Purpose: We aim to report noncoding pathogenic variants in patients with FRMD7-related infantile nystagmus (FIN).
Methods: Genome sequencing (n = 2 families) and reanalysis of targeted panel next generation sequencing (n = 2 families) was performed in genetically unsolved cases of suspected FIN. Previous sequence analysis showed no pathogenic coding variants in genes associated with infantile nystagmus. SpliceAI, SpliceRover, and Alamut consensus programs were used to annotate noncoding variants. Minigene splicing assay was performed to confirm aberrant splicing. In silico analysis of exonic splicing enhancer and silencer was also performed.
Results: FRMD7 intronic variants were identified based on genome sequencing and targeted next-generation sequencing analysis. These included c.285-12A>G (pedigree 1), c.284+63T>A (pedigrees 2 and 3), and c. 383-1368A>G (pedigree 4). All variants were absent in gnomAD, and the both c.285-12A>G and c.284+63T>A variants were predicted to enhance new splicing acceptor gains with SpliceAI, SpliceRover, and Alamut consensus approaches. However, the c.383-1368 A>G variant only had a significant impact score on the SpliceRover program. The c.383-1368A>G variant was predicted to promote pseudoexon inclusion by binding of exonic splicing enhancer. Aberrant exonizations were validated through minigene constructs, and all variants were segregated in the families.
Conclusions: Deep learning-based annotation of noncoding variants facilitates the discovery of hidden genetic variations in patients with FIN. This study provides evidence of effectiveness of combined deep learning-based splicing tools to identify hidden pathogenic variants in previously unsolved patients with infantile nystagmus.
Translational relevance: These results demonstrate robust analysis using two deep learning splicing predictions and in vitro functional study can lead to finding hidden genetic variations in unsolved patients.