Optimization of in silico tools for predicting genetic variants: individualizing for genes with molecular sub-regional stratification
| Autor: | Yong-Hong Yi, Yue-Sheng Long, Tao Su, Bin Li, Na He, Yang Zeng, Wei-Ping Liao, Bin Tang, Liang-Di Gao, Xiao-Rong Liu |
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| Rok vydání: | 2019 |
| Předmět: |
In silico
Computational biology Biology Stratification (mathematics) 03 medical and health sciences symbols.namesake 0302 clinical medicine Databases Genetic Humans KCNQ2 Potassium Channel Missense mutation Computer Simulation Molecular Biology Familial Epilepsy Gene 030304 developmental biology 0303 health sciences NAV1.2 Voltage-Gated Sodium Channel Genetic variants Genetic Variation Pathogenicity NAV1.1 Voltage-Gated Sodium Channel Mendelian inheritance symbols 030217 neurology & neurosurgery Information Systems |
| Zdroj: | Briefings in Bioinformatics. 21:1776-1786 |
| ISSN: | 1477-4054 1467-5463 |
| DOI: | 10.1093/bib/bbz115 |
| Popis: | 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 Nav1.1. 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. |
| Databáze: | OpenAIRE |
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