ABSTRACT
Families studies conducted in different ethnic populations worldwide have helped elucidate the molecular and genetic factors involved in the development of skeletal class III malocclusion. Therefore, the aim of this study is to provide an updated summary. The study followed the JBI Manual for Evidence Synthesis and PRISMA‐scR guidelines. PubMed, Scopus, WOS, Google Scholar and DANS databases were explored using specific strategies. Eligible studies included linkage and genome‐wide analyses, while association studies, case reports and in vivo/in vitro research were excluded. The included studies must have involved at least one family with one or more members exhibiting the skeletal malocclusion phenotypes. An autosomal dominant inheritance pattern with variable penetrance for skeletal class III malocclusions across East Asian, Southeast Asian, Middle Eastern, European and South American populations was identified. In contrast, skeletal class II malocclusions exhibited autosomal dominant and X‐linked inheritance patterns, with a higher prevalence in Eastern Mediterranean and South American populations. Key molecular findings include missense mutations in DUSP6 (c.545C>T and c.1094C>T), which affect mandibular prognathism and maxillary deficiency via the FGF/FGFR and MAPK/ERK pathways. Additionally, mutations in ADAMTS1 (c.742I>T), ADAMTS2 (c.3506G>T) and ADAMTSL1 (c.176G>A) impact mandibular growth through aggrecan metabolism and osteogenesis, disrupting bone remodelling via the EGFR/ErbB signalling pathway. This comprehensive review highlights the complex genetic basis of skeletal malocclusions, provides insights into the underlying molecular mechanisms, suggests potential targets for therapeutic intervention, and contributes to our understanding of the genetic architecture of these conditions.
Keywords: genomic, malocclusion, molecular, orthodontics, phenotype, skeletal class III
1. Introduction
1.1. Background
Craniofacial development is a complex process shaped by genetic and environmental factors, with skeletal malocclusions being a key manifestation [1, 2, 3]. While environmental factors play a role, genetic background is the dominant factor in their intergenerational transmission [1, 2]. Strong genetic influences have been reported in Class II division 2 (Class II/2) and, notably, Class III malocclusions, as well as open bite conditions [3]. A well known historical example is the ‘Habsburg jaw’, demonstrating genetic inheritance over multiple generations [2]. However, inheritance patterns vary across families, involving distinct chromosomal regions and genetic mechanisms [2].
Despite progress, the genetic basis of skeletal malocclusions remains incomplete. Systematic analysis of genetic variants through family‐based studies is crucial, as these offer insights beyond case–control genomic studies, mitigating issues like population stratification and parent‐of‐origin effects [4, 5]. Many rare, high‐penetrance variants remain undetected in genome‐wide association studies (GWAS) due to their reliance on array‐based genotyping and imputation [6]. Structural variations exceeding 50 bp, epistasis, and unsampled genetic variants contribute to missing heritability [7], which may be better captured through family‐based approaches [8].
Family studies are particularly effective for identifying rare genetic variants, as the segregation of low‐frequency alleles in pedigrees increases detection power [8]. Investigating parent‐of‐origin effects [9] and transmission bias [10] helps differentiate genetic from environmental influences [11], while also enabling the identification of de novo mutations [12] and error sources [13]. Additionally, transmission data improves phasing [14] and enhances rare variant imputation [15]. While traditionally considered a risk factor for Mendelian diseases, family history has also been shown to enhance predictions based on polygenic risk scores (PRS) [6].
In this critical review, we have employed a scoping review methodology to synthesise global evidence from family‐based research on non‐syndromic craniofacial malocclusions. It aims to present an updated, meta‐ethnic analysis of inheritance patterns, gene variants and phenotypic expression—focusing primarily on skeletal sagittal malocclusions across diverse populations.
1.2. Endophenotype Diversity in Skeletal Malocclusions and Ethnic‐Linked Variations
The craniofacial complex is considered an endophenotype, representing an intermediate trait emerging from facial morphological variation [16]. This concept has been extensively applied in research on skeletal malocclusions to improve the characterisation and understanding of its phenotypic expression. In family‐based studies, phenotypic expression has been primarily focused on sagittal discrepancies, especially skeletal Class III (SCIII) malocclusion.
Among the studies reviewed, which span 109 kindreds across two to seven generations, skeletal class III (SCIII) malocclusions were the most frequently analysed phenotype. In contrast, skeletal class II (SCII) malocclusions have been less extensively characterised and are typically defined by mandibular micrognathism, with mandibular size being the core clinical trait [17, 18, 19]. Compared with studies on SCII, research on SCIII offers richer phenotypic subtyping based on radiographic landmarks and cephalometric measurements [20, 21, 22, 23, 24, 25, 26].
In both SCII and SCIII studies, affected individuals are selected based on predominant anatomical structures (mandibular, maxillary, or mixed) contributing to maxillomandibular discrepancies [20, 21, 22, 23, 24, 25, 26] Researchers have attempted to classify phenotypes of SCIII based on whether the maxillary, mandibular, or both skeletal components are involved. However, these sub‐phenotypes are not always derived from consistent anatomical landmarks or cephalometric criteria, creating methodological heterogeneity across studies and populations.
Ethnic background remains a major determinant of sub‐phenotypic variation, particularly in SCIII malocclusions, with clustering studies identifying up to 14 morphological subtypes [1, 27, 28]. This review includes pedigrees from East Asian, Southeast Asian, Middle Eastern, European and South American families [17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33, 34, 35, 36].
2. Methods
This scoping review followed the JBI Manual for Evidence Synthesis and the PRISMA‐ScR guidelines. Studies were eligible if they involved at least one family with one or more members exhibiting skeletal malocclusion phenotypes and employed genetic family‐based methodologies, including linkage analysis, genome‐wide linkage analysis, or quantitative trait locus (QTL) mapping. Studies involving other craniofacial conditions or based on genetic association, animal or in vitro models, case reports, or reviews were excluded. No restrictions on language or year were applied.
A comprehensive search was conducted in PubMed, Scopus, Web of Science, Google Scholar and the grey literature platform DANS. Search terms were obtained by an initial PubMed search and refined using the Yale MeSH Analyser. Full search strings are detailed in File S1 and Figure S1.
Study selection was performed in three phases (duplicate removal, title/abstract screening and full‐text review) by two independent reviewers using the Rayyan web tool. Discrepancies were resolved by consensus or a third reviewer. Data were extracted using a predefined form and independently verified by a second reviewer. Extracted information included study characteristics, phenotyping details, genetic methodologies and identified loci and variants. A comprehensive description of the methodology and search results is detailed in File S2 and File S3.
3. Results
The complex interactions between genetic and epigenetic influence layers are critical for understanding the heritability of the composite phenotype that leads to skeletal malocclusion facial forms [37]. To date, 43 different genetic variants identified in family‐based transmission models have been associated with sagittal skeletal malocclusions; these variants span the genome, excluding chromosomes 13, 20, 21 and 22 (Figures 1 and 2 and Table S1). A strong ethnic component was associated with putative genetic markers of malocclusions. Most of the identified gene variants have low or null frequencies in populations other than those being studied, emphasising the role of ethnicity in the genetic architecture of these conditions.
FIGURE 1.
Pedigree and identified genes summary. Grey dots represent individuals with undefined phenotypes, black dots represent unaffected individuals, and red dots represent affected individuals. Phenotype segregation is indicated by red lines. ✢Genes and regions with evidence of linkage exclusion for that population. ✢✢No linkage or exclusion could be determined. ⦿Studies examining SCII phenotype. ●Studies examining SCIII phenotype. ▲▲X‐linked inheritance pattern.
FIGURE 2.
Variants' distribution across chromosomes. X‐axis represent chromosome numbers; * represents variants identified; ○ indicates absence of linkage.
3.1. Skeletal Class III Malocclusion
3.1.1. Genetic Factors
Several genes have been associated with SCIII malocclusion through family‐based studies, often showing ethnic‐specific patterns. Three members of the ADAMTS superfamily—ADAMTS2 (5q35.3), ADAMTS1 (21q21.2) and ADAMTSL1 (9p22.2)—have been linked to maxillary deficiency and mandibular prognathism in Han Chinese [30], Chinese [21] and Thai [31] families, respectively. However, findings from whole‐exome sequencing (WES) studies in Thai populations [31] did not replicate the associations observed in Chinese cohorts [21, 30], possibly due to differing ancestral compositions or phenotype definitions.
Of all the candidate genes, DUSP6 is the only one replicated in independent studies. Two heterozygous missense mutations, c.545C>T and c.1094C>T, were reported in Estonian [33] and Malaysian Malay [32] families, respectively. The same chromosomal region (12q22–q23) has also been implicated in Colombian families [36], where IGF1 was proposed as a candidate gene in patients with maxillary deficiency.
Additionally, a heterozygous mutation (c.35C>A) in FGF23, located in 12pter–p12.3, was found to segregate with mandibular prognathism in Chinese families [20]. These findings indicate that loci 12q22–q23 and 12pter–p12.3 may harbour critical genes influencing SCIII malocclusion.
Attempts to replicate microsatellite associations observed in Korean and Japanese siblings [26, 35] in Brazilian families were unsuccessful. Six microsatellites (D1S234, D6S1689, D19S566, D4S3038, D7S503 and D10S1483) were tested, but replication failed, likely due to the ethnic composition of Brazilian families (with limited Asian admixture) or phenotype heterogeneity. Notably, the genes near these microsatellites—TWIST1 (D7S503), FGFR3 (D4S3038) and FGFR2 (D10S1483)—were found to be strongly linked to craniosynostosis and maxillary deficiency, rather than mandibular prognathism [35].
3.1.2. Molecular Pathways and Mechanisms
Multiple genes implicated in SCIII malocclusion converge on signalling pathways governing bone development, osteogenesis and craniofacial morphogenesis [38, 39]. A central pathway involves FGF/FGFR signalling, which is critical for jaw growth and suture regulation [40]. Variants in FGFR2 (e.g., rs2162540) and FGFR3 (e.g., p.Arg671Gly) have been associated with maxillary deficiency in family studies [17, 35]. Although functional studies remain limited [41], evidence suggests that these variants interfere with mandibular and midfacial bone growth [42].
DUSP6 modulates this pathway by negatively regulating ERK activity. Its replicated mutations (c.545C>T, c.1094C>T) may disrupt ERK balance, leading to craniofacial dysmorphogenesis [32, 33]. This mechanism parallels findings in craniosynostosis syndromes [43].
The EGFR/ErbB signalling cascade has also been implicated. In zebrafish knockdowns, ADAMTS2 loss was found to disrupt osteogenesis by activating EGFR signalling, and ADAMTS1 and ADAMTSL1 variants appeared to affect ECM remodelling and condylar growth [21, 30, 31].
Lastly, FGF23, linked to mandibular prognathism in Chinese families [20], regulates phosphate homeostasis and bone remodelling, further implicating mineral metabolism as a relevant mechanism underlying SCIII pathogenesis [39].
3.2. Skeletal Class II Malocclusion
3.2.1. Genetic Factors
SCII malocclusion, defined primarily by mandibular retrognathism, often follows an autosomal dominant inheritance pattern with incomplete penetrance [17, 19], although an X‐linked pattern has also been proposed in families from Eastern Mediterranean and South American descent [17]. This latter finding contrasts with existing evidence [44] and may reflect differences in phenotype definitions.
One major candidate gene is NOG, encoding a glycoprotein that modulates bone morphogenetic protein (BMP) signalling, thereby influencing cartilage and bone development. Mutations in this gene have been linked to mandibular hypoplasia and mandibular micrognathism [18, 19]. In Colombian families, variants such as c.672C>A and c.567G/C were identified in cases of mandibular micrognathism without severe osseous features. However, methylation analysis revealed no CH3 group patterns, suggesting that NOG is not solely responsible for this phenotype [19].
Other candidate genes found in Eastern Mediterranean families with cases of SCII include GLUD2, ADGRG4, ARSH, TGIF1, FGFR3, ZNF181, INTS7 and WNT6 [17].
3.2.2. Molecular Pathways and Mechanisms
Several FGFR2 and FGFR3 variants have been implicated in SCII. For example, FGFR3 p.Arg671Gly was identified in Middle Eastern families [17] and may disrupt protein function, although functional validation has not been published [41]. FGFR3 inhibition impairs Meckel's cartilage elongation, whereas FGFR2 mutations can affect intramembranous ossification of the mandible [42]. FGFR2 intronic variants rs11200014 and rs2162540 have been associated with SCII and SCIII malocclusion phenotypes, respectively [1, 2, 35].
Dysregulation of the DUSP6–ERK pathway also impacts mandibular growth. Mutations in DUSP6 (c.545C>T, c.1094C>T) may alter negative regulation of ERK signalling [43]. RUNX2, activated by KAT6B, plays a role in mandibular condylar cartilage development and is differentially expressed in SCII/SCIII masseter muscles [45].
Other pathways include BMP inhibition by NOG and potential osteoblast suppression via ERLEC1 [25, 46]. These mechanisms disrupt jaw development during the key stages of morphogenesis.
4. Discussion
Craniofacial morphogenesis is a multifaceted process directed by an array of cytokines and growth factors. The coordination and synergy between these signalling pathways is crucial for bone growth and preservation [47]. Several signalling molecules regulate skeletal development, including the fibroblast growth factor (FGF), wingless‐type MMTV integration site family members (Wnt), and bone morphogenetic protein (BMP) signalling pathways [48].
The Gene Ontology (GO) enrichment analysis tool was used to identify enriched biological processes such as skeletal system morphogenesis, ossification, mesenchymal cell differentiation and regulation of ERK/MAPK signalling to select the most relevant genes for the pathogenesis of skeletal malocclusion. The enriched GO categories showed that FGFR2, TWIST1 and DUSP6 were the most recurrent.
The association between FGF/FGFR family members and SCIII malocclusion appears to be ethnically dependent on or related to specific subtypes of malocclusion, as evidenced by the challenges encountered in replication studies.
FGFR3 and FGFR2 are associated with skeletal undergrowth. Specifically, the variant p.Arg671Gly of FGFR3 has been linked to mandibular micrognathism in Middle Eastern families [17]. This DNA change swaps an arginine with a glycine at codon 671 of the TERT protein (p.Arg671Gly). The arginine residue is weakly conserved, with moderate physicochemical differences between the arginine and glycine residues. The SIFT, PolyPhen‐2 and Align‐GVGD algorithms predict a tolerated effect of missense changes on protein structure and function. However, to date, no functional or associated clinical impact studies have been published. This missense mutation has been hypothesised to disrupt FGFR3 function, although the available evidence is not sufficient to decipher the precise role of this variant [41]. In this respect, FGFR2 and FGFR3 signalling mediate key processes during mandibular osteogenesis. Inhibition of FGFR3 signalling has been shown to truncate the rostral/growing end of Meckel's cartilage, which is crucial for the elongation of Meckel's cartilage, whereas FGFR2/FGFR3 signalling influences the intramembranous ossification of mandibular bones, affecting appositional growth of the lower jaw bone [42]. Particularly relevant to skeletal class II is the fact that chondrocyte‐specific activation of Fgfr3 in mice causes premature synchondrosis closure, enhances osteoblast differentiation around the synchondroses [48] and induces impaired differentiation inhibiting chondroblast proliferation in Meckel's cartilage [42]. FGFR2 variants have been linked to an increased risk of SCII (rs11200014) and SCIII (rs2162540) [1, 2]. rs11200014 and rs2162540 are intron variants, and their contribution to the predisposition to skeletal malocclusions is difficult to predict. However, Fgfr2 mutations in chondrocyte proliferation and differentiation in a mouse model (Fgfr2IIIc P253R mouse) have been shown to disrupt chondrocyte differentiation and growth, resulting in a short anterior cranial base with fusion of cranial base synchondroses, maxillary hypoplasia and synostosis of calvarial sutures [49]. The rs2162540 variant has been consistently associated with a maxillary‐deficient phenotype [35]. In addition, FGFR2/FGFR3 signalling directly affects the intramembranous ossification of mandibular bones, thus exerting a potential direct impact on the appositional growth of the lower jaw bone during formation and elongation [42].
The FGF/FGFR signalling pathway is modulated by dual‐specificity phosphatase 6 (DUSP6), the only gene identified in two independent studies. DUSP6 serves as a balancing factor by negatively regulating the extracellular signal‐regulated kinase (ERK) pathway, which has been linked to skeletal alterations such as craniosynostosis [50]. The feedback mechanism enabled by DUSP6 is vital for the correct spatial and temporal formation of cranial structures, as it ensures that ERK signalling is neither powerful nor weakened, thus supporting optimal craniofacial development [43]. Alterations in DUSP6 functionality, such as those introduced by heterozygous missense mutations c.545C>T (p.Ser182Phe) and c.1094C>T (p.Thr365Ile), have been hypothesised to disrupt this balance. Such disruptions might potentially contribute to the development of SCIII malocclusion observed in diverse ethnic groups, including Estonians, who primarily present with maxillary hypoplasia, and Malaysian Malays. The specific variant c.545C>T (p.Ser182Phe) exhibits a higher occurrence rate than that typically expected for pathogenic variants, suggesting a possible association with malocclusion phenotypes [41]. Given the established roles of DUSP6 in craniofacial development and ERK pathway regulation, it is plausible that any gain‐ or loss‐of‐function may influence the occurrence of skeletal malocclusion. However, the exact molecular mechanisms by which these variants alter craniofacial growth and occlusion require further elucidation.
In contrast, murine studies have demonstrated the role of Twist1 as an enhancer of the FGF signalling pathway, specifically by increasing the activity of Fgfr2 and Fgf10 within the mesenchymal tissue, which plays a significant role during the early stages of human development [51, 52]. Mesenchymal tissue formation during the seventh week after fertilisation is crucial for mandibular ramus growth. Thus, any genetic modulation at this embryonic stage could potentially set the trajectory for future skeletal malocclusion [52]. TWIST1 variants rs2189000 and rs985246 have been correlated with traits related to mandibular ramus height and body size, along with cranial base–orientation discrepancies [1, 2].
Disruptions in key signalling pathways and genetic variants influencing osteogenesis and bone cell differentiation are central to skeletal undergrowth and premature cranial suture closure, leading to maxillary deficiency. Future studies focusing on the integrative analysis of multiomics datasets may further enhance our understanding of the genetic architecture and molecular pathways underlying skeletal malocclusion and craniofacial variations.
5. Conclusions
Family‐based studies reveal that specific genetic variants, often population‐dependent, underlie the development of SCII and SCIII malocclusions through disruptions in key osteogenic pathways. This review identified 43 candidate genes—most notably members of the DUSP6 and ADAMTS families—linked to maxillary deficiency and mandibular overgrowth. Inheritance patterns were primarily autosomal dominant, with occasional recessive or X‐linked transmission. GO enrichment supported the involvement of skeletal morphogenesis and ERK/MAPK signalling. These insights underscore the importance of integrating molecular data and ethnic context to understand the aetiology of malocclusions.
Author Contributions
Alexandra Dehesa‐Santos, Lucía Carreño‐Gomez contributed to the data gathering and review process. Alexandra Dehesa‐Santos, Lucía Carreño‐Gomez, Rosa Yañez‐Vico and Alejandro Iglesias‐Linares contributed substantially to the draft writing, conceptualization of the manuscript, and research. Rosa Yañez‐Vico designed the meta‐analysis information. All authors critically revised and accepted the final version of the paper.
Ethics Statement
The authors have nothing to report.
Consent
The authors have nothing to report.
Conflicts of Interest
All authors declare no conflicts of interest. The paper has been solely submitted to this Journal and is not under consideration elsewhere.
Supporting information
Table S1: Genetic family‐based studies and genetic variants associated with skeletal malocclusions.
Figure S1: Adapted PRISMA 2020 flow diagram for databases and registers.
File S1: Methodology and search results.
File S2: Search String.
File S3: Reasons for exclusion.
Acknowledgements
Authors would like to thank Pedro for help with the graphical design and Python data management.
Funding: This work was supported by a predoctoral grant from Banco Santander awarded to Alexandra Dehesa‐Santos and by the Complutense University of Madrid.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
References
- 1. Dehesa‐Santos A., Iber‐Diaz P., and Iglesias‐Linares A., “Genetic Factors Contributing to Skeletal Class III Malocclusion: A Systematic Review and Meta‐Analysis,” Clinical Oral Investigations 25, no. 4 (2021): 1587–1612. [DOI] [PubMed] [Google Scholar]
- 2. Zhou X., Zhang C., Yao S., Fan L., Ma L., and Pan Y., “Genetic Architecture of Non‐Syndromic Skeletal Class III Malocclusion,” Oral Diseases 29, no. 6 (2023): 2423–2437. [DOI] [PubMed] [Google Scholar]
- 3. Hartsfield J. K., Morford L. A., Otero L. M., and Fardo D. W., “Genetics and Non‐Syndromic Facial Growth,” Journal of Pediatric Genetics 2, no. 1 (2013): 9–20. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Ott J., Kamatani Y., and Lathrop M., “Family‐Based Designs for Genome‐Wide Association Studies,” Nature Reviews. Genetics 12, no. 7 (2011): 465–474. [DOI] [PubMed] [Google Scholar]
- 5. Saint Pierre A. and Génin E., “How Important Are Rare Variants in Common Disease?,” Briefings in Functional Genomics 13, no. 5 (2014): 353–361. [DOI] [PubMed] [Google Scholar]
- 6. Lu T., Forgetta V., Richards J. B., and Greenwood C. M. T., “Capturing Additional Genetic Risk From Family History for Improved Polygenic Risk Prediction,” Communications Biology 5, no. 1 (2022): 595. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Kainer D., Templeton A. R., Prates E. T., et al., “Structural Variants Identified Using Non‐Mendelian Inheritance Patterns Advance the Mechanistic Understanding of Autism Spectrum Disorder,” HGG Advances 4, no. 1 (2022): 100150. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Wijsman E. M., “The Role of Large Pedigrees in an Era of High‐Throughput Sequencing,” Human Genetics 131, no. 10 (2012): 1555–1563. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Haghighi F. and Hodge S. E., “Likelihood Formulation of Parent‐of‐Origin Effects on Segregation Analysis, Including Ascertainment,” American Journal of Human Genetics 70, no. 1 (2002): 142–156. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Spielman R. S., McGinnis R. E., and Ewens W. J., “Transmission Test for Linkage Disequilibrium: The Insulin Gene Region and Insulin‐Dependent Diabetes Mellitus (IDDM),” American Journal of Human Genetics 52, no. 3 (1993): 506–516. [PMC free article] [PubMed] [Google Scholar]
- 11. McGuffin P. and Huckle P., “Simulation of Mendelism Revisited: The Recessive Gene for Attending Medical School,” American Journal of Human Genetics 46, no. 5 (1990): 994–999. [PMC free article] [PubMed] [Google Scholar]
- 12. Hertz J. M., Juncker I., Persson U., et al., “Detection of Mutations in the COL4A5 Gene by SSCP in X‐Linked Alport Syndrome,” Human Mutation 18, no. 2 (2001): 141–148. [DOI] [PubMed] [Google Scholar]
- 13. Cheung C. Y., Thompson E. A., and Wijsman E. M., “Detection of Mendelian Consistent Genotyping Errors in Pedigrees,” Genetic Epidemiology 38, no. 4 (2014): 291–299. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Gao G., Allison D. B., and Hoeschele I., “Haplotyping Methods for Pedigrees,” Human Heredity 67, no. 4 (2009): 248–266. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Cheung C. Y., Thompson E. A., and Wijsman E. M., “GIGI: An Approach to Effective Imputation of Dense Genotypes on Large Pedigrees,” American Journal of Human Genetics 92, no. 4 (2013): 504–516. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Qiao H., Tan J., Wen S., Zhang M., Xu S., and Jin L., “De Novo Dissecting the Three‐Dimensional Facial Morphology of 2379 Han Chinese Individuals,” Phenomics 4, no. 1 (2023): 1–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. El Chekie M. R., Nemer G., Khalil A., Macari A. T., and Ghafari J. G., “Novel Genes Linked to Class II Division 1 Malocclusion With Mandibular Micrognathism,” American Journal of Orthodontics and Dentofacial Orthopedics 163, no. 5 (2023): 667–676.e3. [DOI] [PubMed] [Google Scholar]
- 18. Gutiérrez S. J., Gómez M., Rey J. A., Ochoa M., Gutiérrez S. M., and Prieto J. C., “Polymorphisms of the Noggin Gene and Mandibular Micrognathia: A First Approximation,” Acta Odontológica Latinoamericana 23, no. 1 (2010): 13–19. [PubMed] [Google Scholar]
- 19. Gutiérrez‐Prieto S. J., Torres‐López D. M., García‐Robayo D. A., Rey‐Cubillos J. A., and Gómez‐Rodríguez M., “Clinical and Molecular Study of the NOG Gene in Families With Mandibular Micrognathism,” European Journal of Dentistry 15, no. 4 (2021): 746–754. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Chen F., Li Q., Gu M., Li X., Yu J., and Zhang Y. B., “Identification of a Mutation in FGF23 Involved in Mandibular Prognathism,” Scientific Reports 5 (2015): 11250. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Guan X., Song Y., Ott J., et al., “The ADAMTS1 Gene Is Associated With Familial Mandibular Prognathism,” Journal of Dental Research 94, no. 9 (2015): 1196–1201. [DOI] [PubMed] [Google Scholar]
- 22. Kajii T. S., Oka A., Saito F., Mitsui J., and Iida J., “Whole‐Exome Sequencing in a Japanese Pedigree Implicates a Rare Non‐Synonymous Single‐Nucleotide Variant in BEST3 as a Candidate for Mandibular Prognathism,” Bone 122 (2019): 193–198. [DOI] [PubMed] [Google Scholar]
- 23. Li Q., Li X., Zhang F., and Chen F., “The Identification of a Novel Locus for Mandibular Prognathism in the Han Chinese Population,” Journal of Dental Research 90, no. 1 (2011): 53–57. [DOI] [PubMed] [Google Scholar]
- 24. Li Q., Zhang F., Li X., and Chen F., “Genome Scan for Locus Involved in Mandibular Prognathism in Pedigrees From China,” PLoS One 5, no. 9 (2010): e12678. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Rao C., Guan B., Luo D., et al., “Identification of Pathogenic Variants of ERLEC1 in Individuals With Class III Malocclusion by Exome Sequencing,” Human Mutation 41, no. 8 (2020): 1435–1446. [DOI] [PubMed] [Google Scholar]
- 26. Yamaguchi T., Park S. B., Narita A., Maki K., and Inoue I., “Genome‐Wide Linkage Analysis of Mandibular Prognathism in Korean and Japanese Patients,” Journal of Dental Research 84, no. 3 (2005): 255–259. [DOI] [PubMed] [Google Scholar]
- 27. de Frutos‐Valle L., Martin C., Alarcón J. A., Palma‐Fernández J. C., Ortega R., and Iglesias‐Linares A., “Sub‐Clustering in Skeletal Class III Malocclusion Phenotypes via Principal Component Analysis in a Southern European Population,” Scientific Reports 10, no. 1 (2020): 17882. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Dehesa‐Santos A., Park J. A., Lee S. J., and Iglesias‐Linares A., “East Asian and Southern European Craniofacial Class III Phenotype: Two Sides of the Same Coin?,” Clinical Oral Investigations 28, no. 1 (2024): 84. [DOI] [PubMed] [Google Scholar]
- 29. Genno P. G., Nemer G. M., Zein Eddine S. B., Macari A. T., and Ghafari J. G., “Three Novel Genes Tied to Mandibular Prognathism in Eastern Mediterranean Families,” American Journal of Orthodontics and Dentofacial Orthopedics 156, no. 1 (2019): 104–112.e3. [DOI] [PubMed] [Google Scholar]
- 30. Yao S., Zhou X., Vona B., et al., “Skeletal Class III Malocclusion Is Associated With ADAMTS2 Variants and Reduced Expression in a Familial Case,” International Journal of Molecular Sciences 23, no. 18 (2022): 10673. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Kantaputra P. N., Pruksametanan A., Phondee N., et al., “ADAMTSL1 and Mandibular Prognathism,” Clinical Genetics 95, no. 4 (2019): 507–515. [DOI] [PubMed] [Google Scholar]
- 32. Nowrin S. A., Alam M. K., and Basri R., “Genetic Effect and Prevalence of Class III Malocclusion in Different Population: An Overview,” International Journal of Pharma and Bio Sciences 6, no. 2 (2015): B910–B918. [Google Scholar]
- 33. Nikopensius T., Saag M., Jagomägi T., et al., “A Missense Mutation in DUSP6 Is Associated With Class III Malocclusion,” Journal of Dental Research 92, no. 10 (2013): 893–898. [DOI] [PubMed] [Google Scholar]
- 34. Perillo L., Monsurrò A., Bonci E., Torella A., Mutarelli M., and Nigro V., “Genetic Association of ARHGAP21 Gene Variant With Mandibular Prognathism,” Journal of Dental Research 94, no. 4 (2015): 569–576. [DOI] [PubMed] [Google Scholar]
- 35. Cruz R. M., J. K. Hartsfield, Jr. , Falcão‐Alencar G., et al., “Exclusion of Class III Malocclusion Candidate Loci in Brazilian Families,” Journal of Dental Research 90, no. 10 (2011): 1202–1205. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Frazier‐Bowers S., Rincon‐Rodriguez R., Zhou J., Alexander K., and Lange E., “Evidence of Linkage in a Hispanic Cohort With a Class III Dentofacial Phenotype,” Journal of Dental Research 88, no. 1 (2009): 56–60. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37. Selleri L. and Rijli F. M., “Shaping Faces: Genetic and Epigenetic Control of Craniofacial Morphogenesis,” Nature Reviews. Genetics 24, no. 9 (2023): 610–626. [DOI] [PubMed] [Google Scholar]
- 38. Faria‐Teixeira M. C., Tordera C., Salvado E Silva F., Vaz‐Carneiro A., and Iglesias‐Linares A., “Craniofacial Syndromes and Class III Phenotype: Common Genotype Fingerprints? A Scoping Review and Meta‐Analysis,” Pediatric Research 95, no. 6 (2024): 1455–1475. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39. Ma X., Zhu M., Mi X., and Chen F., “Role of FGF23 c.35C>A in Bone Remodeling During Orthodontic Tooth Movement,” Journal of Hard Tissue Biology 29, no. 2 (2020): 55–62. [Google Scholar]
- 40. Hallett S. A., Ono W., Franceschi R. T., and Ono N., “Cranial Base Synchondrosis: Chondrocytes at the Hub,” International Journal of Molecular Sciences 23, no. 14 (2022): 7817. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41. Nykamp K., Anderson M., Powers M., et al., “Sherloc: A Comprehensive Refinement of the ACMG‐AMP Variant Classification Criteria,” Genetics in Medicine 19, no. 10 (2017): 1105–1117. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42. Havens B. A., Velonis D., Kronenberg M. S., Lichtler A. C., Oliver B., and Mina M., “Roles of FGFR3 During Morphogenesis of Meckel's Cartilage and Mandibular Bones,” Developmental Biology 316, no. 2 (2008): 336–349. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43. Li C., Scott D. A., Hatch E., Tian X., and Mansour S. L., “Dusp6 (Mkp3) is a Negative Feedback Regulator of FGF‐Stimulated ERK Signaling During Mouse Development,” Development 134, no. 1 (2007): 167–176. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44. Cakan D. G., Ulkur F., and Taner T. U., “The Genetic Basis of Facial Skeletal Characteristics and Its Relation With Orthodontics,” European Journal of Dentistry 6, no. 3 (2012): 340–345. [PMC free article] [PubMed] [Google Scholar]
- 45. Desh H., Gray S. L., Horton M. J., et al., “Molecular Motor MYO1C, Acetyltransferase KAT6B and Osteogenetic Transcription Factor RUNX2 Expression in Human Masseter Muscle Contributes to Development of Malocclusion,” Archives of Oral Biology 59, no. 6 (2014): 601–607. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46. Komori T., “Regulation of Bone Development and Extracellular Matrix Protein Genes by RUNX2,” Cell and Tissue Research 339, no. 1 (2010): 189–195. [DOI] [PubMed] [Google Scholar]
- 47. Wu M., Chen G., and Li Y. P., “TGF‐β and BMP Signaling in Osteoblast, Skeletal Development, and Bone Formation, Homeostasis and Disease,” Bone Research 4 (2016): 16009. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48. Su N., Jin M., and Chen L., “Role of FGF/FGFR Signaling in Skeletal Development and Homeostasis: Learning From Mouse Models,” Bone Research 2 (2014): 14003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49. Nagata M., Nuckolls G. H., Wang X., et al., “The Primary Site of the Acrocephalic Feature in Apert Syndrome Is a Dwarf Cranial Base With Accelerated Chondrocytic Differentiation due to Aberrant Activation of the FGFR2 Signaling,” Bone 48, no. 4 (2011): 847–856. [DOI] [PubMed] [Google Scholar]
- 50. Shukla V., Coumoul X., Wang R. H., Kim H. S., and Deng C. X., “RNA Interference and Inhibition of Mek‐Erk Signaling Prevent Abnormal Skeletal Phenotypes in a Mouse Model of Craniosynostosis,” Nature Genetics 39, no. 9 (2007): 1145–1150. [DOI] [PubMed] [Google Scholar]
- 51. Dehesa‐Santos A., Faria‐Teixeira M. C., and Iglesias‐Linares A., “Skeletal Class III Phenotype: Link Between Animal Models and Human Genetics: A Scoping Review,” Journal of Experimental Zoology. Part B, Molecular and Developmental Evolution 342, no. 1 (2024): 21–44. [DOI] [PubMed] [Google Scholar]
- 52. Lee S. K., Kim Y. S., Oh H. S., Yang K. H., Kim E. C., and Chi J. G., “Prenatal Development of the Human Mandible,” Anatomical Record 263, no. 3 (2001): 314–325. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Table S1: Genetic family‐based studies and genetic variants associated with skeletal malocclusions.
Figure S1: Adapted PRISMA 2020 flow diagram for databases and registers.
File S1: Methodology and search results.
File S2: Search String.
File S3: Reasons for exclusion.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.