title: Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies.
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.
authors.affiliation: Zilkha Neurogenetic Institute, Biostatistics Division, Department of Preventive Medicine and.
authors.forename: Chengliang
authors.initials: C
authors.lastname: Dong
authors.affiliation: Human Genetics Center, Division of Biostatistics, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA and.
authors.forename: Peng
authors.initials: P
authors.lastname: Wei
authors.affiliation: Division of Epidemiology, Human Genetics and Environmental Sciences and.
authors.forename: Xueqiu
authors.initials: X
authors.lastname: Jian
authors.affiliation: Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA.
authors.forename: Richard
authors.initials: R
authors.lastname: Gibbs
authors.affiliation: Human Genetics Center, Division of Epidemiology, Human Genetics and Environmental Sciences and Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA.
authors.forename: Eric
authors.initials: E
authors.lastname: Boerwinkle
authors.affiliation: Zilkha Neurogenetic Institute, Biostatistics Division, Department of Preventive Medicine and, Department of Psychiatry, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA, xiaoming.liu@uth.tmc.edu kaiwang@usc.edu.
authors.forename: Kai
authors.initials: K
authors.lastname: Wang
authors.affiliation: Human Genetics Center, Division of Epidemiology, Human Genetics and Environmental Sciences and xiaoming.liu@uth.tmc.edu kaiwang@usc.edu.
authors.forename: Xiaoming
authors.initials: X
authors.lastname: Liu
mesh_terms: D019295:Computational Biology / Q000295:instrumentation / Q000379:methods*; D059472:Exome; D015894:Genome, Human; D006801:Humans; D020641:Polymorphism, Single Nucleotide; D012984:Software