Secondary Logo

Journal Logo

Original Articles

Polymorphism of rs6426749 at 1p36.12 is associated with the risk of osteoarthritis in Taiwanese female population

Tsai, Dung-Janga,b; Tai, Ming-Chengc; Kao, Chung-Chengd; Chen, Wei-Teinge; Wu, Li-Weif; Chiu, Chih-Chieng; Tu, Ming-Yuh; Chen, Yi-Choui,j; Wu, Chia-Chunk,*; Su, Sui-Lunga,b,*

Author Information
Journal of the Chinese Medical Association: May 2021 - Volume 84 - Issue 5 - p 523-527
doi: 10.1097/JCMA.0000000000000515
  • Open



Osteoarthritis (OA) is a degenerative joint disease and is the most common form of OA in the world, with an increased prevalence with age.1 From 1996 to 2010, the overall crude incidence for total knee replacements increased from 26.4 to 74.55 per 100 000 Taiwanese individuals.2 Several factors such as age, sex, obesity, joint damage, and genetic inheritance affect OA occurrence and progression.3 Genetic inheritance plays a particularly important role, with as many as 65% OA cases potentially involving genetic factors.4–8

The complex relationship between OA and osteoporosis remains controversial.9,10 Although underlying pathophysiologic mechanisms for OA remain unclear, previous studies have reported that both conditions have an inverse relationship.11–14 Longitudinal studies have also indicated that high bone mineral density (BMD) is related to the risk of developing OA.9,10,15 Moreover, cohort studies have demonstrated that high BMD is associated with an increased prevalence and incidence of knee OA.9,14 Higher BMD may result in denser and tougher bones, thereby reducing their ability to absorb load, increasing load on joint cartilage, and culminating in OA.16 In addition, biomechanical pathways play important roles in knee OA pathogenesis as reductions in surface load-bearing regions in joints, or excessive, increased mechanical load may lead to joint cartilage injury.17

Recently, rs6426749, a single-nucleotide polymorphism (SNP) related to bone density, was identified at the 1p36.12 chromosomal region.18 Using an integrative functional genomic and epigenomic analyses, the authors confirmed that rs6426749 was potentially a causal SNP affecting bone metabolism and OA.18 Importantly, CDC42 at the same 1p36.12 region was identified as an influential factor in bone metabolism and development.19 WNT4 inhibits NF-κB via the noncanonical WNT signaling pathway, mitigating bone loss in an OA and bone aging mouse model.20

Based on these studies, we speculated that rs6426749 may exert a potential pathologic mechanism via its C allele that affects TFAP2A binding ability, leading to increased CDC42 expression, which increases bone density and articular cartilage load, thus promoting OA.

Thus, in this case–control study, we investigated associations between rs6426749 and the risk of knee OA in Taiwanese individuals and investigated whether this polymorphism affected WNT4 gene expression. Our data may help unravel underlying pathologic mechanisms of OA.


2.1. Participants

A total of 747 participants (308 males and 439 females) comprising controls and patients ≥65 years of age were enrolled in this study. All received Taipei City senior medical check-ups between March 2017 and October 2018 at the Tri-Service General Hospital (TSGH), a medical teaching hospital at the National Defense Medical Center, Taipei, Taiwan. The check-up is a government-driven welfare program for individuals ≥65 years of age who have been registered Taipei City residents for >1 year.

We examined patient information while participants underwent check-ups. All participants who had received study information, understood the process, and provided written consent were enrolled. Participants who unable to draw an enough blood samples were excluded. The study was approved by the TSGH Institutional Review Board (TSGH-2-102-05-028).

2.2. Radiographic assessments

All participants underwent a radiographic examination of both knees that included anterior-posterior and lateral views and weight-bearing and foot-map positioning. Knee radiographs were read and scored by a radiologist and rheumatologist who were blinded to patient clinical information. The Kellgren-Lawrence (KL) grading system was used and comprised the following:21

  • Grade 1: Doubtful narrowing of joint space and possible osteophytic lipping;
  • Grade 2: Definite osteophytes and definite narrowing of joint space;
  • Grade 3: Moderate multiple osteophytes, definite narrowing of joint space, some sclerosis, and possible deformity of bone contour;
  • Grade 4: Large osteophytes, marked narrowing of joint space, severe sclerosis, and definite deformity of bone contour.

2.3. Genomic DNA extraction and genotyping

Approximately 10 mL peripheral blood was intravenously extracted from participants by a physician or nurse. Genomic DNA was isolated using standard procedures, using proteinase K (Invitrogen, Carlsbad, CA) digestion and phenol/chloroform methods. The rs6426749 SNP was genotyped using the iPLEX Gold SNP genotyping method.22 Genotyping was performed under blind conditions. To validate results, at least 10% of samples were randomly selected for repeated genotyping.

2.4. Messenger RNA expression

We assessed correlations between genetic variants and mRNA expression of corresponding genes. Expression quantitative trait loci analysis was also performed using data from the genotype-tissue expression (GTEx) portal database ( and the HapMap Project using a general linear regression model within an additive genetic model.

2.5. Statistical analysis

Sample size estimates were based on rs6426749 frequency, according to Taiwanese biobank database ( data, which defined an allele risk of 20% in the Taiwanese population. Applying the allele risk data in OpenEpi: Open Source Epidemiologic Statistics for Public Health, version 3.1. (, and considering an 80% power and a two-tailed alpha of 0.05, a sample size of 268 individuals per group was adequate to detect an association between the alleles and rs6426749.

Continuous variables were evaluated using Student’s t-test and reported as the mean ± SD. Genotypes and allelic frequencies were compared between OA patients and controls using the χ2 or Fisher’s exact test, as appropriate. Logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) as measures of associations with OA risk. Allele, genotype, dominant, and recessive models were used to calculate associations between genetic polymorphisms and OA risk. A p < 0.05 value was considered statistically significant. Statistical analyses were conducted using R software, version 3.4.4


3.1. Participant characteristics

A total of 747 participants were recruited in this case–control study, comprising 368 patients with knee OA (121 males and 247 females; mean age, 75.90 ± 7.61 years) and 379 controls (187 males and 192 females; mean age, 74.43 ± 7.61 years). The baseline characteristics of both groups are summarized (Table 1). The mean age was significantly higher in patients relative to controls (p = 0.007), and the proportion of females was significantly higher in the OA (67.1%) than in controls (50.7%) (p < 0.001). Body mass index (BMI) was significantly higher in patients than in controls (p = 0.015). According to our radiographic KL grading system, approximately 75% of patients had a KL = 2 grade, whereas approximately 25% had serious knee OA (KL grade > 2).

Table 1 - Demographics of OA patients and controls
Controls (n = 379) Patients (n = 368) p
Age (y) 74.43 ± 7.19 75.90 ± 7.61 0.007
Sex <0.001*
Male 187 (49.3%) 121 (32.9%)
Female 192 (50.7%) 247 (67.1%)
Height (cm) 159.37 ± 7.88 157.46 ± 8.28 0.001
Weight (kg) 61.47 ± 10.55 61.51 ± 11.15 0.968
Body mass index 24.13 ± 3.27 24.74 ± 3.65 0.015
Knee grade
 0 18 (4.7%)
 1 361 (95.3%)
 2 275 (74.7%)
 3 61 (16.6%)
 4 32 (8.7%)
*p < 0.05.
OA = osteoarthritis.

3.2. Association of the rs6426749 polymorphisms with OA susceptibility

Genotype distributions and allele frequencies of the rs6426749 polymorphism in patients and controls were evaluated (Table 2). Genotype frequencies for rs6426749 were in Hardy-Weinberg equilibrium (HWE, p > 0.05). The genotype distribution of rs6426749 GC was significantly different between patients and controls (p < 0.05). When the rs6426749 GG genotype was used as the reference group, the rs6426749 CC genotype was associated with a higher risk of knee OA (adjusted OR = 2.72, 95% CI, 1.17-6.36; p = 0.021).

Table 2 - Genotype distributions of rs6426749 and associations with OA risk
Controls (n = 377) Patients (n = 363) OR (95% CI) p OR (95% CI)a p
 GG 251 (66.6%) 219 (60.3%) 1 1
 GC 118 (31.3%) 121 (33.3%) 1.18 (0.86-1.61) 0.310 1.15 (0.84-1.59) 0.385
 CC 8 (2.1%) 23 (6.3%) 3.30 (1.44-7.52) 0.005* 2.72 (1.17-6.36) 0.021*
 GG 251 (66.6%) 219 (60.3%) 1 1
 GC + CC 126 (33.4%) 144 (39.7%) 1.31 (0.97-1.77) 0.078 1.26 (0.92-1.72) 0.144
 GG + GC 369 (97.9%) 340 (93.7%) 1 1
 CC 8 (2.1%) 23 (6.3%) 3.12 (1.38-7.07) 0.006* 2.60 (1.12-6.03) 0.026*
 G 620 (82.2%) 559 (77.0%) 1 1
 C 134 (17.8%) 167 (23.0%) 1.38 (1.07-1.78) 0.013* 1.31 (1.01-1.71) 0.042*
*p < 0.05.
CI = confidence interval; OA = osteoarthritis; OR = odds ratio.
aAdjusted by gender, age, and body mass index (BMI).

In the recessive model, when rs6426749 GG + GC genotypes were used as the reference group, the rs6426749 CC genotype was associated with a higher risk of knee OA (adjusted OR = 2.60, 95% CI 1.12-6.03; p = 0.026).

Genotype distributions of rs6426749 SNP in patients with different grades were evaluated and are shown (Table 3). The rs6426749 SNP was slightly correlated with an increased risk of knee OA for the grades, KL = 2, KL = 3, and KL = 4. However, due to low subject numbers with KL = 3 and KL = 4, we combined them. In the genotype model, the CC genotype was significantly correlated with an increased susceptibility to mild OA (KL = 2, CC vs GG: adjusted OR = 2.65, 95% CI, 1.09-6.44; p = 0.032). In the dominant model, the GC + CC genotype was significantly correlated with an increased susceptibility to moderate OA (KL = 3 and 4, GC + CC vs GG: adjusted OR = 1.67, 95% CI, 1.02-2.73; p = 0.041). In the recessive model, the CC genotype was significantly correlated with an increased susceptibility to mild OA (KL = 2, CC vs GG + GC: adjusted OR = 2.61, 95% CI, 1.08-6.29; p = 0.033). In the allele model, the C allele was significantly correlated with an increased susceptibility to moderate OA (KL = 3 and 4, C vs G: adjusted OR = 1.60, 95% CI, 1.07-2.40; p = 0.021).

Table 3 - Distribution of rs6426749 polymorphisms by KL grades 2 to 4 in OA patients and controls
Controls (n = 377) Grade 2 (n = 271) Grade 3 (n = 61) Grade 4 (n = 31) KL = 2 OR (95% CI)a p KL = 3 and 4 OR (95% CI)a p
 GG 251 (66.6%) 170 (62.7%) 32 (52.5%) 17 (54.8%) 1 1
 GC 118 (31.3%) 85 (31.4%) 25 (41.0%) 11 (35.5%) 1.05 (0.74-1.49) 0.786 1.55 (0.93-2.58) 0.095
 CC 8 (2.1%) 16 (5.9%) 4 (6.6%) 3 (9.7%) 2.65 (1.09-6.44) 0.032* 3.16 (0.97-10.27) 0.056
 GG 251 (66.6%) 170 (62.7%) 32 (52.5%) 17 (54.8%) 1 1
 GC + CC 126 (33.4%) 101 (37.3%) 29 (47.5%) 14 (45.2%) 1.16 (0.83-1.61) 0.394 1.67 (1.02-2.73) 0.041*
 GG + GC 369 (97.9%) 255 (94.1%) 57 (93.4%) 28 (90.3%) 1 1
 CC 8 (2.1%) 16 (5.9%) 4 (6.6%) 3 (9.7%) 2.61 (1.08-6.29) 0.033* 2.69 (0.84-8.61) 0.095
 G 620(82.2%) 425 (78.4%) 89 (73.0%) 45 (72.6%) 1 1
 C 134(17.8%) 117 (21.6%) 33 (27.0%) 17 (27.4%) 1.24 (0.93-1.64) 0.140 1.60 (1.07-2.40) 0.021*
*p < 0.05.
CI = confidence interval; KL = Kellgren-Lawrence; OA = osteoarthritis; OR = odds ratio.
aAdjusted by gender, age, and body mass index (BMI).

After standardization for sex, age, BMI, and genotype, we observed that the OR for sex was 1.40 (95% CI, 1.21-1.62), p = 0.007; the OR for age was 1.22 (95% CI, 1.06-1.41), p = 0.007; and the OR for BMI was 1.20 (95% CI, 1.03-1.38), p = 0.016. After standardization, the OR for the C allele was 1.14 (95% CI, 1.03-1.26), p = 0.013. Therefore, standardized coefficients affecting the risk of developing OA in descending order were as follows: sex, age, BMI, and the C allele (Table 4). Further evaluation of s6426749 and knee OA associations were performed using sex and BMI stratification (Table 5). Significant associations between rs6426749 and knee OA were found in female participants (OR = 1.56; 95% CI, 1.12-2.18; p = 0.009). In stratified analyses of BMI and men, both were not associated with OA. In addition, in female participants with a BMI < 24, significant associations between rs6426749 and knee OA were observed (OR = 1.73; 95% CI, 1.08-2.76; p = 0.022).

Table 4 - Associations between study demographics and knee OA risk
Independent variable OR (95% CI) p Standardized OR (95% CI) p
 Male 1 1
 Female 1.99 (1.48-2.67) <0.001* 1.40 (1.21-1.62) <0.001*
Age 1.03 (1.01-1.05) 0.007* 1.22 (1.06-1.41) 0.007*
BMI 1.05 (1.01-1.10) 0.016* 1.20 (1.03-1.38) 0.016*
 G allele 1 1
 C allele 1.38 (1.07-1.78) 0.013* 1.14 (1.03-1.26) 0.013*
*p < 0.05.
BMI = body mass index; CI = confidence interval; OA = osteoarthritis; OR = odds ratio.

Table 5 - Stratified analysis on the association of rs6426749 with OA risk
Independent variable Allele type (control/patients) OR (95% CI) p
Gender G Allele C Allele
 Male 304/191 68/47 1.10 (0.73-1.66) 0.651
 Female 316/368 66/120 1.56 (1.12-2.18) 0.009*
Total G Allele C Allele
 BMI < 24 311/228 65/66 1.39 (0.94-2.03) 0.095
 BMI ≥ 24 309/331 69/101 1.37 (0.97-1.93) 0.075
Male G Allele C Allele
 BMI < 24 131/66 29/10 0.68 (0.31-1.49) 0.339
 BMI ≥ 24 173/125 39/37 1.31 (0.79-2.18) 0.291
Female G Allele C Allele
 BMI < 24 180/162 36/56 1.73 (1.08-2.76) 0.022*
 BMI ≥ 24 136/206 30/64 1.41 (0.87-2.29) 0.166
*p < 0.05.
BMI = body mass index; CI = confidence interval; OA = osteoarthritis; OR = odds ratio.

Data from the GTEx project23 showed that the C allele of rs6426749 was associated with increased WNT4 gene expression (p = 0.0072 in whole blood) (Fig. 1).

Fig. 1:
GTEx box plot showing the relationship between the rs6426749 SNP genotype and WNT4 expression in whole blood (p = 0.0072). Sample groups of different genotypes are indicated on the X-axis, and the relative expression of WNT4 is shown on the Y-axis. Presence of the C allele leads to higher WNT4 expression. GTEx = genotype-tissue expression; SNP = single-nucleotide polymorphism.


Previous studies have indicated that there are several genetic variants involved in OA susceptibility, but the exact mechanisms underlying OA risk remain unclear. A recent study showed that rs6426749 affected LINC00339 and CDC42 expression via transcription factors to regulate bone metabolism.18 However, the role of rs6426749 in OA etiology is unclear. Thus, we investigated putative association of rs6426749 with OA risk in Taiwanese individuals. Moreover, we performed a stratified analysis investigating the effects of sex, BMI, and rs6426749 on OA. We speculated that rs6426749 may be a genetic factor affecting OA risk. To the best of our knowledge, this is the first genetic association study between rs6426749 and OA susceptibility in Taiwanese individuals.

From our data, the rs6426749 SNP was significantly associated with OA; study statistical power was 70% as determined by OpenEpi. From a previous study, a reasonable power level was set at 70% to 90%;24 therefore, a 70% power was sufficient for our sample. The control group in the main and female population conformed to HWE (p-value = 0.387 and 0.69, respectively), suggesting that findings could be generalized to the normal population. From the literature, OA occurs from combined risk factors, i.e., increasing age, obesity, and being female.25 Similar results were also observed here. Gender, age, BMI, and rs6426749 appeared to aggravate knee OA, with gender having the greatest impact. From our stratification analyses, BMI data indicated that for overweight or normal participants, rs6426749 did not affect OA risk. In other stratified analyses, we observed that in the normal BMI female population, C allele carriers had a higher risk of knee OA. Furthermore, there were no effects with the risk of knee OA in the dominant model. This status is observed for recessive diseases when genes on both alleles are defective, that is, thalassemia,26 spinal muscular atrophy,27 phenylketonuria,28 and sickle cell disease.29 In this study, rs6426749 had to be mutated in both alleles for severe knee arthritis effects to occur. Indeed, rs6426749 increased the risk of OA. However, its effects were lower when compared with other environmental factors. Although testing innate pathologic genes can be used to predict the risk of disease, proper health management can improve disease occurrence.

Although previous studies indicated that rs6426749 was a protective factor for osteoporosis, our study suggested that the C allele was a risk factor for OA. In addition, our GTEx data indicated that this allele could increase WNT4 expression. WNT4 promotes osteoblast differentiation of mesenchymal stem cells.30 A previous study confirmed that WNT4 enhanced in vivo bone formation and that it similarly inhibited in vivo osteoclast formation and inflammation, thus attenuating bone loss.20 WNT4 signaling may represent an attractive target for treating bone loss, as it promotes osteoblast generation and inhibits osteoclast formation.

This study had several limitations. First, since it was hospital-based, there may have been inherent biases. However, no selection bias was observed because the gene frequency of rs6426749 in the control group was similar to the database, and it also reached HWE. Second, our study had a modest sample size, potentially leading to insignificant statistical data in the low-sample-size severe OA group after stratification with different KL grades. Future, large sample size studies should address this issue. Third, we only investigated rs6426749; however, other SNPs located at 1p36.12 may be similarly associated with OA.

In conclusion, we investigated the role of genetic variants of rs6426749 on knee OA in a Taiwanese population. Our results suggested that the variant C allele of rs6426749 was associated with an increased risk of knee OA in women with normal BMIs. Further studies are required to validate our findings and investigate possible pathophysiologic gene processes. Similarly, further studies should focus on sex-specific mechanisms and the etiology of knee OA to elucidate if rs6426749 could be targeted in future therapeutic strategies.


This study was supported by the Ministry of Science and Technology (MOST107-2314-B016-052-MY3), Taoyuan Armed Forces General Hospital (TYAFGH-D-109009 and TYAFGH-E-109052), National Defense Medical Center (MAB-109-081, MAB-109-094, and MAB-109-095), Taichung Armed Forces General Hospital (NDMC010), and Cheng Hsin General Hospital (CHNDMC-109-8, CHNDMC-110-16).


1. D’Ambrosia RD. Epidemiology of osteoarthritis. Orthopedics. 2005; 28(2 Suppl):s201–5.
2. Lin FH, Chen HC, Lin C, Chiu YL, Lee HS, Chang H, et al. The increase in total knee replacement surgery in Taiwan: a 15-year retrospective study. Medicine (Baltimore). 2018; 97:e11749.
3. Roos EM, Arden NK. Strategies for the prevention of knee osteoarthritis. Nat Rev Rheumatol. 2016; 12:92–101.
4. Spector TD, MacGregor AJ. Risk factors for osteoarthritis: genetics. Osteoarthritis Cartilage. 2004; 12(Suppl A):S39–44.
5. Valdes AM, Van Oene M, Hart DJ, Surdulescu GL, Loughlin J, Doherty M, et al. Reproducible genetic associations between candidate genes and clinical knee osteoarthritis in men and women. Arthritis Rheum. 2006; 54:533–9.
6. Michou L. Genetics of digital osteoarthritis. Joint Bone Spine. 2011; 78:347–51.
7. van Meurs JB. Osteoarthritis year in review 2016: genetics, genomics and epigenetics. Osteoarthritis Cartilage. 2017; 25:181–9.
8. Tachmazidou I, Hatzikotoulas K, Southam L, Esparza-Gordillo J, Haberland V, Zheng J, et al.; arcOGEN Consortium. Identification of new therapeutic targets for osteoarthritis through genome-wide analyses of UK Biobank data. Nat Genet. 2019; 51:230–6.
9. Zhang Y, Hannan MT, Chaisson CE, McAlindon TE, Evans SR, Aliabadi P, et al. Bone mineral density and risk of incident and progressive radiographic knee osteoarthritis in women: the Framingham Study. J Rheumatol. 2000; 27:1032–7.
10. Bergink AP, Uitterlinden AG, Van Leeuwen JP, Hofman A, Verhaar JA, Pols HA. Bone mineral density and vertebral fracture history are associated with incident and progressive radiographic knee osteoarthritis in elderly men and women: the Rotterdam Study. Bone. 2005; 37:446–56.
11. Hannan MT, Anderson JJ, Zhang Y, Levy D, Felson DT. Bone mineral density and knee osteoarthritis in elderly men and women. The Framingham Study. Arthritis Rheum. 1993; 36:1671–80.
12. Hart DJ, Mootoosamy I, Doyle DV, Spector TD. The relationship between osteoarthritis and osteoporosis in the general population: the Chingford Study. Ann Rheum Dis. 1994; 53:158–62.
13. Nevitt MC, Lane NE, Scott JC, Hochberg MC, Pressman AR, Genant HK, et al. Radiographic osteoarthritis of the hip and bone mineral density. The Study of Osteoporotic Fractures Research Group. Arthritis Rheum. 1995; 38:907–16.
14. Burger H, van Daele PL, Odding E, Valkenburg HA, Hofman A, Grobbee DE, et al. Association of radiographically evident osteoarthritis with higher bone mineral density and increased bone loss with age. The Rotterdam Study. Arthritis Rheum. 1996; 39:81–6.
15. Sowers M, Lachance L, Jamadar D, Hochberg MC, Hollis B, Crutchfield M, et al. The associations of bone mineral density and bone turnover markers with osteoarthritis of the hand and knee in pre- and perimenopausal women. Arthritis Rheum. 1999; 42:483–9.
16. Karlsson MK, Magnusson H, Cöster M, Karlsson C, Rosengren BE. Patients with knee osteoarthritis have a phenotype with higher bone mass, higher fat mass, and lower lean body mass. Clin Orthop Relat Res. 2015; 473:258–64.
17. Brandt KD, Dieppe P, Radin E. Etiopathogenesis of osteoarthritis. Med Clin North Am. 2009; 93:1–24, xv.
18. Chen XF, Zhu DL, Yang M, Hu WX, Duan YY, Lu BJ, et al. An osteoporosis risk SNP at 1p36.12 acts as an allele-specific enhancer to modulate LINC00339 expression via long-range loop formation. Am J Hum Genet. 2018; 102:776–93.
19. Greenblatt MB, Shim JH, Glimcher LH. Mitogen-activated protein kinase pathways in osteoblasts. Annu Rev Cell Dev Biol. 2013; 29:63–79.
20. Yu B, Chang J, Liu Y, Li J, Kevork K, Al-Hezaimi K, et al. Wnt4 signaling prevents skeletal aging and inflammation by inhibiting nuclear factor-κB. Nat Med. 2014; 20:1009–17.
21. Kellgren JH, Lawrence JS. Radiological assessment of rheumatoid arthritis. Ann Rheum Dis. 1957; 16:485–93.
22. Perkel J. SNP genotyping: six technologies that keyed a revolution. Nat Methods. 2008; 5:447–53.
23. Consortium GT. The genotype-tissue expression (GTEx) project. Nat Genet. 2013; 45:580–5.
24. Mann J, Ten Have T, Plunkett JW, Meisels SJ. Time sampling: a methodological critique. Child Dev. 1991; 62:227–41.
25. Martel-Pelletier J, Barr AJ, Cicuttini FM, Conaghan PG, Cooper C, Goldring MB, et al. Osteoarthritis. Nat Rev Dis Primers. 2016; 2:16072.
26. Cao A, Galanello R. Beta-thalassemia. Genet Med. 2010; 12:61–76.
27. Lefebvre S, Bürglen L, Reboullet S, Clermont O, Burlet P, Viollet L, et al. Identification and characterization of a spinal muscular atrophy-determining gene. Cell. 1995; 80:155–65.
28. Woolf LI. The heterozygote advantage in phenylketonuria. Am J Hum Genet. 1986; 38:773–5.
29. Allison AC. Genetic control of resistance to human malaria. Curr Opin Immunol. 2009; 21:499–505.
30. Chang J, Sonoyama W, Wang Z, Jin Q, Zhang C, Krebsbach PH, et al. Noncanonical Wnt-4 signaling enhances bone regeneration of mesenchymal stem cells in craniofacial defects through activation of p38 MAPK. J Biol Chem. 2007; 282:30938–48.

Chromosomal location 1p36.12; rs6426749; Knee osteoarthritis; Single-nucleotide polymorphism

Copyright © 2021, the Chinese Medical Association.