eDiVA-Classification and prioritization of pathogenic variants for clinical diagnostics
eDiVA-Classification and prioritization of pathogenic variants for clinical diagnostics
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DOI：
10.1002/humu.23772
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发表时间：
2019-07-01
期刊：
影响因子：
3.9
通讯作者：
Ossowski, Stephan
中科院分区：
文献类型：
--
作者：
Bosio, Mattia;Drechsel, Oliver;Ossowski, Stephan
孟德尔遗传病是一种将基因型与表型联系起来并阐明基因功能的有效模型。全外显子组测序（WES）加速了对家族中罕见孟德尔疾病的研究，允许直接精确定位基因区域中罕见的因果突变，而无需进行连锁分析。然而，多项WES疾病研究报告的20-30%的低诊断率表明需要改进变异致病性分类和因果变异优先化方法。在这里，我们提出了外显子组疾病变异分析（eDiVA;），这是一种自动计算框架，用于使用家庭或亲子三人组的WES识别罕见疾病的因果遗传变异（编码/剪接单核苷酸变异和小插入和缺失）。eDiVA结合了下一代测序数据分析、全面的功能注释和针对家族性遗传疾病研究优化的因果变异优先排序。eDiVA具有基于机器学习的变异致病性预测器，结合了各种基因组和进化特征。临床信息，如疾病表型或遗传模式，被纳入，以提高优先级算法的精度。与最先进的竞争对手进行基准测试表明，eDiVA在检测率和精度方面始终表现良好或优于现有方法。此外，我们将eDiVA应用于几个家族性疾病病例，以证明其临床适用性。
Mendelian diseases have shown to be an and efficient model for connecting genotypes to phenotypes and for elucidating the function of genes. Whole-exome sequencing (WES) accelerated the study of rare Mendelian diseases in families, allowing for directly pinpointing rare causal mutations in genic regions without the need for linkage analysis. However, the low diagnostic rates of 20-30% reported for multiple WES disease studies point to the need for improved variant pathogenicity classification and causal variant prioritization methods. Here, we present the exome Disease Variant Analysis (eDiVA; ), an automated computational framework for identification of causal genetic variants (coding/splicing single-nucleotide variants and small insertions and deletions) for rare diseases using WES of families or parent-child trios. eDiVA combines next-generation sequencing data analysis, comprehensive functional annotation, and causal variant prioritization optimized for familial genetic disease studies. eDiVA features a machine learning-based variant pathogenicity predictor combining various genomic and evolutionary signatures. Clinical information, such as disease phenotype or mode of inheritance, is incorporated to improve the precision of the prioritization algorithm. Benchmarking against state-of-the-art competitors demonstrates that eDiVA consistently performed as a good or better than existing approach in terms of detection rate and precision. Moreover, we applied eDiVA to several familial disease cases to demonstrate its clinical applicability.