S-CAP extends pathogenicity prediction to genetic variants that affect RNA splicing

Jagadeesh, Karthik A., Paggi, Joseph M., Ye, James S., Stenson, Peter D., Cooper, David N. ORCID: https://orcid.org/0000-0002-8943-8484, Bernstein, Jonathan A. and Bejerano, Gill 2019. S-CAP extends pathogenicity prediction to genetic variants that affect RNA splicing. Nature Genetics 51 (4) , pp. 755-763. 10.1038/s41588-019-0348-4

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Abstract

Exome analysis of patients with a likely monogenic disease does not identify a causal variant in over half of cases. Splice-disrupting mutations make up the second largest class of known disease-causing mutations. Each individual (singleton) exome harbors over 500 rare variants of unknown significance (VUS) in the splicing region. The existing relevant pathogenicity prediction tools tackle all non-coding variants as one amorphic class and/or are not calibrated for the high sensitivity required for clinical use. Here we calibrate seven such tools and devise a novel tool called Splicing Clinically Applicable Pathogenicity prediction (S-CAP) that is over twice as powerful as all previous tools, removing 41% of patient VUS at 95% sensitivity. We show that S-CAP does this by using its own features and not via meta-prediction over previous tools, and that splicing pathogenicity prediction is distinct from predicting molecular splicing changes. S-CAP is an important step on the path to deriving non-coding causal diagnoses.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Medicine
Publisher:	Nature
ISSN:	1061-4036
Date of First Compliant Deposit:	30 May 2019
Date of Acceptance:	10 January 2019
Last Modified:	27 Nov 2024 12:15
URI:	https://orca.cardiff.ac.uk/id/eprint/122998

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