Guidance for clinical variant classification in genes for spliceosomal small nuclear RNAs

Small nuclear RNAs (snRNAs) are RNA components of the major and minor spliceosomes that play a core role in splice-site recognition and control of the splicing process. Variants in genes that produce snRNAs are increasingly recognised as major contributors to rare disorders, including neurodevelopmental disorders (NDD) and retinal dystrophies (collectively termed 'RNUopathies', a subset of 'spliceosomopathies'). Clinical interpretation of variants in snRNAs is, however, challenging and existing guidance to support clinical variant classification does not adequately capture the unique features of snRNAs that necessitate a bespoke approach.

We quantified the elevated background mutation rate in snRNA genes using de novo variants from 12,007 trios and assessed mutation density in 76,215 genome sequenced individuals in gnomAD. We convened a panel of clinical, research, and industry scientists with wide-ranging expertise in clinical variant interpretation and classification and expert knowledge in snRNA genes to draft and refine a guidance document.

We detail important considerations for variant classification in snRNA genes. These include: the difficulties of variant identification which requires genome or targeted sequencing approaches, the large number of gene paralogs with high sequence identity that complicate read mapping and variant calling, and historical inaccuracies in snRNA gene annotation. Further we show a ∼50-fold increase in de novo mutation rate in snRNA genes compared to intergenic sequence and discuss the implications of this for variant classification. We provide a set of specific recommendations for classifying variants in snRNA genes. Finally, we introduce RNUdb, an interactive web-based tool to support snRNA variant annotation and classification.

We provide the first guidance for clinical variant classification in snRNA genes and anticipate that this will support routine screening and analysis of snRNA genes in clinical genetic testing.