Replication Study Confirms the Association of the Common rs1800629 Variant of the TNFα Gene with Postmenopausal Osteoporosis Susceptibility in the Han Chinese Population

Abstract

Background:

Previous studies have suggested that tumor necrosis factor α (TNF-α), encoded by the TNFα gene, can increase osteoclast formation, and that specific alleles of the TNFα gene are associated with postmenopausal osteoporosis susceptibility in some populations; however, the exact molecular mechanism remains unknown.

Aims:

To investigate the potential association of nineteen polymorphisms of the TNFα gene with postmenopausal osteoporosis and bone mineral density (BMD) traits in a sample of 1288 postmenopausal women from the Han Chinese population.

Methods:

A total of 437 postmenopausal osteoporosis patients and 851 unrelated age-matched healthy women were recruited to the study. Single marker and haplotype based analyses were conducted to evaluate the association of nineteen single nucleotide polymorphisms (SNPs) in both patient and control groups.

Results:

The SNP rs1800629 was identified as being highly significantly associated with postmenopausal osteoporosis after accounting for age and body mass index (p = 0.000087). In addition, the GG genotype of this SNP was associated with significantly lower measures of femoral neck BMD and lumbar spine BMD. Moreover, haplotype based analyses suggested significant association signals between the haplotype block, including rs1800629 with postmenopausal osteoporosis (p < 0.001).

Conclusion:

We have shown that a TNFα gene polymorphism, rs1800629, is highly significantly associated with postmenopausal osteoporosis and BMD in the female Han Chinese population. Additional sequencing-based studies are needed to investigate the genetic architecture of this genomic region and its relationship with osteoporosis-related phenotypes.

Introduction

Osteoporosis is one of the most common metabolic bone-related disorders characterized by decreased bone mineral density (BMD) and increased risk of bone fracture (Golob and Laya, 2015). Many studies suggested that age and gender are two of the most correlated risk factors for osteoporosis (Venken et al., 2008; Gammage et al., 2012), and the elderly population, especially postmenopausal women, is at high risk for this disorder (Garcia-Unzueta et al., 2008; Dy et al., 2011; Woo et al., 2012). Accumulating evidence shows that BMD is a major risk factor for osteoporosis, and BMD variation is known to be highly heritable in different populations (Duncan and Brown, 2010). Meanwhile, some research have demonstrated that genetic factors play a key role in the susceptibility to osteoporosis (Cheung et al., 2010; Hosoi, 2010; Ralston, 2010), and twin studies have determined the heritability of BMD for different bones to be 51-76% (Park et al., 2012).

With the widespread application of sequencing and genetic association analyses for studying the genetics of complex diseases, such as schizophrenia (Guan et al., 2013, 2014, 2016b, c), candidate gene-based association studies have successfully mapped susceptibility for many complex diseases, including osteoporosis to several loci (Omasu et al., 2003; Vidal et al., 2007; Jin et al., 2009). So far, more than 40 genome-wide association (GWA) studies focusing on BMD have been published, and 66 total loci have been replicated across all of these studies (Kiel et al., 2007; Richards et al., 2008, 2012; Styrkarsdottir et al., 2008; Rivadeneira et al., 2009; Estrada et al., 2012; Hsu and Kiel, 2012; McClung et al., 2014). These potential risk genes were not randomly distributed across the whole human genome, but were clustered around several regions, including cytokines, signaling pathways, growth factors, and transcription factors involved in bone turnover.

Among these candidate genes, tumor necrosis factor (TNF) gene has gained much attention. TNF gene contains four exons spanning a 3 kb region located on 6p21.33, which encodes a cytokine that increases osteoclast formation by affecting production of receptor activator of NF-κB ligand (RANKL) and the responsiveness to RANKL (Warren et al., 2015), and it has been reported to be associated with BMD or osteoporosis (Furuta et al., 2004; Moffett et al., 2005; Canhao et al., 2008). Recently, Kotrych et al. identified TNF gene to be significantly associated to postmenopausal osteoporosis in Europeans (Kotrych et al., 2016). However, some other early studies have examined the association between a few TNF polymorphisms and postmenopausal osteoporosis, but conflicting results have been obtained (Furuta et al., 2004; Chen et al., 2005; Moffett et al., 2005; Canhao et al., 2008; Kim et al., 2009; Kotrych et al., 2016; Lin et al., 2016).

Some studies involved animal models, and human observations suggested that cytokines may play an important role in osteoclast function (Jilka et al., 1995); therefore, TNF gene might contribute to the onset and development of postmenopausal osteoporosis. Because the biological mechanism of TNF gene contributing to postmenopausal osteoporosis remains unknown, follow-up studies are necessary to confirm previous findings and to extend them in different populations. To our knowledge, the association between TNF gene and postmenopausal osteoporosis has not been systematically evaluated in Han Chinese population containing more than 1000 individuals. In our study, we investigated the potential genetic association between TNF gene and postmenopausal osteoporosis and its related phenotypes using a sample of 1288 Han Chinese postmenopausal women to determine whether or not TNF gene is associated with postmenopausal osteoporosis in Han Chinese population.

Methods

Subjects and measurements of clinical characteristics

In the study, 437 women with postmenopausal osteoporosis and 851 age-matched healthy women were recruited from the First Affiliated Hospital of Xi'an Jiaotong University. The BMD of all subjects was measured using dual-energy X-ray absorptiometry (Lunar Expert 1313; Lunar Corp.) at the lumbar spine (LS) (L2-4) and femoral neck (FN). Postmenopausal osteoporosis was diagnosed according to the criteria of the World Health Organization. The areal BMD was expressed in grams per square centimeter (g/cm²), and the BMD of the LS and FN is summarized in Table 1. In addition, demographic data, including age and body mass index (BMI), were also collected (Table 1). None of the subjects had a history of taking medicines for the treatment of osteoporosis, and subjects with diseases or medications known to affect bone metabolism were excluded from the study. Subjects with BMI ≥27 were also ruled out in our study.

Table 1.

Anthropometric Characteristics of the Study Subjects

Parameters	Cases	Controls	p
Age, years (SD)	59.97 (6.16)	59.78 (5.99)	0.600
YSM, years (SD)	8.92 (3.92)	8.61 (4.36)	0.198
BMI, kg/m² (SD)	24.32 (1.44)	24.17 (1.47)	0.068
LS-BMD, g/cm² (SD)	0.613 (0.062)	0.819 (0.056)	<0.001
FN-BMD, g/cm² (SD)	0.747 (0.057)	0.897 (0.045)	<0.001

Clinical variables and age were compared between cases and the controls using a two-sample t-test.

BMD, bone mineral density; BMI, body mass index; FN, femoral neck; LS, lumbar spine; SD, standard deviation; YSM, years since menopause.

To restrict the genetic background of our study subjects, all included unrelated individuals were born locally, and their immediate family members from the previous three generations were also born locally. No one was excluded from the further analyses. Written informed consent was obtained from all subjects. This study was performed in accordance with the ethical guidelines of the Declaration of Helsinki (version 2002) and was approved by the Medical Ethics Committee of Xi'an Jiaotong University.

Single nucleotide polymorphism selection and genotyping

Single nucleotide polymorphisms (SNPs) in the TNF gene were selected from the 1000 Genomes Chinese Han Beijing population (CHB) using Haploview. In total, 19 SNPs with minor allele frequencies (MAF) ≥0.01 were selected to be included in the study, including rs3093547, rs1799964, rs1800630, rs1799724, rs4248158, rs4248160, rs4248161, rs1800629, rs361525, rs3093661, rs1800610, rs3093662, rs3093664, rs3093668, rs3093671, rs3093672, rs769178, rs769177, and rs769176.

Genomic DNA was extracted from peripheral blood leukocytes according to the manufacturer's protocol (Genomic DNA Kit; Axygen Scientific, Inc., CA). Genotyping was performed for all SNPs using the Sequenom MassARRAY RS1000 system (Sequenom, San Diego, CA). The results were processed using Typer Analyzer software (Sequenom), and genotype data were generated from the samples (Guan et al., 2012a). As the final genotype call rate of each SNP was greater than 99.1% and the overall genotyping call rate was 99.8%, the reliability of further statistical analysis was ensured. To verify the quality control of the genotyping results, we randomly selected 5% of samples for repeat, and the reproducibility was 100%.

Statistical analyses

Single marker-based association analyses were conducted to investigate the potential association of TNF gene with postmenopausal osteoporosis by PLINK. Logistic models were fitted for each genetic marker, and age and BMI of subjects were included in each model as covariates to control for potential confounding effects. Bonferroni correction was applied when necessary to address multiple-comparison problems. In addition, we also tested the potential associations between the associated SNP and the BMD of the femoral neck and lumbar spine (FN-BMD, LS-BMD). Furthermore, we have also conducted haplotype-based analyses to investigate the potential association of a combination of multiple genetic markers.

Hardy-Weinberg equilibrium (HWE) test and linkage disequilibrium (LD) calculations were conducted by Haploview. Haplotype frequency and haplotype-based analyses were calculated by GENECOUNTING v2.2, and haplotypic association analyses were conducted for all haplotypes (Guan et al., 2012b). Furthermore, we performed the power analyses by PGA v2.0, and the results indicated that our sample size could detect SNP and haplotype associations with 82% and 88% power, respectively, at a false positive rate of 5% and a presumed minimum odds ratio of 1.5.

Results

In Table 1, no statistical difference of demographic data was found between the groups. Allelic and genotypic frequencies of 19 SNPs in both groups are summarized together with HWE test in Table 2 and Supplementary Table S1 (Supplementary Data are available online at www.liebertpub.com/gtmb). All the SNPs were in HWE, with MAF greater than 0.01 (Table 2 and Supplementary Table S1). As presented in Table 2, we identified one SNP, rs1800629 (p = 0.000087), to be significantly associated with postmenopausal osteoporosis after accounting for age and BMI, which still suggested a significant association signal after multiple-comparison correction (corrected p = 0.001653, p × 19). There were no significant differences of another 18 SNPs in the study (Table 2 and Supplementary Table S1).

Table 2.

Allele and Genotype Frequency of Single Nucleotide Polymorphism Association Analysis

SNP/position		Allele freq. (%)		Genotype freq. (%)
rs4248161		A	C	AA	AC	CC	STAT	p-Value^a	HWE	OR ^b (95% CI)
Case	31,574,990	857 (98.05)	17 (1.95)	420 (96.11)	17 (3.89)	0 (0)	0.3703	0.7111	0.678	1.14 (0.63-2.09)
CTR		1673 (98.3)	29 (1.7)	822 (96.59)	29 (3.41)	0 (0)			0.613	1.12 (0.61-2.06)^c

rs1800629		G	A	GG	GA	AA
Case	31,575,254	810 (92.68)	64 (7.32)	375 (85.81)	60 (13.73)	2 (0.46)	−3.924	0.000087	0.809	1.80 (1.34-2.41)
CTR		1490 (87.54)	212 (12.46)	655 (76.97)	180 (21.15)	16 (1.88)		0.001653	0.379	1.72 (1.29-2.43)^c

p-value and ORs were adjusted for age and BMI by logistic analysis.

Risk allele and significant p-values are in italic bold, and corrected p-values are underlined after Bonferroni correction.

OR refers to risk allele OR in cases and controls.

ORs and 95% CI before adjustments.

CI, confidence interval; CTR, control; HWE, Hardy-Weinberg equilibrium; OR, odds ratio; SNP, single nucleotide polymorphism; STAT, statistic.

To evaluate the relationship between the significantly associated SNP of rs1800629 with BMD, we conducted multivariate analysis of FN-BMD and LS-BMD in each genotype group of the controls with adjustments for age and BMI. As shown in Table 3, significant differences of FN-BMD and LS-BMD between the different genotypes were identified by analysis of covariance in the study. We found significantly lower FN-BMD and LS-BMD in the GG genotype compared with non-GG genotypes. Furthermore, we examined LD structure using genotype data based on 19 candidate SNPs. Two haplotype blocks were identified, one of which included the associated SNP of rs1800629 (Fig. 1). Haplotype analyses were performed to test the block containing rs1800629, and the block was identified to be significant (global p < 0.001), indicating further supportive evidence of significant association of rs1800629 with postmenopausal osteoporosis (Table 4).

FIG. 1.

LD structure based on genotype datasets. The LD blocks are indicated as shaded matrices. LD, linkage disequilibrium.

Table 3.

Characteristics of the Significantly Associated Single Nucleotide Polymorphism of rs1800629 in Total Group of Subjects

Genotype (number)	FN-BMD (SD)	LS-BMD (SD)
GG (655)	0.807 (0.051)	0.886 (0.039)
GA (180)	0.855 (0.053)	0.931 (0.044)
AA (16)	0.898 (0.038)	0.957 (0.053)
p-Value	<0.001	<0.001
GG (655)	0.807 (0.051)	0.886 (0.039)
Non-GG (6196)	0.858 (0.053)	0.933 (0.046)
p-Value	< 0.001	<0.001
Non-AA (835)	0.817 (0.055)	0.896 (0.044)
AA (16)	0.898 (0.038)	0.957 (0.053)
p-Value	<0.001	<0.001

p-value of BMD was adjusted for age and BMI by analysis of covariance, and significant p-values are in italic bold.

Table 4.

Haplotype Frequency and Association Analysis

Haplotype	Frequency (%)
rs4248161-rs1800629	Case	CTR	p-value^a	Global p^b
AG	92.56	87.48	<0.001	<0.001
AA	5.50	10.81	< 0.001
CA	1.82	1.64	0.371
CG	0.12	0.06	0.543

Significant p-values are in italic bold. The haplotypic association analyses were evaluated by a likelihood ratio test followed by permutation testing through comparing estimated haplotype frequencies in cases and controls.

Based on 10,000 permutations.

Based on comparison of frequency distribution of all haplotypes for the combination of SNPs.

CTR, control.

Discussion

TNF gene located on chromosome 6p21.33 encodes a multifunctional pro-inflammatory cytokine, an important regulator of bone metabolism and bone remodeling, which is involved in directly activating osteoclast precursors or inducing the production of RANKL by osteoblasts (Caetano-Lopes et al., 2009). TNF has been suggested as an important role in some age-related disorders. Previous genetic studies have evaluated the relationship between a few TNF polymorphisms and postmenopausal osteoporosis and BMD in some populations, but with conflicting results (Furuta et al., 2004; Chen et al., 2005; Moffett et al., 2005; Canhao et al., 2008; Kim et al., 2009; Kotrych et al., 2016; Lin et al., 2016). Recently, Kotrych et al. reported that TNF gene contributes the risk of postmenopausal osteoporosis in European postmenopausal women (Kotrych et al., 2016). The purpose of our study was to thoroughly examine the association of TNF gene polymorphisms with the risk of postmenopausal osteoporosis in Han Chinese postmenopausal women.

In our study, the SNP of rs1800629 significantly associated with postmenopausal osteoporosis with the risk G allele. We observed significant differences of FN-BMD and LS-BMD among their different genotypes. Furthermore, there were significantly lower FN-BMD and LS-BMD in the GG genotype of rs1800629 compared with non-GG genotypes. Our results were highly similar with those from the study of Kotrych et al. (Kotrych et al., 2016), and Fontova et al. have reported the association of TNF gene with FN-BMD and LS-BMD in the postmenopausal Caucasian Mediterranean women (Fontova et al., 2002). Furthermore, TNF gene was found to be significantly associated with LS-BMD in the early postmenopausal Japanese women (Furuta et al., 2004) and possibly contribute the genetic risk of postmenopausal osteoporosis in Korean postmenopausal women (Kim et al., 2009).

Given the limitations of SNP analyses (Yang et al., 2013; Chen et al., 2015; Guan et al., 2015,2016a, 2016d; Zhang et al., 2015; Jia et al., 2016), we conducted haplotype analyses, which also provide further statistical evidence for our findings. However, there were also some apparent differences from other studies in the study. Chen et al. (2005) examined the relationship of TNF gene with postmenopausal osteoporosis in Taiwan population and suggested no correlation between TNF gene with postmenopausal osteoporosis. Canhao et al. (2008) demonstrated that there exists no association of TNF gene with BMD postmenopausal osteoporosis in Europeans.

Although our study is similar to the previous studies, there also exist obvious differences. Those differences may come from genetic heterogeneity that resulted from the structure between different populations, the examined common variants, and the sample size of the different studies. Furthermore, the similar conclusions drawn from two different ethnic groups (Han Chinese and Caucasian) demonstrated the genetic correlation between TNF gene and postmenopausal osteoporosis. Notably, we did not take some potential risk confounders, such as breastfeeding, alcohol consumption, and physical activity, into consideration in this study, which may lead to increased rate of postmenopausal osteoporosis. Thus, future studies are required to replicate our results in different populations.

The pathological characteristics of postmenopausal osteoporosis include low BMD and damage of microstructure, resulting in high incidence of nonstress fracture. Previous studies reported that estrogen deficiency in postmenopausal women could lead to exceed the bone resorption by osteoclasts over bone formation by osteoblasts and ultimately cause osteoporosis (Pacifici, 1996). However, the exact molecular mechanism regarding estrogen inducing BMD remains unknown. Estrogen plays an important role in maintaining the balance of bone metabolism. Notably, researchers have indicated that estrogen can affect the immune cell activation and inflammatory factor release, which are the key pathogenic factors for the development of osteoporosis (Straub, 2007). Earlier studies showed that TNF-α can directly stimulate bone marrow stromal cell production of RANKL and macrophage-colony stimulating fact, which can directly promote osteoclastic differentiation and the ability of bone resorption (Qian et al., 2014; Warren et al., 2015).

Recently, it has been shown that increased TNF-α can damage the vascular basement membrane of kidney and increase the vascular permeability, which could reduce the renal tubular reabsorption function of calcium and phosphate. Negative balance on calcium and phosphate metabolism would lead to low BMD (Laroche et al., 2012). Cohen-Solal et al. (1998) demonstrated that the culture media of monocytes obtained from postmenopausal women had an increased in vitro bone-resorbing activity that was blocked by addition of anti-TNF-α antibody. Studies on the mice showed that TNF-α knockout mice and TNF-α receptor p55 deficient mice did not exhibit osteoporosis after ovariectomy (Ammann et al., 1997; Roggia et al., 2001). Moreover, researchers have suggested that TNF-α in peripheral blood of postmenopausal osteoporosis patients is significantly higher than healthy postmenopausal women (Nanes, 2003).

rs1800629 is located at the 5′ end of TNF gene, which is involved in the regulation of a wide spectrum of biological processes, including cell proliferation, differentiation, apoptosis, lipid metabolism, and coagulation. In our study, G allele of rs1800629 in the TNF gene was identified as the risk allele of postmenopausal osteoporosis, and the GG genotype was also found to be associated with the lower BMD value. Furthermore, we examined the potential functional significance of rs1800629 in RegulomeDB. RegulomeDB scores are in a range of 1-6, and the smaller score indicates more evidence of biological significance for the variant. The SNP of 1800629 had a RegulomeDB score of 1d and was located in transcription binding sites of nuclear transcription factor Y subunit beta (NFYB) and IKAROS family zinc finger 1 (IKZF1), which indicate that it might be involved with the transcription of TNF. Moreover, a previous study has indicated that G allele of rs1800629 was correlated with increased transcriptional level of TNF gene, which may lead to increase the expression of TNF gene (Kroeger et al., 1997). All of these studies indicate that high levels of TNF-α may play an important role in postmenopausal osteoporosis.

To sum up, in this study, we have shown that TNF is an important locus for postmenopausal osteoporosis and useful for informative assessment of genetic risk for reduced BMD in Han Chinese postmenopausal women. Given of unknown complex network underlying bone remodeling and bone mass in the etiology of postmenopausal osteoporosis, sequencing-based studies are needed in the future to investigate the genetic architecture of this genomic region and its relationship with postmenopausal osteoporosis-related phenotypes.

Footnotes

Acknowledgments

This research was supported by the Project of Shaanxi Key Scientific and Technological Innovation Team (No.: 2013KCT-26). The funding sources had no role in the design of this study, the collection, analysis, and interpretation of data, the writing of the report, or the decision to submit the article for publication.

Author Disclosure Statement

No competing financial interests exist.

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