Identifying a Novel Causal FAM83H Variant for Autosomal Dominant Amelogenesis Imperfecta Using Exome‐Sequencing
ABSTRACT Background Amelogenesis imperfecta (AI) is a rare genetic disorder causing tooth enamel defects. AI has been classified into 14 different clinical subtypes with different modes of inheritance. In this study, we performed whole‐exome sequencing to identify the causative gene defect in a larg...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-06-01
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| Series: | Molecular Genetics & Genomic Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/mgg3.70108 |
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| Summary: | ABSTRACT Background Amelogenesis imperfecta (AI) is a rare genetic disorder causing tooth enamel defects. AI has been classified into 14 different clinical subtypes with different modes of inheritance. In this study, we performed whole‐exome sequencing to identify the causative gene defect in a large Dutch family with autosomal dominant hypocalcified AI (ADHCAI). Methods Whole‐exome sequencing (WES) was performed on genomic DNA of the proband with AI. We focused on eight candidate genes known to be involved in inherited autosomal dominant AI. Sanger sequencing was used to confirm the selected exome candidate variant. Additionally, genotype and phenotype analyses were performed in the selected affected and non‐affected individuals and compared according to previously listed literature for this candidate gene of the proband. Results The clinical phenotype of the affected individuals showed a generalized and extensive enamel defect of all teeth. In the exome dataset of the proband, a novel nonsense variant in FAM83H, c.1055C>A p.(Ser352*) was detected, which was verified by conventional Sanger sequencing. Co‐segregation analysis confirmed that the variant was present in all affected individuals and not in unaffected individuals. Conclusion A novel pathogenic, protein‐truncating variant was detected in FAM83H, a gene with similar truncating variants known to be associated with ADHCAI. |
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| ISSN: | 2324-9269 |