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

This study reports a ∼50-fold increase in mutation rate in snRNA genes, emphasizing the need for tailored guidance in variant classification.

Summary and findings

This study quantified the mutation rate in small nuclear RNA (snRNA) genes and provided guidance for clinical variant classification. It involved analysis of de novo variants from 12,007 trios and mutation density in 76,215 genome sequenced individuals. A ∼50-fold increase in de novo mutation rate in snRNA genes compared to intergenic sequence was reported.

Abstract

Background 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. Methods 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. Results 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. Conclusions 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.

Key findings

• Not reported in abstract.

Limitations

• Not reported in abstract.

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