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

Nature Genetics
Karthik A JagadeeshGill Bejerano

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.

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Citations

Jul 12, 2019·Human Mutation·Melissa S ClineUNKNOWN ENIGMA Consortium
Sep 9, 2020·American Journal of Medical Genetics. Part C, Seminars in Medical Genetics·Michael A WalterGavin Arno
Jul 28, 2020·International Journal of Molecular Sciences·Katarzyna Taylor, Krzysztof Sobczak
May 21, 2021·Scientific Reports·Zhenhua ZhangK Joeri van der Velde
Jul 15, 2021·Journal of Human Genetics·Shengyu XieYuan Yang
Jul 22, 2021·American Journal of Human Genetics·Daniel DanisPeter N Robinson
Jul 8, 2021·Genetics in Medicine : Official Journal of the American College of Medical Genetics·Boyoung YooGill Bejerano

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Methods Mentioned

BETA
RNA-seq

Software Mentioned

Ensembl
MaxEntScan
ExAC
Eigen
SPIDEX
MutPred Splice
Phrank
PhyloP
sklearn
MutPredSplice

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