AUTHOR=Wang Xu , Shen Xiang , Fang Fang , Ding Chang-Hong , Zhang Hao , Cao Zhen-Hua , An Dong-Yan
TITLE=Phenotype-Driven Virtual Panel Is an Effective Method to Analyze WES Data of Neurological Disease
JOURNAL=Frontiers in Pharmacology
VOLUME=9
YEAR=2019
URL=https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2018.01529
DOI=10.3389/fphar.2018.01529
ISSN=1663-9812
ABSTRACT=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.