X-CAP improves pathogenicity prediction of stopgain variants

Ruchir Rastogi; Peter D Stenson; David N Cooper; Gill Bejerano

doi:10.1186/s13073-022-01078-y

X-CAP improves pathogenicity prediction of stopgain variants

Genome Med. 2022 Jul 29;14(1):81. doi: 10.1186/s13073-022-01078-y.

Authors

Ruchir Rastogi¹, Peter D Stenson², David N Cooper², Gill Bejerano^{3

4

5

6}

Affiliations

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

Abstract

Stopgain substitutions are the third-largest class of monogenic human disease mutations and often examined first in patient exomes. Existing computational stopgain pathogenicity predictors, however, exhibit poor performance at the high sensitivity required for clinical use. Here, we introduce a new classifier, termed X-CAP, which uses a novel training methodology and unique feature set to improve the AUROC by 18% and decrease the false-positive rate 4-fold on large variant databases. In patient exomes, X-CAP prioritizes causal stopgains better than existing methods do, further illustrating its clinical utility. X-CAP is available at https://github.com/bejerano-lab/X-CAP .

Keywords: Machine learning; Nonsense; Pathogenicity prediction; Stopgain.

Publication types

Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural

MeSH terms

Computational Biology / methods
Exome*
Humans
Mutation
Mutation, Missense
Software*
Virulence

Abstract

Publication types

MeSH terms

Grants and funding