Using empirical evidence to predict if and how a DNA variant will disrupt RNA splicing in rare disorders

Background The diagnostic rate in Mendelian disorders continues to hover around 50% after genomic testing, meaning that around half of families and clinicians are left with no actionable answer. Variants affecting splicing motifs are particularly challenging to interpret. To conclusively link a splicing variant to disease it’s necessary to determine the consequences of altered splicing on the final mRNA transcript and subsequent protein. Consequently, most probable splicing variants are classified as VUS and unactionable. A range of powerful but opaque algorithms have proliferated for predicting whether a variant alters splicing. Many are based on machine learning and deep learning, with the data and features used to make a specific prediction usually unavailable to be verified and weighted by clinicians. Without detailing the nature and source(s) of evidence used to make each prediction, these algorithms are relegated to the lowest evidence weighting according to globally-accepted, gold standard variant classification rules, established by the ACMG-AMP. In addition, most algorithms currently make no attempt to predict mis-splicing outcomes which will occur as the result of a variant, meaning that bespoke functional testing is still required to discover the variant impact on pre-mRNA splicing and allow ACMG-AMP guided variant reclassification for a definitive molecular diagnosis. There is an urgent need for evidence-based, clinically-validated tools for pathology interpretation of splicing variants. Aims To bridge the gap between data science and genetic pathology, by developing methods based on empirical evidence to predict if and how a DNA variant will disrupt RNA splicing in rare disease. To determine empirical features that accurately inform: 1) spliceosomal selection of a cryptic-donor, in preference to the ‘authentic-donor’ (positioned at the exon-intron junction), and other nearby decoy-donors (any GT or GC) that are not used by the spliceosome, and 2) The mis-splicing events which will occur because of a variant precluding use of the authentic-donor or authentic-acceptor. Methods We use empirical and clinically relevant data to define and evaluate measurable features enriched in (1) cryptic-donors selected by the spliceosome vs decoy-donors (any GT/GC motif) which were not selected by the spliceosome and (2) mis-splicing events (exon skipping or cryptic activation) which occurred because of a splicing variant. Results For 1) we evaluated the use of current algorithms to show that while intrinsic splice-site strength and proximity to the authentic-donor strongly influence spliceosomal selection of a cryptic-donor, these factors alone are not sufficient for accurate prediction. For 2) we find that natural, stochastic mis-splicing events seen in population-based RNA-Seq are remarkably prescient of the mis-splicing events that will occur predominantly after the inactivation of an authentic splice site. Conclusions We’ve created an accurate, evidence-based method to predict the nature of variant -induced mis-splicing. The ability to confidently predict the outcome of a splicing variant is a major step forward which will greatly aid in genetic diagnosis of families with Mendelian disorders

Standardized practices for RNA diagnostics using clinically accessible specimens reclassifies 75% of putative splicing variants

Purpose: Genetic variants causing aberrant premessenger RNA splicing are increasingly being recognized as causal variants in genetic disorders. In this study, we devise standardized practices for polymerase chain reaction (PCR)-based RNA diagnostics using clinically accessible specimens (blood, fibroblasts, urothelia, biopsy). Methods: A total of 74 families with diverse monogenic conditions (31% prenatal-congenital onset, 47% early childhood, and 22% teenage-adult onset) were triaged into PCR-based RNA testing, with comparative RNA sequencing for 19 cases. Results: Informative RNA assay data were obtained for 96% of cases, enabling variant reclassification for 75% variants that can be used for genetic counseling (71%), to inform clinical care (32%) and prenatal counseling (41%). Variant-associated mis-splicing was highly reproducible for 28 cases with samples from ≥2 affected individuals or heterozygotes and 10 cases with ≥2 biospecimens. PCR amplicons encompassing another segregated heterozygous variant was vital for clinical interpretation of 22 of 79 variants to phase RNA splicing events and discern complete from partial mis-splicing. Conclusion: RNA diagnostics enabled provision of a genetic diagnosis for 64% of recruited cases. PCR-based RNA diagnostics has capacity to analyze 81.3% of clinically significant genes, with long amplicons providing an advantage over RNA sequencing to phase RNA splicing events. The Australasian Consortium for RNA Diagnostics (SpliceACORD) provide clinically-endorsed, standardized protocols and recommendations for interpreting RNA assay data

De novo variants in the non-coding spliceosomal snRNA gene RNU4-2 are a frequent cause of syndromic neurodevelopmental disorders.

Around 60% of individuals with neurodevelopmental disorders (NDD) remain undiagnosed after comprehensive genetic testing, primarily of protein-coding genes 1 . Increasingly, large genome-sequenced cohorts are improving our ability to discover new diagnoses in the non-coding genome. Here, we identify the non-coding RNA RNU4-2 as a novel syndromic NDD gene. RNU4-2 encodes the U4 small nuclear RNA (snRNA), which is a critical component of the U4/U6.U5 tri-snRNP complex of the major spliceosome 2 . We identify an 18 bp region of RNU4-2 mapping to two structural elements in the U4/U6 snRNA duplex (the T-loop and Stem III) that is severely depleted of variation in the general population, but in which we identify heterozygous variants in 119 individuals with NDD. The vast majority of individuals (77.3%) have the same highly recurrent single base-pair insertion (n.64_65insT). We estimate that variants in this region explain 0.41% of individuals with NDD. We demonstrate that RNU4-2 is highly expressed in the developing human brain, in contrast to its contiguous counterpart RNU4-1 and other U4 homologs, supporting RNU4-2 's role as the primary U4 transcript in the brain. Overall, this work underscores the importance of non-coding genes in rare disorders. It will provide a diagnosis to thousands of individuals with NDD worldwide and pave the way for the development of effective treatments for these individuals. </p