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GHR and IGF2R genes may contribute to normal variations in craniofacial dimensions: Insights from an admixed
Guido Artemio Marañón-Vásquez1, Alexandre R Vieira2, Alice Gomes de Carvalho Ramos3
1Department of Pediatric Dentistry, School of Dentistry of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil.
Introduction:
This study aimed to determine whether single nucleotide polymorphisms in the growth hormone receptor (GHR) and insulin-like growth factor 2 receptor (IGF2R) genes are associated with different craniofacial phenotypes.
Methods:
A total of 596 orthodontic and 98 orthognathic patients from 4 cities in Brazil were included for analyses. Angular and linear cephalometric measurements were obtained, and phenotype characterizations were performed. Genomic DNA was collected from buccal cells and single nucleotide polymorphisms in GHR (rs2910875, rs2973015, rs1509460) and IGF2R (rs2277071, rs6909681, rs6920141) were genotyped by polymerase chain reactions using TaqMan assay. Genotype-phenotype associations were assessed in the total sample (statistical significance was set at P <8.333 × 10-3) and by a meta-analytic approach implemented to calculate the single effect size measurement for the different cohorts.
Results:
Rare homozygotes for the GHR rs2973015 showed increased measurements for the lower anterior facial height (ANS-Me) and mandibular sagittal lengths (Co-Gn and Go-Pg). In contrast, common homozygotes for the IGF2R rs6920141 presented reduced measurements for these dimensions (ANS-Me and Go-Pg). Furthermore, the less common homozygotes for IGF2R rs2277071 had reduced maxillary sagittal length (Ptm'-A'). The meta-analytical approach replicated the associations of rs2973015 with ANS-Me, rs2277071 with Ptm'-A', and rs6920141 with Go-Pg.
Conclusions:
Our results provide further evidence that GHR contributes to the determination of mandibular morphology. In addition, we report that IGF2R is a possible gene associated with variations in craniofacial dimensions. Applying meta-analytical approaches to genetic variation data originating from likely underpowered samples may provide additional insight regarding genotype and/or phenotype associations.
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