What could be the role of genetic tests and machine learning of AXIN2 variant dominance in non-syndromic hypodontia? A case-control study in orthodontically treated patients.

IF 4.8 2区医学 Q1 Dentistry Progress in Orthodontics Pub Date : 2024-08-26 DOI:10.1186/s40510-024-00532-4

Nora Alhazmi, Ali Alaqla, Bader Almuzzaini, Mohammed Aldrees, Ghaida Alnaqa, Farah Almasoud, Omar Aldibasi, Hala Alshamlan

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Abstract

Background: 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^TM 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.

Results: 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.

Conclusions: 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.

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基因检测和机器学习 AXIN2 变异显性在非综合征性牙齿发育不全中的作用是什么？一项针对正畸治疗患者的病例对照研究。

背景：牙列不齐是人类最常见的牙齿畸形，主要归因于遗传因素。尽管全基因组关联研究（GWAS）已经发现了与牙列不齐相关的单核苷酸多态性（SNP），但由于特定人群的 SNP 变异，遗传风险评估仍具有挑战性。因此，我们旨在进行一项遗传分析，并开发一种基于机器学习的预测模型，以研究之前报道的沙特阿拉伯人群中的 SNP 与牙列不齐之间的关联。我们的病例对照研究纳入了 106 名参与者（年龄在 8-50 岁之间；64 名女性和 42 名男性），其中包括 54 名乳齿发育不全病例和 52 名对照者。我们利用 TaqManTM 实时聚合酶链式反应和等位基因分型技术分析了未刺激唾液样本中的三个选定 SNPs（AXIN2：rs2240308、PAX9：rs61754301 和 MSX1：rs12532）。通过使用多个目标变量的几率比（ORs），采用卡方检验、多项式逻辑回归和机器学习技术评估遗传风险：结果：多变量逻辑回归表明，同源 AXIN2 rs2240308 与牙列不齐表型之间存在显著关联（ORs [95% 置信区间] 2.893 [1.28-6.53]）。机器学习算法显示，AXIN2 同源（A/A）基因型是 12 号牙、22 号牙和 35 号牙软骨发育不全的遗传风险因素，而 AXIN2 同源（G/G）基因型会增加 22 号牙、35 号牙和 45 号牙软骨发育不全的风险。PAX9同源（C/C）基因型与22号和35号牙齿发育不全的风险增加有关：我们的研究证实了 AXIN2 与沙特正畸患者牙齿发育不全之间的联系，并表明将机器学习模型与唾液样本的 SNP 分析相结合可有效识别非综合征性牙齿发育不全患者。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Progress in Orthodontics Dentistry-Orthodontics

CiteScore

7.30

自引率

4.20%

发文量

审稿时长

13 weeks

期刊介绍： Progress in Orthodontics is a fully open access, international journal owned by the Italian Society of Orthodontics and published under the brand SpringerOpen. The Society is currently covering all publication costs so there are no article processing charges for authors. It is a premier journal of international scope that fosters orthodontic research, including both basic research and development of innovative clinical techniques, with an emphasis on the following areas: • Mechanisms to improve orthodontics • Clinical studies and control animal studies • Orthodontics and genetics, genomics • Temporomandibular joint (TMJ) control clinical trials • Efficacy of orthodontic appliances and animal models • Systematic reviews and meta analyses • Mechanisms to speed orthodontic treatment Progress in Orthodontics will consider for publication only meritorious and original contributions. These may be: • Original articles reporting the findings of clinical trials, clinically relevant basic scientific investigations, or novel therapeutic or diagnostic systems • Review articles on current topics • Articles on novel techniques and clinical tools • Articles of contemporary interest