Optimization of in silico tools for predicting genetic variants: individualizing for genes with molecular sub-regional stratification
Optimization of in silico tools for predicting genetic variants: individualizing for genes with molecular sub-regional stratification
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用于预测遗传变异的计算机工具的优化：通过分子亚区域分层对基因进行个体化
DOI：
10.1093/bib/bbz115
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发表时间：
2020-09-01
影响因子：
9.5
通讯作者：
Liao, Wei-Ping
中科院分区：
文献类型：
--
作者：
Tang, Bin;Li, Bin;Liao, Wei-Ping
基因具有独特的功能作用，对遗传缺陷的敏感性不同，但致病性预测存在困难。本研究试图改善现有的硅片算法的性能，并找到一个共同的解决方案，基于个性化的策略。我们通过亚区域分层开始了癫痫相关SCN 1A变异的个体化。从突变数据库中检索与癫痫相关的SCN 1A错义变体，并从ExAC数据库中收集良性错义变体。使用10个传统的逐步优化工具进行预测。使用SCN 1A、SCN 2A和KCNQ 2变体的五重交叉验证评价模型预测能力。在损伤确认/家族性癫痫的SCN 1A变体中进行了额外的验证。SCN 1A的常用预测因子的性能不太令人满意，准确度低于80%，并且根据Na（v）1.1的功能域而变化很大。多步个性化优化，包括截断重置，基于域的分层，预测算法的组合，显着提高了预测性能。对于SCN 2A和KCNQ 2中的变体，获得了类似的改善。最近开发的集成工具，如孟德尔临床适用的致病性，结合注释依赖性消耗和本征的预测性能，也显着提高了应用的策略与分子亚区域分层。SCN 1A变异体的预测得分与功能缺陷程度和临床表型的严重程度呈线性相关。该研究强调了在实践中对每个基因进行分子亚区域分层的个性化优化的必要性。
Genes are unique in functional role and differ in their sensitivities to genetic defects, but with difficulties in pathogenicity prediction. This study attempted to improve the performance of existing in silico algorithms and find a common solution based on individualization strategy. We initiated the individualization with the epilepsy-related SCN1A variants by sub-regional stratification. SCN1A missense variants related to epilepsy were retrieved from mutation databases, and benign missense variants were collected from ExAC database. Predictions were performed by using 10 traditional tools with stepwise optimizations. Model predictive ability was evaluated using the five-fold cross-validations on variants of SCN1A, SCN2A, and KCNQ2. Additional validation was performed in SCN1A variants of damage-confirmed/familial epilepsy. The performance of commonly used predictors was less satisfactory for SCN1A with accuracy less than 80% and varied dramatically by functional domains of Na(v)1.1. Multistep individualized optimizations, including cutoff resetting, domain-based stratification, and combination of predicting algorithms, significantly increased predictive performance. Similar improvements were obtained for variants in SCN2A and KCNQ2. The predictive performance of the recently developed ensemble tools, such as Mendelian clinically applicable pathogenicity, combined annotation-dependent depletion and Eigen, was also improved dramatically by application of the strategy with molecular sub-regional stratification. The prediction scores of SCN1A variants showed linear correlations with the degree of functional defects and the severity of clinical phenotypes. This study highlights the need of individualized optimization with molecular sub-regional stratification for each gene in practice.