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Phenotypes caused by de novo SCN1A pathogenic variants are very variable, ranging from severely affected patients with Dravet syndrome to much milder genetic epilepsy febrile seizures plus cases. The most important determinant of disease severity is the type of variant, with variants that cause a complete loss of function of the SCN1A protein (α-subunit of the neuronal sodium channel Nav1.1) being detected almost exclusively in Dravet syndrome patients. However, even within Dravet syndrome disease severity ranges greatly, and consequently other disease modifiers must exist. A better prediction of disease severity is very much needed in daily practice to improve counseling, stressing the importance of identifying modifying factors in this patient group. We evaluated 128 participants with de novo, pathogenic SCN1A variants to investigate whether mosaicism, caused by postzygotic mutation, is a major modifier in SCN1A-related epilepsy.

Mosaicism was investigated by reanalysis of the pathogenic SCN1A variants using single molecule molecular inversion probes and next generation sequencing with high coverage. Allelic ratios of pathogenic variants were used to determine whether mosaicism was likely. Selected mosaic variants were confirmed by droplet digital polymerase chain reaction and sequencing of different tissues. Developmental outcome was classified based on available data on intelligence quotient and school functioning/education.

Mosaicism was present for 7.5% of de novo pathogenic SCN1A variants in symptomatic patients. Mosaic participants were less severely affected than nonmosaic participants if only participants with truncating variants are considered (distribution of developmental outcome scores, Mann-Whitney U, P = .023).

Postzygotic mutation is a common phenomenon in SCN1A-related epilepsies. Participants with mosaicism have on average milder phenotypes, suggesting that mosaicism can be a major modifier of SCN1A-related diseases. Detection of mosaicism has important implications for genetic counseling and can be achieved by deep sequencing of unique reads.