Dataset Information

REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants.

ABSTRACT: The vast majority of coding variants are rare, and assessment of the contribution of rare variants to complex traits is hampered by low statistical power and limited functional data. Improved methods for predicting the pathogenicity of rare coding variants are needed to facilitate the discovery of disease variants from exome sequencing studies. We developed REVEL (rare exome variant ensemble learner), an ensemble method for predicting the pathogenicity of missense variants on the basis of individual tools: MutPred, FATHMM, VEST, PolyPhen, SIFT, PROVEAN, MutationAssessor, MutationTaster, LRT, GERP, SiPhy, phyloP, and phastCons. REVEL was trained with recently discovered pathogenic and rare neutral missense variants, excluding those previously used to train its constituent tools. When applied to two independent test sets, REVEL had the best overall performance (p < 10^-12) as compared to any individual tool and seven ensemble methods: MetaSVM, MetaLR, KGGSeq, Condel, CADD, DANN, and Eigen. Importantly, REVEL also had the best performance for distinguishing pathogenic from rare neutral variants with allele frequencies <0.5%. The area under the receiver operating characteristic curve (AUC) for REVEL was 0.046-0.182 higher in an independent test set of 935 recent SwissVar disease variants and 123,935 putatively neutral exome sequencing variants and 0.027-0.143 higher in an independent test set of 1,953 pathogenic and 2,406 benign variants recently reported in ClinVar than the AUCs for other ensemble methods. We provide pre-computed REVEL scores for all possible human missense variants to facilitate the identification of pathogenic variants in the sea of rare variants discovered as sequencing studies expand in scale.

SUBMITTER: Ioannidis NM

PROVIDER: S-EPMC5065685 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants.

Ioannidis Nilah M NM Rothstein Joseph H JH Pejaver Vikas V Middha Sumit S McDonnell Shannon K SK Baheti Saurabh S Musolf Anthony A Li Qing Q Holzinger Emily E Karyadi Danielle D Cannon-Albright Lisa A LA Teerlink Craig C CC Stanford Janet L JL Isaacs William B WB Xu Jianfeng J Cooney Kathleen A KA Lange Ethan M EM Schleutker Johanna J Carpten John D JD Powell Isaac J IJ Cussenot Olivier O Cancel-Tassin Geraldine G Giles Graham G GG MacInnis Robert J RJ Maier Christiane C Hsieh Chih-Lin CL Wiklund Fredrik F Catalona William J WJ Foulkes William D WD Mandal Diptasri D Eeles Rosalind A RA Kote-Jarai Zsofia Z Bustamante Carlos D CD Schaid Daniel J DJ Hastie Trevor T Ostrander Elaine A EA Bailey-Wilson Joan E JE Radivojac Predrag P Thibodeau Stephen N SN Whittemore Alice S AS Sieh Weiva W

American journal of human genetics 20160922 4

The vast majority of coding variants are rare, and assessment of the contribution of rare variants to complex traits is hampered by low statistical power and limited functional data. Improved methods for predicting the pathogenicity of rare coding variants are needed to facilitate the discovery of disease variants from exome sequencing studies. We developed REVEL (rare exome variant ensemble learner), an ensemble method for predicting the pathogenicity of missense variants on the basis of indivi ...[more]

PMID: 27666373

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Project description:BackgroundGermline variants of ten keratin genes (K1, K2, K5, K6A, K6B, K9, K10, K14, K16, and K17) have been reported for causing different types of genodermatoses with an autosomal dominant mode of inheritance. Among all the variants of these ten keratin genes, most of them are missense variants. Unlike pathogenic and likely pathogenic variants, understanding the clinical importance of novel missense variants or variants of uncertain significance (VUS) is the biggest challenge for clinicians or medical geneticists. Functional characterization is the only way to understand the clinical association of novel missense variants or VUS but it is time consuming, costly, and depends on the availability of patient's samples. Existing databases report the pathogenic variants of the keratin genes, but never emphasize the systematic effects of these variants on keratin protein structure and genotype-phenotype correlation.ResultsTo address this need, we developed a comprehensive database KVarPredDB, which contains information of all ten keratin genes associated with genodermatoses. We integrated and curated 400 reported pathogenic missense variants as well as 4629 missense VUS. KVarPredDB predicts the pathogenicity of novel missense variants as well as to understand the severity of disease phenotype, based on four criteria; firstly, the difference in physico-chemical properties between the wild type and substituted amino acids; secondly, the loss of inter/intra-chain interactions; thirdly, evolutionary conservation of the wild type amino acids and lastly, the effect of the substituted amino acids in the heptad repeat. Molecular docking simulations based on resolved crystal structures were adopted to predict stability changes and get the binding energy to compare the wild type protein with the mutated one. We use this basic information to determine the structural and functional impact of novel missense variants on the keratin coiled-coil heterodimer. KVarPredDB was built under the integrative web application development framework SSM (SpringBoot, Spring MVC, MyBatis) and implemented in Java, Bootstrap, React-mutation-mapper, MySQL, Tomcat. The website can be accessed through http://bioinfo.zju.edu.cn/KVarPredDB . The genomic variants and analysis results are freely available under the Creative Commons license.ConclusionsKVarPredDB provides an intuitive and user-friendly interface with computational analytical investigation for each missense variant of the keratin genes associated with genodermatoses.

Dataset Information

REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants.

Publications

REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants.

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