Association between DVWA gene polymorphisms and knee osteoarthritis in the North Chinese Han population

Association between DVWA gene polymorphisms and knee osteoarthritis in the North Chinese Han population | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between DVWA gene polymorphisms and knee osteoarthritis in the North Chinese Han population Dejian Zhang, Xiaomeng Liu, Hongwei Min, Kemin Liu, Tiebing Qu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2451652/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective Previous studies have shown that knee arthritis is a disease influenced by environmental and genetic factors. In this paper, we mainly investigate the association between SNPs in the DVWA gene region and the susceptibility to KOA in north Chinese Han people. Methods We performed two-center cross-sectional observational study that included 103 Chinese Han patients with KOA and 128 healthy Chinese Han volunteers. We investigated four SNPs (rs11718863, rs7639618, rs7651842 and rs7639807) in the DVWA gene region and extracted the genes using QIAamp DNA Mini Kit. We amplified the target gene fragment and sequenced the genotype.The corresponding frequency were counted and the counting results were statistically analyzed. Results The patient group was significantly older than the control group, and the difference was statistically significant(P 0.05, respectively). The chi-square test was used to analyze the four SNPs of DVWA gene, and rs11718863 and rs7639618 polymorphism was statistically different between the two groups(P = 0.04, P = 0.04, respectively). The rs11718863 and rs7639618 was consistent with Hardy-Weinberg equilibrium. Logistic regression analysis showed that KOA risk was significantly increased in the rs11718863 TT genotype (3.31, 95%CI 1.32, 8.34, P = 0.011) and the rs7639618 TT genotype (OR 2.86 ,95% CI 1.16, 7.04, P = 0.023). After age adjustment, the same result was observed in the rs11718863 SNP(OR 2.79, 95% CI 1.04, 7.43, P = 0.041), and the risk of KOA disappeared in the rs7639618 SNP (OR 1.50, 95% CI 0.79, 2.86, P = 0.217). The rs7651842 and rs7639807SNP were monomorphic for the T and C allele respectively. Conclusions We found that the rs11718863 SNP was associated with KOA. The TT genotype and T allele were the highest risk factors for the development of KOA in the north Chinese Han population. knee osteoarthritis DVWA gene polymorphism heredity Figures Figure 1 Introduction Knee osteoarthritis (KOA) is a type of prevalent, chronic degenerative arthritis which mainly caused by destruction of cartilage and bone[ 1 ]. Knee-joint pain and functional restrictions are the most common feature of KOA, and affect approximately one-third of people older than 60. The global age-standardized prevalence rates(ASRs) of OA was 6348.25 per 100,000 in 2019, while the KOA was the leading site, accounting for 60.6% of the total prevalent cases in 2019[ 2 ]. Among them, the Chinese have the highest prevalence of KOA. The prevalence of KOA was highest in China, 21.5% in males and 42.8% in females in the elderly Chinese population [ 3 ]. Given the increased life expectancy and ageing of the global population, KOA has caused increasing socioeconomic burdens. Epidemiological studies have shown that KOA is a complex multi-etiological disorder, and genetic and environmental factors can influence the onset and progression of the disease[ 4 ]. OA studies have shown that genetic factors can determine 39%- 65% of radiographic hand, hip, and KOA in women [ 5 ]. Genetic factors play a key role in the initiation and progression of KOA[ 5 , 6 ], and multiple susceptibility genes, including DVWA, have previously been studied [ 7 , 8 ]. The DVWA gene, located on chromosome 3p24.3, is specifically expressed in cartilage and produces a protein containing 276 amino acids. A missense mutation of DVWA encodes a new amino acid that reduces the linkage between product protein and β-tubulin, whereas The binding force between these two proteins is responsible for chondrocyte differentiation and plays a role in preventing the incidence of KOA[ 9 , 10 ]. Miyamoto and colleagues analyzed functional variations in DVWA gene region in a large genome-wide association study (GWAS). The DVWA gene has four SNPs (rs11718863, rs7639618, rs7651842, rs7639807 and rs17040821). The rs11718863 SNP was clearly related to KOA (OR = 1.43, p < 7*10 − 11 ) [ 11 ]. Several studies have shown associations between SNPs and KOA, but these results remain unconfirmed and controversial. Because gene polymorphisms can be affected by ethnogenetic and regional divergence [ 12 , 13 ], it is important to study geographically- and ethnically-linked allele distribution. Therefore, our study aimed to clarify the relationship between these four SNPs and KOA in the Han population in northern China. Participants and Methods Participants This was a prospective, two-center, cross-sectional observational study conducted in the Orthopaedic ward and outpatient department. We calculated the sample size based on the prevalence rate of KOA in the elderly in China, and the final result was 204 people according to the sampling error of 20% of the morbidity. The subjects consisted of 103 knee osteoarthritis (KOA) patients and 128 healthy volunteers who were all recruited at the Beijing Bo'Ai and Chao-Yang Hospitals between January 2017 and September 2018. The KOA group included 17 males and 86 females who were diagnosed with KOA based on American College of Rheumatology criteria[ 14 ]. The diagnostic criteria for KOA include knee pain plus at least 3 out of the 7 following items: ①age 50 and above, ② knee stiffness less than 30 minutes, ③knee crepitus, ④knee swelling, ⑤bony tenderness, ⑥The local temperature of the knee joint was not normal,⑦Imaging examination revealed osteophyte formation. The 128 healthy subjects had no history of tumors, immunopathy, or osteoarthritis. The control samples which included 23 males and 105 females, were admitted to the medical center of the same hospitals. The KOA group was selected from patients who had Grade 3 or Grade 4 OA on knee plain radiographs. The inclusion criteria of the patient group: 1) Han nationality; 2) having lived in the north of china longer than twenty years; 3) aged 50–80; 4)BMI < 30kg/m 2 ; 5) definite KOA disease; 6) volunteer to join the study. The inclusion items of the control group: 1) Han nationality; 2) living and working in Northern China over two decades; 3) between 50 and 75 years old; 4) BMI༜30kg/m 2 ; 5) no history of knee disease. Exclusion criteria of patient group included: 1) Age less than 50 or older than 75; 2) Unwilling to sign an informed consent form; 3) long-term migrant workers who have been away over 5 years, or the cumulative resident period is shorter than two decades; 4) Various secondary OA, such as previous knee trauma or surgery, congenital developmental diseases, etc; 5) Inflammatory joint disease (gouty arthritis, rheumatoid arthritis, systemic lupus erythematosus, hemophilia arthritis, etc.); 6) history of diabetes, heart disease, infectious disease, tumors, mental illness and other serious mental diseases or do not cooperate; 7)history of corticosteroid use; 8) limb paralysis after cerebral infarction;9) BMI ≥ 30kg/m 2 ; 10) incompatible diagnostic criteria of knee joint osteoarthritis. The exclusion criteria for control group included: 1)Age less than 50 and older than 80; 2) Unwilling to sign an informed consent form; 4) BMI ≥ 30kg/m 2 . All participants signed the written informed consent. If the subjects had no ability to sign, the family members signed on behalf of the patients. The Ethics Committee of China Rehabilitation Research Center (No.2017-075-1) approved the research protocol. Clinical data and sample collection We collected background data from all subjects, including age, gender, weight, BMI and history of relevant diseases. 2ml blood samples were drawn from a peripheral vein of all participants in 5ml vacutainer tubes containing EDTA(ethylene diamine tetraethylene) as an anticoagulant. The samples were packaged separately and stored in a -80℃refrigerator for future use. The samples were sorted on ice bags in batches, and We followed the instructions in the QIAamp DNA Mini Kit (Kaj) operation protocol for genomic DNA extraction. DNA was removed from the centrifuge column with 1.5µl deionized water, and preserved at -20℃in a refrigerator for future analysis. The concentration of the blood samples were measured using a NanoDrop™ 1000 ultraviolet spectrophotometer (Thermo Scientific, Waltham, MA, USA) to obtain DNA purity and DNA concentration based on A260/A280 ratios. The elution buffer was applied to calibrate the instruments. Specimens were thoroughly mixed and tested at room temperature. DNA was stored in a 4℃ fridge after detecting genomic integrity with agarose gel electropho- resis. DNA amplification and genotyping The DVWA gene fragment amplified by PCR was sequenced using the first generation method (Shanghai Majorbio bio Pharm Technology Co., Ltd.). The four mutation sites(SNP rs11718863, rs7639618, rs7651842 and rs7639807) are all contained in the DVWA gene and close together. One primer is enough. Four SNPs in DVWA gene were genotyped using primers: forward primer5’-AGGCTGCCTGC CATTATTACTT- 3’, and reverse primer sequence was5’-CCCATGCTGTT TCCTTTGAACA − 3’. This set of primers can cover the four SNP we want to study. The 25µl reaction system included 1µl of both forward and reverse primers, 5µl of DNA templates, 12.5µL of 2×Taq PCR Mastermix (50 mM Tris-HCl, pH 8.7, 100 mM KCl, 4 mM MgCl 2 , 0.1 U Taq Platinum polymerase/µL, 500µM dNTP each), and 5.5µl of sterilized water (DNase /RNase-free). We set the annealing temperature between 54℃-59 ℃ and performed quantitative PCR detection using agarose gel electrophoresis. Our results supported a 57℃ annealing temperature as the ideal temperature. The size of the PCR were about 363bp, The results were detected with agarose gel electrophoresis. SNP alleles and genotypes were analyzed from the sequencing results. Statistical Analysis SPSS 27.0 (IBM Corp.,Armonk, USA) was used for statistical analysis. Genotypes and allele frequencies of each group were directly counted. The independent segregation of the alleles was confirmed by the Hardy-Weinberg equilibrium (HWE) test. The independent-Sample T-test or Pearson's chi-squared test were used to assess the difference in baseline data between the two groups. Data conforming to normal distribution were denoted as mean ± standard deviation, and chi square test was adoted to evalutated the difference in genotype distribution and sex ratio between the patient group and the control group. A multivariate logistic regression was used to assess the association between SNP and KOA. Due to the age difference between the two groups of patients, we regarded age as the covariate to conduct logsitic regression again. The difference was statistically significant at P < 0.05. Results Table 1 shows baseline data between the two groups. The ages of participants in the KOA group ranged between age 50–75, with the mean age of 67.18 ± 9.53 years. The ages of participants in the control group ranged between age 40–70, with the mean age of 60.70 ± 6.38 years. The two groups were notably different in age (P 0.05, respectively). After gene extraction and target fragment amplification, we performed agarose gel electrophoresis respectively (Fig. 1 ). The PCR products meeting the requirements were sequenced. Among the four SNP, rs7651842 and rs7639807 each have a single genotype (TT and CC, respectively). As for rs7639618 and rs11718863 SNP, there were three genotypes in each SNP. Thus, +/+ represented a wild homozygous genotype, and +/- indicated a mutated heterozygous genotype, and -/- stood for the mutated homozygote. Statistical analysis showed that rs7639618 and rs11718863 didn't followed the Hardy-Weinberg equilibrium (Table 2 ). It was mainly because the small sample size. In our study, the genotype distribution of the two SNP (rs11718863, rs7639618) in the DVWA gene region were almost the same, which was the consequence of the linkage disequilibrium(LD) between these two SNPs. The D-index was 1 and the two SNPs be combined into haplotypes. In our study, two SNPs (rs11718863, rs7639618) in the DVWA were evaluated with chi-square tests (normal homozygote, mutation heterozygote and homozygous), and the first two genotypes were found to be statistically significant (P = 0.04, P = 0.04, respectively). Table 2 Hardy-Weinberg equilibrium in the KOA group and control group +/+ +/- -/- χ 2 P value Both groups rs11718863 27 127 77 5.45 0.02 rs7639618 75 128 28 5.63 0.02 Test group rs11718863 18 56 29 1.03 0.31 rs7639618 29 56 18 1.03 0.31 Control group rs11718863 9 71 48 6.36 0.01 rs7639618 46 72 10 6.29 0.01 KOA: knee osteoarthritis; +/+, +/-, -/- represent wild type, mutated heterozygous, mutated homozygous genotype. Table 3 Frequency distributions of SNPs in DVWA genotypes SNP Frequency (n%) χ 2 P Value KOA Control rs11718863 genotype TT AT 18(17.48) 9(7.03) 6.83 0.033 56(54.37) 71(55.47) AA 29(28.15) 48(47.5) allele T 92(44.66) 89(34.77) 4.70 0.030 A 114(55.34) 167(65.23) rs7639618 genotype CC 29(28.15) 46(35.94) 5.5 0.064 CT 56(54.37) 72(56.25) TT 18(17.47) 10(7.81) allele C 114(55.34) 164(64.06) 3.62 0.057 T 92(44.66) 92(35.94) SNPs: single nucleotide polymorphisms; KOA: knee osteoarthritis There was statistically significance between-group difference in the frequency distribution of the TT genotype and T allele in the rs7639618 locus (P 0.05). Logistic regression analysis showed that KOA risk was significantly increased in the rs11718863 TT genotype (3.31, 95%CI 1.32, 8.34) and the rs7639618 TT genotype (OR 2.86 ,95% CI 1.16, 7.04). In view of the obvious difference in age between the two groups, we used age as a concomitant variable for multiple logistic analysis again. However, the same result was observed in the rs11718863 SNP, and the risk of KOA disappeared in the rs7639618 SNP (OR 1.50, 95% CI 0.79, 2.86) after age adjustment (Table 4 , 5 ). Table 4 The association between DVWA SNPs and the risk of KOA gene Genotypes KOA group(103) Control group(128) OR(95%CI) P value DVWA rs11718863 AA 29 48 1 AT 56 71 1.30(0.73,2.32) 0.367 TT 18 9 3.31(1.32,8.34) 0.011 AT + TT 74 80 1.53(0.89, 2.68) 0.135 rs7639618 CC 29 46 1 CT 56 72 1.23(0.69, 2.21) 0.479 TT 18 10 2.86(1.16, 7.04) 0.023 CT + TT 74 82 1.43(0.82, 2.51) 0.21 SNPs: single nucleotide polymorphisms; CI: confidence interval; OR: odds ratio; KOA: knee osteoarthritis Table 5 The association of DVWA SNPs with KOA risk after adjustment for age gene Genotypes KOA group (103) Control group (128) OR(95%CI) P value DVWA rs11718863 AA 29 48 1 AT 56 71 1.68(0.88, 3.62) 0.115 TT 18 9 2.79(1.04, 7.43) 0.041 AT + TT 74 80 0.56(0.30, 1.03) 0.063 rs7639618 CC 29 46 1 CT 56 72 1.50(0.79, 2.86) 0.217 TT 18 10 2.28(0.86, 6.01) 0.097 CT + TT 74 82 0.63(0.34, 1.16) 0.139 SNPs: single nucleotide polymorphisms; KOA: knee osteoarthritis; CI: confidence interval; OR: odds ratio Discussion GWA studies demonstrated several SNPs in the DVWA [ 15 ], but the biological significances were unclear in the north Chinese Han population. We chose subjects who had lived in northern China over 20 years, thus reducing the influence of any potential environmental modifiers or population heterogeneity. The rs11718863 and rs7639618 genotype distribution in our research population was in line with Hardy Weinberg's law of equilibrium. Our study discovered that TT genotype and T allele frequency at the rs11718863 locus were high in the KOA group, suggesting that the rs11718863 SNP was related to KOA. Logistic regression analysis showed that the DVWA rs11718863 and rs7639618 TT genotypes were risk factors for KOA (OR = 2.79, 95% CI 1.04–7.43), OR = 2.86, 95% CI 1.16, 7.04). Rs11718863 TT genotypes were risk factors for KOA after adjusting for age, indicating that the rs7639618 locus might interact with age-related factors. The rs7651842 and rs7639807 genotypes, which are single TT and CC, respectively, showed no correlation with KOA susceptibility. We consider that this result was mainly due to the small sample capacity. In the further study, we will expand the sample size to reduce bias. A number of studies have shown that DVWA SNPs variation related to population and regional differences is correlated with susceptibility to KOA [ 10 , 16 – 20 ]. A study showed that the risk of KOA was significantly associated with the DVWA rs7639618 SNP in a Japanese population (OR = 1.26, 95%CI:1.12–1.43) [ 11 ]. Another study also illustrated the same result in Asians, but not in UK population [ 21 ] (OR = 0.88, 95% CI: 0.74–1.04); P = 0.12) and the in a Korean cohort [ 22 ]. A previous study manifested that the DVWA rs11718863 SNP was closely related to Kellgren&Lawrence analysis of KOA [ 9 ] and Italy scholars also manifested that the rs11718863 SNP was closely related to the occurrence of KOA in Sicilians [ 10 ]. Conversely, some reports had found that the DVWA rs11718863 SNP was not associated with KOA[ 18 ]. Thus, ethnicity should be considered in evaluated the DVWA gene polymorphisms and KOA susceptibility. Our research also supported this view. Current approaches for interrupting the onset and progression of KOA are limited and inadequate. Genetic studies of KOA have clarified the molecular mechanisms that result to the specific symptoms, including pain, stiffness, swelling, and limitation in the motion, and can provide new insights into treatment. In the future, extensive functional genomics research should be conducted to search for new SNPs and to further illuminate the underlying mechanisms of already-identified genes and pathways that are associated with KOA susceptibility [ 23 ]. Identifying KOA susceptibility genes facilitates the prediction of disease phenotypes and to set up KOA prediction models based on genotypic information [ 24 ]. This study has many limitations. The number of subjects was not insufficient, which may not indicate the connection between gene DVWA and KOA. Participants were from two hospitals(Beijing Bo'Ai and Chao-Yang Hospital), so they might not be a good representation of all KOA target groups. OA is a multifactorial disease associated with multiple genes and has different heritability according to different joints. This study only evaluated KOA and DVWA gene on account of failure to exam other joints with X-ray and the results are not applicable to other joints. Finally, the effect of gene and environment interaction on KOA was not considered in. We need to supplement and improve the experimental design in future studies. Conclusion This study elucidated the effect of DVWA variants in the development of KOA in a Han Chinese population. We found that the rs11718863 SNP was associated with KOA. The TT genotype and T allele were the highest risk factors for the development of KOA. Our research has identified a useful genetic marker that can improve understanding of the potential causative genes and therapeutic targets for KOA. Given that genetic factors vary with different disorders, severity, genders and populations, further investigations with larger sample sizes and participants of different ethnicities should be implemented to confirm the research results. Declarations AUTHOR CONTRIBUTIONS DZ and XL: conceptualization, data analysis, and writing; KL: data analysis and writing, TQ and HM: data analysis; ZL: writing and editing the article; HM: supervision of the project and reviewing, writing, and editing the article. All authors revised the article. FUNDING This work was supported by the Fundamental Research Funds for Central Public Welfare Research Institutes, China Rehabilitation Research Center 2016CZ-2 ACKNOWLEDGMENTS We thank all the members for participating in this study. CONFLICTS OF INTREST No conflict of interest. CONSENT FOR PUBLICATION Informed consent for publication was obtained from all authors of this manuscript. References Poornima S, Subramanyam K, Khan IA, G S, Hasan Q. 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Osteoarthritis Cartilage. 2012 Mar;20(3):218 – 22. doi: 10.1016/j.joca.2012.01.007 . Epub 2012 Jan 14. PMID: 22261407. Takahashi H, Nakajima M, Ozaki K, Tanaka T, Kamatani N, Ikegawa S. Prediction model for knee osteoarthritis based on genetic and clinical information. Arthritis Res Ther. 2010;12(5):R187. doi: 10.1186/ar3157 . Epub 2010 Oct 12. PMID: 20939878; PMCID: PMC2991022. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2451652","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":166024926,"identity":"8c5da247-243e-4e9f-ae66-d484055a09bc","order_by":0,"name":"Dejian Zhang","email":"","orcid":"","institution":"China Rehabilitation Research Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Dejian","middleName":"","lastName":"Zhang","suffix":""},{"id":166024927,"identity":"c72beaed-f5ce-4f20-9c2b-9eacc713299e","order_by":1,"name":"Xiaomeng Liu","email":"","orcid":"","institution":"China Rehabilitation Research Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaomeng","middleName":"","lastName":"Liu","suffix":""},{"id":166024928,"identity":"abecb1ee-44c8-400f-b12e-7c9a8b2f5f63","order_by":2,"name":"Hongwei Min","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAlElEQVRIiWNgGAWjYDCCAwxsDAwVEnLyJGo5Y2Fs2ECSFsa2ikQgg0jAd/t02mPeeRIJjA3MDx/dIEaL5Lnc7ca82yTy2BnYjI1ziNFicIZ3m3TuNolixgYeNmkStMyRSGw4QJqWBlK0SIK0/DkmYWzYTKxf+IBaJGfU1MnJszc/fEyUFgRgJk35KBgFo2AUjAJ8AACpcy1ber7rOgAAAABJRU5ErkJggg==","orcid":"","institution":"China Rehabilitation Research Center","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Hongwei","middleName":"","lastName":"Min","suffix":""},{"id":166024929,"identity":"17f3f839-1499-4787-8cb3-90fbba1005b8","order_by":3,"name":"Kemin Liu","email":"","orcid":"","institution":"China Rehabilitation Research Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kemin","middleName":"","lastName":"Liu","suffix":""},{"id":166024930,"identity":"e86e8456-356e-48f9-af8e-cd8190f46bc3","order_by":4,"name":"Tiebing Qu","email":"","orcid":"","institution":"China Rehabilitation Research Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Tiebing","middleName":"","lastName":"Qu","suffix":""},{"id":166024931,"identity":"c8c2f762-6fec-4b06-891e-509e6812000c","order_by":5,"name":"huijuan mu","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"huijuan","middleName":"","lastName":"mu","suffix":""}],"badges":[],"createdAt":"2023-01-06 20:29:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2451652/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2451652/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":31430306,"identity":"c5a640a5-11d9-46b4-8c14-e6af90b517da","added_by":"auto","created_at":"2023-01-11 15:31:00","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":36216,"visible":true,"origin":"","legend":"\u003cp\u003ewhole-genome agarose gel electrophoresis (A), target fragment agarose gel electrophoresis (B)\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2451652/v1/5d42ba17d0b4319cd3ec25f4.jpg"},{"id":31926782,"identity":"dab6bbc3-09ff-4574-bbc5-43f2768e5f4c","added_by":"auto","created_at":"2023-01-23 06:29:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":311887,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2451652/v1/c220f5fe-e04d-4aae-b311-991532f30181.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between DVWA gene polymorphisms and knee osteoarthritis in the North Chinese Han population","fulltext":[{"header":"Introduction","content":"\u003cp\u003eKnee osteoarthritis (KOA) is a type of prevalent, chronic degenerative arthritis which mainly caused by destruction of cartilage and bone[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Knee-joint pain and functional restrictions are the most common feature of KOA, and affect approximately one-third of people older than 60. The global age-standardized prevalence rates(ASRs) of OA was 6348.25 per 100,000 in 2019, while the KOA was the leading site, accounting for 60.6% of the total prevalent cases in 2019[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Among them, the Chinese have the highest prevalence of KOA. The prevalence of KOA was highest in China, 21.5% in males and 42.8% in females in the elderly Chinese population [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Given the increased life expectancy and ageing of the global population, KOA has caused increasing socioeconomic burdens.\u003c/p\u003e \u003cp\u003eEpidemiological studies have shown that KOA is a complex multi-etiological disorder, and genetic and environmental factors can influence the onset and progression of the disease[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. OA studies have shown that genetic factors can determine 39%- 65% of radiographic hand, hip, and KOA in women [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Genetic factors play a key role in the initiation and progression of KOA[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], and multiple susceptibility genes, including DVWA, have previously been studied [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe DVWA gene, located on chromosome 3p24.3, is specifically expressed in cartilage and produces a protein containing 276 amino acids. A missense mutation of DVWA encodes a new amino acid that reduces the linkage between product protein and β-tubulin, whereas The binding force between these two proteins is responsible for chondrocyte differentiation and plays a role in preventing the incidence of KOA[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Miyamoto and colleagues analyzed functional variations in DVWA gene region in a large genome-wide association study (GWAS). The DVWA gene has four SNPs (rs11718863, rs7639618, rs7651842, rs7639807 and rs17040821). The rs11718863 SNP was clearly related to KOA (OR\u0026thinsp;=\u0026thinsp;1.43, p\u0026thinsp;\u0026lt;\u0026thinsp;7*10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral studies have shown associations between SNPs and KOA, but these results remain unconfirmed and controversial. Because gene polymorphisms can be affected by ethnogenetic and regional divergence [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], it is important to study geographically- and ethnically-linked allele distribution. Therefore, our study aimed to clarify the relationship between these four SNPs and KOA in the Han population in northern China.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eParticipants and Methods\u003c/h2\u003e \u003cdiv id=\"Sec3\" class=\"Section3\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThis was a prospective, two-center, cross-sectional observational study conducted in the Orthopaedic ward and outpatient department. We calculated the sample size based on the prevalence rate of KOA in the elderly in China, and the final result was 204 people according to the sampling error of 20% of the morbidity. The subjects consisted of 103 knee osteoarthritis (KOA) patients and 128 healthy volunteers who were all recruited at the Beijing Bo'Ai and Chao-Yang Hospitals between January 2017 and September 2018. The KOA group included 17 males and 86 females who were diagnosed with KOA based on American College of Rheumatology criteria[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The diagnostic criteria for KOA include knee pain plus at least 3 out of the 7 following items: ①age 50 and above, ② knee stiffness less than 30 minutes, ③knee crepitus, ④knee swelling, ⑤bony tenderness, ⑥The local temperature of the knee joint was not normal,⑦Imaging examination revealed osteophyte formation. The 128 healthy subjects had no history of tumors, immunopathy, or osteoarthritis. The control samples which included 23 males and 105 females, were admitted to the medical center of the same hospitals. The KOA group was selected from patients who had Grade 3 or Grade 4 OA on knee plain radiographs.\u003c/p\u003e \u003cp\u003eThe inclusion criteria of the patient group: 1) Han nationality; 2) having lived in the north of china longer than twenty years; 3) aged 50\u0026ndash;80; 4)BMI\u0026thinsp;\u0026lt;\u0026thinsp;30kg/m\u003csup\u003e2\u003c/sup\u003e; 5) definite KOA disease; 6) volunteer to join the study.\u003c/p\u003e \u003cp\u003eThe inclusion items of the control group: 1) Han nationality; 2) living and working in Northern China over two decades; 3) between 50 and 75 years old; 4) BMI༜30kg/m\u003csup\u003e2\u003c/sup\u003e; 5) no history of knee disease.\u003c/p\u003e \u003cp\u003eExclusion criteria of patient group included: 1) Age less than 50 or older than 75; 2) Unwilling to sign an informed consent form; 3) long-term migrant workers who have been away over 5 years, or the cumulative resident period is shorter than two decades; 4) Various secondary OA, such as previous knee trauma or surgery, congenital developmental diseases, etc; 5) Inflammatory joint disease (gouty arthritis, rheumatoid arthritis, systemic lupus erythematosus, hemophilia arthritis, etc.); 6) history of diabetes, heart disease, infectious disease, tumors, mental illness and other serious mental diseases or do not cooperate; 7)history of corticosteroid use; 8) limb paralysis after cerebral infarction;9) BMI\u0026thinsp;\u0026ge;\u0026thinsp;30kg/m\u003csup\u003e2\u003c/sup\u003e; 10) incompatible diagnostic criteria of knee joint osteoarthritis.\u003c/p\u003e \u003cp\u003eThe exclusion criteria for control group included: 1)Age less than 50 and older than 80; 2) Unwilling to sign an informed consent form; 4) BMI\u0026thinsp;\u0026ge;\u0026thinsp;30kg/m\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e All participants signed the written informed consent. If the subjects had no ability to sign, the family members signed on behalf of the patients. The Ethics Committee of China Rehabilitation Research Center (No.2017-075-1) approved the research protocol.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eClinical data and sample collection\u003c/h2\u003e \u003cp\u003eWe collected background data from all subjects, including age, gender, weight, BMI and history of relevant diseases. 2ml blood samples were drawn from a peripheral vein of all participants in 5ml vacutainer tubes containing EDTA(ethylene diamine tetraethylene) as an anticoagulant. The samples were packaged separately and stored in a -80℃refrigerator for future use.\u003c/p\u003e \u003cp\u003e The samples were sorted on ice bags in batches, and We followed the instructions in the QIAamp DNA Mini Kit (Kaj) operation protocol for genomic DNA extraction. DNA was removed from the centrifuge column with 1.5\u0026micro;l deionized water, and preserved at -20℃in a refrigerator for future analysis. The concentration of the blood samples were measured using a NanoDrop\u0026trade; 1000 ultraviolet spectrophotometer (Thermo Scientific, Waltham, MA, USA) to obtain DNA purity and DNA concentration based on A260/A280 ratios. The elution buffer was applied to calibrate the instruments. Specimens were thoroughly mixed and tested at room temperature. DNA was stored in a 4℃ fridge after detecting genomic integrity with agarose gel electropho- resis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eDNA amplification and genotyping\u003c/h2\u003e \u003cp\u003eThe DVWA gene fragment amplified by PCR was sequenced using the first generation method (Shanghai Majorbio bio Pharm Technology Co., Ltd.). The four mutation sites(SNP rs11718863, rs7639618, rs7651842 and rs7639807) are all contained in the DVWA gene and close together. One primer is enough. Four SNPs in DVWA gene were genotyped using primers: forward primer5\u0026rsquo;-AGGCTGCCTGC CATTATTACTT- 3\u0026rsquo;, and reverse primer sequence was5\u0026rsquo;-CCCATGCTGTT TCCTTTGAACA \u0026minus;\u0026thinsp;3\u0026rsquo;. This set of primers can cover the four SNP we want to study. The 25\u0026micro;l reaction system included 1\u0026micro;l of both forward and reverse primers, 5\u0026micro;l of DNA templates, 12.5\u0026micro;L of 2\u0026times;Taq PCR Mastermix (50 mM Tris-HCl, pH 8.7, 100 mM KCl, 4 mM MgCl\u003csub\u003e2\u003c/sub\u003e, 0.1 U Taq Platinum polymerase/\u0026micro;L, 500\u0026micro;M dNTP each), and 5.5\u0026micro;l of sterilized water (DNase /RNase-free). We set the annealing temperature between 54℃-59 ℃ and performed quantitative PCR detection using agarose gel electrophoresis. Our results supported a 57℃ annealing temperature as the ideal temperature. The size of the PCR were about 363bp, The results were detected with agarose gel electrophoresis. SNP alleles and genotypes were analyzed from the sequencing results.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eSPSS 27.0 (IBM Corp.,Armonk, USA) was used for statistical analysis. Genotypes and allele frequencies of each group were directly counted. The independent segregation of the alleles was confirmed by the Hardy-Weinberg equilibrium (HWE) test. The independent-Sample T-test or Pearson's chi-squared test were used to assess the difference in baseline data between the two groups. Data conforming to normal distribution were denoted as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, and chi square test was adoted to evalutated the difference in genotype distribution and sex ratio between the patient group and the control group. A multivariate logistic regression was used to assess the association between SNP and KOA. Due to the age difference between the two groups of patients, we regarded age as the covariate to conduct logsitic regression again. The difference was statistically significant at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows baseline data between the two groups. The ages of participants in the KOA group ranged between age 50\u0026ndash;75, with the mean age of 67.18\u0026thinsp;\u0026plusmn;\u0026thinsp;9.53 years. The ages of participants in the control group ranged between age 40\u0026ndash;70, with the mean age of 60.70\u0026thinsp;\u0026plusmn;\u0026thinsp;6.38 years. The two groups were notably different in age (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). There were no difference in gender, height, weight, or BMI between the two groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05, respectively).\u003c/p\u003e \u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e\u003cp\u003eAfter gene extraction and target fragment amplification, we performed agarose gel electrophoresis respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The PCR products meeting the requirements were sequenced. Among the four SNP, rs7651842 and rs7639807 each have a single genotype (TT and CC, respectively). As for rs7639618 and rs11718863 SNP, there were three genotypes in each SNP. Thus, +/+ represented a wild homozygous genotype, and +/- indicated a mutated heterozygous genotype, and -/- stood for the mutated homozygote. Statistical analysis showed that rs7639618 and rs11718863 didn't followed the Hardy-Weinberg equilibrium (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). It was mainly because the small sample size. In our study, the genotype distribution of the two SNP (rs11718863, rs7639618) in the DVWA gene region were almost the same, which was the consequence of the linkage disequilibrium(LD) between these two SNPs. The D-index was 1 and the two SNPs be combined into haplotypes. In our study, two SNPs (rs11718863, rs7639618) in the DVWA were evaluated with chi-square tests (normal homozygote, mutation heterozygote and homozygous), and the first two genotypes were found to be statistically significant (P\u0026thinsp;=\u0026thinsp;0.04, P\u0026thinsp;=\u0026thinsp;0.04, respectively).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHardy-Weinberg equilibrium in the KOA group and control group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+/+\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+/-\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-/-\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoth groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers11718863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers7639618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers11718863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers7639618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers11718863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers7639618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eKOA: knee osteoarthritis; +/+, +/-, -/- represent wild type, mutated heterozygous, mutated homozygous genotype.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFrequency distributions of SNPs in DVWA genotypes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" morerows=\"1\" nameend=\"c3\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eSNP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eFrequency (n%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e \u003cem\u003eP\u003c/em\u003e Value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKOA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003ers11718863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003egenotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003cp\u003eAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18(17.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9(7.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e6.83 0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56(54.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71(55.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29(28.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48(47.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eallele\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92(44.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89(34.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e4.70 0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e114(55.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e167(65.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003ers7639618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003egenotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29(28.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46(35.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e5.5 0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56(54.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72(56.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18(17.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10(7.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eallele\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e114(55.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e164(64.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3.62 0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92(44.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92(35.94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eSNPs: single nucleotide polymorphisms; KOA: knee osteoarthritis\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThere was statistically significance between-group difference in the frequency distribution of the TT genotype and T allele in the rs7639618 locus (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). There were no significance between-group differences in genotype or allele frequency distribution in rs7639618(P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eLogistic regression analysis showed that KOA risk was significantly increased in the rs11718863 TT genotype (3.31, 95%CI 1.32, 8.34) and the rs7639618 TT genotype (OR 2.86 ,95% CI 1.16, 7.04). In view of the obvious difference in age between the two groups, we used age as a concomitant variable for multiple logistic analysis again. However, the same result was observed in the rs11718863 SNP, and the risk of KOA disappeared in the rs7639618 SNP (OR 1.50, 95% CI 0.79, 2.86) after age adjustment (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe association between DVWA SNPs and the risk of KOA\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003egene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGenotypes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKOA group(103)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eControl group(128)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDVWA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers11718863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.30(0.73,2.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.31(1.32,8.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAT\u0026thinsp;+\u0026thinsp;TT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.53(0.89, 2.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers7639618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.23(0.69, 2.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.86(1.16, 7.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCT\u0026thinsp;+\u0026thinsp;TT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.43(0.82, 2.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eSNPs: single nucleotide polymorphisms; CI: confidence interval; OR: odds ratio; KOA: knee osteoarthritis\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe association of DVWA SNPs with KOA risk after adjustment for age\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003egene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGenotypes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKOA group\u003c/p\u003e \u003cp\u003e(103)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eControl group\u003c/p\u003e \u003cp\u003e(128)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR(95%CI) P value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDVWA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers11718863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.68(0.88, 3.62) 0.115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.79(1.04, 7.43) 0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAT\u0026thinsp;+\u0026thinsp;TT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.56(0.30, 1.03) 0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers7639618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.50(0.79, 2.86) 0.217\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.28(0.86, 6.01) 0.097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCT\u0026thinsp;+\u0026thinsp;TT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.63(0.34, 1.16) 0.139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eSNPs: single nucleotide polymorphisms; KOA: knee osteoarthritis; CI: confidence interval; OR: odds ratio\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eGWA studies demonstrated several SNPs in the DVWA [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], but the biological significances were unclear in the north Chinese Han population. We chose subjects who had lived in northern China over 20 years, thus reducing the influence of any potential environmental modifiers or population heterogeneity. The rs11718863 and rs7639618 genotype distribution in our research population was in line with Hardy Weinberg's law of equilibrium. Our study discovered that TT genotype and T allele frequency at the rs11718863 locus were high in the KOA group, suggesting that the rs11718863 SNP was related to KOA. Logistic regression analysis showed that the DVWA rs11718863 and rs7639618 TT genotypes were risk factors for KOA (OR\u0026thinsp;=\u0026thinsp;2.79, 95% CI 1.04\u0026ndash;7.43), OR\u0026thinsp;=\u0026thinsp;2.86, 95% CI 1.16, 7.04). Rs11718863 TT genotypes were risk factors for KOA after adjusting for age, indicating that the rs7639618 locus might interact with age-related factors. The rs7651842 and rs7639807 genotypes, which are single TT and CC, respectively, showed no correlation with KOA susceptibility. We consider that this result was mainly due to the small sample capacity. In the further study, we will expand the sample size to reduce bias.\u003c/p\u003e \u003cp\u003eA number of studies have shown that DVWA SNPs variation related to population and regional differences is correlated with susceptibility to KOA [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR17 CR18 CR19\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. A study showed that the risk of KOA was significantly associated with the DVWA rs7639618 SNP in a Japanese population (OR\u0026thinsp;=\u0026thinsp;1.26, 95%CI:1.12\u0026ndash;1.43) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Another study also illustrated the same result in Asians, but not in UK population [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] (OR\u0026thinsp;=\u0026thinsp;0.88, 95% CI: 0.74\u0026ndash;1.04); P\u0026thinsp;=\u0026thinsp;0.12) and the in a Korean cohort [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA previous study manifested that the DVWA rs11718863 SNP was closely related to Kellgren\u0026amp;Lawrence analysis of KOA [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and Italy scholars also manifested that the rs11718863 SNP was closely related to the occurrence of KOA in Sicilians [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Conversely, some reports had found that the DVWA rs11718863 SNP was not associated with KOA[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Thus, ethnicity should be considered in evaluated the DVWA gene polymorphisms and KOA susceptibility. Our research also supported this view.\u003c/p\u003e \u003cp\u003eCurrent approaches for interrupting the onset and progression of KOA are limited and inadequate. Genetic studies of KOA have clarified the molecular mechanisms that result to the specific symptoms, including pain, stiffness, swelling, and limitation in the motion, and can provide new insights into treatment. In the future, extensive functional genomics research should be conducted to search for new SNPs and to further illuminate the underlying mechanisms of already-identified genes and pathways that are associated with KOA susceptibility [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Identifying KOA susceptibility genes facilitates the prediction of disease phenotypes and to set up KOA prediction models based on genotypic information [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study has many limitations. The number of subjects was not insufficient, which may not indicate the connection between gene DVWA and KOA. Participants were from two hospitals(Beijing Bo'Ai and Chao-Yang Hospital), so they might not be a good representation of all KOA target groups. OA is a multifactorial disease associated with multiple genes and has different heritability according to different joints. This study only evaluated KOA and DVWA gene on account of failure to exam other joints with X-ray and the results are not applicable to other joints. Finally, the effect of gene and environment interaction on KOA was not considered in. We need to supplement and improve the experimental design in future studies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study elucidated the effect of DVWA variants in the development of KOA in a Han Chinese population. We found that the rs11718863 SNP was associated with KOA. The TT genotype and T allele were the highest risk factors for the development of KOA. Our research has identified a useful genetic marker that can improve understanding of the potential causative genes and therapeutic targets for KOA. Given that genetic factors vary with different disorders, severity, genders and populations, further investigations with larger sample sizes and participants of different ethnicities should be implemented to confirm the research results.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDZ and XL: conceptualization, data analysis, and writing; KL: data analysis and writing, TQ and HM: data analysis; ZL: writing and editing the article; HM: supervision of the project and reviewing, writing, and editing the article. All authors revised the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Fundamental Research Funds for Central Public Welfare Research Institutes, China Rehabilitation Research Center 2016CZ-2\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all the members for participating in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICTS OF INTREST\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONSENT FOR PUBLICATION\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent for publication was obtained from all authors of this manuscript.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePoornima S, Subramanyam K, Khan IA, G S, Hasan Q. 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PMID: 20939878; PMCID: PMC2991022.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"knee osteoarthritis, DVWA, gene polymorphism, heredity","lastPublishedDoi":"10.21203/rs.3.rs-2451652/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2451652/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003ePrevious studies have shown that knee arthritis is a disease influenced by environmental and genetic factors. In this paper, we mainly investigate the association between SNPs in the DVWA gene region and the susceptibility to KOA in north Chinese Han people.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe performed two-center cross-sectional observational study that included 103 Chinese Han patients with KOA and 128 healthy Chinese Han volunteers. We investigated four SNPs (rs11718863, rs7639618, rs7651842 and rs7639807) in the DVWA gene region and extracted the genes using QIAamp DNA Mini Kit. We amplified the target gene fragment and sequenced the genotype.The corresponding frequency were counted and the counting results were statistically analyzed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe patient group was significantly older than the control group, and the difference was statistically significant(P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). There were no statistically significance between-group differences in gender, height, weight, or BMI (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05, respectively). The chi-square test was used to analyze the four SNPs of DVWA gene, and rs11718863 and rs7639618 polymorphism was statistically different between the two groups(P\u0026thinsp;=\u0026thinsp;0.04, P\u0026thinsp;=\u0026thinsp;0.04, respectively). The rs11718863 and rs7639618 was consistent with Hardy-Weinberg equilibrium. Logistic regression analysis showed that KOA risk was significantly increased in the rs11718863 TT genotype (3.31, 95%CI 1.32, 8.34, P\u0026thinsp;=\u0026thinsp;0.011) and the rs7639618 TT genotype (OR 2.86 ,95% CI 1.16, 7.04, P\u0026thinsp;=\u0026thinsp;0.023). After age adjustment, the same result was observed in the rs11718863 SNP(OR 2.79, 95% CI 1.04, 7.43, P\u0026thinsp;=\u0026thinsp;0.041), and the risk of KOA disappeared in the rs7639618 SNP (OR 1.50, 95% CI 0.79, 2.86, P\u0026thinsp;=\u0026thinsp;0.217). The rs7651842 and rs7639807SNP were monomorphic for the T and C allele respectively.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eWe found that the rs11718863 SNP was associated with KOA. The TT genotype and T allele were the highest risk factors for the development of KOA in the north Chinese Han population.\u003c/p\u003e","manuscriptTitle":"Association between DVWA gene polymorphisms and knee osteoarthritis in the North Chinese Han population","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-01-11 15:30:55","doi":"10.21203/rs.3.rs-2451652/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"080613f2-f4a1-47a1-a65a-234281780198","owner":[],"postedDate":"January 11th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-01-23T06:29:18+00:00","versionOfRecord":[],"versionCreatedAt":"2023-01-11 15:30:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2451652","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2451652","identity":"rs-2451652","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}