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Hum Hered. 2012;73(1):47-51. doi: 10.1159/000334984. Epub 2012 Jan 18.

A combined functional annotation score for non-synonymous variants.

Author information

1
Wellcome Trust Sanger Institute, Hinxton, Hinxton, UK. ml10@sanger.ac.uk

Abstract

AIMS:

Next-generation sequencing has opened the possibility of large-scale sequence-based disease association studies. A major challenge in interpreting whole-exome data is predicting which of the discovered variants are deleterious or neutral. To address this question in silico, we have developed a score called Combined Annotation scoRing toOL (CAROL), which combines information from 2 bioinformatics tools: PolyPhen-2 and SIFT, in order to improve the prediction of the effect of non-synonymous coding variants.

METHODS:

We used a weighted Z method that combines the probabilistic scores of PolyPhen-2 and SIFT. We defined 2 dataset pairs to train and test CAROL using information from the dbSNP: 'HGMD-PUBLIC' and 1000 Genomes Project databases. The training pair comprises a total of 980 positive control (disease-causing) and 4,845 negative control (non-disease-causing) variants. The test pair consists of 1,959 positive and 9,691 negative controls.

RESULTS:

CAROL has higher predictive power and accuracy for the effect of non-synonymous variants than each individual annotation tool (PolyPhen-2 and SIFT) and benefits from higher coverage.

CONCLUSION:

The combination of annotation tools can help improve automated prediction of whole-genome/exome non-synonymous variant functional consequences.

PMID:
22261837
PMCID:
PMC3390741
DOI:
10.1159/000334984
[Indexed for MEDLINE]
Free PMC Article
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