Identifying Mendelian disease genes with the variant effect scoring tool.
Identifying Mendelian disease genes with the variant effect scoring tool.
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DOI：
10.1186/1471-2164-14-s3-s3
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发表时间：
2013
期刊：
影响因子：
4.4
通讯作者：
Karchin R
中科院分区：
文献类型：
--
作者：
Carter H;Douville C;Stenson PD;Cooper DN;Karchin R
全外显子组测序研究确定了数百至数千种对人类健康意义模糊的罕见蛋白质编码变体。需要计算工具来加速识别导致人类疾病的特定变异和基因。我们开发了变体效应评分工具（VEST），一种基于监督机器学习的分类器，用于优先考虑可能参与人类疾病的罕见错义变体。VEST分类器训练集包括来自最新人类基因突变数据库发布的~ 45，000个疾病突变和来自外显子组测序项目的另外~ 45，000个高频（等位基因频率>1%）puerectin中性错义变体。在精心设计的holdout基准测试实验中，VEST优于一些最流行的优先排序错义变体的方法（VEST ROC AUC = 0.91，PolyPhen 2 ROC AUC = 0.86，SIFT 4.0 ROC AUC = 0.84）。VEST针对VEST训练集中未包括的中性变体的VEST评分的空分布估计变体评分p值。这些p值可以在多个疾病外显子组的基因水平上聚合，以对可能涉及疾病的基因进行排名。我们测试了一个总的VEST基因评分，以确定候选孟德尔疾病基因的能力，基于全外显子组测序的少数疾病病例。我们使用了两个孟德尔疾病的致病基因是已知的全外显子组数据。仅考虑所有病例中包含变异的基因，VEST基因评分将米勒综合征4例患者的2253个基因中的二氢乳清酸脱氢酶（DHODH）排名第2，将弗里曼谢尔顿综合征3例患者的2313个基因中的肌球蛋白-3（MYH 3）排名第2。我们的研究结果证明了将生物信息学变异评分汇总为基因水平评分的潜在功率增益，以及生物信息学在大规模外显子组测序研究中协助寻找疾病基因的一般效用。VEST作为一个独立的软件包在http://wiki.chasmsoftware.org上提供，并由CRAVAT网络服务器托管在http://www.cravat.us上
Whole exome sequencing studies identify hundreds to thousands of rare protein coding variants of ambiguous significance for human health. Computational tools are needed to accelerate the identification of specific variants and genes that contribute to human disease. We have developed the Variant Effect Scoring Tool (VEST), a supervised machine learning-based classifier, to prioritize rare missense variants with likely involvement in human disease. The VEST classifier training set comprised ~ 45,000 disease mutations from the latest Human Gene Mutation Database release and another ~45,000 high frequency (allele frequency >1%) putatively neutral missense variants from the Exome Sequencing Project. VEST outperforms some of the most popular methods for prioritizing missense variants in carefully designed holdout benchmarking experiments (VEST ROC AUC = 0.91, PolyPhen2 ROC AUC = 0.86, SIFT4.0 ROC AUC = 0.84). VEST estimates variant score p-values against a null distribution of VEST scores for neutral variants not included in the VEST training set. These p-values can be aggregated at the gene level across multiple disease exomes to rank genes for probable disease involvement. We tested the ability of an aggregate VEST gene score to identify candidate Mendelian disease genes, based on whole-exome sequencing of a small number of disease cases. We used whole-exome data for two Mendelian disorders for which the causal gene is known. Considering only genes that contained variants in all cases, the VEST gene score ranked dihydroorotate dehydrogenase (DHODH) number 2 of 2253 genes in four cases of Miller syndrome, and myosin-3 (MYH3) number 2 of 2313 genes in three cases of Freeman Sheldon syndrome. Our results demonstrate the potential power gain of aggregating bioinformatics variant scores into gene-level scores and the general utility of bioinformatics in assisting the search for disease genes in large-scale exome sequencing studies. VEST is available as a stand-alone software package at http://wiki.chasmsoftware.org and is hosted by the CRAVAT web server at http://www.cravat.us