Journal
NATURE GENETICS
Volume 51, Issue 4, Pages 755-+Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/s41588-019-0348-4
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Funding
- Qiagen
- Stanford Graduate Fellowship
- Computational and Evolutionary Genomics Fellowship
- Stanford Pediatrics Department, DARPA
- Packard Foundation
- Microsoft Faculty Fellowships
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Exome analysis of patients with a likely monogenic disease does not identify a causal variant in over half of cases. Splicedisrupting 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.
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