Association analysis between rs6184 and rs6180 polymorphisms of growth hormone receptor gene regarding skeletal-facial profile in a Colombian population

Association analysis between rs6184 and rs6180 polymorphisms of growth hormone receptor gene... Summary Background/Objective There is strong evidence that genetic factors may affect the craniofacial morphology. This study aimed to examine the association between the rs6184 and rs6180 polymorphic variants of the growth hormone receptor (GHR) gene and skeletal-facial profile in a Colombian population. Subjects/Methods Saliva samples from 306 individuals ranging in age from 15 to 53 (mean 24.33) years were collected. Cephalometric parameters were used to categorize the participants as Class I, Class II, or Class III skeletal-facial profile. The polymerase chain reaction-restriction fragment length polymorphism method was used to identify genotypes of the rs6184 and rs6180 single nucleotide polymorphisms (SNPs). The association of polymorphisms with the skeletal-facial profile was assessed separately and adjusted for confounding using a multivariate binary logistic regression model, alongside with analysis of linkage disequilibrium and haplotype associations. Results Although individuals carrying the CA genotype of the rs6184 SNP showed both significantly decreased values for ANB angle and increased measures concerning mandibular body length and mandibular length, no significant differences amongst genotype groups of rs6180 SNP were observed. Moreover, chi-square test and logistic regression analysis revealed that the CA genotype of rs6184 SNP and the A-A haplotype were highly associated with Class III skeletal-facial profile. Conclusions Although these results do not support that rs6180 SNP could be identified as a predictor for skeletal-facial profile, they suggest that the allele A of rs6184 SNP alone or in combination with other SNPs in the GHR gene yields significant horizontal and longitudinal variations of the mandibular morphology and might be a strong/independent prognostic indicator for Class III skeletal-facial profile in the present population. Introduction Variations in craniofacial morphology are evident during development and growth (1). From the orthodontic viewpoint, the skeletal-facial profile refers to the relative sagittal and vertical relationships between the cranial base, middle face (maxilla), and lower face (mandible) (2). The synchronic development of both jaws will allow a harmonious maxillo-mandibular growth (Class I facial profile) (3, 4). On the contrast, the posterior or anterior mandibular relationship with respect to the maxilla in a greater proportion than stated as harmonic has been classified as Class II and Class III skeletal-facial profiles, respectively (2, 4–6). Although normal facial profile can be attained and maintained in spite of the variations in the facial pattern seen as a result of the change in size, position, rotation of cranial base, maxilla, and/or mandible or cumulative effect of any two or all three, in some individuals, a disturbed pattern of differential growth may lead to a maxillo-mandibular discrepancy (7). It has been acknowledged that the human craniofacial morphology constitutes a complex physical trait that may be determined by genetic, environmental, mechanical, and epigenetic factors (8), and that from their relative interplay may result in the establishment of a craniofacial dysmorphosis (9, 10). Whilst it has been established that the hereditary pattern is heterogeneous and diverse transmission models have been proposed (11–13), the advances in clinical genetics have allowed us to identify the existence of a genetic predisposition for skeletal-facial profile (11, 14–20). Likewise, it has been reported that single nucleotide polymorphisms (SNPs) in genes which encode mediators of bone growth and metabolism may be associated with variations in the craniofacial profile (16, 21–28). Taking into account that growth hormone (GH) plays a major role in regulating both growth and metabolism during childhood/adolescence through the binding to its specific cell surface growth hormone receptor (GHR) (29), it is clear that mutations in functionally critical areas of GHR gene may influence the growth and development of the craniofacial complex (30, 31). The GHR gene is located at chromosome region 5p13.1-p12 (OMIM 600946) and consists of 10 exons, nine of which are coding (32). Exon 2 encodes the signal peptide, exons 3 to 7 the extracellular domain, exon 8 the transmembrane domain, and exon 9 as well as part of exon 10 the intracellular domain (33). Increasing evidence suggests that the polymorphic variants rs6184 and rs6180 in exon 10 of the GHR gene might be considered as genetic factors of mandibular morphogenesis (16, 21–24, 31). However, the association of these two polymorphic variants with different skeletal-facial profiles has not been described. Moreover, the results of their association with mandibular growth have been conflictive, because while some researchers suggest that the polymorphism rs6184 may be associated with mandibular height growth (16, 21–23), others have postulated that the minor allele has an inhibitory effect on mandibular growth (24). Although the available information regarding the rs6180 polymorphic variant is sparse (16, 23), it has been suggested that it may be one of the factors that account for differences of anthropometric measurements (28, 34). Given that identification of genetic variants that lead to a specific skeletal-facial profile would enable a more effective prognosis and treatment of cranial dysmorphosis, this study aimed to examine the association between the rs6184 and rs6180 polymorphic variants of the GHR gene and the skeletal-facial profile in a Colombian population. Subjects and methods Study design, study population, and inclusion/exclusion criteria This cross-sectional, observational, analytic study was conducted in accordance with the ethical guidelines of the Helsinki Declaration, and ethical approval was obtained from the Ethics Committee for Human Studies of the Faculty of Dentistry of the University of Antioquia in Medellín (Colombia). The sample size was calculated on the basis of a previous study regarding the association of GHR gene polymorphisms with mandibular height growth (23). It was increased by 20% to safeguard the estimations at an optimal level of precision (5%) against the potential effect of sample size reduction due to exclusions and dropouts. Thus, the theoretical sample size for clinical screening was set to 200 individuals to determine significant differences in outcomes at the 95% confidence level, with an α value of 0.05 and 80% power. However, every effort was made to recruit the maximum number of participants so that the study sample included a total of 306 participants from the population of individuals that sought treatment and/or consultation at the Graduate Orthodontic as well as Maxillofacial Surgery Clinics. Prior to enrolment, the purpose was fully explained, and a signed informed consent was obtained individually from all recruits or the parents/custodian of those fewer than 18 years of age. Participants were privately interviewed to obtain medical and demographic information and underwent a clinical examination synchronously by two trained and calibrated raters (GA J-A, VA A-G) to rule out the presence of anatomic, pathologic, and/or functional conditions that could affect the results. Eligibility criteria included healthy unrelated individuals of Colombian ancestry with full permanent dentition (except for third molars), older than 15 years (12), who had completed their growth and development as evidenced by the cervical vertebral maturation (CVM) stage (35). Furthermore, exclusion criteria applied were pregnancy, ongoing or preceding orthodontic/orthopaedic therapy, as well as preceding history of maxillofacial surgery, facial fractures, jaw tumors and cysts, and congenital disorders of the jaws that could affect the craniofacial growth pattern. Craniofacial measurements To analyse skeletal-facial profile, lateral cephalograms were obtained with a digital pan/ceph system [Orthopos® XG 5 orthopantomograph (Sirona Dental Systems®, Bensheim, Germany)] under standardized conditions (73 kVp,15 mA, exposure time of 9.4 seconds, focus-sensor distance1.71 Mt, and magnification factor 1:1 for all images). The volunteers stood with the Frankfort horizontal plane parallel to the floor with their lips in rest position. Using Radiocef Studio 2® software (Radio Memory®, Belo Horizonte, Brazil), the cephalometric tracings and landmark identifications were digitized and analysed simultaneously by two observers (MI P-C, GA F-M). When discordant measure data were established between the examiners, new evaluations were performed and any further controversy was resolved by consensus. Based on previous studies (2, 36–40), eleven cephalometric parameters were used to categorize the participants as having a Class I (n = 112), Class II (n = 150), or Class III (n = 44) skeletal-facial profile (Supplementary Table 1). In some cases, a measurement was prioritized over another to classify the skeletal-facial profile of the study population. In those cases, the most appropriate analysis according to the clinical aspect of each participant was chosen. The Class I group consisted of individuals with SNA angle values within 82 ± 2 degrees; SNB angle values of 80 ± 2 degrees; ANB angle values of 2 ± 2 degrees (36); A ┴ Na-FH values of 0.4 ± 2.3 mm for females and 1.1 ± 2.7 mm for males; and Pog ┴ Na-FH values from −2 to +4 mm (37). At the same time, the Class II group included participants with SNA angle values greater than 84 degrees; SNB angle values less than 78 degrees; ANB angle values greater than 4 degrees (36); A ┴ Na-FH values greater than 1 mm; and Pog ┴ Na-FH values inferior to −2 mm (37). Otherwise, the Class III group consisted of volunteers with SNA angle values less than 80 degrees; SNB angle values greater than 82 degrees; ANB angle values less than 0 degrees (36); A ┴ Na-FH values less than 1 mm; Pog ┴ Na-FH values greater than 4 mm (37). Synchronously both mandibular body length (Go-Mn distance) and maxillary length (ANS-PNS distance) were assessed for all individuals following previously described criteria (2, 38) in order to determine the sagittal mandibular and maxillary sizes for each diagnostic group, respectively. When indicated, data were calculated separately for male and female individuals so as to determine the skeletal-facial profile according to the sexual dimorphism. In specific cases which some of the measurements were outside the limits previously established for each parameter, the results for both ANB and the distances to Na perpendicular, which could better represent the skeletal-facial profile of the individual, and that would be in agreement with the linear measurements for classifying the mandibular and the maxillary dimensions, were prioritized. DNA isolation and genotyping assay For DNA isolation, 5 ml of unstimulated whole saliva was collected from each individual into a 50 ml sterile plastic centrifuge tube (Greiner Bio-one®, Frickenhausen, Germany). After collection, whole saliva was clarified by centrifugation for 10 minutes at 800 × g. The obtained pellet was dispersed by using a vortex for 15 seconds, and 200 µl were used for DNA extraction using the QIAamp® DNA mini kit (Qiagen Sciences®, Germantown, Maryland, USA). DNA was stored frozen at −20°C until use. Polymorphic sites were determined by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method. Specific primers used for determining the rs6184 (41) and rs6180 (34) polymorphic sites in the GHR gene are listed in Supplementary Table 2. PCR amplification was carried out in a 96-well thermal cycler (Mastercycler® gradient, Eppendorf®, Hamburg, Germany). PCR products were run in 2% agarose gel electrophoresis in TBE (Tris–Borate–EDTA) buffer, stained with 0.5 mg/ml ethidium bromide and visualized in an ultraviolet transilluminator. The sizes of the expected fragments before enzymatic digestion were 1037 bp for rs6184 and 672 bp for rs6180 SNPs. As a molecular size marker, 100-to-1500 bp in multiples of 100 bp DNA ladder was used. For negative control, DNA sample was replaced by sterile water. PCR products from rs6184 and rs6180 polymorphic sites were respectively digested with Eco147I (Thermo Scientific®, Barrington, IL, USA) or HpyCH4V (New England Biolabs®, Ipswich, MA, USA) restriction enzymes. Fragments were analysed in agarose electrophoresis as described above. Genotypes were determined as CC (808, 229 bp), CA (1037, 808, 229 bp), or AA (1037 bp) for rs6184 SNP; and AA (317, 275, 80 bp), CA (397, 317, 275, 80 bp), or CC (397, 275 bp) for rs6180 SNP. Statistical analysis and data management Data collected were analysed in standard statistical software (IBM® SPSS® 23.0, Chicago, IL, USA). The intra-raters agreement for cephalometric measures, CVM stage, as well as for PCR assays was determined through double evaluations for each specific test performed by the same observers with 10 participants selected randomly using a computer-generated randomization code (Epidat 4.0®, PAHO/WHO, Washington, DC, USA). The interval between tests 1 and 2 was 6 months. For comparisons, the reliability between the two series of data was assessed by using the Cohen’s kappa statistic (κ) for categorical variables and the intraclass correlation coefficient (ICC) for quantitative variables. A value less than 0.40 indicated poor reproducibility, 0.40–0.80 indicated fair to good reproducibility, and greater than 0.80 indicated excellent reproducibility. All grouped data were tested for normality using the Kolmogorov–Smirnov and Shapiro–Wilk tests when indicated. Because the data were normally distributed, statistical tests were performed using parametric methods. Statistical analysis was then performed on three levels: First, between-group comparisons were explored in bivariate analyses in order to detect differences regarding demographic, clinical, and cephalometric parameters, as well as for genetic variants, as well as to identify potential predictor variables for association with skeletal-facial profile. Pearson’s chi-square test (χ2) or Fisher’s exact test (when frequency was less than 5) was used for categorical variables, and one-way analysis of variance (ANOVA) with Bonferroni multiple-comparison or unpaired t tests was applied to determine differences amongst continuous data. Furthermore, deviation from Hardy–Weinberg equilibrium (HWE) was assessed by goodness-of-fit by comparing the rs6184 and rs6180 genotype frequencies with those expected on the basis of the observed alleles using χ2 critical value test with 1 degree of freedom. Linkage disequilibrium (LD) amongst both SNPs and haplotype frequencies/distribution was further analysed using Haploview 4.2 software (MIT/Harvard Broad Institute, Cambridge, Massachusetts, USA). LD was measured based upon calculating disequilibrium (D’) and correlation (r2) coefficients values. Second, univariate analysis amongst significant genetic predictors with skeletal-facial profile was conducted to assess the association as estimated by the odds ratio (OR) and 95% confidence interval (CI). Positive associations existed when the OR was greater than 2 and the confidence range did not include 1.0. Third, the strength and the independence of the association were further analysed by multivariate binary logistic regression analysis, whilst adjusting for covariables with a level of significance less than 0.20 in the bivariate analysis which were categorized according to the mean age obtained from all participants (i.e. less than 24.33 versus 24.33 or more years), the history of digital-sucking habit (i.e. yes versus no), and the history of oral-breathing habit (i.e. yes versus no). In this model, P < 0.05 was used as the entry criterion, whereas P > 0.10 was the removal criterion. The calibration and discrimination ability of the multivariate model was evaluated through the Hosmer–Lemeshow goodness-of-fit statistic and the c-statistic tests, respectively. All tests were two-sided and statistical significance was assumed a P-value of less than 0.05. Results Reproducibility of the measurements Overall, intra-raters reproducibility was excellent not only for all cephalometric measurements recorded simultaneously per patient by the same two examiners (ICC ranging from 0.901 to 0.998, all P < 0.001), but also for CVM stage (κ = 1.00). Also, intra-observer agreement was excellent for genotyping assays (κ = 1.00). Demographic, clinical, and cephalometric characteristics of the study population Bivariate comparisons between demographic and clinical variables as well as cephalometric measurements assessed from participants recruited for this study are outlined in Table 1. As can be seen from this table, with respect to the evaluated variables, the only significant variable for association with skeletal-facial profile was the oral-breathing habit, since it was significantly more common (P = 0.010, χ2 test) for Class II and Class III profiles than in Class I profile so that it was considered as a confounder of the association between genetic findings and the skeletal-facial profile. In addition, although there were no significant differences between skeletal-facial profiles with respect to age and digital-sucking habit (P > 0.05, one-way ANOVA and χ2 tests), these two latter variables were considered additional confounding variables as they met the criteria to be included in the multivariate analysis model (P < 0.20). As also depicted in Table 1, although there were no significant differences between the groups with respect to mean values of cranial base length, not surprisingly all maxillary/mandibular measurements were statistically different between them (all P values <0.05, one-way ANOVA). Table 1. Bivariate comparisons of demographic, functional, anatomic, and cephalometric parameters obtained from the study population according to skeletal-facial profile [n (%) or mean ± SD]. Parameters Skeletal-facial profile P-Value Class I (n = 112) Class II (n = 150) Class III (n = 44) Gender Male 40 (35.7) 62 (41.3) 21 (47.7) 0.358* Female 72 (64.3) 88 (58.7) 23 (52.3) Age 24.00 ± 7.98 25.14 ± 8.88 22.43 ± 7.39 0.148** History of digital-sucking habit Yes 20 (17.9) 31 (20.7) 3 (6.8) 0.106* No 92 (82.1) 119 (79.3) 41 (93.2) History of lip-sucking habit Yes 23 (20.5) 28 (18.7) 10 (22.7) 0.822* No 89 (79.5) 122 (81.3) 34 (77.3) History of prolonged pacifier habit Yes 16 (14.3) 29 (19.3) 6 (13.6) 0.469* No 96 (85.7) 121 (80.7) 38 (86.4) History of adenoidectomy and/or tonsillectomy Yes 5 (4.5) 8 (5.3) 1 (2.3) 0.692* No 107 (95.5) 142 (94.7) 43 (97.7) History of oral-breathing habit Yes 22 (19.6) 51 (34.0)a 15 (34.1) 0.028* No 90 (80.4) 99 (66.0) 29 (65.9) Dental crowding Yes 61 (54.5) 78 (52.0) 23 (52.3) 0.921* No 51 (45.5) 72 (48.0) 21 (47.7) Dental spacing Yes 17 (15.2) 11 (7.3) 7 (15.9) 0.086* No 95 (84.8) 139 (92.7) 37 (84.1) S-Na distance 65.42 ± 3.29 64.96 ± 3.42 65.03 ± 3.46 0.262** SNA angle 81.94 ± 3.31 83.22 ± 4.07b 81.38 ± 3.02 0.002** A ┴ Na-FH 0.40 ± 2.59 1.56 ± 3.43b −0.57 ± 3.34 <0.001** Pog ┴ Na-FH −1.66 ± 4.98 −6.71 ± 5.89b 3.77 ± 6.33c <0.001** SNB angle 79.72 ± 3.25 76.93 ± 7.43b 83.12 ± 3.78c <0.001** ANS-PNS distance 50.08 ± 2.69 50.88 ± 3.23 48.96 ± 3.26d 0.001** Go-Mn distance 70.97 ± 4.37 68.86 ± 4.25b 73.59 ± 5.13c <0.001** ANB angle 2.22 ± 1.18 5.80 ± 1.76b −1.60 ± 2.44c <0.001** Cd-Gn distance 110.71 ± 6.10 108.44 ± 6.20b 116.47 ± 7.45c <0.001** Cd-Go distance 54.29 ± 4.85 52.94 ± 5.27 55.90 ± 5.03d 0.002** Ar-Go-Mn angle 117.50 ± 13.48 120.20 ± 8.90 123.31 ± 10.66e 0.010** Parameters Skeletal-facial profile P-Value Class I (n = 112) Class II (n = 150) Class III (n = 44) Gender Male 40 (35.7) 62 (41.3) 21 (47.7) 0.358* Female 72 (64.3) 88 (58.7) 23 (52.3) Age 24.00 ± 7.98 25.14 ± 8.88 22.43 ± 7.39 0.148** History of digital-sucking habit Yes 20 (17.9) 31 (20.7) 3 (6.8) 0.106* No 92 (82.1) 119 (79.3) 41 (93.2) History of lip-sucking habit Yes 23 (20.5) 28 (18.7) 10 (22.7) 0.822* No 89 (79.5) 122 (81.3) 34 (77.3) History of prolonged pacifier habit Yes 16 (14.3) 29 (19.3) 6 (13.6) 0.469* No 96 (85.7) 121 (80.7) 38 (86.4) History of adenoidectomy and/or tonsillectomy Yes 5 (4.5) 8 (5.3) 1 (2.3) 0.692* No 107 (95.5) 142 (94.7) 43 (97.7) History of oral-breathing habit Yes 22 (19.6) 51 (34.0)a 15 (34.1) 0.028* No 90 (80.4) 99 (66.0) 29 (65.9) Dental crowding Yes 61 (54.5) 78 (52.0) 23 (52.3) 0.921* No 51 (45.5) 72 (48.0) 21 (47.7) Dental spacing Yes 17 (15.2) 11 (7.3) 7 (15.9) 0.086* No 95 (84.8) 139 (92.7) 37 (84.1) S-Na distance 65.42 ± 3.29 64.96 ± 3.42 65.03 ± 3.46 0.262** SNA angle 81.94 ± 3.31 83.22 ± 4.07b 81.38 ± 3.02 0.002** A ┴ Na-FH 0.40 ± 2.59 1.56 ± 3.43b −0.57 ± 3.34 <0.001** Pog ┴ Na-FH −1.66 ± 4.98 −6.71 ± 5.89b 3.77 ± 6.33c <0.001** SNB angle 79.72 ± 3.25 76.93 ± 7.43b 83.12 ± 3.78c <0.001** ANS-PNS distance 50.08 ± 2.69 50.88 ± 3.23 48.96 ± 3.26d 0.001** Go-Mn distance 70.97 ± 4.37 68.86 ± 4.25b 73.59 ± 5.13c <0.001** ANB angle 2.22 ± 1.18 5.80 ± 1.76b −1.60 ± 2.44c <0.001** Cd-Gn distance 110.71 ± 6.10 108.44 ± 6.20b 116.47 ± 7.45c <0.001** Cd-Go distance 54.29 ± 4.85 52.94 ± 5.27 55.90 ± 5.03d 0.002** Ar-Go-Mn angle 117.50 ± 13.48 120.20 ± 8.90 123.31 ± 10.66e 0.010** *Two-sided Pearson χ2 test. **One-way ANOVA test. aStatistically significant difference (P = 0.01, χ2 test) as compared with Class I skeletal-facial profile. bStatistically significant difference (P < 0.05, Bonferroni multiple-comparison test) as compared with Class I/Class III skeletal-facial profiles. cStatistically significant difference (P < 0.05, Bonferroni multiple-comparison test) as compared with Class I/Class II skeletal-facial profiles. dStatistically significant difference (P < 0.05, Bonferroni multiple-comparison test) as compared with Class II skeletal-facial profile. eStatistically significant difference (P < 0.05, Bonferroni multiple-comparison test) as compared with Class I skeletal-facial profile. View Large Table 1. Bivariate comparisons of demographic, functional, anatomic, and cephalometric parameters obtained from the study population according to skeletal-facial profile [n (%) or mean ± SD]. Parameters Skeletal-facial profile P-Value Class I (n = 112) Class II (n = 150) Class III (n = 44) Gender Male 40 (35.7) 62 (41.3) 21 (47.7) 0.358* Female 72 (64.3) 88 (58.7) 23 (52.3) Age 24.00 ± 7.98 25.14 ± 8.88 22.43 ± 7.39 0.148** History of digital-sucking habit Yes 20 (17.9) 31 (20.7) 3 (6.8) 0.106* No 92 (82.1) 119 (79.3) 41 (93.2) History of lip-sucking habit Yes 23 (20.5) 28 (18.7) 10 (22.7) 0.822* No 89 (79.5) 122 (81.3) 34 (77.3) History of prolonged pacifier habit Yes 16 (14.3) 29 (19.3) 6 (13.6) 0.469* No 96 (85.7) 121 (80.7) 38 (86.4) History of adenoidectomy and/or tonsillectomy Yes 5 (4.5) 8 (5.3) 1 (2.3) 0.692* No 107 (95.5) 142 (94.7) 43 (97.7) History of oral-breathing habit Yes 22 (19.6) 51 (34.0)a 15 (34.1) 0.028* No 90 (80.4) 99 (66.0) 29 (65.9) Dental crowding Yes 61 (54.5) 78 (52.0) 23 (52.3) 0.921* No 51 (45.5) 72 (48.0) 21 (47.7) Dental spacing Yes 17 (15.2) 11 (7.3) 7 (15.9) 0.086* No 95 (84.8) 139 (92.7) 37 (84.1) S-Na distance 65.42 ± 3.29 64.96 ± 3.42 65.03 ± 3.46 0.262** SNA angle 81.94 ± 3.31 83.22 ± 4.07b 81.38 ± 3.02 0.002** A ┴ Na-FH 0.40 ± 2.59 1.56 ± 3.43b −0.57 ± 3.34 <0.001** Pog ┴ Na-FH −1.66 ± 4.98 −6.71 ± 5.89b 3.77 ± 6.33c <0.001** SNB angle 79.72 ± 3.25 76.93 ± 7.43b 83.12 ± 3.78c <0.001** ANS-PNS distance 50.08 ± 2.69 50.88 ± 3.23 48.96 ± 3.26d 0.001** Go-Mn distance 70.97 ± 4.37 68.86 ± 4.25b 73.59 ± 5.13c <0.001** ANB angle 2.22 ± 1.18 5.80 ± 1.76b −1.60 ± 2.44c <0.001** Cd-Gn distance 110.71 ± 6.10 108.44 ± 6.20b 116.47 ± 7.45c <0.001** Cd-Go distance 54.29 ± 4.85 52.94 ± 5.27 55.90 ± 5.03d 0.002** Ar-Go-Mn angle 117.50 ± 13.48 120.20 ± 8.90 123.31 ± 10.66e 0.010** Parameters Skeletal-facial profile P-Value Class I (n = 112) Class II (n = 150) Class III (n = 44) Gender Male 40 (35.7) 62 (41.3) 21 (47.7) 0.358* Female 72 (64.3) 88 (58.7) 23 (52.3) Age 24.00 ± 7.98 25.14 ± 8.88 22.43 ± 7.39 0.148** History of digital-sucking habit Yes 20 (17.9) 31 (20.7) 3 (6.8) 0.106* No 92 (82.1) 119 (79.3) 41 (93.2) History of lip-sucking habit Yes 23 (20.5) 28 (18.7) 10 (22.7) 0.822* No 89 (79.5) 122 (81.3) 34 (77.3) History of prolonged pacifier habit Yes 16 (14.3) 29 (19.3) 6 (13.6) 0.469* No 96 (85.7) 121 (80.7) 38 (86.4) History of adenoidectomy and/or tonsillectomy Yes 5 (4.5) 8 (5.3) 1 (2.3) 0.692* No 107 (95.5) 142 (94.7) 43 (97.7) History of oral-breathing habit Yes 22 (19.6) 51 (34.0)a 15 (34.1) 0.028* No 90 (80.4) 99 (66.0) 29 (65.9) Dental crowding Yes 61 (54.5) 78 (52.0) 23 (52.3) 0.921* No 51 (45.5) 72 (48.0) 21 (47.7) Dental spacing Yes 17 (15.2) 11 (7.3) 7 (15.9) 0.086* No 95 (84.8) 139 (92.7) 37 (84.1) S-Na distance 65.42 ± 3.29 64.96 ± 3.42 65.03 ± 3.46 0.262** SNA angle 81.94 ± 3.31 83.22 ± 4.07b 81.38 ± 3.02 0.002** A ┴ Na-FH 0.40 ± 2.59 1.56 ± 3.43b −0.57 ± 3.34 <0.001** Pog ┴ Na-FH −1.66 ± 4.98 −6.71 ± 5.89b 3.77 ± 6.33c <0.001** SNB angle 79.72 ± 3.25 76.93 ± 7.43b 83.12 ± 3.78c <0.001** ANS-PNS distance 50.08 ± 2.69 50.88 ± 3.23 48.96 ± 3.26d 0.001** Go-Mn distance 70.97 ± 4.37 68.86 ± 4.25b 73.59 ± 5.13c <0.001** ANB angle 2.22 ± 1.18 5.80 ± 1.76b −1.60 ± 2.44c <0.001** Cd-Gn distance 110.71 ± 6.10 108.44 ± 6.20b 116.47 ± 7.45c <0.001** Cd-Go distance 54.29 ± 4.85 52.94 ± 5.27 55.90 ± 5.03d 0.002** Ar-Go-Mn angle 117.50 ± 13.48 120.20 ± 8.90 123.31 ± 10.66e 0.010** *Two-sided Pearson χ2 test. **One-way ANOVA test. aStatistically significant difference (P = 0.01, χ2 test) as compared with Class I skeletal-facial profile. bStatistically significant difference (P < 0.05, Bonferroni multiple-comparison test) as compared with Class I/Class III skeletal-facial profiles. cStatistically significant difference (P < 0.05, Bonferroni multiple-comparison test) as compared with Class I/Class II skeletal-facial profiles. dStatistically significant difference (P < 0.05, Bonferroni multiple-comparison test) as compared with Class II skeletal-facial profile. eStatistically significant difference (P < 0.05, Bonferroni multiple-comparison test) as compared with Class I skeletal-facial profile. View Large Identification of GHR genotypes and analysis for association with the skeletal-facial profile The genotypes for each polymorphism were clearly distinguished from each other by distinct banding patterns, according to the presence or absence of the restriction site (Supplementary Fig. 1). The distribution of genotypes and alleles of each SNP according to the skeletal-facial profiles is displayed in Table 2. All of the groups were in HWE with non-significant χ2 values comparing the observed and expected genotype frequencies (P > 0.05, χ2 < 3.841). In relation to rs6184 polymorphism, although there were no individuals with the homozygous mutation (AA), the results showed that only the genotype CA was a significant candidate for association with Class III skeletal-facial profile (P < 0.001, χ2). Also, a significant over-representation of the allele A was observed in Class III compared with Class I or with Class II skeletal-facial profiles (P < 0.001). On the other hand, concerning rs6180 polymorphism, no significant differences neither in the distribution of genotypes nor in the allele frequencies for this polymorphism amongst skeletal-facial profiles were observed (all P values >0.05). Also as a result, in the whole study group, the minor allele frequency (MAF) for the A allele of rs6184 polymorphism was 1.8%, whereas the C allele of rs6180 polymorphism showed a MAF of 38.4% (data not shown). Table 2. Bivariate associations between genotype distributions and allele frequencies of the GHR gene variants and skeletal-facial profiles. SNP id Skeletal-facial profilea P-value* Skeletal Class I (n = 112) Skeletal Class II (n = 150) Skeletal Class III (n = 44) rs6184 Genotype CC 110 (98.20) 148 (98.70) 37 (84.10) <0.001 CA 2 (1.80) 2 (1.30) 7 (15.90)b,c AA — — — Allele frequency C 222 (99.10) 298 (99.30) 81 (92.00) <0.001 A 2 (0.90) 2 (0.70) 7 (8.00)d,e Hardy-Weinberg equilibrium χ2 critical value 0.008 0.004 0.745 χ2 test P-value 0.927 0.947 0.387 rs6180 Genotype AA 43 (38.40) 63 (42.00) 16 (36.40) 0.643 AC 50 (44.60) 60 (40.00) 23 (52.30) CC 19 (17.00) 27 (18.00) 5 (11.40) Allele frequency A 136 (60.70) 186 (62.00) 55 (52.50) 0.940 C 88 (39.30) 114 (38.00) 33 (37.50) Hardy–Weinberg equilibrium χ2 critical value 0.425 2.283 1.311 χ2 test P-value 0.514 0.131 0.252 SNP id Skeletal-facial profilea P-value* Skeletal Class I (n = 112) Skeletal Class II (n = 150) Skeletal Class III (n = 44) rs6184 Genotype CC 110 (98.20) 148 (98.70) 37 (84.10) <0.001 CA 2 (1.80) 2 (1.30) 7 (15.90)b,c AA — — — Allele frequency C 222 (99.10) 298 (99.30) 81 (92.00) <0.001 A 2 (0.90) 2 (0.70) 7 (8.00)d,e Hardy-Weinberg equilibrium χ2 critical value 0.008 0.004 0.745 χ2 test P-value 0.927 0.947 0.387 rs6180 Genotype AA 43 (38.40) 63 (42.00) 16 (36.40) 0.643 AC 50 (44.60) 60 (40.00) 23 (52.30) CC 19 (17.00) 27 (18.00) 5 (11.40) Allele frequency A 136 (60.70) 186 (62.00) 55 (52.50) 0.940 C 88 (39.30) 114 (38.00) 33 (37.50) Hardy–Weinberg equilibrium χ2 critical value 0.425 2.283 1.311 χ2 test P-value 0.514 0.131 0.252 aValues are given as n (%) of individuals within diagnosis group. *Two-sided Pearson’s chi-square test (χ2). bStatistically significant difference as compared with Class I skeletal-facial profile (P = 0.002, Fisher’s exact test). cStatistically significant difference as compared with Class II skeletal-facial profile (P = 0.001, Fisher’s exact test). dStatistically significant difference as compared with Class I skeletal-facial profile (P = 0.003, Fisher’s exact test). eStatistically significant difference as compared with Class II skeletal-facial profile (P = 0.001, Fisher’s exact test). View Large Table 2. Bivariate associations between genotype distributions and allele frequencies of the GHR gene variants and skeletal-facial profiles. SNP id Skeletal-facial profilea P-value* Skeletal Class I (n = 112) Skeletal Class II (n = 150) Skeletal Class III (n = 44) rs6184 Genotype CC 110 (98.20) 148 (98.70) 37 (84.10) <0.001 CA 2 (1.80) 2 (1.30) 7 (15.90)b,c AA — — — Allele frequency C 222 (99.10) 298 (99.30) 81 (92.00) <0.001 A 2 (0.90) 2 (0.70) 7 (8.00)d,e Hardy-Weinberg equilibrium χ2 critical value 0.008 0.004 0.745 χ2 test P-value 0.927 0.947 0.387 rs6180 Genotype AA 43 (38.40) 63 (42.00) 16 (36.40) 0.643 AC 50 (44.60) 60 (40.00) 23 (52.30) CC 19 (17.00) 27 (18.00) 5 (11.40) Allele frequency A 136 (60.70) 186 (62.00) 55 (52.50) 0.940 C 88 (39.30) 114 (38.00) 33 (37.50) Hardy–Weinberg equilibrium χ2 critical value 0.425 2.283 1.311 χ2 test P-value 0.514 0.131 0.252 SNP id Skeletal-facial profilea P-value* Skeletal Class I (n = 112) Skeletal Class II (n = 150) Skeletal Class III (n = 44) rs6184 Genotype CC 110 (98.20) 148 (98.70) 37 (84.10) <0.001 CA 2 (1.80) 2 (1.30) 7 (15.90)b,c AA — — — Allele frequency C 222 (99.10) 298 (99.30) 81 (92.00) <0.001 A 2 (0.90) 2 (0.70) 7 (8.00)d,e Hardy-Weinberg equilibrium χ2 critical value 0.008 0.004 0.745 χ2 test P-value 0.927 0.947 0.387 rs6180 Genotype AA 43 (38.40) 63 (42.00) 16 (36.40) 0.643 AC 50 (44.60) 60 (40.00) 23 (52.30) CC 19 (17.00) 27 (18.00) 5 (11.40) Allele frequency A 136 (60.70) 186 (62.00) 55 (52.50) 0.940 C 88 (39.30) 114 (38.00) 33 (37.50) Hardy–Weinberg equilibrium χ2 critical value 0.425 2.283 1.311 χ2 test P-value 0.514 0.131 0.252 aValues are given as n (%) of individuals within diagnosis group. *Two-sided Pearson’s chi-square test (χ2). bStatistically significant difference as compared with Class I skeletal-facial profile (P = 0.002, Fisher’s exact test). cStatistically significant difference as compared with Class II skeletal-facial profile (P = 0.001, Fisher’s exact test). dStatistically significant difference as compared with Class I skeletal-facial profile (P = 0.003, Fisher’s exact test). eStatistically significant difference as compared with Class II skeletal-facial profile (P = 0.001, Fisher’s exact test). View Large The analysis by Haploview showed that rs6184 allele A was with high probability present only in chromosomes that carry also rs6180 allele A. This means that a haplotype A (rs6184)-C (rs6180) would not have existed in this study group, and the parameters D’ and r2 get values of 1 indicating strong linkage equilibrium between the polymorphisms. In addition, haplotype analysis showed that whereas two major haplotypes (C-A, C-C) accounting for 98.2% of estimated haplotypes in this Colombian population, the A-A haplotype had a frequency of 1.8%. Nevertheless, as shown in Table 3, the A-A haplotype was found to be significantly more frequent in Class III skeletal-facial profile (P < 0.001, χ2) than in Class I and Class II groups. Table 3. Bivariate associations between haplotypes estimated on the basis of genotypic data for rs6184 and rs6180 single nucleotide polymorphisms (SNPs) spanning the linkage disequilibrium (LP) block covering exon 10 of the GHR gene and skeletal-facial profile. Haplotypea Skeletal-facial profileb P-value* Class I (n = 224) Class II (n = 300) Class III (n = 88) C-A 134 (59.80) 184 (61.30) 48 (54.50) <0.001 C-C 88 (39.30) 114 (38.00) 33 (37.50) A-A 2 (0.90) 2 (0.70) 7 (8.00)c Haplotypea Skeletal-facial profileb P-value* Class I (n = 224) Class II (n = 300) Class III (n = 88) C-A 134 (59.80) 184 (61.30) 48 (54.50) <0.001 C-C 88 (39.30) 114 (38.00) 33 (37.50) A-A 2 (0.90) 2 (0.70) 7 (8.00)c aThe order of the polymorphism is as follows: rs6184-rs6180 bValues are given as n (%) of haplotypes within diagnosis. *Two-sided Pearson’s chi-square test (χ2). cStatistically significant difference when compared with Class I and II skeletal-facial profiles (P < 0.001 χ2 test). View Large Table 3. Bivariate associations between haplotypes estimated on the basis of genotypic data for rs6184 and rs6180 single nucleotide polymorphisms (SNPs) spanning the linkage disequilibrium (LP) block covering exon 10 of the GHR gene and skeletal-facial profile. Haplotypea Skeletal-facial profileb P-value* Class I (n = 224) Class II (n = 300) Class III (n = 88) C-A 134 (59.80) 184 (61.30) 48 (54.50) <0.001 C-C 88 (39.30) 114 (38.00) 33 (37.50) A-A 2 (0.90) 2 (0.70) 7 (8.00)c Haplotypea Skeletal-facial profileb P-value* Class I (n = 224) Class II (n = 300) Class III (n = 88) C-A 134 (59.80) 184 (61.30) 48 (54.50) <0.001 C-C 88 (39.30) 114 (38.00) 33 (37.50) A-A 2 (0.90) 2 (0.70) 7 (8.00)c aThe order of the polymorphism is as follows: rs6184-rs6180 bValues are given as n (%) of haplotypes within diagnosis. *Two-sided Pearson’s chi-square test (χ2). cStatistically significant difference when compared with Class I and II skeletal-facial profiles (P < 0.001 χ2 test). View Large On the other hand, additional bivariate tests (Table 4) revealed that, irrespective of diagnosis group, both mandibular body length and mandibular length were significantly larger, whereas the ANB angle was significantly smaller in those individuals carrying the CA genotype of the rs6184 SNP (P < 0.005, unpaired t-test). Conversely, no significant differences were observed between measurement parameters for craniofacial morphology according to the genetic variants of the rs6180 SNP (all P > 0.05). Also, after haplotype comparison, the results showed similar significant variations for these three cephalometric parameters in individuals having the A-A haplotype when compared with others without the A-A haplotype (all P < 0.05, one-way ANOVA/Bonferroni multiple comparison test, data not shown). Table 4. Bivariate comparisons of cephalometric parameters according to genotypes of the rs6184 and rs6180 single nucleotide polymorphisms (SNPs) of GHR gene (mean ± SD). Parameter SNP/Genotype rs6184 rs6180 CC (n = 295) CA (n = 11) AA (n = 0) P-Value* AA (n = 122) AC (n = 133) CC (n = 51) P-Value** S-Na distance 65.14 ± 3.39 65.25 ± 3.24 — 0.911 65.12 ± 3.35 65.14 ± 3.51 65.20 ± 3.15 0.990 SNA angle 82.50 ± 3.73 82.31 ± 3.97 — 0.877 82.61 ± 4.13 82.13 ± 3.50 83.14 ± 3.27 0.236 A ┴ Na-FH 0.87 ± 3.08 −0.74 ± 3.97 — 0.092 0.85 ± 3.53 0.46 ± 2.97 1.75 ± 2.96 0.052 Pog ┴ Na-FH −3.46 ± 6.49 −0.67 ± 11.98 — 0.461 −3.69 ± 7.20 −3.46 ± 6.35 −2.30 ± 6.67 0.457 SNB angle 78.70 ± 6.09 82.38 ± 6.33 — 0.050 79.21 ± 4.59 78.94 ± 3.91 77.69 ± 11.67 0.322 ANS-PNS distance 50.34 ± 3.07 49.43 ± 4.05 — 0.339 50.33 ± 3.19 50.16 ± 3.00 50.66 ± 3.24 0.623 Go-Mn distance 70.16 ± 4.57 74.40 ± 6.78 — 0.003 70.70 ± 4.59 69.87 ± 4.80 70.58 ± 4.80 0.341 ANB angle 3.55 ± 3.03 −0.07 ± 3.94 — <0.001 3.40 ± 3.31 3.24 ± 3.03 3.99 ± 2.95 0.344 Cd-Gn distance 110.20 ± 6.66 116.45 ± 9.98 — 0.003 111.06 ± 6.73 110.14 ± 7.19 109.67 ± 6.39 0.388 Cd-Go distance 53.78 ± 5.07 55.97 ± 7.39 — 0.169 54.17 ± 5.29 53.89 ± 5.34 53.05 ± 4.40 0.429 Ar-Go-Mn angle 119.55 ± 11.19 122.43 ± 10.48 — 0.402 118.74 ± 13.68 120.85 ± 9.29 118.76 ± 8.54 0.262 Parameter SNP/Genotype rs6184 rs6180 CC (n = 295) CA (n = 11) AA (n = 0) P-Value* AA (n = 122) AC (n = 133) CC (n = 51) P-Value** S-Na distance 65.14 ± 3.39 65.25 ± 3.24 — 0.911 65.12 ± 3.35 65.14 ± 3.51 65.20 ± 3.15 0.990 SNA angle 82.50 ± 3.73 82.31 ± 3.97 — 0.877 82.61 ± 4.13 82.13 ± 3.50 83.14 ± 3.27 0.236 A ┴ Na-FH 0.87 ± 3.08 −0.74 ± 3.97 — 0.092 0.85 ± 3.53 0.46 ± 2.97 1.75 ± 2.96 0.052 Pog ┴ Na-FH −3.46 ± 6.49 −0.67 ± 11.98 — 0.461 −3.69 ± 7.20 −3.46 ± 6.35 −2.30 ± 6.67 0.457 SNB angle 78.70 ± 6.09 82.38 ± 6.33 — 0.050 79.21 ± 4.59 78.94 ± 3.91 77.69 ± 11.67 0.322 ANS-PNS distance 50.34 ± 3.07 49.43 ± 4.05 — 0.339 50.33 ± 3.19 50.16 ± 3.00 50.66 ± 3.24 0.623 Go-Mn distance 70.16 ± 4.57 74.40 ± 6.78 — 0.003 70.70 ± 4.59 69.87 ± 4.80 70.58 ± 4.80 0.341 ANB angle 3.55 ± 3.03 −0.07 ± 3.94 — <0.001 3.40 ± 3.31 3.24 ± 3.03 3.99 ± 2.95 0.344 Cd-Gn distance 110.20 ± 6.66 116.45 ± 9.98 — 0.003 111.06 ± 6.73 110.14 ± 7.19 109.67 ± 6.39 0.388 Cd-Go distance 53.78 ± 5.07 55.97 ± 7.39 — 0.169 54.17 ± 5.29 53.89 ± 5.34 53.05 ± 4.40 0.429 Ar-Go-Mn angle 119.55 ± 11.19 122.43 ± 10.48 — 0.402 118.74 ± 13.68 120.85 ± 9.29 118.76 ± 8.54 0.262 *Two-sided unpaired t-test. **One-way ANOVA. View Large Table 4. Bivariate comparisons of cephalometric parameters according to genotypes of the rs6184 and rs6180 single nucleotide polymorphisms (SNPs) of GHR gene (mean ± SD). Parameter SNP/Genotype rs6184 rs6180 CC (n = 295) CA (n = 11) AA (n = 0) P-Value* AA (n = 122) AC (n = 133) CC (n = 51) P-Value** S-Na distance 65.14 ± 3.39 65.25 ± 3.24 — 0.911 65.12 ± 3.35 65.14 ± 3.51 65.20 ± 3.15 0.990 SNA angle 82.50 ± 3.73 82.31 ± 3.97 — 0.877 82.61 ± 4.13 82.13 ± 3.50 83.14 ± 3.27 0.236 A ┴ Na-FH 0.87 ± 3.08 −0.74 ± 3.97 — 0.092 0.85 ± 3.53 0.46 ± 2.97 1.75 ± 2.96 0.052 Pog ┴ Na-FH −3.46 ± 6.49 −0.67 ± 11.98 — 0.461 −3.69 ± 7.20 −3.46 ± 6.35 −2.30 ± 6.67 0.457 SNB angle 78.70 ± 6.09 82.38 ± 6.33 — 0.050 79.21 ± 4.59 78.94 ± 3.91 77.69 ± 11.67 0.322 ANS-PNS distance 50.34 ± 3.07 49.43 ± 4.05 — 0.339 50.33 ± 3.19 50.16 ± 3.00 50.66 ± 3.24 0.623 Go-Mn distance 70.16 ± 4.57 74.40 ± 6.78 — 0.003 70.70 ± 4.59 69.87 ± 4.80 70.58 ± 4.80 0.341 ANB angle 3.55 ± 3.03 −0.07 ± 3.94 — <0.001 3.40 ± 3.31 3.24 ± 3.03 3.99 ± 2.95 0.344 Cd-Gn distance 110.20 ± 6.66 116.45 ± 9.98 — 0.003 111.06 ± 6.73 110.14 ± 7.19 109.67 ± 6.39 0.388 Cd-Go distance 53.78 ± 5.07 55.97 ± 7.39 — 0.169 54.17 ± 5.29 53.89 ± 5.34 53.05 ± 4.40 0.429 Ar-Go-Mn angle 119.55 ± 11.19 122.43 ± 10.48 — 0.402 118.74 ± 13.68 120.85 ± 9.29 118.76 ± 8.54 0.262 Parameter SNP/Genotype rs6184 rs6180 CC (n = 295) CA (n = 11) AA (n = 0) P-Value* AA (n = 122) AC (n = 133) CC (n = 51) P-Value** S-Na distance 65.14 ± 3.39 65.25 ± 3.24 — 0.911 65.12 ± 3.35 65.14 ± 3.51 65.20 ± 3.15 0.990 SNA angle 82.50 ± 3.73 82.31 ± 3.97 — 0.877 82.61 ± 4.13 82.13 ± 3.50 83.14 ± 3.27 0.236 A ┴ Na-FH 0.87 ± 3.08 −0.74 ± 3.97 — 0.092 0.85 ± 3.53 0.46 ± 2.97 1.75 ± 2.96 0.052 Pog ┴ Na-FH −3.46 ± 6.49 −0.67 ± 11.98 — 0.461 −3.69 ± 7.20 −3.46 ± 6.35 −2.30 ± 6.67 0.457 SNB angle 78.70 ± 6.09 82.38 ± 6.33 — 0.050 79.21 ± 4.59 78.94 ± 3.91 77.69 ± 11.67 0.322 ANS-PNS distance 50.34 ± 3.07 49.43 ± 4.05 — 0.339 50.33 ± 3.19 50.16 ± 3.00 50.66 ± 3.24 0.623 Go-Mn distance 70.16 ± 4.57 74.40 ± 6.78 — 0.003 70.70 ± 4.59 69.87 ± 4.80 70.58 ± 4.80 0.341 ANB angle 3.55 ± 3.03 −0.07 ± 3.94 — <0.001 3.40 ± 3.31 3.24 ± 3.03 3.99 ± 2.95 0.344 Cd-Gn distance 110.20 ± 6.66 116.45 ± 9.98 — 0.003 111.06 ± 6.73 110.14 ± 7.19 109.67 ± 6.39 0.388 Cd-Go distance 53.78 ± 5.07 55.97 ± 7.39 — 0.169 54.17 ± 5.29 53.89 ± 5.34 53.05 ± 4.40 0.429 Ar-Go-Mn angle 119.55 ± 11.19 122.43 ± 10.48 — 0.402 118.74 ± 13.68 120.85 ± 9.29 118.76 ± 8.54 0.262 *Two-sided unpaired t-test. **One-way ANOVA. View Large Univariate and multivariate binary logistic regression analyses for association of GHR gene polymorphisms with the skeletal-facial profile Based on the bivariate analyses and taking into account that significant associations were detected between rs6184 polymorphism and the A-A haplotype regarding Class III skeletal-facial profile, only these parameters were fitted into a multivariate binary logistic regression model to examine the strength and independence of the relationship and to assess for the presence of confounding after adjusting for age, digital-sucking habit, and oral breathing habit. The initial and final models derived from this analysis are displayed in Table 5. The Hosmer–Lemeshow goodness-of-fit test probability values varied from 0.290 to 0.391, confirming good calibration and fit of the multivariable model. Likewise, the c-statistic values ranged from 0.861 to 0.866 in the adjusted model, thus indicating good discrimination. As it could be noted from this table, the OR of Class III skeletal-facial profile was significantly increased (P < 0.001, Wald’s test) for individuals with the CA genotype of the rs6184 SNP as well as the A-A haplotype. Otherwise, based on the multivariate binary logistic regression model, it was noteworthy that the associations between the CA genotype of the rs6184 SNP or the A-A haplotype with Class III skeletal-facial profile were not confounded by other variables. In both cases, the associations persisted (P < 0.001, Wald’s test) when adjusted for other covariables. Table 5. Univariate and multivariate binary logistic regression analyses for the association of significant genetic predictors with Class III skeletal-facial profile after adjusting for age, digital-sucking habit, and oral breathing habit. Parameter Casesa Univariate analysis Multivariate binary logistic regression analysis Calibrationb Discriminationc Class I/Class II skeletal-facial profiles Class III skeletal-facial profile Unadjusted OR (95% CI) P-Value* Adjusted OR (95% CI) P-Value* rs6184 genotype CC 258 (98.50) 37 (84.10) Referent 0.391 0.866 CA 4 (1.50) 7 (15.90) 12.20 (3.41 − 43.71) <0.001 16.63 (4.07 − 68.02) <0.001 AA — — N/Ad N/Ad N/Ad N/Ad Haplotype C-A/C-C 520 (99.23) 81 (92.04) Referent 0.861 A-A 4 (0.77) 7 (7.96) 11.24 (3.22 − 39.24) <0.001 14.54 (3.77 − 56.03) <0.001 0.290 A-C — — N/Ad N/Ad N/Ad N/Ad Parameter Casesa Univariate analysis Multivariate binary logistic regression analysis Calibrationb Discriminationc Class I/Class II skeletal-facial profiles Class III skeletal-facial profile Unadjusted OR (95% CI) P-Value* Adjusted OR (95% CI) P-Value* rs6184 genotype CC 258 (98.50) 37 (84.10) Referent 0.391 0.866 CA 4 (1.50) 7 (15.90) 12.20 (3.41 − 43.71) <0.001 16.63 (4.07 − 68.02) <0.001 AA — — N/Ad N/Ad N/Ad N/Ad Haplotype C-A/C-C 520 (99.23) 81 (92.04) Referent 0.861 A-A 4 (0.77) 7 (7.96) 11.24 (3.22 − 39.24) <0.001 14.54 (3.77 − 56.03) <0.001 0.290 A-C — — N/Ad N/Ad N/Ad N/Ad aValues are given as n (%) of genotypes or haplotypes within diagnosis groups. *Wald test. bHosmer and Lemeshow goodness-of-fit test. cc-statistic. dN/A, not applicable View Large Table 5. Univariate and multivariate binary logistic regression analyses for the association of significant genetic predictors with Class III skeletal-facial profile after adjusting for age, digital-sucking habit, and oral breathing habit. Parameter Casesa Univariate analysis Multivariate binary logistic regression analysis Calibrationb Discriminationc Class I/Class II skeletal-facial profiles Class III skeletal-facial profile Unadjusted OR (95% CI) P-Value* Adjusted OR (95% CI) P-Value* rs6184 genotype CC 258 (98.50) 37 (84.10) Referent 0.391 0.866 CA 4 (1.50) 7 (15.90) 12.20 (3.41 − 43.71) <0.001 16.63 (4.07 − 68.02) <0.001 AA — — N/Ad N/Ad N/Ad N/Ad Haplotype C-A/C-C 520 (99.23) 81 (92.04) Referent 0.861 A-A 4 (0.77) 7 (7.96) 11.24 (3.22 − 39.24) <0.001 14.54 (3.77 − 56.03) <0.001 0.290 A-C — — N/Ad N/Ad N/Ad N/Ad Parameter Casesa Univariate analysis Multivariate binary logistic regression analysis Calibrationb Discriminationc Class I/Class II skeletal-facial profiles Class III skeletal-facial profile Unadjusted OR (95% CI) P-Value* Adjusted OR (95% CI) P-Value* rs6184 genotype CC 258 (98.50) 37 (84.10) Referent 0.391 0.866 CA 4 (1.50) 7 (15.90) 12.20 (3.41 − 43.71) <0.001 16.63 (4.07 − 68.02) <0.001 AA — — N/Ad N/Ad N/Ad N/Ad Haplotype C-A/C-C 520 (99.23) 81 (92.04) Referent 0.861 A-A 4 (0.77) 7 (7.96) 11.24 (3.22 − 39.24) <0.001 14.54 (3.77 − 56.03) <0.001 0.290 A-C — — N/Ad N/Ad N/Ad N/Ad aValues are given as n (%) of genotypes or haplotypes within diagnosis groups. *Wald test. bHosmer and Lemeshow goodness-of-fit test. cc-statistic. dN/A, not applicable View Large Discussion Over recent decades, the genetic control of craniofacial growth and development has been the focus of a large number of studies (11, 16, 21–28, 42). From these studies, it has been recognized that craniofacial morphology follows the pattern of multifactorial inheritance (20). Notwithstanding, the majority of the studies have only examined the correlation between mandibular height and genotype, and few published studies have focused on the description of SNPs in normal human populations and their association with the skeletal-facial profile (14, 18, 20, 28). In view of this fact, this cephalometric study investigated whether rs6184 and rs6180 SNPs in GHR gene might be linked with the skeletal-facial profile in a group of Colombian individuals. The cephalometric measurements calculated in this study were compared with Caucasian norms (37) because of the lack of data available from Colombian population. It is possible to assume that the study included a sufficiently large sample of individuals with different maxillo-mandibular relationships, which were categorized as Class I, Class II, or Class III skeletal-facial profile. Even so, information derived from these data may not necessarily portray the true cephalometric averages of the Colombian population. Hence, large-scale studies need to be performed to have greater statistical power and precision. In the search for candidate genes involved in craniofacial dysmorphogenesis, genetic polymorphisms affecting gene activity are prime targets (22). This investigation constitutes an explorative approach evaluating variations in detection frequencies of GHR SNPs from human genomic DNA obtained from saliva of the study participants in order to identify potential associations with skeletal-facial profile. The foremost findings reported here were that both the CA genotype of rs6184 SNP and the A-A haplotype were highly associated with Class III skeletal-facial profile in a Colombian population. The current findings suggest that the functionality of these SNPs might depend on gene–gene and gene–environmental interactions which could affect the skeletal–facial profile directly or indirectly, changing the function of the GHR protein, or also affecting their expression levels (21). In addition, there would also be the possibility that the effect would be caused by some other genetic variant, not studied here, in the GHR or some other gene, which is located in the same chromosome (haplotype). The present findings partially parallel those reported by others, which found significant associations between the CA genotype of rs6184 SNP (16, 31) and lack of association with any of rs6180 polymorphic variants (16) regarding mandibular morphology. However, different results have also been described since some have observed significant associations between CC genotype of rs6184 (23) and CC genotype of rs6180 (22) SNPs regarding mandibular morphology. Given that craniofacial morphology has ethnic differences (16, 43–45), it is important to highlight that the observed differences may be mainly due to different genotype/allele frequencies in distinct populations which may be attributed essentially to ethnic and geographical factors and that may complicate interpretation of the results of genetic studies. The Colombian population represents a mixture of different ethnic backgrounds (46) and this circumstance makes it very difficult to match the ethnicity of participants. It has been acknowledged that SNPs represent natural sequence variants in which the minor allele has a frequency greater than 1% in a human population (47). Nevertheless, both genotype and allele frequencies may vary between different ethnic groups, being more or less similar between populations sharing common ancestries or underlying different phylogeographical origins (48). In agreement with the former, in the present study, MAF of A allele of rs6184 SNP was 1.8%, nearly analogous to what was observed for other populations including Colombians (2.0%), Mexicans (2.0%), Peruvians (2.0%), Puerto Ricans (3.0%), and Europeans (2.0%), whereas East Asian populations show population frequencies of the allele ranging from 8.2 to 17.7% (www.1000genomes.org/1000-genomes-browsers) (49). For rs6180 SNP, the MAF of the C allele (38.4%) was also quite similar to what previously reported in that database for Colombians (37.0%), Mexicans (44.0%), Peruvians (35.0%), Puerto Ricans (44.0%), and Europeans (42.0%), but remarkably different from that of East Asian populations where the population frequencies of the allele vary from 54.8 to 62.6%. Thus, the results may have been partly affected by ethnic stratification. On the other hand, although there are controversies regarding the genetic association studies, the evaluation of these SNPs in the craniofacial skeleton could be helpful to unravel their putative role in the determination of skeletal-facial profile. In accordance with the former view, it has been previously shown that individuals carrying the CA genotype of rs6184 SNP present statistically higher values of mandibular ramus height (16, 23) and mandibular length (31). Equally, carriers of CC genotype of rs6180 SNP had showed a longer ramus than those with the genotype AC or AA (22). In the current study, however, although individuals carrying the CA genotype of the rs6184 SNP showed both significantly decreased values for ANB angle and increased measures concerning mandibular body length (Go–Mn distance) and mandibular length (Cd–Gn distance), no significant differences in cephalometric parameters amongst genotype groups of rs6180 SNP were observed. Adhering to the findings herein presented, it looks that rs6184 SNP of the GHR gene can affect not only the horizontal but also the longitudinal development of the mandible, thus leading strongly/independently to the acquisition of a Class III skeletal–facial profile. In this sense, it has been described that mandibular growth greatly depends on cartilage growth and is a multifactorial phenomenon in which genetic disposition, nutrition, homeostasis, hormones, and growth factors interact (23, 50) so that the cartilage-mediated growth in the mandibular condyle might play an important role in the determination of growth and morphology of the craniofacial complex (51). Given that the GHRs not only have been shown to be present in the mandibular condyle (52), but also play an important role in cartilage growth (50), amino acid changes in GHR gene, including the rs6184 and rs6180 variants, might affect mandibular growth with site-, area-, or region-specific effects (16), therefore resulting in a morphological difference between heterozygote and wild type genotypes. Conclusions Although the current results do not support that rs6180 SNP in the GHR gene could be identified as a predictor for skeletal-facial profile, they suggest that the allele A of rs6184 SNP alone or in combination with other SNPs in the GHR gene may account for significant horizontal and longitudinal variations of the mandibular morphology and might be a strong/independent prognostic indicator for Class III skeletal-facial profile in the present population. Supplementary material Supplementary material is available at European Journal of Orthodontics online. Funding This study has been fully supported by the Technical Research Council of the Faculty of Dentistry-University of Antioquia (CIFO-Code 021-2014). Conflict of Interest None to declare. Acknowledgements The authors would like to express their thanks to ECCO-Radiología Oral Inteligente at Medellín, for their assistance, cooperation, and provision of the cephalometric imaging. References 1. Staudt , C.B. and Kiliaridis , S . ( 2009 ) Different skeletal types underlying Class III malocclusion in a random population . American Journal of Orthodontics and Dentofacial Orthopedics , 136 , 715 – 721 . Google Scholar CrossRef Search ADS PubMed 2. Downs , W.B . ( 1956 ) Analysis of the dentofacial profile . The Angle Orthodontist , 26 , 191 – 212 . 3. Øland , J. , Jensen , J. , Papadopoulos , M.A. and Melsen , B . ( 2011 ) Does skeletal facial profile influence preoperative motives and postoperative satisfaction? A prospective study of 66 surgical-orthodontic patients . Journal of Oral and Maxillofacial Surgery , 69 , 2025 – 2032 . Google Scholar CrossRef Search ADS PubMed 4. Bishara , S.E . ( 2006 ) Class II malocclusions: diagnostic and clinical considerations with and without treatment . Seminars in Orthodontics , 12 , 11 – 24 . Google Scholar CrossRef Search ADS 5. Peck , H. and Peck , S . ( 1970 ) A concept of facial esthetics . The Angle Orthodontist , 40 , 284 – 318 . Google Scholar PubMed 6. Johnston , C. , Burden , D. , Kennedy , D. , Harradine , N. and Stevenson , M . ( 2006 ) Class III surgical-orthodontic treatment: a cephalometric study . American Journal of Orthodontics and Dentofacial Orthopedics , 130 , 300 – 309 . Google Scholar CrossRef Search ADS PubMed 7. Rana , T. , Khanna , R. , Tikku , T. and Sachan , K . ( 2012 ) Relationship of maxilla to cranial base in different facial types-a cephalometric evaluation . Journal of Oral Biology and Craniofacial Research , 2 , 30 – 35 . Google Scholar CrossRef Search ADS PubMed 8. Saunders , S.R. , Popovich , F. and Thompson , G.W . ( 1980 ) A family study of craniofacial dimensions in the Burlington Growth Centre sample . American Journal of Orthodontics , 78 , 394 – 403 . Google Scholar CrossRef Search ADS PubMed 9. Mossey , P.A . ( 1999 ) The heritability of malocclusion: Part 1—Genetics, principles and terminology . British Journal of Orthodontics , 26 , 103 – 113 . Google Scholar CrossRef Search ADS PubMed 10. Mossey , P.A . ( 1999 ) The heritability of malocclusion: part 2. The influence of genetics in malocclusion . British Journal of Orthodontics , 26 , 195 – 203 . Google Scholar CrossRef Search ADS PubMed 11. Yamaguchi , T. , Park , S.B. , Narita , A. , Maki , K. and Inoue , I . ( 2005 ) Genome-wide linkage analysis of mandibular prognathism in Korean and Japanese patients . Journal of Dental Research , 84 , 255 – 259 . Google Scholar CrossRef Search ADS PubMed 12. Cruz , R.M. , Krieger , H. , Ferreira , R. , Mah , J. , Hartsfield , J. Jr and Oliveira , S . ( 2008 ) Major gene and multifactorial inheritance of mandibular prognathism . American Journal of Medical Genetics. Part A , 146A , 71 – 77 . Google Scholar CrossRef Search ADS PubMed 13. Frazier-Bowers , S. , Rincon-Rodriguez , R. , Zhou , J. , Alexander , K. and Lange , E . ( 2009 ) Evidence of linkage in a Hispanic cohort with a Class III dentofacial phenotype . Journal of Dental Research , 88 , 56 – 60 . Google Scholar CrossRef Search ADS PubMed 14. Coussens , A.K. and van Daal , A . ( 2005 ) Linkage disequilibrium analysis identifies an FGFR1 haplotype-tag SNP associated with normal variation in craniofacial shape . Genomics , 85 , 563 – 573 . Google Scholar CrossRef Search ADS PubMed 15. Lee , D.G. , Kim , T.W. , Kang , S.C. and Kim , S.T . ( 2006 ) Estrogen receptor gene polymorphism and craniofacial morphology in female TMJ osteoarthritis patients . International Journal of Oral and Maxillofacial Surgery , 35 , 165 – 169 . Google Scholar CrossRef Search ADS PubMed 16. Kang , E.H. , Yamaguchi , T. , Tajima , A. , Nakajima , T. , Tomoyasu , Y. , Watanabe , M. , Yamaguchi , M. , Park , S.B. , Maki , K. and Inoue , I . ( 2009 ) Association of the growth hormone receptor gene polymorphisms with mandibular height in a Korean population . Archives of Oral Biology , 54 , 556 – 562 . Google Scholar CrossRef Search ADS PubMed 17. Hauspie , R.C. , Susanne , C. and Defrise-Gussenhoven , E . ( 1985 ) Testing for the presence of genetic variance in factors of face measurements of Belgian twins . Annals of Human Biology , 12 , 429 – 440 . Google Scholar CrossRef Search ADS PubMed 18. Devor , E.J . ( 1987 ) Transmission of human craniofacial dimensions . Journal of Craniofacial Genetics and Developmental Biology , 7 , 95 – 106 . Google Scholar PubMed 19. Sharma , K . ( 1998 ) Sex differences in genetic determinants of craniofacial variations–a study based on twin kinships . Acta Geneticae Medicae et Gemellologiae , 47 , 31 – 41 . Google Scholar CrossRef Search ADS PubMed 20. Johannsdottir , B. , Thorarinsson , F. , Thordarson , A. and Magnusson , T.E . ( 2005 ) Heritability of craniofacial characteristics between parents and offspring estimated from lateral cephalograms . American Journal of Orthodontics and Dentofacial Orthopedics , 127 , 200 – 7 . Google Scholar CrossRef Search ADS PubMed 21. Yamaguchi , T. , Maki , K. and Shibasaki , Y . ( 2001 ) Growth hormone receptor gene variant and mandibular height in the normal Japanese population . American Journal of Orthodontics and Dentofacial Orthopedics , 119 , 650 – 653 . Google Scholar CrossRef Search ADS PubMed 22. Zhou , J. , Lu , Y. , Gao , X.H. , Chen , Y.C. , Lu , J.J. , Bai , Y.X. , Shen , Y. and Wang , B.K . ( 2005 ) The growth hormone receptor gene is associated with mandibular height in a Chinese population . Journal of Dental Research , 84 , 1052 – 1056 . Google Scholar CrossRef Search ADS PubMed 23. Tomoyasu , Y. , Yamaguchi , T. , Tajima , A. , Nakajima , T. , Inoue , I. and Maki , K . ( 2009 ) Further evidence for an association between mandibular height and the growth hormone receptor gene in a Japanese population . American Journal of Orthodontics and Dentofacial Orthopedics , 136 , 536 – 541 . Google Scholar CrossRef Search ADS PubMed 24. Sasaki , Y. , Satoh , K. , Hayasaki , H. , Fukumoto , S. , Fujiwara , T. and Nonaka , K . ( 2009 ) The P561T polymorphism of the growth hormone receptor gene has an inhibitory effect on mandibular growth in young children . European Journal of Orthodontics , 31 , 536 – 541 . Google Scholar CrossRef Search ADS PubMed 25. Xue , F. , Wong , R. and Rabie , A.B . ( 2010 ) Identification of SNP markers on 1p36 and association analysis of EPB41 with mandibular prognathism in a Chinese population . Archives of Oral Biology , 55 , 867 – 872 . Google Scholar CrossRef Search ADS PubMed 26. Jang , J.Y. , Park , E.K. , Ryoo , H.M. , Shin , H.I. , Kim , T.H. , Jang , J.S. , Park , H.S. , Choi , J.Y. and Kwon , T.G . ( 2010 ) Polymorphisms in the Matrilin-1 gene and risk of mandibular prognathism in Koreans . Journal of Dental Research , 89 , 1203 – 1207 . Google Scholar CrossRef Search ADS PubMed 27. Tassopoulou-Fishell , M. , Deeley , K. , Harvey , E.M. , Sciote , J. and Vieira , A.R . ( 2012 ) Genetic variation in myosin 1H contributes to mandibular prognathism . American Journal of Orthodontics and Dentofacial Orthopedics , 141 , 51 – 59 . Google Scholar CrossRef Search ADS PubMed 28. Peng , S. , Tan , J. , Hu , S. , Zhou , H. , Guo , J. , Jin , L. and Tang , K . ( 2013 ) Detecting genetic association of common human facial morphological variation using high density 3D image registration . PLoS Computational Biology , 9 , e1003375 . Google Scholar CrossRef Search ADS PubMed 29. Piwien-Pilipuk , G. , Huo , J.S. and Schwartz , J . ( 2002 ) Growth hormone signal transduction . Journal of Pediatric Endocrinology & Metabolism: JPEM , 15 , 771 – 786 . Google Scholar CrossRef Search ADS PubMed 30. Ramirez-Yañez , G.O. , Smid , J.R. , Young , W.G. and Waters , M.J . ( 2005 ) Influence of growth hormone on the craniofacial complex of transgenic mice . European Journal of Orthodontics , 27 , 494 – 500 . Google Scholar CrossRef Search ADS PubMed 31. Bayram , S. , Basciftci , F.A. and Kurar , E . ( 2014 ) Relationship between P561T and C422F polymorphisms in growth hormone receptor gene and mandibular prognathism . The Angle Orthodontist , 84 , 803 – 809 . Google Scholar CrossRef Search ADS PubMed 32. Leung , D.W. , Spencer , S.A. , Cachianes , G. , Hammonds , R.G. , Collins , C. , Henzel , W.J. , Barnard , R. , Waters , M.J. and Wood , W.I . ( 1987 ) Growth hormone receptor and serum binding protein: purification, cloning and expression . Nature , 330 , 537 – 543 . Google Scholar CrossRef Search ADS PubMed 33. Godowski , P.J. , Leung , D.W. , Meacham , L.R. , Galgani , J.P. , Hellmiss , R. , Keret , R. , Rotwein , P.S. , Parks , J.S. , Laron , Z. and Wood , W.I . ( 1989 ) Characterization of the human growth hormone receptor gene and demonstration of a partial gene deletion in two patients with Laron-type dwarfism . Proceedings of the National Academy of Sciences of the United States of America , 86 , 8083 – 8087 . Google Scholar CrossRef Search ADS PubMed 34. Xu-Sheng , Q. , Yong , Q. , Xu , S. , Cai-Wei , X. , Wei-Jun , W. , Bi-Yu , R. and Shou-Feng , W . ( 2007 ) An analysis of growth hormone receptor gene polymorphism for Han population in Jiangsu province . Journal of Nanjing University (Natural Sciences) , 43 , 145 – 151 . 35. Baccetti , T. , Franchi , L. and McNamara , J.A. Jr . ( 2002 ) An improved version of the cervical vertebral maturation (CVM) method for the assessment of mandibular growth . The Angle Orthodontist , 72 , 316 – 323 . Google Scholar PubMed 36. Steiner , C.C . ( 1953 ) Cephalometrics for you and me . American Journal of Orthodontics , 39 , 720 – 755 . 37. McNamara , J.A. Jr . ( 1984 ) A method of cephalometric evaluation . American Journal of Orthodontics , 86 , 449 – 469 . Google Scholar CrossRef Search ADS PubMed 38. Burstone , C.J. , James , R.B. , Legan , H. , Murphy , G.A. and Norton , L.A . ( 1978 ) Cephalometrics for orthognathic surgery . Journal of Oral Surgery , 36 , 269 – 277 . Google Scholar PubMed 39. BJORK , A . ( 1963 ) Variations in the growth pattern of the human mandible: longitudinal radiographic study by the implant method . Journal of Dental Research , 42 , 400 – 411 . Google Scholar CrossRef Search ADS PubMed 40. Björk , A . ( 1969 ) Prediction of mandibular growth rotation . American Journal of Orthodontics , 55 , 585 – 599 . Google Scholar CrossRef Search ADS PubMed 41. Chujo , S. , Kaji , H. , Takahashi , Y. , Okimura , Y. , Abe , H. and Chihara , K . ( 1996 ) No correlation of growth hormone receptor gene mutation P561T with body height . European Journal of Endocrinology , 134 , 560 – 562 . Google Scholar CrossRef Search ADS PubMed 42. Francis-West , P.H. , Robson , L. and Evans , D.J . ( 2003 ) Craniofacial development: the tissue and molecular interactions that control development of the head . Advances in Anatomy, Embryology, and Cell Biology , 169 , III – VI, 1 . Google Scholar PubMed 43. Miyajima , K. , McNamara , J.A. Jr , Kimura , T. , Murata , S. and Iizuka , T . ( 1996 ) Craniofacial structure of Japanese and European-American adults with normal occlusions and well-balanced faces . American Journal of Orthodontics and Dentofacial Orthopedics , 110 , 431 – 438 . Google Scholar CrossRef Search ADS PubMed 44. Ishii , N. , Deguchi , T. and Hunt , N.P . ( 2002 ) Morphological differences in the craniofacial structure between Japanese and Caucasian girls with Class II Division 1 malocclusions . European Journal of Orthodontics , 24 , 61 – 67 . Google Scholar CrossRef Search ADS PubMed 45. Ioi , H. , Nakata , S. , Nakasima , A. and Counts , A.L . ( 2007 ) Comparison of cephalometric norms between Japanese and Caucasian adults in antero-posterior and vertical dimension . European Journal of Orthodontics , 29 , 493 – 499 . Google Scholar CrossRef Search ADS PubMed 46. Ibarra , A. et al. ( 2014 ) Comparison of the genetic background of different Colombian populations using the SNPforID 52plex identification panel . International Journal of Legal Medicine , 128 , 19 – 25 . Google Scholar CrossRef Search ADS PubMed 47. Nachman , M.W . ( 2001 ) Single nucleotide polymorphisms and recombination rate in humans . Trends in Genetics: TIG , 17 , 481 – 485 . Google Scholar CrossRef Search ADS PubMed 48. Pena , S.D. , Bastos-Rodrigues , L. , Pimenta , J.R. and Bydlowski , S.P . ( 2009 ) DNA tests probe the genomic ancestry of Brazilians . Brazilian Journal of Medical and Biological Research , 42 , 870 – 876 . Google Scholar CrossRef Search ADS PubMed 49. 1000 Genomes Project Consortium , Abecasis , G.R. , Altshuler , D. , Auton , A. , Brooks , L.D. , Durbin , R.M. , Gibbs , R.A. , Hurles , M.E. and McVean , G.A . ( 2010 ) A map of human genome variation from population-scale sequencing . Nature , 467 , 1061 – 1073 . Google Scholar CrossRef Search ADS PubMed 50. Visnapuu , V. , Peltomäki , T. , Rönning , O. , Vahlberg , T. and Helenius , H . ( 2001 ) Growth hormone and insulin-like growth factor I receptors in the temporomandibular joint of the rat . Journal of Dental Research , 80 , 1903 – 1907 . Google Scholar CrossRef Search ADS PubMed 51. Mizoguchi , I. , Toriya , N. and Nakao , Y . ( 2013 ) Growth of the mandible and biological characteristics of the mandibular condylar cartilage . Japanese Dental Science Review , 49 , 139 – 150 . Google Scholar CrossRef Search ADS 52. Lewinson , D. , Bialik , G.M. and Hochberg , Z . ( 1994 ) Differential effects of hypothyroidism on the cartilage and the osteogenic process in the mandibular condyle: recovery by growth hormone and thyroxine . Endocrinology , 135 , 1504 – 1510 . Google Scholar CrossRef Search ADS PubMed © The Author 2017. Published by Oxford University Press on behalf of the European Orthodontic Society. All rights reserved. For permissions, please email: journals.permissions@oup.com http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The European Journal of Orthodontics Oxford University Press

Association analysis between rs6184 and rs6180 polymorphisms of growth hormone receptor gene regarding skeletal-facial profile in a Colombian population

Loading next page...
 
/lp/ou_press/association-analysis-between-rs6184-and-rs6180-polymorphisms-of-growth-cVdEBuCgLq
Publisher
Oxford University Press
Copyright
© The Author 2017. Published by Oxford University Press on behalf of the European Orthodontic Society. All rights reserved. For permissions, please email: journals.permissions@oup.com
ISSN
0141-5387
eISSN
1460-2210
D.O.I.
10.1093/ejo/cjx070
Publisher site
See Article on Publisher Site

Abstract

Summary Background/Objective There is strong evidence that genetic factors may affect the craniofacial morphology. This study aimed to examine the association between the rs6184 and rs6180 polymorphic variants of the growth hormone receptor (GHR) gene and skeletal-facial profile in a Colombian population. Subjects/Methods Saliva samples from 306 individuals ranging in age from 15 to 53 (mean 24.33) years were collected. Cephalometric parameters were used to categorize the participants as Class I, Class II, or Class III skeletal-facial profile. The polymerase chain reaction-restriction fragment length polymorphism method was used to identify genotypes of the rs6184 and rs6180 single nucleotide polymorphisms (SNPs). The association of polymorphisms with the skeletal-facial profile was assessed separately and adjusted for confounding using a multivariate binary logistic regression model, alongside with analysis of linkage disequilibrium and haplotype associations. Results Although individuals carrying the CA genotype of the rs6184 SNP showed both significantly decreased values for ANB angle and increased measures concerning mandibular body length and mandibular length, no significant differences amongst genotype groups of rs6180 SNP were observed. Moreover, chi-square test and logistic regression analysis revealed that the CA genotype of rs6184 SNP and the A-A haplotype were highly associated with Class III skeletal-facial profile. Conclusions Although these results do not support that rs6180 SNP could be identified as a predictor for skeletal-facial profile, they suggest that the allele A of rs6184 SNP alone or in combination with other SNPs in the GHR gene yields significant horizontal and longitudinal variations of the mandibular morphology and might be a strong/independent prognostic indicator for Class III skeletal-facial profile in the present population. Introduction Variations in craniofacial morphology are evident during development and growth (1). From the orthodontic viewpoint, the skeletal-facial profile refers to the relative sagittal and vertical relationships between the cranial base, middle face (maxilla), and lower face (mandible) (2). The synchronic development of both jaws will allow a harmonious maxillo-mandibular growth (Class I facial profile) (3, 4). On the contrast, the posterior or anterior mandibular relationship with respect to the maxilla in a greater proportion than stated as harmonic has been classified as Class II and Class III skeletal-facial profiles, respectively (2, 4–6). Although normal facial profile can be attained and maintained in spite of the variations in the facial pattern seen as a result of the change in size, position, rotation of cranial base, maxilla, and/or mandible or cumulative effect of any two or all three, in some individuals, a disturbed pattern of differential growth may lead to a maxillo-mandibular discrepancy (7). It has been acknowledged that the human craniofacial morphology constitutes a complex physical trait that may be determined by genetic, environmental, mechanical, and epigenetic factors (8), and that from their relative interplay may result in the establishment of a craniofacial dysmorphosis (9, 10). Whilst it has been established that the hereditary pattern is heterogeneous and diverse transmission models have been proposed (11–13), the advances in clinical genetics have allowed us to identify the existence of a genetic predisposition for skeletal-facial profile (11, 14–20). Likewise, it has been reported that single nucleotide polymorphisms (SNPs) in genes which encode mediators of bone growth and metabolism may be associated with variations in the craniofacial profile (16, 21–28). Taking into account that growth hormone (GH) plays a major role in regulating both growth and metabolism during childhood/adolescence through the binding to its specific cell surface growth hormone receptor (GHR) (29), it is clear that mutations in functionally critical areas of GHR gene may influence the growth and development of the craniofacial complex (30, 31). The GHR gene is located at chromosome region 5p13.1-p12 (OMIM 600946) and consists of 10 exons, nine of which are coding (32). Exon 2 encodes the signal peptide, exons 3 to 7 the extracellular domain, exon 8 the transmembrane domain, and exon 9 as well as part of exon 10 the intracellular domain (33). Increasing evidence suggests that the polymorphic variants rs6184 and rs6180 in exon 10 of the GHR gene might be considered as genetic factors of mandibular morphogenesis (16, 21–24, 31). However, the association of these two polymorphic variants with different skeletal-facial profiles has not been described. Moreover, the results of their association with mandibular growth have been conflictive, because while some researchers suggest that the polymorphism rs6184 may be associated with mandibular height growth (16, 21–23), others have postulated that the minor allele has an inhibitory effect on mandibular growth (24). Although the available information regarding the rs6180 polymorphic variant is sparse (16, 23), it has been suggested that it may be one of the factors that account for differences of anthropometric measurements (28, 34). Given that identification of genetic variants that lead to a specific skeletal-facial profile would enable a more effective prognosis and treatment of cranial dysmorphosis, this study aimed to examine the association between the rs6184 and rs6180 polymorphic variants of the GHR gene and the skeletal-facial profile in a Colombian population. Subjects and methods Study design, study population, and inclusion/exclusion criteria This cross-sectional, observational, analytic study was conducted in accordance with the ethical guidelines of the Helsinki Declaration, and ethical approval was obtained from the Ethics Committee for Human Studies of the Faculty of Dentistry of the University of Antioquia in Medellín (Colombia). The sample size was calculated on the basis of a previous study regarding the association of GHR gene polymorphisms with mandibular height growth (23). It was increased by 20% to safeguard the estimations at an optimal level of precision (5%) against the potential effect of sample size reduction due to exclusions and dropouts. Thus, the theoretical sample size for clinical screening was set to 200 individuals to determine significant differences in outcomes at the 95% confidence level, with an α value of 0.05 and 80% power. However, every effort was made to recruit the maximum number of participants so that the study sample included a total of 306 participants from the population of individuals that sought treatment and/or consultation at the Graduate Orthodontic as well as Maxillofacial Surgery Clinics. Prior to enrolment, the purpose was fully explained, and a signed informed consent was obtained individually from all recruits or the parents/custodian of those fewer than 18 years of age. Participants were privately interviewed to obtain medical and demographic information and underwent a clinical examination synchronously by two trained and calibrated raters (GA J-A, VA A-G) to rule out the presence of anatomic, pathologic, and/or functional conditions that could affect the results. Eligibility criteria included healthy unrelated individuals of Colombian ancestry with full permanent dentition (except for third molars), older than 15 years (12), who had completed their growth and development as evidenced by the cervical vertebral maturation (CVM) stage (35). Furthermore, exclusion criteria applied were pregnancy, ongoing or preceding orthodontic/orthopaedic therapy, as well as preceding history of maxillofacial surgery, facial fractures, jaw tumors and cysts, and congenital disorders of the jaws that could affect the craniofacial growth pattern. Craniofacial measurements To analyse skeletal-facial profile, lateral cephalograms were obtained with a digital pan/ceph system [Orthopos® XG 5 orthopantomograph (Sirona Dental Systems®, Bensheim, Germany)] under standardized conditions (73 kVp,15 mA, exposure time of 9.4 seconds, focus-sensor distance1.71 Mt, and magnification factor 1:1 for all images). The volunteers stood with the Frankfort horizontal plane parallel to the floor with their lips in rest position. Using Radiocef Studio 2® software (Radio Memory®, Belo Horizonte, Brazil), the cephalometric tracings and landmark identifications were digitized and analysed simultaneously by two observers (MI P-C, GA F-M). When discordant measure data were established between the examiners, new evaluations were performed and any further controversy was resolved by consensus. Based on previous studies (2, 36–40), eleven cephalometric parameters were used to categorize the participants as having a Class I (n = 112), Class II (n = 150), or Class III (n = 44) skeletal-facial profile (Supplementary Table 1). In some cases, a measurement was prioritized over another to classify the skeletal-facial profile of the study population. In those cases, the most appropriate analysis according to the clinical aspect of each participant was chosen. The Class I group consisted of individuals with SNA angle values within 82 ± 2 degrees; SNB angle values of 80 ± 2 degrees; ANB angle values of 2 ± 2 degrees (36); A ┴ Na-FH values of 0.4 ± 2.3 mm for females and 1.1 ± 2.7 mm for males; and Pog ┴ Na-FH values from −2 to +4 mm (37). At the same time, the Class II group included participants with SNA angle values greater than 84 degrees; SNB angle values less than 78 degrees; ANB angle values greater than 4 degrees (36); A ┴ Na-FH values greater than 1 mm; and Pog ┴ Na-FH values inferior to −2 mm (37). Otherwise, the Class III group consisted of volunteers with SNA angle values less than 80 degrees; SNB angle values greater than 82 degrees; ANB angle values less than 0 degrees (36); A ┴ Na-FH values less than 1 mm; Pog ┴ Na-FH values greater than 4 mm (37). Synchronously both mandibular body length (Go-Mn distance) and maxillary length (ANS-PNS distance) were assessed for all individuals following previously described criteria (2, 38) in order to determine the sagittal mandibular and maxillary sizes for each diagnostic group, respectively. When indicated, data were calculated separately for male and female individuals so as to determine the skeletal-facial profile according to the sexual dimorphism. In specific cases which some of the measurements were outside the limits previously established for each parameter, the results for both ANB and the distances to Na perpendicular, which could better represent the skeletal-facial profile of the individual, and that would be in agreement with the linear measurements for classifying the mandibular and the maxillary dimensions, were prioritized. DNA isolation and genotyping assay For DNA isolation, 5 ml of unstimulated whole saliva was collected from each individual into a 50 ml sterile plastic centrifuge tube (Greiner Bio-one®, Frickenhausen, Germany). After collection, whole saliva was clarified by centrifugation for 10 minutes at 800 × g. The obtained pellet was dispersed by using a vortex for 15 seconds, and 200 µl were used for DNA extraction using the QIAamp® DNA mini kit (Qiagen Sciences®, Germantown, Maryland, USA). DNA was stored frozen at −20°C until use. Polymorphic sites were determined by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method. Specific primers used for determining the rs6184 (41) and rs6180 (34) polymorphic sites in the GHR gene are listed in Supplementary Table 2. PCR amplification was carried out in a 96-well thermal cycler (Mastercycler® gradient, Eppendorf®, Hamburg, Germany). PCR products were run in 2% agarose gel electrophoresis in TBE (Tris–Borate–EDTA) buffer, stained with 0.5 mg/ml ethidium bromide and visualized in an ultraviolet transilluminator. The sizes of the expected fragments before enzymatic digestion were 1037 bp for rs6184 and 672 bp for rs6180 SNPs. As a molecular size marker, 100-to-1500 bp in multiples of 100 bp DNA ladder was used. For negative control, DNA sample was replaced by sterile water. PCR products from rs6184 and rs6180 polymorphic sites were respectively digested with Eco147I (Thermo Scientific®, Barrington, IL, USA) or HpyCH4V (New England Biolabs®, Ipswich, MA, USA) restriction enzymes. Fragments were analysed in agarose electrophoresis as described above. Genotypes were determined as CC (808, 229 bp), CA (1037, 808, 229 bp), or AA (1037 bp) for rs6184 SNP; and AA (317, 275, 80 bp), CA (397, 317, 275, 80 bp), or CC (397, 275 bp) for rs6180 SNP. Statistical analysis and data management Data collected were analysed in standard statistical software (IBM® SPSS® 23.0, Chicago, IL, USA). The intra-raters agreement for cephalometric measures, CVM stage, as well as for PCR assays was determined through double evaluations for each specific test performed by the same observers with 10 participants selected randomly using a computer-generated randomization code (Epidat 4.0®, PAHO/WHO, Washington, DC, USA). The interval between tests 1 and 2 was 6 months. For comparisons, the reliability between the two series of data was assessed by using the Cohen’s kappa statistic (κ) for categorical variables and the intraclass correlation coefficient (ICC) for quantitative variables. A value less than 0.40 indicated poor reproducibility, 0.40–0.80 indicated fair to good reproducibility, and greater than 0.80 indicated excellent reproducibility. All grouped data were tested for normality using the Kolmogorov–Smirnov and Shapiro–Wilk tests when indicated. Because the data were normally distributed, statistical tests were performed using parametric methods. Statistical analysis was then performed on three levels: First, between-group comparisons were explored in bivariate analyses in order to detect differences regarding demographic, clinical, and cephalometric parameters, as well as for genetic variants, as well as to identify potential predictor variables for association with skeletal-facial profile. Pearson’s chi-square test (χ2) or Fisher’s exact test (when frequency was less than 5) was used for categorical variables, and one-way analysis of variance (ANOVA) with Bonferroni multiple-comparison or unpaired t tests was applied to determine differences amongst continuous data. Furthermore, deviation from Hardy–Weinberg equilibrium (HWE) was assessed by goodness-of-fit by comparing the rs6184 and rs6180 genotype frequencies with those expected on the basis of the observed alleles using χ2 critical value test with 1 degree of freedom. Linkage disequilibrium (LD) amongst both SNPs and haplotype frequencies/distribution was further analysed using Haploview 4.2 software (MIT/Harvard Broad Institute, Cambridge, Massachusetts, USA). LD was measured based upon calculating disequilibrium (D’) and correlation (r2) coefficients values. Second, univariate analysis amongst significant genetic predictors with skeletal-facial profile was conducted to assess the association as estimated by the odds ratio (OR) and 95% confidence interval (CI). Positive associations existed when the OR was greater than 2 and the confidence range did not include 1.0. Third, the strength and the independence of the association were further analysed by multivariate binary logistic regression analysis, whilst adjusting for covariables with a level of significance less than 0.20 in the bivariate analysis which were categorized according to the mean age obtained from all participants (i.e. less than 24.33 versus 24.33 or more years), the history of digital-sucking habit (i.e. yes versus no), and the history of oral-breathing habit (i.e. yes versus no). In this model, P < 0.05 was used as the entry criterion, whereas P > 0.10 was the removal criterion. The calibration and discrimination ability of the multivariate model was evaluated through the Hosmer–Lemeshow goodness-of-fit statistic and the c-statistic tests, respectively. All tests were two-sided and statistical significance was assumed a P-value of less than 0.05. Results Reproducibility of the measurements Overall, intra-raters reproducibility was excellent not only for all cephalometric measurements recorded simultaneously per patient by the same two examiners (ICC ranging from 0.901 to 0.998, all P < 0.001), but also for CVM stage (κ = 1.00). Also, intra-observer agreement was excellent for genotyping assays (κ = 1.00). Demographic, clinical, and cephalometric characteristics of the study population Bivariate comparisons between demographic and clinical variables as well as cephalometric measurements assessed from participants recruited for this study are outlined in Table 1. As can be seen from this table, with respect to the evaluated variables, the only significant variable for association with skeletal-facial profile was the oral-breathing habit, since it was significantly more common (P = 0.010, χ2 test) for Class II and Class III profiles than in Class I profile so that it was considered as a confounder of the association between genetic findings and the skeletal-facial profile. In addition, although there were no significant differences between skeletal-facial profiles with respect to age and digital-sucking habit (P > 0.05, one-way ANOVA and χ2 tests), these two latter variables were considered additional confounding variables as they met the criteria to be included in the multivariate analysis model (P < 0.20). As also depicted in Table 1, although there were no significant differences between the groups with respect to mean values of cranial base length, not surprisingly all maxillary/mandibular measurements were statistically different between them (all P values <0.05, one-way ANOVA). Table 1. Bivariate comparisons of demographic, functional, anatomic, and cephalometric parameters obtained from the study population according to skeletal-facial profile [n (%) or mean ± SD]. Parameters Skeletal-facial profile P-Value Class I (n = 112) Class II (n = 150) Class III (n = 44) Gender Male 40 (35.7) 62 (41.3) 21 (47.7) 0.358* Female 72 (64.3) 88 (58.7) 23 (52.3) Age 24.00 ± 7.98 25.14 ± 8.88 22.43 ± 7.39 0.148** History of digital-sucking habit Yes 20 (17.9) 31 (20.7) 3 (6.8) 0.106* No 92 (82.1) 119 (79.3) 41 (93.2) History of lip-sucking habit Yes 23 (20.5) 28 (18.7) 10 (22.7) 0.822* No 89 (79.5) 122 (81.3) 34 (77.3) History of prolonged pacifier habit Yes 16 (14.3) 29 (19.3) 6 (13.6) 0.469* No 96 (85.7) 121 (80.7) 38 (86.4) History of adenoidectomy and/or tonsillectomy Yes 5 (4.5) 8 (5.3) 1 (2.3) 0.692* No 107 (95.5) 142 (94.7) 43 (97.7) History of oral-breathing habit Yes 22 (19.6) 51 (34.0)a 15 (34.1) 0.028* No 90 (80.4) 99 (66.0) 29 (65.9) Dental crowding Yes 61 (54.5) 78 (52.0) 23 (52.3) 0.921* No 51 (45.5) 72 (48.0) 21 (47.7) Dental spacing Yes 17 (15.2) 11 (7.3) 7 (15.9) 0.086* No 95 (84.8) 139 (92.7) 37 (84.1) S-Na distance 65.42 ± 3.29 64.96 ± 3.42 65.03 ± 3.46 0.262** SNA angle 81.94 ± 3.31 83.22 ± 4.07b 81.38 ± 3.02 0.002** A ┴ Na-FH 0.40 ± 2.59 1.56 ± 3.43b −0.57 ± 3.34 <0.001** Pog ┴ Na-FH −1.66 ± 4.98 −6.71 ± 5.89b 3.77 ± 6.33c <0.001** SNB angle 79.72 ± 3.25 76.93 ± 7.43b 83.12 ± 3.78c <0.001** ANS-PNS distance 50.08 ± 2.69 50.88 ± 3.23 48.96 ± 3.26d 0.001** Go-Mn distance 70.97 ± 4.37 68.86 ± 4.25b 73.59 ± 5.13c <0.001** ANB angle 2.22 ± 1.18 5.80 ± 1.76b −1.60 ± 2.44c <0.001** Cd-Gn distance 110.71 ± 6.10 108.44 ± 6.20b 116.47 ± 7.45c <0.001** Cd-Go distance 54.29 ± 4.85 52.94 ± 5.27 55.90 ± 5.03d 0.002** Ar-Go-Mn angle 117.50 ± 13.48 120.20 ± 8.90 123.31 ± 10.66e 0.010** Parameters Skeletal-facial profile P-Value Class I (n = 112) Class II (n = 150) Class III (n = 44) Gender Male 40 (35.7) 62 (41.3) 21 (47.7) 0.358* Female 72 (64.3) 88 (58.7) 23 (52.3) Age 24.00 ± 7.98 25.14 ± 8.88 22.43 ± 7.39 0.148** History of digital-sucking habit Yes 20 (17.9) 31 (20.7) 3 (6.8) 0.106* No 92 (82.1) 119 (79.3) 41 (93.2) History of lip-sucking habit Yes 23 (20.5) 28 (18.7) 10 (22.7) 0.822* No 89 (79.5) 122 (81.3) 34 (77.3) History of prolonged pacifier habit Yes 16 (14.3) 29 (19.3) 6 (13.6) 0.469* No 96 (85.7) 121 (80.7) 38 (86.4) History of adenoidectomy and/or tonsillectomy Yes 5 (4.5) 8 (5.3) 1 (2.3) 0.692* No 107 (95.5) 142 (94.7) 43 (97.7) History of oral-breathing habit Yes 22 (19.6) 51 (34.0)a 15 (34.1) 0.028* No 90 (80.4) 99 (66.0) 29 (65.9) Dental crowding Yes 61 (54.5) 78 (52.0) 23 (52.3) 0.921* No 51 (45.5) 72 (48.0) 21 (47.7) Dental spacing Yes 17 (15.2) 11 (7.3) 7 (15.9) 0.086* No 95 (84.8) 139 (92.7) 37 (84.1) S-Na distance 65.42 ± 3.29 64.96 ± 3.42 65.03 ± 3.46 0.262** SNA angle 81.94 ± 3.31 83.22 ± 4.07b 81.38 ± 3.02 0.002** A ┴ Na-FH 0.40 ± 2.59 1.56 ± 3.43b −0.57 ± 3.34 <0.001** Pog ┴ Na-FH −1.66 ± 4.98 −6.71 ± 5.89b 3.77 ± 6.33c <0.001** SNB angle 79.72 ± 3.25 76.93 ± 7.43b 83.12 ± 3.78c <0.001** ANS-PNS distance 50.08 ± 2.69 50.88 ± 3.23 48.96 ± 3.26d 0.001** Go-Mn distance 70.97 ± 4.37 68.86 ± 4.25b 73.59 ± 5.13c <0.001** ANB angle 2.22 ± 1.18 5.80 ± 1.76b −1.60 ± 2.44c <0.001** Cd-Gn distance 110.71 ± 6.10 108.44 ± 6.20b 116.47 ± 7.45c <0.001** Cd-Go distance 54.29 ± 4.85 52.94 ± 5.27 55.90 ± 5.03d 0.002** Ar-Go-Mn angle 117.50 ± 13.48 120.20 ± 8.90 123.31 ± 10.66e 0.010** *Two-sided Pearson χ2 test. **One-way ANOVA test. aStatistically significant difference (P = 0.01, χ2 test) as compared with Class I skeletal-facial profile. bStatistically significant difference (P < 0.05, Bonferroni multiple-comparison test) as compared with Class I/Class III skeletal-facial profiles. cStatistically significant difference (P < 0.05, Bonferroni multiple-comparison test) as compared with Class I/Class II skeletal-facial profiles. dStatistically significant difference (P < 0.05, Bonferroni multiple-comparison test) as compared with Class II skeletal-facial profile. eStatistically significant difference (P < 0.05, Bonferroni multiple-comparison test) as compared with Class I skeletal-facial profile. View Large Table 1. Bivariate comparisons of demographic, functional, anatomic, and cephalometric parameters obtained from the study population according to skeletal-facial profile [n (%) or mean ± SD]. Parameters Skeletal-facial profile P-Value Class I (n = 112) Class II (n = 150) Class III (n = 44) Gender Male 40 (35.7) 62 (41.3) 21 (47.7) 0.358* Female 72 (64.3) 88 (58.7) 23 (52.3) Age 24.00 ± 7.98 25.14 ± 8.88 22.43 ± 7.39 0.148** History of digital-sucking habit Yes 20 (17.9) 31 (20.7) 3 (6.8) 0.106* No 92 (82.1) 119 (79.3) 41 (93.2) History of lip-sucking habit Yes 23 (20.5) 28 (18.7) 10 (22.7) 0.822* No 89 (79.5) 122 (81.3) 34 (77.3) History of prolonged pacifier habit Yes 16 (14.3) 29 (19.3) 6 (13.6) 0.469* No 96 (85.7) 121 (80.7) 38 (86.4) History of adenoidectomy and/or tonsillectomy Yes 5 (4.5) 8 (5.3) 1 (2.3) 0.692* No 107 (95.5) 142 (94.7) 43 (97.7) History of oral-breathing habit Yes 22 (19.6) 51 (34.0)a 15 (34.1) 0.028* No 90 (80.4) 99 (66.0) 29 (65.9) Dental crowding Yes 61 (54.5) 78 (52.0) 23 (52.3) 0.921* No 51 (45.5) 72 (48.0) 21 (47.7) Dental spacing Yes 17 (15.2) 11 (7.3) 7 (15.9) 0.086* No 95 (84.8) 139 (92.7) 37 (84.1) S-Na distance 65.42 ± 3.29 64.96 ± 3.42 65.03 ± 3.46 0.262** SNA angle 81.94 ± 3.31 83.22 ± 4.07b 81.38 ± 3.02 0.002** A ┴ Na-FH 0.40 ± 2.59 1.56 ± 3.43b −0.57 ± 3.34 <0.001** Pog ┴ Na-FH −1.66 ± 4.98 −6.71 ± 5.89b 3.77 ± 6.33c <0.001** SNB angle 79.72 ± 3.25 76.93 ± 7.43b 83.12 ± 3.78c <0.001** ANS-PNS distance 50.08 ± 2.69 50.88 ± 3.23 48.96 ± 3.26d 0.001** Go-Mn distance 70.97 ± 4.37 68.86 ± 4.25b 73.59 ± 5.13c <0.001** ANB angle 2.22 ± 1.18 5.80 ± 1.76b −1.60 ± 2.44c <0.001** Cd-Gn distance 110.71 ± 6.10 108.44 ± 6.20b 116.47 ± 7.45c <0.001** Cd-Go distance 54.29 ± 4.85 52.94 ± 5.27 55.90 ± 5.03d 0.002** Ar-Go-Mn angle 117.50 ± 13.48 120.20 ± 8.90 123.31 ± 10.66e 0.010** Parameters Skeletal-facial profile P-Value Class I (n = 112) Class II (n = 150) Class III (n = 44) Gender Male 40 (35.7) 62 (41.3) 21 (47.7) 0.358* Female 72 (64.3) 88 (58.7) 23 (52.3) Age 24.00 ± 7.98 25.14 ± 8.88 22.43 ± 7.39 0.148** History of digital-sucking habit Yes 20 (17.9) 31 (20.7) 3 (6.8) 0.106* No 92 (82.1) 119 (79.3) 41 (93.2) History of lip-sucking habit Yes 23 (20.5) 28 (18.7) 10 (22.7) 0.822* No 89 (79.5) 122 (81.3) 34 (77.3) History of prolonged pacifier habit Yes 16 (14.3) 29 (19.3) 6 (13.6) 0.469* No 96 (85.7) 121 (80.7) 38 (86.4) History of adenoidectomy and/or tonsillectomy Yes 5 (4.5) 8 (5.3) 1 (2.3) 0.692* No 107 (95.5) 142 (94.7) 43 (97.7) History of oral-breathing habit Yes 22 (19.6) 51 (34.0)a 15 (34.1) 0.028* No 90 (80.4) 99 (66.0) 29 (65.9) Dental crowding Yes 61 (54.5) 78 (52.0) 23 (52.3) 0.921* No 51 (45.5) 72 (48.0) 21 (47.7) Dental spacing Yes 17 (15.2) 11 (7.3) 7 (15.9) 0.086* No 95 (84.8) 139 (92.7) 37 (84.1) S-Na distance 65.42 ± 3.29 64.96 ± 3.42 65.03 ± 3.46 0.262** SNA angle 81.94 ± 3.31 83.22 ± 4.07b 81.38 ± 3.02 0.002** A ┴ Na-FH 0.40 ± 2.59 1.56 ± 3.43b −0.57 ± 3.34 <0.001** Pog ┴ Na-FH −1.66 ± 4.98 −6.71 ± 5.89b 3.77 ± 6.33c <0.001** SNB angle 79.72 ± 3.25 76.93 ± 7.43b 83.12 ± 3.78c <0.001** ANS-PNS distance 50.08 ± 2.69 50.88 ± 3.23 48.96 ± 3.26d 0.001** Go-Mn distance 70.97 ± 4.37 68.86 ± 4.25b 73.59 ± 5.13c <0.001** ANB angle 2.22 ± 1.18 5.80 ± 1.76b −1.60 ± 2.44c <0.001** Cd-Gn distance 110.71 ± 6.10 108.44 ± 6.20b 116.47 ± 7.45c <0.001** Cd-Go distance 54.29 ± 4.85 52.94 ± 5.27 55.90 ± 5.03d 0.002** Ar-Go-Mn angle 117.50 ± 13.48 120.20 ± 8.90 123.31 ± 10.66e 0.010** *Two-sided Pearson χ2 test. **One-way ANOVA test. aStatistically significant difference (P = 0.01, χ2 test) as compared with Class I skeletal-facial profile. bStatistically significant difference (P < 0.05, Bonferroni multiple-comparison test) as compared with Class I/Class III skeletal-facial profiles. cStatistically significant difference (P < 0.05, Bonferroni multiple-comparison test) as compared with Class I/Class II skeletal-facial profiles. dStatistically significant difference (P < 0.05, Bonferroni multiple-comparison test) as compared with Class II skeletal-facial profile. eStatistically significant difference (P < 0.05, Bonferroni multiple-comparison test) as compared with Class I skeletal-facial profile. View Large Identification of GHR genotypes and analysis for association with the skeletal-facial profile The genotypes for each polymorphism were clearly distinguished from each other by distinct banding patterns, according to the presence or absence of the restriction site (Supplementary Fig. 1). The distribution of genotypes and alleles of each SNP according to the skeletal-facial profiles is displayed in Table 2. All of the groups were in HWE with non-significant χ2 values comparing the observed and expected genotype frequencies (P > 0.05, χ2 < 3.841). In relation to rs6184 polymorphism, although there were no individuals with the homozygous mutation (AA), the results showed that only the genotype CA was a significant candidate for association with Class III skeletal-facial profile (P < 0.001, χ2). Also, a significant over-representation of the allele A was observed in Class III compared with Class I or with Class II skeletal-facial profiles (P < 0.001). On the other hand, concerning rs6180 polymorphism, no significant differences neither in the distribution of genotypes nor in the allele frequencies for this polymorphism amongst skeletal-facial profiles were observed (all P values >0.05). Also as a result, in the whole study group, the minor allele frequency (MAF) for the A allele of rs6184 polymorphism was 1.8%, whereas the C allele of rs6180 polymorphism showed a MAF of 38.4% (data not shown). Table 2. Bivariate associations between genotype distributions and allele frequencies of the GHR gene variants and skeletal-facial profiles. SNP id Skeletal-facial profilea P-value* Skeletal Class I (n = 112) Skeletal Class II (n = 150) Skeletal Class III (n = 44) rs6184 Genotype CC 110 (98.20) 148 (98.70) 37 (84.10) <0.001 CA 2 (1.80) 2 (1.30) 7 (15.90)b,c AA — — — Allele frequency C 222 (99.10) 298 (99.30) 81 (92.00) <0.001 A 2 (0.90) 2 (0.70) 7 (8.00)d,e Hardy-Weinberg equilibrium χ2 critical value 0.008 0.004 0.745 χ2 test P-value 0.927 0.947 0.387 rs6180 Genotype AA 43 (38.40) 63 (42.00) 16 (36.40) 0.643 AC 50 (44.60) 60 (40.00) 23 (52.30) CC 19 (17.00) 27 (18.00) 5 (11.40) Allele frequency A 136 (60.70) 186 (62.00) 55 (52.50) 0.940 C 88 (39.30) 114 (38.00) 33 (37.50) Hardy–Weinberg equilibrium χ2 critical value 0.425 2.283 1.311 χ2 test P-value 0.514 0.131 0.252 SNP id Skeletal-facial profilea P-value* Skeletal Class I (n = 112) Skeletal Class II (n = 150) Skeletal Class III (n = 44) rs6184 Genotype CC 110 (98.20) 148 (98.70) 37 (84.10) <0.001 CA 2 (1.80) 2 (1.30) 7 (15.90)b,c AA — — — Allele frequency C 222 (99.10) 298 (99.30) 81 (92.00) <0.001 A 2 (0.90) 2 (0.70) 7 (8.00)d,e Hardy-Weinberg equilibrium χ2 critical value 0.008 0.004 0.745 χ2 test P-value 0.927 0.947 0.387 rs6180 Genotype AA 43 (38.40) 63 (42.00) 16 (36.40) 0.643 AC 50 (44.60) 60 (40.00) 23 (52.30) CC 19 (17.00) 27 (18.00) 5 (11.40) Allele frequency A 136 (60.70) 186 (62.00) 55 (52.50) 0.940 C 88 (39.30) 114 (38.00) 33 (37.50) Hardy–Weinberg equilibrium χ2 critical value 0.425 2.283 1.311 χ2 test P-value 0.514 0.131 0.252 aValues are given as n (%) of individuals within diagnosis group. *Two-sided Pearson’s chi-square test (χ2). bStatistically significant difference as compared with Class I skeletal-facial profile (P = 0.002, Fisher’s exact test). cStatistically significant difference as compared with Class II skeletal-facial profile (P = 0.001, Fisher’s exact test). dStatistically significant difference as compared with Class I skeletal-facial profile (P = 0.003, Fisher’s exact test). eStatistically significant difference as compared with Class II skeletal-facial profile (P = 0.001, Fisher’s exact test). View Large Table 2. Bivariate associations between genotype distributions and allele frequencies of the GHR gene variants and skeletal-facial profiles. SNP id Skeletal-facial profilea P-value* Skeletal Class I (n = 112) Skeletal Class II (n = 150) Skeletal Class III (n = 44) rs6184 Genotype CC 110 (98.20) 148 (98.70) 37 (84.10) <0.001 CA 2 (1.80) 2 (1.30) 7 (15.90)b,c AA — — — Allele frequency C 222 (99.10) 298 (99.30) 81 (92.00) <0.001 A 2 (0.90) 2 (0.70) 7 (8.00)d,e Hardy-Weinberg equilibrium χ2 critical value 0.008 0.004 0.745 χ2 test P-value 0.927 0.947 0.387 rs6180 Genotype AA 43 (38.40) 63 (42.00) 16 (36.40) 0.643 AC 50 (44.60) 60 (40.00) 23 (52.30) CC 19 (17.00) 27 (18.00) 5 (11.40) Allele frequency A 136 (60.70) 186 (62.00) 55 (52.50) 0.940 C 88 (39.30) 114 (38.00) 33 (37.50) Hardy–Weinberg equilibrium χ2 critical value 0.425 2.283 1.311 χ2 test P-value 0.514 0.131 0.252 SNP id Skeletal-facial profilea P-value* Skeletal Class I (n = 112) Skeletal Class II (n = 150) Skeletal Class III (n = 44) rs6184 Genotype CC 110 (98.20) 148 (98.70) 37 (84.10) <0.001 CA 2 (1.80) 2 (1.30) 7 (15.90)b,c AA — — — Allele frequency C 222 (99.10) 298 (99.30) 81 (92.00) <0.001 A 2 (0.90) 2 (0.70) 7 (8.00)d,e Hardy-Weinberg equilibrium χ2 critical value 0.008 0.004 0.745 χ2 test P-value 0.927 0.947 0.387 rs6180 Genotype AA 43 (38.40) 63 (42.00) 16 (36.40) 0.643 AC 50 (44.60) 60 (40.00) 23 (52.30) CC 19 (17.00) 27 (18.00) 5 (11.40) Allele frequency A 136 (60.70) 186 (62.00) 55 (52.50) 0.940 C 88 (39.30) 114 (38.00) 33 (37.50) Hardy–Weinberg equilibrium χ2 critical value 0.425 2.283 1.311 χ2 test P-value 0.514 0.131 0.252 aValues are given as n (%) of individuals within diagnosis group. *Two-sided Pearson’s chi-square test (χ2). bStatistically significant difference as compared with Class I skeletal-facial profile (P = 0.002, Fisher’s exact test). cStatistically significant difference as compared with Class II skeletal-facial profile (P = 0.001, Fisher’s exact test). dStatistically significant difference as compared with Class I skeletal-facial profile (P = 0.003, Fisher’s exact test). eStatistically significant difference as compared with Class II skeletal-facial profile (P = 0.001, Fisher’s exact test). View Large The analysis by Haploview showed that rs6184 allele A was with high probability present only in chromosomes that carry also rs6180 allele A. This means that a haplotype A (rs6184)-C (rs6180) would not have existed in this study group, and the parameters D’ and r2 get values of 1 indicating strong linkage equilibrium between the polymorphisms. In addition, haplotype analysis showed that whereas two major haplotypes (C-A, C-C) accounting for 98.2% of estimated haplotypes in this Colombian population, the A-A haplotype had a frequency of 1.8%. Nevertheless, as shown in Table 3, the A-A haplotype was found to be significantly more frequent in Class III skeletal-facial profile (P < 0.001, χ2) than in Class I and Class II groups. Table 3. Bivariate associations between haplotypes estimated on the basis of genotypic data for rs6184 and rs6180 single nucleotide polymorphisms (SNPs) spanning the linkage disequilibrium (LP) block covering exon 10 of the GHR gene and skeletal-facial profile. Haplotypea Skeletal-facial profileb P-value* Class I (n = 224) Class II (n = 300) Class III (n = 88) C-A 134 (59.80) 184 (61.30) 48 (54.50) <0.001 C-C 88 (39.30) 114 (38.00) 33 (37.50) A-A 2 (0.90) 2 (0.70) 7 (8.00)c Haplotypea Skeletal-facial profileb P-value* Class I (n = 224) Class II (n = 300) Class III (n = 88) C-A 134 (59.80) 184 (61.30) 48 (54.50) <0.001 C-C 88 (39.30) 114 (38.00) 33 (37.50) A-A 2 (0.90) 2 (0.70) 7 (8.00)c aThe order of the polymorphism is as follows: rs6184-rs6180 bValues are given as n (%) of haplotypes within diagnosis. *Two-sided Pearson’s chi-square test (χ2). cStatistically significant difference when compared with Class I and II skeletal-facial profiles (P < 0.001 χ2 test). View Large Table 3. Bivariate associations between haplotypes estimated on the basis of genotypic data for rs6184 and rs6180 single nucleotide polymorphisms (SNPs) spanning the linkage disequilibrium (LP) block covering exon 10 of the GHR gene and skeletal-facial profile. Haplotypea Skeletal-facial profileb P-value* Class I (n = 224) Class II (n = 300) Class III (n = 88) C-A 134 (59.80) 184 (61.30) 48 (54.50) <0.001 C-C 88 (39.30) 114 (38.00) 33 (37.50) A-A 2 (0.90) 2 (0.70) 7 (8.00)c Haplotypea Skeletal-facial profileb P-value* Class I (n = 224) Class II (n = 300) Class III (n = 88) C-A 134 (59.80) 184 (61.30) 48 (54.50) <0.001 C-C 88 (39.30) 114 (38.00) 33 (37.50) A-A 2 (0.90) 2 (0.70) 7 (8.00)c aThe order of the polymorphism is as follows: rs6184-rs6180 bValues are given as n (%) of haplotypes within diagnosis. *Two-sided Pearson’s chi-square test (χ2). cStatistically significant difference when compared with Class I and II skeletal-facial profiles (P < 0.001 χ2 test). View Large On the other hand, additional bivariate tests (Table 4) revealed that, irrespective of diagnosis group, both mandibular body length and mandibular length were significantly larger, whereas the ANB angle was significantly smaller in those individuals carrying the CA genotype of the rs6184 SNP (P < 0.005, unpaired t-test). Conversely, no significant differences were observed between measurement parameters for craniofacial morphology according to the genetic variants of the rs6180 SNP (all P > 0.05). Also, after haplotype comparison, the results showed similar significant variations for these three cephalometric parameters in individuals having the A-A haplotype when compared with others without the A-A haplotype (all P < 0.05, one-way ANOVA/Bonferroni multiple comparison test, data not shown). Table 4. Bivariate comparisons of cephalometric parameters according to genotypes of the rs6184 and rs6180 single nucleotide polymorphisms (SNPs) of GHR gene (mean ± SD). Parameter SNP/Genotype rs6184 rs6180 CC (n = 295) CA (n = 11) AA (n = 0) P-Value* AA (n = 122) AC (n = 133) CC (n = 51) P-Value** S-Na distance 65.14 ± 3.39 65.25 ± 3.24 — 0.911 65.12 ± 3.35 65.14 ± 3.51 65.20 ± 3.15 0.990 SNA angle 82.50 ± 3.73 82.31 ± 3.97 — 0.877 82.61 ± 4.13 82.13 ± 3.50 83.14 ± 3.27 0.236 A ┴ Na-FH 0.87 ± 3.08 −0.74 ± 3.97 — 0.092 0.85 ± 3.53 0.46 ± 2.97 1.75 ± 2.96 0.052 Pog ┴ Na-FH −3.46 ± 6.49 −0.67 ± 11.98 — 0.461 −3.69 ± 7.20 −3.46 ± 6.35 −2.30 ± 6.67 0.457 SNB angle 78.70 ± 6.09 82.38 ± 6.33 — 0.050 79.21 ± 4.59 78.94 ± 3.91 77.69 ± 11.67 0.322 ANS-PNS distance 50.34 ± 3.07 49.43 ± 4.05 — 0.339 50.33 ± 3.19 50.16 ± 3.00 50.66 ± 3.24 0.623 Go-Mn distance 70.16 ± 4.57 74.40 ± 6.78 — 0.003 70.70 ± 4.59 69.87 ± 4.80 70.58 ± 4.80 0.341 ANB angle 3.55 ± 3.03 −0.07 ± 3.94 — <0.001 3.40 ± 3.31 3.24 ± 3.03 3.99 ± 2.95 0.344 Cd-Gn distance 110.20 ± 6.66 116.45 ± 9.98 — 0.003 111.06 ± 6.73 110.14 ± 7.19 109.67 ± 6.39 0.388 Cd-Go distance 53.78 ± 5.07 55.97 ± 7.39 — 0.169 54.17 ± 5.29 53.89 ± 5.34 53.05 ± 4.40 0.429 Ar-Go-Mn angle 119.55 ± 11.19 122.43 ± 10.48 — 0.402 118.74 ± 13.68 120.85 ± 9.29 118.76 ± 8.54 0.262 Parameter SNP/Genotype rs6184 rs6180 CC (n = 295) CA (n = 11) AA (n = 0) P-Value* AA (n = 122) AC (n = 133) CC (n = 51) P-Value** S-Na distance 65.14 ± 3.39 65.25 ± 3.24 — 0.911 65.12 ± 3.35 65.14 ± 3.51 65.20 ± 3.15 0.990 SNA angle 82.50 ± 3.73 82.31 ± 3.97 — 0.877 82.61 ± 4.13 82.13 ± 3.50 83.14 ± 3.27 0.236 A ┴ Na-FH 0.87 ± 3.08 −0.74 ± 3.97 — 0.092 0.85 ± 3.53 0.46 ± 2.97 1.75 ± 2.96 0.052 Pog ┴ Na-FH −3.46 ± 6.49 −0.67 ± 11.98 — 0.461 −3.69 ± 7.20 −3.46 ± 6.35 −2.30 ± 6.67 0.457 SNB angle 78.70 ± 6.09 82.38 ± 6.33 — 0.050 79.21 ± 4.59 78.94 ± 3.91 77.69 ± 11.67 0.322 ANS-PNS distance 50.34 ± 3.07 49.43 ± 4.05 — 0.339 50.33 ± 3.19 50.16 ± 3.00 50.66 ± 3.24 0.623 Go-Mn distance 70.16 ± 4.57 74.40 ± 6.78 — 0.003 70.70 ± 4.59 69.87 ± 4.80 70.58 ± 4.80 0.341 ANB angle 3.55 ± 3.03 −0.07 ± 3.94 — <0.001 3.40 ± 3.31 3.24 ± 3.03 3.99 ± 2.95 0.344 Cd-Gn distance 110.20 ± 6.66 116.45 ± 9.98 — 0.003 111.06 ± 6.73 110.14 ± 7.19 109.67 ± 6.39 0.388 Cd-Go distance 53.78 ± 5.07 55.97 ± 7.39 — 0.169 54.17 ± 5.29 53.89 ± 5.34 53.05 ± 4.40 0.429 Ar-Go-Mn angle 119.55 ± 11.19 122.43 ± 10.48 — 0.402 118.74 ± 13.68 120.85 ± 9.29 118.76 ± 8.54 0.262 *Two-sided unpaired t-test. **One-way ANOVA. View Large Table 4. Bivariate comparisons of cephalometric parameters according to genotypes of the rs6184 and rs6180 single nucleotide polymorphisms (SNPs) of GHR gene (mean ± SD). Parameter SNP/Genotype rs6184 rs6180 CC (n = 295) CA (n = 11) AA (n = 0) P-Value* AA (n = 122) AC (n = 133) CC (n = 51) P-Value** S-Na distance 65.14 ± 3.39 65.25 ± 3.24 — 0.911 65.12 ± 3.35 65.14 ± 3.51 65.20 ± 3.15 0.990 SNA angle 82.50 ± 3.73 82.31 ± 3.97 — 0.877 82.61 ± 4.13 82.13 ± 3.50 83.14 ± 3.27 0.236 A ┴ Na-FH 0.87 ± 3.08 −0.74 ± 3.97 — 0.092 0.85 ± 3.53 0.46 ± 2.97 1.75 ± 2.96 0.052 Pog ┴ Na-FH −3.46 ± 6.49 −0.67 ± 11.98 — 0.461 −3.69 ± 7.20 −3.46 ± 6.35 −2.30 ± 6.67 0.457 SNB angle 78.70 ± 6.09 82.38 ± 6.33 — 0.050 79.21 ± 4.59 78.94 ± 3.91 77.69 ± 11.67 0.322 ANS-PNS distance 50.34 ± 3.07 49.43 ± 4.05 — 0.339 50.33 ± 3.19 50.16 ± 3.00 50.66 ± 3.24 0.623 Go-Mn distance 70.16 ± 4.57 74.40 ± 6.78 — 0.003 70.70 ± 4.59 69.87 ± 4.80 70.58 ± 4.80 0.341 ANB angle 3.55 ± 3.03 −0.07 ± 3.94 — <0.001 3.40 ± 3.31 3.24 ± 3.03 3.99 ± 2.95 0.344 Cd-Gn distance 110.20 ± 6.66 116.45 ± 9.98 — 0.003 111.06 ± 6.73 110.14 ± 7.19 109.67 ± 6.39 0.388 Cd-Go distance 53.78 ± 5.07 55.97 ± 7.39 — 0.169 54.17 ± 5.29 53.89 ± 5.34 53.05 ± 4.40 0.429 Ar-Go-Mn angle 119.55 ± 11.19 122.43 ± 10.48 — 0.402 118.74 ± 13.68 120.85 ± 9.29 118.76 ± 8.54 0.262 Parameter SNP/Genotype rs6184 rs6180 CC (n = 295) CA (n = 11) AA (n = 0) P-Value* AA (n = 122) AC (n = 133) CC (n = 51) P-Value** S-Na distance 65.14 ± 3.39 65.25 ± 3.24 — 0.911 65.12 ± 3.35 65.14 ± 3.51 65.20 ± 3.15 0.990 SNA angle 82.50 ± 3.73 82.31 ± 3.97 — 0.877 82.61 ± 4.13 82.13 ± 3.50 83.14 ± 3.27 0.236 A ┴ Na-FH 0.87 ± 3.08 −0.74 ± 3.97 — 0.092 0.85 ± 3.53 0.46 ± 2.97 1.75 ± 2.96 0.052 Pog ┴ Na-FH −3.46 ± 6.49 −0.67 ± 11.98 — 0.461 −3.69 ± 7.20 −3.46 ± 6.35 −2.30 ± 6.67 0.457 SNB angle 78.70 ± 6.09 82.38 ± 6.33 — 0.050 79.21 ± 4.59 78.94 ± 3.91 77.69 ± 11.67 0.322 ANS-PNS distance 50.34 ± 3.07 49.43 ± 4.05 — 0.339 50.33 ± 3.19 50.16 ± 3.00 50.66 ± 3.24 0.623 Go-Mn distance 70.16 ± 4.57 74.40 ± 6.78 — 0.003 70.70 ± 4.59 69.87 ± 4.80 70.58 ± 4.80 0.341 ANB angle 3.55 ± 3.03 −0.07 ± 3.94 — <0.001 3.40 ± 3.31 3.24 ± 3.03 3.99 ± 2.95 0.344 Cd-Gn distance 110.20 ± 6.66 116.45 ± 9.98 — 0.003 111.06 ± 6.73 110.14 ± 7.19 109.67 ± 6.39 0.388 Cd-Go distance 53.78 ± 5.07 55.97 ± 7.39 — 0.169 54.17 ± 5.29 53.89 ± 5.34 53.05 ± 4.40 0.429 Ar-Go-Mn angle 119.55 ± 11.19 122.43 ± 10.48 — 0.402 118.74 ± 13.68 120.85 ± 9.29 118.76 ± 8.54 0.262 *Two-sided unpaired t-test. **One-way ANOVA. View Large Univariate and multivariate binary logistic regression analyses for association of GHR gene polymorphisms with the skeletal-facial profile Based on the bivariate analyses and taking into account that significant associations were detected between rs6184 polymorphism and the A-A haplotype regarding Class III skeletal-facial profile, only these parameters were fitted into a multivariate binary logistic regression model to examine the strength and independence of the relationship and to assess for the presence of confounding after adjusting for age, digital-sucking habit, and oral breathing habit. The initial and final models derived from this analysis are displayed in Table 5. The Hosmer–Lemeshow goodness-of-fit test probability values varied from 0.290 to 0.391, confirming good calibration and fit of the multivariable model. Likewise, the c-statistic values ranged from 0.861 to 0.866 in the adjusted model, thus indicating good discrimination. As it could be noted from this table, the OR of Class III skeletal-facial profile was significantly increased (P < 0.001, Wald’s test) for individuals with the CA genotype of the rs6184 SNP as well as the A-A haplotype. Otherwise, based on the multivariate binary logistic regression model, it was noteworthy that the associations between the CA genotype of the rs6184 SNP or the A-A haplotype with Class III skeletal-facial profile were not confounded by other variables. In both cases, the associations persisted (P < 0.001, Wald’s test) when adjusted for other covariables. Table 5. Univariate and multivariate binary logistic regression analyses for the association of significant genetic predictors with Class III skeletal-facial profile after adjusting for age, digital-sucking habit, and oral breathing habit. Parameter Casesa Univariate analysis Multivariate binary logistic regression analysis Calibrationb Discriminationc Class I/Class II skeletal-facial profiles Class III skeletal-facial profile Unadjusted OR (95% CI) P-Value* Adjusted OR (95% CI) P-Value* rs6184 genotype CC 258 (98.50) 37 (84.10) Referent 0.391 0.866 CA 4 (1.50) 7 (15.90) 12.20 (3.41 − 43.71) <0.001 16.63 (4.07 − 68.02) <0.001 AA — — N/Ad N/Ad N/Ad N/Ad Haplotype C-A/C-C 520 (99.23) 81 (92.04) Referent 0.861 A-A 4 (0.77) 7 (7.96) 11.24 (3.22 − 39.24) <0.001 14.54 (3.77 − 56.03) <0.001 0.290 A-C — — N/Ad N/Ad N/Ad N/Ad Parameter Casesa Univariate analysis Multivariate binary logistic regression analysis Calibrationb Discriminationc Class I/Class II skeletal-facial profiles Class III skeletal-facial profile Unadjusted OR (95% CI) P-Value* Adjusted OR (95% CI) P-Value* rs6184 genotype CC 258 (98.50) 37 (84.10) Referent 0.391 0.866 CA 4 (1.50) 7 (15.90) 12.20 (3.41 − 43.71) <0.001 16.63 (4.07 − 68.02) <0.001 AA — — N/Ad N/Ad N/Ad N/Ad Haplotype C-A/C-C 520 (99.23) 81 (92.04) Referent 0.861 A-A 4 (0.77) 7 (7.96) 11.24 (3.22 − 39.24) <0.001 14.54 (3.77 − 56.03) <0.001 0.290 A-C — — N/Ad N/Ad N/Ad N/Ad aValues are given as n (%) of genotypes or haplotypes within diagnosis groups. *Wald test. bHosmer and Lemeshow goodness-of-fit test. cc-statistic. dN/A, not applicable View Large Table 5. Univariate and multivariate binary logistic regression analyses for the association of significant genetic predictors with Class III skeletal-facial profile after adjusting for age, digital-sucking habit, and oral breathing habit. Parameter Casesa Univariate analysis Multivariate binary logistic regression analysis Calibrationb Discriminationc Class I/Class II skeletal-facial profiles Class III skeletal-facial profile Unadjusted OR (95% CI) P-Value* Adjusted OR (95% CI) P-Value* rs6184 genotype CC 258 (98.50) 37 (84.10) Referent 0.391 0.866 CA 4 (1.50) 7 (15.90) 12.20 (3.41 − 43.71) <0.001 16.63 (4.07 − 68.02) <0.001 AA — — N/Ad N/Ad N/Ad N/Ad Haplotype C-A/C-C 520 (99.23) 81 (92.04) Referent 0.861 A-A 4 (0.77) 7 (7.96) 11.24 (3.22 − 39.24) <0.001 14.54 (3.77 − 56.03) <0.001 0.290 A-C — — N/Ad N/Ad N/Ad N/Ad Parameter Casesa Univariate analysis Multivariate binary logistic regression analysis Calibrationb Discriminationc Class I/Class II skeletal-facial profiles Class III skeletal-facial profile Unadjusted OR (95% CI) P-Value* Adjusted OR (95% CI) P-Value* rs6184 genotype CC 258 (98.50) 37 (84.10) Referent 0.391 0.866 CA 4 (1.50) 7 (15.90) 12.20 (3.41 − 43.71) <0.001 16.63 (4.07 − 68.02) <0.001 AA — — N/Ad N/Ad N/Ad N/Ad Haplotype C-A/C-C 520 (99.23) 81 (92.04) Referent 0.861 A-A 4 (0.77) 7 (7.96) 11.24 (3.22 − 39.24) <0.001 14.54 (3.77 − 56.03) <0.001 0.290 A-C — — N/Ad N/Ad N/Ad N/Ad aValues are given as n (%) of genotypes or haplotypes within diagnosis groups. *Wald test. bHosmer and Lemeshow goodness-of-fit test. cc-statistic. dN/A, not applicable View Large Discussion Over recent decades, the genetic control of craniofacial growth and development has been the focus of a large number of studies (11, 16, 21–28, 42). From these studies, it has been recognized that craniofacial morphology follows the pattern of multifactorial inheritance (20). Notwithstanding, the majority of the studies have only examined the correlation between mandibular height and genotype, and few published studies have focused on the description of SNPs in normal human populations and their association with the skeletal-facial profile (14, 18, 20, 28). In view of this fact, this cephalometric study investigated whether rs6184 and rs6180 SNPs in GHR gene might be linked with the skeletal-facial profile in a group of Colombian individuals. The cephalometric measurements calculated in this study were compared with Caucasian norms (37) because of the lack of data available from Colombian population. It is possible to assume that the study included a sufficiently large sample of individuals with different maxillo-mandibular relationships, which were categorized as Class I, Class II, or Class III skeletal-facial profile. Even so, information derived from these data may not necessarily portray the true cephalometric averages of the Colombian population. Hence, large-scale studies need to be performed to have greater statistical power and precision. In the search for candidate genes involved in craniofacial dysmorphogenesis, genetic polymorphisms affecting gene activity are prime targets (22). This investigation constitutes an explorative approach evaluating variations in detection frequencies of GHR SNPs from human genomic DNA obtained from saliva of the study participants in order to identify potential associations with skeletal-facial profile. The foremost findings reported here were that both the CA genotype of rs6184 SNP and the A-A haplotype were highly associated with Class III skeletal-facial profile in a Colombian population. The current findings suggest that the functionality of these SNPs might depend on gene–gene and gene–environmental interactions which could affect the skeletal–facial profile directly or indirectly, changing the function of the GHR protein, or also affecting their expression levels (21). In addition, there would also be the possibility that the effect would be caused by some other genetic variant, not studied here, in the GHR or some other gene, which is located in the same chromosome (haplotype). The present findings partially parallel those reported by others, which found significant associations between the CA genotype of rs6184 SNP (16, 31) and lack of association with any of rs6180 polymorphic variants (16) regarding mandibular morphology. However, different results have also been described since some have observed significant associations between CC genotype of rs6184 (23) and CC genotype of rs6180 (22) SNPs regarding mandibular morphology. Given that craniofacial morphology has ethnic differences (16, 43–45), it is important to highlight that the observed differences may be mainly due to different genotype/allele frequencies in distinct populations which may be attributed essentially to ethnic and geographical factors and that may complicate interpretation of the results of genetic studies. The Colombian population represents a mixture of different ethnic backgrounds (46) and this circumstance makes it very difficult to match the ethnicity of participants. It has been acknowledged that SNPs represent natural sequence variants in which the minor allele has a frequency greater than 1% in a human population (47). Nevertheless, both genotype and allele frequencies may vary between different ethnic groups, being more or less similar between populations sharing common ancestries or underlying different phylogeographical origins (48). In agreement with the former, in the present study, MAF of A allele of rs6184 SNP was 1.8%, nearly analogous to what was observed for other populations including Colombians (2.0%), Mexicans (2.0%), Peruvians (2.0%), Puerto Ricans (3.0%), and Europeans (2.0%), whereas East Asian populations show population frequencies of the allele ranging from 8.2 to 17.7% (www.1000genomes.org/1000-genomes-browsers) (49). For rs6180 SNP, the MAF of the C allele (38.4%) was also quite similar to what previously reported in that database for Colombians (37.0%), Mexicans (44.0%), Peruvians (35.0%), Puerto Ricans (44.0%), and Europeans (42.0%), but remarkably different from that of East Asian populations where the population frequencies of the allele vary from 54.8 to 62.6%. Thus, the results may have been partly affected by ethnic stratification. On the other hand, although there are controversies regarding the genetic association studies, the evaluation of these SNPs in the craniofacial skeleton could be helpful to unravel their putative role in the determination of skeletal-facial profile. In accordance with the former view, it has been previously shown that individuals carrying the CA genotype of rs6184 SNP present statistically higher values of mandibular ramus height (16, 23) and mandibular length (31). Equally, carriers of CC genotype of rs6180 SNP had showed a longer ramus than those with the genotype AC or AA (22). In the current study, however, although individuals carrying the CA genotype of the rs6184 SNP showed both significantly decreased values for ANB angle and increased measures concerning mandibular body length (Go–Mn distance) and mandibular length (Cd–Gn distance), no significant differences in cephalometric parameters amongst genotype groups of rs6180 SNP were observed. Adhering to the findings herein presented, it looks that rs6184 SNP of the GHR gene can affect not only the horizontal but also the longitudinal development of the mandible, thus leading strongly/independently to the acquisition of a Class III skeletal–facial profile. In this sense, it has been described that mandibular growth greatly depends on cartilage growth and is a multifactorial phenomenon in which genetic disposition, nutrition, homeostasis, hormones, and growth factors interact (23, 50) so that the cartilage-mediated growth in the mandibular condyle might play an important role in the determination of growth and morphology of the craniofacial complex (51). Given that the GHRs not only have been shown to be present in the mandibular condyle (52), but also play an important role in cartilage growth (50), amino acid changes in GHR gene, including the rs6184 and rs6180 variants, might affect mandibular growth with site-, area-, or region-specific effects (16), therefore resulting in a morphological difference between heterozygote and wild type genotypes. Conclusions Although the current results do not support that rs6180 SNP in the GHR gene could be identified as a predictor for skeletal-facial profile, they suggest that the allele A of rs6184 SNP alone or in combination with other SNPs in the GHR gene may account for significant horizontal and longitudinal variations of the mandibular morphology and might be a strong/independent prognostic indicator for Class III skeletal-facial profile in the present population. Supplementary material Supplementary material is available at European Journal of Orthodontics online. Funding This study has been fully supported by the Technical Research Council of the Faculty of Dentistry-University of Antioquia (CIFO-Code 021-2014). Conflict of Interest None to declare. Acknowledgements The authors would like to express their thanks to ECCO-Radiología Oral Inteligente at Medellín, for their assistance, cooperation, and provision of the cephalometric imaging. References 1. Staudt , C.B. and Kiliaridis , S . ( 2009 ) Different skeletal types underlying Class III malocclusion in a random population . American Journal of Orthodontics and Dentofacial Orthopedics , 136 , 715 – 721 . Google Scholar CrossRef Search ADS PubMed 2. Downs , W.B . ( 1956 ) Analysis of the dentofacial profile . The Angle Orthodontist , 26 , 191 – 212 . 3. Øland , J. , Jensen , J. , Papadopoulos , M.A. and Melsen , B . ( 2011 ) Does skeletal facial profile influence preoperative motives and postoperative satisfaction? A prospective study of 66 surgical-orthodontic patients . Journal of Oral and Maxillofacial Surgery , 69 , 2025 – 2032 . Google Scholar CrossRef Search ADS PubMed 4. Bishara , S.E . ( 2006 ) Class II malocclusions: diagnostic and clinical considerations with and without treatment . Seminars in Orthodontics , 12 , 11 – 24 . Google Scholar CrossRef Search ADS 5. Peck , H. and Peck , S . ( 1970 ) A concept of facial esthetics . The Angle Orthodontist , 40 , 284 – 318 . Google Scholar PubMed 6. Johnston , C. , Burden , D. , Kennedy , D. , Harradine , N. and Stevenson , M . ( 2006 ) Class III surgical-orthodontic treatment: a cephalometric study . American Journal of Orthodontics and Dentofacial Orthopedics , 130 , 300 – 309 . Google Scholar CrossRef Search ADS PubMed 7. Rana , T. , Khanna , R. , Tikku , T. and Sachan , K . ( 2012 ) Relationship of maxilla to cranial base in different facial types-a cephalometric evaluation . Journal of Oral Biology and Craniofacial Research , 2 , 30 – 35 . Google Scholar CrossRef Search ADS PubMed 8. Saunders , S.R. , Popovich , F. and Thompson , G.W . ( 1980 ) A family study of craniofacial dimensions in the Burlington Growth Centre sample . American Journal of Orthodontics , 78 , 394 – 403 . Google Scholar CrossRef Search ADS PubMed 9. Mossey , P.A . ( 1999 ) The heritability of malocclusion: Part 1—Genetics, principles and terminology . British Journal of Orthodontics , 26 , 103 – 113 . Google Scholar CrossRef Search ADS PubMed 10. Mossey , P.A . ( 1999 ) The heritability of malocclusion: part 2. The influence of genetics in malocclusion . British Journal of Orthodontics , 26 , 195 – 203 . Google Scholar CrossRef Search ADS PubMed 11. Yamaguchi , T. , Park , S.B. , Narita , A. , Maki , K. and Inoue , I . ( 2005 ) Genome-wide linkage analysis of mandibular prognathism in Korean and Japanese patients . Journal of Dental Research , 84 , 255 – 259 . Google Scholar CrossRef Search ADS PubMed 12. Cruz , R.M. , Krieger , H. , Ferreira , R. , Mah , J. , Hartsfield , J. Jr and Oliveira , S . ( 2008 ) Major gene and multifactorial inheritance of mandibular prognathism . American Journal of Medical Genetics. Part A , 146A , 71 – 77 . Google Scholar CrossRef Search ADS PubMed 13. Frazier-Bowers , S. , Rincon-Rodriguez , R. , Zhou , J. , Alexander , K. and Lange , E . ( 2009 ) Evidence of linkage in a Hispanic cohort with a Class III dentofacial phenotype . Journal of Dental Research , 88 , 56 – 60 . Google Scholar CrossRef Search ADS PubMed 14. Coussens , A.K. and van Daal , A . ( 2005 ) Linkage disequilibrium analysis identifies an FGFR1 haplotype-tag SNP associated with normal variation in craniofacial shape . Genomics , 85 , 563 – 573 . Google Scholar CrossRef Search ADS PubMed 15. Lee , D.G. , Kim , T.W. , Kang , S.C. and Kim , S.T . ( 2006 ) Estrogen receptor gene polymorphism and craniofacial morphology in female TMJ osteoarthritis patients . International Journal of Oral and Maxillofacial Surgery , 35 , 165 – 169 . Google Scholar CrossRef Search ADS PubMed 16. Kang , E.H. , Yamaguchi , T. , Tajima , A. , Nakajima , T. , Tomoyasu , Y. , Watanabe , M. , Yamaguchi , M. , Park , S.B. , Maki , K. and Inoue , I . ( 2009 ) Association of the growth hormone receptor gene polymorphisms with mandibular height in a Korean population . Archives of Oral Biology , 54 , 556 – 562 . Google Scholar CrossRef Search ADS PubMed 17. Hauspie , R.C. , Susanne , C. and Defrise-Gussenhoven , E . ( 1985 ) Testing for the presence of genetic variance in factors of face measurements of Belgian twins . Annals of Human Biology , 12 , 429 – 440 . Google Scholar CrossRef Search ADS PubMed 18. Devor , E.J . ( 1987 ) Transmission of human craniofacial dimensions . Journal of Craniofacial Genetics and Developmental Biology , 7 , 95 – 106 . Google Scholar PubMed 19. Sharma , K . ( 1998 ) Sex differences in genetic determinants of craniofacial variations–a study based on twin kinships . Acta Geneticae Medicae et Gemellologiae , 47 , 31 – 41 . Google Scholar CrossRef Search ADS PubMed 20. Johannsdottir , B. , Thorarinsson , F. , Thordarson , A. and Magnusson , T.E . ( 2005 ) Heritability of craniofacial characteristics between parents and offspring estimated from lateral cephalograms . American Journal of Orthodontics and Dentofacial Orthopedics , 127 , 200 – 7 . Google Scholar CrossRef Search ADS PubMed 21. Yamaguchi , T. , Maki , K. and Shibasaki , Y . ( 2001 ) Growth hormone receptor gene variant and mandibular height in the normal Japanese population . American Journal of Orthodontics and Dentofacial Orthopedics , 119 , 650 – 653 . Google Scholar CrossRef Search ADS PubMed 22. Zhou , J. , Lu , Y. , Gao , X.H. , Chen , Y.C. , Lu , J.J. , Bai , Y.X. , Shen , Y. and Wang , B.K . ( 2005 ) The growth hormone receptor gene is associated with mandibular height in a Chinese population . Journal of Dental Research , 84 , 1052 – 1056 . Google Scholar CrossRef Search ADS PubMed 23. Tomoyasu , Y. , Yamaguchi , T. , Tajima , A. , Nakajima , T. , Inoue , I. and Maki , K . ( 2009 ) Further evidence for an association between mandibular height and the growth hormone receptor gene in a Japanese population . American Journal of Orthodontics and Dentofacial Orthopedics , 136 , 536 – 541 . Google Scholar CrossRef Search ADS PubMed 24. Sasaki , Y. , Satoh , K. , Hayasaki , H. , Fukumoto , S. , Fujiwara , T. and Nonaka , K . ( 2009 ) The P561T polymorphism of the growth hormone receptor gene has an inhibitory effect on mandibular growth in young children . European Journal of Orthodontics , 31 , 536 – 541 . Google Scholar CrossRef Search ADS PubMed 25. Xue , F. , Wong , R. and Rabie , A.B . ( 2010 ) Identification of SNP markers on 1p36 and association analysis of EPB41 with mandibular prognathism in a Chinese population . Archives of Oral Biology , 55 , 867 – 872 . Google Scholar CrossRef Search ADS PubMed 26. Jang , J.Y. , Park , E.K. , Ryoo , H.M. , Shin , H.I. , Kim , T.H. , Jang , J.S. , Park , H.S. , Choi , J.Y. and Kwon , T.G . ( 2010 ) Polymorphisms in the Matrilin-1 gene and risk of mandibular prognathism in Koreans . Journal of Dental Research , 89 , 1203 – 1207 . Google Scholar CrossRef Search ADS PubMed 27. Tassopoulou-Fishell , M. , Deeley , K. , Harvey , E.M. , Sciote , J. and Vieira , A.R . ( 2012 ) Genetic variation in myosin 1H contributes to mandibular prognathism . American Journal of Orthodontics and Dentofacial Orthopedics , 141 , 51 – 59 . Google Scholar CrossRef Search ADS PubMed 28. Peng , S. , Tan , J. , Hu , S. , Zhou , H. , Guo , J. , Jin , L. and Tang , K . ( 2013 ) Detecting genetic association of common human facial morphological variation using high density 3D image registration . PLoS Computational Biology , 9 , e1003375 . Google Scholar CrossRef Search ADS PubMed 29. Piwien-Pilipuk , G. , Huo , J.S. and Schwartz , J . ( 2002 ) Growth hormone signal transduction . Journal of Pediatric Endocrinology & Metabolism: JPEM , 15 , 771 – 786 . Google Scholar CrossRef Search ADS PubMed 30. Ramirez-Yañez , G.O. , Smid , J.R. , Young , W.G. and Waters , M.J . ( 2005 ) Influence of growth hormone on the craniofacial complex of transgenic mice . European Journal of Orthodontics , 27 , 494 – 500 . Google Scholar CrossRef Search ADS PubMed 31. Bayram , S. , Basciftci , F.A. and Kurar , E . ( 2014 ) Relationship between P561T and C422F polymorphisms in growth hormone receptor gene and mandibular prognathism . The Angle Orthodontist , 84 , 803 – 809 . Google Scholar CrossRef Search ADS PubMed 32. Leung , D.W. , Spencer , S.A. , Cachianes , G. , Hammonds , R.G. , Collins , C. , Henzel , W.J. , Barnard , R. , Waters , M.J. and Wood , W.I . ( 1987 ) Growth hormone receptor and serum binding protein: purification, cloning and expression . Nature , 330 , 537 – 543 . Google Scholar CrossRef Search ADS PubMed 33. Godowski , P.J. , Leung , D.W. , Meacham , L.R. , Galgani , J.P. , Hellmiss , R. , Keret , R. , Rotwein , P.S. , Parks , J.S. , Laron , Z. and Wood , W.I . ( 1989 ) Characterization of the human growth hormone receptor gene and demonstration of a partial gene deletion in two patients with Laron-type dwarfism . Proceedings of the National Academy of Sciences of the United States of America , 86 , 8083 – 8087 . Google Scholar CrossRef Search ADS PubMed 34. Xu-Sheng , Q. , Yong , Q. , Xu , S. , Cai-Wei , X. , Wei-Jun , W. , Bi-Yu , R. and Shou-Feng , W . ( 2007 ) An analysis of growth hormone receptor gene polymorphism for Han population in Jiangsu province . Journal of Nanjing University (Natural Sciences) , 43 , 145 – 151 . 35. Baccetti , T. , Franchi , L. and McNamara , J.A. Jr . ( 2002 ) An improved version of the cervical vertebral maturation (CVM) method for the assessment of mandibular growth . The Angle Orthodontist , 72 , 316 – 323 . Google Scholar PubMed 36. Steiner , C.C . ( 1953 ) Cephalometrics for you and me . American Journal of Orthodontics , 39 , 720 – 755 . 37. McNamara , J.A. Jr . ( 1984 ) A method of cephalometric evaluation . American Journal of Orthodontics , 86 , 449 – 469 . Google Scholar CrossRef Search ADS PubMed 38. Burstone , C.J. , James , R.B. , Legan , H. , Murphy , G.A. and Norton , L.A . ( 1978 ) Cephalometrics for orthognathic surgery . Journal of Oral Surgery , 36 , 269 – 277 . Google Scholar PubMed 39. BJORK , A . ( 1963 ) Variations in the growth pattern of the human mandible: longitudinal radiographic study by the implant method . Journal of Dental Research , 42 , 400 – 411 . Google Scholar CrossRef Search ADS PubMed 40. Björk , A . ( 1969 ) Prediction of mandibular growth rotation . American Journal of Orthodontics , 55 , 585 – 599 . Google Scholar CrossRef Search ADS PubMed 41. Chujo , S. , Kaji , H. , Takahashi , Y. , Okimura , Y. , Abe , H. and Chihara , K . ( 1996 ) No correlation of growth hormone receptor gene mutation P561T with body height . European Journal of Endocrinology , 134 , 560 – 562 . Google Scholar CrossRef Search ADS PubMed 42. Francis-West , P.H. , Robson , L. and Evans , D.J . ( 2003 ) Craniofacial development: the tissue and molecular interactions that control development of the head . Advances in Anatomy, Embryology, and Cell Biology , 169 , III – VI, 1 . Google Scholar PubMed 43. Miyajima , K. , McNamara , J.A. Jr , Kimura , T. , Murata , S. and Iizuka , T . ( 1996 ) Craniofacial structure of Japanese and European-American adults with normal occlusions and well-balanced faces . American Journal of Orthodontics and Dentofacial Orthopedics , 110 , 431 – 438 . Google Scholar CrossRef Search ADS PubMed 44. Ishii , N. , Deguchi , T. and Hunt , N.P . ( 2002 ) Morphological differences in the craniofacial structure between Japanese and Caucasian girls with Class II Division 1 malocclusions . European Journal of Orthodontics , 24 , 61 – 67 . Google Scholar CrossRef Search ADS PubMed 45. Ioi , H. , Nakata , S. , Nakasima , A. and Counts , A.L . ( 2007 ) Comparison of cephalometric norms between Japanese and Caucasian adults in antero-posterior and vertical dimension . European Journal of Orthodontics , 29 , 493 – 499 . Google Scholar CrossRef Search ADS PubMed 46. Ibarra , A. et al. ( 2014 ) Comparison of the genetic background of different Colombian populations using the SNPforID 52plex identification panel . International Journal of Legal Medicine , 128 , 19 – 25 . Google Scholar CrossRef Search ADS PubMed 47. Nachman , M.W . ( 2001 ) Single nucleotide polymorphisms and recombination rate in humans . Trends in Genetics: TIG , 17 , 481 – 485 . Google Scholar CrossRef Search ADS PubMed 48. Pena , S.D. , Bastos-Rodrigues , L. , Pimenta , J.R. and Bydlowski , S.P . ( 2009 ) DNA tests probe the genomic ancestry of Brazilians . Brazilian Journal of Medical and Biological Research , 42 , 870 – 876 . Google Scholar CrossRef Search ADS PubMed 49. 1000 Genomes Project Consortium , Abecasis , G.R. , Altshuler , D. , Auton , A. , Brooks , L.D. , Durbin , R.M. , Gibbs , R.A. , Hurles , M.E. and McVean , G.A . ( 2010 ) A map of human genome variation from population-scale sequencing . Nature , 467 , 1061 – 1073 . Google Scholar CrossRef Search ADS PubMed 50. Visnapuu , V. , Peltomäki , T. , Rönning , O. , Vahlberg , T. and Helenius , H . ( 2001 ) Growth hormone and insulin-like growth factor I receptors in the temporomandibular joint of the rat . Journal of Dental Research , 80 , 1903 – 1907 . Google Scholar CrossRef Search ADS PubMed 51. Mizoguchi , I. , Toriya , N. and Nakao , Y . ( 2013 ) Growth of the mandible and biological characteristics of the mandibular condylar cartilage . Japanese Dental Science Review , 49 , 139 – 150 . Google Scholar CrossRef Search ADS 52. Lewinson , D. , Bialik , G.M. and Hochberg , Z . ( 1994 ) Differential effects of hypothyroidism on the cartilage and the osteogenic process in the mandibular condyle: recovery by growth hormone and thyroxine . Endocrinology , 135 , 1504 – 1510 . Google Scholar CrossRef Search ADS PubMed © The Author 2017. Published by Oxford University Press on behalf of the European Orthodontic Society. All rights reserved. For permissions, please email: journals.permissions@oup.com

Journal

The European Journal of OrthodonticsOxford University Press

Published: Oct 20, 2017

There are no references for this article.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off