Nat Genet. 2019 Feb 25. doi: 10.1038/s41588-019-0348-4. [Epub ahead of print]

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

Jagadeesh KA¹, Paggi JM¹, Ye JS², Stenson PD³, Cooper DN³, Bernstein JA⁴, Bejerano G^5,^6,^7,⁸.

Author information

1: Department of Computer Science, Stanford University, Stanford, CA, USA.
2: Department of Biology, Stanford University, Stanford, CA, USA.
3: Institute of Medical Genetics, Cardiff University, Cardiff, UK.
4: Department of Pediatrics, Stanford University, Stanford, CA, USA.
5: Department of Computer Science, Stanford University, Stanford, CA, USA. bejerano@stanford.edu.
6: Department of Pediatrics, Stanford University, Stanford, CA, USA. bejerano@stanford.edu.
7: Department of Developmental Biology, Stanford University, Stanford, CA, USA. bejerano@stanford.edu.
8: Department of Biomedical Data Science, Stanford University, Stanford, CA, USA. bejerano@stanford.edu.

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.