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Detection of copy number variations in epilepsy using exome data

Tsuchida N, Nakashima M, Kato M, Heyman E, Inui T, Haginoya K, et al.

Clin Genet, 2018 03;93(3):577-587.
PMID: 28940419 DOI: 10.1111/cge.13144

Epilepsies are common neurological disorders and genetic factors contribute to their pathogenesis. Copy number variations (CNVs) are increasingly recognized as an important etiology of many human diseases including epilepsy. Whole-exome sequencing (WES) is becoming a standard tool for detecting pathogenic mutations and has recently been applied to detecting CNVs. Here, we analyzed 294 families with epilepsy using WES, and focused on 168 families with no causative single nucleotide variants in known epilepsy-associated genes to further validate CNVs using 2 different CNV detection tools using WES data. We confirmed 18 pathogenic CNVs, and 2 deletions and 2 duplications at chr15q11.2 of clinically unknown significance. Of note, we were able to identify small CNVs less than 10 kb in size, which might be difficult to detect by conventional microarray. We revealed 2 cases with pathogenic CNVs that one of the 2 CNV detection tools failed to find, suggesting that using different CNV tools is recommended to increase diagnostic yield. Considering a relatively high discovery rate of CNVs (18 out of 168 families, 10.7%) and successful detection of CNV with <10 kb in size, CNV detection by WES may be able to surrogate, or at least complement, conventional microarray analysis.
Efficient detection of copy-number variations using exome data: Batch- and sex-based analyses

Uchiyama Y, Yamaguchi D, Iwama K, Miyatake S, Hamanaka K, Tsuchida N, et al.

Hum Mutat, 2021 01;42(1):50-65.
PMID: 33131168 DOI: 10.1002/humu.24129

Many algorithms to detect copy number variations (CNVs) using exome sequencing (ES) data have been reported and evaluated on their sensitivity and specificity, reproducibility, and precision. However, operational optimization of such algorithms for a better performance has not been fully addressed. ES of 1199 samples including 763 patients with different disease profiles was performed. ES data were analyzed to detect CNVs by both the eXome Hidden Markov Model (XHMM) and modified Nord's method. To efficiently detect rare CNVs, we aimed to decrease sequencing biases by analyzing, at the same time, the data of all unrelated samples sequenced in the same flow cell as a batch, and to eliminate sex effects of X-linked CNVs by analyzing female and male sequences separately. We also applied several filtering steps for more efficient CNV selection. The average number of CNVs detected in one sample was <5. This optimization together with targeted CNV analysis by Nord's method identified pathogenic/likely pathogenic CNVs in 34 patients (4.5%, 34/763). In particular, among 142 patients with epilepsy, the current protocol detected clinically relevant CNVs in 19 (13.4%) patients, whereas the previous protocol identified them in only 14 (9.9%) patients. Thus, this batch-based XHMM analysis efficiently selected rare pathogenic CNVs in genetic diseases.
Genetic and clinical landscape of childhood cerebellar hypoplasia and atrophy

Sakamoto M, Iwama K, Sasaki M, Ishiyama A, Komaki H, Saito T, et al.

Genet Med, 2022 Dec;24(12):2453-2463.
PMID: 36305856 DOI: 10.1016/j.gim.2022.08.007

PURPOSE: Cerebellar hypoplasia and atrophy (CBHA) in children is an extremely heterogeneous group of disorders, but few comprehensive genetic studies have been reported. Comprehensive genetic analysis of CBHA patients may help differentiating atrophy and hypoplasia and potentially improve their prognostic aspects.
METHODS: Patients with CBHA in 176 families were genetically examined using exome sequencing. Patients with disease-causing variants were clinically evaluated.
RESULTS: Disease-causing variants were identified in 96 of the 176 families (54.5%). After excluding 6 families, 48 patients from 42 families were categorized as having syndromic associations with CBHA, whereas the remaining 51 patients from 48 families had isolated CBHA. In 51 patients, 26 aberrant genes were identified, of which, 20 (76.9%) caused disease in 1 family each. The most prevalent genes were CACNA1A, ITPR1, and KIF1A. Of the 26 aberrant genes, 21 and 1 were functionally annotated to atrophy and hypoplasia, respectively. CBHA+S was more clinically severe than CBHA-S. Notably, ARG1 and FOLR1 variants were identified in 2 families, leading to medical treatments.
CONCLUSION: A wide genetic and clinical diversity of CBHA was revealed through exome sequencing in this cohort, which highlights the importance of comprehensive genetic analyses. Furthermore, molecular-based treatment was available for 2 families.

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