Department of Preventive Dental Sciences, College of Dentistry, King Saud bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, Ministry of the National Guard Health Affairs, Riyadh, Saudi Arabia.
Department of Restorative and Prosthetic Dental Sciences, College of Dentistry, King Saud bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, Ministry of the National Guard Health Affairs, Riyadh, Saudi Arabia.
Prog Orthod. 2024 Aug 26;25(1):31. doi: 10.1186/s40510-024-00532-4.
Hypodontia is the most prevalent dental anomaly in humans, and is primarily attributed to genetic factors. Although genome-wide association studies (GWAS) have identified single-nucleotide polymorphisms (SNP) associated with hypodontia, genetic risk assessment remains challenging due to population-specific SNP variants. Therefore, we aimed to conducted a genetic analysis and developed a machine-learning-based predictive model to examine the association between previously reported SNPs and hypodontia in the Saudi Arabian population. Our case-control study included 106 participants (aged 8-50 years; 64 females and 42 males), comprising 54 hypodontia cases and 52 controls. We utilized TaqMan Real-Time Polymerase Chain Reaction and allelic genotyping to analyze three selected SNPs (AXIN2: rs2240308, PAX9: rs61754301, and MSX1: rs12532) in unstimulated whole saliva samples. The chi-square test, multinomial logistic regression, and machine-learning techniques were used to assess genetic risk by using odds ratios (ORs) for multiple target variables.
Multivariate logistic regression indicated a significant association between homozygous AXIN2 rs2240308 and the hypodontia phenotype (ORs [95% confidence interval] 2.893 [1.28-6.53]). Machine-learning algorithms revealed that the AXIN2 homozygous (A/A) genotype is a genetic risk factor for hypodontia of teeth #12, #22, and #35, whereas the AXIN2 homozygous (G/G) genotype increases the risk for hypodontia of teeth #22, #35, and #45. The PAX9 homozygous (C/C) genotype is associated with an increased risk for hypodontia of teeth #22 and #35.
Our study confirms a link between AXIN2 and hypodontia in Saudi orthodontic patients and suggests that combining machine-learning models with SNP analysis of saliva samples can effectively identify individuals with non-syndromic hypodontia.
缺牙是人类最常见的牙齿异常,主要归因于遗传因素。尽管全基因组关联研究(GWAS)已经确定了与缺牙相关的单核苷酸多态性(SNP),但由于特定人群的 SNP 变异,遗传风险评估仍然具有挑战性。因此,我们旨在进行遗传分析,并开发基于机器学习的预测模型,以检查先前报道的 SNP 与沙特阿拉伯人群中缺牙之间的关联。我们的病例对照研究包括 106 名参与者(年龄 8-50 岁;64 名女性和 42 名男性),包括 54 例缺牙病例和 52 例对照。我们利用 TaqMan 实时聚合酶链反应和等位基因基因分型,分析了未刺激全唾液样本中三个选定的 SNP(AXIN2:rs2240308、PAX9:rs61754301 和 MSX1:rs12532)。卡方检验、多项逻辑回归和机器学习技术用于使用多个目标变量的比值比(ORs)评估遗传风险。
多变量逻辑回归表明,AXIN2 rs2240308 纯合子与缺牙表型之间存在显著关联(ORs[95%置信区间]2.893[1.28-6.53])。机器学习算法表明,AXIN2 纯合子(A/A)基因型是 #12、#22 和 #35 牙齿缺牙的遗传危险因素,而 AXIN2 纯合子(G/G)基因型增加了 #22、#35 和 #45 牙齿缺牙的风险。PAX9 纯合子(C/C)基因型与 #22 和 #35 牙齿缺牙的风险增加相关。
我们的研究证实了 AXIN2 与沙特正畸患者缺牙之间的联系,并表明结合 SNP 分析和唾液样本的机器学习模型可以有效地识别非综合征性缺牙个体。