Genetic heterogeneity within a consanguineous family involving TTPA and SETX genes.
Cyrine JeridiAmine RachdiFatma NabliZacharia SaiedRania ZouariDina Ben MohamedMariem Ben SaidSaber MasmoudiSamia Ben SassiRim AmouriPublished in: Journal of neurogenetics (2023)
Autosomal recessive cerebellar ataxias (ARCA) constitute a highly heterogeneous group of progressive neurodegenerative disorders that typically occur prior to adulthood. Despite some clinical resemblance between these disorders, different genes are involved. We report in this study four Tunisian patients belonging to the same large consanguineous family, sharing autosomal recessive cerebellar ataxia phenotypes but with clinical, biological, electrophysiological, and radiological differences leading to the diagnosis of two distinct ARCA caused by two distinct gene defects. Two of our patients presented ataxia with the vitamin E deficiency (AVED) phenotype, and the other two presented ataxia with oculo-motor apraxia 2 (AOA2). Genetic testing confirmed the clinical diagnosis by the detection of a frameshift c.744delA pathogenic variant in the TTPA gene, which is the most frequent in Tunisia, and a new variant c.1075dupT in the SETX gene. In Tunisia, data suggest that genetic disorders are common. The combined effects of the founder effect and inbreeding, added to genetic drift, may increase the frequency of detrimental rare disorders. The genetic heterogeneity observed in this family highlights the difficulty of genetic counseling in an inbred population. The examination and genetic testing of all affected patients, not just the index patient, is essential to not miss a treatable ataxia such as AVED, as in the case of this family.
Keyphrases
- genome wide
- end stage renal disease
- copy number
- newly diagnosed
- ejection fraction
- chronic kidney disease
- prognostic factors
- peritoneal dialysis
- dna methylation
- multiple sclerosis
- intellectual disability
- healthcare
- autism spectrum disorder
- gene expression
- machine learning
- hepatitis c virus
- big data
- single cell
- hiv infected
- genome wide analysis
- data analysis