REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants
REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants
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DOI：
10.1016/j.ajhg.2016.08.016
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发表时间：
2016-10-06
影响因子：
9.8
通讯作者：
Sieh, Weiva
中科院分区：
文献类型：
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作者：
Ioannidis, Nilah M.;Rothstein, Joseph H.;Sieh, Weiva
绝大多数的编码变异是罕见的，和评估的贡献罕见的变异复杂的性状是阻碍了低统计功率和有限的功能数据。需要用于预测罕见编码变体的致病性的改进方法，以促进从外显子组测序研究中发现疾病变体。我们开发了REVEL（罕见外显子组变体集成学习器），这是一种基于单个工具预测错义变体致病性的集成方法：MutPred，FATHMM，VEST，PolyPhen，SIFT，PROVEAN，MutationAssessor，MutationTaster，LRT，GERP，SiPhy，PARP和phastCons。REVEL使用最近发现的致病性和罕见的中性误解变体进行训练，不包括之前用于训练其组成工具的变体。当应用于两个独立的测试集时，与任何单个工具和七种集成方法相比，REVEL具有最佳的整体性能（p < 10（-12））：MetaSVM，MetaLR，KGGSeq，Condel，CADD，DANN和Eigen。重要的是，REVEL在区分致病性和具有等位基因频率的罕见中性变体方面也具有最佳性能
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