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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., 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: 25 Aug 2019 02:28
URI: http://orca.cf.ac.uk/id/eprint/122998

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