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Optimization of in silico tools for predicting genetic variants: individualizing for genes with molecular sub-regional stratification.
Tang, Bin; Li, Bin; Gao, Liang-Di; He, Na; Liu, Xiao-Rong; Long, Yue-Sheng; Zeng, Yang; Yi, Yong-Hong; Su, Tao; Liao, Wei-Ping.
Afiliação
  • Tang B; Institute of Neuroscience and the Second Affiliated Hospital of Guangzhou Medical University.
  • Li B; Institute of Neuroscience and Department of Neurology of the Second Affiliated Hospital of Guangzhou Medical University, Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzo, China.
  • Gao LD; Institute of Neuroscience and the Second Affiliated Hospital of Guangzhou Medical University.
  • He N; Institute of Neuroscience and Department of Neurology of the Second Affiliated Hospital of Guangzhou Medical University, Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzo, China.
  • Liu XR; Institute of Neuroscience and the Second Affiliated Hospital of Guangzhou Medical University.
  • Long YS; Institute of Neuroscience and the Second Affiliated Hospital of Guangzhou Medical University.
  • Zeng Y; Institute of Neuroscience and the Second Affiliated Hospital of Guangzhou Medical University.
  • Yi YH; Institute of Neuroscience and Department of Neurology of the Second Affiliated Hospital of Guangzhou Medical University, Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzo, China.
  • Su T; Institute of Neuroscience and the Second Affiliated Hospital of Guangzhou Medical University.
  • Liao WP; Institute of Neuroscience and Department of Neurology of the Second Affiliated Hospital of Guangzhou Medical University, Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzo, China.
Brief Bioinform ; 21(5): 1776-1786, 2020 09 25.
Article em En | MEDLINE | ID: mdl-31686106
ABSTRACT
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.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Variação Genética Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Brief Bioinform Assunto da revista: BIOLOGIA / INFORMATICA MEDICA Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Variação Genética Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Brief Bioinform Assunto da revista: BIOLOGIA / INFORMATICA MEDICA Ano de publicação: 2020 Tipo de documento: Article