Open Access
Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

Single nucleotide polymorphisms in bone turnover-related genes in Koreans: ethnic differences in linkage disequilibrium and haplotype

  • Kyung-Seon Kim1,
  • Ghi-Su Kim2, 3,
  • Joo-Yeon Hwang1,
  • Hye-Ja Lee1,
  • Mi-Hyun Park1,
  • Kwang-joong Kim1,
  • Jongsun Jung1,
  • Hyo-Soung Cha1,
  • Hyoung Doo Shin4,
  • Jong-Ho Kang5,
  • Eui Kyun Park2, 6,
  • Tae-Ho Kim2,
  • Jung-Min Hong2,
  • Jung-Min Koh2, 3,
  • Bermseok Oh1,
  • Kuchan Kimm1,
  • Shin-Yoon Kim2, 7Email author and
  • Jong-Young Lee1Email author
Contributed equally
BMC Medical GeneticsBMC series ¿ open, inclusive and trusted20078:70

DOI: 10.1186/1471-2350-8-70

Received: 29 September 2006

Accepted: 26 November 2007

Published: 26 November 2007

Abstract

Background

Osteoporosis is defined as the loss of bone mineral density that leads to bone fragility with aging. Population-based case-control studies have identified polymorphisms in many candidate genes that have been associated with bone mass maintenance or osteoporotic fracture. To investigate single nucleotide polymorphisms (SNPs) that are associated with osteoporosis, we examined the genetic variation among Koreans by analyzing 81 genes according to their function in bone formation and resorption during bone remodeling.

Methods

We resequenced all the exons, splice junctions and promoter regions of candidate osteoporosis genes using 24 unrelated Korean individuals. Using the common SNPs from our study and the HapMap database, a statistical analysis of deviation in heterozygosity depicted.

Results

We identified 942 variants, including 888 SNPs, 43 insertion/deletion polymorphisms, and 11 microsatellite markers. Of the SNPs, 557 (63%) had been previously identified and 331 (37%) were newly discovered in the Korean population. When compared SNPs in the Korean population with those in HapMap database, 1% (or less) of SNPs in the Japanese and Chinese subpopulations and 20% of those in Caucasian and African subpopulations were significantly differentiated from the Hardy-Weinberg expectations. In addition, an analysis of the genetic diversity showed that there were no significant differences among Korean, Han Chinese and Japanese populations, but African and Caucasian populations were significantly differentiated in selected genes. Nevertheless, in the detailed analysis of genetic properties, the LD and Haplotype block patterns among the five sub-populations were substantially different from one another.

Conclusion

Through the resequencing of 81 osteoporosis candidate genes, 118 unknown SNPs with a minor allele frequency (MAF) > 0.05 were discovered in the Korean population. In addition, using the common SNPs between our study and HapMap, an analysis of genetic diversity and deviation in heterozygosity was performed and the polymorphisms of the above genes among the five populations were substantially differentiated from one another. Further studies of osteoporosis could utilize the polymorphisms identified in our data since they may have important implications for the selection of highly informative SNPs for future association studies.

Background

Bone is continuously remodeled in vertebrates through coordinated phases of bone formation and resorption in order to maintain bone volume and phosphorus and calcium homeostasis [1]. Bone remodeling by direct contact with bone cells or by the release of soluble effectors is also altered by other cell protagonists present in the bone microenvironment such as monocytes/macrophages, lymphocytes, and endothelial cells [2]. In the disease state, the loss of bone homeostasis is potentially associated with changes in the numerous cellular protagonists that are responsible for the interactions between bone tissue, the immune system, and the vascular compartment. The study of bone homeostasis can therefore be utilized to elicit a better understanding of the pathologies associated with bone diseases such as osteoporosis [2]. Bone mass also has a very strong genetic determination: Twin and family studies showed that genetic factor could cause 50 to 90% of variance in bone mineral density (BMD) [38]. In addition, both the calcium-sensing receptor (CASR) and the interleukin 6 (IL-6) are important candidate genes for osteoporosis as well as in bone and mineral metabolism. These genes may have effects on BMD variation in Chinese nuclear families [9]. Determining SNPs for bone remodeling-related genes is becoming a more feasible and efficient tool for analyzing the processes associated with osteoporosis. However, an investigation of the distribution of SNPs within human populations is laborious and costly, mainly due to the necessity of testing large numbers of individuals and SNPs. Some SNPs for bone remodeling genes have already been reported; however, there are significant differences in allele frequency distributions among population groups, indicating that the populations exhibit genetic heterogeneity with respect to the incidence of these SNPs. Moreover, racial differences in the prevalence of certain alleles could account for a certain proportion of bone disease trait variation between different ethnicities [10]. The genetic variability of Asian and Caucasian populations was observed at restriction sites exhibiting polymorphisms of five important candidate genes for BMD: CASR-BsaHI, alpha 2HS-glycoprotein (AHSG)-SacI, estrogen receptor alpha (ESR1)-PvuII and XbaI, vitamin D receptor (VDR)-ApaI and parathyroid hormone (PTH)-BstBI. The results of the statistical analysis between the two populations revealed a significant allelic and genotypic differentiation in polymorphisms associated with osteoporosis. Intra- and inter-population variability implies that the studied pattern of variation at some loci may be affected by various types of natural selection [11]. A case-control approach is normally used to investigate the association of osteoporosis with SNPs in osteoporosis-related genes. A few of the newly discovered candidate genes (PLXNA2, CAT and SEMA7A) in our study were also used in case-control association studies in a Korean population [1214]. These genes were screened in 24 individuals and then were genotyped in 560 postmenopausal women to compare gene and bone properties. Statistical analyses found a genetic linkage of the SNPs and haplotypes from the above genes with a risk of vertebral fracture or with BMD at the lumbar spine and at the femur neck [1214]. Thus, to facilitate further association studies using SNPs of genes involved in osteoporosis, we selected 81 candidate genes involved in bone formation and resorption. We have characterized the genetic variants of these candidate osteoporosis genes, including gene-based haplotype diversity. These SNPs may be useful for genetic association studies that compare the SNP and haplotype information of ethnic groups.

Methods

Subjects and candidate genes

The study population consists of 24 unrelated Korean individuals, 11 men and 13 women, who were recruited from Ansan and Ansung area. The men were aged between 41 and 65 years (mean ± SD: 57.8 ± 8.5 years) and the women were aged between 41 and 62 years (mean ± SD: 52.6 ± 6.9 years). They were used for SNP screening and immortalized B lymphocyte cell line generation (cell line IDs GRB2015717, GRB2014744, GRB2014719, GRB2014754, GRB2015301, GRB2014712, GRB2012585, GRB2012949, GRB2012816, GRB2013123, GRB2012811, GRB2012998, GRB2015263, GRB2014890, GRB2014112, GRB2014896, GRB2014197, GRB2010947, GRB2021291, GRB2021404, GRB2021105, GRB2022466, GRB2026940, GRB2021302). Informed consent was obtained from all of the subjects, and this study was approved by the Institutional Review Board of the Korea National Institute of Health. Candidate osteoporosis genes were selected based on their function in bone/chondrocyte formation or bone resorption according to reports in the literature. We included the following genes of interest: those that promote or inhibit bone/chondrocyte formation; those that promote or inhibit bone resorption; and those involved in adipocyte differentiation. Genes of interest that promote bone/chondrocyte formation are as follows: FGFs [15], SOX5,6,9 [15], BMPs [16], LGALS3 [17], LGALS1 [18], DLX5 [16, 19], MSX2 [19], SP7 [19], CBFB [20], TGFBI [21], MSX1 [22], BGLAP [23], SPP1 [24], IBSP [24], IL1RN [25], CTNNB1 [16, 26], WNTs [26], TCF4 [27], OMD [28], VEGFs [29], DMP1 [30], IL13 [31], AR [32], CYP17A1 [32, 33] and CYP19A1 [32, 33]. Genes of interest that inhibit bone/chondrocyte formation are as follows: PTHrP/PTHR1 [15, 19], NPY2R [19], PPARG [19], TWIST1 [16, 24], DKK1 [26], PTH [19, 34], AHSG [35], PPP3CA [36], WIF1 [37], MEPE [38] and IL10 [39, 40]. Genes of interest that promote bone resorption are as follows: PTHrP/PTHR1 [15, 19], PTH [19, 34], PTGS2 [26], IL4 [34], IL6ST [41], CTSK [42], H+ATPase [42], ITGA1 [42, 43], NFKB [42, 44], CALCR [44], CLCN7 [44], FOS [44, 45], FOSB [42, 45], FOSL2 [46, 47], ITGAV [42, 44], CSK [42], TRAF6 [42, 44], MITF [44], CCR1 [48], NFATC1 [49], JDP2 [50], IL15 [51], PTK2B [52], CASR [53], SEMA7A [54], PTGER4 [55] and PLXNA2 [12]. Genes of interest that inhibit bone resorption are as follows: IL13 [31], AR [32], IL3 [56], ZNF675 [57], GPX1 [58] and CAT [59]. In additional, a decrease in bone volume that occurs with age and in osteoporosis is accompanied by an increase in adipose tissue in the bone marrow, suggesting a dysregulation of the mesenchymal stem cell differentiation pathway in favour of adipogenesis. Therefore, we also included the following adipocyte differentiation genes: PPARs [19], CEBPB [19, 47] and DBI [60].

Resequencing analysis

To identify SNPs in the 81 candidate osteoporosis genes (Table 1), we resequenced all exons, including the coding region, the 5' UTR and the 3' UTR up to the splice junctions, as well as the promoter regions of approximately 0.5 kb proximal to the transcription start site in genomic DNA samples. For sequencing analysis, genomic DNA information was obtained from GenBank. Polymerase chain reaction (PCR) primers were designed using the Primer 3 program [61]. Genomic DNA was isolated from the 24 immortalized B lymphocyte cell lines of the selected subjects. PCR products were sequenced using the BigDye Terminator v3.1 cycle sequencing kit (Applied Biosystems, Foster City, CA) and an ABI 3730 automated sequencer (Applied Biosystems). SNPs were detected by multiple alignments of the sequences using the Phred/Phrap/Consed package [62, 63] and polyphred [64]. All data for the SNPs discovered in the Korean samples have been deposited in the KSNP database [65].
Table 1

Gene information for candidate osteoporosis genes

Gene Symbol

Gene Name

Locus ID

NM_#

Genomic Size

Exon #

BGLAP

Bone gamma-carboxyglutamate (gla) protein (osteocalcin)

1q25-q31

NM_000711

263037

8

CALCR

Calcitonin receptor

7q21.3

NM_001742

149952

13

IL6ST

Interleukin 6 signal transducer (gp130, oncostatin M receptor)

5q11

NM_002184

54069

17

LGALS3

Lectin, galactoside-binding, soluble, 3 (galectin 3)

14q21-q22

NM_002306

16124

6

PPARG

Peroxisome proliferative activated receptor, gamma

3p25

NM_005037

146417

7

PTH

Parathyroid hormone

11p15.3-p15.1

NM_000315

3966

3

SP7

Sp7 transcription factor(osterix)

12q13.13

NM_152860

9176

2

TGFBI

Transforming growth factor, beta-induced, 68 kDa

5q31

NM_000358

34810

17

AR

Androgen receptor

Xq11.2-q12

NM_000044

180246

8

BMP7

Bone morphogenetic protein 7

20q13

NM_001719

95747

7

AHSG

Alpha 2 HS-glycoprotein

3q27

NM_001622

8219

7

BMP2

Bone morphogenetic protein 2

20p12

NM_001200

10563

3

BMP4

Bone morphogenetic protein 4

14q22-q23

NM_001202

7156

4

BMP6

Bone morphogenetic protein 6

6p24-p23

NM_001718

154718

7

CBFB

Core-binding factor, beta subunit

16q22.1

NM_022845

71907

6

CTSK

Cathepsin K

1q21

NM_000396

12126

8

DLX5

Distal-less homeobox 5

7q22

NM_005221

4436

3

IBSP

Integrin-binding sialoprotein (bone sialoprotein, bone sialoprotein II)

4q21-q25

NM_004967

12373

7

IL1RN

Interleukin 1 receptor antagonist

2q14.2

NM_000577

16123

5

LGALS1

Lectin, galactoside-binding, soluble, 1 (galectin 1)

22q13.1

NM_002305

4165

4

MSX1

Msh homeobox homolog 1

4p16.3-p16.1

NM_002448

4053

3

MSX2

Msh homeobox homolog 2

5q34-q35

NM_002449

6300

2

PTHLH

Parathyroid hormone-like hormone

12p12.1-p11.2

NM_002820

9663

4

PTHR1

Parathyroid hormone receptor 1

3p22-p21.1

NM_000316

26052

16

RUNX1

Runt-related transcription factor 1

21q22.3

NM_001754

261497

8

SPP1

Secreted phosphoprotein 1 (osteopontin, bone sialoprotein I)

4q21-q25

NM_000582

7761

6

TWIST1

Twist homolog 1 (acrocephalosyndactyly 3; Saethre-Chotzen syndrome)

7p21.2

NM_000474

2203

2

CEBPB

CCAAT/enhancer binding protein (C/EBP), beta

20q13.1

NM_005194

1837

1

CYP17A1

Cytochrome P450, family 17, subfamily A, polypeptide 1

10q24.3

NM_000102

6885

8

CYP19A1

Cytochrome P450, family 19, subfamily A, polypeptide 1

15q21.1

NM_000103

129125

10

IL10

Interleukin 10

1q31-q32

NM_000572

4892

5

IL4

Interleukin 4

5q31.1

NM_000589

5996

4

NFKB1

Nuclear factor of kappa light polypeptide gene enhancer in B-cells 1 (p105)

4q24

NM_003998

115989

24

VEGF

Vascular endothelial growth factor

6p12

NM_003376

14391

8

NPY2R

Neuropeptide Y receptor Y2

4q31

NM_000910

8447

2

FGF2

Fibroblast growth factor 2

4q26-q27

NM_002006

71528

3

FOS

V-fos FBJ murine osteosarcoma viral oncogene homolog

14q24.3

NM_005252

3383

4

FOSB

FBJ murine osteosarcoma viral oncogene homolog B

19q13.32

NM_006732

7184

4

SOX5

SRY (sex determining region Y)-box 5

12p12.1

NM_152989

1030149

18

SOX6

SRY (sex determining region Y)-box 6

11p15.3

NM_033326

506124

16

SOX9

SRY (sex determining region Y)-box 9

17q24.3-q25.1

NM_000346

5401

3

PTGER4

Prostaglandin E receptor 4 (subtype EP4)

5p13.1

NM_000958

13804

3

CSK

C-src tyrosine kinase

15q23-q25

NM_004383

20790

13

FGF23

Fibroblast growth factor 23

12p13.3

NM_020638

11502

3

FOSL2

Fos-like antigen 2

2p23-p22

NM_005253

21736

4

REL

V-rel reticuloendotheliosis viral oncogene homolog (avian)

2p13-p12

NM_002908

41427

11

RELA

V-rel reticuloendotheliosis viral oncogene homolog A, p65 (avian)

11q13

NM_021975

8559

11

RELB

V-rel reticuloendotheliosis viral oncogene homolog B (avian)

19q13.32

NM_006509

36741

11

NFKB2

Nuclear factor of kappa light polypeptide gene enhancer in B-cells 2 (p49/p100)

10q24

NM_002502

6805

23

ITGAV

Integrin, alpha V (vitronectin receptor, alpha polypeptide, antigen CD51)

2q31-q32

NM_002210

90828

30

JDP2

Jun dimerization protein 2

14q24.3

NM_130469

38320

4

NFATC1

Nuclear factor of activated T-cells, cytoplasmic, calcineurin-dependent 1

18q23

NM_172390

72406

8

PPP3CA

Protein phosphatase 3 (formerly 2B), catalytic subunit, alpha isoform (calcineurin A alpha)

4q24

NM_000944

323767

14

CASR

Calcium sensing receptor

3q21.1

NM_000388

102813

7

ZNF675

Zinc finger protein 675

19p12

AB_209601

33969

4

TRAF6

TNF receptor associated factor 6

11p12

NM_004620

21100

7

CLCN7

Chloride channel 7

16p13.3

NM_001287

29668

25

DBI

Diazepam binding inhibitor (acyl-Coenzyme A binding protein)

2q14.2

NM_020548

5461

5

CTNNB1

Catenin (cadherin-associated protein), beta 1, 88 kDa

3p21

NM_001904

40935

15

TCF4

Transcription factor 4

18q21.2

NM_003199

360475

20

OMD

Osteomodulin

9q22.31

NM_005014

10023

3

DMP1

Dentin matrix acidic phosphoprotein

4q22.1

NM_004407

14049

6

WIF1

WNT inhibitory factor 1

12q14.3

NM_007191

70681

10

MEPE

Matrix extracellular phosphoglycoprotein with ASARM motif (bone)

4q22.1

NM_020203

13807

4

CCR1

Chemokine (C-C motif) receptor 1

3p21.31

NM_001295

6101

2

ATP6V0D2

ATPase, H+ transporting, lysosomal 38 kDa, V0 subunit d2

8q21.3

NM_152565

55316

8

IL15

Interleukin 15

4q31.21

NM_172174

96859

8

VEGFC

Vascular endothelial growth factor C

4q34.3

BC_063685

109205

7

PTK2B

PTK2B protein tyrosine kinase 2 beta

8p21.2

NM_004103

147905

35

WNT9A

Wingless-type MMTV integration site family, member 9A

1q42.13

NM_003395

26903

4

PPARA

Peroxisome proliferative activated receptor, alpha

22q13.31

NM_001001928

93155

8

CAT

Catalase

11p13

NM_001752

33115

13

GPX1

Glutathione peroxidase 1

3p21.3

NM_201397

1181

1

ITGA1

Integrin, alpha 1\

5q11.2

NM_181501

165350

29

MITF

Microphthalmia-associated transcription factor

3p14.2-p14.1

NM_198159

228855

10

PLXNA2

Plexin A2

1q32.2

NM_025179

216898

32

PTGS2

Prostaglandin-endoperoxide synthase 2

1q25.2-q25.3

NM_000963

8588

10

SEMA7A

Semaphorin 7A, GPI membrane anchor

15q22.3-q23

NM_003612

23954

14

DKK1

Dickkopf homolog 1

10q11.2

NM_012242

3063

4

IL3

Interleukin 3 (colony-stimulating factor, multiple)

5q31.1

NM_000588

2550

5

IL13

Interleukin 13

5q31

NM_002188

2937

4

Statistical analysis

The HapMap database [66] was used to compare the Korean population with other populations. To measure the genetic differentiation between populations, Wright's F ST (the classic measure of population divergence) was calculated from the genotypic data. Haplotypes were suggested using the Partition Ligation-Expectation Maximization (PL-EM) algorithm [67]. We used the KSNP database to analyze LD and haplotype blocks and for tagging the detected SNPs. We defined LD blocks according to the method of LD-based blocking with bootstrapping [68], and haplotype tagging of selected SNPs was accomplished using the Entropy method [69].

Results

Identification of SNPs in candidate osteoporosis genes in the Korean population

We directly sequenced 81 candidate osteoporosis genes including all exons, their intron boundaries, and ~1.5 kb of the 5' flanking region. We identified 942 variants, including 888 SNPs, 43 insertion/deletion polymorphisms, and 11 microsatellite markers (Table 2). Of the 888 SNPs, 118 were located in promoter regions, 21 in 5' untranslated regions (UTRs), 157 in coding regions, 435 in introns, 119 in 3' UTRs and 38 in intergenic regions (Table 2). With regard to the minor allele frequency (MAF), we classified the 888 SNPs into low (MAF < 0.05), intermediate (0.05–0.15), and high (>0.15) frequency classes as described by Cargill et al [70] (Fig. 1A). Of the 888 SNPs, we identified 331 unknown SNPs which were not reported in dbSNP (build 124), and the rest were known (Fig. 1B). Of the 888 SNPs, 401 belonged to the high MAF class, of which 53 (13.2%) were unknown SNPs. In addition, the majority of the low MAF class (70.3%) were also unknown SNPs, suggesting that a large portion of newly identified SNPs exist in a recessive model. Overall, about two-third of the SNPs identified in this study are common in the Korean population (MAF > 0.05). When functionally classified, 76% of the nonsynonymous SNPs (cSNP) belonged to the low MAF class whereas only 52.2% of SNPs in the promoter regions belonged to this class (Fig. 1C). In addition, newly identified SNPs with MAF > 0.15 represented 16% of all the discovered SNPs. In functional aspect, we found some unknown SNPs in the coding region of the genes encoding interleukin 6 signal transducer (IL6ST), the androgen receptor (AR), and the core-binding factor beta subunit (CBFB) which were not reported in dbSNP database. However, there were no SNPs in the coding region of NFKB2 in both our dataset and dbSNP, suggesting that they are functionally and evolutionary highly conserved genes.
Table 2

Summary of polymorphisms discovered in candidate osteoporosis genes

Gene

IND

MIC

SNP

     

Promoter

5'UTR

Syn

Nonsyn

3'UTR

Intron

Intergenic

Total

 

aT

bN

T

N

T

N

T

N

T

N

T

N

T

N

T

N

T

N

T

N

BGLAP

    

1

     

1

1

      

2

1

CALCR

3

2

    

1

   

1

 

4

2

3

1

  

9

3

IL6ST

    

1

1

1

1

  

2

1

  

5

5

  

9

8

LGALS3

        

1

1

3

1

  

4

1

  

8

3

PPARG

  

2

2

  

1

 

1

     

3

2

  

5

2

PTH

        

1

   

1

1

1

   

3

1

SP7

        

1

         

1

0

TGFBI

        

5

1

  

2

 

12

4

  

19

5

AR

        

2

2

5

5

1

1

4

4

1

1

13

13

BMP7

            

1

 

7

1

  

8

1

AHSG

        

2

 

2

   

2

   

6

0

BMP2

        

1

 

3

1

  

1

   

5

1

BMP4

          

1

   

9

5

  

10

5

BMP6

        

2

1

1

1

2

1

4

1

  

9

4

CBFB

          

1

1

5

5

4

4

  

10

10

CTSK

          

1

1

  

1

1

  

2

2

DLX5

          

1

1

  

1

1

  

2

2

IBSP

    

2

2

  

2

 

5

       

9

2

IL1RN

    

4

1

3

 

1

   

1

 

24

 

1

 

34

1

LGALS1

    

1

         

4

2

  

5

2

MSX1

  

1

           

2

1

  

2

1

MSX2

1

1

1

         

2

     

2

0

PTHLH

1

         

1

1

1

 

2

1

  

4

2

PTHR1

      

1

1

1

     

4

   

6

1

RUNX1

            

11

10

3

1

  

14

11

SPP1

    

6

5

  

2

   

3

1

7

2

  

18

8

TWIST1

2

2

  

1

1

1

           

2

1

FGF2

            

6

1

2

1

1

1

9

3

FOS

1

   

1

 

2

 

1

     

1

   

5

0

FOSB

    

1

1

      

3

1

1

   

5

2

CSK

        

3

2

  

3

 

3

3

  

9

5

PTGER4

    

2

1

      

2

2

    

4

3

FGF23

          

2

1

      

2

1

FOSL2

          

1

1

  

2

   

3

1

ITGAV

          

1

 

3

1

14

7

  

18

8

REL

          

1

1

  

2

1

  

3

2

RELA

              

2

   

2

0

RELB

  

1

 

1

1

      

1

1

3

1

  

5

3

SOX5

1

1

      

2

2

  

1

1

2

   

5

3

SOX6

        

2

1

    

8

6

  

10

7

SOX9

            

2

 

1

1

  

3

1

IL3

          

1

       

1

0

IL4

    

1

     

1

1

  

3

   

5

1

IL13

          

1

 

1

 

1

   

3

0

NFKB1

3

3

  

3

2

1

1

  

2

2

  

23

5

  

29

10

VEGF

3

3

  

1

 

2

       

10

3

6

1

19

4

IL10

1

1

          

3

1

5

2

  

8

3

NPY2R

1

1

1

1

    

2

 

1

1

1

1

10

6

  

14

8

CAT

    

2

 

1

 

1

 

2

2

  

9

2

2

 

17

4

CEBPB

    

4

2

            

4

2

CYP17A1

    

2

 

1

 

3

1

1

1

  

10

1

  

17

3

CYP19A1

2

1

      

2

1

2

2

  

15

4

2

 

21

7

GPX1

    

1

       

1

     

2

0

ITGA1

2

2

  

5

2

  

2

 

5

2

13

3

31

3

5

1

61

11

MITF

    

4

2

    

1

1

5

3

3

2

2

1

15

9

PLXNA2

1

       

5

2

8

2

  

33

16

1

1

47

21

PTGS2

2

   

4

 

1

 

3

 

2

 

3

 

6

 

3

 

22

0

SEMA7A

    

1

1

  

5

1

1

1

2

 

6

3

  

15

6

DKK1

    

1

   

1

     

2

1

  

4

1

CASR

2

2

  

2

 

1

1

2

2

3

1

2

 

5

1

  

15

5

CCR1

2

2

      

1

1

1

1

5

 

10

5

1

 

18

7

CLCN7

2

2

  

10

5

  

2

1

  

2

1

12

5

1

 

27

12

CTNNB1

1

1

  

1

       

3

1

3

1

  

7

2

DBI

    

11

4

  

1

1

    

4

1

1

 

17

6

DMP1

2

2

  

6

     

2

1

2

 

4

   

14

1

IL15

    

2

1

      

4

1

10

2

  

16

4

JDP2

    

2

     

1

 

2

     

5

0

MEPE

        

1

 

1

1

1

 

9

2

  

12

3

NFATC1

    

4

1

  

3

 

1

1

1

1

9

6

  

18

9

OMD

    

4

     

1

1

1

1

    

6

2

PPARA

1

1

1

1

2

1

  

1

1

2

1

    

6

5

11

8

PPP3CA

1

1

1

1

2

       

4

3

7

5

  

13

8

PTK2B

    

8

4

3

1

10

3

1

 

1

 

24

6

3

2

50

16

TCF4

1

1

1

1

    

1

     

11

7

  

12

7

TRAF6

3

3

  

3

1

    

1

1

1

1

    

5

3

VEGFC

1

1

  

2

2

  

1

1

    

3

1

  

6

4

WIF1

1

1

1

1

4

4

      

1

 

3

1

  

8

5

WNT9A

    

2

   

2

   

3

1

1

1

1

 

9

2

ATP6V0D2

2

1

1

1

3

1

1

1

1

1

  

2

1

3

 

1

1

11

5

ZNF675

        

1

1

1

   

2

2

  

4

3

Total

43

35

11

8

118

46

21

6

81

27

76

40

119

47

435

151

38

14

888

331

SNPs, single nucleotide polymorphisms; IND, insertion/deletion polymorphism; MIC, microsatellite; UTR, untranslated region. Syn, Synonymous cSNP, indicates there is no change in the amino acid; Nonsyn, Nonsynonymous cSNP, substitutions in coding regions that result in a different amino acid

aT, sum of known SNPs and newly identified SNPs; bN, newly identified SNP in this study

https://static-content.springer.com/image/art%3A10.1186%2F1471-2350-8-70/MediaObjects/12881_2006_Article_264_Fig1_HTML.jpg
Figure 1

Distribution of the SNPs identified in the 81 candidate osteoporosis genes. (A) Classification of the SNPs into minor allele frequency (MAF) classes. (B) Number of known and unknown SNPs. (C) Distribution of SNPs according to location or type. The percentages in (A), (B), and (C) refer to the percentage of SNPs within each MAF class in the given categories.

It has been reported that the Japanese SNP database (JSNP) was constructed through the gene-based resquencing method of 24 individuals [71]. Therefore, the newly discovered SNPs for candidate genes of osteoporosis from this study were compared with those in the JSNP database. Of 70 SNPs in the exon region (excluding UTR) with MAF > 0.05 in our data, 28 SNPs were common between our study and the JSNP database. The ratio of the common SNPs to all SNPs from our data and those from JSNP for the selected genes was 28/70 and 28/43, respectively.

Deviation in Heterozygosity and Genetic diversity

We used HapMap to compare the allele frequencies of diverse ethnic groups with that of the Korean population [72]. Among the 557 known SNPs detected in this study, 313 were found in HapMap. We thus evaluated genetic differences between Koreans and the diverse populations by measuring the Wright's F ST coefficients using the 313 common SNPs assuming the Hardy-Weinberg principle. F IS is the average deviation in heterozygosity within subpopulations, F ST is the deviation due to subdivision alone, and F IT is the overall deviation in heterozygosity in the total population [73]. The mean values of F IS , F ST and F IT for multiple loci with five subpopulations (KR, CHB, JPT, CEU and YRI) are -0.0121, 0.3366 and 0.3287, respectively, indicating that the SNPs in genes associated with osteoporosis were significantly differentiated among the five subpopulations while the SNPs within the subpopulations were consistent with the Hardy-Weinberg expectations. In addition, the pairwise F ST (s) of KR compared with each of the four subpopulations using the 313 individual SNPs were calculated. The distribution of the pairwise F ST (s) values is plotted in Fig. 2. Interestingly, two distribution patterns were observed that grouped KR-CHB with KR-JPT and KR-CEU with KR-YRI. In addition, the F ST values for KR-CHB and KR-JPT continually decreased to 0.05 whereas those of KR-CEU and KR-YRI continued to 0.2 or more from which point the overall major and minor alleles are reversed, suggesting that there is a large genetic barrier among continental populations. When a threshold (F ST = 0.1 or higher) as the level of significance was applied [74] to our data, 2, 2, 73 and 92 out of 313 SNPs were significantly deviated between KR compared with CHB, JPT, YRI and CEU, respectively. In order to investigate the difference in linkage disequilibrium (LD) patterns between the significantly diverse SNPs in the sub-populations, two highly polymorphic genes (PTK2B and IL1RN) in terms of the number of SNPs per gene were selected and their Haplotype blocks using Haploview [75, 76] were plotted against five subpopulations, KR, CHB, JPT, CEU and YRI, as shown in Fig. 3. Interestingly, all five haplotype blocks for each gene were different from one another. Overall, the largest block was found in the CEU population whereas smaller blocks were found in the two genes of the KR and YRI populations. This result implies that determining genetic properties, such as, LD is a powerful method to elucidate the subtle differences in genetic diversity between sub-populations.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2350-8-70/MediaObjects/12881_2006_Article_264_Fig2_HTML.jpg
Figure 2

Distribution of F ST among the sub-populations.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2350-8-70/MediaObjects/12881_2006_Article_264_Fig3_HTML.jpg
Figure 3

Comparison of LD patterns of PTK2B and IL1RN among the sub-populations.

In order to determine the genetic diversity between subpopulations, both Nei's standard genetic distance and Latter's F ST distance were also calculated [77, 78] and listed in Table 3. Overall, both distance measures agreed with each other in terms of the trend, but overall, Nei's distances were lower than those of Latter's. The genetic distance between the KR and either the CHB (0.012) or the JPT (0.013) subpopulations was very close to each other. On the other hand, the genetic distance of the KR population was closer to the YRI population (0.594) than that of the CEU population (0.646) in these SNPs of selected genes. Therefore, the genetic diversity between KR compared with the other populations for the selected genes also agreed with the F ST analysis result.
Table 3

Pairwise genetic distance among five population

 

KOR

CHB

JPT

CEU

YRI

KOR

 

0.01197

0.01331

0.64578

0.59374

CHB

0.05259

 

0.01207

0.68158

0.58691

JPT

0.05775

0.04840

 

0.71527

0.60823

CEU

0.85137

0.86316

0.86480

 

1.00000

YRI

0.83252

0.82958

0.83475

1.00000

 

The upper triangle is for Nei's standard genetic distance [19] and the lower triangle is for Latter's F ST distance [20].

Discussion

In this study, 81 candidate genes of osteoporosis were sequenced to identify common genetic polymorphisms that might alter bone remodeling. In the analysis of differences among ethnic group allele frequencies using the measure of genetic distance, we showed that the Han Chinese and Japanese populations were close to the Korean population. This implies a strong genetic linkage among the Han Chinese, Japanese and Korean populations, which may reflect either a recent common ancestry or high levels of mutual immigration among these groups [79].

The 888 polymorphisms identified in this study were obtained from 24 unrelated individuals. Three hundred and thirty-one (37.3%) variants were newly identified polymorphisms that were not present in the public database examined, whereas 557 (62.7%) of the polymorphisms found by resequencing were already present in the database. Of the 331 variants that were not reported in the database, 64.4% belonged to the low minor allele frequency group (MAF < 0.05) in Koreans and variants, 35.6% were common SNPs in the Korean population. These common SNPs could be useful for further case-control association studies of osteoporosis in Koreans. We identified new SNPs that had low allele frequencies. This may be due to the fact that previous studies used various factors, such as a mixture of populations, or had a relatively smaller sample size, thereby limiting their ability to discover low allele frequency SNPs. Alternatively, as an ethnically homogeneous population, the Korean samples may have allele frequencies that significantly differ from those from mixed samples. Of the 557 variants that were already present in the dbSNP database, only 16.2% had a minor allele frequency lower than 0.05, 21.4% between 0.05 to 0.15 and 62.5% greater than 0.15. Therefore, our resequencing effort provided experimental validation for more than 460 polymorphisms that were already in the database.

In our study, we measured the LD block structure of the candidate genes, excluding cases of one or two SNPs and uncommon SNPs (MAF < 0.05) in each gene, from the limited sample using normalized D' statistics between all pairwise SNP markers with MAF > 0.05 that satisfied the Hardy-Weinberg's equilibrium (p < 0.05). The LD and haplotype results are shown in the KSNP database [65]. A comparison of the haplotype blocks of two highly polymorphic genes (PTK2B and IL1RN) from the KR population with those from the 4 subpopulations in HapMap, showed diverse block patterns (Fig. 3). Therefore, the LD and haplotype information could be valuable resources for ethnicity comparison, tagging SNPs and recombination signals of the osteoporosis-related genes in future studies.

In this study, the nonsynonymous cSNPs tended to have a larger proportion of low allele frequencies compared with the synonymous cSNPs, the noncoding SNPs, and the promoter SNPs. This trend is consistent with a selection pressure against SNPs that cause amino acid changes [80]. In contrast, the promoter regions, which had a wide range of allele frequencies overall, had more SNPs with high allele frequency compared with the other regions. These results indicate that the promoter variants found in this study might be utilized as genetic determinants for future studies [81]. The several million human SNPs reported in the HapMap international project will likely prove useful for association studies; however SNPs located close to functionally important genes are more valuable as markers than random genomic SNPs. Moreover, SNPs located in the coding or promoter regions have the added benefit of potentially causing the genetic variation that directly contributes to disease. Therefore, additional resequencing efforts are still needed for comprehensive studies of osteoporosis candidate genes across ethnic groups as such data should prove important for future association studies of osteoporosis.

Conclusion

We directly resequenced 81 candidate osteoporosis genes and identified 942 variants including 888 SNPs, 43 insertion/deletion polymorphisms, and 11 microsatellite markers. Of the 888 SNPs, 331 SNPs have not been previously identified and 557 SNPs were already reported in the dbSNP database, of which more than 460 were validated by our resequencing effort.

Statistical analysis of deviation in heterozygosity with the HapMap data depicted that compared with SNPs in Koreans, 1%(or less) of SNPs in Japanese and Chinese and 20% of those in Caucasian and African were significantly differentiated from the Hardy-Weinberg expectations. In addition, the analysis of genetic diversity between Korean and the other four populations showed that the order of the closest neighbor (in terms of genetic distance) is Han Chinese, Japanese, African and Caucasian. In general, we didn't find any significant differences among three sub-populations from KR, CHB and JPT, but these Asian populations, CEU and YRI were significantly different in both the F ST and genetic diversity results in selected genes. Nevertheless, analysis using genetic properties, such as LD and haplotype patterns showed that all-sub populations were substantially different.

Overall, through the resequencing of 81 osteoporosis candidate genes, 118 unknown SNPs with MAF > 0.05 were discovered in a Korean population. In addition, our newly discovered SNPs were compared with those in HapMap to elucidate diversity and deviation in heterozygosity, resulting in strong genetic linkages between the Han Chinese, Japanese and Korean populations. This result may reflect either a recent common ancestry or high levels of mutual immigration among these groups. Yet, using a genetic property, such as LD patterns, is a powerful method to elucidate the subtle differences between the Korean, Chinese and Japanese populations. Our results could aid in the design of case-controlled and population stratification studies in the Korean population.

Notes

Declarations

Acknowledgements

This work was supported by intramural grants from the Korea National Institute of Health, Korea Center for Disease Control, Republic of Korea (Project No.: 347-6111-211) and a grant from the Korea Health 21 R&D Project, Ministry of Health & Welfare, Republic of Korea (Project No.: A010252)

Authors’ Affiliations

(1)
Center for Genome Science, National Institute of Health
(2)
Skeletal Diseases Genome Research Center, Kyungpook National University Hospital
(3)
Division of Endocrinology and Metabolism, University of Ulsan College of Medicine, Asan Medical Center
(4)
Department of Genetic Epidemiology, SNP Genetics, Inc.
(5)
World Meridian Venture Center 10F
(6)
Department of Pathology and Regenerative Medicine, School of Dentistry, Kyungpook National University
(7)
Department of Orthopedic Surgery, Kyungpook National University School of Medicine

References

  1. Aubin JE, Triffit JT: Mesenchymal stem cells and osteoblast differentiation. Principles of bone biology. Edited by: Bilezikian JP, Raisz LG, Rodan GA. 2002, San Diego: Academic Press, 1: 59-81.View Article
  2. Theoleyre S, Wittrant Y, Tat SK, Fortun Y, Redini F, Heymann D: The molecular triad OPG/RANK/RANKL: involvement in the orchestration of pathophysiological bone remodeling. Cytokine Growth Factor Rev. 2004, 15: 457-475. 10.1016/j.cytogfr.2004.06.004. Review.View ArticlePubMed
  3. Pocock NA, Eisman JA, Hopper JL, Yeates MG, Sambrook PN, Eberl S: Genetic determinants of bone mass in adults. A twin study. J Clin Invest. 1987, 80: 706-10.PubMed CentralView ArticlePubMed
  4. Slemenda CW, Christian JC, Williams CJ, Norton JA, Johnston CC: Genetic determinants of bone mass in adult women: A re-evaluation of the twin model and the potential importance of gene interaction on heritability estimates. J Bone Miner Res. 1991, 6: 561-7.View ArticlePubMed
  5. Lutz J, Tesar R: Mother-daughter pairs: Spinal and femoral bone densities and dietary intakes. Am J Clin Nutr. 1990, 52: 872-877.PubMed
  6. Guéguen R, Jouanny P, Guillemin F, Kuntz C, Pourel J, Siest G: Segregation analysis and variance components analysis of bone mineral density in healthy families. J Bone Miner Res. 1995, 10 (12): 2017-2022.View ArticlePubMed
  7. Hunter DJ, de Lange M, Andrew T, Snieder H, Mac-Gregor AJ, Spector TD: Genetic variation in bone mineral density and calcaneal ultrasound: a study of the influence of menupause using female twins. Osteoporos Int. 2001, 12: 406-11. 10.1007/s001980170110.View ArticlePubMed
  8. Ng MY, Sham PC, Paterson AD, Chan V, Kung AW: Effect of environmental factors and gender on the heritability of bone mineral density and bone size. Ann Hum Genet. 2006, 70: 428-38. 10.1111/j.1469-1809.2005.00242.x.View ArticlePubMed
  9. Wang YB, Guo JJ, Liu YJ, Deng FY, Jiang DK, Deng HW: The human calcium-sensing receptor and interleukin-6 genes are associated with bone mineral density in Chinese. Yi Chuan Xue Bao. 2006, 33: 870-80.PubMed
  10. Beavan S, Prentice A, Dibba B, Yan L, Coper C, Ralston SH: Polymorphism of the collagen type I α1 gene and ethnic differences in hip-fracture rates. N Engl J Med. 1998, 339: 351-352. 10.1056/NEJM199807303390517.View ArticlePubMed
  11. Dvornyk V, Liu XH, Shen H, Lei SF, Zhao LJ, Huang QR, Qin YJ, Jiang DK, Long JR, Zhang YY, Gong G, Recker RR, Deng HW: Differentiation of Caucasians and Chinese at bone mass candidate genes: implication for ethnic difference of bone mass. Ann Hum Genet. 2003, 67: 216-27. 10.1046/j.1469-1809.2003.00037.x.View ArticlePubMed
  12. Hwang JY, Lee JY, Park MH, Kim KS, Kim KK, Ryu HJ, Lee JK, Han BG, Kim JW, Oh B, Kimm K, Park BL, Shin HD, Kim TH, Hong JM, Park EK, Kim DJ, Koh JM, Kim GS, Kim SY: Association of PLXNA2 polymorphisms with vertebral fracture risk and bone mineral density in postmenopausal Korean population. Osteoporos Int. 2006, 17: 1592-601. 10.1007/s00198-006-0126-x.View ArticlePubMed
  13. Oh B, Kim SY, Kim DJ, Lee JY, Lee JK, Kimm K, Park BL, Shin HD, Kim TH, Park EK, Koh JM, Kim GS: Associations of catalase gene polymorphisms with bone mineral density and bone turnover markers in postmenopausal women. J Med Genet. 2007, 44: e62-10.1136/jmg.2006.042259.PubMed CentralView ArticlePubMed
  14. Koh JM, Oh B, Lee JY, Lee JK, Kimm K, Kim GS, Park BL, Cheong HS, Shin HD, Hong JM, Kim TH, Park EK, Kim SY: Association study of semaphorin 7a (sema7a) polymorphisms with bone mineral density and fracture risk in postmenopausal Korean women. J Hum Genet. 2006, 51: 112-7. 10.1007/s10038-005-0331-z.View ArticlePubMed
  15. de Crombrugghe B, Lefebvre V, Nakashima K: Regulatory mechanisms in the pathways of cartilage and boneformation. Curr Opin Cell Biol. 2001, 13: 721-7. 10.1016/S0955-0674(00)00276-3. Review.View ArticlePubMed
  16. Kobayashi T, Kronenberg H: Minireview: transcriptional regulation in development of bone. Endocrinology. 2005, 146: 1012-7. 10.1210/en.2004-1343. Review.View ArticlePubMed
  17. Ortega N, Behonick DJ, Colnot C, Cooper DN, Werb Z: Galectin-3 is a downstream regulator of matrix metalloproteinase-9 function during endochondral bone formation. Mol Biol Cell. 2005, 16: 3028-39. 10.1091/mbc.E04-12-1119.PubMed CentralView ArticlePubMed
  18. Andersen H, Jensen ON, Moiseeva EP, Eriksen EF: A proteome study of secreted prostatic factors affecting osteoblastic activity: galectin-1 is involved in differentiation of human bone marrow stromal cells. J Bone Miner Res. 2003, 18: 195-203. 10.1359/jbmr.2003.18.2.195.View ArticlePubMed
  19. Harada S, Rodan GA: Control of osteoblast function and regulation of bone mass. Nature. 2003, 423: 349-55. 10.1038/nature01660. Review.View ArticlePubMed
  20. Yoshida CA, Furuichi T, Fujita T, Fukuyama R, Kanatani N, Kobayashi S, Satake M, Takada K, Komori T: Core-binding factor beta interacts with Runx2 and is required for skeletal development. Nat Genet. 2002, 32: 633-8. 10.1038/ng1015.View ArticlePubMed
  21. Zhou S, Glowacki J, Yates KE: Comparison of TGF-beta/BMP pathways signaled by demineralized bone powder and BMP-2 in human dermal fibroblasts. J Bone Miner Res. 2004, 19: 1732-41. 10.1359/JBMR.040702.View ArticlePubMed
  22. Zhang Z, Song Y, Zhang X, Tang J, Chen J, Chen Y: Msx1/Bmp4 genetic pathway regulates mammalian alveolar bone formation via induction of Dlx5 and Cbfa1. Mech Dev. 2003, 120: 1469-79. 10.1016/j.mod.2003.09.002.View ArticlePubMed
  23. Pagani F, Francucci CM, Moro L: Markers of bone turnover: biochemical and clinical perspectives. J Endocrinol Invest. 2005, 28 (10 Suppl): 8-13. Review.PubMed
  24. Jabs EW: A TWIST in the fate of human osteoblasts identifies signaling molecules involved in skull development. J Clin Invest. 2001, 107: 1075-7.PubMed CentralView ArticlePubMed
  25. Nemetz A, Toth M, Garcia-Gonzalez MA, Zagoni T, Feher J, Pena AS, Tulassay Z: Allelic variation at the interleukin 1beta gene is associated with decreased bone mass in patients with inflammatory bowel diseases. Gut. 2001, 49: 644-9. 10.1136/gut.49.5.644.PubMed CentralView ArticlePubMed
  26. Raisz LG: Pathogenesis of osteoporosis: concepts, conflicts, and prospects. J Clin Invest. 2005, 115: 3318-25. 10.1172/JCI27071.PubMed CentralView ArticlePubMed
  27. Bienz M, Clevers H: Linking colorectal cancer to Wnt signaling. Cell. 2000, 103: 311-20. 10.1016/S0092-8674(00)00122-7. ReviewView ArticlePubMed
  28. Tasheva ES, Klocke B, Conrad GW: Analysis of transcriptional regulation of the small leucine rich proteoglycans. Mol Vis. 2004, 10: 758-72.PubMed
  29. Deckers MM, Karperien M, van der Bent C, Yamashita T, Papapoulos SE, Lowik CW: Expression of vascular endothelial growth factors and their receptors during osteoblast differentiation. Endocrinology. 2000, 141: 1667-74. 10.1210/en.141.5.1667.View ArticlePubMed
  30. Ye L, Mishina Y, Chen D, Huang H, Dallas SL, Dallas MR, Sivakumar P, Kunieda T, Tsutsui TW, Boskey A, Bonewald LF, Feng JQ: Dmp1-deficient mice display severe defects in cartilage formation responsible for a chondrodysplasia-like phenotype. J Biol Chem. 2005, 280: 6197-203. 10.1074/jbc.M412911200.PubMed CentralView ArticlePubMed
  31. Rifas L, Cheng SL: IL-13 regulates vascular cell adhesion molecule-1 expression in human osteoblasts. J Cell Biochem. 2003, 89: 213-9. 10.1002/jcb.10531.View ArticlePubMed
  32. Yanase T, Suzuki S, Goto K, Nomura M, Okabe T, Takayanagi R, Nawata H: Aromatase in bone: roles of Vitamin D3 and androgens. J Steroid Biochem Mol Biol. 2003, 86: 393-7. 10.1016/S0960-0760(03)00349-2. Review.View ArticlePubMed
  33. Somner J, McLellan S, Cheung J, Mak YT, Frost ML, Knapp KM, Wierzbicki AS, Wheeler M, Fogelman I, Ralston SH, Hampson GN: Polymorphisms in the P450 c17 (17-hydroxylase/17,20-Lyase) and P450 c19 (aromatase) genes: association with serum sex steroid concentrations and bone mineral density in postmenopausal women. J Clin Endocrinol Metab. 2004, 89: 344-51. 10.1210/jc.2003-030164.View ArticlePubMed
  34. Voskaridou E, Terpos E: New insights into the pathophysiology and management of osteoporosis in patients with beta thalassaemia. Br J Haematol. 2004, 127: 127-39. 10.1111/j.1365-2141.2004.05143.x. Review.View ArticlePubMed
  35. Szweras M, Liu D, Partridge EA, Pawling J, Sukhu B, Clokie C, Jahnen-Dechent W, Tenenbaum HC, Swallow CJ, Grynpas MD, Dennis JW: alpha 2-HS glycoprotein/fetuin, a transforming growth factor-beta/bone morphogenetic protein antagonist, regulates postnatal bone growth and remodeling. J Biol Chem. 2002, 277: 19991-7. 10.1074/jbc.M112234200.View ArticlePubMed
  36. Parisi MS, Gazzerro E, Rydziel S, Canalis E: Expression and regulation of CCN genes in murine osteoblasts. Bone. 2006, 38: 671-7. 10.1016/j.bone.2005.10.005.View ArticlePubMed
  37. Vaes BL, Dechering KJ, van Someren EP, Hendriks JM, van de Ven CJ, Feijen A, Mummery CL, Reinders MJ, Olijve W, van Zoelen EJ, Steegenga WT: Microarray analysis reveals expression regulation of Wnt antagonists in differentiating osteoblasts. Bone. 2005, 36: 803-11. 10.1016/j.bone.2005.02.001.View ArticlePubMed
  38. Nampei A, Hashimoto J, Hayashida K, Tsuboi H, Shi K, Tsuji I, Miyashita H, Yamada T, Matsukawa N, Matsumoto M, Morimoto S, Ogihara T, Ochi T, Yoshikawa H: Matrix extracellular phosphoglycoprotein (MEPE) is highly expressed in osteocytes in human bone. J Bone Miner Metab. 2004, 22: 176-84. 10.1007/s00774-003-0468-9.View ArticlePubMed
  39. Van Vlasselaer P, Borremans B, Van Den Heuvel R, Van Gorp U, de Waal Malefyt R: Interleukin-10 inhibits the osteogenic activity of mouse bone marrow. Blood. 1993, 82: 2361-70.PubMed
  40. Van Vlasselaer P, Borremans B, van Gorp U, Dasch JR, De Waal-Malefyt R: Interleukin 10 inhibits transforming growth factor-beta (TGF-beta) synthesis required for osteogenic commitment of mouse bone marrow cells. J Cell Biol. 1994, 124: 569-77. 10.1083/jcb.124.4.569.View ArticlePubMed
  41. Blair HC, Robinson LJ, Zaidi M: Osteoclast signalling pathways. Biochem Biophys Res Commun. 2005, 328: 728-38. 10.1016/j.bbrc.2004.11.077. Review.View ArticlePubMed
  42. Teitelbaum SL: Bone resorption by osteoclasts. Science. 2000, 289: 1504-8. 10.1126/science.289.5484.1504. Review.View ArticlePubMed
  43. Titelbaum SL: Osteoporosis and integrins. J Clin Endocrinol Metab. 2005, 90: 2466-2468. 10.1210/jc.2005-0338.View Article
  44. Boyle WJ, Simonet WS, Lacey DL: Osteoclast differentiation and activation. Nature. 2003, 423: 337-42. 10.1038/nature01658. Review.View ArticlePubMed
  45. Ito Y, Inoue D, Kido S, Matsumoto T: c-Fos degradation by the ubiquitin-proteasome proteolytic pathway in osteoclast progenitors. Bone. 2005, 37: 842-9. 10.1016/j.bone.2005.04.030.View ArticlePubMed
  46. Takayanagi H, Kim S, Matsuo K, Suzuki H, Suzuki T, Sato K, Yokochi T, Oda H, Nakamura K, Ida N, Wagner EF, Taniguchi T: RANKL maintains bone homeostasis through c-Fos-dependent induction of interferon-beta. Nature. 2002, 416: 744-9. 10.1038/416744a.View ArticlePubMed
  47. Chang W, Rewari A, Centrella M, McCarthy TL: Fos-related antigen 2 controls protein kinase A-induced CCAAT/enhancer-binding protein beta expression in osteoblasts. J Biol Chem. 2004, 279: 42438-44. 10.1074/jbc.M405549200.View ArticlePubMed
  48. Ishida N, Hayashi K, Hattori A, Yogo K, Kimura T, Takeya T: CCR1 acts downstream of NFAT2 in osteoclastogenesis and enhances cell migration. J Bone Miner Res. 2006, 21: 48-57. 10.1359/JBMR.051001.View ArticlePubMed
  49. Koga T, Matsui Y, Asagiri M, Kodama T, de Crombrugghe B, Nakashima K, Takayanagi H: NFAT and Osterix cooperatively regulate bone formation. Nat Med. 2005, 11: 880-5. 10.1038/nm1270.View ArticlePubMed
  50. Kawaida R, Ohtsuka T, Okutsu J, Takahashi T, Kadono Y, Oda H, Hikita A, Nakamura K, Tanaka S, Furukawa H: Jun dimerization protein 2 (JDP2), a member of the AP-1 family of transcription factor, mediates osteoclast differentiation induced by RANKL. J Exp Med. 2003, 197: 1029-35. 10.1084/jem.20021321.PubMed CentralView ArticlePubMed
  51. Ogata Y, Kukita A, Kukita T, Komine M, Miyahara A, Miyazaki S, Kohashi O: A novel role of IL-15 in the development of osteoclasts: inability to replace its activity with IL-2. J Immunol. 1999, 162: 2754-60.PubMed
  52. Lakkakorpi PT, Bett AJ, Lipfert L, Rodan GA, Duong le T: PYK2 autophosphorylation, but not kinase activity, is necessary for adhesion-induced association with c-Src, osteoclast spreading, and bone resorption. J Biol Chem. 2003, 278: 11502-12. 10.1074/jbc.M206579200.View ArticlePubMed
  53. Kameda T, Mano H, Yamada Y, Takai H, Amizuka N, Kobori M, Izumi N, Kawashima H, Ozawa H, Ikeda K, Kameda A, Hakeda Y, Kumegawa M: Calcium-sensing receptor in mature osteoclasts, which are bone resorbing cells. Biochem Biophys Res Commun. 1998, 245: 419-22. 10.1006/bbrc.1998.8448.View ArticlePubMed
  54. Delorme G, Saltel F, Bonnelye E, Jurdic P, Machuca-Gayet I: Expression and function of semaphorin 7A in bone cells. Biol Cell. 2005, 97: 589-97.View ArticlePubMed
  55. Miyaura C, Inada M, Suzawa T, Sugimoto Y, Ushikubi F, Ichikawa A, Narumiya S, Suda T: Impaired bone resorption to prostaglandin E2 in prostaglandin E receptor EP4-knockout mice. J Biol Chem. 2000, 275: 19819-23. 10.1074/jbc.M002079200.View ArticlePubMed
  56. Khapli SM, Mangashetti LS, Yogesha SD, Wani MR: IL-3 acts directly on osteoclast precursors and irreversibly inhibits receptor activator of NF-kappa B ligand-induced osteoclast differentiation by diverting the cells to macrophage lineage. J Immunol. 2003, 171: 142-51.View ArticlePubMed
  57. Shin JN, Kim I, Lee JS, Koh GY, Lee ZH, Kim HH: A novel zinc finger protein that inhibits osteoclastogenesis and the function of tumor necrosis factor receptor-associated factor 6. J Biol Chem. 2002, 277: 8346-53. 10.1074/jbc.M110964200.View ArticlePubMed
  58. Lean JM, Jagger CJ, Kirstein B, Fuller K, Chambers TJ: Hydrogen peroxide is essential for estrogen-deficiency bone loss and osteoclast formation. Endocrinology. 2005, 146: 728-35. 10.1210/en.2004-1021.View ArticlePubMed
  59. Fujimiya K, Sugihara K, Nishikawa T: Experimental study on the role of osteoclasts and free radicals in the mandibular invasion of VX2 carcinoma in Japanese white rabbits. Bone. 1997, 20: 245-50. 10.1016/S8756-3282(96)00366-3.View ArticlePubMed
  60. Sandberg MB, Bloksgaard M, Duran-Sandoval D, Duval C, Staels B, Mandrup S: The gene encoding acyl-CoA-binding protein is subject to metabolic regulation by both sterol regulatory element-binding protein and peroxisome proliferator-activated receptor alpha in hepatocytes. J Biol Chem. 2005, 280: 5258-66. 10.1074/jbc.M407515200.View ArticlePubMed
  61. Rozen S, Skaletsky H: Primer3 on the WWW for general users and for biologist programmers. Methods Mol Biol. 2000, 132: 365-386.PubMed
  62. Ewing B, Hillier L, Wendl MC, Green P: Base-calling of automated sequencer traces using phred. I. Accuracy assessment. Genome Res. 1998, 8: 175-185.View ArticlePubMed
  63. Gordon D, Abajian C, Green P: Consed: a graphical tool for sequence finishing. Genome Res. 1998, 8: 195-202.View ArticlePubMed
  64. Nickerson DA, Tobe VO, Taylor SL: PolyPhred: automating the detection and genotyping of single nucleotide substitutions using fluorescence-based resequencing. Nucleic Acids Res. 1997, 25: 2745-2751. 10.1093/nar/25.14.2745.PubMed CentralView ArticlePubMed
  65. KSNP database. [http://www.ngri.re.kr/SNP/]
  66. International HapMap Project. [http://www.hapmap.org/index.html.en/]
  67. Qin ZS, Niu T, Liu JS: Partition-ligation-expectation maximization algorithm for haplotype inference with singlenucleotide polymorphisms. Am J Hum Genet. 2002, 71: 1242-1247. 10.1086/344207.PubMed CentralView ArticlePubMed
  68. Zhu X, Yan D, Cooper RS, Luke A, Ikeda MA, Chang YP, Weder A, Chakravarti A: Linkage disequilibrium and haplotype diversity in the genes of the renin-angiotensin system: findings from the family blood pressure program. Genome Res. 2003, 13: 173-81. 10.1101/gr.302003.PubMed CentralView ArticlePubMed
  69. Avi-Itzhak HI, Su X, De La Vega FM: Selection of minimum subsets of single nucleotide polymorphisms to capture haplotype block diversity. Pac Symp Biocomput. 2003, 466-77.
  70. Cargill M, Altshuler D, Ireland J, Sklar P, Ardlie K, Patil N, Shaw N, Lane CR, Lim EP, Kalyanaraman N, Nemesh J, Ziaugra L, Friedland L, Rolfe A, Warrington J, Lipshutz R, Daley GQ, Lander ES: Characterization of single-nucleotide polymorphisms in coding regions of human genes. Nat Genet. 1999, 22: 231-8. 10.1038/10290.View ArticlePubMed
  71. A database of Japanese Single Nucleotide Polymorphisms. [http://snp.ims.u-tokyo.ac.jp/]
  72. Barrett JC, Fry B, Maller J, Daly MJ: Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics. 2005, 21: 263-5. 10.1093/bioinformatics/bth457.View ArticlePubMed
  73. Gabriel SB, Schaffner SF, Nguyen H, Moore JM, Roy J, Blumenstiel B, Higgins J, DeFelice M, Lochner A, Faggart M, Liu-Cordero SN, Rotimi C, Adeyemo A, Cooper R, Ward R, Lander ES, Daly MJ, Altshuler D: The structure of haplotype blocks in the human genome. Science. 2002, 296: 2225-9. 10.1126/science.1069424.View ArticlePubMed
  74. The International HapMap Consortium: The International HapMap Project. Nature. 2003, 426: 789-796. 10.1038/nature02168.View Article
  75. Wright S: Evolution and the Genetics of populations IV: variability within and among natural populations. 1978, Chicago: University of Chicago Press
  76. Hartl D, Clark AG: Principles of population genetics. 1989, Sunderland, MA: Sinauer Associates, 118-119.
  77. Nei M, Roychoudhury AK: Sampling variances of heterozygosity and genetic distance. Genetics. 1974, 76: 379-90.PubMed CentralPubMed
  78. Latter BD: Selection in finite populations with multiple alleles. 3. Genetic divergence with centripetal selection and mutation. Genetics. 1972, 70: 475-90.PubMed CentralPubMed
  79. Kim KJ, Lee HJ, Park MH, Cha SH, Kim KS, Kim HT, Kimm K, Oh B, Lee JY: SNP identification, linkage disequilibrium, and haplotype analysis for a 200-kb genomic region in a Korean population. Genomics. 2006, 88: 535-40. 10.1016/j.ygeno.2006.03.003.View ArticlePubMed
  80. Kim JJ, Kim HH, Park JH, Ryu HJ, Kim J, Moon S, Gu H, Kim HT, Lee JY, Han BG, Park C, Kimm K, Park CS, Lee JK, Oh B: Large-scale identification and characterization of genetic variants in asthma candidate genes. Immunogenetics. 2005, 57: 636-43. 10.1007/s00251-005-0024-y.View ArticlePubMed
  81. Kim GS, Koh JM, Chang JS, Park BL, Kim LH, Park EK, Kim SY, Shin HD: Association of the OSCAR promoter polymorphism with BMD in postmenopausal women. J Bone Miner Res. 2005, 20: 1342-8. 10.1359/JBMR.050320.View ArticlePubMed
  82. Pre-publication history

    1. The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2350/8/70/prepub

Copyright

© Kim et al; licensee BioMed Central Ltd. 2007

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Advertisement