scholarly journals Copy-number variation contributes 9% of pathogenicity in the inherited retinal degenerations

2019 ◽  
Author(s):  
Erin Zampaglione ◽  
Benyam Kinde ◽  
Emily M. Place ◽  
Daniel Navarro-Gomez ◽  
Matthew Maher ◽  
...  
Keyword(s):  
Copy Number ◽  
Snp Array ◽  
Copy Number Variations ◽  
Detection Methods ◽  
Single Nucleotide Variants ◽  
Targeted Next Generation Sequencing ◽  
Small Indels ◽  
Pathogenic Variants ◽  
Number Variation ◽  
Cnv Detection

ABSTRACTPurposeCurrent sequencing strategies can genetically solve 55-60% of inherited retinal degeneration (IRD) cases, despite recent progress in sequencing. This can partially be attributed to elusive pathogenic variants (PVs) in known IRD genes, including copy number variations (CNVs), which we believe are a major contributor to unsolved IRD cases.MethodsFive hundred IRD patients were analyzed with targeted next generation sequencing (NGS). The NGS data was used to detect CNVs with ExomeDepth and gCNV and the results were compared to CNV detection with a SNP-Array. Likely causal CNV predictions were validated by quantitative (q)PCR.ResultsLikely disease-causing single nucleotide variants (SNVs) and small indels were found in 55.8% of subjects. PVs in USH2A (11.6%), RPGR (4%) and EYS (4%) were the most common. Likely causal CNVs were found in an additional 8.8% of patients. Of the three CNV detection methods, gCNV showed the highest accuracy. Approximately 30% of unsolved subjects had a single likely PV in a recessive IRD gene.ConclusionsCNV detection using NGS-based algorithms is a reliable method that greatly increases the genetic diagnostic rate of IRDs. Experimentally validating CNVs helps estimate the rate at which IRDs might be solved by a CNV plus a more elusive variant.

scholarly journals Integrating genomic correlation structure improves copy number variations detection

2020 ◽  
Author(s):  
Xizhi Luo ◽  
Fei Qin ◽  
Guoshuai Cai ◽  
Feifei Xiao
Keyword(s):  
Copy Number ◽  
Snp Array ◽  
Copy Number Variations ◽  
Correlation Structure ◽  
Supplementary Information ◽  
Dependence Structure ◽  
Random Pattern ◽  
Genomic Correlation ◽  
Equal Chance ◽  
Cnv Detection

Abstract Motivation Copy number variation plays important roles in human complex diseases. The detection of copy number variants (CNVs) is identifying mean shift in genetic intensities to locate chromosomal breakpoints, the step of which is referred to as chromosomal segmentation. Many segmentation algorithms have been developed with a strong assumption of independent observations in the genetic loci, and they assume each locus has an equal chance to be a breakpoint (i.e. boundary of CNVs). However, this assumption is violated in the genetics perspective due to the existence of correlation among genomic positions, such as linkage disequilibrium (LD). Our study showed that the LD structure is related to the location distribution of CNVs, which indeed presents a non-random pattern on the genome. To generate more accurate CNVs, we proposed a novel algorithm, LDcnv, that models the CNV data with its biological characteristics relating to genetic dependence structure (i.e. LD). Results We theoretically demonstrated the correlation structure of CNV data in SNP array, which further supports the necessity of integrating biological structure in statistical methods for CNV detection. Therefore, we developed the LDcnv that integrated the genomic correlation structure with a local search strategy into statistical modeling of the CNV intensities. To evaluate the performance of LDcnv, we conducted extensive simulations and analyzed large-scale HapMap datasets. We showed that LDcnv presented high accuracy, stability and robustness in CNV detection and higher precision in detecting short CNVs compared to existing methods. This new segmentation algorithm has a wide scope of potential application with data from various high-throughput technology platforms. Availability and implementation https://github.com/FeifeiXiaoUSC/LDcnv. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Copy number variation analysis in Chinese children with complete atrioventricular canal and single ventricle

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Xingyu Zhang ◽  
Bo Wang ◽  
Guoling You ◽  
Ying Xiang ◽  
Qihua Fu ◽  
...  
Keyword(s):  
Copy Number ◽  
Trisomy 21 ◽  
Single Ventricle ◽  
Snp Array ◽  
Copy Number Variations ◽  
Chinese Children ◽  
Atrioventricular Canal ◽  
Genome Wide ◽  
Number Variation ◽  
Complete Atrioventricular

Abstract Background Congenital heart disease (CHD) is one of the most common birth defects. Copy number variations (CNVs) have been proved to be important genetic factors that contribute to CHD. Here we screened genome-wide CNVs in Chinese children with complete atrioventricular canal (CAVC) and single ventricle (SV), since there were scarce researches dedicated to these two types of CHD. Methods We screened CNVs in 262 sporadic CAVC cases and 259 sporadic SV cases respectively, using a customized SNP array. The detected CNVs were annotated and filtered using available databases. Results Among 262 CAVC patients, we identified 6 potentially-causative CNVs in 43 individuals (16.41%, 43/262), including 2 syndrome-related CNVs (7q11.23 and 8q24.3 deletion). Surprisingly, 90.70% CAVC patients with detected CNVs (39/43) were found to carry duplications of 21q11.2–21q22.3, which were recognized as trisomy 21 (Down syndrome, DS). In CAVC with DS patients, the female to male ratio was 1.6:1.0 (24:15), and the rate of pulmonary hypertension (PH) was 41.03% (16/39). Additionally, 6 potentially-causative CNVs were identified in the SV patients (2.32%, 6/259), and none of them was trisomy 21. Most CNVs identified in our cohort were classified as rare (< 1%), occurring just once among CAVC or SV individuals except the 21q11.2–21q22.3 duplication (14.89%) in CAVC cohort. Conclusions Our study identified 12 potentially-causative CNVs in 262 CAVC and 259 SV patients, representing the largest cohort of these two CHD types in Chinese population. The results provided strong correlation between CAVC and DS, which also showed sex difference and high incidence of PH. The presence of potentially-causative CNVs suggests the etiology of complex CHD is incredibly diverse, and CHD candidate genes remain to be discovered.

Rapid Diagnosis of Imprinting Disorders Involving Copy Number Variation and Uniparental Disomy Using Genome-Wide SNP Microarrays

2015 ◽  
Vol 146 (1) ◽  
pp. 9-18 ◽  
Author(s):  
Weiqiang Liu ◽  
Rui Zhang ◽  
Jun Wei ◽  
Huimin Zhang ◽  
Guojiu Yu ◽  
...  
Keyword(s):  
Copy Number ◽  
Snp Array ◽  
Uniparental Disomy ◽  
Copy Number Variations ◽  
Chromosomal Microarray ◽  
Chromosomal Microarray Analysis ◽  
Imprinting Disorders ◽  
Genome Wide ◽  
Number Variation ◽  
Efficient Alternative

Imprinting disorders, such as Beckwith-Wiedemann syndrome (BWS), Prader-Willi syndrome (PWS) and Angelman syndrome (AS), can be detected via methylation analysis, methylation-specific multiplex ligation-dependent probe amplification (MS-MLPA), or other methods. In this study, we applied single nucleotide polymorphism (SNP)-based chromosomal microarray analysis to detect copy number variations (CNVs) and uniparental disomy (UPD) events in patients with suspected imprinting disorders. Of 4 patients, 2 had a 5.25-Mb microdeletion in the 15q11.2q13.2 region, 1 had a 38.4-Mb mosaic UPD in the 11p15.4 region, and 1 had a 60-Mb detectable UPD between regions 14q13.2 and 14q32.13. Although the 14q32.2 region was classified as normal by SNP array for the 14q13 UPD patient, it turned out to be a heterodisomic UPD by short tandem repeat marker analysis. MS-MLPA analysis was performed to validate the variations. In conclusion, SNP-based microarray is an efficient alternative method for quickly and precisely diagnosing PWS, AS, BWS, and other imprinted gene-associated disorders when considering aberrations due to CNVs and most types of UPD.

scholarly journals Copy Number Variation of UGT 2B Genes in Indian Families Using Whole Genome Scans

2016 ◽  
Vol 2016 ◽  
pp. 1-7
Author(s):  
Avinash M. Veerappa ◽  
Prakash Padakannaya ◽  
Nallur B. Ramachandra
Keyword(s):  
Copy Number ◽  
Gene Deletion ◽  
Indian Population ◽  
Snp Array ◽  
Copy Number Variations ◽  
Genome Scans ◽  
Genome Wide ◽  
Disease Associations ◽  
Number Variation

Background and Objectives. Uridine diphospho-glucuronosyltransferase 2B (UGT2B) is a family of genes involved in metabolizing steroid hormones and several other xenobiotics. These UGT2B genes are highly polymorphic in nature and have distinct polymorphisms associated with specific regions around the globe. Copy number variations (CNVs) status of UGT2B17 in Indian population is not known and their disease associations have been inconclusive. It was therefore of interest to investigate the CNV profile of UGT2B genes.Methods. We investigated the presence of CNVs in UGT2B genes in 31 members from eight Indian families using Affymetrix Genome-Wide Human SNP Array 6.0 chip.Results. Our data revealed >50% of the study members carried CNVs in UGT2B genes, of which 76% showed deletion polymorphism. CNVs were observed more in UGT2B17 (76.4%) than in UGT2B15 (17.6%). Molecular network and pathway analysis found enrichment related to steroid metabolic process, carboxylesterase activity, and sequence specific DNA binding.Interpretation and Conclusion. We report the presence of UGT2B gene deletion and duplication polymorphisms in Indian families. Network analysis indicates the substitutive role of other possible genes in the UGT activity. The CNVs of UGT2B genes are very common in individuals indicating that the effect is neutral in causing any suspected diseases.

scholarly journals HadoopCNV: A dynamic programming imputation algorithm to detect copy number variants from sequencing data

2017 ◽  
Author(s):  
Hui Yang ◽  
Gary Chen ◽  
Leandro Lima ◽  
Han Fang ◽  
Laura Jimenez ◽  
...  
Keyword(s):  
Dynamic Programming ◽  
Copy Number ◽  
Large Scale ◽  
Read Depth ◽  
Copy Number Variations ◽  
Detection Methods ◽  
Depth Information ◽  
Sequencing Data ◽  
Cnv Detection ◽  
Hadoop Framework

ABSTRACTBACKGROUNDWhole-genome sequencing (WGS) data may be used to identify copy number variations (CNVs). Existing CNV detection methods mostly rely on read depth or alignment characteristics (paired-end distance and split reads) to infer gains/losses, while neglecting allelic intensity ratios and cannot quantify copy numbers. Additionally, most CNV callers are not scalable to handle a large number of WGS samples.METHODSTo facilitate large-scale and rapid CNV detection from WGS data, we developed a Dynamic Programming Imputation (DPI) based algorithm called HadoopCNV, which infers copy number changes through both allelic frequency and read depth information. Our implementation is built on the Hadoop framework, enabling multiple compute nodes to work in parallel.RESULTSCompared to two widely used tools – CNVnator and LUMPY, HadoopCNV has similar or better performance on both simulated data sets and real data on the NA12878 individual. Additionally, analysis on a 10-member pedigree showed that HadoopCNV has a Mendelian precision that is similar or better than other tools. Furthermore, HadoopCNV can accurately infer loss of heterozygosity (LOH), while other tools cannot. HadoopCNV requires only 1.6 hours for a human genome with 30X coverage, on a 32-node cluster, with a linear relationship between speed improvement and the number of nodes. We further developed a method to combine HadoopCNV and LUMPY result, and demonstrated that the combination resulted in better performance than any individual tools.CONCLUSIONSThe combination of high-resolution, allele-specific read depth from WGS data and Hadoop framework can result in efficient and accurate detection of CNVs.

scholarly journals BMI-CNV: A Bayesian framework for multiple genotyping platforms detection of copy number variation

2021 ◽  
Author(s):  
Xizhi Luo ◽  
Guoshuai Cai ◽  
Alexander C Mclain ◽  
Christopher I Amos ◽  
Bo Cai ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Copy Number ◽  
Process Model ◽  
Snp Array ◽  
Gaussian Mixture ◽  
Copy Number Variations ◽  
Protein Coding ◽  
Number Variation ◽  
Project Data ◽  
Improved Accuracy

Whole-exome sequencing (WES) enables detection of Copy number variations (CNVs) with high resolution in protein-coding regions. However, variations in the intergenic or intragenic regions are excluded from studies. Fortunately, samples have been previously sequenced by other genotyping platforms, such as SNP array. Moreover, conventional single sample-based methods suffer from high false discovery rate due to prominent data noise. Therefore, methods for integrating multiple genotyping platforms and samples are highly demanded for improved CNV detection. We developed BMI-CNV, a Bayesian Multi-sample and Integrative CNV (BMI-CNV) profiling method with data sequenced by both WES and microarray. For the multi-sample integration, we identify the shared CNVs regions across samples using a Bayesian probit stick-breaking process model coupled with a Gaussian Mixture model estimation. With extensive simulations, BMI-CNV outperformed existing methods with remarkably improved accuracy. By applying to the matched 1000 genomes project and HapMap project data, we showed that BMI-CNV accurately detected common variants. We further applied it to The Research of International Cancer of Lung (TRICL) consortium with matched WES and OncoArray data and identified lung cancer risk associated genes in 17q11.2, 1p36.12, 8q23.1 and 5q22.2 regions, which may provide new insights into the etiology of lung cancer.

scholarly journals Genome-Wide Analysis of Copy Number Variations in Normal Population Identified by SNP Arrays

2009 ◽  
Vol 2 (1) ◽  
pp. 54-65 ◽  
Author(s):  
Jian Wang ◽  
Tsz-Kwong Man ◽  
Kwong Kwok Wong ◽  
Pulivarthi H. Rao ◽  
Hon-Chiu Eastwood Leung ◽  
...  
Keyword(s):  
Copy Number Variation ◽  
Human Genome ◽  
Copy Number ◽  
Repetitive Sequences ◽  
Snp Array ◽  
Copy Number Variations ◽  
Gene Copy ◽  
Chemical Stimulus ◽  
Genome Wide ◽  
Number Variation

Gene copy number change is an essential characteristic of many types of cancer. However, it is important to distinguish copy number variation (CNV) in the human genome of normal individuals from bona fide abnormal copy number changes of genes specific to cancers. Based on Affymetrix 50K single nucleotide polymorphism (SNP) array data, we identified genome-wide copy number variations among 104 normal subjects from three ethnic groups that were used in the HapMap project. Our analysis revealed 155 CNV regions, of which 37% were gains and 63% were losses. About 21% (30) of the CNV regions are concordant with earlier reports. These 155 CNV regions are located on more than 100 cytobands across all 23 chromosomes. The CNVs range from 68bp to 18 Mb in length, with a median length of 86 Kb. Eight CNV regions were selected for validation by quantitative PCR. Analysis of genomic sequences within and adjacent to CNVs suggests that repetitive sequences such as long interspersed nuclear elements (LINEs) and long terminal repeats (LTRs) may play a role in the origin of CNVs by facilitating non-allelic homologous recombination. Thirty-two percent of the CNVs identified in this study are associated with segmental duplications. CNVs were not preferentially enriched in gene-encoding regions. Among the 364 genes that are completely encompassed by these 155 CNVs, genes related to olfactory sensory, chemical stimulus, and other physiological responses are significantly enriched. A statistical analysis of CNVs by ethnic group revealed distinct patterns regarding the CNV location and gain-to-loss ratio. The CNVs reported here will help build a more comprehensive map of genomic variations in the human genome and facilitate the differentiation between copy number variation and somatic changes in cancers. The potential roles of certain repeat elements in CNV formation, as corroborated by other studies, shed light on the origin of CNVs and will improve our understanding of the mechanisms of genomic rearrangements in the human genome.

scholarly journals Detection of copy number variation associated with ventriculomegaly in fetuses using single nucleotide polymorphism arrays

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Huili Xue ◽  
Aili Yu ◽  
Na Lin ◽  
Xuemei Chen ◽  
Min Lin ◽  
...  
Keyword(s):  
Single Nucleotide Polymorphism ◽  
Copy Number ◽  
Snp Array ◽  
Copy Number Variations ◽  
Nucleotide Polymorphism ◽  
Single Nucleotide ◽  
Group I ◽  
Number Variation ◽  
Fetal Ventriculomegaly ◽  
Cns Anomalies

AbstractEtiopathogenesis of fetal ventriculomegaly is poorly understood. Associations between fetal isolated ventriculomegaly and copy number variations (CNVs) have been previously described. We investigated the correlations between fetal ventriculomegaly—with or without other ultrasound anomalies—and chromosome abnormalities. 222 fetuses were divided into four groups: (I) 103 (46.4%) cases with isolated ventriculomegaly, (II) 41 (18.5%) cases accompanied by soft markers, (III) 33 (14.9%) cases complicated with central nervous system (CNS) anomalies, and (IV) 45 (20.3%) cases with accompanying anomalies. Karyotyping and single nucleotide polymorphism (SNP) array were used in parallel. Karyotype abnormalities were identified in 15/222 (6.8%) cases. Karyotype abnormalities in group I, II, III, and IV were 4/103 (3.9%), 2/41 (4.9%), 4/33 (12.1%), and 5/45 (11.1%), respectively. Concerning the SNP array analysis results, 31/222 (14.0%) were CNVs, CNVs in groups I, II, III, and IV were 11/103 (10.7%), 6/41 (14.6%), 9/33 (27.3%), and 5/45 fetuses (11.1%), respectively. Detections of clinical significant CNVs were higher in non-isolated ventriculomegaly than in isolated ventriculomegaly (16.81% vs 10.7%, P = 0.19). SNP arrays can effectively identify CNVs in fetuses with ventriculomegaly and increase the abnormal chromosomal detection rate by approximately 7.2%, especially ventriculomegaly accompanied by CNS anomalies.

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.

Application of Copy Number Variation Sequencing in Genetic Analysis of Miscarriages in Early and Middle Pregnancy

2020 ◽  
Vol 160 (11-12) ◽  
pp. 634-642
Author(s):  
Shiqiang Luo ◽  
Xingyuan Chen ◽  
Tizhen Yan ◽  
Jiaolian Ya ◽  
Zehui Xu ◽  
...  
Keyword(s):  
Copy Number Variation ◽  
High Throughput ◽  
Copy Number ◽  
High Throughput Sequencing ◽  
Chromosomal Abnormalities ◽  
Pregnancy Termination ◽  
Mendelian Inheritance ◽  
Copy Number Variations ◽  
Abnormal Chromosome ◽  
Number Variation

High-throughput sequencing based on copy number variation (CNV-seq) is commonly used to detect chromosomal abnormalities. This study identifies chromosomal abnormalities in aborted embryos/fetuses in early and middle pregnancy and explores the application value of CNV-seq in determining the causes of pregnancy termination. High-throughput sequencing was used to detect chromosome copy number variations (CNVs) in 116 aborted embryos in early and middle pregnancy. The detection data were compared with the Database of Genomic Variants (DGV), the Database of Chromosomal Imbalance and Phenotype in Humans using Ensemble Resources (DECIPHER), and the Online Mendelian Inheritance in Man (OMIM) database to determine the CNV type and the clinical significance. High-throughput sequencing results were successfully obtained in 109 out of 116 specimens, with a detection success rate of 93.97%. In brief, there were 64 cases with abnormal chromosome numbers and 23 cases with CNVs, in which 10 were pathogenic mutations and 13 were variants of uncertain significance. An abnormal chromosome number is the most important reason for embryo termination in early and middle pregnancy, followed by pathogenic chromosome CNVs. CNV-seq can quickly and accurately detect chromosome abnormalities and identify microdeletion and microduplication CNVs that cannot be detected by conventional chromosome analysis, which is convenient and efficient for genetic etiology diagnosis in miscarriage.

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