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. 2019 Jan 9:9:1529.
doi: 10.3389/fphar.2018.01529. eCollection 2018.

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

Xu Wang  1 , Xiang Shen  2 , Fang Fang  1 , Chang-Hong Ding  1 , Hao Zhang  2 , Zhen-Hua Cao  2 , Dong-Yan An  2
Affiliations

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

Xu Wang et al. Front Pharmacol. .

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.

Keywords: WES; annotation; phenotype-driven; rare disease; virtual panel.

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Figures

FIGURE 1
FIGURE 1
The flow chart of phenotype-driven designing virtual panel.
FIGURE 2
FIGURE 2
Phenotype of the patients. (a) large ear; (b) internal strabismus; (c) inverted nipples; (d) fat pad in the buttock.
FIGURE 3
FIGURE 3
MRI result of the patient. (a) Coronal T1W image. Black arrows pointed the anterior limb of the internal capsule. The faint hyperintense signal of T1W indicated delayed myelination of the patient. (b) Sagittal T1W image. The patient presented cerebellar atrophy.
FIGURE 4
FIGURE 4
Chest CT result of the patient. It is shown that the patient had spine kyphosis.
FIGURE 5
FIGURE 5
The mass spectrum result of the metabolite in urine. The result of the urine metabolite indicated liver abnormality of patient.
FIGURE 6
FIGURE 6
The blood test result of the patient’s serum. Alleviation of alanine aminotransferase (ALT) and Aspartate aminotransferase (AST) and deduction of plasma cholinesterase indicated liver dysfunction of the patient.
FIGURE 7
FIGURE 7
Pedigree of family members. The elder sister of the patient also had similar phenotypes.
FIGURE 8
FIGURE 8
Next-Generation result of patient. The result shows the two heterozygous mutations in the PMM2 gene. (a) c.640G > C; (b) c.430T > G.
FIGURE 9
FIGURE 9
Sanger Sequence result of the patient’s family. The result shows that (A) the proband’s father was the heterozygous carrier of the c.430T > C mutation, while (B) the proband’s mother carried the c.640G > C mutation. The proband’s sister is also the carrier of the compound heterozygous mutations.
FIGURE 10
FIGURE 10
Indicated splicing change by MutationTaster (Schwarz et al., 2014). This mutation might disturb the exon-intron border.
See this image and copyright information in PMC

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