Nora Alhazmi, Ali Alaqla, Bader Almuzzaini, Mohammed Aldrees, Ghaida Alnaqa, Farah Almasoud, Omar Aldibasi, Hala Alshamlan
{"title":"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。","authors":"Nora Alhazmi, Ali Alaqla, Bader Almuzzaini, Mohammed Aldrees, Ghaida Alnaqa, Farah Almasoud, Omar Aldibasi, Hala Alshamlan","doi":"10.1186/s40510-024-00532-4","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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<sup>TM</sup> 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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":56071,"journal":{"name":"Progress in Orthodontics","volume":"25 1","pages":"31"},"PeriodicalIF":4.8000,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11345341/pdf/","citationCount":"0","resultStr":"{\"title\":\"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.\",\"authors\":\"Nora Alhazmi, Ali Alaqla, Bader Almuzzaini, Mohammed Aldrees, Ghaida Alnaqa, Farah Almasoud, Omar Aldibasi, Hala Alshamlan\",\"doi\":\"10.1186/s40510-024-00532-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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<sup>TM</sup> 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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>\",\"PeriodicalId\":56071,\"journal\":{\"name\":\"Progress in Orthodontics\",\"volume\":\"25 1\",\"pages\":\"31\"},\"PeriodicalIF\":4.8000,\"publicationDate\":\"2024-08-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11345341/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Progress in Orthodontics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s40510-024-00532-4\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Dentistry\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Progress in Orthodontics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s40510-024-00532-4","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Dentistry","Score":null,"Total":0}
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.
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 TaqManTM 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.
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
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