Candidate Gene Analyses of Skeletal Variation in Malocclusion
Candidate Gene Analyses of Skeletal Variation in Malocclusion
复制标题
DOI：
10.1177/0022034515581643
复制
发表时间：
2015-07-01
影响因子：
7.6
通讯作者：
Uribe, L. M. Moreno
中科院分区：
文献类型：
--
作者：
da Fontoura, C. S. G.;Miller, S. F.;Uribe, L. M. Moreno
本研究评估了错牙合患者颅面候选基因与骨骼变异之间的关联。对269例未经治疗的骨性I、II、III类错牙合成人的头颅侧位片进行数字化处理，其中包括14个标志。二维坐标进行分析，使用Procrustes拟合和主成分（PC）分析，以产生连续的错？Skeleton分类（I、II或III）用作分类表型。对71个颅面基因和位点的198个单核苷酸多态性（SNPs）进行了基因分型。表型-基因型关联通过多变量线性回归连续表型和多项logistic回归骨性错牙合类进行了测试。PC分析导致4个主成分（PC）解释69%的总骨骼面部变异。PC 1解释了32.7%的变异，描述了从骨骼深到开放咬合的垂直差异。PC 1与PAX 5附近的SNP相关（P = 0.01）。PC 2解释了21.7%，并捕获了水平上下颌差异。PC 2与SNAI 3（P = 0.0002）和MYO 1H（P = 0.006）上游的SNP相关。PC 3解释了8.2%，并捕获了升支高度、体长和前颅底方向的变化。PC 3与TWIST 1相关（P = 0.000076）。最后，PC 4解释了6.6%，并检测到髁突倾斜度和联合投影的变化。PC 4与PAX 7相关（P = 0.007）。此外，骨骼II类风险相对于I类增加，其中FGFR 2中的SNP的次要等位基因（比值比[OR] = 2.1，P = 0.004），EDN 1中的SNP降低（OR = 0.5，P = 0.007）。相反，与I类相比，FGFR 2（OR 2.2，P = 0.005）和COL 1A 1（OR = 2.1，P = 0.008）中的SNP增加了骨骼III类风险，TBX 5（OR = 0.5，P = 0.014）中的SNP降低了骨骼III类风险。PAX 5、SNAI 3、MYO 1H、TWIST 1和PAX 7与错牙合患者的颅面骨骼变异相关，而FGFR 2、EDN 1、TBX 5和COL 1A 1与骨骼错牙合类型相关。
This study evaluated associations between craniofacial candidate genes and skeletal variation in patients with malocclusion. Lateral cephalometric radiographs of 269 untreated adults with skeletal classes I, II, and III malocclusion were digitized with 14 landmarks. Two-dimensional coordinates were analyzed using Procrustes fit and principal component (PC) analysis to generate continuous malocclusion phenotypes. Skeletal class classifications (I, II, or III) were used as a categorical phenotype. Individuals were genotyped for 198 single-nucleotide polymorphisms (SNPs) in 71 craniofacial genes and loci. Phenotype-genotype associations were tested via multivariate linear regression for continuous phenotypes and multinomial logistic regression for skeletal malocclusion class. PC analysis resulted in 4 principal components (PCs) explaining 69% of the total skeletal facial variation. PC1 explained 32.7% of the variation and depicted vertical discrepancies ranging from skeletal deep to open bites. PC1 was associated with a SNP near PAX5 (P = 0.01). PC2 explained 21.7% and captured horizontal maxillomandibular discrepancies. PC2 was associated with SNPs upstream of SNAI3 (P = 0.0002) and MYO1H (P = 0.006). PC3 explained 8.2% and captured variation in ramus height, body length, and anterior cranial base orientation. PC3 was associated with TWIST1 (P = 0.000076). Finally, PC4 explained 6.6% and detected variation in condylar inclination as well as symphysis projection. PC4 was associated with PAX7 (P = 0.007). Furthermore, skeletal class II risk increased relative to class I with the minor alleles of SNPs in FGFR2 (odds ratio [OR] = 2.1, P = 0.004) and declined with SNPs in EDN1 (OR = 0.5, P = 0.007). Conversely, skeletal class III risk increased versus class I with SNPs in FGFR2 (OR 2.2, P = 0.005) and COL1A1 (OR = 2.1, P = 0.008) and declined with SNPs in TBX5 (OR = 0.5, P = 0.014). PAX5, SNAI3, MYO1H, TWIST1, and PAX7 are associated with craniofacial skeletal variation among patients with malocclusion, while FGFR2, EDN1, TBX5, and COL1A1 are associated with type of skeletal malocclusion.