osteolysis (92), and osteoblast- or chondrocyte-mediated pathological processes in bone diseases such as ossification of the posterior longitudinal ligament (OPLL) of the spine (93) and osteoarthritis (OA) (94,95,96). These studies will not only illuminate new methods for bone biology research but also are likely to provide significant insights that will contribute to the development of new therapeutic interventions in the near future.
As our previous review pointed out, most high-throughput gene expression studies have used cultured cell lines of humans, mice, and rats in vitro. The disadvantage of using cultured cell lines is that gene expression profiles undoubtedly change because cultured cells gradually lose their osteogenic potential due to lack of stimulation by factors that are available in vivo, but not in vitro. Given this significant limitation, studying fresh cell- or bone-related tissues may be a promising way to obtain data that more closely resemble in vivo conditions.
VII. Proteomics Studies
Because cell recognition and signal transduction pathways occur at the protein level (e.g., receptor/ligand interactions, antigen recognition, and cell adhesion), it is appropriate and important to utilize proteomics approaches to directly investigate protein expression. In addition, because of complicated processes such as alternative mRNA splicing and posttranslational modification of proteins, the correlation between expression levels of mRNA and proteins could be low (97,98). The Human Genome Project revealed that there are far fewer protein-coding genes in the human genome than there are proteins in the human proteome [∼35,000 genes give rise to ∼1,000,000 proteins (99)]. This profound discrepancy clearly indicates that the diversity of proteins cannot be fully characterized by gene-expression analysis alone. Consequently, to understand functional genomics, proteomics approaches have become an indispensable complement to mRNA expression microarrays. Combining the data generated by these two approaches provides much richer information than that provided by genomic studies alone.
A. Studying cultured cells using in vitro systems
Proteomics has only recently been applied to the bone field, so only a few relevant studies have been published in the past 2 yr. Most of these studies involve expression proteomics, in which samples from case and control groups are quantitatively analyzed to identify proteins that are differentially expressed between groups. The most common approach was to use in vitro cell culture systems, and several proteins that are important for the development of MSCs (100,101,102,103,104), osteoblasts (105,106,107,108,109), osteoclasts (110,111,112,113), chondrocytes (114,115), bone marrow osteoprogenitor cells (116), and osteosarcoma cells (117) were identified in this manner. For example, by applying two-dimensional liquid chromatography/matrix-assisted laser desorption/ionization mass spectrometry (MS) on MSC-derived fast-growing clones (with tripotential differentiation capacity) vs. MSC-derived slow-growing clones (with only unipotential differentiation capacity), Mareddy et al. (102) identified 11 proteins (e.g., calmodulin, tropomyosin, and caldesmon) with differential expression. Using iTRAQ-coupled two-dimensional-liquid chromatography-tandem MS/MS analysis, Xu et al. (105) found several cytoskeletal proteins, metabolic enzymes, signaling and cell growth proteins that were differentially expressed in human osteoblasts cultured on plane HA vs. osteoblasts cultured on carbon nanotube-reinforced HA. These findings of in vitro proteomics studies, however, may be limited by the fact that factors regulating gene expression in complex physiological/pathological environments in vivo can be changed or eliminated in vitro. Consequently, in vitro proteomics studies are likely to be compromised by this comparative lack of regulatory control.
B. Studying fresh cells, serum, or tissues
Compared with in vitro studies, ex vivo studies using fresh cells or tissues are more likely to provide data that represent protein expression under physiological/pathological conditions. Our group recently used two-dimensional electrophoresis (2-DE) coupled with MS to perform a comparative protein expression profiling study of circulating monocytes from premenopausal Chinese females with extremely high BMD vs. those with extremely low BMD. In total, 38 differentially expressed proteins were identified, and five of these proteins (GSN, FSU1, SOD2, GPX1, and P4HB) were confirmed by Western blotting (118). Moreover, several additional studies of serum and fresh tissues have been performed in the past 2 yr to investigate the global-scale molecular profiling of bone-related diseases, such as OA (114,119,120,121), osteonecrosis of the femoral head (122), and osteosarcoma (123). Using 2-DE, a comparative analysis of the proteins extracted from patients with OA cartilage vs. those with normal cartilage was performed (119). After further identification by linear ion trap-Fourier transform ion cyclotron resonance MS, 14 proteins associated with OA were unambiguously determined, including proteins involved in glycolysis and energy production (ADH, ADK, ENOA, KPYM, and FR), signaling (ANNX-I, PEBP, and TUB), Redox (PRDX3 and SODM), and cartilage matrix (COLL-I and COLL-VI). In another study using integrated genetic, bioinformatic, and proteomic approaches, five novel proteins (SOX11, FGF23, KLF6, WWOX, and GDF15) were implicated in the genesis of OA (120). Based on the methods of isoelectric focusing, 2-DE, and silver staining, as well as matrix-assisted laser desorption ionization time-of-flight mass spectrometry, levels of kininogen 1 variant, complement factor C3 precursor, and complement factor H were found to be increased, whereas levels of antithrombin III chain B, apolipoprotein A–IV precursor, and gelsolin isoform α precursor were decreased in osteonecrosis of the femoral head patients (122). By comparing the protein expression profiles of two distinct groups of osteosarcoma biopsy samples, a chemosensitive group vs. a chemoresistant group, Kawai et al. (123) identified 10 protein spots associated with the chemosensitivity of osteosarcoma to preoperative chemotherapy. The proteins represented by these 10 spots could potentially be new diagnostic or prognostic markers for osteosarcoma, or new therapeutic targets for the disease.
Proteomics represents a promising field that is poised to boost our understanding of the dynamic nature of protein expression, cellular and subcellular protein distribution, posttranslational modifications, and protein-protein interactions. However, the application of proteomics to the bone field is only in its initial stages. Current proteomics studies in osteoporosis focus mainly on expression proteomics. Because key proteins involved in osteoporosis development generally interact with other proteins, functional proteomics studies will be necessary to provide a global understanding of protein-protein interactions. To better understand and even predict the functions of proteins, additional knowledge regarding three-dimensional structures of the proteome is required. Structural proteomics may prospectively fulfill this goal by mapping out the structures of protein complexes or proteins in a specific cellular organelle.
The impact of advances in the field of proteomics on our knowledge of osteoporosis is relatively small at this point. As new techniques are developed and applied to bone, however, we anticipate that the abundance of new information generated will be crucial toward understanding the pathogenesis of osteoporosis and developing novel therapeutic strategies for treating this disease.
VIII. Future Directions
A. Functional studies
After genes for osteoporosis have been identified, it is important to perform functional studies to determine the influence of such genes on the differentiation of osteoblasts or osteoclasts and the effects of such genes on the variation of bone-related traits. The strategies may involve in vitro and/or in vivo assays to test the effects of the gene of interest in cell cultures or animal models. Strategies for in vitro assays often include overexpression or knockdown of gene expression in osteoblast or osteoclast precursor cells, which is followed by evaluation of alkaline phosphatase (ALP) or tartrate-resistant acid phosphatase (TRAP) activity to assess osteoblast or osteoclast differentiation. To determine the potential molecular mechanisms by which the expression of target genes are regulated through specific transcription factors, EMSAs and chromatin immunoprecipitation can also be employed. Should strong functional evidence be obtained in vitro, one will consider further in vivo investigations using knockout and/or transgenic approaches to examine how the target gene changes the bone-related phenotypes, e.g., BMD or BS. In vivo functional analyses are admittedly challenging for complex traits partially due to biological redundancy and the significant potential for knockdown of critical genes to prevent in utero development.
Furthermore, the specific impact of potential functional variants from the gene of interest will be ascertained through, e.g., promoter assay, RNA splicing assay, and gene activity assay. First, if SNPs change recognition sequences of potential transcriptional factors, binding affinity to the transcriptional factors may be modified, leading to changes in transcriptional efficiency. Investigators can perform EMSAs and luciferase reporter assays to examine this potential effect. Second, for SNPs located in the splice-site, one can detect alternative mRNA splicing using mRNA phenotyping protocols (124). Third, if SNPs lie in the coding region, constructs that differ in the target SNPs can be prepared and expressed in vitro. The activity of different protein products can then be compared using appropriate assays for determining the function of that specific protein.
B. Epigenetic variation
Epigenetics refers to reversible, heritable changes in gene regulation that occur without a change in DNA sequence. Epigenetic regulation has been implicated as a key regulatory mechanism in the etiology of human complex diseases (125). miRNA regulation, DNA methylation, and histone methylation are three common types of epigenetic modifications. Previous pioneering studies have shown that DNA methylation may be involved in the osteoclastogenesis (126), and acetylation of histone H3 and H4 may be involved in osteogenesis (127). Genome-wide measurement of epigenetic variation has recently been made possible using techniques such as Affymetrix Human Tiling 2.0R Array Set, which will allow us to progress toward a thorough understanding of the roles of epigenetics in osteoporosis.
C. New phenotypes
Active shape modeling has been used to create a template describing the outline of the hip joint from dual energy x-ray absorptiometry images. Using this method, Goodyear et al. (128) identified risk factors for hip fractures independent of BMD. Composite indices of FN strength that are also constructed from hip dual energy x-ray absorptiometry images, such as compression strength index (CSI), bending strength index, and impact strength index, have the potential to improve hip fracture risk assessment (129). Although two studies have made efforts in finding genetic variants for CSI (130,131), more genetic studies for these new phenotypes are necessary.
IX. Future Prospects for the Application of Genetic Risk Assessment in Osteoporosis Prediction and Treatment
Genes that have been implicated in osteoporosis by genetic studies only make minor contributions individually to bone density and fracture risk. For example, the five regions identified in the study of Styrkarsdottir et al. (39) accounted for only approximately 3% of the total variation in hip and spine BMD. Similarly, the contribution of the CNV of UGT2B17 gene to variations in BMD, cortical thickness (CT), and buckling ratio (BR) in Caucasians was only 0.67, 0.71, and 0.77%, respectively (47). It is thought that many genes, each with small effects, may be responsible for osteoporosis, rather than a small number of genes with large effects (132). Thus, osteoporosis predictive tests are likely to involve many genes, and tests for single genes are unlikely to be of clinical significance. Therefore, in theory, if most of the major genes that cause osteoporosis can be identified and their interaction with each other and with environmental factors can be understood, this information can be used to identify those who are at risk. However, the depth of our knowledge currently falls far short of this goal. Because molecular genetic studies in the field of osteoporosis are coming at a rapid rate, we are hopeful that, in the next one or two decades, sufficient genetic information will become available to develop genetic algorithms to assess the risk for osteoporosis.
Genetic studies have established a link between osteoporosis in humans and the Wnt, BMP-Smad, and 12/15-lipoxygenase pathways. Recently, the EphrinA-EphR pathway was found to be associated with FN bone geometry section modulus by pathway-based GWA analysis (133). It is likely that new pathways related to osteoporosis will continue to be identified by genetic studies, such as pathway-based GWA analysis. Furthermore, proteomics approaches could characterize the disease process directly by finding sets of proteins that work together to produce disease. Although our fundamental knowledge is fairly limited, there is a general feeling that bone-related pathways provide a substantial potential for developing novel ways to treat osteoporosis (134). Current emphasis in the development of new anabolic therapies for osteoporosis is directed at modifying the effects of Wnt pathway on osteoblast differentiation and bone formation (135). However, due to the pleiotropic function of these various pathways, there is substantial concern that it will be difficult to manipulate these pathways for osteoporosis treatment without producing unwanted side effects on other cells and tissues. In the coming years, basic and clinical efforts will be required to explore new pathways and modes of translating knowledge about the contribution of these pathways to the pathogenesis of osteoporosis into therapeutic applications.
X. Summary
In this article, we have updated the progress of genetic studies of osteoporosis published from October 2007 to November 2009 at three levels, corresponding to DNA, mRNA, and protein. Since our last update toward the end of September 2007, remarkable progress has been made in revealing the genetic basis of osteoporosis. A number of promising candidate genes, genomic regions, or proteins were identified, and several of them have been replicated by multiple studies. However, the majority of these findings are still inconclusive, pending further investigation. Efforts must be made in various aspects, including controlling population stratification and genetic heterogeneity, performing larger-scale studies in possibly hundreds of thousands of subjects, exploring new phenotypes, considering gene-gene and gene-environment interactions, deep resequencing of the genome, investigating the contribution of CNVs, DNA methylation, and histone modifications, and identifying the causal variants and their biological roles in osteoporosis. We expect that the results of these studies will be used to develop rational approaches toward predicting whether or not individuals are predisposed to developing osteoporosis and novel therapeutic approaches toward the treatment of this important disease.
Footnotes
The study benefited from Grant 30570875 from the National Science Foundation of China, Shanghai Leading Academic Discipline Project (S30501), and a startup fund from Shanghai University of Science and Technology. H.-W.D. was partially supported by National Institutes of Health Grants P50AR055081, R01AG026564, R01AR050496, RC2DE020756, R01AR057049, and R03TW008221, and by the Franklin D. Dickson/Missouri Endowment.
Disclosure Summary: The authors have nothing to disclose.
First Published Online March 31, 2010
Abbreviations: 2-DE, Two dimensional electrophoresis; AAM, age at menarche; ALP, alkaline phosphatase; BFM, body fat mass; BMC, bone mineral content; BMD, bone mineral density; BR, buckling ratio; BS, bone size; CNV, copy number variation; COX-2, cyclooxygenase-2; CSI, compression strength index; CT, cortical thickness; ER, estrogen receptor; FGF, fibroblast growth factor; FN, femoral neck; GWA, genome-wide association; GWL, genome-wide linkage; HA, hydroxyapatite; LRP5, low-density lipoprotein receptor-related protein 5; LS, lumbar spine; miRNA, microRNA; MS, mass spectrometry; MSC, mesenchymal stem cell; OA, osteoarthritis; 1,25-(OH)2D3, 1,25-dihydroxyvitamin D3; OPG, osteoprotegerin; OPLL, ossification of the posterior longitudinal ligament; PBMD, peak BMD; QTLs, quantitative trait loci; RANK, receptor activator of nuclear factor κ-β; RANKL, RANK ligand; SNP, single nucleotide polymorphism; SRC, steroid receptor coactivator; TBLM, total body lean mass; TRAP, tartrate-resistant acid phosphatase; VDR, vitamin D receptor.
References
- Liu YJ, Shen H, Xiao P, Xiong DH, Li LH, Recker RR, Deng HW 2006 Molecular genetic studies of gene identification for osteoporosis: a 2004 update. J Bone Miner Res 21:1511–1535 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Liu YZ, Liu YJ, Recker RR, Deng HW 2003 Molecular studies of identification of genes for osteoporosis: the 2002 update. J Endocrinol 177:147–196 [DOI] [PubMed] [Google Scholar]
- Guo Y, Yang TL, Pan F, Xu XH, Dong SS, Deng HW 2008 Molecular genetic studies of gene identification for osteoporosis. Expert Rev Endocrinol Metab 3:223–267 [DOI] [PubMed] [Google Scholar]
- Ralston SH 2003 Genetic determinants of susceptibility to osteoporosis. Curr Opin Pharmacol 3:286–290 [DOI] [PubMed] [Google Scholar]
- Mencej-Bedrac S, Prezelj J, Kocjan T, Teskac K, Ostanek B, Smelcer M, Marc J 2009 The combinations of polymorphisms in vitamin D receptor, osteoprotegerin and tumour necrosis factor superfamily member 11 genes are associated with bone mineral density. J Mol Endocrinol 42:239–247 [DOI] [PubMed] [Google Scholar]
- Gentil P, Lima RM, Lins TC, Abreu BS, Pereira RW, Oliveira RJ 2007 Physical activity, Cdx-2 genotype, and BMD. Int J Sports Med 28:1065–1069 [DOI] [PubMed] [Google Scholar]
- Bland R 2000 Steroid hormone receptor expression and action in bone. Clin Sci (Lond) 98:217–240 [PubMed] [Google Scholar]
- Dong J, Huang ZW, Piao JH, Li F, Zeng J, Gong J, Yang XG 2008 Relationship between estrogen receptor gene Px haplotype and the effect of calcium and soy isoflavone supplementation on bone mineral density of Chinese postmenopausal women. Zhonghua Yu Fang Yi Xue Za Zhi 42:329–334 [PubMed] [Google Scholar]
- Xing L, He GP, Chen YM, Su YX 2008 Interaction of interleukin-6 and estrogen receptor gene polymorphisms on bone mass accrual in Chinese adolescent girls. J Bone Miner Metab 26:493–498 [DOI] [PubMed] [Google Scholar]
- Yin J, Zhang Q, Hu XQ, Du WJ, Liu AL, Wang XY, Tong ZQ, Ma GS 2008 Interactions between effects of estrogen receptor gene polymorphisms on BMD and experiences of the first spermorrhea in Chinese Han boys. Biomed Environ Sci 21:129–136 [DOI] [PubMed] [Google Scholar]
- Kastelan D, Grubic Z, Kraljevic I, Polasek O, Dusek T, Stingl K, Kerhin-Brkljacic V, Korsic M 2009 The role of estrogen receptor-α gene TA polymorphism and aromatase gene TTTA polymorphism on peak bone mass attainment in males: is there an additive negative effect of certain allele combinations? J Bone Miner Metab 27:198–204 [DOI] [PubMed] [Google Scholar]
- Tranah GJ, Taylor BC, Lui LY, Zmuda JM, Cauley JA, Ensrud KE, Hillier TA, Hochberg MC, Li J, Rhees BK, Erlich HA, Sternlicht MD, Peltz G, Cummings SR 2008 Genetic variation in candidate osteoporosis genes, bone mineral density, and fracture risk: the study of osteoporotic fractures. Calcif Tissue Int 83:155–166 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Gómez R, Magaña JJ, Cisneros B, Pérez-Salazar E, Faugeron S, Véliz D, Castro C, Rubio J, Casas L, Valdés-Flores M 2007 Association of the estrogen receptor α gene polymorphisms with osteoporosis in the Mexican population. Clin Genet 72:574–581 [DOI] [PubMed] [Google Scholar]
- Wang JT, Guo Y, Yang TL, Xu XH, Dong SS, Li M, Li TQ, Chen Y, Deng HW 2008 Polymorphisms in the estrogen receptor genes are associated with hip fractures in Chinese. Bone 43:910–914 [DOI] [PubMed] [Google Scholar]
- Hofbauer LC, Schoppet M 2004 Clinical implications of the osteoprotegerin/RANKL/RANK system for bone and vascular diseases. JAMA 292:490–495 [DOI] [PubMed] [Google Scholar]
- Moffett SP, Oakley JI, Cauley JA, Lui LY, Ensrud KE, Taylor BC, Hillier TA, Hochberg MC, Li J, Cayabyab S, Lee JM, Peltz G, Cummings SR, Zmuda JM 2008 Osteoprotegerin Lys3Asn polymorphism and the risk of fracture in older women. J Clin Endocrinol Metab 93:2002–2008 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Mencej S, Albagha OM, Prezelj J, Kocjan T, Marc J 2008 Tumour necrosis factor superfamily member 11 gene promoter polymorphisms modulate promoter activity and influence bone mineral density in postmenopausal women with osteoporosis. J Mol Endocrinol 40:273–279 [DOI] [PubMed] [Google Scholar]
- Sims AM, Shephard N, Carter K, Doan T, Dowling A, Duncan EL, Eisman J, Jones G, Nicholson G, Prince R, Seeman E, Thomas G, Wass JA, Brown MA 2008 Genetic analyses in a sample of individuals with high or low BMD shows association with multiple Wnt pathway genes. J Bone Miner Res 23:499–506 [DOI] [PubMed] [Google Scholar]
- Holmen SL, Giambernardi TA, Zylstra CR, Buckner- Berghuis BD, Resau JH, Hess JF, Glatt V, Bouxsein ML, Ai M, Warman ML, Williams BO 2004 Decreased BMD and limb deformities in mice carrying mutations in both Lrp5 and Lrp6. J Bone Miner Res 19:2033–2040 [DOI] [PubMed] [Google Scholar]
- 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, Masi L, McGuigan FE, Mellström D, Mosekilde L, Nogues X, Pols HA, Reeve J, Renner W, Rivadeneira F, van Schoor NM, Weber K, Ioannidis JP, Uitterlinden AG 2008 Large-scale analysis of association between LRP5 and LRP6 variants and osteoporosis. JAMA 299:1277–1290 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Grundberg E, Lau EM, Lorentzon M, Lorentzson M, Karlsson M, Holmberg A, Groop L, Mellström D, Orwoll E, Mallmin H, Ohlsson C, Ljunggren O, Akesson K 2008 Large-scale association study between two coding LRP5 gene polymorphisms and bone phenotypes and fractures in men. Osteoporos Int 19:829–837 [DOI] [PubMed] [Google Scholar]
- Munafò MR, Flint J 2004 Meta-analysis of genetic association studies. Trends Genet 20:439–444 [DOI] [PubMed] [Google Scholar]
- Jin H, Stewart TL, Hof RV, Reid DM, Aspden RM, Ralston S 2009 A rare haplotype in the upstream regulatory region of COL1A1 is associated with reduced bone quality and hip fracture. J Bone Miner Res 24:448–454 [DOI] [PubMed] [Google Scholar]
- Lindahl K, Rubin CJ, Brändström H, Karlsson MK, Holmberg A, Ohlsson C, Mellström D, Orwoll E, Mallmin H, Kindmark A, Ljunggren O 2009 Heterozygosity for a coding SNP in COL1A2 confers a lower BMD and an increased stroke risk. Biochem Biophys Res Commun 384:501–505 [DOI] [PubMed] [Google Scholar]
- Nyholt DR 2004 A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other. Am J Hum Genet 74:765–769 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kaufman JM, Ostertag A, Saint-Pierre A, Cohen-Solal M, Boland A, Van Pottelbergh I, Toye K, de Vernejoul MC, Martinez M 2008 Genome-wide linkage screen of bone mineral density (BMD) in European pedigrees ascertained through a male relative with low BMD values: evidence for quantitative trait loci on 17q21–23, 11q12–13, 13q12–14, and 22q11. J Clin Endocrinol Metab 93:3755–3762 [DOI] [PubMed] [Google Scholar]
- Willaert A, Van Pottelbergh I, Zmierczak H, Goemaere S, Kaufman JM, De Paepe A, Coucke P 2008 A genome-wide linkage scan for low spinal bone mineral density in a single extended family confirms linkage to 1p36.3. Eur J Hum Genet 16:970–976 [DOI] [PubMed] [Google Scholar]
- Yan H, Liu YJ, Zhou Q, Xiao P, Recker RR, Deng HW 2009 Comparison of whole genome linkage scans in premenopausal and postmenopausal women: no bone-loss-specific QTLs were implicated. Osteoporos Int 20: 771–777 [DOI] [PubMed] [Google Scholar]
- Zhang F, Xiao P, Yang F, Shen H, Xiong DH, Deng HY, Papasian CJ, Drees BM, Hamilton JJ, Recker RR, Deng HW 2008 A whole genome linkage scan for QTLs underlying peak bone mineral density. Osteoporos Int 19:303–310 [DOI] [PubMed] [Google Scholar]
- Shaffer JR, Kammerer CM, Bruder JM, Cole SA, Dyer TD, Almasy L, MacCluer JW, Blangero J, Bauer RL, Mitchell BD 2008 Genetic influences on bone loss in the San Antonio Family Osteoporosis study. Osteoporos Int 19:1759–1767 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Chinappen-Horsley U, Blake GM, Fogelman I, Kato B, Ahmadi KR, Spector TD 2008 Quantitative trait loci for bone lengths on chromosome 5 using dual energy x-ray absorptiometry imaging in the Twins UK cohort. PLoS ONE 3:e1752 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Liu XG, Liu YJ, Liu J, Pei Y, Xiong DH, Shen H, Deng HY, Papasian CJ, Drees BM, Hamilton JJ, Recker RR, Deng HW 2008 A bivariate whole genome linkage study identified genomic regions influencing both BMD and bone structure. J Bone Miner Res 23:1806–1814 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Wang XL, Deng FY, Tan LJ, Deng HY, Liu YZ, Papasian CJ, Recker RR, Deng HW 2008 Bivariate whole genome linkage analyses for total body lean mass and BMD. J Bone Miner Res 23:447–452 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Tang ZH, Xiao P, Lei SF, Deng FY, Zhao LJ, Deng HY, Tan LJ, Shen H, Xiong DH, Recker RR, Deng HW 2007 A bivariate whole-genome linkage scan suggests several shared genomic regions for obesity and osteoporosis. J Clin Endocrinol Metab 92:2751–2757 [DOI] [PubMed] [Google Scholar]
- Pan F, Xiao P, Guo Y, Liu YJ, Deng HY, Recker RR, Deng HW 2008 Chromosomal regions 22q13 and 3p25 may harbor quantitative trait loci influencing both age at menarche and bone mineral density. Hum Genet 123:419–427 [DOI] [PubMed] [Google Scholar]
- Zhang ZX, Lei SF, Deng FY, Zhang F, Liu YJ, Recker RR, Papasian CJ, Deng HW 2009 Bivariate genome-wide linkage analysis for traits BMD and AAM: effect of menopause on linkage signals. Maturitas 62:16–20 [DOI] [PubMed] [Google Scholar]
- Hirschhorn JN, Daly MJ 2005 Genome-wide association studies for common diseases and complex traits. Nat Rev Genet 6:95–108 [DOI] [PubMed] [Google Scholar]
- Kiel DP, Demissie S, Dupuis J, Lunetta KL, Murabito JM, Karasik D 2007 Genome-wide association with bone mass and geometry in the Framingham Heart Study. BMC Med Genet 8(Suppl 1):S14 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Styrkarsdottir U, Halldorsson BV, Gretarsdottir S, Gudbjartsson DF, Walters GB, Ingvarsson T, Jonsdottir T, Saemundsdottir J, Center JR, Nguyen TV, Bagger Y, Gulcher JR, Eisman JA, Christiansen C, Sigurdsson G, Kong A, Thorsteinsdottir U, Stefansson K 2008 Multiple genetic loci for bone mineral density and fractures. N Engl J Med 358:2355–2365 [DOI] [PubMed] [Google Scholar]
- 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 2008 Bone mineral density, osteoporosis, and osteoporotic fractures: a genome-wide association study. Lancet 371:1505–1512 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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, Oh B, Kim HL 2009 A large-scale genome-wide association study of Asian populations uncovers genetic factors influencing eight quantitative traits. Nat Genet 41:527–534 [DOI] [PubMed] [Google Scholar]
- 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 2009 New sequence variants associated with bone mineral density. Nat Genet 41:15–17 [DOI] [PubMed] [Google Scholar]
- Thorleifsson G, Holm H, Edvardsson V, Walters GB, Styrkarsdottir U, Gudbjartsson DF, Sulem P, Halldorsson BV, de Vegt F, d'Ancona FC, den Heijer M, Franzson L, Christiansen C, Alexandersen P, Rafnar T, Kristjansson K, Sigurdsson G, Kiemeney LA, Bodvarsson M, Indridason OS, Palsson R, Kong A, Thorsteinsdottir U, Stefansson K 2009 Sequence variants in the CLDN14 gene associate with kidney stones and bone mineral density. Nat Genet 41:926–930 [DOI] [PubMed] [Google Scholar]
- Rivadeneira F, Styrkársdottir U, Estrada K, Halldórsson BV, Hsu YH, Richards JB, Zillikens MC, Kavvoura FK, Amin N, Aulchenko YS, Cupples LA, Deloukas P, Demissie S, Grundberg E, Hofman A, Kong A, Karasik D, van Meurs JB, Oostra B, Pastinen T, Pols HA, Sigurdsson G, Soranzo N, Thorleifsson G, Thorsteinsdottir U, Williams FM, Wilson SG, Zhou Y, Ralston SH, van Duijn CM, Spector T, Kiel DP, Stefansson K, Ioannidis JP, Uitterlinden AG 2009 Twenty bone mineral density loci identified by large-scale meta-analysis of genome-wide association studies. Nat Genet 41:1199–1206 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Richards JB, Kavvoura FK, Rivadeneira F, Styrkársdóttir U, Estrada K, Halldórsson BV, Hsu YH, Zillikens MC, Wilson SG, Mullin BH, Amin N, Aulchenko YS, Cupples LA, Deloukas P, Demissie S, Hofman A, Kong A, Karasik D, van Meurs JB, Oostra BA, Pols HA, Sigurdsson G, Thorsteinsdottir U, Soranzo N, Williams FM, Zhou Y, Ralston SH, Thorleifsson G, van Duijn CM, Kiel DP, Stefansson K, Uitterlinden AG, Ioannidis JP, Spector TD 2009 Collaborative meta-analysis: associations of 150 candidate genes with osteoporosis and osteoporotic fracture. Ann Intern Med 151:528–537 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Xiong DH, Liu XG, Guo YF, Tan LJ, Wang L, Sha BY, Tang ZH, Pan F, Yang TL, Chen XD, Lei SF, Yerges LM, Zhu XZ, Wheeler VW, Patrick AL, Bunker CH, Guo Y, Yan H, Pei YF, Zhang YP, Levy S, Papasian CJ, Xiao P, Lundberg YW, Recker RR, Liu YZ, Liu YJ, Zmuda JM, Deng HW 2009 Genome-wide association and follow-up replication studies identified ADAMTS18 and TGFBR3 as bone mass candidate genes in different ethnic groups. Am J Hum Genet 84:388–398 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Yang TL, Chen XD, Guo Y, Lei SF, Wang JT, Zhou Q, Pan F, Chen Y, Zhang ZX, Dong SS, Xu XH, Yan H, Liu X, Qiu C, Zhu XZ, Chen T, Li M, Zhang H, Zhang L, Drees BM, Hamilton JJ, Papasian CJ, Recker RR, Song XP, Cheng J, Deng HW 2008 Genome-wide copy-number-variation study identified a susceptibility gene, UGT2B17, for osteoporosis. Am J Hum Genet 83:663–674 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Cho SW, Her SJ, Sun HJ, Choi OK, Yang JY, Kim SW, Kim SY, Shin CS 2008 Differential effects of secreted frizzled-related proteins (sFRPs) on osteoblastic differentiation of mouse mesenchymal cells and apoptosis of osteoblasts. Biochem Biophys Res Commun 367:399–405 [DOI] [PubMed] [Google Scholar]
- 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 2007 Meta-analysis of genome-wide scans provides evidence for sex- and site-specific regulation of bone mass. J Bone Miner Res 22:173–183 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Xiao P, Shen H, Guo YF, Xiong DH, Liu YZ, Liu YJ, Zhao LJ, Long JR, Guo Y, Recker RR, Deng HW 2006 Genomic regions identified for BMD in a large sample including epistatic interactions and gender-specific effects. J Bone Miner Res 21:1536–1544 [DOI] [PubMed] [Google Scholar]
- Liu YZ, Wilson SG, Wang L, Liu XG, Guo YF, Li J, Yan H, Deloukas P, Soranzo N, Chinappen-Horsley U, Chinnapen-Horsley U, Cervino A, Cervino A, Williams FM, Xiong DH, Zhang YP, Jin TB, Levy S, Papasian CJ, Drees BM, Hamilton JJ, Recker RR, Spector TD, Deng HW 2008 Identification of PLCL1 gene for hip bone size variation in females in a genome-wide association study. PLoS ONE 3:e3160 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Liu YZ, Pei YF, Liu JF, Yang F, Guo Y, Zhang L, Liu XG, Yan H, Wang L, Zhang YP, Levy S, Recker RR, Deng HW 2009 Powerful bivariate genome-wide association analyses suggest the SOX6 gene influencing both obesity and osteoporosis phenotypes in males. PLoS One 4:e6827 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Zeggini E, Ioannidis JP 2009 Meta-analysis in genome-wide association studies. Pharmacogenomics 10:191–201 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ionita-Laza I, Rogers AJ, Lange C, Raby BA, Lee C 2009 Genetic association analysis of copy-number variation (CNV) in human disease pathogenesis. Genomics 93:22–26 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kimura H, Kwan KM, Zhang Z, Deng JM, Darnay BG, Behringer RR, Nakamura T, de Crombrugghe B, Akiyama H 2008 Cthrc1 is a positive regulator of osteoblastic bone formation. PLoS ONE 3:e3174 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ding KH, Shi XM, Zhong Q, Kang B, Xie D, Bollag WB, Bollag RJ, Hill W, Washington W, Mi QS, Insogna K, Chutkan N, Hamrick M, Isales CM 2008 Impact of glucose-dependent insulinotropic peptide on age-induced bone loss. J Bone Miner Res 23:536–543 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Mödder UI, Sanyal A, Xu J, O'Malley BW, Spelsberg TC, Khosla S 2008 The skeletal response to estrogen is impaired in female but not in male steroid receptor coactivator (SRC)-1 knock out mice. Bone 42:414–421 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Palmieri A, Pezzetti F, Spinelli G, Arlotti M, Avantaggiato A, Scarano A, Scapoli L, Zollino I, Carinci F 2008 PerioGlas regulates osteoblast RNA interfering. J Prosthodont 17:522–526 [DOI] [PubMed] [Google Scholar]
- Annalisa P, Furio P, Ilaria Z, Anna A, Luca S, Marcella M, Marzia A, Elena M, Carinci F 2008 An organic bovine bone and a silicate-based synthetic bone activate different microRNAs. J Oral Sci 50:301–307 [DOI] [PubMed] [Google Scholar]
- Palmieri A, Pezzetti F, Avantaggiato A, Lo Muzio L, Scarano A, Rubini C, Guerzoni L, Arlotti M, Ventorre D, Carinci F 2008 Titanium acts on osteoblast translational process. J Oral Implantol 34:190–195 [DOI] [PubMed] [Google Scholar]
- Palmieri A, Pezzetti F, Brunelli G, Arlotti M, Lo Muzio L, Scarano A, Rubini C, Sollazzo V, Massari L, Carinci F 2008 Anatase nanosurface regulates microRNAs. J Craniofac Surg 19:328–333 [DOI] [PubMed] [Google Scholar]
- Palmieri A, Pezzetti F, Brunelli G, Lo Muzio L, Scarano A, Scapoli L, Martinelli M, Arlotti M, Guerzoni L, Rubini C, Carinci F 2008 Short-period effects of zirconia and titanium on osteoblast microRNAs. Clin Implant Dent Relat Res 10:200–205 [DOI] [PubMed] [Google Scholar]
- Palmieri A, Pezzetti F, Brunelli G, Martinelli M, Scapoli L, Arlotti M, Masiero E, Carinci F 2008 Medpor regulates osteoblast’s microRNAs. Biomed Mater Eng 18:91–97 [PubMed] [Google Scholar]
- Palmieri A, Pezzetti F, Brunelli G, Scapoli L, Lo Muzio L, Scarano A, Martinelli M, Carinci F 2008 Calcium sulfate acts on the miRNA of MG63E osteoblast-like cells. J Biomed Mater Res B Appl Biomater 84:369–374 [DOI] [PubMed] [Google Scholar]
- Palmieri A, Pezzetti F, Brunelli G, Zollino I, Lo Muzio L, Martinelli M, Scapoli L, Arlotti M, Masiero E, Carinci F 2008 Zirconium oxide regulates RNA interfering of osteoblast-like cells. J Mater Sci Mater Med 19:2471–2476 [DOI] [PubMed] [Google Scholar]
- Bombonato-Prado KF, Bellesini LS, Junta CM, Marques MM, Passos GA, Rosa AL 2009 Microarray-based gene expression analysis of human osteoblasts in response to different biomaterials. J Biomed Mater Res A 88:401–408 [DOI] [PubMed] [Google Scholar]
- Carinci F, Piattelli A, Degidi M, Palmieri A, Perrotti V, Scapoli L, Martinelli M, Zuccarino L, Pezzetti F 2007 Effects of demineralized freeze-dried bone allograft on gene expression of osteoblastlike MG63 cells. Int J Periodontics Restorative Dent 27:596–601 [PubMed] [Google Scholar]
- Tsuruoka N, Yamato R, Sakai Y, Yoshitake Y, Yonekura H 2007 Promotion by collagen tripeptide of type I collagen gene expression in human osteoblastic cells and fracture healing of rat femur. Biosci Biotechnol Biochem 71:2680–2687 [DOI] [PubMed] [Google Scholar]
- Matsubara T, Kida K, Yamaguchi A, Hata K, Ichida F, Meguro H, Aburatani H, Nishimura R, Yoneda T 2008 BMP2 regulates Osterix through Msx2 and Runx2 during osteoblast differentiation. J Biol Chem 283:29119–29125 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Silvestris F, Cafforio P, De Matteo M, Calvani N, Frassanito MA, Dammacco F 2008 Negative regulation of the osteoblast function in multiple myeloma through the repressor gene E4BP4 activated by malignant plasma cells. Clin Cancer Res 14:6081–6091 [DOI] [PubMed] [Google Scholar]
- Lei SF, Wu S, Li LM, Deng FY, Xiao SM, Jiang C, Chen Y, Jiang H, Yang F, Tan LJ, Sun X, Zhu XZ, Liu MY, Liu YZ, Chen XD, Deng HW 2009 An in vivo genome wide gene expression study of circulating monocytes suggested GBP1, STAT1 and CXCL10 as novel risk genes for the differentiation of peak bone mass. Bone 44:1010–1014 [DOI] [PubMed] [Google Scholar]
- Nakao K, Aoyama M, Fukuoka H, Fujita M, Miyazawa K, Asai K, Goto S 2009 IGF2 modulates the microenvironment for osteoclastogenesis. Biochem Biophys Res Commun 378:462–466 [DOI] [PubMed] [Google Scholar]
- Battaglino RA, Pham L, Morse LR, Vokes M, Sharma A, Odgren PR, Yang M, Sasaki H, Stashenko P 2008 NHA-oc/NHA2: a mitochondrial cation-proton antiporter selectively expressed in osteoclasts. Bone 42:180–192 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kominsky SL, Abdelmagid SM, Doucet M, Brady K, Weber KL 2008 Macrophage inflammatory protein-1 δ: a novel osteoclast stimulating factor secreted by renal cell carcinoma bone metastasis. Cancer Res 68:1261–1266 [DOI] [PubMed] [Google Scholar]
- Tanabe S, Sato Y, Suzuki T, Suzuki K, Nagao T, Yamaguchi T 2008 Gene expression profiling of human mesenchymal stem cells for identification of novel markers in early- and late-stage cell culture. J Biochem 144:399–408 [DOI] [PubMed] [Google Scholar]
- Xiao J, Wu Y, Chen R, Lin Y, Wu L, Tian W, Liu L 2008 Expression of Pcp4 gene during osteogenic differentiation of bone marrow mesenchymal stem cells in vitro. Mol Cell Biochem 309:143–150 [DOI] [PubMed] [Google Scholar]
- Zhao D, Wu H, Li F, Li R, Tao C 2008 Electromagnetic field change the expression of osteogenesis genes in murine bone marrow mesenchymal stem cells. J Huazhong Univ Sci Technolog Med Sci 28:152–155 [DOI] [PubMed] [Google Scholar]
- Dai ZQ, Wang R, Ling SK, Wan YM, Li YH 2007 Simulated microgravity inhibits the proliferation and osteogenesis of rat bone marrow mesenchymal stem cells. Cell Prolif 40:671–684 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kawazoe Y, Katoh S, Onodera Y, Kohgo T, Shindoh M, Shiba T 2008 Activation of the FGF signaling pathway and subsequent induction of mesenchymal stem cell differentiation by inorganic polyphosphate. Int J Biol Sci 4:37–47 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Pekkinen M, Ahlström ME, Riehle U, Huttunen MM, Lamberg-Allardt CJ 2008 Effects of phosphodiesterase 7 inhibition by RNA interference on the gene expression and differentiation of human mesenchymal stem cell-derived osteoblasts. Bone 43:84–91 [DOI] [PubMed] [Google Scholar]
- Hopwood B, Tsykin A, Findlay DM, Fazzalari NL 2007 Microarray gene expression profiling of osteoarthritic bone suggests altered bone remodelling, WNT and transforming growth factor-β/bone morphogenic protein signalling. Arthritis Res Ther 9:R100 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hopwood B, Tsykin A, Findlay DM, Fazzalari NL 2009 Gene expression profile of the bone microenvironment in human fragility fracture bone. Bone 44:87–101 [DOI] [PubMed] [Google Scholar]
- Ryaby JT, Sheller MR, Levine BP, Bramlet DG, Ladd AL, Carney DH 2006 Thrombin peptide TP508 stimulates cellular events leading to angiogenesis, revascularization, and repair of dermal and musculoskeletal tissues. J Bone Joint Surg Am 88(Suppl 3):132–139 [DOI] [PubMed] [Google Scholar]
- Li X, Wang H, Touma E, Qi Y, Rousseau E, Quigg RJ, Ryaby JT 2007 TP508 accelerates fracture repair by promoting cell growth over cell death. Biochem Biophys Res Commun 364:187–193 [DOI] [PubMed] [Google Scholar]
- Kojima N, Ozawa S, Miyata Y, Hasegawa H, Tanaka Y, Ogawa T 2008 High-throughput gene expression analysis in bone healing around titanium implants by DNA microarray. Clin Oral Implants Res 19:173–181 [DOI] [PubMed] [Google Scholar]
- Li X, Liu H, Qin L, Tamasi J, Bergenstock M, Shapses S, Feyen JH, Notterman DA, Partridge NC 2007 Determination of dual effects of parathyroid hormone on skeletal gene expression in vivo by microarray and network analysis. J Biol Chem 282:33086–33097 [DOI] [PubMed] [Google Scholar]
- Allan EH, Häusler KD, Wei T, Gooi JH, Quinn JM, Crimeen-Irwin B, Pompolo S, Sims NA, Gillespie MT, Onyia JE, Martin TJ 2008 EphrinB2 regulation by PTH and PTHrP revealed by molecular profiling in differentiating osteoblasts. J Bone Miner Res 23:1170–1181 [DOI] [PubMed] [Google Scholar]
- Yao W, Cheng Z, Busse C, Pham A, Nakamura MC, Lane NE 2008 Glucocorticoid excess in mice results in early activation of osteoclastogenesis and adipogenesis and prolonged suppression of osteogenesis: a longitudinal study of gene expression in bone tissue from glucocorticoid-treated mice. Arthritis Rheum 58:1674–1686 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Mori K, Berreur M, Blanchard F, Chevalier C, Guisle- Marsollier I, Masson M, Rédini F, Heymann D 2007 Receptor activator of nuclear factor-κB ligand (RANKL) directly modulates the gene expression profile of RANK-positive Saos-2 human osteosarcoma cells. Oncol Rep 18:1365–1371 [PubMed] [Google Scholar]
- Onan D, Allan EH, Quinn JM, Gooi JH, Pompolo S, Sims NA, Gillespie MT, Martin TJ 2009 The chemokine Cxcl1 is a novel target gene of parathyroid hormone (PTH)/PTH-related protein in committed osteoblasts. Endocrinology 150:2244–2253 [DOI] [PubMed] [Google Scholar]
- Zou X, Zou L, Foldager C, Bendtsen M, Feng W, Bünger CE 2009 Different mechanisms of spinal fusion using equine bone protein extract, rhBMP-2 and autograft during the process of anterior lumbar interbody fusion. Biomaterials 30:991–1004 [DOI] [PubMed] [Google Scholar]
- Wilson TJ, Nannuru KC, Futakuchi M, Sadanandam A, Singh RK 2008 Cathepsin G enhances mammary tumor-induced osteolysis by generating soluble receptor activator of nuclear factor-κB ligand. Cancer Res 68:5803–5811 [DOI] [PubMed] [Google Scholar]
- Kishiya M, Sawada T, Kanemaru K, Kudo H, Numasawa T, Yokoyama T, Tanaka S, Motomura S, Ueyama K, Harata S, Toh S, Furukawa K 2008 A functional RNAi screen for Runx2-regulated genes associated with ectopic bone formation in human spinal ligaments. J Pharmacol Sci 106:404–414 [DOI] [PubMed] [Google Scholar]
- Fukui N, Miyamoto Y, Nakajima M, Ikeda Y, Hikita A, Furukawa H, Mitomi H, Tanaka N, Katsuragawa Y, Yamamoto S, Sawabe M, Juji T, Mori T, Suzuki R, Ikegawa S 2008 Zonal gene expression of chondrocytes in osteoarthritic cartilage. Arthritis Rheum 58:3843–3853 [DOI] [PubMed] [Google Scholar]
- Karlsson C, Brantsing C, Egell S, Lindahl A 2008 Notch1, Jagged1, and HES5 are abundantly expressed in osteoarthritis. Cells Tissues Organs 188:287–298 [DOI] [PubMed] [Google Scholar]
- Sánchez-Sabaté E, Alvarez L, Gil-Garay E, Munuera L, Vilaboa N 2009 Identification of differentially expressed genes in trabecular bone from the iliac crest of osteoarthritic patients. Osteoarthritis Cartilage 17:1106–1114 [DOI] [PubMed] [Google Scholar]
- Abbott A 1999 A post-genomic challenge: learning to read patterns of protein synthesis. Nature 402:715–720 [DOI] [PubMed] [Google Scholar]
- Anderson L, Seilhamer J 1997 A comparison of selected mRNA and protein abundances in human liver. Electrophoresis 18:533–537 [DOI] [PubMed] [Google Scholar]
- Weissinger EM, Zürbig P, Ganser A 2009 Proteomics studies after hematopoietic stem cell transplantation. Methods Mol Biol 506:437–452 [DOI] [PubMed] [Google Scholar]
- Bennett KP, Bergeron C, Acar E, Klees RF, Vandenberg SL, Yener B, Plopper GE 2007 Proteomics reveals multiple routes to the osteogenic phenotype in mesenchymal stem cells. BMC Genomics 8:380 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Coutu DL, Wu JH, Monette A, Rivard GE, Blostein MD, Galipeau J 2008 Periostin, a member of a novel family of vitamin K-dependent proteins, is expressed by mesenchymal stromal cells. J Biol Chem 283:17991–18001 [DOI] [PubMed] [Google Scholar]
- Mareddy S, Broadbent J, Crawford R, Xiao Y 2009 Proteomic profiling of distinct clonal populations of bone marrow mesenchymal stem cells. J Cell Biochem 106:776–786 [DOI] [PubMed] [Google Scholar]
- Celebi B, Elçin YM 2009 Proteome analysis of rat bone marrow mesenchymal stem cell subcultures. J Proteome Res 8:2164–2172 [DOI] [PubMed] [Google Scholar]
- Lee SK, Kim Y, Kim SS, Lee JH, Cho K, Lee SS, Lee ZW, Kwon KH, Kim YH, Suh-Kim H, Yoo JS, Park YM 2009 Differential expression of cell surface proteins in human bone marrow mesenchymal stem cells cultured with or without basic fibroblast growth factor containing medium. Proteomics 9:4389–4405 [DOI] [PubMed] [Google Scholar]
- Xu J, Khor KA, Sui J, Zhang J, Tan TL, Chen WN 2008 Comparative proteomics profile of osteoblasts cultured on dissimilar hydroxyapatite biomaterials: an iTRAQ-coupled 2-D LC-MS/MS analysis. Proteomics 8:4249–4258 [DOI] [PubMed] [Google Scholar]
- Gorski JP, Huffman NT, Cui C, Henderson EP, Midura RJ, Seidah NG 2009 Potential role of proprotein convertase SKI-1 in the mineralization of primary bone. Cells Tissues Organs 189:25–32 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Folio C, Mora MI, Zalacain M, Corrales FJ, Segura V, Sierrasesúmaga L, Toledo G, San-Julián M, Patiño-García A 2009 Proteomic analysis of chemonaive pediatric osteosarcomas and corresponding normal bone reveals multiple altered molecular targets. J Proteome Res 8:3882–3888 [DOI] [PubMed] [Google Scholar]
- Xu JL, Khor KA, Sui JJ, Zhang JH, Chen WN 2009 Protein expression profiles in osteoblasts in response to differentially shaped hydroxyapatite nanoparticles. Biomaterials 30:5385–5391 [DOI] [PubMed] [Google Scholar]
- Sun ZF, Jiang H, Ye ZQ, Jia B, Zhang XL, Zhang KQ 2009 Expression of Rho GDIα in rat osteoblasts intermittently exposed to parathyroid hormone in vitro and in vivo. Acta Pharmacol Sin 30:1001–1007 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Chang EJ, Ha J, Oerlemans F, Lee YJ, Lee SW, Ryu J, Kim HJ, Lee Y, Kim HM, Choi JY, Kim JY, Shin CS, Pak YK, Tanaka S, Wieringa B, Lee ZH, Kim HH 2008 Brain-type creatine kinase has a crucial role in osteoclast-mediated bone resorption. Nat Med 14:966–972 [DOI] [PubMed] [Google Scholar]
- Ha BG, Hong JM, Park JY, Ha MH, Kim TH, Cho JY, Ryoo HM, Choi JY, Shin HI, Chun SY, Kim SY, Park EK 2008 Proteomic profile of osteoclast membrane proteins: identification of Na+/H+ exchanger domain containing 2 and its role in osteoclast fusion. Proteomics 8:2625–2639 [DOI] [PubMed] [Google Scholar]
- Yoon SH, Lee Y, Kim HJ, Lee ZH, Hyung SW, Lee SW, Kim HH 2009 Lyn inhibits osteoclast differentiation by interfering with PLCγ1-mediated Ca2+ signaling. FEBS Lett 583:1164–1170 [DOI] [PubMed] [Google Scholar]
- Heckel T, Czupalla C, Expirto Santo AI, Anitei M, Arantzazu Sanchez-Fernandez M, Mosch K, Krause E, Hoflack B 2009 Src-dependent repression of ARF6 is required to maintain podosome-rich sealing zones in bone-digesting osteoclasts. Proc Natl Acad Sci USA 106:1451–1456 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Masuko K, Murata M, Xiang Y, Nakamura H, Yudoh K, Nishioka K, Beppu M, Kato T 2007 Tryptase enhances release of vascular endothelial growth factor from human osteoarthritic chondrocytes. Clin Exp Rheumatol 25:860–865 [PubMed] [Google Scholar]
- Yang G, Sun Q, Teng Y, Li F, Weng T, Yang X 2008 PTEN deficiency causes dyschondroplasia in mice by enhanced hypoxia-inducible factor 1α signaling and endoplasmic reticulum stress. Development 135:3587–3597 [DOI] [PubMed] [Google Scholar]
- Kantawong F, Burchmore R, Wilkinson CD, Oreffo RO, Dalby MJ 2009 Differential in-gel electrophoresis (DIGE) analysis of human bone marrow osteoprogenitor cell contact guidance. Acta Biomater 5:1137–1146 [DOI] [PubMed] [Google Scholar]
- Niforou KM, Anagnostopoulos AK, Vougas K, Kittas C, Gorgoulis VG, Tsangaris GT 2008 The proteome profile of the human osteosarcoma U2OS cell line. Cancer Genomics Proteomics 5:63–78 [PubMed] [Google Scholar]
- Deng FY, Liu YZ, Li LM, Jiang C, Wu S, Chen Y, Jiang H, Yang F, Xiong JX, Xiao P, Xiao SM, Tan LJ, Sun X, Zhu XZ, Liu MY, Lei SF, Chen XD, Xie JY, Xiao GG, Liang SP, Deng HW 2008 Proteomic analysis of circulating monocytes in Chinese premenopausal females with extremely discordant bone mineral density. Proteomics 8:4259- 4272 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Guo D, Tan W, Wang F, Lv Z, Hu J, Lv T, Chen Q, Gu X, Wan B, Zhang Z 2008 Proteomic analysis of human articular cartilage: identification of differentially expressed proteins in knee osteoarthritis. Joint Bone Spine 75:439–444 [DOI] [PubMed] [Google Scholar]
- Iliopoulos D, Malizos KN, Oikonomou P, Tsezou A 2008 Integrative microRNA and proteomic approaches identify novel osteoarthritis genes and their collaborative metabolic and inflammatory networks. PLoS ONE 3:e3740 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ling SM, Patel DD, Garnero P, Zhan M, Vaduganathan M, Muller D, Taub D, Bathon JM, Hochberg M, Abernethy DR, Metter EJ, Ferrucci L 2009 Serum protein signatures detect early radiographic osteoarthritis. Osteoarthritis Cartilage 17:43–48 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Wu RW, Wang FS, Ko JY, Wang CJ, Wu SL 2008 Comparative serum proteome expression of osteonecrosis of the femoral head in adults. Bone 43:561–566 [DOI] [PubMed] [Google Scholar]
- Kawai A, Kondo T, Suehara Y, Kikuta K, Hirohashi S 2008 Global protein-expression analysis of bone and soft tissue sarcomas. Clin Orthop Relat Res 466:2099–2106 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Noonan KE, Roninson IB 1988 mRNA phenotyping by enzymatic amplification of randomly primed cDNA. Nucleic Acids Res 16:10366 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Soejima H 2009 Epigenetics-related diseases and analytic methods. Rinsho Byori 57:769–778 [PubMed] [Google Scholar]
- Kitazawa R, Kitazawa S 2007 Methylation status of a single CpG locus 3 bases upstream of TATA-box of receptor activator of nuclear factor-κB ligand (RANKL) gene promoter modulates cell- and tissue-specific RANKL expression and osteoclastogenesis. Mol Endocrinol 21:148–158 [DOI] [PubMed] [Google Scholar]
- Shen J, Hovhannisyan H, Lian JB, Montecino MA, Stein GS, Stein JL, Van Wijnen AJ 2003 Transcriptional induction of the osteocalcin gene during osteoblast differentiation involves acetylation of histones h3 and h4. Mol Endocrinol 17:743–756 [DOI] [PubMed] [Google Scholar]
- Goodyear S, Gregory JS, Barr RJ, McCloskey E, Alesci S, Aspden R, Reid D, Can active shape modeling be used to generate an additional risk factor for hip fracture? Proc 31st Meeting of the American Society for Bone and Mineral Research, Denver, CO, 2009, p S133 (Abstract) [Google Scholar]
- Karlamangla AS, Barrett-Connor E, Young J, Greendale GA 2004 Hip fracture risk assessment using composite indices of femoral neck strength: the Rancho Bernardo study. Osteoporos Int 15:62–70 [DOI] [PubMed] [Google Scholar]
- Dong SS, Liu XG, Chen Y, Guo Y, Wang L, Zhao J, Xiong DH, Xu XH, Recker RR, Deng HW 2009 Association analyses of RANKL/RANK/OPG gene polymorphisms with femoral neck compression strength index variation in Caucasians. Calcif Tissue Int 85:104–112 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Xu XH, Xiong DH, Liu XG, Guo Y, Chen Y, Zhao J, Recker RR, Deng HW 2010 Association analyses of vitamin D-binding protein gene with compression strength index variation in Caucasian nuclear families. Osteoporos Int 21:99–107 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Guéguen R, Jouanny P, Guillemin F, Kuntz C, Pourel J, Siest G 1995 Segregation analysis and variance components analysis of bone mineral density in healthy families. J Bone Miner Res 10:2017–2022 [DOI] [PubMed] [Google Scholar]
- Chen Y, Xiong DH, Guo YF, Pan F, Zhou Q, Zhang F, Deng HW 2010 Pathway-based genome-wide association analysis identified the importance of EphrinA-EphR pathway for femoral neck bone geometry. Bone 46:129–136 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Piters E, Boudin E, Van Hul W 2008 Wnt signaling: a win for bone. Arch Biochem Biophys 473:112–116 [DOI] [PubMed] [Google Scholar]
- Martin TJ, Sims NA, Ng KW 2008 Regulatory pathways revealing new approaches to the development of anabolic drugs for osteoporosis. Osteoporos Int 19:1125–1138 [DOI] [PubMed] [Google Scholar]
- Ichikawa S, Koller DL, Curry LR, Lai D, Xuei X, Edenberg HJ, Hui SL, Peacock M, Foroud T, Econs MJ 2009 Association of adenylate cyclase 10 (ADCY10) polymorphisms and bone mineral density in healthy adults. Calcif Tissue Int 84:97–102 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Lazáry A, Kósa JP, Tóbiás B, Lazáry J, Balla B, Bácsi K, Takács I, Nagy Z, Mezo T, Speer G, Lakatos P 2008 Single nucleotide polymorphisms in new candidate genes are associated with bone mineral density and fracture risk. Eur J Endocrinol 159:187–196 [DOI] [PubMed] [Google Scholar]
- Foroud T, Ichikawa S, Koller D, Lai D, Curry L, Xuei X, Edenberg HJ, Hui S, Peacock M, Econs MJ 2008 Association studies of ALOX5 and bone mineral density in healthy adults. Osteoporos Int 19:637–643 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Cheung CL, Chan V, Kung AW 2008 A differential association of ALOX15 polymorphisms with bone mineral density in pre- and post-menopausal women. Hum Hered 65:1–8 [DOI] [PubMed] [Google Scholar]
- Kim TH, Hong JM, Shin ES, Kim HJ, Cho YS, Lee JY, Lee SH, Park EK, Kim SY 2009 Polymorphisms in the Annexin gene family and the risk of osteonecrosis of the femoral head in the Korean population. Bone 45:125–131 [DOI] [PubMed] [Google Scholar]
- Yerges LM, Klei L, Cauley JA, Roeder K, Kammerer CM, Moffett SP, Ensrud KE, Nestlerode CS, Marshall LM, Hoffman AR, Lewis C, Lang TF, Barrett-Connor E, Ferrell RE, Orwoll ES, Zmuda JM 2009 High-density association study of 383 candidate genes for volumetric bone density at the femoral neck and lumbar spine among older men. J Bone Miner Res 24:2039–2049 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Stiger F, Brändström H, Gillberg P, Melhus H, Wolk A, Michaelsson K, Kindmark A 2008 Association between repeat length of exon 1 CAG microsatellite in the androgen receptor and bone density in men is modulated by sex hormone levels. Calcif Tissue Int 82:427–435 [DOI] [PubMed] [Google Scholar]
- Limer KL, Pye SR, Thomson W, Boonen S, Borghs H, Vanderschueren D, Huhtaniemi IT, Adams JE, Ward KA, Platt H, Payne D, John SL, Bartfai G, Casanueva F, Finn JD, Forti G, Giwercman A, Han TS, Kula K, Lean ME, Pendleton N, Punab M, Silman AJ, Wu FC, O'Neill TW 2009 Genetic variation in sex hormone genes influences heel ultrasound parameters in middle-aged and elderly men: results from the European Male Aging Study (EMAS). J Bone Miner Res 24:314–323 [DOI] [PubMed] [Google Scholar]
- Mullin BH, Prince RL, Dick IM, Hart DJ, Spector TD, Dudbridge F, Wilson SG 2008 Identification of a role for the ARHGEF3 gene in postmenopausal osteoporosis. Am J Hum Genet 82:1262–1269 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Wang H, Liu D, Yang Z, Tian B, Li J, Meng X, Wang Z, Yang H, Lin X 2008 Association of bone morphogenetic protein-2 gene polymorphisms with susceptibility to ossification of the posterior longitudinal ligament of the spine and its severity in Chinese patients. Eur Spine J 17:956–964 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Freedman BI, Bowden DW, Ziegler JT, Langefeld CD, Lehtinen AB, Rudock ME, Lenchik L, Hruska KA, Register TC, Carr JJ 2009 Bone morphogenetic protein 7 (BMP7) gene polymorphisms are associated with inverse relationships between vascular calcification and BMD: the Diabetes Heart Study. J Bone Miner Res 24:1719–1727 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Mori S, Kou I, Sato H, Emi M, Ito H, Hosoi T, Ikegawa S 2009 Nucleotide variations in genes encoding carbonic anhydrase 8 and 10 associated with femoral bone mineral density in Japanese female with osteoporosis. J Bone Miner Metab 27:213–216 [DOI] [PubMed] [Google Scholar]
- Poulou M, Kaliakatsos M, Tsezou A, Kanavakis E, Malizos KN, Tzetis M 2008 Association of the CALM1 core promoter polymorphism with knee osteoarthritis in patients of Greek origin. Genet Test 12:263–265 [DOI] [PubMed] [Google Scholar]
- Urano T, Usui T, Shiraki M, Ouchi Y, Inoue S 2009 Association of a single nucleotide polymorphism in the constitutive androstane receptor gene with bone mineral density. Geriatr Gerontol Int 9:235–241 [DOI] [PubMed] [Google Scholar]
- Li X, He GP, Su YX 2008 Effect of interleukin-6 and calcium sensing receptor gene polymorphisms on bone mass accrual in Chinese adolescent girls. Zhonghua Yi Xue Yi Chuan Xue Za Zhi 25:136–140 [PubMed] [Google Scholar]
- Pineda B, Laporta P, Hermenegildo C, Cano A, García-Pérez MA 2008 A C>T polymorphism located at position −1 of the Kozak sequence of CD40 gene is associated with low bone mass in Spanish postmenopausal women. Osteoporos Int 19:1147–1152 [DOI] [PubMed] [Google Scholar]
- Chu K, Koller DL, Ichikawa S, Snyder R, Curry L, Lai D, Austin A, Xuei X, Edenberg HJ, Hui SL, Foroud TM, Peacock M, Econs MJ 2008 CLCN7 polymorphisms and bone mineral density in healthy premenopausal white women and in white men. Bone 43:995–998 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Karsak M, Malkin I, Toliat MR, Kubisch C, Nürnberg P, Zimmer A, Livshits G 2009 The cannabinoid receptor type 2 (CNR2) gene is associated with hand bone strength phenotypes in an ethnically homogeneous family sample. Hum Genet 126:629–636 [DOI] [PubMed] [Google Scholar]
- Huang QY, Li GH, Kung AW 2009 Multiple osteoporosis susceptibility genes on chromosome 1p36 in Chinese. Bone 44:984–988 [DOI] [PubMed] [Google Scholar]
- Dinçel E, Sepici-Dinçel A, Sepici V, Ozsoy H, Sepici B 2008 Hip fracture risk and different gene polymorphisms in the Turkish population. Clinics 63:645–650 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Selezneva LI, Khusainova RI, Nurlygaianov RZ, Fazlyeva EA, Usenko KP, Lesniak OM, Khucnutdinova EK 2008 Association of polymorphisms and haplotypes in the 5` region of COLIA1 gene with the risk of osteoporotic fractures in Russian women from Volga-Ural region. Genetika 44:219–225 [PubMed] [Google Scholar]
- Eriksson AL, Mellström D, Lorentzon M, Orwoll ES, Redlund-Johnell I, Grundberg E, Holmberg A, Ljunggren O, Karlsson MK, Ohlsson C 2008 The COMT val158met polymorphism is associated with prevalent fractures in Swedish men. Bone 42:107–112 [DOI] [PubMed] [Google Scholar]
- Jones TS, Kaste SC, Liu W, Cheng C, Yang W, Tantisira KG, Pui CH, Relling MV 2008 CRHR1 polymorphisms predict bone density in survivors of acute lymphoblastic leukemia. J Clin Oncol 26:3031–3037 [DOI] [PubMed] [Google Scholar]
- Li GH, Kung AW, Huang QY 1 September 2009 Common variants in FLNB/CRTAP, not ARHGEF3 at 3p, are associated with osteoporosis in southern Chinese women. Osteoporos Int DOI: 10.1007/s00198-009-1043-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kang BY, Kim JY, Lee KO 2007 Association between an AluI polymorphism in the calcitonin receptor gene and quantitative ultrasound parameters in Korean men. Med Princ Pract 16:389–393 [DOI] [PubMed] [Google Scholar]
- Charopoulos I, Trovas G, Stathopoulou M, Kyriazopoulos P, Galanos A, Dedoussis G, Antonogiannakis E, Lyritis GP 2008 Lack of association between vitamin D and calcitonin receptor gene polymorphisms and forearm bone values of young Greek males. J Musculoskelet Neuronal Interact 8:196–203 [PubMed] [Google Scholar]
- Napoli N, Rini GB, Serber D, Giri T, Yarramaneni J, Bucchieri S, Camarda L, Di Fede G, Camarda MR, Jain S, Mumm S, Armamento-Villareal R 2009 The Val432Leu polymorphism of the CYP1B1 gene is associated with differences in estrogen metabolism and bone density. Bone 44:442–448 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Quan J, Yahata T, Tamura N, Nagata H, Tanaka K 2009 Relationship between single nucleotide polymorphisms in CYP1A1 and CYP1B1 genes and the bone mineral density and serum lipid profiles in postmenopausal Japanese women taking hormone therapy. Menopause 16:171–176 [DOI] [PubMed] [Google Scholar]
- Riancho JA, Sañudo C, Valero C, Pipaón C, Olmos JM, Mijares V, Fernández-Luna JL, Zarrabeitia MT 2009 Association of the aromatase gene alleles with BMD: epidemiological and functional evidence. J Bone Miner Res 24:1709–1718 [DOI] [PubMed] [Google Scholar]
- Cheung CL, Livshits G, Zhou Y, Meigs JB, McAteer JB, Florez JC, Cupples LA, Demissie S, Kiel DP, Karasik D 5 November 2009 Hip geometry variation is associated with bone mineralization pathway gene variants: The Framingham Study. J Bone Miner Res DOI: 10.1359/jbmr.091102 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Yang LC, Piao JH, Dong J, Huang ZW, Zhang Q, Ma GS 2007 Study on the association between estrogen receptor-α gene polymorphisms and pubertal skeletal developmental status in China. Zhonghua Liu Xing Bing Xue Za Zhi 28:540–543 [PubMed] [Google Scholar]
- Geng L, Yao Z, Yang H, Luo J, Han L, Lu Q 2007 Association of CA repeat polymorphism in estrogen receptor β gene with postmenopausal osteoporosis in Chinese. J Genet Genomics 34:868–876 [DOI] [PubMed] [Google Scholar]
- Giroux S, Elfassihi L, Cole DE, Rousseau F 2008 Replication of associations between LRP5 and ESRRA variants and bone density in premenopausal women. Osteoporos Int 19:1769–1775 [DOI] [PubMed] [Google Scholar]
- Wilson SG, Jones MR, Mullin BH, Dick IM, Richards JB, Pastinen TM, Grundberg E, Ljunggren O, Surdulescu GL, Dudbridge F, Elliott KS, Cervino AC, Spector TD, Prince RL 2009 Common sequence variation in FLNB regulates bone structure in women in the general population and FLNB mRNA expression in osteoblasts in vitro. J Bone Miner Res 24:1989–1997 [DOI] [PubMed] [Google Scholar]
- Yerges LM, Zhang Y, Cauley JA, Kammerer CM, Nestlerode CS, Wheeler VW, Patrick AL, Bunker CH, Moffett SP, Ferrell RE, Zmuda JM 2009 Functional characterization of genetic variation in the Frizzled 1 (FZD1) promoter and association with bone phenotypes: more to the LRP5 story? J Bone Miner Res 24:87–96 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kim JG, Kim H, Jee BC, Suh CS, Choi YM, Moon SY 2009 Non-association between polymorphisms of the frizzled receptor genes and bone mineral density in postmenopausal Korean women. J Korean Med Sci 24:443–447 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Southam L, Rodriguez-Lopez J, Wilkins JM, Pombo- Suarez M, Snelling S, Gomez-Reino JJ, Chapman K, Gonzalez A, Loughlin J 2007 An SNP in the 5`-UTR of GDF5 is associated with osteoarthritis susceptibility in Europeans and with in vivo differences in allelic expression in articular cartilage. Hum Mol Genet 16:2226–2232 [DOI] [PubMed] [Google Scholar]
- Vaes RB, Rivadeneira F, Kerkhof JM, Hofman A, Pols HA, Uitterlinden AG, van Meurs JB 2009 Genetic variation in the GDF5 region is associated with osteoarthritis, height, hip axis length and fracture risk: the Rotterdam study. Ann Rheum Dis 68:1754–1760 [DOI] [PubMed] [Google Scholar]
- Dennison EM, Syddall HE, Jameson KA, Sayer AA, Gaunt TR, Rodriguez S, Day IN, Cooper C, Lips MA 2009 A study of relationships between single nucleotide polymorphisms from the growth hormone-insulin-like growth factor axis and bone mass: the Hertfordshire Cohort Study. J Rheumatol 36:1520–1526 [DOI] [PubMed] [Google Scholar]
- Peng YM, Lei SF, Guo Y, Xiong DH, Yan H, Wang L, Guo YF, Deng HW 2008 Sex-specific association of the glucocorticoid receptor gene with extreme BMD. J Bone Miner Res 23:247–252 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kuipers A, Zhang Y, Cauley JA, Nestlerode CS, Chu Y, Bunker CH, Patrick AL, Wheeler VW, Hoffman AR, Orwoll ES, Zmuda JM 2009 Association of a high mobility group gene (HMGA2) variant with bone mineral density. Bone 45:295–300 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hwang JY, Lee SH, Kim GS, Koh JM, Go MJ, Kim YJ, Kim HC, Kim TH, Hong JM, Park EK, Lee JY, Kim SY 2009 HSD11B1 polymorphisms predicted bone mineral density and fracture risk in postmenopausal women without a clinically apparent hypercortisolemia. Bone 45:1098–1103 [DOI] [PubMed] [Google Scholar]
- Magaña JJ, Gómez R, Cisneros B, Casas L, Valdés-Flores M 2008 Association of interleukin-6 gene polymorphisms with bone mineral density in Mexican women. Arch Med Res 39:618–624 [DOI] [PubMed] [Google Scholar]
- Kusek J, Seremak-Mrozikiewicz A, Drews K, Mikołajczak P, Czerny B, Maciejewska M, Bogacz A, Derebecka-Hołysz N, Barlik M, Mrozikiewicz PM 2008 The influence of interleukin-6 and tumor necrosis factor α gene polymorphisms on bone mineral density in postmenopausal women. Ginekol Pol 79:426–431 [PubMed] [Google Scholar]
- Bustamante M, Nogués X, Mellibovsky L, Agueda L, Jurado S, Cáceres E, Blanch J, Carreras R, Díez-Pérez A, Grinberg D, Balcells S 2007 Polymorphisms in the interleukin-6 receptor gene are associated with bone mineral density and body mass index in Spanish postmenopausal women. Eur J Endocrinol 157:677–684 [DOI] [PubMed] [Google Scholar]
- Koh JM, Oh B, Ha MH, Cho KW, Lee JY, Park BL, Shin HD, Bae MA, Kim HJ, Hong JM, Kim TH, Shin HI, Lee SH, Kim GS, Kim SY, Park EK 2009 Association of IL-15 polymorphisms with bone mineral density in postmenopausal Korean women. Calcif Tissue Int 85:369–378 [DOI] [PubMed] [Google Scholar]
- Kim TH, Hong JM, Oh B, Cho YS, Lee JY, Kim HL, Lee JE, Ha MH, Park EK, Kim SY 2008 Association of polymorphisms in the interleukin 23 receptor gene with osteonecrosis of femoral head in Korean population. Exp Mol Med 40:418–426 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Lee HJ, Kim SY, Koh JM, Bok J, Kim KJ, Kim KS, Park MH, Shin HD, Park BL, Kim TH, Hong JM, Park EK, Kim DJ, Oh B, Kimm K, Kim GS, Lee JY 2007 Polymorphisms and haplotypes of integrin α1 (ITGA1) are associated with bone mineral density and fracture risk in postmenopausal Koreans. Bone 41:979–986 [DOI] [PubMed] [Google Scholar]
- Kim SM, Kim SH, Lee JR, Jee BC, Ku SY, Suh CS, Choi YM, Kim JG, Moon SY 2008 Association of leptin receptor polymorphisms Lys109Arg and Gln223Arg with serum leptin profile and bone mineral density in Korean women. Am J Obstet Gynecol 198:421.e1–e8 [DOI] [PubMed] [Google Scholar]
- Urano T, Shiraki M, Usui T, Sasaki N, Ouchi Y, Inoue S 2009 A1330V variant of the low-density lipoprotein receptor-related protein 5 (LRP5) gene decreases Wnt signaling and affects the total body bone mineral density in Japanese women. Endocr J 56:625–631 [DOI] [PubMed] [Google Scholar]
- Tran BN, Nguyen ND, Eisman JA, Nguyen TV 2008 Association between LRP5 polymorphism and bone mineral density: a Bayesian meta-analysis. BMC Med Genet 9:55 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Lee YH, Woo JH, Choi SJ, Ji JD, Song GG 2009 Association between the A1330V polymorphism of the low-density lipoprotein receptor-related protein 5 gene and bone mineral density: a meta-analysis. Rheumatol Int 29:539–544 [DOI] [PubMed] [Google Scholar]
- Brixen K, Beckers S, Peeters A, Piters E, Balemans W, Nielsen TL, Wraae K, Bathum L, Brasen C, Hagen C, Andersen M, Van Hul W, Abrahamsen B 2007 Polymorphisms in the low-density lipoprotein receptor-related protein 5 (LRP5) gene are associated with peak bone mass in non-sedentary men: results from the Odense androgen study. Calcif Tissue Int 81:421–429 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Agueda L, Bustamante M, Jurado S, Garcia-Giralt N, Ciria M, Saló G, Carreras R, Nogués X, Mellibovsky L, Díez-Pérez A, Grinberg D, Balcells S 2008 A haplotype-based analysis of the LRP5 gene in relation to osteoporosis phenotypes in Spanish postmenopausal women. J Bone Miner Res 23:1954–1963 [DOI] [PubMed] [Google Scholar]
- Cheung CL, Huang QY, Chan V, Kung AW 2008 Association of low-density lipoprotein receptor-related protein 5 (LRP5) promoter SNP with peak bone mineral density in Chinese women. Hum Hered 65:232–239 [DOI] [PubMed] [Google Scholar]
- Velasco J, Zarrabeitia MT, Prieto JR, Perez-Castrillon JL, Perez-Aguilar MD, Perez-Nuñez MI, Sañudo C, Hernandez- Elena J, Calvo I, Ortiz F, Gonzalez-Macias J, Riancho JA 2010 Wnt pathway genes in osteoporosis and osteoarthritis: differential expression and genetic association study. Osteoporos Int 21:109–118 [DOI] [PubMed] [Google Scholar]
- Nissen N, Madsen JS, Bladbjerg EM, Beck Jensen JE, Jørgensen NR, Langdahl B, Abrahamsen B, Brixen K 2009 No association between hip geometry and four common polymorphisms associated with fracture: the Danish osteoporosis prevention study. Calcif Tissue Int 84:276–285 [DOI] [PubMed] [Google Scholar]
- Utriainen P, Jääskeläinen J, Saarinen A, Vanninen E, Mäkitie O, Voutilainen R 2009 Body composition and bone mineral density in children with premature adrenarche and the association of LRP5 gene polymorphisms with bone mineral density. J Clin Endocrinol Metab 94:4144–4151 [DOI] [PubMed] [Google Scholar]
- Mencej-Bedrac S, Prezelj J, Kocjan T, Komadina R, Marc J 2009 Analysis of association of LRP5, LRP6, SOST, DKK1, and CTNNB1 genes with bone mineral density in a Slovenian population. Calcif Tissue Int 85:501–506 [DOI] [PubMed] [Google Scholar]
- Cheung CL, Sham PC, Chan V, Paterson AD, Luk KD, Kung AW 2008 Identification of LTBP2 on chromosome 14q as a novel candidate gene for bone mineral density variation and fracture risk association. J Clin Endocrinol Metab 93:4448–4455 [DOI] [PubMed] [Google Scholar]
- Yang XY, Xu DH 2007 MDR1(ABCB1) gene polymorphisms associated with steroid-induced osteonecrosis of femoral head in systemic lupus erythematosus. Pharmazie 62:930–932 [PubMed] [Google Scholar]
- Zhang ZL, He JW, Qin YJ, Hu YQ, Li M, Zhang H, Hu WW, Liu YJ, Gu JM 2008 Association between myostatin gene polymorphisms and peak BMD variation in Chinese nuclear families. Osteoporos Int 19:39–47 [DOI] [PubMed] [Google Scholar]
- Steer CD, Emmett PM, Lewis SJ, Smith GD, Tobias JH 2009 Methylenetetrahydrofolate reductase (MTHFR) C677T polymorphism is associated with spinal BMD in 9-year-old children. J Bone Miner Res 24:117–124 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Moffett SP, Dillon KA, Yerges LM, Goodrich LJ, Nestlerode C, Bunker CH, Wheeler VW, Patrick AL, Zmuda JM 2009 Identification and association analysis of single nucleotide polymorphisms in the human noggin (NOG) gene and osteoporosis phenotypes. Bone 44:999–1002 [DOI] [PubMed] [Google Scholar]
- Ackert-Bicknell CL, Demissie S, Marín de Evsikova C, Hsu YH, DeMambro VE, Karasik D, Cupples LA, Ordovas JM, Tucker KL, Cho K, Canalis E, Paigen B, Churchill GA, Forejt J, Beamer WG, Ferrari S, Bouxsein ML, Kiel DP, Rosen CJ 2008 PPARG by dietary fat interaction influences bone mass in mice and humans. J Bone Miner Res 23:1398–1408 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Glueck CJ, Freiberg RA, Oghene J, Fontaine RN, Wang P 2007 Association between the T-786C eNOS polymorphism and idiopathic osteonecrosis of the head of the femur. J Bone Joint Surg Am 89:2460–2468 [DOI] [PubMed] [Google Scholar]
- Firat SC, Cetin Z, Samanci N, Aydin F, Balci N, Gungor F, Firat MZ, Luleci G, Karauzum SB 2009 Evaluation of eNOS gene polymorphisms in relation to BMD in postmenopausal women. Maturitas 63:352–356 [DOI] [PubMed] [Google Scholar]
- Goodrich LJ, Yerges-Armstrong LM, Miljkovic I, Nestlerode CS, Kuipers AL, Bunker CH, Patrick AL, Wheeler VW, Zmuda JM 2009 Molecular variation in neuropeptide Y and bone mineral density among men of African ancestry. Calcif Tissue Int 85:507–513 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Pineda B, Laporta P, Cano A, García-Pérez MA 2008 The Asn19Lys substitution in the osteoclast inhibitory lectin (OCIL) gene is associated with a reduction of bone mineral density in postmenopausal women. Calcif Tissue Int 82:348–353 [DOI] [PubMed] [Google Scholar]
- García-Unzueta MT, Riancho JA, Zarrabeitia MT, Sañudo C, Berja A, Valero C, Pesquera C, Paule B, González- Macías J, Amado JA 2008 Association of the 163A/G and 1181G/C osteoprotegerin polymorphism with bone mineral density. Horm Metab Res 40:219–224 [DOI] [PubMed] [Google Scholar]
- Zajickova K, Zemanova A, Hill M, Zofkova I 2008 Is A163G polymorphism in the osteoprotegerin gene associated with heel velocity of sound in postmenopausal women? Physiol Res 57(Suppl 1):S153–S157 [DOI] [PubMed] [Google Scholar]
- Jurado S, Nogués X, Agueda L, Garcia-Giralt N, Urreizti R, Yoskovitz G, Pérez-Edo L, Saló G, Carreras R, Mellibovsky L, Balcells S, Grinberg D, Díez-Pérez A 2010 Polymorphisms and haplotypes across the osteoprotegerin gene associated with bone mineral density and osteoporotic fractures. Osteoporos Int 21:287–296 [DOI] [PubMed] [Google Scholar]
- Cheung CL, Chan BY, Chan V, Ikegawa S, Kou I, Ngai H, Smith D, Luk KD, Huang QY, Mori S, Sham PC, Kung AW 2009 Pre-B-cell leukemia homeobox 1 (PBX1) shows functional and possible genetic association with bone mineral density variation. Hum Mol Genet 18:679–687 [DOI] [PubMed] [Google Scholar]
- Tang NL, Liao CD, Ching JK, Suen EW, Chan IH, Orwoll E, Ho SC, Chan FW, Kwok AW, Kwok T, Woo J, Leung PC 2010 Sex-specific effect of Pirin gene on bone mineral density in a cohort of 4000 Chinese. Bone 46:543–550 [DOI] [PubMed] [Google Scholar]
- Rhee EJ, Oh KW, Yun EJ, Jung CH, Park CY, Lee WY, Oh ES, Baek KH, Kang MI, Park SW, Kim SW 2007 The association of Pro12Ala polymorphism of peroxisome proliferator-activated receptor-γ gene with serum osteoprotegerin levels in healthy Korean women. Exp Mol Med 39:696–704 [DOI] [PubMed] [Google Scholar]
- Kim TH, Hong JM, Park EK, Kim SY 2007 Peroxisome proliferator-activated receptor-γ gene polymorphisms are not associated with osteonecrosis of the femoral head in the Korean population. Mol Cells 24:388–393 [PubMed] [Google Scholar]
- Tenne M, McGuigan F, Jansson L, Gerdhem P, Obrant KJ, Luthman H, Akesson K 2008 Genetic variation in the PTH pathway and bone phenotypes in elderly women: evaluation of PTH, PTHLH, PTHR1 and PTHR2 genes. Bone 42:719–727 [DOI] [PubMed] [Google Scholar]
- Gupta A, Välimäki VV, J Välimäki M, Löyttyniemi E, Richard M, L Bukka P, Goltzman D, Karaplis AC 2008 Variable number of tandem repeats polymorphism in parathyroid hormone-related protein as predictor of peak bone mass in young healthy Finnish males. Eur J Endocrinol 158:755–764 [DOI] [PubMed] [Google Scholar]
- Zupan J, Mencej-Bedrac S, Jurkoviæ-Mlakar S, Prezelj J, Marc J 2010 Gene-gene interactions in RANK/RANKL/OPG system influence bone mineral density in postmenopausal women. J Steroid Biochem Mol Biol 118:102–106 [DOI] [PubMed] [Google Scholar]
- Stolk L, van Meurs JB, Arp PP, Hofman A, Pols HA, Uitterlinden AG 2008 The RIZ Pro704 insertion-deletion polymorphism, bone mineral density and fracture risk: the Rotterdam study. Bone 42:286–293 [DOI] [PubMed] [Google Scholar]
- Ermakov S, Malkin I, Keter M, Kobyliansky E, Livshits G 2007 Family-based association study of ROR2 polymorphisms with an array of radiographic hand bone strength phenotypes. Osteoporos Int 18:1683–1692 [DOI] [PubMed] [Google Scholar]
- Ermakov S, Malkin I, Keter M, Kobyliansky E, Livshits G 2008 Family-based association study of polymorphisms in the RUNX2 locus with hand bone length and hand BMD. Ann Hum Genet 72:510–518 [DOI] [PubMed] [Google Scholar]
- Kumarasuriyar A, Lee I, Nurcombe V, Cool SM 2009 De-sulfation of MG-63 cell glycosaminoglycans delays in vitro osteogenesis, up-regulates cholesterol synthesis and disrupts cell cycle and the actin cytoskeleton. J Cell Physiol 219:572–583 [DOI] [PubMed] [Google Scholar]
- Ohnaka K, Yamamoto K, Nakamura K, Adachi M, Kawate H, Kono S, Takayanagi R 2009 Association of single nucleotide polymorphisms in secreted frizzled-related protein 1 gene with bone mineral density in Japanese women. Geriatr Gerontol Int 9:304–309 [DOI] [PubMed] [Google Scholar]
- Napoli N, Varadharajan A, Rini GB, Del Fiacco R, Yarramaneni J, Mumm S, Villareal DT, Armamento- Villareal R 2009 Effects of polymorphisms of the sex hormone-binding globulin (SHBG) gene on free estradiol and bone mineral density. Bone 45:1169–1174 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Urano T, Shiraki M, Usui T, Sasaki N, Ouchi Y, Inoue S 2009 Bone mass effects of a Smad6 gene polymorphism in Japanese postmenopausal women. J Bone Miner Metab 27:562–566 [DOI] [PubMed] [Google Scholar]
- Huang QY, Li GH, Kung AW 2009 The −9247 T/C polymorphism in the SOST upstream regulatory region that potentially affects C/EBPα and FOXA1 binding is associated with osteoporosis. Bone 45:289–294 [DOI] [PubMed] [Google Scholar]
- Delany AM, McMahon DJ, Powell JS, Greenberg DA, Kurland ES 2008 Osteonectin/SPARC polymorphisms in Caucasian men with idiopathic osteoporosis. Osteoporos Int 19:969–978 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Lee HJ, Choi SJ, Hong JM, Lee WK, Baek JI, Kim SY, Park EK, Kim SY, Kim TH, Kim UK 2009 Association of a polymorphism in the intron 7 of the SREBF1 gene with osteonecrosis of the femoral head in Koreans. Ann Hum Genet 73:34–41 [DOI] [PubMed] [Google Scholar]
- Langdahl BL, Uitterlinden AG, Ralston SH, Trikalinos TA, Balcells S, Brandi ML, Scollen S, Lips P, Lorenc R, Obermayer-Pietsch B, Reid DM, Armas JB, Arp PP, Bassiti A, Bustamante M, Husted LB, Carey AH, Pérez Cano R, Dobnig H, Dunning AM, Fahrleitner-Pammer A, Falchetti A, Karczmarewicz E, Kruk M, van Leeuwen JP, Masi L, van Meurs JB, Mangion J, McGuigan FE, Mellibovsky L, Mosekilde L, Nogués X, Pols HA, Reeve J, Renner W, Rivadeneira F, van Schoor NM, Ioannidis JP 2008 Large-scale analysis of association between polymorphisms in the transforming growth factor β 1 gene (TGFB1) and osteoporosis: the GENOMOS study. Bone 42:969–981 [DOI] [PubMed] [Google Scholar]
- Mori S, Kou I, Sato H, Emi M, Ito H, Hosoi T, Ikegawa S 2008 Association of genetic variations of genes encoding thrombospondin, type 1, domain-containing 4 and 7A with low bone mineral density in Japanese women with osteoporosis. J Hum Genet 53:694–697 [DOI] [PubMed] [Google Scholar]
- Canhao H, Fonseca JE, Caetano-Lopes J, Saldanha C, Queiroz MV 2008 Assessment of laboratory measurements and −308 TNFα gene promoter polymorphisms in normal bone mineral density. Clin Rheumatol 27:301–307 [DOI] [PubMed] [Google Scholar]
- Kim H, Chun S, Ku SY, Suh CS, Choi YM, Kim JG 2009 Association between polymorphisms in tumor necrosis factor (TNF) and TNF receptor genes and circulating TNF, soluble TNF receptor levels, and bone mineral density in postmenopausal Korean women. Menopause 16:1014- 1020 [DOI] [PubMed] [Google Scholar]
- van der Deure WM, Uitterlinden AG, Hofman A, Rivadeneira F, Pols HA, Peeters RP, Visser TJ 2008 Effects of serum TSH and FT4 levels and the TSHR-Asp727Glu polymorphism on bone: the Rotterdam Study. Clin Endocrinol (Oxf) 68:175–181 [DOI] [PubMed] [Google Scholar]
- Hwang JY, Kim SY, Lee SH, Kim GS, Go MJ, Kim SE, Kim HC, Shin HD, Park BL, Kim TH, Hong JM, Park EK, Kim HL, Lee JY, Koh JM 14 July 2009 Association of TWIST1 gene polymorphisms with bone mineral density in postmenopausal women. Osteoporos Int DOI: 10.1007/s00198-009-1009–8 [DOI] [PubMed] [Google Scholar]
- Kim TH, Hong JM, Lee JY, Oh B, Park EK, Lee CK, Bae SC, Kim SY 2008 Promoter polymorphisms of the vascular endothelial growth factor gene is associated with an osteonecrosis of the femoral head in the Korean population. Osteoarthritis Cartilage 16:287–291 [DOI] [PubMed] [Google Scholar]
- Costa N, Paramanathan S, Mac Donald D, Wierzbicki AS, Hampson G 2009 Factors regulating circulating vascular endothelial growth factor (VEGF): association with bone mineral density (BMD) in post-menopausal osteoporosis. Cytokine 46:376–381 [DOI] [PubMed] [Google Scholar]
- Gentil P, de Lima Lins TC, Lima RM, de Abreu BS, Grattapaglia D, Bottaro M, de Oliveira RJ, Pereira RW 2009 Vitamin-D-receptor genotypes and bone-mineral density in postmenopausal women: interaction with physical activity. J Aging Phys Act 17:31–45 [DOI] [PubMed] [Google Scholar]
- Uysal AR, Sahin M, Gürsoy A, Güllü S 2008 Vitamin D receptor gene polymorphism and osteoporosis in the Turkish population. Genet Test 12:591–594 [DOI] [PubMed] [Google Scholar]
- Pluskiewicz W, Zdrzałek J, Karasek D 2009 Spine bone mineral density and VDR polymorphism in subjects with ulcerative colitis. J Bone Miner Metab 27:567–573 [DOI] [PubMed] [Google Scholar]
- Zambrano-Morales M, Borjas L, Fernández E, Zabala W, de Romero P, Pineda L, Morales-Machín A 2008 Association of the vitamin D receptor gene BBAAtt haplotype with osteoporosis in post-menopausic women. Invest Clin 49:29–38 [PubMed] [Google Scholar]
- Horst-Sikorska W, Ignaszak-Szczepaniak M, Marcinkowska M, Kaczmarek M, Stajgis M, Slomski R 2008 Association analysis of vitamin D receptor gene polymorphisms with bone mineral density in young women with Graves’ disease. Acta Biochim Pol 55:371–380 [PubMed] [Google Scholar]
- Quevedo LI, Martínez BM, Castillo NM, Rivera FN 2008 Vitamin D receptor gene polymorphisms and risk of hip fracture in Chilean elderly women. Rev Med Chil 136:475–481 [PubMed] [Google Scholar]
- Kim HS, Kim JS, Kim NS, Kim JH, Lee BK 2007 Association of vitamin D receptor polymorphism with calcaneal broadband ultrasound attenuation in Korean postmenopausal women with low calcium intake. Br J Nutr 98:878–881 [DOI] [PubMed] [Google Scholar]
- Pérez A, Ulla M, García B, Lavezzo M, Elías E, Binci M, Rivoira M, Centeno V, Alisio A, de Talamoni NT 2008 Genotypes and clinical aspects associated with bone mineral density in Argentine postmenopausal women. J Bone Miner Metab 26:358–365 [DOI] [PubMed] [Google Scholar]
- Bezerra FF, Cabello GM, Mendonça LM, Donangelo CM 2008 Bone mass and breast milk calcium concentration are associated with vitamin D receptor gene polymorphisms in adolescent mothers. J Nutr 138:277–281 [DOI] [PubMed] [Google Scholar]
- Dundar U, Solak M, Kavuncu V, Ozdemir M, Cakir T, Yildiz H, Evcik D 2009 Evidence of association of vitamin D receptor Apa I gene polymorphism with bone mineral density in postmenopausal women with osteoporosis. Clin Rheumatol 28:1187–1191 [DOI] [PubMed] [Google Scholar]
- Zmuda JM, Yerges LM, Kammerer CM, Cauley JA, Wang X, Nestlerode CS, Wheeler VW, Patrick AL, Bunker CH, Moffett SP, Ferrell RE 2009 Association analysis of WNT10B with bone mass and structure among individuals of African ancestry. J Bone Miner Res 24:437–447 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Perez-Castrillon JL, Olmos JM, Nan DN, Castillo J, Arozamena J, Montero A, Perez-Nuñez MI, Riancho JA 2009 Polymorphisms of the WNT10B gene, bone mineral density, and fractures in postmenopausal women. Calcif Tissue Int 85:113–118 [DOI] [PubMed] [Google Scholar]
- Streeten EA, Beck TJ, O'Connell JR, Rampersand E, McBride DJ, Takala SL, Pollin TI, Uusi-Rasi K, Mitchell BD, Shuldiner AR 2008 Autosome-wide linkage analysis of hip structural phenotypes in the Old Order Amish. Bone 43:607–612 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Xiong DH, Wang JT, Wang W, Guo YF, Xiao P, Shen H, Jiang H, Chen Y, Deng H, Drees B, Recker RR, Deng HW 2007 Genetic determination of osteoporosis: lessons learned from a large genome-wide linkage study. Hum Biol 79:593–608 [DOI] [PubMed] [Google Scholar]
- Karasik D, Dupuis J, Cupples LA, Beck TJ, Mahaney MC, Havill LM, Kiel DP, Demissie S 2007 Bivariate linkage study of proximal hip geometry and body size indices: the Framingham study. Calcif Tissue Int 81:162–173 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Tan LJ, Liu YZ, Xiao P, Yang F, Tang ZH, Liu PY, Recker RR, Deng HW 2008 Evidence for major pleiotropic effects on bone size variation from a principal component analysis of 451 Caucasian families. Acta Pharmacol Sin 29:745–751 [DOI] [PubMed] [Google Scholar]
- Lei S, Deng F, Xiao P, Zhong K, Deng H, Recker RR, Deng H 2009 Bivariate whole-genome linkage scan for bone geometry and total body fat mass. J Genet Genomics 36:89–97 [DOI] [PubMed] [Google Scholar]
- Shaffer JR, Kammerer CM, Bruder JM, Cole SA, Dyer TD, Almasy L, Maccluer JW, Blangero J, Bauer RL, Mitchell BD 2009 Quantitative trait locus on chromosome 1q influences bone loss in young Mexican American adults. Calcif Tissue Int 84:75–84 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Karasik D, Zhou Y, Cupples LA, Hannan MT, Kiel DP, Demissie S 2009 Bivariate genome-wide linkage analysis of femoral bone traits and leg lean mass: Framingham study. J Bone Miner Res 24:710–718 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Williams FM, Kato BS, Livshits G, Sambrook PN, Spector TD, MacGregor AJ 2008 Lumbar disc disease shows linkage to chromosome 19 overlapping with a QTL for hand OA. Ann Rheum Dis 67:117–119 [DOI] [PubMed] [Google Scholar]
- Lucas GJ, Riches PL, Hocking LJ, Cundy T, Nicholson GC, Walsh JP, Ralston SH 2008 Identification of a major locus for Paget’s disease on chromosome 10p13 in families of British descent. J Bone Miner Res 23:58–63 [DOI] [PubMed] [Google Scholar]
- Peacock M, Koller DL, Lai D, Hui S, Foroud T, Econs MJ 2009 Bone mineral density variation in men is influenced by sex-specific and non sex-specific quantitative trait loci. Bone 45:443–448 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Timpson NJ, Tobias JH, Richards JB, Soranzo N, Duncan EL, Sims AM, Whittaker P, Kumanduri V, Zhai G, Glaser B, Eisman J, Jones G, Nicholson G, Prince R, Seeman E, Spector TD, Brown MA, Peltonen L, Smith GD, Deloukas P, Evans DM 2009 Common variants in the region around Osterix are associated with bone mineral density and growth in childhood. Hum Mol Genet 18:1510–1517 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Guo Y, Zhang LS, Yang TL, Tian Q, Xiong DH, Pei YF, Deng HW 29 October 2009 PTH and IL21R may underlie variation of femoral neck bone mineral density as revealed by a genome-wide association study. J Bone Miner Res DOI: 10.1359/jbmr.091040 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ealey KN, Kaludjerovic J, Archer MC, Ward WE 2008 Adiponectin is a negative regulator of bone mineral and bone strength in growing mice. Exp Biol Med (Maywood) 233:1546–1553 [DOI] [PubMed] [Google Scholar]
- Callewaert F, Venken K, Ophoff J, De Gendt K, Torcasio A, van Lenthe GH, Van Oosterwyck H, Boonen S, Bouillon R, Verhoeven G, Vanderschueren D 2009 Differential regulation of bone and body composition in male mice with combined inactivation of androgen and estrogen receptor-α. FASEB J 23:232–240 [DOI] [PubMed] [Google Scholar]
- Izu Y, Mizoguchi F, Kawamata A, Hayata T, Nakamoto T, Nakashima K, Inagami T, Ezura Y, Noda M 2009 Angiotensin II type 2 receptor blockade increases bone mass. J Biol Chem 284:4857–4864 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Valverde P, Zhang J, Fix A, Zhu J, Ma W, Tu Q, Chen J 2008 Overexpression of bone sialoprotein leads to an uncoupling of bone formation and bone resorption in mice. J Bone Miner Res 23:1775–1788 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Idris AI, Sophocleous A, Landao-Bassonga E, Canals M, Milligan G, Baker D, van't Hof RJ, Ralston SH 2009 Cannabinoid receptor type 1 protects against age-related osteoporosis by regulating osteoblast and adipocyte differentiation in marrow stromal cells. Cell Metab 10:139–147 [DOI] [PubMed] [Google Scholar]
- Binder NB, Niederreiter B, Hoffmann O, Stange R, Pap T, Stulnig TM, Mack M, Erben RG, Smolen JS, Redlich K 2009 Estrogen-dependent and C-C chemokine receptor-2-dependent pathways determine osteoclast behavior in osteoporosis. Nat Med 15:417–424 [DOI] [PubMed] [Google Scholar]
- Cui W, Cuartas E, Ke J, Zhang Q, Einarsson HB, Sedgwick JD, Li J, Vignery A 2007 CD200 and its receptor, CD200R, modulate bone mass via the differentiation of osteoclasts. Proc Natl Acad Sci USA 104:14436–14441 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Saeed Z, Guilbault C, De Sanctis JB, Henri J, Marion D, St-Arnaud R, Radzioch D 2008 Fenretinide prevents the development of osteoporosis in Cftr-KO mice. J Cyst Fibros 7:222–230 [DOI] [PubMed] [Google Scholar]
- Daley E, Streeten EA, Sorkin JD, Kuznetsova N, Shapses SA, Carleton SM, Shuldiner AR, Marini JC, Phillips CL, Goldstein SA, Leikin S, McBride DJ 13 July 2009 Variable bone fragility associated with an Amish COL1A2 variant and a knock-in mouse model. J Bone Miner Res DOI: 10.1359/jbmr.090720 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Alexopoulos LG, Youn I, Bonaldo P, Guilak F 2009 Developmental and osteoarthritic changes in Col6a1-knockout mice: biomechanics of type VI collagen in the cartilage pericellular matrix. Arthritis Rheum 60:771–779 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Wang CJ, Iida K, Egusa H, Hokugo A, Jewett A, Nishimura I 2008 Trabecular bone deterioration in col9a1+/− mice associated with enlarged osteoclasts adhered to collagen IX-deficient bone. J Bone Miner Res 23:837–849 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Cho SW, Sun HJ, Yang JY, Jung JY, An JH, Cho HY, Choi HJ, Kim SW, Kim SY, Kim D, Shin CS 2009 Transplantation of mesenchymal stem cells overexpressing RANK-Fc or CXCR4 prevents bone loss in ovariectomized mice. Mol Ther 17:1979–1987 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Jin W, Chang M, Paul EM, Babu G, Lee AJ, Reiley W, Wright A, Zhang M, You J, Sun SC 2008 Deubiquitinating enzyme CYLD negatively regulates RANK signaling and osteoclastogenesis in mice. J Clin Invest 118:1858–1866 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Sjögren K, Lagerquist M, Moverare-Skrtic S, Andersson N, Windahl SH, Swanson C, Mohan S, Poutanen M, Ohlsson C 2009 Elevated aromatase expression in osteoblasts leads to increased bone mass without systemic adverse effects. J Bone Miner Res 24:1263–1270 [DOI] [PubMed] [Google Scholar]
- MacDonald BT, Joiner DM, Oyserman SM, Sharma P, Goldstein SA, He X, Hauschka PV 2007 Bone mass is inversely proportional to Dkk1 levels in mice. Bone 41:331–339 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Samee N, Geoffroy V, Marty C, Schiltz C, Vieux-Rochas M, Clément-Lacroix P, Belleville C, Levi G, de Vernejoul MC 2009 Increased bone resorption and osteopenia in Dlx5 heterozygous mice. J Cell Biochem 107:865–872 [DOI] [PubMed] [Google Scholar]
- Aguirre JI, Plotkin LI, Gortazar AR, Millan MM, O'Brien CA, Manolagas SC, Bellido T 2007 A novel ligand-independent function of the estrogen receptor is essential for osteocyte and osteoblast mechanotransduction. J Biol Chem 282:25501–25508 [DOI] [PubMed] [Google Scholar]
- Lories RJ, Peeters J, Bakker A, Tylzanowski P, Derese I, Schrooten J, Thomas JT, Luyten FP 2007 Articular cartilage and biomechanical properties of the long bones in Frzb-knockout mice. Arthritis Rheum 56:4095–4103 [DOI] [PubMed] [Google Scholar]
- Abdelmagid SM, Barbe MF, Rico MC, Salihoglu S, Arango-Hisijara I, Selim AH, Anderson MG, Owen TA, Popoff SN, Safadi FF 2008 Osteoactivin, an anabolic factor that regulates osteoblast differentiation and function. Exp Cell Res 314:2334–2351 [DOI] [PubMed] [Google Scholar]
- Mårtensson UE, Salehi SA, Windahl S, Gomez MF, Swärd K, Daszkiewicz-Nilsson J, Wendt A, Andersson N, Hellstrand P, Grände PO, Owman C, Rosen CJ, Adamo ML, Lundquist I, Rorsman P, Nilsson BO, Ohlsson C, Olde B, Leeb-Lundberg LM 2009 Deletion of the G protein-coupled receptor 30 impairs glucose tolerance, reduces bone growth, increases blood pressure, and eliminates estradiol-stimulated insulin release in female mice. Endocrinology 150:687–698 [DOI] [PubMed] [Google Scholar]
- Luo J, Zhou W, Zhou X, Li D, Weng J, Yi Z, Cho SG, Li C, Yi T, Wu X, Li XY, de Crombrugghe B, Höök M, Liu M 2009 Regulation of bone formation and remodeling by G-protein-coupled receptor 48. Development 136:2747–2756 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Liu XD, Deng LF, Wang J, Qi J, Zhou Q, Wang JS, Wei L, Zhu YP, Clemens T 2007 The regulation of hypoxia inducible factor-1α on osteoblast function in postmenopausal osteoporosis. Zhonghua Wai Ke Za Zhi 45:1274–1278 [PubMed] [Google Scholar]
- Chen AB, Hamamura K, Wang G, Xing W, Mohan S, Yokota H, Liu Y 2007 Model-based comparative prediction of transcription-factor binding motifs in anabolic responses in bone. Genomics Proteomics Bioinformatics 5:158–165 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Oristian DS, Sloofman LG, Zhou X, Wang L, Farach-Carson MC, Kirn-Safran CB 2009 Ribosomal protein L29/HIP deficiency delays osteogenesis and increases fragility of adult bone in mice. J Orthop Res 27:28–35 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Shen Z, Peng Z, Sun Y, Väänänen HK, Poutanen M 2008 Overexpression of human hydroxysteroid (17β) dehydrogenase 2 induces disturbance in skeletal development in young male mice. J Bone Miner Res 23:1217–1226 [DOI] [PubMed] [Google Scholar]
- Baylink D, Lau KH, Mohan S 2007 The role of IGF system in the rise and fall in bone density with age. J Musculoskelet Neuronal Interact 7:304–305 [PubMed] [Google Scholar]
- Govoni KE, Lee SK, Chung YS, Behringer RR, Wergedal JE, Baylink DJ, Mohan S 2007 Disruption of insulin-like growth factor-I expression in type IIαI collagen-expressing cells reduces bone length and width in mice. Physiol Genomics 30:354–362 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Yao W, Zhong J, Yu J, Warner T, Bozic T, Ye P, D'Ercole AJ, Hock JM, Lee WH 2008 IGF-I improved bone mineral density and body composition of weaver mutant mice. Growth Horm IGF Res 18:517–525 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Salopek D, Grceviæ D, Kataviæ V, Kovaciæ N, Lukiæ IK, Marusiæ A 2008 Increased bone resorption and osteopenia are a part of the lymphoproliferative phenotype of mice with systemic over-expression of interleukin-7 gene driven by MHC class II promoter. Immunol Lett 121:134–139 [DOI] [PubMed] [Google Scholar]
- Zhao B, Takami M, Yamada A, Wang X, Koga T, Hu X, Tamura T, Ozato K, Choi Y, Ivashkiv LB, Takayanagi H, Kamijo R 2009 Interferon regulatory factor-8 regulates bone metabolism by suppressing osteoclastogenesis. Nat Med 15:1066–1071 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kawamata A, Izu Y, Yokoyama H, Amagasa T, Wagner EF, Nakashima K, Ezura Y, Hayata T, Noda M 2008 JunD suppresses bone formation and contributes to low bone mass induced by estrogen depletion. J Cell Biochem 103:1037–1045 [DOI] [PubMed] [Google Scholar]
- O'Connor RD, Zayzafoon M, Farach-Carson MC, Schanen NC 2009 Mecp2 deficiency decreases bone formation and reduces bone volume in a rodent model of Rett syndrome. Bone 45:346–356 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Aliprantis AO, Ueki Y, Sulyanto R, Park A, Sigrist KS, Sharma SM, Ostrowski MC, Olsen BR, Glimcher LH 2008 NFATc1 in mice represses osteoprotegerin during osteoclastogenesis and dissociates systemic osteopenia from inflammation in cherubism. J Clin Invest 118:3775–3789 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Robling AG, Childress P, Yu J, Cotte J, Heller A, Philip BK, Bidwell JP 2009 Nmp4/CIZ suppresses parathyroid hormone-induced increases in trabecular bone. J Cell Physiol 219:734–743 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Sato S, Hanada R, Kimura A, Abe T, Matsumoto T, Iwasaki M, Inose H, Ida T, Mieda M, Takeuchi Y, Fukumoto S, Fujita T, Kato S, Kangawa K, Kojima M, Shinomiya K, Takeda S 2007 Central control of bone remodeling by neuromedin U. Nat Med 13:1234–1240 [DOI] [PubMed] [Google Scholar]
- Tamma R, Colaianni G, Zhu LL, DiBenedetto A, Greco G, Montemurro G, Patano N, Strippoli M, Vergari R, Mancini L, Colucci S, Grano M, Faccio R, Liu X, Li J, Usmani S, Bachar M, Bab I, Nishimori K, Young LJ, Buettner C, Iqbal J, Sun L, Zaidi M, Zallone A 2009 Oxytocin is an anabolic bone hormone. Proc Natl Acad Sci USA 106:7149–7154 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Nordstrom SM, Carleton SM, Carson WL, Eren M, Phillips CL, Vaughan DE 2007 Transgenic over-expression of plasminogen activator inhibitor-1 results in age-dependent and gender-specific increases in bone strength and mineralization. Bone 41:995–1004 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Tanner SJ, Hefferan TE, Rosen CJ, Conover CA 2008 Impact of pregnancy-associated plasma protein-a deletion on the adult murine skeleton. J Bone Miner Res 23:655–662 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Wu Y, Tworkoski K, Michaud M, Madri JA 2009 Bone marrow monocyte PECAM-1 deficiency elicits increased osteoclastogenesis resulting in trabecular bone loss. J Immunol 182:2672–2679 [DOI] [PubMed] [Google Scholar]
- Wan Y, Chong LW, Evans RM 2007 PPAR-γ regulates osteoclastogenesis in mice. Nat Med 13:1496–1503 [DOI] [PubMed] [Google Scholar]
- O'Brien CA, Plotkin LI, Galli C, Goellner JJ, Gortazar AR, Allen MR, Robling AG, Bouxsein M, Schipani E, Turner CH, Jilka RL, Weinstein RS, Manolagas SC, Bellido T 2008 Control of bone mass and remodeling by PTH receptor signaling in osteocytes. PLoS ONE 3:e2942 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Yan J, Chen S, Zhang Y, Li X, Li Y, Wu X, Yuan J, Robling AG, Kapur R, Karpur R, Chan RJ, Yang FC 2008 Rac1 mediates the osteoclast gains-in-function induced by haploinsufficiency of Nf1. Hum Mol Genet 17:936–948 [DOI] [PubMed] [Google Scholar]
- Kawano T, Troiano N, Adams DJ, Wu JJ, Sun BH, Insogna K 2008 The anabolic response to parathyroid hormone is augmented in Rac2 knockout mice. Endocrinology 149:4009–4015 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Galli C, Zella LA, Fretz JA, Fu Q, Pike JW, Weinstein RS, Manolagas SC, O'Brien CA 2008 Targeted deletion of a distant transcriptional enhancer of the receptor activator of nuclear factor-κB ligand gene reduces bone remodeling and increases bone mass. Endocrinology 149:146–153 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Tu Q, Zhang J, Paz J, Wade K, Yang P, Chen J 2008 Haploinsufficiency of Runx2 results in bone formation decrease and different BSP expression pattern changes in two transgenic mouse models. J Cell Physiol 217:40–47 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Holmes C, Khan TS, Owen C, Ciliberti N, Grynpas MD, Stanford WL 2007 Longitudinal analysis of mesenchymal progenitors and bone quality in the stem cell antigen-1-null osteoporotic mouse. J Bone Miner Res 22:1373–1386 [DOI] [PubMed] [Google Scholar]
- Gaur T, Wixted JJ, Hussain S, O'Connell SL, Morgan EF, Ayers DC, Komm BS, Bodine PV, Stein GS, Lian JB 2009 Secreted frizzled related protein 1 is a target to improve fracture healing. J Cell Physiol 220:174–181 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Nakanishi R, Akiyama H, Kimura H, Otsuki B, Shimizu M, Tsuboyama T, Nakamura T 2008 Osteoblast-targeted expression of Sfrp4 in mice results in low bone mass. J Bone Miner Res 23:271–277 [DOI] [PubMed] [Google Scholar]
- Eddleston A, Marenzana M, Moore AR, Stephens P, Muzylak M, Marshall D, Robinson MK 2009 A short treatment with an antibody to sclerostin can inhibit bone loss in an ongoing model of colitis. J Bone Miner Res 24:1662–1671 [DOI] [PubMed] [Google Scholar]
- Baek WY, Lee MA, Jung JW, Kim SY, Akiyama H, de Crombrugghe B, Kim JE 2009 Positive regulation of adult bone formation by osteoblast-specific transcription factor osterix. J Bone Miner Res 24:1055–1065 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Mansergh FC, Wells T, Elford C, Evans SL, Perry MJ, Evans MJ, Evans BA 2007 Osteopenia in Sparc (osteonectin)-deficient mice: characterization of phenotypic determinants of femoral strength and changes in gene expression. Physiol Genomics 32:64–73 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Franzén A, Hultenby K, Reinholt FP, Onnerfjord P, Heinegård D 2008 Altered osteoclast development and function in osteopontin deficient mice. J Orthop Res 26:721–728 [DOI] [PubMed] [Google Scholar]
- Mödder UI, Monroe DG, Fraser DG, Spelsberg TC, Rosen CJ, Géhin M, Chambon P, O'Malley BW, Khosla S 2009 Skeletal consequences of deletion of steroid receptor coactivator-2/transcription intermediary factor-2. J Biol Chem 284:18767–18777 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Sintuu C, Murray SS, Behnam K, Simon R, Jawien J, Silva JD, Duarte ME, Brochmann EJ 2008 Full-length bovine spp24 [spp24 (24–203)] inhibits BMP-2 induced bone formation. J Orthop Res 26:753–758 [DOI] [PubMed] [Google Scholar]
- Pignolo RJ, Suda RK, McMillan EA, Shen J, Lee SH, Choi Y, Wright AC, Johnson FB 2008 Defects in telomere maintenance molecules impair osteoblast differentiation and promote osteoporosis. Aging Cell 7:23–31 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Schiltz C, Marty C, de Vernejoul MC, Geoffroy V 2008 Inhibition of osteoblastic metalloproteinases in mice prevents bone loss induced by oestrogen deficiency. J Cell Biochem 104:1803–1817 [DOI] [PubMed] [Google Scholar]
- Rude RK, Wei L, Norton HJ, Lu SS, Dempster DW, Gruber HE 23 July 2009 TNFα receptor knockout in mice reduces adverse effects of magnesium deficiency on bone. Growth Factors DOI: 10.1080/08977190903152644 [DOI] [PubMed] [Google Scholar]
- Hussain Mian A, Saito H, Alles N, Shimokawa H, Aoki K, Ohya K 2008 Lipopolysaccharide-induced bone resorption is increased in TNF type 2 receptor-deficient mice in vivo. J Bone Miner Metab 26:469–477 [DOI] [PubMed] [Google Scholar]
- Bassett JH, Nordström K, Boyde A, Howell PG, Kelly S, Vennström B, Williams GR 2007 Thyroid status during skeletal development determines adult bone structure and mineralization. Mol Endocrinol 21:1893–1904 [DOI] [PubMed] [Google Scholar]
- Mizoguchi F, Mizuno A, Hayata T, Nakashima K, Heller S, Ushida T, Sokabe M, Miyasaka N, Suzuki M, Ezura Y, Noda M 2008 Transient receptor potential vanilloid 4 deficiency suppresses unloading-induced bone loss. J Cell Physiol 216:47–53 [DOI] [PubMed] [Google Scholar]
- Furlan F, Galbiati C, Jorgensen NR, Jensen JE, Mrak E, Rubinacci A, Talotta F, Verde P, Blasi F 2007 Urokinase plasminogen activator receptor affects bone homeostasis by regulating osteoblast and osteoclast function. J Bone Miner Res 22:1387–1396 [DOI] [PubMed] [Google Scholar]
- Minasyan A, Keisala T, Zou J, Zhang Y, Toppila E, Syvälä H, Lou YR, Kalueff AV, Pyykkö I, Tuohimaa P 2009 Vestibular dysfunction in vitamin D receptor mutant mice. J Steroid Biochem Mol Biol 114:161–166 [DOI] [PubMed] [Google Scholar]
- Bennett CN, Ouyang H, Ma YL, Zeng Q, Gerin I, Sousa KM, Lane TF, Krishnan V, Hankenson KD, MacDougald OA 2007 Wnt10b increases postnatal bone formation by enhancing osteoblast differentiation. J Bone Miner Res 22:1924–1932 [DOI] [PubMed] [Google Scholar]
- Rivas D, Li W, Akter R, Henderson JE, Duque G 2009 Accelerated features of age-related bone loss in zmpste24 metalloproteinase-deficient mice. J Gerontol A Biol Sci Med Sci 64:1015–1024 [DOI] [PubMed] [Google Scholar]
- Collet C, Schiltz C, Geoffroy V, Maroteaux L, Launay JM, de Vernejoul MC 2008 The serotonin 5-HT2B receptor controls bone mass via osteoblast recruitment and proliferation. FASEB J 22:418–427 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ichikawa T, Horie-Inoue K, Ikeda K, Blumberg B, Inoue S 2007 Vitamin K2 induces phosphorylation of protein kinase A and expression of novel target genes in osteoblastic cells. J Mol Endocrinol 39:239–247 [DOI] [PubMed] [Google Scholar]
- Leskinen JJ, Karjalainen HM, Olkku A, Hynynen K, Mahonen A, Lammi MJ 2008 Genome-wide microarray analysis of MG-63 osteoblastic cells exposed to ultrasound. Biorheology 45:345–354 [PubMed] [Google Scholar]
- Palmieri A, Pezzetti F, Brunelli G, Zollino I, Scapoli L, Martinelli M, Arlotti M, Carinci F 2007 Differences in osteoblast miRNA induced by cell binding domain of collagen and silicate-based synthetic bone. J Biomed Sci 14:777–782 [DOI] [PubMed] [Google Scholar]
- Sollazzo V, Palmieri A, Pezzetti F, Bignozzi CA, Argazzi R, Massari L, Brunelli G, Carinci F 2008 Genetic effect of zirconium oxide coating on osteoblast-like cells. J Biomed Mater Res B Appl Biomater 84:550–558 [DOI] [PubMed] [Google Scholar]
- Chang HH, Wu CB, Chen YJ, Weng CY, Wong WP, Chen YJ, Chang BE, Chen MH, Yao CC 2008 MMP-3 response to compressive forces in vitro and in vivo. J Dent Res 87:692–696 [DOI] [PubMed] [Google Scholar]
- Qian A, Di S, Gao X, Zhang W, Tian Z, Li J, Hu L, Yang P, Yin D, Shang P 2009 cDNA microarray reveals the alterations of cytoskeleton-related genes in osteoblast under high magneto-gravitational environment. Acta Biochim Biophys Sin (Shanghai) 41:561–577 [DOI] [PubMed] [Google Scholar]
- Palmieri A, Pezzetti F, Brunelli G, Martinelli M, Lo Muzio L, Scarano A, Degidi M, Piattelli A, Carinci F 2008 Peptide-15 changes miRNA expression in osteoblast-like cells. Implant Dent 17:100–108 [DOI] [PubMed] [Google Scholar]
- Sollazzo V, Palmieri A, Pezzetti F, Scarano A, Martinelli M, Scapoli L, Massari L, Brunelli G, Caramelli E, Carinci F 2008 Genetic effect of anatase on osteoblast-like cells. J Biomed Mater Res B Appl Biomater 85:29–36 [DOI] [PubMed] [Google Scholar]
- Choi RC, Gao QT, Cheung AW, Zhu JT, Lau FT, Li J, Li WZ, Chu GK, Duan R, Cheung JK, Ding AW, Zhao KJ, Dong TT, Tsim KW 8 January 2009 A Chinese herbal decoction, Danggui Buxue Tang, stimulates proliferation, differentiation and gene expression of cultured osteosarcoma cells: genomic approach to reveal specific gene activation. Evid Based Complement Alternat Med DOI: 10.1093/ecam/nen085 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Walters DK, Steinmann P, Langsam B, Schmutz S, Born W, Fuchs B 2008 Identification of potential chemoresistance genes in osteosarcoma. Anticancer Res 28:673–679 [PubMed] [Google Scholar]
- Vertemati M, Minola E, Dolci C, Stabellini G, Pezzetti F, Moscheni C, Calastrini C, Bramerio M, Palmieri A, Vizzotto L 2009 Gene expression, cytoskeletal changes and extracellular matrix synthesis in human osteoblasts treated with cyclosporin A. Biomed Pharmacother 63:619–626 [DOI] [PubMed] [Google Scholar]
- Trost Z, Trebse R, Prezelj J, Komadina R, Logar DB, Marc J 2010 A microarray based identification of osteoporosis-related genes in primary culture of human osteoblasts. Bone 46:72–80 [DOI] [PubMed] [Google Scholar]
- Hofmann A, Ritz U, Hessmann MH, Alini M, Rommens PM, Rompe JD 2008 Extracorporeal shock wave-mediated changes in proliferation, differentiation, and gene expression of human osteoblasts. J Trauma 65:1402–1410 [DOI] [PubMed] [Google Scholar]
- Bellesini LS, Beloti MM, Crippa GE, Bombonato-Prado KF, Junta CM, Marques MM, Passos GA, Rosa AL 2009 The effect of TAK-778 on gene expression of osteoblastic cells is mediated through estrogen receptor. Exp Biol Med (Maywood) 234:190–199 [DOI] [PubMed] [Google Scholar]
- Carinci F, Papaccio G, Laino G, Palmieri A, Brunelli G, D'Aquino R, Graziano A, Lanza V, Scapoli L, Martinelli M, Pezzetti F 2008 Comparison between genetic portraits of osteoblasts derived from primary cultures and osteoblasts obtained from human pulpar stem cells. J Craniofac Surg 19:616–625; discussion 626–617 [DOI] [PubMed] [Google Scholar]
- Song JH, Kim JH, Park S, Kang W, Kim HW, Kim HE, Jang JH 2008 Signaling responses of osteoblast cells to hydroxyapatite: the activation of ERK and SOX9. J Bone Miner Metab 26:138–142 [DOI] [PubMed] [Google Scholar]
- Hofmann A, Ritz U, Hessmann MH, Schmid C, Tresch A, Rompe JD, Meurer A, Rommens PM 2008 Cell viability, osteoblast differentiation, and gene expression are altered in human osteoblasts from hypertrophic fracture non-unions. Bone 42:894–906 [DOI] [PubMed] [Google Scholar]
- Patiño-García A, Zalacain M, Folio C, Zandueta C, Sierrasesúmaga L, San Julián M, Toledo G, De Las Rivas J, Lecanda F 2009 Profiling of chemonaive osteosarcoma and paired-normal cells identifies EBF2 as a mediator of osteoprotegerin inhibition to tumor necrosis factor-related apoptosis-inducing ligand-induced apoptosis. Clin Cancer Res 15:5082–5091 [DOI] [PubMed] [Google Scholar]
- Zieker D, Schäfer R, Glatzle J, Nieselt K, Coerper S, Kluba T, Northoff H, Königsrainer A, Hunt TK, Beckert S 2008 Lactate modulates gene expression in human mesenchymal stem cells. Langenbecks Arch Surg 393:297–301 [DOI] [PubMed] [Google Scholar]
- Dalby MJ, Andar A, Nag A, Affrossman S, Tare R, McFarlane S, Oreffo RO 2008 Genomic expression of mesenchymal stem cells to altered nanoscale topographies. J R Soc Interface 5:1055–1065 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Duque G, Huang DC, Macoritto M, Rivas D, Yang XF, Ste-Marie LG, Kremer R 2009 Autocrine regulation of interferon γ in mesenchymal stem cells plays a role in early osteoblastogenesis. Stem Cells 27:550–558 [DOI] [PubMed] [Google Scholar]
- Seib FP, Müller K, Franke M, Grimmer M, Bornhäuser M, Werner C 2009 Engineered extracellular matrices modulate the expression profile and feeder properties of bone marrow-derived human multipotent mesenchymal stromal cells. Tissue Eng Part A 15:3161–3171 [DOI] [PubMed] [Google Scholar]
- Piek E, Sleumer LS, van Someren EP, Heuver L, de Haan JR, de Grijs I, Gilissen C, Hendriks JM, van Ravestein-van Os RI, Bauerschmidt S, Dechering KJ, van Zoelen EJ 2010 Osteo-transcriptomics of human mesenchymal stem cells: accelerated gene expression and osteoblast differentiation induced by vitamin D reveals c-MYC as an enhancer of BMP2-induced osteogenesis. Bone 46:613–627 [DOI] [PubMed] [Google Scholar]
- Egusa H, Iida K, Kobayashi M, Lin TY, Zhu M, Zuk PA, Wang CJ, Thakor DK, Hedrick MH, Nishimura I 2007 Downregulation of extracellular matrix-related gene clusters during osteogenic differentiation of human bone marrow- and adipose tissue-derived stromal cells. Tissue Eng 13:2589–2600 [DOI] [PubMed] [Google Scholar]
- Lisignoli G, Codeluppi K, Todoerti K, Manferdini C, Piacentini A, Zini N, Grassi F, Cattini L, Piva R, Rizzoli V, Facchini A, Giuliani N, Neri A 2009 Gene array profile identifies collagen type XV as a novel human osteoblast-secreted matrix protein. J Cell Physiol 220:401–409 [DOI] [PubMed] [Google Scholar]
- Schaap-Oziemlak A, Raymakers RA, Bergevoet SM, Gilissen C, Jansen BJ, Adema GJ, Kogler G, le Sage C, Agami R, van der Reijden BA, Jansen J 1 October 2009 MicroRNA hsa-miR-135b regulates mineralization in osteogenic differentiation of human unrestricted somatic stem cells (USSCs). Stem Cells Dev DOI: 10.1089/scd.2009.0112 [DOI] [PubMed] [Google Scholar]
- Chen XD, Xiao P, Lei SF, Liu YZ, Guo YF, Deng FY, Tan LJ, Zhu XZ, Chen FR, Recker RR, Deng HW 13 July 2009 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 DOI: 10.1359/jbmr.090724 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Li Y, Meng G, Guo QN 2008 Changes in genomic imprinting and gene expression associated with transformation in a model of human osteosarcoma. Exp Mol Pathol 84:234–239 [DOI] [PubMed] [Google Scholar]
- MacArthur BD, Tare RS, Murawski K, Oreffo RO 2008 Identification of candidate regulators of multipotency in human skeletal progenitor cells. Biochem Biophys Res Commun 377:68–72 [DOI] [PubMed] [Google Scholar]
- Nihrane A, Sezgin G, Dsilva S, Dellorusso P, Yamamoto K, Ellis SR, Liu JM 2009 Depletion of the Shwachman-Diamond syndrome gene product, SBDS, leads to growth inhibition and increased expression of OPG and VEGF-A. Blood Cells Mol Dis 42:85–91 [DOI] [PubMed] [Google Scholar]
- Verdelli D, Nobili L, Todoerti K, Intini D, Cosenza M, Civallero M, Bertacchini J, Deliliers GL, Sacchi S, Lombardi L, Neri A 2009 Molecular targeting of the PKC-β inhibitor enzastaurin (LY317615) in multiple myeloma involves a coordinated downregulation of MYC and IRF4 expression. Hematol Oncol 27:23–30 [DOI] [PubMed] [Google Scholar]
- Goessler UR, Bugert P, Bieback K, Stern-Straeter J, Bran G, Sadick H, Hormann K, Riedel F 2009 In vitro-analysis of integrin-expression in stem-cells from bone marrow and cord blood during chondrogenic differentiation. J Cell Mol Med 13:1175–1184 [DOI] [PMC free article] [PubMed] [Google Scholar] [Retracted]
- Shinoda Y, Ogata N, Higashikawa A, Manabe I, Shindo T, Yamada T, Kugimiya F, Ikeda T, Kawamura N, Kawasaki Y, Tsushima K, Takeda N, Nagai R, Hoshi K, Nakamura K, Chung UI, Kawaguchi H 2008 Kruppel-like factor 5 causes cartilage degradation through transactivation of matrix metalloproteinase 9. J Biol Chem 283:24682– 24689 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Babu AN, Meng X, Zou N, Yang X, Wang M, Song Y, Cleveland JC, Weyant M, Banerjee A, Fullerton DA 2008 Lipopolysaccharide stimulation of human aortic valve interstitial cells activates inflammation and osteogenesis. Ann Thorac Surg 86:71–76 [DOI] [PubMed] [Google Scholar]
- Sandell LJ, Xing X, Franz C, Davies S, Chang LW, Patra D 2008 Exuberant expression of chemokine genes by adult human articular chondrocytes in response to IL-1β. Osteoarthritis Cartilage 16:1560–1571 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Borsy A, Podani J, Stéger V, Balla B, Horváth A, Kósa JP, Gyurján Jr I, Molnár A, Szabolcsi Z, Szabó L, Jakó E, Zomborszky Z, Nagy J, Semsey S, Vellai T, Lakatos P, Orosz L 2009 Identifying novel genes involved in both deer physiological and human pathological osteoporosis. Mol Genet Genomics 281:301–313 [DOI] [PubMed] [Google Scholar]
- Ku SJ, Chang YI, Chae CH, Kim SG, Park YW, Jung YK, Choi JY 2009 Static tensional forces increase osteogenic gene expression in three-dimensional periodontal ligament cell culture. BMB Rep 42:427–432 [DOI] [PubMed] [Google Scholar]
- Morioka K, Tanikawa C, Ochi K, Daigo Y, Katagiri T, Kawano H, Kawaguchi H, Myoui A, Yoshikawa H, Naka N, Araki N, Kudawara I, Ieguchi M, Nakamura K, Nakamura Y, Matsuda K 2009 Orphan receptor tyrosine kinase ROR2 as a potential therapeutic target for osteosarcoma. Cancer Sci 100:1227–1233 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Zhu R, Xu R, Jiang X, Cai Y, Zou Y, Du M, Qin L 2007 Expression profile of cancer-related genes in human adult bone marrow-derived neural stemlike cells highlights the need for tumorigenicity study. J Neurosci Res 85:3064–3070 [DOI] [PubMed] [Google Scholar]
- Gruber HE, Hoelscher G, Ingram JA, Hanley Jr EN 2008 Immunolocalization and biochemical evidence of pregnancy- associated plasma protein A in the intervertebral disc. Spine 33:E447–E454 [DOI] [PubMed] [Google Scholar]
- Yang G, Zaidi M, Zhang W, Zhu LL, Li J, Iqbal J, Varbanov A, Gross G, Phipps R, Troen BR, Sun L 2008 Functional grouping of osteoclast genes revealed through microarray analysis. Biochem Biophys Res Commun 366:352–359 [DOI] [PubMed] [Google Scholar]
- Pederson L, Ruan M, Westendorf JJ, Khosla S, Oursler MJ 2008 Regulation of bone formation by osteoclasts involves Wnt/BMP signaling and the chemokine sphingosine-1-phosphate. Proc Natl Acad Sci USA 105:20764–20769 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hamamura K, Liu Y, Yokota H 2008 Microarray analysis of thapsigargin-induced stress to the endoplasmic reticulum of mouse osteoblasts. J Bone Miner Metab 26:231–240 [DOI] [PubMed] [Google Scholar]
- Schroeder TM, Nair AK, Staggs R, Lamblin AF, Westendorf JJ 2007 Gene profile analysis of osteoblast genes differentially regulated by histone deacetylase inhibitors. BMC Genomics 8:362 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ott CE, Bauer S, Manke T, Ahrens S, Rödelsperger C, Grünhagen J, Kornak U, Duda G, Mundlos S, Robinson PN 2009 Promiscuous and depolarization-induced immediate-early response genes are induced by mechanical strain of osteoblasts. J Bone Miner Res 24:1247–1262 [DOI] [PubMed] [Google Scholar]
- Yoshitake F, Itoh S, Narita H, Ishihara K, Ebisu S 2008 Interleukin-6 directly inhibits osteoclast differentiation by suppressing receptor activator of NF-κB signaling pathways. J Biol Chem 283:11535–11540 [DOI] [PubMed] [Google Scholar]
- Ackert-Bicknell CL, Shockley KR, Horton LG, Lecka- Czernik B, Churchill GA, Rosen CJ 2009 Strain-specific effects of rosiglitazone on bone mass, body composition, and serum insulin-like growth factor-I. Endocrinology 150:1330–1340 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Zaman G, Saxon LK, Sunters A, Hilton H, Underhill P, Williams D, Price JS, Lanyon LE 2010 Loading-related regulation of gene expression in bone in the contexts of estrogen deficiency, lack of estrogen receptor α and disuse. Bone 46:628–642 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Zhang P, Turner CH, Yokota H 2009 Joint loading-driven bone formation and signaling pathways predicted from genome-wide expression profiles. Bone 44:989–998 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Sepulveda DE, Andrews BA, Asenjo JA, Papoutsakis ET 2008 Comparative transcriptional analysis of embryoid body versus two-dimensional differentiation of murine embryonic stem cells. Tissue Eng Part A 14:1603–1614 [DOI] [PubMed] [Google Scholar]
- Owen HC, Roberts SJ, Ahmed SF, Farquharson C 2008 Dexamethasone-induced expression of the glucocorticoid response gene lipocalin 2 in chondrocytes. Am J Physiol Endocrinol Metab 294:E1023–E1034 [DOI] [PubMed] [Google Scholar]
- Kim MS, Wu KY, Auyeung V, Chen Q, Gruppuso PA, Phornphutkul C 2009 Leucine restriction inhibits chondrocyte proliferation and differentiation through mechanisms both dependent and independent of mTOR signaling. Am J Physiol Endocrinol Metab 296:E1374–E1382 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Wallin R, Schurgers L, Wajih N 2008 Effects of the blood coagulation vitamin K as an inhibitor of arterial calcification. Thromb Res 122:411–417 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Sosnoski DM, Gay CV 2007 Evaluation of bone-derived and marrow-derived vascular endothelial cells by microarray analysis. J Cell Biochem 102:463–472 [DOI] [PubMed] [Google Scholar]
- Li Z, Schem C, Shi YH, Medina D, Zhang M 2008 Increased COX2 expression enhances tumor-induced osteoclastic lesions in breast cancer bone metastasis. Clin Exp Metastasis 25:389–400 [DOI] [PubMed] [Google Scholar]
- Ambrosetti D, Holmes G, Mansukhani A, Basilico C 2008 Fibroblast growth factor signaling uses multiple mechanisms to inhibit Wnt-induced transcription in osteoblasts. Mol Cell Biol 28:4759–4771 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Capulli M, Rufo A, Teti A, Rucci N 2009 Global transcriptome analysis in mouse calvarial osteoblasts highlights sets of genes regulated by modeled microgravity and identifies a “mechanoresponsive osteoblast gene signature”. J Cell Biochem 107:240–252 [DOI] [PubMed] [Google Scholar]
- Bianchi EN, Ferrari SL 2009 β-Arrestin2 regulates parathyroid hormone effects on a p38 MAPK and NFκB gene expression network in osteoblasts. Bone 45:716–725 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Paic F, Igwe JC, Nori R, Kronenberg MS, Franceschetti T, Harrington P, Kuo L, Shin DG, Rowe DW, Harris SE, Kalajzic I 2009 Identification of differentially expressed genes between osteoblasts and osteocytes. Bone 45:682–692 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Bivi N, Bereszczak JZ, Romanello M, Zeef LA, Delneri D, Quadrifoglio F, Moro L, Brancia FL, Tell G 2009 Transcriptome and proteome analysis of osteocytes treated with nitrogen-containing bisphosphonates. J Proteome Res 8:1131–1142 [DOI] [PubMed] [Google Scholar]
- Dean AK, Harris SE, Kalajzic I, Ruan J 2009 A systems biology approach to the identification and analysis of transcriptional regulatory networks in osteocytes. BMC Bioinformatics 10(Suppl 9):S5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kato T, Tsuda T, Inaba H, Kawai S, Okahashi N, Shibata Y, Abiko Y, Amano A 2008 Porphyromonas gingivalis gingipains cause G(1) arrest in osteoblastic/stromal cells. Oral Microbiol Immunol 23:158–164 [DOI] [PubMed] [Google Scholar]
- Guo J, Jin J, Cooper LF 2008 Dissection of sets of genes that control the character of wnt5a-deficient mouse calvarial cells. Bone 43:961–971 [DOI] [PubMed] [Google Scholar]
- Kwan MD, Wan DC, Wang Z, Gupta DM, Slater BJ, Longaker MT 2008 Microarray analysis of the role of regional dura mater in cranial suture fate. Plast Reconstr Surg 122:389–399 [DOI] [PubMed] [Google Scholar]
- Kobayashi T, Lu J, Cobb BS, Rodda SJ, McMahon AP, Schipani E, Merkenschlager M, Kronenberg HM 2008 Dicer-dependent pathways regulate chondrocyte proliferation and differentiation. Proc Natl Acad Sci USA 105: 1949–1954 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Munugalavadla V, Vemula S, Sims EC, Krishnan S, Chen S, Yan J, Li H, Niziolek PJ, Takemoto C, Robling AG, Yang FC, Kapur R 2008 The p85α subunit of class IA phosphatidylinositol 3-kinase regulates the expression of multiple genes involved in osteoclast maturation and migration. Mol Cell Biol 28:7182–7198 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Fowlkes JL, Bunn RC, Liu L, Wahl EC, Coleman HN, Cockrell GE, Perrien DS, Lumpkin Jr CK, Thrailkill KM 2008 Runt-related transcription factor 2 (RUNX2) and RUNX2-related osteogenic genes are down-regulated throughout osteogenesis in type 1 diabetes mellitus. Endocrinology 149:1697–1704 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Wan DC, Kwan MD, Gupta DM, Wang Z, Slater BJ, Panetta NJ, Morrell NT, Longaker MT 2008 Global age-dependent differences in gene expression in response to calvarial injury. J Craniofac Surg 19:1292–1301 [DOI] [PubMed] [Google Scholar]
- Mace KA, Restivo TE, Rinn JL, Paquet AC, Chang HY, Young DM, Boudreau NJ 2009 HOXA3 modulates injury-induced mobilization and recruitment of bone marrow-derived cells. Stem Cells 27:1654–1665 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Sun Y, Lee SM, Wong YM, Lau CP, Shaw PC, Qin L, Leung PC, Fung KP 2008 Dosing effects of an antiosteoporosis herbal formula—a preclinical investigation using a rat model. Phytother Res 22:267–273 [DOI] [PubMed] [Google Scholar]
- Zhang M, Pritchard MR, Middleton FA, Horton JA, Damron TA 2008 Microarray analysis of perichondral and reserve growth plate zones identifies differential gene expressions and signal pathways. Bone 43:511–520 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Román-García P, Carrillo-López N, Fernández-Martín JL, Naves-Díaz M, Ruiz-Torres MP, Cannata-Andía JB 2010 High phosphorus diet induces vascular calcification, a related decrease in bone mass and changes in the aortic gene expression. Bone 46:121–128 [DOI] [PubMed] [Google Scholar]
- Wong AK, Schönmeyr BH, Soares MA, Li S, Mehrara BJ 2008 Hyperbaric oxygen inhibits growth but not differentiation of normal and irradiated osteoblasts. J Craniofac Surg 19:757–765 [DOI] [PubMed] [Google Scholar]
- Alam I, Sun Q, Liu L, Koller DL, Liu Y, Edenberg HJ, Econs MJ, Foroud T, Turner CH 2008 Genomic expression analysis of rat chromosome 4 for skeletal traits at femoral neck. Physiol Genomics 35:191–196 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Alam I, Sun Q, Koller DL, Liu L, Liu Y, Edenberg HJ, Foroud T, Turner CH 2010 Genes influencing spinal bone mineral density in inbred F344, LEW, COP, and DA rats. Funct Integr Genomics 10:63–72 [DOI] [PMC free article] [PubMed] [Google Scholar]