Abstract
Haohuan Li1, Xiaolong Zhang1, Yiping Cao2, Song Hu3, Fei Peng1, Jianlin Zhou1 and Jianping Li1
1Department of Orthopedics, Renmin Hospital of Wuhan University, Wuhan, 430060, China
2Key Laboratory of Optoelectronic Chemical Materials and Devices, Ministry of Education, Jianghan University, Wuhan, 430056, China
3Department of Physiology, Jianghan University, Wuhan Medical College, Wuhan, 430056, China
Correspondence to:
Haohuan Li, email: [email protected]
Keywords: osteoarthritis, EN1, polymorphism, BMD, case-control
Received: November 08, 2016 Accepted: March 10, 2017 Published: April 05, 2017
ABSTRACT
Osteoarthritis (OA) is a complex disease that affects the whole joint, resulting from the combined influence of biomechanical factors and genetic factors. The heritable component for primary OA accounts for about 60% of variation in population liability to the disease. So far, genome-wide association studies (GWAS) and candidate gene studies have established many OA-related loci. However, these findings account for only a rather small fraction of the genetic component. To further reveal the genetic architecture of OA, we conducted this case-control study to explore the association of locus EN1 rs4144782 and knee OA susceptibility in a Chinese population. EN1 rs4144782 was significantly associated with increased risk of knee OA (OR=1.26; 95% CI: 1.05-1.50, P value=0.012). In dominant model, compared with carriers of GG genotype, those with AG or AA genotype have an 1.44-fold increased risk of OA (OR: 1.44; 95% CI: 1.10-1.88; P value=0.008). Subgroup analyses didn’t change the results materially. This should be the first association study of EN1 locus on risk of OA, and our finding suggested that the EN1 rs4144782 might contribute to the susceptibility of knee OA.
INTRODUCTION
The most common joint disorder, osteoarthritis (OA), which results from breakdown of joint cartilage and underlying bone, could cause substantial pain and physical disability [1]. However, no disease-modifying drugs exist for the treatment of OA [2]. To present, the concept of the pathophysiology is still evolving and undetermined, from being viewed as cartilage-limited to a multifactorial disease which could affect the whole joint [3]. Many genome-wide association studies (GWASs) of OA have been conducted in European Caucasian populations, and identified lot of susceptibility loci of OA consecutively, including LRCH1, PTGS2, A2BP1, 7q22, DOT1L, LSP1P3, and so on [4–14]. Candidate gene studies also identified the most consistently reported gene, GDF5, whose polymorphisms have been identified to be associated with susceptibility of OA [15–29]. These reports mean that genetic factors may play an important role in the development of OA, although just a little was identified.
Despite of numerous efforts to identify genetic factors associated with OA, the identified loci could still explain a small part of the hereditary of OA, due to the disease heterogeneity and limitation of large sample size [4, 13, 17, 27, 28, 30-36]. Recently, whole-genome sequencing identifies locus Homeobox protein engrailed-1 (EN1) gene as a determinant of bone mineral density (BMD) [37]. Higher BMD was reported to be associated with OA in many cross-sectional and longitudinal epidemiological studies [38–41]. One important mechanism that could underpin the observed association between BMD and OA is genetic pleiotropy—the existence of genetic variants that contribute to both BMD variation and OA risk [42]. For example, Yerges-Armstrong et al [43] evaluated the associations between GWAS-identified genetic loci for variance in BMD and knee OA, and identified four SNPs significantly associated with prevalent radiographic knee OA, including rs2016266, rs10226308, rs3736228, and rs10835187. This results further supported the hypothesis that BMD, or its determinants, might be a risk factor for OA development. Thus, to clarify the potential relationship between locus EN1 rs4144782 and knee OA susceptibility, we conducted this case-control study in a Chinese population.
RESULTS
Clinical characteristics between OA cases and healthy controls
Totally, we included 500 OA patients and 500 matched healthy controls in this study. As shown in Table 1, the proportion of the matching variable which included age, gender, smoking status, alcohol status, and regular physical activity in cases and healthy controls was comparable (all P value > 0.05). However, BMI in OA cases was larger than that in healthy controls (P < 0.001), and it’s more likely to be diabetes patients for OA cases (P value = 0.027).
Table 1: Characteristics of the subjects with knee OA, and control subjects
Variables | Cases (n=500) | Controls (n=500) | P value |
|---|---|---|---|
Age | 47.8±7.8 | 48.2±8.2 | 0.430 |
Gender | |||
Male | 150 (30.0%) | 143 (28.6%) | 0.627 |
female | 350 (70.0%) | 357 (71.4%) | |
Smoking status | |||
Yes | 78 (15.6%) | 82 (16.4%) | 0.730 |
No | 422 (84.4%) | 418 (83.6%) | |
Alcohol status | |||
drinkers | 178 (35.6%) | 180 (36.0%) | 0.895 |
Non-drinkers | 322 (64.4%) | 320 (64.0%) | |
Regular physical activity | |||
Yes | 113 (22.6%) | 121 (24.2%) | 0.550 |
No | 387 (77.4%) | 379 (75.8%) | |
BMI | 24.99±2.28 | 24.26±2.6 | P<0.001 |
Diabetes | |||
Yes | 114 (22.8%) | 86 (17.2%) | 0.027 |
No | 386 (77.2%) | 414 (82.8%) |
BMI=body mass index.
Association between allelic and genotypic frequency of EN1 rs4144782 and knee OA risk
Table 2 presents the association between allelic and genotypic frequency of EN1 rs4144782 and knee OA risk. Genotype frequency of EN1 rs4144782 in the control group was in accordance with HWE (P = 0.168). EN1 rs4144782 was significantly associated with OA risk under the log-additive model (OR=1.26; 95% CI: 1.05-1.50, P value=0.012). Compared with individuals with the GG genotype, the adjusted OR for developing OA was 1.35 (95% CI: 1.03-1.77; P value=0.030) for those with the AG genotype, as well as 1.46 (95% CI: 1.03-2.06; P value=0.031) for those with the AA genotype. In dominant model, compared with carriers of GG genotype, those with AG or AA genotype have an 1.44-fold increased risk of OA (OR: 1.44; 95% CI: 1.10-1.88; P value=0.008).
Table 2: Associations between EN1 rs4144782 and susceptibility of knee OA among Chinese population
Genotypes | Cases (n, %) | Controls (n, %) | OR (95% CI)a | P value |
|---|---|---|---|---|
GG | 152 (30.4%) | 188 (37.6%) | 1.00 (Reference) | |
AG | 245 (49.0%) | 225 (45.0%) | 1.35 (1.03-1.77) | 0.030 |
AA | 103 (20.6%) | 87 (17.4%) | 1.46 (1.03-2.06) | 0.031 |
A vs G | 1.26 (1.05-1.50) | 0.012 | ||
Dominant model | ||||
GG | 152 (30.4%) | 188 (37.6%) | 1.00 (Reference) | |
AG+AA | 348 (69.6%) | 312 (62.4%) | 1.44 (1.10-1.88) | 0.008 |
Recessive model | ||||
GG+AG | 397 (79.4%) | 413 (82.6%) | 1.00 (Reference) | |
AA | 103 (20.6%) | 87 (17.4%) | 1.25 (0.91-1.73) | 0.168 |
aAdjusted for age, gender, smoking and drinking status, physical activity, diabetes, and BMI.
BMI=body mass index; OR=odds ratio; CI: confidence level.
Stratified analyses of associations between EN1 rs4144782 and knee OA risk
Table 3 presents stratified analyses of association between EN1 rs4144782 and knee OA risk by age, gender, smoking and drinking status, physical activity, diabetes, and BMI. Generally, the results were not changed materially. The significant ORs ranged from 1.23 to 1.28 in allelic model (P value: 0.013-0.049). While in dominant model, the significant ORs ranged from 1.34 to 1.53 (P value: 0.007-0.049). We also detected an significant association for non-smokers in recessive model (OR:1.43; 95% CI: 1.00-2.05; P value = 0.048).
Table 3: Stratified associations between EN1 rs4144782 and susceptibility of OA among Chinese population
Allelic model | Dominant model | Recessive model | ||||
|---|---|---|---|---|---|---|
OR (95% CI) a | P value | OR (95% CI) a | P value | OR (95% CI) a | P value | |
Age group | ||||||
≥40 | 1.28 (1.05-1.57) | 0.013 | 1.51 (1.12-2.03) | 0.007 | 1.25 (0.88-1.79) | 0.212 |
<40 | 1.18 (0.76-1.81) | 0.461 | 1.24 (0.66-2.33) | 0.510 | 1.25 (0.559-2.81) | 0.583 |
Gender | ||||||
Male | 1.34 (0.96-1.87) | 0.082 | 1.52 (0.92-2.53) | 0.103 | 1.43 (0.80-2.58) | 0.230 |
female | 1.23 (1.00-1.51) | 0.047 | 1.40 (1.02-1.92) | 0.037 | 1.19 (0.81-1.75) | 0.388 |
BMI | ||||||
≥25 | 1.31 (0.83-2.08) | 0.245 | 1.64 (0.83-3.21) | 0.152 | 1.18 (0.51-2.74) | 0.696 |
<25 | 1.24 (1.00-1.54) | 0.049 | 1.38 (1.00-1.91) | 0.049 | 1.27 (0.86-1.87) | 0.234 |
Diabetes | ||||||
Yes | 1.07 (0.70-1.62) | 0.765 | 1.09 (0.58-2.03) | 0.789 | 1.09 (0.50-2.39) | 0.825 |
No | 1.28 (1.05-1.56) | 0.014 | 1.50 (1.11-2.02) | 0.008 | 1.26 (0.88-1.80) | 0.206 |
Smoking status | ||||||
Yes | 1.20 (0.76-1.88) | 0.436 | 2.13 (1.04-4.37) | 0.038 | 0.66 (0.29-1.47) | 0.309 |
No | 1.27 (1.05-1.55) | 0.016 | 1.34 (1.00-1.79) | 0.047 | 1.43 (1.00-2.05) | 0.048 |
Alcohol status | ||||||
drinkers | 1.25 (0.93-1.69) | 0.140 | 1.26 (0.81-1.96) | 0.302 | 1.51 (0.86-2.63) | 0.150 |
Non-drinkers | 1.25 (1.00-1.55) | 0.045 | 1.53 (1.09-2.15) | 0.014 | 1.12 (0.75-1.67) | 0.583 |
Physical activity | ||||||
Yes | 1.11 (0.75-1.62) | 0.606 | 1.16 (0.65-2.07) | 0.623 | 1.12 (0.57-2.22) | 0.734 |
No | 1.28 (1.05-1.57) | 0.016 | 1.49 (1.10-2.01) | 0.011 | 1.28 (0.88-1.85) | 0.192 |
aAdjusted for age, gender, smoking and drinking status, physical activity, diabetes, and BMI.
BMI=body mass index; OR=odds ratio; CI: confidence level.
DISCUSSION
In this case-control study, we investigated the association between EN1 rs4144782 and susceptibility to OA in a Chinese population. Our results showed that EN1 rs4144782 was significantly associated with increased risk of knee OA. The results reconfirmed the close connection of BMD and OA. To the best of our knowledge, this should be the first association study of EN1 locus on risk of OA.
The emerging findings of susceptibility loci of many disease have provided insight into the early diagnosis in the past years [44, 45]. OA is considered as a multifactorial disease, resulting from the combined influence of biomechanical factors and genetic factors [46]. COL2A1 was the first gene identified to be associated with OA by genetic linkage study in 1990 [47]. Following, SNPs in gene VDR, ER, TGF-beta1, GDF5, and VNTR were found in sequence [48–51]. Recently, Gok et al [52] first reported the CA repeat length of >/= 20 in the promoter region of ADAMTS9 gene has a six-fold increase in probability for having severe OA. The most intriguing hypothesis that high systemic BMD increased OA risk conclude that genetic variations associated with high BMD will lead to a corresponding increase in OA risk, which was confirmed by the detection of SNP rs2016266, rs10226308, rs3736228, and rs10835187 [43].
The engrailed gene plays an important role in the development of segmentation course, where it is required for the formation of posterior compartments [53–55]. En-1 is in the central portion of the mouse chromosome 1, while En-2 is in the proximal portion of mouse chromosome 5 [56]. Zhang et al [57] also indicate that EN1 is a positive regulator of brown adipogenesis. It was shown that polymorphisms (rs1861972, rs1861973) of homeobox transcription factor gene ENGRAILED 2 and its haplotype A-C was associated with autistic disorder in Chinese children [58]. In current study, Our results showed that EN1 rs4144782 was significantly associated with increased risk of OA. EN1 is a homeobox gene that contributes to the development in the dorsal midbrain and anterior hindbrain of human beings [59].
SNP rs4144782 was located in the intron region of EN1 gene. Using HaploReg v4 software, we found that a G to A transform will result in the expression decrement of motifs CAC-binding-protein, EWSR1-FLI1, PU.1_disc3, SP1_disc3, SP1, STAT_disc7, TATA_disc7, and TFII-I, as well as increased expression of motifs HDAC2_disc6, TCF12_disc5, and VDR_2 [60]. RegulomeDB also shows that allele exchange of EN1 rs4144782 will affect the binding efficiency of many transcription factors [61]. These will then affect the expression of En1 gene in sequence.
There are several limitations that can be derived from the present study. Although the sample size of the current study was moderate, the number of subjects on the interaction analyses was relatively small. In addition, all the patients and controls involved in this study were enrolled from the hospital, thus inherent bias may affect the results. Also, this study just considered common variation (minor allele frequency > 0.05) like other candidate gene studies. Rare variants, which owns larger effect sizes than the average effect size of common variants, should be explored for its contribution to the development of knee OA. Considering the different genetic architecture (different minor allele) of EN1 rs4144782 in different ethnicities (Asian, European and American), results should be interpreted discreetly and further replicated in other independent population with different ethnicities. Larger-scale studies examining gene-gene and gene-environment interactions are also needed.
Conclusively, in this case-control study, we identified that EN1 rs4144782 was significantly associated with increased risk of knee OA. This conclusion contributes to the hypothesis for an association of high BMD-associated SNPs with higher knee OA risk, which will help to elucidate the connection of BMD and OA, and to perform risk prediction, early prevention, diagnosis and individualized treatment of OA. Further work is needed to determine the precise role of EN1 locus on the occurrence of OA, and how rs4144782 regulates the function of EN1 gene and regulates its downstream genes, related pathways in vitro and in vivo.
MATERIALS AND METHODS
Study population
The case-control study was conducted in accordance with STROBE (strengthening the reporting of observational studies in epidemiology) guidelines (STROBE checklist in Supplementary File 1). The sample size was calculated using Quanto software [62]. Finally, OA cases (n=500) were recruited at Renmin Hospital of Wuhan University (Wuhan, Hubei province, China) between January 2008 and April 2015 in current study. The diagnosis of knee OA was based on having a K/L grade of 2 or higher in at least one knee [63]. The representative X-ray images were presented in Supplementary File 2. Healthy controls (n=500) were randomly selected from healthy persons under routine health Screening during the same study period. Propensity-score matching was used to select controls to minimize selection bias due to perceived confounders, with the following variables as contributors to the propensity score: age, gender, smoking status, alcohol status, and physical activity. To eliminate the potential recall bias, the participants were interviewed face to face using standard questionnaire. A 5% samples were checked for the concordance. Ethical approval was granted by the Renmin Hospital of Wuhan University research ethics committee, and was conducted in compliance with the Declaration of Helsinki. Informed written consent for blood use and data publication was provided by all donors.
DNA extraction and genotyping
Peripheral venous blood (2ml) from all participants was collected by vacuum tube with ethylene diamine tetraacetic acid (EDTA). Genomic DNA was extracted from the whole blood samples of all participants using QIAamp DNA Blood Mini kit (Qiagen, Berlin, Germany) according to the manufacturer's instructions. The DNA concentration and purity were estimated by measuring ratio of the absorbance at 260 and 280 nm. The tagSNP rs4144782 was selected using the web-based software SNP info (https://snpinfo.niehs.nih.gov/). Genotyping was done with Sequenom iPLEX matrix assisted laser desorption/ionization–time-of-flight mass spectrometry technology using 15 ng DNA. The MassARRAY Typer 4.0 software was used for proper data acquisition and analysis. A random 10% of quality control samples were checked for the concordance. Laboratory personnel were blinded to the case-control status of the specimens, and to the quality control samples.
Statistical analyses
All analyses were performed using SAS version 9.2 and P values were two-sided in current study. The Hardy–Weinberg equilibrium (HWE) was assessed using a goodness-of-fit χ2 test performed to identify possible genotyping errors among the controls of each study. Categorical variables were compared between patients with knee OA and healthy controls using the χ2 test or Fisher’s exact test where appropriate. Logistic regression was used to estimate ORs and 95% confidence intervals (CIs) as a measure of the association with the susceptibility of knee OA, adjusted for the potential confounding factors, including age, gender, smoking and drinking status, physical activity, diabetes, and BMI. We also conducted subgroup analyses by age, gender, smoking and drinking status, physical activity, diabetes, and BMI.
ACKNOWLEDGMENTS
The study was support by the National Natural Science Foundation of China (81171760).
CONFLICTS OF INTEREST
The authors declare that they have no conflicts of interest.
REFERENCES
1. Birmingham K. New guidelines for osteoarthritis. Nursing older people. 2008; 20:5.
2. Martel-Pelletier J, Barr AJ, Cicuttini FM, Conaghan PG, Cooper C, Goldring MB, Goldring SR, Jones G, Teichtahl AJ, Pelletier JP. Osteoarthritis. Nature reviews Disease primers. 2016; 2:16072.
3. Malfait AM, Miller RJ. Emerging Targets for the Management of Osteoarthritis Pain. Current osteoporosis reports. 2016; 14: 260-268.
4. Yau MS, Yerges-Armstrong LM, Liu Y, Lewis CE, Duggan DJ, Renner JB, Torner J, Felson DT, McCulloch CE, Kwoh CK, Nevitt MC, Hochberg MC, Mitchell BD, et al. Genome-wide Association Study of Radiographic Knee Osteoarthritis in North American Caucasians. Arthritis Rheumatol. 2016; 69: 343-351.
5. Evans DS, Cailotto F, Parimi N, Valdes AM, Castano-Betancourt MC, Liu Y, Kaplan RC, Bidlingmaier M, Vasan RS, Teumer A, Tranah GJ, Nevitt MC, Cummings SR, et al. Genome-wide association and functional studies identify a role for IGFBP3 in hip osteoarthritis. Ann Rheum Dis. 2015; 74:1861-1867.
6. Evangelou E, Kerkhof HJ, Styrkarsdottir U, Ntzani EE, Bos SD, Esko T, Evans DS, Metrustry S, Panoutsopoulou K, Ramos YF, Thorleifsson G, Tsilidis KK, Arden N, et al. A meta-analysis of genome-wide association studies identifies novel variants associated with osteoarthritis of the hip. Ann Rheum Dis. 2014; 73:2130-2136.
7. Zeggini E, Panoutsopoulou K, Southam L, Rayner NW, Day-Williams AG, Lopes MC, Boraska V, Esko T, Evangelou E, Hoffman A, Houwing-Duistermaat JJ, Ingvarsson T, Jonsdottir I, et al. Identification of new susceptibility loci for osteoarthritis (arcOGEN): a genome-wide association study. Lancet. 2012; 380:815-823.
8. Castano Betancourt MC, Cailotto F, Kerkhof HJ, Cornelis FM, Doherty SA, Hart DJ, Hofman A, Luyten FP, Maciewicz RA, Mangino M, Metrustry S, Muir K, Peters MJ, et al. Genome-wide association and functional studies identify the DOT1L gene to be involved in cartilage thickness and hip osteoarthritis. Proc Natl Acad Sci U S A. 2012; 109:8218-8223.
9. Evangelou E, Valdes AM, Kerkhof HJ, Styrkarsdottir U, Zhu Y, Meulenbelt I, Lories RJ, Karassa FB, Tylzanowski P, Bos SD, Akune T, Arden NK, Carr A, et al. Meta-analysis of genome-wide association studies confirms a susceptibility locus for knee osteoarthritis on chromosome 7q22. Ann Rheum Dis. 2011; 70:349-355.
10. Kerkhof HJ, Lories RJ, Meulenbelt I, Jonsdottir I, Valdes AM, Arp P, Ingvarsson T, Jhamai M, Jonsson H, Stolk L, Thorleifsson G, Zhai G, Zhang F, et al. A genome-wide association study identifies an osteoarthritis susceptibility locus on chromosome 7q22. Arthritis Rheum. 2010; 62:499-510.
11. Zhai G, van Meurs JB, Livshits G, Meulenbelt I, Valdes AM, Soranzo N, Hart D, Zhang F, Kato BS, Richards JB, Williams FM, Inouye M, Kloppenburg M, et al. A genome-wide association study suggests that a locus within the ataxin 2 binding protein 1 gene is associated with hand osteoarthritis: the Treat-OA consortium. J Med Genet. 2009; 46:614-616.
12. Valdes AM, Loughlin J, Timms KM, van Meurs JJ, Southam L, Wilson SG, Doherty S, Lories RJ, Luyten FP, Gutin A, Abkevich V, Ge D, Hofman A, et al. Genome-wide association scan identifies a prostaglandin-endoperoxide synthase 2 variant involved in risk of knee osteoarthritis. Am J Hum Genet. 2008; 82:1231-1240.
13. Abel K, Reneland R, Kammerer S, Mah S, Hoyal C, Cantor CR, Nelson MR, Braun A. Genome-wide SNP association: identification of susceptibility alleles for osteoarthritis. Autoimmun Rev. 2006; 5:258-263.
14. Spector TD, Reneland RH, Mah S, Valdes AM, Hart DJ, Kammerer S, Langdown M, Hoyal CR, Atienza J, Doherty M, Rahman P, Nelson MR, Braun A. Association between a variation in LRCH1 and knee osteoarthritis: a genome-wide single-nucleotide polymorphism association study using DNA pooling. Arthritis Rheum. 2006; 54:524-532.
15. Zhang R, Yao J, Xu P, Ji B, Luck JV, Chin B, Lu S, Kelsoe JR, Ma J. A comprehensive meta-analysis of association between genetic variants of GDF5 and osteoarthritis of the knee, hip and hand. Inflammation research. 2015; 64:405-414.
16. Pan F, Tian J, Winzenberg T, Ding C, Jones G. Association between GDF5 rs143383 polymorphism and knee osteoarthritis: an updated meta-analysis based on 23,995 subjects. BMC musculoskeletal disorders. 2014; 15:404.
17. Syddall CM, Reynard LN, Young DA, Loughlin J. The identification of trans-acting factors that regulate the expression of GDF5 via the osteoarthritis susceptibility SNP rs143383. PLoS Genet. 2013; 9:e1003557.
18. Liu J, Cai W, Zhang H, He C, Deng L. Rs143383 in the growth differentiation factor 5 (GDF5) gene significantly associated with osteoarthritis (OA)-a comprehensive meta-analysis. Int J Med Sci. 2013; 10:312-319.
19. Hao SW, Jin QH. Association between the +104T/C polymorphism in the 5'UTR of GDF5 and susceptibility to knee osteoarthritis: a meta-analysis. Mol Med Rep. 2013; 7:485-488.
20. Shin MH, Lee SJ, Kee SJ, Song SK, Kweon SS, Park DJ, Park YW, Lee SS, Kim TJ. Genetic association analysis of GDF5 and ADAM12 for knee osteoarthritis. Joint Bone Spine. 2012; 79:488-491.
21. Valdes AM, Evangelou E, Kerkhof HJ, Tamm A, Doherty SA, Kisand K, Kerna I, Uitterlinden A, Hofman A, Rivadeneira F, Cooper C, Dennison EM, Zhang W, et al. The GDF5 rs143383 polymorphism is associated with osteoarthritis of the knee with genome-wide statistical significance. Ann Rheum Dis. 2011; 70:873-875.
22. Egli RJ, Southam L, Wilkins JM, Lorenzen I, Pombo-Suarez M, Gonzalez A, Carr A, Chapman K, Loughlin J. Functional analysis of the osteoarthritis susceptibility-associated GDF5 regulatory polymorphism. Arthritis Rheum. 2009; 60:2055-2064.
23. Evangelou E, Chapman K, Meulenbelt I, Karassa FB, Loughlin J, Carr A, Doherty M, Doherty S, Gomez-Reino JJ, Gonzalez A, Halldorsson BV, Hauksson VB, Hofman A, et al. Large-scale analysis of association between GDF5 and FRZB variants and osteoarthritis of the hip, knee, and hand. Arthritis Rheum. 2009; 60:1710-1721.
24. Valdes AM, Spector TD, Doherty S, Wheeler M, Hart DJ, Doherty M. Association of the DVWA and GDF5 polymorphisms with osteoarthritis in UK populations. Ann Rheum Dis. 2009; 68:1916-1920.
25. Vaes RB, Rivadeneira F, Kerkhof JM, Hofman A, Pols HA, Uitterlinden AG, van Meurs JB. Genetic variation in the GDF5 region is associated with osteoarthritis, height, hip axis length and fracture risk: the Rotterdam study. Ann Rheum Dis. 2009; 68:1754-1760.
26. Martinez A, Varade J, Lamas JR, Fernandez-Arquero M, Jover JA, de la Concha EG, Fernandez-Gutierrez B, Urcelay E. GDF5 Polymorphism associated with osteoarthritis: risk for rheumatoid arthritis. Ann Rheum Dis. 2008; 67:1352-1353.
27. Chapman K, Takahashi A, Meulenbelt I, Watson C, Rodriguez-Lopez J, Egli R, Tsezou A, Malizos KN, Kloppenburg M, Shi D, Southam L, van der Breggen R, Donn R, et al. A meta-analysis of European and Asian cohorts reveals a global role of a functional SNP in the 5' UTR of GDF5 with osteoarthritis susceptibility. Hum Mol Genet. 2008; 17:1497-1504.
28. Southam L, Rodriguez-Lopez J, Wilkins JM, Pombo-Suarez M, Snelling S, Gomez-Reino JJ, Chapman K, Gonzalez A, Loughlin J. An SNP in the 5'-UTR of GDF5 is associated with osteoarthritis susceptibility in Europeans and with in vivo differences in allelic expression in articular cartilage. Hum Mol Genet. 2007; 16:2226-2232.
29. Miyamoto Y, Mabuchi A, Shi D, Kubo T, Takatori Y, Saito S, Fujioka M, Sudo A, Uchida A, Yamamoto S, Ozaki K, Takigawa M, Tanaka T, et al. A functional polymorphism in the 5' UTR of GDF5 is associated with susceptibility to osteoarthritis. Nat Genet. 2007; 39:529-533.
30. Inanir A, Yigit S, Tural S, Cecen O, Yildirim E. MTHFR gene C677T mutation and ACE gene I/D polymorphism in Turkish patients with osteoarthritis. Dis Markers. 2013; 34:17-22.
31. Etokebe GE, Jotanovic Z, Mihelic R, Mulac-Jericevic B, Nikolic T, Balen S, Sestan B, Dembic Z. Susceptibility to large-joint osteoarthritis (hip and knee) is associated with BAG6 rs3117582 SNP and the VNTR polymorphism in the second exon of the FAM46A gene on chromosome 6. Journal of orthopaedic research. 2015; 33:56-62.
32. Reynard LN, Bui C, Syddall CM, Loughlin J. CpG methylation regulates allelic expression of GDF5 by modulating binding of SP1 and SP3 repressor proteins to the osteoarthritis susceptibility SNP rs143383. Hum Genet. 2014; 133:1059-1073.
33. Lee SW, Song JH, Choi WS, Yoon JH, Kim O, Park YG, Nam SW, Lee JY, Park WS. The single nucleotide polymorphism (SNP) of the estrogen receptor-beta gene, rs1256049, is associated with knee osteoarthritis in Korean population. Knee. 2014; 21:242-246.
34. Nakajima M, Shi D, Dai J, Tsezou A, Zheng M, Norman PE, Takahashi A, Ikegawa S, Jiang Q. Replication studies in various ethnic populations do not support the association of the HIF-2alpha SNP rs17039192 with knee osteoarthritis. Nat Med. 2011; 17:26-27; author reply 27-29.
35. Mototani H, Iida A, Nakajima M, Furuichi T, Miyamoto Y, Tsunoda T, Sudo A, Kotani A, Uchida A, Ozaki K, Tanaka Y, Nakamura Y, Tanaka T, et al. A functional SNP in EDG2 increases susceptibility to knee osteoarthritis in Japanese. Hum Mol Genet. 2008; 17:1790-1797.
36. Iida A, Kizawa H, Nakamura Y, Ikegawa S. High-resolution SNP map of ASPN, a susceptibility gene for osteoarthritis. J Hum Genet. 2006; 51:151-154.
37. Zheng HF, Forgetta V, Hsu YH, Estrada K, Rosello-Diez A, Leo PJ, Dahia CL, Park-Min KH, Tobias JH, Kooperberg C, Kleinman A, Styrkarsdottir U, Liu CT, et al. Whole-genome sequencing identifies EN1 as a determinant of bone density and fracture. Nature. 2015; 526:112-117.
38. Belmonte-Serrano MA, Bloch DA, Lane NE, Michel BE, Fries JF. The relationship between spinal and peripheral osteoarthritis and bone density measurements. J Rheumatol. 1993; 20:1005-1013.
39. Dequeker J, Mokassa L, Aerssens J. Bone density and osteoarthritis. The Journal of rheumatology Supplement. 1995; 43:98-100.
40. Hamerman D, Stanley ER. Implications of increased bone density in osteoarthritis. J Bone Miner Res. 1996; 11:1205-1208.
41. Hardcastle SA, Dieppe P, Gregson CL, Davey Smith G, Tobias JH. Osteoarthritis and bone mineral density: are strong bones bad for joints? BoneKEy reports. 2015; 4:624.
42. Spector TD, MacGregor AJ. Risk factors for osteoarthritis: genetics. Osteoarthritis Cartilage. 2004; 12:S39-44.
43. Yerges-Armstrong LM, Yau MS, Liu Y, Krishnan S, Renner JB, Eaton CB, Kwoh CK, Nevitt MC, Duggan DJ, Mitchell BD, Jordan JM, Hochberg MC, Jackson RD. Association analysis of BMD-associated SNPs with knee osteoarthritis. J Bone Miner Res. 2014; 29:1373-1379.
44. van der Steen SL, Riedijk SR, Verhagen-Visser J, Govaerts LC, Srebniak MI, Van Opstal D, Joosten M, Knapen MF, Tibben A, Diderich KE, Galjaard RJ. The Psychological Impact of Prenatal Diagnosis and Disclosure of Susceptibility Loci: First Impressions of Parents' Experiences. J Genet Couns. 2016; 25: 1227-1234.
45. Phipps AI, Newcomb PA, Garcia-Albeniz X, Hutter CM, White E, Fuchs CS, Hazra A, Ogino S, Nan H, Ma J, Campbell PT, Figueiredo JC, Peters U, et al. Association between colorectal cancer susceptibility loci and survival time after diagnosis with colorectal cancer. Gastroenterology. 2012; 143:51-54 e54.
46. MacGregor AJ, Li Q, Spector TD, Williams FM. The genetic influence on radiographic osteoarthritis is site specific at the hand, hip and knee. Rheumatology (Oxford). 2009; 48:277-280.
47. Knowlton RG, Katzenstein PL, Moskowitz RW, Weaver EJ, Malemud CJ, Pathria MN, Jimenez SA, Prockop DJ. Genetic linkage of a polymorphism in the type II procollagen gene (COL2A1) to primary osteoarthritis associated with mild chondrodysplasia. N Engl J Med. 1990; 322:526-530.
48. Kirk KM, Doege KJ, Hecht J, Bellamy N, Martin NG. Osteoarthritis of the hands, hips and knees in an Australian twin sample--evidence of association with the aggrecan VNTR polymorphism. Twin research. 2003; 6:62-66.
49. Yamada Y. Association of a Leu(10)-->Pro polymorphism of the transforming growth factor-beta1 with genetic susceptibility to osteoporosis and spinal osteoarthritis. Mech Ageing Dev. 2000; 116:113-123.
50. Ushiyama T, Ueyama H, Inoue K, Nishioka J, Ohkubo I, Hukuda S. Estrogen receptor gene polymorphism and generalized osteoarthritis. J Rheumatol. 1998; 25:134-137.
51. Keen RW, Hart DJ, Lanchbury JS, Spector TD. Association of early osteoarthritis of the knee with a Taq I polymorphism of the vitamin D receptor gene. Arthritis Rheum. 1997; 40:1444-1449.
52. Gok K, Cemeroglu O, Cakirbay H, Gunduz E, Acar M, Cetin EN, Gunduz M, Demircan K. Relationship between cytosine-adenine repeat polymorphism of ADAMTS9 gene and clinical and radiologic severity of knee osteoarthritis. International journal of rheumatic diseases. 2016 May 27. [Epub ahead of print].
53. Lawrence PA, Johnston P. On the role of the engrailed+ gene in the internal organs of Drosophila. EMBO J. 1984; 3:2839-2844.
54. Kornberg T. Engrailed: a gene controlling compartment and segment formation in Drosophila. Proc Natl Acad Sci U S A. 1981; 78:1095-1099.
55. Lawrence PA, Morata G. Compartments in the wing of Drosophila: a study of the engrailed gene. Dev Biol. 1976; 50:321-337.
56. Joyner AL, Martin GR. En-1 and En-2, two mouse genes with sequence homology to the Drosophila engrailed gene: expression during embryogenesis. Genes Dev. 1987; 1:29-38.
57. Zhang C, Weng Y, Shi F, Jin W. The Engrailed-1 Gene Stimulates Brown Adipogenesis. Stem cells international. 2016; 2016:7369491.
58. Yang P, Lung FW, Jong YJ, Hsieh HY, Liang CL, Juo SH. Association of the homeobox transcription factor gene ENGRAILED 2 with autistic disorder in Chinese children. Neuropsychobiology. 2008; 57:3-8.
59. Webb BT, Sullivan PF, Skelly T, van den Oord EJ. Model-based gene selection shows engrailed 1 is associated with antipsychotic response. Pharmacogenet Genomics. 2008; 18:751-759.
60. Ward LD, Kellis M. HaploReg v4: systematic mining of putative causal variants, cell types, regulators and target genes for human complex traits and disease. Nucleic Acids Res. 2016; 44:D877-881.
61. Boyle AP, Hong EL, Hariharan M, Cheng Y, Schaub MA, Kasowski M, Karczewski KJ, Park J, Hitz BC, Weng S, Cherry JM, Snyder M. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 2012; 22:1790-1797.
62. Gauderman W, Morrison J. QUANTO 1.1: A computer program for power and sample size calculations for genetic-epidemiology studies. http://hydrauscedu/gxe/. 2006.
63. Kellgren JH, Lawrence JS. Radiological assessment of osteo-arthrosis. Ann Rheum Dis. 1957; 16:494-502.
