scholarly journals Variable Phenotypes of Epilepsy, Intellectual Disability, and Schizophrenia Caused by 12p13.33–p13.32 Terminal Microdeletion in a Korean Family: A Case Report and Literature Review

Genes ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 1001
Author(s):  
Jiyoon Han ◽  
Joonhong Park
Keyword(s):  
Intellectual Disability ◽  
Exome Sequencing ◽  
Environmental Influence ◽  
Copy Number Variations ◽  
Genetic Modifiers ◽  
Sequencing Data ◽  
Exome Sequencing Data ◽  
Korean Family ◽  
Coverage Analysis ◽  
Patient Will

A simultaneous analysis of nucleotide changes and copy number variations (CNVs) based on exome sequencing data was demonstrated as a potential new first-tier diagnosis strategy for rare neuropsychiatric disorders. In this report, using depth-of-coverage analysis from exome sequencing data, we described variable phenotypes of epilepsy, intellectual disability (ID), and schizophrenia caused by 12p13.33–p13.32 terminal microdeletion in a Korean family. We hypothesized that CACNA1C and KDM5A genes of the six candidate genes located in this region were the best candidates for explaining epilepsy, ID, and schizophrenia and may be responsible for clinical features reported in cases with monosomy of the 12p13.33 subtelomeric region. On the background of microdeletion syndrome, which was described in clinical cases with mild, moderate, and severe neurodevelopmental manifestations as well as impairments, the clinician may determine whether the patient will end up with a more severe or milder end‐phenotype, which in turn determines disease prognosis. In our case, the 12p13.33–p13.32 terminal microdeletion may explain the variable expressivity in the same family. However, further comprehensive studies with larger cohorts focusing on careful phenotyping across the lifespan are required to clearly elucidate the possible contribution of genetic modifiers and the environmental influence on the expressivity of 12p13.33 microdeletion and associated characteristics.

scholarly journals DeAnnCNV: a tool for online detection and annotation of copy number variations from whole-exome sequencing data

2015 ◽  
Vol 43 (W1) ◽  
pp. W289-W294 ◽  
Author(s):  
Yuanwei Zhang ◽  
Zhenhua Yu ◽  
Rongjun Ban ◽  
Huan Zhang ◽  
Furhan Iqbal ◽  
...  
Keyword(s):  
Exome Sequencing ◽  
Whole Exome Sequencing ◽  
Copy Number ◽  
Copy Number Variations ◽  
Online Detection ◽  
Sequencing Data ◽  
Exome Sequencing Data ◽  
Whole Exome ◽  
Whole Exome Sequencing Data

scholarly journals Identifying long indels in exome sequencing data of patients with intellectual disability

2018 ◽  
Author(s):  
Sander Pajusalu ◽  
Rolph Pfundt ◽  
Lisenka E.L.M. Vissers ◽  
Michael P. Kwint ◽  
Tiia Reimand ◽  
...  
Keyword(s):  
Intellectual Disability ◽  
Exome Sequencing ◽  
Size Range ◽  
Clinical Phenotype ◽  
Diagnostic Yield ◽  
Sequencing Data ◽  
Indel Detection ◽  
Exome Sequencing Data ◽  
Genome Wide

AbstractExome sequencing is a powerful tool for detecting both single and multiple nucleotide variation genome wide. However long indels, in the size range 20 – 200bp, remain difficult to accurately detect. By assessing a set of common exonic long indels, we estimate the sensitivity of long indel detection in exome sequencing data to be 92%. To clarify the role of pathogenic long indels in patients with intellectual disability (ID), we analysed exome sequencing data from 820 patients using two variant callers, Pindel and Platypus. We identified three indels explaining the patients’ clinical phenotype by disrupting the UBE3A, PGAP3 and MECP2 genes. Comparison of different tools demonstrated the importance of both correct genotyping and annotation variants. In conclusion, specialized long indel detection can improve diagnostic yield in ID patients.

scholarly journals ExCNVSS: A Noise-Robust Method for Copy Number Variation Detection in Whole Exome Sequencing Data

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Jinhwa Kong ◽  
Jaemoon Shin ◽  
Jungim Won ◽  
Keonbae Lee ◽  
Unjoo Lee ◽  
...  
Keyword(s):  
Exome Sequencing ◽  
Copy Number ◽  
Matched Control ◽  
De Novo ◽  
Simulated Data ◽  
Scale Space ◽  
Copy Number Variations ◽  
Data Sets ◽  
Sequencing Data ◽  
Exome Sequencing Data

Copy number variations (CNVs) are structural variants associated with human diseases. Recent studies verified that disease-related genes are based on the extraction of rare de novo and transmitted CNVs from exome sequencing data. The need for more efficient and accurate methods has increased, which still remains a challenging problem due to coverage biases, as well as the sparse, small-sized, and noncontinuous nature of exome sequencing. In this study, we developed a new CNV detection method, ExCNVSS, based on read coverage depth evaluation and scale-space filtering to resolve these problems. We also developed the method ExCNVSS_noRatio, which is a version of ExCNVSS, for applying to cases with an input of test data only without the need to consider the availability of a matched control. To evaluate the performance of our method, we tested it with 11 different simulated data sets and 10 real HapMap samples’ data. The results demonstrated that ExCNVSS outperformed three other state-of-the-art methods and that our method corrected for coverage biases and detected all-sized CNVs even without matched control data.

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