Efficient detection of copy‐number variations using exome data: Batch‐ and sex‐based analyses

2020 ◽  
Vol 42 (1) ◽  
pp. 50-65
Author(s):  
Yuri Uchiyama ◽  
Daisuke Yamaguchi ◽  
Kazuhiro Iwama ◽  
Satoko Miyatake ◽  
Kohei Hamanaka ◽  
...  
Keyword(s):  
Copy Number ◽  
Copy Number Variations ◽  
Exome Data ◽  
Efficient Detection

scholarly journals Polishing Copy Number Variant Calls on Exome Sequencing Data via Deep Learning

2020 ◽  
Author(s):  
Furkan Özden ◽  
Can Alkan ◽  
A. Ercüment Çiçek
Keyword(s):  
Deep Learning ◽  
Exome Sequencing ◽  
Copy Number ◽  
Gc Content ◽  
Copy Number Variant ◽  
Copy Number Variations ◽  
Exome Capture ◽  
Sequencing Data ◽  
Independent Manner ◽  
Efficient Detection

AbstractAccurate and efficient detection of copy number variants (CNVs) is of critical importance due to their significant association with complex genetic diseases. Although algorithms working on whole genome sequencing (WGS) data provide stable results with mostly-valid statistical assumptions, copy number detection on whole exome sequencing (WES) data has mostly been a losing game with extremely high false discovery rates. This is unfortunate as WES data is cost efficient, compact and is relatively ubiquitous. The bottleneck is primarily due to non-contiguous nature of the targeted capture: biases in targeted genomic hybridization, GC content, targeting probes, and sample batching during sequencing. Here, we present a novel deep learning model, DECoNT, which uses the matched WES and WGS data and learns to correct the copy number variations reported by any over-the-shelf WES-based germline CNV caller. We train DECoNT on the 1000 Genomes Project data, and we show that (i) we can efficiently triple the duplication call precision and double the deletion call precisions of the state-of-the-art algorithms. We also show that model consistently improves the performance in a (i) sequencing technology, (ii) exome capture kit and (iii) CNV caller independent manner. Using DECoNT as a universal exome CNV call polisher has the potential to improve the reliability of germline CNV detection on WES data sets and surge its application. The code and the models are available at https://github.com/ciceklab/DECoNT.

scholarly journals Detection of copy number variations in epilepsy using exome data

2018 ◽  
Vol 93 (3) ◽  
pp. 577-587 ◽  
Author(s):  
N. Tsuchida ◽  
M. Nakashima ◽  
M. Kato ◽  
E. Heyman ◽  
T. Inui ◽  
...  
Keyword(s):  
Copy Number ◽  
Copy Number Variations ◽  
Exome Data

X-linked CNV and skewed X-chromosome inactivation

2020 ◽  
pp. 19-21
Author(s):  
Е.А. Фонова ◽  
Е.Н. Толмачева ◽  
А.А. Кашеварова ◽  
М.Е. Лопаткина ◽  
К.А. Павлова ◽  
...  
Keyword(s):  
Cell Proliferation ◽  
Intellectual Disability ◽  
X Chromosome ◽  
Copy Number ◽  
Copy Number Variations ◽  
X Chromosome Inactivation ◽  
Chromosome Inactivation ◽  
Chromosome X ◽  
Skewed X Chromosome Inactivation

Смещение инактивации Х-хромосомы может быть следствием и маркером нарушения клеточной пролиферации при вариациях числа копий ДНК на Х-хромосоме. Х-сцепленные CNV выявляются как у женщин с невынашиванием беременности и смещением инактивации Х-хромосомы (с частотой 33,3%), так и у пациентов с умственной отсталостью и смещением инактивацией у их матерей (с частотой 40%). A skewed X-chromosome inactivation can be a consequence and a marker of impaired cell proliferation in the presence of copy number variations (CNV) on the X chromosome. X-linked CNVs are detected in women with miscarriages and a skewed X-chromosome inactivation (with a frequency of 33.3%), as well as in patients with intellectual disability and skewed X-chromosome inactivation in their mothers (with a frequency of 40%).

scholarly journals Combination of Genome-Wide Polymorphisms and Copy Number Variations of Pharmacogenes in Koreans

2021 ◽  
Vol 11 (1) ◽  
pp. 33
Author(s):  
Nayoung Han ◽  
Jung Mi Oh ◽  
In-Wha Kim
Keyword(s):  
Copy Number ◽  
Genome Wide Association Study ◽  
Copy Number Gain ◽  
Copy Number Variations ◽  
Gene Gain ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Haplotype Blocks ◽  
Genome Wide ◽  
Control And Prevention

For predicting phenotypes and executing precision medicine, combination analysis of single nucleotide variants (SNVs) genotyping with copy number variations (CNVs) is required. The aim of this study was to discover SNVs or common copy CNVs and examine the combined frequencies of SNVs and CNVs in pharmacogenes using the Korean genome and epidemiology study (KoGES), a consortium project. The genotypes (N = 72,299) and CNV data (N = 1000) were provided by the Korean National Institute of Health, Korea Centers for Disease Control and Prevention. The allele frequencies of SNVs, CNVs, and combined SNVs with CNVs were calculated and haplotype analysis was performed. CYP2D6 rs1065852 (c.100C>T, p.P34S) was the most common variant allele (48.23%). A total of 8454 haplotype blocks in 18 pharmacogenes were estimated. DMD ranked the highest in frequency for gene gain (64.52%), while TPMT ranked the highest in frequency for gene loss (51.80%). Copy number gain of CYP4F2 was observed in 22 subjects; 13 of those subjects were carriers with CYP4F2*3 gain. In the case of TPMT, approximately one-half of the participants (N = 308) had loss of the TPMT*1*1 diplotype. The frequencies of SNVs and CNVs in pharmacogenes were determined using the Korean cohort-based genome-wide association study.

scholarly journals Performance of In Silico Prediction Tools for the Detection of Germline Copy Number Variations in Cancer Predisposition Genes in 4208 Female Index Patients with Familial Breast and Ovarian Cancer

Cancers ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 118
Author(s):  
Louisa Lepkes ◽  
Mohamad Kayali ◽  
Britta Blümcke ◽  
Jonas Weber ◽  
Malwina Suszynska ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
In Silico ◽  
Copy Number ◽  
Gc Content ◽  
Copy Number Variations ◽  
Cancer Predisposition ◽  
Predictive Values ◽  
Prediction Tools ◽  
Predisposition Genes ◽  
Female Index

The identification of germline copy number variants (CNVs) by targeted next-generation sequencing (NGS) frequently relies on in silico CNV prediction tools with unknown sensitivities. We investigated the performances of four in silico CNV prediction tools, including one commercial (Sophia Genetics DDM) and three non-commercial tools (ExomeDepth, GATK gCNV, panelcn.MOPS) in 17 cancer predisposition genes in 4208 female index patients with familial breast and/or ovarian cancer (BC/OC). CNV predictions were verified via multiplex ligation-dependent probe amplification. We identified 77 CNVs in 76 out of 4208 patients (1.81%); 33 CNVs were identified in genes other than BRCA1/2, mostly in ATM, CHEK2, and RAD51C and less frequently in BARD1, MLH1, MSH2, PALB2, PMS2, RAD51D, and TP53. The Sophia Genetics DDM software showed the highest sensitivity; six CNVs were missed by at least one of the non-commercial tools. The positive predictive values ranged from 5.9% (74/1249) for panelcn.MOPS to 79.1% (72/91) for ExomeDepth. Verification of in silico predicted CNVs is required due to high frequencies of false positive predictions, particularly affecting target regions at the extremes of the GC content or target length distributions. CNV detection should not be restricted to BRCA1/2 due to the relevant proportion of CNVs in further BC/OC predisposition genes.

scholarly journals Cell-free DNA copy number variations predict efficacy of immune checkpoint inhibitor-based therapy in hepatobiliary cancers

2021 ◽  
Vol 9 (5) ◽  
pp. e001942
Author(s):  
Xu Yang ◽  
Ying Hu ◽  
Keyan Yang ◽  
Dongxu Wang ◽  
Jianzhen Lin ◽  
...  
Keyword(s):  
Combination Therapy ◽  
Clinical Outcome ◽  
Risk Score ◽  
Immune Checkpoint ◽  
Copy Number ◽  
Immune Checkpoint Inhibitor ◽  
Checkpoint Inhibitor ◽  
Copy Number Variations ◽  
Risk Scores ◽  
Free Dna

BackgroundThis study was designed to screen potential biomarkers in plasma cell-free DNA (cfDNA) for predicting the clinical outcome of immune checkpoint inhibitor (ICI)-based therapy in advanced hepatobiliary cancers.MethodsThree cohorts including 187 patients with hepatobiliary cancers were recruited from clinical trials at the Peking Union Medical College Hospital. Forty-three patients received combination therapy of programmed cell death protein 1 (PD-1) inhibitor with lenvatinib (ICI cohort 1), 108 patients received ICI-based therapy (ICI cohort 2) and 36 patients received non-ICI therapy (non-ICI cohort). The plasma cfDNA and blood cell DNA mutation profiles were assessed to identify efficacy biomarkers by a cancer gene-targeted next-generation sequencing panel.ResultsBased on the copy number variations (CNVs) in plasma cfDNA, the CNV risk score model was constructed to predict survival by using the least absolute shrinkage and selection operator Cox regression methods. The results of the two independent ICI-based therapy cohorts showed that patients with lower CNV risk scores had longer overall survival (OS) and progression-free survival (PFS) than those with high CNV risk scores (log-rank p<0.01). In the non-ICI cohort, the CNV risk score was not associated with PFS or OS. Furthermore, the results indicated that 53% of patients with low CNV risk scores achieved durable clinical benefit; in contrast, 88% of patients with high CNV risk scores could not benefit from combination therapy (p<0.05).ConclusionsThe CNVs in plasma cfDNA could predict the clinical outcome of the combination therapy of PD-1 inhibitor with lenvatinib and other ICI-based therapies in hepatobiliary cancers.

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