KR20060120390A - Osacr gene polymorphisms for predicting and diagnosing a risk of pathogenesis of osteoporosis - Google Patents

Abstract

OSCAR(osteoclast-associated receptor) gene polymorphisms are provided to predict and diagnose a risk of pathogenesis of osteoporosis by measuring polymorphisms in OSCAR associated with a risk of bone mineral density and spine fracture. The OSCAR gene polymorphisms for predicting and diagnosing the risk of pathogenesis of osteoporosis are provided, wherein the OSCAR gene polymorphisms include OSCAR-2322A>G polymorphism; and the OSCAR-2322A>G polymorphism is associated with the risk of bone mineral density and spine fracture.

Description

OSACR gene polymorphisms for predicting and diagnosing a risk of pathogenesis of osteoporosis}

1A depicts polymorphisms identified in OSCAR on chromosome 19q13.4 (reference genomic sequence NT_011109.15, published February 2004). Coding exons are shown as black blocks and 5 'and 3' UTRs as white blocks. The first base of the translation initiation site was designated as nucleotide +1. * Indicates polymorphisms that were genotyped in large populations (n = 560). The frequency of large non-genotype polymorphisms was based on sequence data (n = 24).

Figure 1b shows the haplotype of OSCAR. Only haplotypes with frequencies above 0.04 were presented and the remaining (1) included rare haplotypes: GCAGC, ACTGA, AAAGC, ACAGC, GCATC, ACTTA, GAAGA and ACTTC.

1C shows the correlational unbalance coefficients (| D '| and r ² ) between OSCAR polymorphisms.

FIG. 2 shows bone density (BMD) at the lumbar spine (L2-L4), femoral neck, total femoral, femoral shaft, femur, and word triangle for each genotype of OSCAR-2322 polymorphism. BMD was measured using two instruments (Lunar Expert XL and Hologic QDR 4500-A). BMD data on the femoral shaft was not available in 129 women because the Hologic instrument could not measure BMD at this site. P-values in the co-dominance model for multiple regression analysis are shown. The number of subjects is described in the bar.

3A-3H show sequence chromatograms of polymorphisms in OSCAR. Red boxes indicate polymorphic sites.

Arden, N. K. and T. D. Spector (1997). "Genetic influences on muscle strength, lean body mass, and bone mineral density: a twin study." J Bone Miner Res 12 (12): 2076-81.

2. Arko, B., J. Prezelj, et al. (2002). "Sequence variations in the osteoprotegerin gene promoter in patients with postmenopausal osteoporosis." J Clin Endocrinol Metab 87 (9): 4080-4.

3. Arron, J. R. and Y. Choi (2000). "Bone versus immune system." Nature 408 (6812): 535-6.

4. Baron, R. (2004). "Arming the osteoclast." Nat Med 10 (5): 458-60.

5. Biswas, R. N., D. N. Baker, et al. (2004). "Polyphosphoinositides-dependent regulation of the osteoclast actin cytoskeleton and bone resorption." BMC Cell Biol 5 (1): 19.

6. Boyle, W. J., W. S. Simonet, et al. (2003). "Osteoclast differentiation and activation." Nature 423 (6937): 337-42.

7. Deng, H. W., W. M. Chen, et al. (2000). "Genetic determination of Colles 'fracture and differential bone mass in women with and without Colles' fracture." J Bone Miner Res 15 (7): 1243-52.

8. Eisman, J. A. (1999). "Genetics of osteoporosis." Endocr Rev 20 (6): 788-804.

9. Garcia-Giralt, N., X. Nogues, et al. (2002). "Two new single-nucleotide polymorphisms in the COL1A1 upstream regulatory region and their relationship to bone mineral density." J Bone Miner Res 17 (3): 384-93.

10. Hedrick, P. and S. Kumar (2001). "Mutation and linkage disequilibrium in human mtDNA." Eur J Hum Genet 9 (12): 969-72.

Hedrick, P. W. (1987). "Gametic disequilibrium measures: proceed with caution." Genetics 117 (2): 331-41.

12. Kanis, J. A., L. J. Melton, 3rd, et al. (1994). "The diagnosis of osteoporosis." J Bone Miner Res 9 (8): 1137-41.

13. Karst, M., G. Gorny, et al. (2004). "Roles of stromal cell RANKL, OPG, and M-CSF expression in biphasic TGF-beta regulation of osteoclast differentiation." J Cell Physiol 200 (1): 99-106.

14. Keen, R. W., K. L. Woodford-Richens, et al. (1998). "Allelic variation at the interleukin-1 receptor antagonist gene is associated with early postmenopausal bone loss at the spine." Bone 23 (4): 367-71.

15. Kim, N., M. Takami, et al. (2002). "A novel member of the leukocyte receptor complex regulates osteoclast differentiation." J Exp Med 195 (2): 201-9.

16. Kobayashi, S., S. Inoue, et al. (1996). "Association of bone mineral density with polymorphism of the estrogen receptor gene." J Bone Miner Res 11 (3): 306-11.

17. Lander, E. S. and N. J. Schork (1994). "Genetic dissection of complex traits." Science 265 (5181): 2037-48.

18. Livak, K. J. (1999). "Allelic discrimination using fluorogenic probes and the 5 'nuclease assay." Genet Anal 14 (5-6): 143-9.

19. Lorenzo, J. (2000). "Interactions between immune and bone cells: new insights with many remaining questions." J Clin Invest 106 (6): 749-52.

20. MacGregor, A., H. Snieder, et al. (2000). "Genetic factors and osteoporotic fractures in elderly people.Twin data support genetic contribution to risk of fracture." Bmj 320 (7250): 1669-70; author reply 1670-1.

21. Martin, A. M., J. K. Kulski, et al. (2002). "Leukocyte Ig-like receptor complex (LRC) in mice and men." Trends Immunol 23 (2): 81-8.

22. Merck, E., C. Gaillard, et al. (2004). "OSCAR is an FcR {gamma} -associated receptor expressed by myeloid cells, involved in antigen presentation and activation of human dendritic cells." Blood: Epub ahead of print.

23. Merrell, K., J. C. Gonzales, et al. (1995). "Genetic analyses of tattered, an X-linked dominant, developmental mouse mutation." Mamm Genome 6 (4): 291-4.

24. Morrison, N. A., J. C. Qi, et al. (1994). "Prediction of bone density from vitamin D receptor alleles." Nature 367 (6460): 284-7.

25. Morrison, N. A., R. Yeoman, et al. (1992). "Contribution of trans-acting factor alleles to normal physiological variability: vitamin D receptor gene polymorphism and circulating osteocalcin." Proc Natl Acad Sci U S A 89 (15): 6665-9.

26. Motyckova, G., K. N. Weilbaecher, et al. (2001). "Linking osteopetrosis and pycnodysostosis: regulation of cathepsin K expression by the microphthalmia transcription factor family." Proc Natl Acad Sci U S A 98 (10): 5798-803.

27. Muller, B. (2002). "Cytokine imbalance in non-immunological chronic disease." Cytokine 18 (6): 334-9.

28. Munro Peacock, C. H. T., Michael J. Econs and Tatiana Foroud (2002). "Genetics of Osteoporosis." Endocrine Reviews 23 (3): 303-326.

29. Murray, R. E., F. McGuigan, et al. (1997). "Polymorphisms of the interleukin-6 gene are associated with bone mineral density." Bone 21 (1): 89-92.

30. Slemenda, C. W., J. C. Christian, et al. (1991). "Genetic determinants of bone mass in adult women: a reevaluation of the twin model and the potential importance of gene interaction on heritability estimates." J Bone Miner Res 6 (6): 561-7.

31. So, H., J. Rho, et al. (2003). "Microphthalmia transcription factor and PU.1 synergistically induce the leukocyte receptor osteoclast-associated receptor gene expression." J Biol Chem 278 (26): 24209-16.

32. Stephens, M., N. J. Smith, et al. (2001). "A new statistical method for haplotype reconstruction from population data." Am J Hum Genet 68 (4): 978-89.

33. Stewart, T. L. and S. H. Ralston (2000). "Role of genetic factors in the pathogenesis of osteoporosis." J Endocrinol 166 (2): 235-45.

34. Teitelbaum, S. L. (2000). "Bone resorption by osteoclasts." Science 289 (5484): 1504-8.

35. Teitelbaum, S. L. and F. P. Ross (2003). "Genetic regulation of osteoclast development and function." Nat Rev Genet 4 (8): 638-49.

36. Walsh, M. C. and Y. Choi (2003). "Biology of the TRANCE axis." Cytokine Growth Factor Rev 14 (3-4): 251-63.

37. Weeks, D. E. and G. M. Lathrop (1995). "Polygenic disease: methods for mapping complex disease traits." Trends Genet 11 (12): 513-9.

38. Yamada, Y., A. Miyauchi, et al. (2001). "Association of the C-509-> T polymorphism, alone of in combination with the T869-> C polymorphism, of the transforming growth factor-beta1 gene with bone mineral density and genetic susceptibility to osteoporosis in Japanese women." J Mol Med 79 (2-3): 149-56.

Technical Field

The present invention relates to polymorphism of genes associated with disease. Specifically, the present invention relates to polymorphism of genes for predicting, diagnosing or predicting and diagnosing the risk of osteoporosis, and to measuring bone density and polymorphism in OSCAR, a gene involved in the risk of bone mineral density (BMD) and spinal fractures. A method of predicting, diagnosing or predicting and diagnosing a risk of vertebral fractures.

Prior art

Osteoporosis is a common metabolic bone disease characterized by low bone mineral density (BMD) and increased fracture risk [28]. Although complex environmental risk factors are associated with the onset, genetic factors are also involved and have been found to correspond to about 50-80% of the variations in BMD based on twin and family studies [1, 8, 12, 17, 30, 33 and 37]. Therefore, it is important to identify (identify) a gene that acts as a regulator of BMD, which could be used to identify and protect the subject at risk for osteoporosis. To date, vitamin D receptor (VDR), estrogen receptor (ER), transforming growth factor beta 1 (TGFB1), interleukin-1 receptor antagonist (IL1RA), interleukin 6 (IL6), collagen 1A1 (COL1A1), and osteoprote Polymorphisms in several genes, such as gerin (OPG), have been reported as genes associated with bone mass (2, 9, 14, 16, 24, 25, 29 and 38, supra).

Osteoclasts, the only cells that can reabsorb bone, are essential for bone homeostasis. They are derived from hematopoietic cells of the monocyte-macrophage lineage that give rise to macrophages and dendritic cells (supra: 4, 5, 13 and 34, supra). Receptor activators and macrophage-colony stimulating factors of NF-κB ligands produced by osteoblasts / stromal cells are essential factors for maintaining osteoclast differentiation [6 and 35]. Recently, osteoclast-associated receptor (OSCAR), a novel member of the leukocyte receptor complex, has been identified as a regulator of osteoclastogenesis. Human OSCAR (MIM 606862) was mapped to leukocyte receptor complex (LRC) on chromosome 19q13.4 [supra: 21]. It is widely expressed in cells of myeloid lineage and continues to be expressed during differentiation of CD14 + monocytes into dendritic cells and maintained after maturation [22]. The formation of osteoclasts from bone marrow progenitor cells is substantially inhibited by the addition of soluble forms of OSCAR in coculture with osteoblasts in the presence of bone-resorption factors [15 and 31], which is responsible for osteoclast differentiation. It suggests the crucial role of OSCAR. Thus, the OSCAR gene can be thought of as a potential candidate for the regulation of BMD and the risk of osteoporosis.

Despite the important role of OSCAR in bone metabolism, its genetic effect on the determination of bone mass has not been investigated so far. In the present invention, the present inventors performed extension screening of genes by direct sequencing to detect polymorphism in Korean postmenopausal women, analyzed the association with the risk of BMD and radioactive spinal fractures, and developed osteoporosis using the same. The present invention has been completed by inventing a method of predicting, diagnosing or predicting and diagnosing risk.

An object of the present invention is to identify polymorphisms in genes involved in osteoporosis.

It is also an object of the present invention to provide a method for predicting, diagnosing or predicting and diagnosing the risk of developing osteoporosis.

To achieve this goal, the inventors have identified genetic polymorphisms in potential candidate genes associated with osteoporosis. Osteoclast-associated receptors (OSCARs) play a decisive role in osteoclast differentiation and are therefore important candidate genes for the regulation of BMD, which is an important factor in determining bone strength and osteoporosis fracture risk, and has quantitative multigenic traits. Have

OSCAR was initially known to be specifically expressed in osteoclast precursors and mature osteoclasts. In coculture with osteoblasts, osteoclast formation was inhibited by OSCAR-Fc in the murine model, suggesting that OSCAR is an important regulator of osteoclast formation [15: 15]. The human OSCAR gene was then found to be widely expressed in cells of myeloid lineage, including all stages of differentiation and maturation of osteoclasts as well as dendritic cells [22]. Dendritic cells can stimulate T cell clones specific for epitopes of mouse IgG1 after uptake and treatment of human OSCAR-specific antibodies, demonstrating their ability to mediate antigen presentation. [Supra: 22]. This suggests that the human OSCAR gene may play a crucial role in the regulation of the immune system in addition to its role in osteoclast formation.

Bone mass is determined by the difference between bone resorption by osteoclasts and formation by osteoblasts. Imbalance in the remodeling process beyond resorption leads to accelerated bone loss (see above and 20 and 28). Previous studies have shown that the immune system plays an important role in bone remodeling [supra: 3 and 19]. For example, proinflammatory cytokines stimulated by an immune response regulate bone metabolism even in individuals without immunological disease [27]. In addition, the activation of RANKL, an essential factor for osteoclast differentiation, is regulated by the immune system [36]. Thus, this suggests that OSCAR regulates osteoclastogenesis directly and indirectly through the immune system.

Microocular disease transcription factor (MITF) gene is a basic helix-loop important for differentiation of osteoclasts by binding to E-box-type enhancers in the 5'- flanking region of the MITF-reactive gene. -Encodes a helix-loop-helix zipper protein [23]. Recently, So et al., Supra: 31, show that binding of MITF to its recognition site in the OSCAR promoter is crucial for OSCAR expression, suggesting the functional importance of the promoter region of OSCAR .

In contrast to the volume of reports on the association between genetic variation and BMD, genetic studies on fracture risk are relatively rare. The heritability of the fracture itself was estimated to be 25 to 35% [7 and 20, supra], which is much lower than the heritability of BMD values. This suggests that many reduction-related factors other than BMD play an important role in determining fracture risk.

In an effort to identify genetic polymorphisms in potential candidate genes for osteoporosis, 10 variants in OSCAR were identified using direct DNA sequencing. Ten sequence variations were identified by direct sequencing in 24 Korean individuals: two in the 5 'flanking region, seven in the exon (including six non-homologous SNPs) and one in the intron. Five of these polymorphisms were then selected for large genotyping (n = 560). Genotypes were analyzed by TaqMan method. One SNP in the 5 'flanking region (OSCAR-2322A> G) showed a significant and sensitive effect on the risk of BMD and radioactive vertebral fractures at various bone sites in postmenopausal women (n = 560).

Multiple regression analyzes controlling age, postmenopausal years, height, weight, and evaluation instrument as covariates showed that one SNP in the 5 'flanking region (OSCAR-2322A> G) was associated with multiple bone sites in postmenopausal women. Showed significant and sensitive effects on the risk of BMD and radioactive vertebral fractures.

These results suggest that the OSCAR gene is a candidate for genetic determinants of BMD and fracture risk in postmenopausal women.

The present invention is described based on the following examples. The following examples are intended to illustrate the invention, not to limit the invention. All documents disclosed in the present invention are incorporated by reference.

Example

Example 1 Selection of Subjects

The study population consisted of 560 Korean postmenopausal women who visited Asan Medical Center (AMC, Seoul, Korea). Menopause was defined as no menstruation for at least 6 months and confirmed by measurement of serum follicle-stimulating hormone (FSH). Immature menopausal women (under 40) were excluded. We also excluded women who took drugs that could affect bone metabolism for more than 6 months or within the previous 12 months. Subjects were excluded from any disease that could affect bone metabolism. Women with stroke or dementia were also excluded due to concerns related to their limited physical activity. Also excluded were women with spinal-degenerative diseases detected by traditional spinal radiographs. This study was approved by the AMC Ethics Review Committee and all subjects submitted a Consent Statement.

Dual energy X-rays of the BMD area (g / cm ² ) of the anterior-rear lumbar spine (L2-L4) and non-dominant proximal femoral (femoral neck, femoral shaft, total femoral, femur and Word's triangle) Measurements were taken in 431 women using an absorbometer Lunaar Expert XL (with software version 1.90, Madison, Wis., USA). In 129 women, BMD at the same site except the femoral shaft was measured using Hologic QDR 4500-A (with software version 4.84, Waltham, Mass., USA). BMD values at the femoral shaft and other proximal femoral sites were not possible in 229 and 101 subjects, respectively. In vivo precision of Lunar and Hologic instruments was 0.82% and 0.85% for lumbar spine and 1.12% and 1.20% for femoral neck, respectively. T-score was used to diagnose osteopenia and osteoporosis at each site according to the World Health Organization (WHO) definition (-2.5 <T-score = -1.0 SD and T-score = -2.5 SD, respectively). Subjects with excess T-scores were classified as normal controls. Lateral thoracolumbar (T4-L4) radiographs were obtained for all subjects. Spinal fractures were quantitatively defined as more than 15% loss in the anterior, posterior or median height of one or more vertebral areas in subjects without a history of major trauma such as traffic accidents.

For comparison of allelic frequencies of OSCAR SNPs identified in Korean populations with other major ethnic groups, we present 50 whites obtained from the Human Genetic Cell Repository (http://locus.umdnj.edu/nigms/). And genotypes of 50 African American DNAs.

Example 2: Sequence Analysis of OSCAR Genes

Genomic DNA was extracted from peripheral blood leukocytes using a commercially available kit (Wizard Genomic DNA purification kit, Promega, Madison, Wis., USA). We used a DNA analyzer (ABI PRISM 3700, Applied Biosystems, Foster City, Calif.) To find the exon of OSCAR genes, including the promoter region (about 1.5 kb), to detect genetic variations in 24 Korean DNA samples. Boundaries were sequenced. A set of eight primers of the OSCAR gene for amplification and sequencing were designed based on the GenBank sequence (reference genome sequence NT_011109.15 for OSCAR, published February 2004).

Table 1. Primer Sequences for Discovery of OSCAR Polymorphism

Sequence variation was confirmed by chromatogram (see FIGS. 3A-3H).

Example 3: Genotyping Using Polarization Detection

To analyze the genotype of the polymorphic site, amplification primers and probes were designed for TaqMan (supra: 18). Primer Express (Applied Biosystems) was used to design both PCR primers and MGB TaqMan probes.

Table 2. Sequences of Amplification and Taqman Probes for OSCAR SNP Genotyping

One allele probe was labeled with FAM dye and the other was labeled with fluorescent VIC dye. PCRs were performed on a TaqMan Universal Master mix without U-cil-Applyed Biosystems (UNG) with a PCR primer concentration of 900 nM and TaqMan MGB-probe concentration of 200 nM. The reaction was performed in 384-well format using 20 ng genomic DNA with a total reaction volume of 5 ul. The plate was then placed in a thermal cycler (PE 9700, Applied Biosystems) and heated at 50 ° C. for 2 minutes and at 95 ° C. for 10 minutes, followed by 40 cycles of 15 seconds at 95 ° C. and 1 minute at 60 ° C. TaqMan assay plates were transferred to Prism 7900HT instrument (Applied Biosystems) to read the fluorescence intensity in each well of the plate. Fluorescence data collected from each plate was analyzed using automated software (SDS 2.1).

Example 4: Statistical Analysis

We examined Lewontin's D '(| D' |) and the associated unbalance coefficient r ² between all pairs of biallelic loci [supra: 10 and 11]. The algorithm (PHASE) developed by Stephens et al. [32] was used to predict the a priori of the haplotype structure based on population genetics and coalescent theory. Reasoning using Bayesian (including Bayesian) approach. The genetic effect of the deduced haplotype was analyzed in the same way as polymorphism. Multiple regression assays were performed on BMDs to control age (continuous variable), postmenopausal years (YSM; continuous variable), weight, height, and type of evaluation instrument as covariates. In addition, a genotype distribution of OSCAR polymorphism and haplotype between cohort of osteopenia and osteoporosis patients and normal subjects was used as a covariate using a logistic regression model that controls the type of age, YSM, weight, height, and evaluation device. Analyzed.

result

The average age of the participants was 59.4 ± 7.2 years (46 to 83 years), and the mean years after menopause (YSM) was 10.4 ± 8.2 years (1 to 35 years) (see Table 3). BMD was measured in 431 subjects (77.0%) and 129 subjects (23.0%) using two instruments, Lunaar Expert XL and Hologic QDR 4500-A, respectively. As expected, age (P = 0.01 and P = 0.005), body weight (P <0.0001 and P <0.0001), height (P = 0.01 and P = 0.09), YSM (P = 0.02 and P <0.0001) and assessment The instrument (P <0.0001 and P <0.0001) was positively correlated with BMD in the lumbar spine and femoral neck (see Table 3). The BMD values measured by the Hologic instrument (0.764 ± 0.116 mg / cm ² and 0.606 ± 0.098 mg / cm ² , respectively) were measured by the Lunar instrument (0.870 ± 0.182 mg / cm ² and 0.723 ± 0.128 mg / cm ² ). Therefore, age, number of years after menopause, weight, height and assessment instrument were used as covariates in multiple regression analysis of association with BMD.

Table 3. Multiple regression analysis of clinical profile and BMD (g / cm ² ) in Korean postmenopausal women (n = 560)

* Values are mean (± SD) unless otherwise indicated.

Direct sequencing of all exons of the OSCAR genes and their boundaries, including -1,500 bp of the 5 'flanking region, identified 10 single-nucleotide polymorphisms (SNPs): two in the 5' flanking region, 7 in exons (including 6 non-homologous SNPs) and 1 in introns (see FIG. 1A). The frequency in the Korean population is shown in Figure 1a. In addition, the frequency of human OSCAR gene polymorphism is shown in Table 4.

Table 4. Frequency of Human OSCAR Gene Polymorphism

* P value for deviation from Hardy-Weinberg equilibrium

Bold typefaces show genotyping polymorphisms in large populations.

One set of absolute LDs (| D '| = 1 and r ² = 1) (-2322A> G: -111C> G) and two sets of complete LDs (pair-wise comparison between SNPs) | D '| = 1 and r ² ≠ 1) were found (see FIGS. 1A and 1C). Five SNPs were selected for large scale genotyping based on LDs, frequency and haplotype tagging status. Haplotypes in the OSCAR gene were constructed using PHASE software (see above, 32) (see FIG. 1B). There were significant differences in alleles and haplotypes among the three ethnic groups (see FIG. 1B and Table 5).

Table 5. Single-nucleotide polymorphism and allele frequencies of OSCAR in Korean (n = 560), African-American (n = 50), and Caucasian (n = 50) subjects

* P value of deviation from Hardy-Weinberg equilibrium in Korean population.

** Heterozygosity Calculated in Korean Community

*** P values for differences in the distribution of polymorphisms among the three major ethnic groups

When using multiple regression analysis to control age, YSM, height, weight, and evaluation instrument as covariates for association with BMD of the lumbar spine and femoral neck, one SNP in the 5 'flanking region, OSCAR-2322A> G Was associated with BMD in both the lumbar and femoral necks (P = 0.005 and P = 0.005, respectively; see Table 7). The genetic effect of OSCAR-2322A> G on BMD in the lumbar spine was gene-dose dependent and the highest BMD was found in homozygotes for major alleles (0.86 ± 0.19 mg / cm ² ), intermediate BMD Was found in the heterozygotes (0.84 ± 0.17 mg / cm ² ) and the lowest BMD was found in the homozygous for the minor allele (0.82 ± 0.17 mg / cm ² ). However, OSCAR-2322A> G in the femoral neck showed a predominant effect on BMD values and higher BMD was found in individuals with the GG genotype than the rest (AA and AG genotypes). In addition, the genetic effect of OSCAR-2322A> G on BMD was detected in other areas of the bone (femoral shaft, word triangle, entire femoral and femur) (see FIG. 2).

Table 6. BMD and OSCAR-2322A of age, postmenopausal years, body weight, height and evaluation instrument-adjusted femoral shaft, lumbar spine (L2-L4), total thigh, femoral neck, thigh and word triangle in Korean postmenopausal women Regression Analysis with G Polymorphism

* Number of subjects, and mean and standard deviation of BMD

** P value of co-dominance model for multiple regression analysis

In further haplotype analysis, OSCAR-ht1 was significantly associated with BMD at both sites (P = 0.01 in the lumbar spine and P = 0.04 in the femoral neck; see Table 7). However, this genetic effect seems to be caused from the OSCAR-2322A> G because OSCAR-ht1 is OSCAR-2322A> being most (> 90%) tagged by G (see Fig. 1b).

Table 7. Regression analysis of age, postmenopausal age, body weight, height, and evaluation instrument-adjusted lumbar spine (L2-L4) and BMD and OSCAR polymorphism of the femoral neck in Korean postmenopausal women

* C / C, C / R and R / R represent homozygotes for universal alleles, and heterozygotes and homozygotes for rare alleles, respectively.

** Number of subjects and mean and standard deviation of BMD

*** P value of co-dominance model for multiple regression analysis

We also analyzed the genetic effect of OSCAR polymorphism on the risk of radioactive vertebral fractures. In the present invention, fractures were detected in 98 (16.9%) subjects. The allele frequency of OSCAR-2322G * was higher in subjects with vertebral fractures (frequency = 0.515) than in subjects without vertebral fractures (frequency = 0.441) (P = 0.05; see Table 8). This increased risk of vertebral fractures could be explained by reduced lumbar BMD in individuals with OSCAR-2322G * . In summary, the sensitive effect of OSCAR-2322G * on BMD can be evidenced by the reduced risk of reduced BMD and vertebral fractures at various bone locations.

Table 8. Association between Oscar gene polymorphism, haplotype, and risk of osteopenia / osteoporosis and radioactive spinal fractures in Korean postmenopausal populations.

Minor allele frequency

** Patient subjects include subjects with t-scores less than -1 in the lumbar spine.

*** P value for logistic analysis controlling age, weight, height, YSM and evaluation device as covariates

In the present invention, the inventors have identified 10 polymorphisms of OSCAR in the Korean population, and after controlling the overlapping variables, one SNP ( OSCAR-2322A> G ) and one haplotype ( OSCAR-ht1 ) were identified as BMD values and Significantly associated with radiological vertebral fractures. Just know that the present inventors, this is the first definitive report suggests a role for the OSCAR in bone metabolism, and that the results of the present invention may be a candidate for genetic determinants of BMD and fracture risk in women after the OSCAR gene menopause Suggests.

In the present invention, the inventors have carefully examined the presence of exons and their boundaries as well as the polymorphism in the promoter region (about 1.5 kb), and only OSCAR-2322A> G polymorphism in the promoter is significantly associated with BMD in all regions tested. Said that. In summary, this suggests that OSCAR promoter polymorphism can modulate BMD by affecting the level of transcription and / or expression of OSCAR.

Despite the relatively low heredity of fractures, we found that OSCAR-2322A / G was significantly associated with the risk of radioactive vertebral fractures. This further suggests the possibility that these polymorphic sites can function in bone metabolism.

In summary, as part of an effort to examine the possible relevance of OSCAR 's genetic polymorphism in osteoporosis, 10 polymorphisms have been newly identified (identified) in OSCAR and five universal sites have been identified in postmenopausal women in Korea (n = 560). Genotyping was performed. Statistical analysis was used to determine the genetic association between OSCAR-2322A> G polymorphism and the risk of BMD and radioactive vertebral fractures. These results provide an important part of understanding the genetic background of bone metabolism.

As discussed above, a significant association between polymorphism in osteoclast-associated receptor (OSCAR) and bone mineral density and spinal fracture risk was identified. Therefore, the risk of developing osteoporosis can be predicted by measuring the polymorphism in OSCAR according to the present invention, and the present invention can also be usefully used in the field of treatment for metabolic bone disease.

Although the above has been described with reference to a preferred embodiment of the present invention, those skilled in the art can variously modify and change the present invention without departing from the spirit and scope of the present invention described in the claims below. It will be appreciated.

<110> SNP GENETICS, INC. <120> OSCAR <130> IPM-28816 <160> 36 <170> KopatentIn 1.71 <210> 1 <211> 21 <212> DNA <213> Artificial Sequence <220> <223> OSCAR-1, Forward primer for sequence variants screening <400> 1 tacattttaa caatcccccg c 21 <210> 2 <211> 21 <212> DNA <213> Artificial Sequence <220> <223> OSCAR-1, Reverse primer for sequence variants screening <400> 2 cgggtcccct ctagtgtgtc t 21 <210> 3 <211> 21 <212> DNA <213> Artificial Sequence <220> <223> OSCAR-2, Forward primer for sequence variants screening <400> 3 gcataagcag ggttgagaac g 21 <210> 4 <211> 21 <212> DNA <213> Artificial Sequence <220> <223> OSCAR-2, Reverse primer for sequence variants screening <400> 4 cgggtagttc tgggaacctc t 21 <210> 5 <211> 19 <212> DNA <213> Artificial Sequence <220> <223> OSCAR-3, Forward primer for sequence variants screening <400> 5 cccggtgacc tctaaccct 19 <210> 6 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> OSCAR-3, Reverse primer for sequence variants screening <400> 6 cgggctcagt ttcttcgtct 20 <210> 7 <211> 21 <212> DNA <213> Artificial Sequence <220> <223> OSCAR-4, Forward primer for sequence variants screening <400> 7 gctcttgaac tcttgggctc a 21 <210> 8 <211> 21 <212> DNA <213> Artificial Sequence <220> <223> OSCAR-4, Reverse primer for sequence variants screening <400> 8 cctagaatcc caaacctgct g 21 <210> 9 <211> 19 <212> DNA <213> Artificial Sequence <220> <223> OSCAR-5, Forward primer for sequence variants screening <400> 9 gtggcgggca cttgtagtc 19 <210> 10 <211> 21 <212> DNA <213> Artificial Sequence <220> <223> OSCAR-5, Reverse primer for sequence variants screening <400> 10 ggtcctagct taggcctctg c 21 <210> 11 <211> 27 <212> DNA <213> Artificial Sequence <220> <223> OSCAR-6, Forward primer for sequence variants screening <400> 11 agtaattgtc caaggtgatt ttcctcc 27 <210> 12 <211> 18 <212> DNA <213> Artificial Sequence <220> <223> OSCAR-6, Reverse primer for sequence variants screening <400> 12 gtttgcccgc ctcgctct 18 <210> 13 <211> 19 <212> DNA <213> Artificial Sequence <220> <223> OSCAR-7, Forward primer for sequence variants screening <400> 13 cgagcgagcg agcggaaga 19 <210> 14 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> OSCAR-7, Reverse primer for sequence variants screening <400> 14 gtcctggggc ctgcattcct 20 <210> 15 <211> 19 <212> DNA <213> Artificial Sequence <220> <223> OSCAR-8, Forward primer for sequence variants screening <400> 15 aaacctggag ttcagggcc 19 <210> 16 <211> 19 <212> DNA <213> Artificial Sequence <220> <223> OSCAR-8, Reverse primer for sequence variants screening <400> 16 gctgggattc gaaccctct 19 <210> 17 <211> 17 <212> DNA <213> Artificial Sequence <220> <223> Forward primer for genotyping <400> 17 ggcgccgact cctgaag 17 <210> 18 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> Reverse primer for genotyping <400> 18 gtggcacgag agggttagag 20 <210> 19 <211> 14 <212> DNA <213> Artificial Sequence <220> <223> Probe-1 (VIC) for genotyping <400> 19 tgacgcttct tgcc 14 <210> 20 <211> 14 <212> DNA <213> Artificial Sequence <220> <223> Probe-2 (FAM) for genotyping <400> 20 tgacgctcct tgcc 14 <210> 21 <211> 18 <212> DNA <213> Artificial Sequence <220> <223> Forward primer for genotyping <400> 21 gcccagaggt tcccagaa 18 <210> 22 <211> 23 <212> DNA <213> Artificial Sequence <220> <223> Reverse primer for genotyping <400> 22 agacaaagat ggctgcgagt aag 23 <210> 23 <211> 13 <212> DNA <213> Artificial Sequence <220> <223> Probe-1 (VIC) for genotyping <400> 23 accggcacct gca 13 <210> 24 <211> 14 <212> DNA <213> Artificial Sequence <220> <223> Probe-2 (FAM) for genotyping <400> 24 caccggaacc tgca 14 <210> 25 <211> 21 <212> DNA <213> Artificial Sequence <220> <223> Forward primer for genotyping <400> 25 gccgtggagg taaaggaagt g 21 <210> 26 <211> 17 <212> DNA <213> Artificial Sequence <220> <223> Reverse primer for genotyping <400> 26 cgcaggtccg caaagtc 17 <210> 27 <211> 14 <212> DNA <213> Artificial Sequence <220> <223> Probe-1 (VIC) for genotyping <400> 27 ttgcgcttgc tcct 14 <210> 28 <211> 13 <212> DNA <213> Artificial Sequence <220> <223> Probe-2 (FAM) for genotyping <400> 28 tgcgcatgct cct 13 <210> 29 <211> 22 <212> DNA <213> Artificial Sequence <220> <223> Forward primer for genotyping <400> 29 tccgagctgg cagaattctt tc 22 <210> 30 <211> 18 <212> DNA <213> Artificial Sequence <220> <223> Reverse primer for genotyping <400> 30 gtctggcctt cggtagca 18 <210> 31 <211> 17 <212> DNA <213> Artificial Sequence <220> <223> Probe-1 (VIC) for genotyping <400> 31 agcggtaact tccccct 17 <210> 32 <211> 18 <212> DNA <213> Artificial Sequence <220> <223> Probe-2 (FAM) for genotyping <400> 32 cagcggtaaa ttccccct 18 <210> 33 <211> 15 <212> DNA <213> Artificial Sequence <220> <223> Forward primer for genotyping <400> 33 ccgcccgcag actct 15 <210> 34 <211> 17 <212> DNA <213> Artificial Sequence <220> <223> Reverse primer for genotyping <400> 34 ccccaggcgg actaggt 17 <210> 35 <211> 16 <212> DNA <213> Artificial Sequence <220> <223> Probe-1 (VIC) for genotyping <400> 35 cctccgacta cacccg 16 <210> 36 <211> 16 <212> DNA <213> Artificial Sequence <220> <223> Probe-2 (FAM) for genotyping <400> 36 cctccgactc cacccg 16  

Claims (3)

OSCAR gene polymorphism to predict, diagnose or predict and diagnose the risk of developing osteoporosis. The OSCAR gene polymorphism of claim 1, comprising OSCAR-2322A> G polymorphism. 3. The OSCAR gene polymorphism of claim 2, wherein the OSCAR-2322A> G polymorphism is significantly associated with bone mineral density and spinal fractures.

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