Determination of disease phenotypes and pathogenic variants from exome sequence data in the CAGI 4 gene panel challenge.
Determination of disease phenotypes and pathogenic variants from exome sequence data in the CAGI 4 gene panel challenge.
复制标题
在CAGI 4基因面板挑战中，从外显子组序列数据中确定疾病表型和致病变异。
DOI：
10.1002/humu.23249
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发表时间：
2017-09
期刊：
影响因子：
3.9
通讯作者：
Moult J
中科院分区：
文献类型：
--
作者：
Kundu K;Pal LR;Yin Y;Moult J
使用基因面板序列进行诊断和预后测试现在已经很普遍，但迄今为止还很少有客观的测试方法来解释这些数据。我们描述了基因面板测序数据分析管道（VarP）的设计和实现，并在CAGI4社区实验中对其进行了评估。将该方法应用于106例患者的临床基因面板测序数据，目的是确定每个患者患有14种疾病中的哪一种以及相应的致病变异。该疾病类别被正确识别为36例，其中包括10例原始临床管道未发现致病变异的病例。在另外七个病例中，我们发现了强有力的证据，证明存在一种与测试结果不同的疾病。许多潜在的致病变异是错误的，以前与疾病没有关联，这些变异被证明是最难正确分配致病性或其他方面的。后期分析表明，三维结构数据可以帮助多达一半的病例。对HGMD注释的过度依赖导致了许多错误的疾病分配。我们使用了一种很大程度上特别的方法来分配每个变异的致病性概率，在这个领域还有很多工作要做。
The use of gene panel sequence for diagnostic and prognostic testing is now widespread, but there are so far few objective tests of methods to interpret these data. We describe the design and implementation of a gene panel sequencing data analysis pipeline (VarP) and its assessment in a CAGI4 community experiment. The method was applied to clinical gene panel sequencing data of 106 patients, with the goal of determining which of 14 disease classes each patient has and the corresponding causative variant(s). The disease class was correctly identified for 36 cases, including 10 where the original clinical pipeline did not find causative variants. For a further seven cases, we found strong evidence of an alternative disease to that tested. Many of the potentially causative variants are missense, with no previous association with disease, and these proved the hardest to correctly assign pathogenicity or otherwise. Post analysis showed that three-dimensional structure data could have helped for up to half of these cases. Over-reliance on HGMD annotation led to a number of incorrect disease assignments. We used a largely ad hoc method to assign probabilities of pathogenicity for each variant, and there is much work still to be done in this area.
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影响因子：
12.3
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