Variant Location Is a Novel Risk Factor for Individuals With Arrhythmogenic Cardiomyopathy Due to a Desmoplakin (DSP) Truncating Variant.

BACKGROUND: Truncating variants in desmoplakin (DSPtv) are an important cause of arrhythmogenic cardiomyopathy; however the genetic architecture and genotype-specific risk factors are incompletely understood. We evaluated phenotype, risk factors for ventricular arrhythmias, and underlying genetics of DSPtv cardiomyopathy. METHODS: Individuals with DSPtv and any cardiac phenotype, and their gene-positive family members were included from multiple international centers. Clinical data and family history information were collected. Event-free survival from ventricular arrhythmia was assessed. Variant location was compared between cases and controls, and literature review of reported DSPtv performed. RESULTS: There were 98 probands and 72 family members (mean age at diagnosis 43±8 years, 59% women) with a DSPtv, of which 146 were considered clinically affected. Ventricular arrhythmia (sudden cardiac arrest, sustained ventricular tachycardia, appropriate implantable cardioverter defibrillator therapy) occurred in 56 (33%) individuals. DSPtv location and proband status were independent risk factors for ventricular arrhythmia. Further, gene region was important with variants in cases (cohort n=98; Clinvar n=167) more likely to occur in the regions resulting in nonsense mediated decay of both major DSP isoforms, compared with n=124 genome aggregation database control variants (148 [83.6%] versus 29 [16.4%]; P<0.0001). CONCLUSIONS: In the largest series of individuals with DSPtv, we demonstrate that variant location is a novel risk factor for ventricular arrhythmia, can inform variant interpretation, and provide critical insights to allow for precision-based clinical management.Edgar T. Hoorntje, Charlotte Burns, Luisa Marsili, Ben Corden, Victoria N. Parikh, Gerard J. te Meerman, Belinda Gray, Ahmet Adiyaman, Richard D. Bagnall, Daniela Q.C.M. Barge-Schaapveld, Maarten P. van den Berg, Marianne Bootsma, Laurens P. Bosman, Gemma Correnti, Johan Duflou, Ruben N. Eppinga, Diane Fatkin, Michael Fietz, Eric Haan, Jan D.H. Jongbloed, Arnaud D. Hauer, Lien Lam, Freyja H.M. van Lint, Amrit Lota, Carlo Marcelis, Hugh J. McCarthy, Anneke M. van Mil, Rogier A. Oldenburg, Nicholas Pachter, R. Nils Planken, Chloe Reuter, Christopher Semsarian, Jasper J. van der Smagt, Tina Thompson, Jitendra Vohra, Paul G.A. Volders, Jaap I. van Waning, Nicola Whiffin, Arthur van den Wijngaard, Ahmad S. Amin, Arthur A.M. Wilde, Gijs van Woerden, Laura Yeates, Dominica Zentner, Euan A. Ashley, Matthew T. Wheeler, James S. Ware, J. Peter van Tintelen, Jodie Ingle

Predicting the impact of rare variants on RNA splicing in CAGI6

Variants which disrupt splicing are a frequent cause of rare disease that have been under-ascertained clinically. Accurate and efficient methods to predict a variant’s impact on splicing are needed to interpret the growing number of variants of unknown significance (VUS) identified by exome and genome sequencing. Here, we present the results of the CAGI6 Splicing VUS challenge, which invited predictions of the splicing impact of 56 variants ascertained clinically and functionally validated to determine splicing impact. The performance of 12 prediction methods, along with SpliceAI and CADD, was compared on the 56 functionally validated variants. The maximum accuracy achieved was 82% from two different approaches, one weighting SpliceAI scores by minor allele frequency, and one applying the recently published Splicing Prediction Pipeline (SPiP). SPiP performed optimally in terms of sensitivity, while an ensemble method combining multiple prediction tools and information from databases exceeded all others for specificity. Several challenge methods equalled or exceeded the performance of SpliceAI, with ultimate choice of prediction method likely to depend on experimental or clinical aims. One quarter of the variants were incorrectly predicted by at least 50% of the methods, highlighting the need for further improvements to splicing prediction methods for successful clinical application

Recommendations for clinical interpretation of variants found in non-coding regions of the genome

Background The majority of clinical genetic testing focuses almost exclusively on regions of the genome that directly encode proteins. The important role of variants in non-coding regions in penetrant disease is, however, increasingly being demonstrated, and the use of whole genome sequencing in clinical diagnostic settings is rising across a large range of genetic disorders. Despite this, there is no existing guidance on how current guidelines designed primarily for variants in protein-coding regions should be adapted for variants identified in other genomic contexts. Methods We convened a panel of nine clinical and research scientists with wide-ranging expertise in clinical variant interpretation, with specific experience in variants within non-coding regions. This panel discussed and refined an initial draft of the guidelines which were then extensively tested and reviewed by external groups. Results We discuss considerations specifically for variants in non-coding regions of the genome. We outline how to define candidate regulatory elements, highlight examples of mechanisms through which non-coding region variants can lead to penetrant monogenic disease, and outline how existing guidelines can be adapted for the interpretation of these variants. Conclusions These recommendations aim to increase the number and range of non-coding region variants that can be clinically interpreted, which, together with a compatible phenotype, can lead to new diagnoses and catalyse the discovery of novel disease mechanisms