Chromosomal microarray data for validation of copy-number variants detection from a low-coverage whole-genome sequencing approach in clinical samples
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE73191 | GEO | 2015/10/01
SECONDARY ACCESSION(S): PRJNA296348
REPOSITORIES: GEO
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Project description:Chromosomal microarray data for validation of copy-number variants detection from a low-coverage whole-genome sequencing approach in clinical samples
| PRJNA296348 | ENA
Project description:Methods for the direct detection of copy number variation (CNV) genome-wide have become effective instruments for identifying genetic risk factors for disease. The application of next-generation sequencing platforms to genetic studies promises to improve sensitivity to detect CNVs as well as inversions, indels, and SNPs. New computational approaches are needed to systematically detect these variants from genome sequence data. Existing sequence-based approaches for CNV detection are primarily based on paired-end read mapping (PEM) as reported previously by Tuzun et al. and Korbel et al. Due to limitations of the PEM approach, some classes of CNVs are difficult to ascertain, including large insertions and variants located within complex genomic regions. To overcome these limitations, we developed a method for CNV detection using read depth of coverage. Event-wise testing (EWT) is a method based on significance testing. In contrast to standard segmentation algorithms that typically operate by performing likelihood evaluation for every point in the genome, EWT works on intervals of data points, rapidly searching for specific classes of events. Overall false-positive rate is controlled by testing the significance of each possible event and adjusting for multiple testing. Deletions and duplications detected in an individual genome by EWT are examined across multiple genomes to identify polymorphism between individuals. We estimated error rates using simulations based on real data, and we applied EWT to the analysis of chromosome 1 from paired-end shotgun sequence data (30x) on five individuals. Our results suggest that analysis of read depth is an effective approach for the detection of CNVs, and it captures structural variants that are refractory to established PEM-based methods.
| S-EPMC2752127 | biostudies-literature
Project description:BackgroundCongenital gastrointestinal obstruction (CGIO) mainly refers to the stenosis or atresia of any part from the esophagus to the anus and is one of the most common surgical causes in the neonatal period. The concept of genetic factors as an etiology of CGIO has been accepted, but investigations about CGIO have mainly focused on aneuploidy, and the focus has been on duodenal obstruction. The objective of this study was to evaluate the risk of chromosome aberrations (including numeric and structural aberrations) in different types of CGIO. A second objective was to assess the risk of abnormal CNVs detected by copy number variation sequencing (CNV-seq) in fetuses with different types of CGIO.MethodsData from pregnancies referred for invasive testing and CNV-seq due to sonographic diagnosis of fetal CGIO from 2015 to 2020 were obtained retrospectively from the computerized database. The rates of chromosome aberrations and abnormal CNV-seq findings for isolated CGIOs and complicated CGIOs and different types of CGIOs were calculated.ResultsOf the 240 fetuses with CGIO that underwent karyotyping, the detection rate of karyotype abnormalities in complicated CGIO was significantly higher than that of the isolated group (33.8% vs. 10.8%, p < 0.01). Ninety-three cases with normal karyotypes further underwent CNV-seq, and CNV-seq revealed an incremental diagnostic value of 9.7% over conventional karyotyping. In addition, the incremental diagnostic yield of CNV-seq analysis in complicated CGIOs (20%) was higher than that in isolated CGIOs (4.8%), and the highest prevalence of pathogenic CNVs/likely pathogenic CNVs was found in the duodenal stenosis/atresia group (17.5%), followed by the anorectal malformation group (15.4%). The 13q deletion, 10q26 deletion, 4q24 deletion, and 2p24 might be additional genetic etiologies of duodenal stenosis/atresia.ConclusionsThe risk of pathogenic chromosomal abnormalities and CNVs increased in the complicated CGIO group compared to that in the isolated CGIO group, especially when fetuses presented duodenal obstruction (DO) and anorectal malformation. CNV-seq was recommended to detect submicroscopic chromosomal aberrations for DO and anorectal malformation when the karyotype was normal. The relationship between genotypes and phenotypes needs to be explored in the future to facilitate prenatal diagnosis of fetal CGIO and yield new clues into their etiologies.
| S-EPMC8772214 | biostudies-literature
Project description:BackgroundCopy number variations (CNVs) represent an important type of genetic variation that deeply impact phenotypic polymorphisms and human diseases. The advent of high-throughput sequencing technologies provides an opportunity to revolutionize the discovery of CNVs and to explore their relationship with diseases. However, most of the existing methods depend on sequencing depth and show instability with low sequence coverage. In this study, using low coverage whole-genome sequencing (LCS) we have developed an effective population-scale CNV calling (PSCC) method.Methodology/principal findingsIn our novel method, two-step correction was used to remove biases caused by local GC content and complex genomic characteristics. We chose a binary segmentation method to locate CNV segments and designed combined statistics tests to ensure the stable performance of the false positive control. The simulation data showed that our PSCC method could achieve 99.7%/100% and 98.6%/100% sensitivity and specificity for over 300 kb CNV calling in the condition of LCS (∼2×) and ultra LCS (∼0.2×), respectively. Finally, we applied this novel method to analyze 34 clinical samples with an average of 2× LCS. In the final results, all the 31 pathogenic CNVs identified by aCGH were successfully detected. In addition, the performance comparison revealed that our method had significant advantages over existing methods using ultra LCS.Conclusions/significanceOur study showed that PSCC can sensitively and reliably detect CNVs using low coverage or even ultra-low coverage data through population-scale sequencing.
| S-EPMC3897425 | biostudies-literature
Project description:Although there are many methods available for inferring copy-number variants (CNVs) from next-generation sequence data, there remains a need for a system that is computationally efficient but that retains good sensitivity and specificity across all types of CNVs. Here, we introduce a new method, estimation by read depth with single-nucleotide variants (ERDS), and use various approaches to compare its performance to other methods. We found that for common CNVs and high-coverage genomes, ERDS performs as well as the best method currently available (Genome STRiP), whereas for rare CNVs and high-coverage genomes, ERDS performs better than any available method. Importantly, ERDS accommodates both unique and highly amplified regions of the genome and does so without requiring separate alignments for calling CNVs and other variants. These comparisons show that for genomes sequenced at high coverage, ERDS provides a computationally convenient method that calls CNVs as well as or better than any currently available method.
| S-EPMC3511991 | biostudies-literature
Project description:Detection of copy number variants as an integral part of noninvasive prenatal testing is increasingly used in clinical practice worldwide. We performed validation on plasma samples from 34 pregnant women with known aberrations using cell-free DNA sequencing to evaluate the sensitivity for copy number variants (CNV) detection using an in-house CNV fraction-based detection algorithm. The sensitivity for CNVs smaller than 3 megabases (Mb), larger than 3Mb, and overall was 78.57%, 100%, and 90.6%, respectively. Regarding the fetal fraction, detection sensitivity in the group with a fetal fraction of less than 10% was 57.14%, whereas there was 100% sensitivity in the group with fetal fraction exceeding 10%. The assay is also capable of indicating whether the origin of an aberration is exclusively fetal or fetomaternal/maternal. This validation demonstrated that a CNV fraction-based algorithm was applicable and feasible in clinical settings as a supplement to testing for common trisomies 21, 18, and 13.
| S-EPMC7460070 | biostudies-literature
Project description:Copy number variants (CNVs) are a major cause of several genetic disorders, making their detection an essential component of genetic analysis pipelines. Current methods for detecting CNVs from exome-sequencing data are limited by high false-positive rates and low concordance because of inherent biases of individual algorithms. To overcome these issues, calls generated by two or more algorithms are often intersected using Venn diagram approaches to identify "high-confidence" CNVs. However, this approach is inadequate, because it misses potentially true calls that do not have consensus from multiple callers. Here, we present CN-Learn, a machine-learning framework that integrates calls from multiple CNV detection algorithms and learns to accurately identify true CNVs using caller-specific and genomic features from a small subset of validated CNVs. Using CNVs predicted by four exome-based CNV callers (CANOES, CODEX, XHMM, and CLAMMS) from 503 samples, we demonstrate that CN-Learn identifies true CNVs at higher precision (∼90%) and recall (∼85%) rates while maintaining robust performance even when trained with minimal data (∼30 samples). CN-Learn recovers twice as many CNVs compared to individual callers or Venn diagram-based approaches, with features such as exome capture probe count, caller concordance, and GC content providing the most discriminatory power. In fact, ∼58% of all true CNVs recovered by CN-Learn were either singletons or calls that lacked support from at least one caller. Our study underscores the limitations of current approaches for CNV identification and provides an effective method that yields high-quality CNVs for application in clinical diagnostics.
| S-EPMC6633262 | biostudies-literature
Project description:We analyzed the chromosomal-arm-level copy number alterations (CNAs) in the cervical exfoliative cell and tissue samples by using the low-coverage whole genomic sequencing technique.In this study, we retrospectively collected 55 archived exfoliated cervical cell suspension samples and the corresponding formalin-fixed and paraffin-embedded tissue section samples including 27 invasive cervical cancer and 28 control cases. We also collected 19 samples of the cervical exfoliative cells randomly from women to verify the new algorithm model. We analyzed the CNAs in cervical exfoliated cell and tissue samples by using the low-coverage next generation of sequencing.In the model-building study, multiple chromosomal-arm-level CNAs were detected in both cervical exfoliated cell and tissue samples of all cervical cancer cases. By analyzing the consistency of CNAs between exfoliated cells and cervical tissue samples, as well as the heterogeneity in individual patient, we also established a C-score algorithm model according to the chromosomal-arm-level changes of 1q, 2q, 3p, 7q. The C-score model was then validated by the pathological diagnosis of all 74 exfoliated cell samples (including 55 cases in model-building group and 19 cases in verification group). In our result, a cutoff value of C-score >6 showed 100% sensitivity and 100% specificity in the diagnosis of cervical cancer.In this study, we found that CNAs of cervical exfoliated cell samples could robustly distinguish invasive cervical cancer from cancer-free tissues. And we have also developed a C-score algorithm model to process the sequencing data in a more standardized and automated way.
| S-EPMC6078888 | biostudies-other
Project description:Copy number variants (CNVs) are known to affect a large portion of the human genome and have been implicated in many diseases. Although whole-genome sequencing (WGS) can help identify CNVs, most analytical methods suffer from limited sensitivity and specificity, especially in regions of low mappability. To address this, we use PopSV, a CNV caller that relies on multiple samples to control for technical variation. We demonstrate that our calls are stable across different types of repeat-rich regions and validate the accuracy of our predictions using orthogonal approaches. Applying PopSV to 640 human genomes, we find that low-mappability regions are approximately 5 times more likely to harbor germline CNVs, in stark contrast to the nearly uniform distribution observed for somatic CNVs in 95 cancer genomes. In addition to known enrichments in segmental duplication and near centromeres and telomeres, we also report that CNVs are enriched in specific types of satellite and in some of the most recent families of transposable elements. Finally, using this comprehensive approach, we identify 3455 regions with recurrent CNVs that were missing from existing catalogs. In particular, we identify 347 genes with a novel exonic CNV in low-mappability regions, including 29 genes previously associated with disease.
| S-EPMC6101599 | biostudies-other
Project description:The identification of DNA copy numbers from short-read sequencing data remains a challenge for both technical and algorithmic reasons. The raw data for these analyses are measured in tens to hundreds of gigabytes per genome; transmitting, storing, and analyzing such large files is cumbersome, particularly for methods that analyze several samples simultaneously. We developed a very efficient representation of depth of coverage (150-1000× compression) that enables such analyses. Current methods for analyzing variants in whole-genome sequencing (WGS) data frequently miss copy number variants (CNVs), particularly hemizygous deletions in the 1-100 kb range. To fill this gap, we developed a method to identify CNVs in individual genomes, based on comparison to joint profiles pre-computed from a large set of genomes. We analyzed depth of coverage in over 6000 high quality (>40×) genomes. The depth of coverage has strong sequence-specific fluctuations only partially explained by global parameters like %GC. To account for these fluctuations, we constructed multi-genome profiles representing the observed or inferred diploid depth of coverage at each position along the genome. These Reference Coverage Profiles (RCPs) take into account the diverse technologies and pipeline versions used. Normalization of the scaled coverage to the RCP followed by hidden Markov model (HMM) segmentation enables efficient detection of CNVs and large deletions in individual genomes. Use of pre-computed multi-genome coverage profiles improves our ability to analyze each individual genome. We make available RCPs and tools for performing these analyses on personal genomes. We expect the increased sensitivity and specificity for individual genome analysis to be critical for achieving clinical-grade genome interpretation.
| S-EPMC4330915 | biostudies-literature