Mutation spectrum and genotype-phenotype correlations in 157 Korean CADASIL patients: a multicenter study.
Ji-You MinSeo-Jin ParkEun-Joo KangSeung-Yong HwangSung-Hee HanPublished in: Neurogenetics (2021)
CADASIL is an inherited disease caused by mutations in the NOTCH3 gene. We aimed to investigate the mutation and clinical spectrum, and genotype-phenotype correlations of Korean CADASIL patients. Samples from 492 clinically suspicious patients were collected from four hospitals. Sanger sequencing was performed to screen exons 2 to 25 of the NOTCH3 gene and variants of unknown significance (VUS) were analyzed using the ACMG guidelines. The medical records and MRI data were received from each hospital, for comprehensive analysis of genotype-phenotype correlations. Previously reported NOTCH3 variants were most commonly detected in exon 11 whereas exon 4 was the most common in European studies. The variants were detected equally between the EGFr domains 1-6 and 7-34, which was different from EGFr 1-6 predominant European studies. The average age-of-onset of patients with EGFr 1-6 variants were 4.81 ± 1.95 years younger than patients with EGFr 7-34 variants. Overall, it took Korean patients 51.2 ± 10 years longer to develop CADASIL in comparison to European patients. The most common mutation was p.R544C, which was associated with a later onset of stroke and a significant time-to-event curve difference. We verified four atypical phenotypes of p.R544C that had been reported in previous studies. Eight novel variants in 15 patients were detected but remained a VUS based on the ACMG criteria. This study reported a different EGFr distribution of Korean patients in comparison to European patients and its correlation with a later age-of-onset. An association between a later onset of stroke/TIA and p.R544C was observed.
Keyphrases
- end stage renal disease
- newly diagnosed
- ejection fraction
- small cell lung cancer
- chronic kidney disease
- prognostic factors
- peritoneal dialysis
- computed tomography
- magnetic resonance imaging
- gene expression
- machine learning
- healthcare
- copy number
- artificial intelligence
- deep learning
- subarachnoid hemorrhage
- patient reported outcomes
- dna methylation
- magnetic resonance
- high throughput