Institute of Vision Research, Department of Ophthalmology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea.
Brain Korea 21 Project for Medical Science, Yonsei University, Seoul, South Korea.
Transl Vis Sci Technol. 2022 Jun 1;11(6):25. doi: 10.1167/tvst.11.6.25.
We aim to report noncoding pathogenic variants in patients with FRMD7-related infantile nystagmus (FIN).
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.
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.
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.
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.
我们旨在报告 FRMD7 相关婴儿性眼球震颤（FIN）患者中的非编码致病性变异。
对疑似 FIN 的遗传未解决病例进行基因组测序（n=2 个家系）和靶向 panel 下一代测序的重新分析（n=2 个家系）。先前的序列分析显示与婴儿性眼球震颤相关的基因中不存在致病性编码变异。使用 SpliceAI、SpliceRover 和 Alamut 共识程序对非编码变异进行注释。进行迷你基因剪接测定以确认异常剪接。还进行了外显子剪接增强子和沉默子的计算机分析。
根据基因组测序和靶向下一代测序分析，确定了 FRMD7 内含子变异。这些变异包括 c.285-12A>G（家系 1）、c.284+63T>A（家系 2 和 3）和 c.383-1368A>G（家系 4）。所有变异均不存在于 gnomAD 中，并且 c.285-12A>G 和 c.284+63T>A 变异均被预测通过 SpliceAI、SpliceRover 和 Alamut 共识方法增强新的剪接受体获得。然而，c.383-1368A>G 变异仅在 SpliceRover 程序中具有显著的影响评分。c.383-1368A>G 变异被预测通过结合外显子剪接增强子促进假外显子的包含。通过迷你基因构建物验证了异常外显子化，并且所有变异均在家系中分离。
基于深度学习的非编码变异注释有助于发现 FIN 患者隐藏的遗传变异。本研究证明了结合基于深度学习的剪接工具的有效性，可用于鉴定先前未解决的婴儿性眼球震颤患者中的隐匿性致病性变异。
我们旨在报告 FRMD7 相关婴儿性眼球震颤（FIN）患者中的非编码致病性变异。
对疑似 FIN 的遗传未解决病例进行基因组测序（n=2 个家系）和靶向 panel 下一代测序的重新分析（n=2 个家系）。先前的序列分析显示与婴儿性眼球震颤相关的基因中不存在致病性编码变异。使用 SpliceAI、SpliceRover 和 Alamut 共识程序对非编码变异进行注释。进行迷你基因剪接测定以确认异常剪接。还进行了外显子剪接增强子和沉默子的计算机分析。
根据基因组测序和靶向下一代测序分析，确定了 FRMD7 内含子变异。这些变异包括 c.285-12A>G（家系 1）、c.284+63T>A（家系 2 和 3）和 c.383-1368A>G（家系 4）。所有变异均不存在于 gnomAD 中，并且 c.285-12A>G 和 c.284+63T>A 变异均被预测通过 SpliceAI、SpliceRover 和 Alamut 共识方法增强新的剪接受体获得。然而，c.383-1368A>G 变异仅在 SpliceRover 程序中具有显著的影响评分。c.383-1368A>G 变异被预测通过结合外显子剪接增强子促进假外显子的包含。通过迷你基因构建物验证了异常外显子化，并且所有变异均在家系中分离。
基于深度学习的非编码变异注释有助于发现 FIN 患者隐藏的遗传变异。本研究证明了结合基于深度学习的剪接工具的有效性，可用于鉴定先前未解决的婴儿性眼球震颤患者中的隐匿性致病性变异。
