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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\u2013control study included 106 participants (aged 8\u201350 years; 64 females and 42 males), comprising 54 hypodontia cases and 52 controls. We utilized TaqMan<jats:sup>TM<\/jats:sup> Real-Time Polymerase Chain Reaction and allelic genotyping to analyze three selected SNPs (<jats:italic>AXIN2<\/jats:italic>: rs2240308, <jats:italic>PAX9<\/jats:italic>: rs61754301, and <jats:italic>MSX1<\/jats:italic>: 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.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Multivariate logistic regression indicated a significant association between homozygous <jats:italic>AXIN2<\/jats:italic> rs2240308 and the hypodontia phenotype (ORs [95% confidence interval] 2.893 [1.28\u20136.53]). Machine-learning algorithms revealed that the <jats:italic>AXIN2<\/jats:italic> homozygous (A\/A) genotype is a genetic risk factor for hypodontia of teeth #12, #22, and #35, whereas the <jats:italic>AXIN2<\/jats:italic> homozygous (G\/G) genotype increases the risk for hypodontia of teeth #22, #35, and #45. The <jats:italic>PAX9<\/jats:italic> homozygous (C\/C) genotype is associated with an increased risk for hypodontia of teeth #22 and #35.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Our study confirms a link between <jats:italic>AXIN2<\/jats:italic> and hypodontia in Saudi orthodontic patients and suggest<jats:bold>s<\/jats:bold> that combining machine-learning models with SNP analysis of saliva samples can effectively identify individuals with non-syndromic hypodontia.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s40510-024-00532-4","type":"journal-article","created":{"date-parts":[[2024,8,26]],"date-time":"2024-08-26T00:02:07Z","timestamp":1724630527000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["What could be the role of genetic tests and machine learning of AXIN2 variant dominance in non-syndromic hypodontia? 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