Detection of copy number variation by SNP-allelotyping.
Ontology highlight
ABSTRACT:
SUBMITTER: Parker B
PROVIDER: S-EPMC4254366 | biostudies-literature | 2015 Mar
REPOSITORIES: biostudies-literature
Publications
Detection of copy number variation by SNP-allelotyping.
Parker Brett B Alexander Ryan R Wu Xingyao X Feely Shawna S Shy Michael M Schnetz-Boutaud Nathalie N Li Jun J
Journal of neurogenetics 20140602 1
Charcot-Marie-Tooth disease type 1A (CMT1A) is caused by an abnormal copy number variation (CNV) with a trisomy of chromosome 17p12. The increase of the DNA-segment copy number is expected to alter the allele frequency of single nucleotide polymorphism (SNP) within the duplicated region. We tested whether SNP allele frequency determined by a Sequenom MassArray can be used to detect the CMT1A mutation. Our results revealed distinct patterns of SNP allele frequency distribution, which reliably dif ...[more]
Similar Datasets
Project description:BackgroundCopy Number Variations (CNVs) are usually inferred from Single Nucleotide Polymorphism (SNP) arrays by use of some software packages based on given algorithms. However, there is no clear understanding of the performance of these software packages; it is therefore difficult to select one or several software packages for CNV detection based on the SNP array platform.We selected four publicly available software packages designed for CNV calling from an Affymetrix SNP array, including Birdsuite, dChip, Genotyping Console (GTC) and PennCNV. The publicly available dataset generated by Array-based Comparative Genomic Hybridization (CGH), with a resolution of 24 million probes per sample, was considered to be the "gold standard". Compared with the CGH-based dataset, the success rate, average stability rate, sensitivity, consistence and reproducibility of these four software packages were assessed compared with the "gold standard". Specially, we also compared the efficiency of detecting CNVs simultaneously by two, three and all of the software packages with that by a single software package.ResultsSimply from the quantity of the detected CNVs, Birdsuite detected the most while GTC detected the least. We found that Birdsuite and dChip had obvious detecting bias. And GTC seemed to be inferior because of the least amount of CNVs it detected. Thereafter we investigated the detection consistency produced by one certain software package and the rest three software suits. We found that the consistency of dChip was the lowest while GTC was the highest. Compared with the CNVs detecting result of CGH, in the matching group, GTC called the most matching CNVs, PennCNV-Affy ranked second. In the non-overlapping group, GTC called the least CNVs. With regards to the reproducibility of CNV calling, larger CNVs were usually replicated better. PennCNV-Affy shows the best consistency while Birdsuite shows the poorest.ConclusionWe found that PennCNV outperformed the other three packages in the sensitivity and specificity of CNV calling. Obviously, each calling method had its own limitations and advantages for different data analysis. Therefore, the optimized calling methods might be identified using multiple algorithms to evaluate the concordance and discordance of SNP array-based CNV calling.
| S-EPMC4015297 | biostudies-literature
Project description:Next-generation sequencing (NGS) technologies offer new opportunities for precise and accurate identification of genomic aberrations, including copy number variations (CNVs). For high-throughput NGS data, using depth of coverage has become a major approach to identify CNVs, especially for whole exome sequencing (WES) data. Due to the high level of noise and biases of read-count data and complexity of the WES data, existing CNV detection tools identify many false CNV segments. Besides, NGS generates a huge amount of data, requiring to use effective and efficient methods. In this work, we propose a novel segmentation algorithm based on the total variation approach to detect CNVs more precisely and efficiently using WES data. The proposed method also filters out outlier read-counts and identifies significant change points to reduce false positives. We used real and simulated data to evaluate the performance of the proposed method and compare its performance with those of other commonly used CNV detection methods. Using simulated and real data, we show that the proposed method outperforms the existing CNV detection methods in terms of accuracy and false discovery rate and has a faster runtime compared to the circular binary segmentation method.
| S-EPMC7278034 | biostudies-literature
Project description:Detection of chromosomal aberrations from a single cell by array comparative genomic hybridization (single-cell array CGH), instead of from a population of cells, is an emerging technique. However, such detection is challenging because of the genome artifacts and the DNA amplification process inherent to the single cell approach. Current normalization algorithms result in inaccurate aberration detection for single-cell data. We propose a normalization method based on channel, genome composition and recurrent genome artifact corrections. We demonstrate that the proposed channel clone normalization significantly improves the copy number variation detection in both simulated and real single-cell array CGH data.
| S-EPMC3245619 | biostudies-literature
Project description:Copy number variations (CNVs) are gain and loss of DNA sequence of a genome. High throughput platforms such as microarrays and next generation sequencing technologies (NGS) have been applied for genome wide copy number losses. Although progress has been made in both approaches, the accuracy and consistency of CNV calling from the two platforms remain in dispute. In this study, we perform a deep analysis on copy number losses on 254 human DNA samples, which have both SNP microarray data and NGS data publicly available from Hapmap Project and 1000 Genomes Project respectively. We show that the copy number losses reported from Hapmap Project and 1000 Genome Project only have < 30% overlap, while these reports are required to have cross-platform (e.g. PCR, microarray and high-throughput sequencing) experimental supporting by their corresponding projects, even though state-of-art calling methods were employed. On the other hand, copy number losses are found directly from HapMap microarray data by an accurate algorithm, i.e. CNVhac, almost all of which have lower read mapping depth in NGS data; furthermore, 88% of which can be supported by the sequences with breakpoint in NGS data. Our results suggest the ability of microarray calling CNVs and the possible introduction of false negatives from the unessential requirement of the additional cross-platform supporting. The inconsistency of CNV reports from Hapmap Project and 1000 Genomes Project might result from the inadequate information containing in microarray data, the inconsistent detection criteria, or the filtration effect of cross-platform supporting. The statistical test on CNVs called from CNVhac show that the microarray data can offer reliable CNV reports, and majority of CNV candidates can be confirmed by raw sequences. Therefore, the CNV candidates given by a good caller could be highly reliable without cross-platform supporting, so additional experimental information should be applied in need instead of necessarily.
| S-EPMC5922522 | biostudies-literature
Project description:We developed Copy Number Segmentation by Regression Tree in Next Generation Sequencing (CONSERTING), an algorithm for detecting somatic copy-number alteration (CNA) using whole-genome sequencing (WGS) data. CONSERTING performs iterative analysis of segmentation on the basis of changes in read depth and the detection of localized structural variations, with high accuracy and sensitivity. Analysis of 43 cancer genomes from both pediatric and adult patients revealed novel oncogenic CNAs, complex rearrangements and subclonal CNAs missed by alternative approaches.
| S-EPMC4591043 | biostudies-literature
Project description:Common copy number variations (CNVs) are small regions of genomic variations at the same loci across multiple samples, which can be detected with high resolution from next-generation sequencing (NGS) technique. Multiple sequencing data samples are often available from genomic studies; examples include sequences from multiple platforms and sequences from multiple individuals. By integrating complementary information from multiple data samples, detection power can be potentially improved. However, most of current CNV detection methods often process an individual sequence sample, or two samples in an abnormal versus matched normal study; researches on detecting common CNVs across multiple samples have been very limited but are much needed. In this paper, we propose a novel method to detect common CNVs from multiple sequencing samples by exploiting the concurrency of genomic variations in read depth signals derived from multiple NGS data. We use a penalized sparse regression model to fit multiple read depth profiles, based on which common CNV identification is formulated as a change-point detection problem. Finally, we validate the proposed method on both simulation and real data, showing that it can give both higher detection power and better break point estimation over several published CNV detection methods.
| S-EPMC4165854 | biostudies-literature
Project description:The analysis of structural variants, in particular of copy-number variations (CNVs), has proven valuable in unraveling the genetic basis of human diseases. Hence, a large number of algorithms have been developed for the detection of CNVs in SNP array signal intensity data. Using the European and African HapMap trio data, we undertook a comparative evaluation of six commonly used CNV detection software tools, namely Affymetrix Power Tools (APT), QuantiSNP, PennCNV, GLAD, R-gada and VEGA, and assessed their level of pair-wise prediction concordance. The tool-specific CNV prediction accuracy was assessed in silico by way of intra-familial validation. Software tools differed greatly in terms of the number and length of the CNVs predicted as well as the number of markers included in a CNV. All software tools predicted substantially more deletions than duplications. Intra-familial validation revealed consistently low levels of prediction accuracy as measured by the proportion of validated CNVs (34-60%). Moreover, up to 20% of apparent family-based validations were found to be due to chance alone. Software using Hidden Markov models (HMM) showed a trend to predict fewer CNVs than segmentation-based algorithms albeit with greater validity. PennCNV yielded the highest prediction accuracy (60.9%). Finally, the pairwise concordance of CNV prediction was found to vary widely with the software tools involved. We recommend HMM-based software, in particular PennCNV, rather than segmentation-based algorithms when validity is the primary concern of CNV detection. QuantiSNP may be used as an additional tool to detect sets of CNVs not detectable by the other tools. Our study also reemphasizes the need for laboratory-based validation, such as qPCR, of CNVs predicted in silico.
| S-EPMC4510559 | biostudies-literature
Project description:The use of array technology to define chromosome deletions and duplications is bringing us closer to establishing a genotype/phenotype map of genomic copy number alterations. We studied 21 patients and five relatives with deletions of the short arm of chromosome 20 using the Illumina HumanHap550 SNP array to: 1) more accurately determine the deletion sizes; 2) identify and compare breakpoints; 3) establish genotype/phenotype correlations; and 4) investigate the use of the HumanHap550 platform for analysis of chromosome deletions. Deletions ranged from 95 kb to 14.62 Mb, and all of the breakpoints were unique. Eleven patients had deletions between 95 kb and 4 Mb and these individuals had normal development, with no anomalies outside of those associated with Alagille syndrome (AGS). The proximal and distal boundaries of these 11 deletions constitute a 5.4-Mb region, and we propose that haploinsufficiency for only 1 of the 12 genes in this region causes phenotypic abnormalities. This defines the JAG1-associated critical region, in which deletions do not confer findings other than those associated with AGS. The other 10 patients had deletions between 3.28 Mb and 14.62 Mb, which extended outside the critical region, and, notably, all of these patients had developmental delay. This group had other findings such as autism, scoliosis, and bifid uvula. We identified 47 additional polymorphic genome-wide copy number variants (>20 SNPs), with 0 to 5 variants called per patient. Deletions of the short arm of chromosome 20 are associated with relatively mild and limited clinical anomalies. The use of SNP arrays provides accurate high-resolution definition of genomic abnormalities.
| S-EPMC2650004 | biostudies-literature
Project description:SNP genotyping has emerged as a technology to incorporate copy number variants (CNVs) into genetic analyses of human traits. However, the extent to which SNP platforms accurately capture CNVs remains unclear. Using independent, sequence-based CNV maps, we find that commonly used SNP platforms have limited or no probe coverage for a large fraction of CNVs. Despite this, in 9 samples we inferred 368 CNVs using Illumina SNP genotyping data and experimentally validated over two-thirds of these. We also developed a method (SNP-Conditional Mixture Modeling, SCIMM) to robustly genotype deletions using as few as two SNP probes. We find that HapMap SNPs are strongly correlated with 82% of common deletions, but the newest SNP platforms effectively tag about 50%. We conclude that currently available genome-wide SNP assays can capture CNVs accurately, but improvements in array designs, particularly in duplicated sequences, are necessary to facilitate more comprehensive analyses of genomic variation.
| S-EPMC2759751 | biostudies-literature
Project description:Copy number variants (CNVs) represent a form of genomic structural variation underlying phenotypic diversity. In this study, we used the Illumina Ovine SNP 600 K BeadChip array for genome-wide detection of CNVs in 48 Chinese Tan sheep. A total of 1,296 CNV regions (CNVRs), ranging from 1.2 kb to 2.3 Mb in length, were detected, representing approximately 4.7% of the entire ovine genome (Oar_v3.1). We combined our findings with five existing CNVR reports to generate a composite genome-wide dataset of 4,321 CNVRs, which revealed 556 (43%) novel CNVRs. Subsequently, ten novel CNVRs were randomly chosen for further quantitative real-time PCR (qPCR) confirmation, and eight were successfully validated. Gene functional enrichment revealed that these CNVRs cluster into Gene Ontology (GO) categories of homeobox and embryonic skeletal system morphogenesis. One CNVR overlapping with the homeobox transcription factor DLX3 and previously shown to be associated with curly hair in sheep was identified as the candidate CNV for the special curly fleece phenotype in Tan sheep. We constructed a Chinese indigenous sheep genomic CNV map based on the Illumina Ovine SNP 600 K BeadChip array, providing an important addition to published sheep CNVs, which will be helpful for future investigations of the genomic structural variations underlying traits of interest in sheep.
| S-EPMC5430420 | biostudies-literature