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Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies.

TitleComparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies.
Publication TypeJournal Article
Year of Publication2015
AuthorsDong C, Wei P, Jian X, Gibbs R, Boerwinkle E, Wang K
Secondary AuthorsLiu X
JournalHum Mol Genet
Volume24
Issue8
Pagination2125-37
Date Published2015 Apr 15
ISSN1460-2083
KeywordsComputational Biology, Exome, Genome, Human, Humans, Polymorphism, Single Nucleotide, Software
Abstract

Accurate deleteriousness prediction for nonsynonymous variants is crucial for distinguishing pathogenic mutations from background polymorphisms in whole exome sequencing (WES) studies. Although many deleteriousness prediction methods have been developed, their prediction results are sometimes inconsistent with each other and their relative merits are still unclear in practical applications. To address these issues, we comprehensively evaluated the predictive performance of 18 current deleteriousness-scoring methods, including 11 function prediction scores (PolyPhen-2, SIFT, MutationTaster, Mutation Assessor, FATHMM, LRT, PANTHER, PhD-SNP, SNAP, SNPs&GO and MutPred), 3 conservation scores (GERP++, SiPhy and PhyloP) and 4 ensemble scores (CADD, PON-P, KGGSeq and CONDEL). We found that FATHMM and KGGSeq had the highest discriminative power among independent scores and ensemble scores, respectively. Moreover, to ensure unbiased performance evaluation of these prediction scores, we manually collected three distinct testing datasets, on which no current prediction scores were tuned. In addition, we developed two new ensemble scores that integrate nine independent scores and allele frequency. Our scores achieved the highest discriminative power compared with all the deleteriousness prediction scores tested and showed low false-positive prediction rate for benign yet rare nonsynonymous variants, which demonstrated the value of combining information from multiple orthologous approaches. Finally, to facilitate variant prioritization in WES studies, we have pre-computed our ensemble scores for 87 347 044 possible variants in the whole-exome and made them publicly available through the ANNOVAR software and the dbNSFP database.

DOI10.1093/hmg/ddu733
Alternate JournalHum Mol Genet
PubMed ID25552646
PubMed Central IDPMC4375422
Grant ListU54HG003273 / HG / NHGRI NIH HHS / United States
R01-HL106034 / HL / NHLBI NIH HHS / United States
R01 HL106034 / HL / NHLBI NIH HHS / United States
R01 GM104411 / GM / NIGMS NIH HHS / United States
R01 HL116720 / HL / NHLBI NIH HHS / United States
R01-CA169122 / CA / NCI NIH HHS / United States
R01 CA169122 / CA / NCI NIH HHS / United States
R01-HL116720 / HL / NHLBI NIH HHS / United States
R01-HG006465 / HG / NHGRI NIH HHS / United States
RC2-HL02419 / HL / NHLBI NIH HHS / United States
R01 HG006465 / HG / NHGRI NIH HHS / United States