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Phenotype-Driven Virtual Panel Is an Effective Method to Analyze WES Data of Neurological Disease

Objective: Whole Exome Sequencing (WES) is an effective diagnostic method for complicated and multi-system involved rare diseases. However, annotation and analysis of the WES result, especially for single case analysis still remain a challenge. Here, we introduce a method called phenotype-driven des...

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Autores principales: Wang, Xu, Shen, Xiang, Fang, Fang, Ding, Chang-Hong, Zhang, Hao, Cao, Zhen-Hua, An, Dong-Yan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6333749/
https://www.ncbi.nlm.nih.gov/pubmed/30687093
http://dx.doi.org/10.3389/fphar.2018.01529
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author Wang, Xu
Shen, Xiang
Fang, Fang
Ding, Chang-Hong
Zhang, Hao
Cao, Zhen-Hua
An, Dong-Yan
author_facet Wang, Xu
Shen, Xiang
Fang, Fang
Ding, Chang-Hong
Zhang, Hao
Cao, Zhen-Hua
An, Dong-Yan
author_sort Wang, Xu
collection PubMed
description Objective: Whole Exome Sequencing (WES) is an effective diagnostic method for complicated and multi-system involved rare diseases. However, annotation and analysis of the WES result, especially for single case analysis still remain a challenge. Here, we introduce a method called phenotype-driven designing “virtual panel” to simplify the procedure and assess the diagnostic rate of this method. Methods: WES was performed in samples of 30 patients, core phenotypes of probands were then extracted and inputted into an in-house software, “Mingjian” to calculate and generate associated gene list of a virtual panel. Mingjian is a self-updating genetic disease computer supportive diagnostic system that based on the databases of HPO, OMIM, HGMD. The virtual panel that generated by Mingjian system was then used to filter and annotate candidate mutations. Sanger sequencing and co-segregation analysis among the family were then used to confirm the filtered mutants. Result: We first used phenotype-driven designing “virtual panel” to analyze the WES data of a patient whose core phenotypes are ataxia, seizures, esotropia, puberty and gonadal disorders, and global developmental delay. Two mutations, c.430T > C and c.640G > C in PMM2 were identified by this method. This result was also confirmed by Sanger sequencing among the family. The same analysing method was then used in the annotation of WES data of other 29 neurological rare disease patients. The diagnostic rate was 65.52%, which is significantly higher than the diagnostic rate before. Conclusion: Phenotype-driven designing virtual panel could achieve low-cost individualized analysis. This method may decrease the time-cost of annotation, increase the diagnostic efficiency and the diagnostic rate.
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spelling pubmed-63337492019-01-25 Phenotype-Driven Virtual Panel Is an Effective Method to Analyze WES Data of Neurological Disease Wang, Xu Shen, Xiang Fang, Fang Ding, Chang-Hong Zhang, Hao Cao, Zhen-Hua An, Dong-Yan Front Pharmacol Pharmacology Objective: Whole Exome Sequencing (WES) is an effective diagnostic method for complicated and multi-system involved rare diseases. However, annotation and analysis of the WES result, especially for single case analysis still remain a challenge. Here, we introduce a method called phenotype-driven designing “virtual panel” to simplify the procedure and assess the diagnostic rate of this method. Methods: WES was performed in samples of 30 patients, core phenotypes of probands were then extracted and inputted into an in-house software, “Mingjian” to calculate and generate associated gene list of a virtual panel. Mingjian is a self-updating genetic disease computer supportive diagnostic system that based on the databases of HPO, OMIM, HGMD. The virtual panel that generated by Mingjian system was then used to filter and annotate candidate mutations. Sanger sequencing and co-segregation analysis among the family were then used to confirm the filtered mutants. Result: We first used phenotype-driven designing “virtual panel” to analyze the WES data of a patient whose core phenotypes are ataxia, seizures, esotropia, puberty and gonadal disorders, and global developmental delay. Two mutations, c.430T > C and c.640G > C in PMM2 were identified by this method. This result was also confirmed by Sanger sequencing among the family. The same analysing method was then used in the annotation of WES data of other 29 neurological rare disease patients. The diagnostic rate was 65.52%, which is significantly higher than the diagnostic rate before. Conclusion: Phenotype-driven designing virtual panel could achieve low-cost individualized analysis. This method may decrease the time-cost of annotation, increase the diagnostic efficiency and the diagnostic rate. Frontiers Media S.A. 2019-01-09 /pmc/articles/PMC6333749/ /pubmed/30687093 http://dx.doi.org/10.3389/fphar.2018.01529 Text en Copyright © 2019 Wang, Shen, Fang, Ding, Zhang, Cao and An. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Pharmacology
Wang, Xu
Shen, Xiang
Fang, Fang
Ding, Chang-Hong
Zhang, Hao
Cao, Zhen-Hua
An, Dong-Yan
Phenotype-Driven Virtual Panel Is an Effective Method to Analyze WES Data of Neurological Disease
title Phenotype-Driven Virtual Panel Is an Effective Method to Analyze WES Data of Neurological Disease
title_full Phenotype-Driven Virtual Panel Is an Effective Method to Analyze WES Data of Neurological Disease
title_fullStr Phenotype-Driven Virtual Panel Is an Effective Method to Analyze WES Data of Neurological Disease
title_full_unstemmed Phenotype-Driven Virtual Panel Is an Effective Method to Analyze WES Data of Neurological Disease
title_short Phenotype-Driven Virtual Panel Is an Effective Method to Analyze WES Data of Neurological Disease
title_sort phenotype-driven virtual panel is an effective method to analyze wes data of neurological disease
topic Pharmacology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6333749/
https://www.ncbi.nlm.nih.gov/pubmed/30687093
http://dx.doi.org/10.3389/fphar.2018.01529
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