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Whole-Genome Sequencing Among Kazakhstani Children with Early-Onset Epilepsy Revealed New Gene Variants and Phenotypic Variability.

Mirgul BayanovaAidos K BolatovAssiya BazenovaLyazzat NazarovaAlissa NauryzbayevaNaanlep Matthew TankoSaule E RakhimovaNazerke SatvaldinaDiana SamakyzyUlan A KozhamkulovUlykbek Y KairovAinur R AkilzhanovaDos D Sarbassov
Published in: Molecular neurobiology (2023)
In Kazakhstan, there is insufficient data on genetic epilepsy, which has its own clinical and management implications. Thus, this study aimed to use whole genome sequencing to identify and evaluate genetic variants and genetic structure of early onset epilepsy in the Kazakhstani pediatric population. In this study, for the first time in Kazakhstan, whole genome sequencing was carried out among epilepsy diagnosed children. The study involved 20 pediatric patients with early onset epilepsy and no established cause of the disease during the July-December, 2021. The average age at enrolment was 34.5 months, with a mean age at seizure onset of 6 months. Six patients (30%) were male, and 7 were familial cases. We identified pathogenic and likely pathogenic variants in 14 (70%) cases, among them, 6 novel disease gene variants (KCNQ2, CASK, WWOX, MT-CO3, GRIN2D, and SLC12A5). Other genes associated with the disease were SCN1A (x2), SLC2A1, ARX, CACNA1B, PCDH19, KCNT1, and CHRNA2. Identification of the genetic causes in 70% of cases confirms the general structure of the etiology of early onset epilepsy and the necessity of using NGS in diagnostics. Moreover, the study describes new genotype-phenotypic correlations in genetic epilepsy. Despite certain limitations of the study, it can be concluded that the genetic etiology of pediatric epilepsy in Kazakhstan is very broad and requires further research.
Keyphrases
  • early onset
  • late onset
  • copy number
  • young adults
  • end stage renal disease
  • single cell
  • prognostic factors
  • peritoneal dialysis
  • ejection fraction
  • temporal lobe epilepsy
  • deep learning
  • big data