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

Small nuclear RNAs are the RNA components of the major and minor spliceosomes; the genes producing them show a very high background mutation rate and numerous paralogues with high sequence identity, two properties that distort direct application of standard classification criteria.

Small nuclear RNAs (snRNAs) are the RNA components of the major and minor spliceosomes, and variants in the genes producing them are increasingly recognised as major contributors to rare disorders, including neurodevelopmental disorders and retinal dystrophies, collectively termed RNUopathies. The authors quantified the elevated background mutation rate of these genes using de novo variants from 12,007 trios and mutation density across 76,215 genome-sequenced individuals in gnomAD, then convened a panel of clinical, research and industry experts to draft a guidance document. They detail the difficulties specific to these genes: identification requiring genome or targeted sequencing, numerous paralogues with high sequence identity that complicate read mapping and variant calling, and historical inaccuracies in gene annotation. They show a roughly 50-fold increase in de novo mutation rate in snRNA genes compared with intergenic sequence and discuss its implications for classification. They finally provide a set of specific recommendations and RNUdb, an interactive web-based tool for snRNA variant annotation and classification.

Synthesis written by Geno'X. For the full original abstract, please refer to the source publication.

This is a document we need: since RNUopathies entered diagnostic practice, ACMG criteria have been applied to non-coding genes for which they were never designed, with a risk of over-classification that the 50-fold de novo mutation rate makes explicit — a de novo variant carries far less weight here than in a coding gene. The format has real limits: expert consensus, not systematic, still a preprint, without retrospective validation on already classified variants. Use it now as a framework for caution in the relevant cases, not as an enforceable standard.

Analysis by Dr Thibaut Benquey