Allelic genome structural variations in maize detected by array comparative genome hybridization

DNA polymorphisms such as insertion/deletions and duplications affecting genome segments larger than 1 kb are known as copy-number variations (CNVs) or structural variations (SVs). They have been recently studied in animals and humans by using array-comparative genome hybridization (aCGH), and have been associated with several human diseases. Their presence and phenotypic effects in plants have not been investigated on a genomic scale, although individual structural variations affecting traits have been described. We used aCGH to investigate the presence of CNVs in maize by comparing the genome of 13 maize inbred lines to B73. Analysis of hybridization signal ratios of 60,472 60-mer oligonucleotide probes between inbreds in relation to their location in the reference genome (B73) allowed us to identify clusters of probes that deviated from the ratio expected for equal copy-numbers. We found CNVs distributed along the maize genome in all chromosome arms. They occur with appreciable frequency in different germplasm subgroups, suggesting ancient origin. Validation of several CNV regions showed both insertion/deletions and copy-number differences. The nature of CNVs detected suggests CNVs might have a considerable impact on plant phenotypes, including disease response and heterosis.     

Reference-unbiased copy number variant analysis using CGH microarrays

Comparative genomic hybridization (CGH) microarrays have been used to determine copy number variations (CNVs) and their effects on complex diseases. Detection of absolute CNVs independent of genomic variants of an arbitrary reference sample has been a critical issue in CGH array experiments. Whole genome analysis using massively parallel sequencing with multiple ultra-high resolution CGH arrays provides an opportunity to catalog highly accurate genomic variants of the reference DNA (NA10851). Using information on variants, we developed a new method, the CGH array reference-free algorithm (CARA), which can determine reference-unbiased absolute CNVs from any CGH array platform. The algorithm enables the removal and rescue of false positive and false negative CNVs, respectively, which appear due to the effects of genomic variants of the reference sample in raw CGH array experiments. We found that the CARA remarkably enhanced the accuracy of CGH array in determining absolute CNVs. Our method thus provides a new approach to interpret CGH array data for personalized medicine.     

A Statistical Change Point Model Approach for the Detection of DNA Copy Number Variations in Array CGH Data

Array comparative genomic hybridization (aCGH) provides a high-resolution and high-throughput technique for screening of copy number variations (CNVs) within the entire genome. This technique, compared to the conventional CGH, significantly improves the identification of chromosomal abnormalities. However, due to the random noise inherited in the imaging and hybridization process, identifying statistically significant DNA copy number changes in aCGH data is challenging. We propose a novel approach that uses the mean and variance change point model (MVCM) to detect CNVs or breakpoints in aCGH data sets. We derive an approximate p-value for the test statistic and also give the estimate of the locus of the DNA copy number change. We carry out simulation studies to evaluate the accuracy of the estimate and the p-value formulation. These simulation results show that the approach is effective in identifying copy number changes. The approach is also tested on fibroblast cancer cell line data, breast tumor cell line data, and breast cancer cell line aCGH data sets that are publicly available. Changes that have not been identified by the circular binary segmentation (CBS) method but are biologically verified are detected by our approach on these cell lines with higher sensitivity and specificity than CBS.     

A Bayesian Analysis for Identifying DNA Copy Number Variations Using a Compound Poisson Process

To study chromosomal aberrations that may lead to cancer formation or genetic diseases, the array-based Comparative Genomic Hybridization (aCGH) technique is often used for detecting DNA copy number variants (CNVs). Various methods have been developed for gaining CNVs information based on aCGH data. However, most of these methods make use of the log-intensity ratios in aCGH data without taking advantage of other information such as the DNA probe (e.g., biomarker) positions/distances contained in the data. Motivated by the specific features of aCGH data, we developed a novel method that takes into account the estimation of a change point or locus of the CNV in aCGH data with its associated biomarker position on the chromosome using a compound Poisson process. We used a Bayesian approach to derive the posterior probability for the estimation of the CNV locus. To detect loci of multiple CNVs in the data, a sliding window process combined with our derived Bayesian posterior probability was proposed. To evaluate the performance of the method in the estimation of the CNV locus, we first performed simulation studies. Finally, we applied our approach to real data from aCGH experiments, demonstrating its applicability.     

Enhancer Chip: Detecting Human Copy Number Variations in Regulatory Elements

Critical functional properties are embedded in the non-coding portion of the human genome. Recent successful studies have shown that variations in distant-acting gene enhancer sequences can contribute to disease. In fact, various disorders, such as thalassaemias, preaxial polydactyly or susceptibility to Hirschsprung’s disease, may be the result of rearrangements of enhancer elements. We have analyzed the distribution of enhancer loci in the genome and compared their localization to that of previously described copy-number variations (CNVs). These data suggest a negative selection of copy number variable enhancers. To identify CNVs covering enhancer elements, we have developed a simple and cost-effective test. Here we describe the gene selection, design strategy and experimental validation of a customized oligonucleotide Array-Based Comparative Genomic Hybridization (aCGH), designated Enhancer Chip. It has been designed to investigate CNVs, allowing the analysis of all the genome with a 300 Kb resolution and specific disease regions (telomeres, centromeres and selected disease loci) at a tenfold higher resolution. Moreover, this is the first aCGH able to test over 1,250 enhancers, in order to investigate their potential pathogenic role. Validation experiments have demonstrated that Enhancer Chip efficiently detects duplications and deletions covering enhancer loci, demonstrating that it is a powerful instrument to detect and characterize copy number variable enhancers.     

Genome-wide mapping of copy number variation in humans: comparative analysis of high resolution array platforms

Accurate and efficient genome-wide detection of copy number variants (CNVs) is essential for understanding human genomic variation, genome-wide CNV association type studies, cytogenetics research and diagnostics, and independent validation of CNVs identified from sequencing based technologies. Numerous, array-based platforms for CNV detection exist utilizing array Comparative Genome Hybridization (aCGH), Single Nucleotide Polymorphism (SNP) genotyping or both. We have quantitatively assessed the abilities of twelve leading genome-wide CNV detection platforms to accurately detect Gold Standard sets of CNVs in the genome of HapMap CEU sample NA12878, and found significant differences in performance. The technologies analyzed were the NimbleGen 4.2 M, 2.1 M and 3×720 K Whole Genome and CNV focused arrays, the Agilent 1×1 M CGH and High Resolution and 2×400 K CNV and SNP+CGH arrays, the Illumina Human Omni1Quad array and the Affymetrix SNP 6.0 array. The Gold Standards used were a 1000 Genomes Project sequencing-based set of 3997 validated CNVs and an ultra high-resolution aCGH-based set of 756 validated CNVs. We found that sensitivity, total number, size range and breakpoint resolution of CNV calls were highest for CNV focused arrays. Our results are important for cost effective CNV detection and validation for both basic and clinical applications.     

Gene copy number variations in adaptive evolution: The genomic distribution of gene copy number variations revealed by genetic mapping and their adaptive role in an undomesticated species,white spruce (Picea glauca)

Gene copy number variation (CNV) has been associated with phenotypic variability in animals and plants, but a genomewide understanding of their impacts on phenotypes is largely restricted to human and agricultural systems. As such, CNVs have rarely been considered in investigations of the genomic architecture of adaptation in wild species. Here, we report on the genetic mapping of gene CNVs in white spruce, which lacks a contiguous assembly of its large genome (~20 Gb), and their relationships with adaptive phenotypic variation. We detected 3,911 gene CNVs including de novo structural variations using comparative genome hybridization on arrays (aCGH) in a large progeny set. We inferred the heterozygosity at CNV loci within parents by comparing haploid and diploid tissues and genetically mapped 82 gene CNVs. Our analysis showed that CNVs were distributed over 10 linkage groups and identified four CNV hotspots that we predict to occur in other species of the Pinaceae. Significant relationships were found between 29 of the gene CNVs and adaptive traits based on regression analyses with timings of bud set and bud flush, and height growth, suggesting a role for CNVs in climate adaptation. The importance of CNVs in adaptive evolution of white spruce was also indicated by functional gene annotations and the clustering of 31% of the mapped adaptive gene CNVs in CNV hotspots. Taken together, these results illustrate the feasibility of studying CNVs in undomesticated species and represent a major step towards a better understanding of the roles of CNVs in adaptive evolution.     

Use of BAC array CGH for evaluation of chromosomal stability of clinically used human mesenchymal stem cells and of cancer cell lines

Array-based comparative genomic hybridization (aCGH) using bacterial artificial chromosomes (BAC) is a powerful method to analyze DNA copy number aberrations of the entire human genome. In fact, CGH and aCGH have revealed various DNA copy number aberrations in numerous cancer cells and cancer cell lines examined so far. In this report, BAC aCGH was applied to evaluate the stability or instability of cell lines. Established cell lines have greatly contributed to advancements in not only biology but also medical science. However, cell lines have serious problems, such as alteration of biological properties during long-term cultivation. Firstly, we investigated two cancer cell lines, HeLa and Caco-2. HeLa cells, established from a cervical cancer, showed significantly increased DNA copy number alterations with passage time. Caco-2 cells, established from a colon cancer, showed no remarkable differences under various culture conditions. These results indicate that BAC aCGH can be used for the evaluation and validation of genomic stability of cultured cells. Secondly, BAC aCGH was applied to evaluate and validate the genomic stabilities of three patient's mesenchymal stem cells (MSCs), which were already used for their treatments. These three MSCs showed no significant differences in DNA copy number aberrations over their entire chromosomal regions. Therefore, BAC aCGH is highly recommended for use for a quality check of various cells before using them for any kind of biological investigation or clinical application.     

Genomic hypomethylation in the human germline associates with selective structural mutability in the human genome

The hotspots of structural polymorphisms and structural mutability in the human genome remain to be explained mechanistically. We examine associations of structural mutability with germline DNA methylation and with non-allelic homologous recombination (NAHR) mediated by low-copy repeats (LCRs). Combined evidence from four human sperm methylome maps, human genome evolution, structural polymorphisms in the human population, and previous genomic and disease studies consistently points to a strong association of germline hypomethylation and genomic instability. Specifically, methylation deserts, the ~1% fraction of the human genome with the lowest methylation in the germline, show a tenfold enrichment for structural rearrangements that occurred in the human genome since the branching of chimpanzee and are highly enriched for fast-evolving loci that regulate tissue-specific gene expression. Analysis of copy number variants (CNVs) from 400 human samples identified using a custom-designed array comparative genomic hybridization (aCGH) chip, combined with publicly available structural variation data, indicates that association of structural mutability with germline hypomethylation is comparable in magnitude to the association of structural mutability with LCR-mediated NAHR. Moreover, rare CNVs occurring in the genomes of individuals diagnosed with schizophrenia, bipolar disorder, and developmental delay and de novo CNVs occurring in those diagnosed with autism are significantly more concentrated within hypomethylated regions. These findings suggest a new connection between the epigenome, selective mutability, evolution, and human disease.     

Development of bioinformatics resources for display and analysis of copy number and other structural variants in the human genome

The discovery of an abundance of copy number variants (CNVs; gains and losses of DNA sequences >1 kb) and other structural variants in the human genome is influencing the way research and diagnostic analyses are being designed and interpreted. As such, comprehensive databases with the most relevant information will be critical to fully understand the results and have impact in a diverse range of disciplines ranging from molecular biology to clinical genetics. Here, we describe the development of bioinformatics resources to facilitate these studies. The Database of Genomic Variants (http://projects.tcag.ca/variation/) is a comprehensive catalogue of structural variation in the human genome. The database currently contains 1,267 regions reported to contain copy number variation or inversions in apparently healthy human cases. We describe the current contents of the database and how it can serve as a resource for interpretation of array comparative genomic hybridization (array CGH) and other DNA copy imbalance data. We also present the structure of the database, which was built using a new data modeling methodology termed Cross-Referenced Tables (XRT). This is a generic and easy-to-use platform, which is strong in handling textual data and complex relationships. Web-based presentation tools have been built allowing publication of XRT data to the web immediately along with rapid sharing of files with other databases and genome browsers. We also describe a novel tool named eFISH (electronic fluorescence in situ hybridization) (http://projects.tcag.ca/efish/), a BLAST-based program that was developed to facilitate the choice of appropriate clones for FISH and CGH experiments, as well as interpretation of results in which genomic DNA probes are used in hybridization-based experiments.     

Identification of copy number variants from exome sequence data

Background

With advances in next generation sequencing technologies and genomic capture techniques, exome sequencing has become a cost-effective approach for mutation detection in genetic diseases. However, computational prediction of copy number variants (CNVs) from exome sequence data is a challenging task. Whilst numerous programs are available, they have different sensitivities, and have low sensitivity to detect smaller CNVs (1–4 exons). Additionally, exonic CNV discovery using standard aCGH has limitations due to the low probe density over exonic regions. The goal of our study was to develop a protocol to detect exonic CNVs (including shorter CNVs that cover 1–4 exons), combining computational prediction algorithms and a high-resolution custom CGH array.

Results

We used six published CNV prediction programs (ExomeCNV, CONTRA, ExomeCopy, ExomeDepth, CoNIFER, XHMM) and an in-house modification to ExomeCopy and ExomeDepth (ExCopyDepth) for computational CNV prediction on 30 exomes from the 1000 genomes project and 9 exomes from primary immunodeficiency patients. CNV predictions were tested using a custom CGH array designed to capture all exons (exaCGH). After this validation, we next evaluated the computational prediction of shorter CNVs. ExomeCopy and the in-house modified algorithm, ExCopyDepth, showed the highest capability in detecting shorter CNVs. Finally, the performance of each computational program was assessed by calculating the sensitivity and false positive rate.

Conclusions

In this paper, we assessed the ability of 6 computational programs to predict CNVs, focussing on short (1–4 exon) CNVs. We also tested these predictions using a custom array targeting exons. Based on these results, we propose a protocol to identify and confirm shorter exonic CNVs combining computational prediction algorithms and custom aCGH experiments.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-661) contains supplementary material, which is available to authorized users.     

An algorithm for inferring complex haplotypes in a region of copy-number variation

Recent studies have extensively examined the large-scale genetic variants in the human genome known as copy-number variations (CNVs), and the universality of CNVs in normal individuals, along with their functional importance, has been increasingly recognized. However, the absence of a method to accurately infer alleles or haplotypes within a CNV region from high-throughput experimental data hampers the finer analyses of CNV properties and applications to disease-association studies. Here we developed an algorithm to infer complex haplotypes within a CNV region by using data obtained from high-throughput experimental platforms. We applied this algorithm to experimental data and estimated the population frequencies of haplotypes that can yield information on both sequences and numbers of DNA copies. These results suggested that the analysis of such complex haplotypes is essential for accurately detecting genetic differences within a CNV region between population groups.     

Breaking the waves: improved detection of copy number variation from microarray-based comparative genomic hybridization

Background

Large-scale high throughput studies using microarray technology have established that copy number variation (CNV) throughout the genome is more frequent than previously thought. Such variation is known to play an important role in the presence and development of phenotypes such as HIV-1 infection and Alzheimer's disease. However, methods for analyzing the complex data produced and identifying regions of CNV are still being refined.

Results

We describe the presence of a genome-wide technical artifact, spatial autocorrelation or 'wave', which occurs in a large dataset used to determine the location of CNV across the genome. By removing this artifact we are able to obtain both a more biologically meaningful clustering of the data and an increase in the number of CNVs identified by current calling methods without a major increase in the number of false positives detected. Moreover, removing this artifact is critical for the development of a novel model-based CNV calling algorithm - CNVmix - that uses cross-sample information to identify regions of the genome where CNVs occur. For regions of CNV that are identified by both CNVmix and current methods, we demonstrate that CNVmix is better able to categorize samples into groups that represent copy number gains or losses.

Conclusion

Removing artifactual 'waves' (which appear to be a general feature of array comparative genomic hybridization (aCGH) datasets) and using cross-sample information when identifying CNVs enables more biological information to be extracted from aCGH experiments designed to investigate copy number variation in normal individuals.     

Progress in the detection of human genome structural variations

The emerging of high-throughput and high-resolution genomic technologies led to the detection of submicroscopic variants ranging from 1 kb to 3 Mb in the human genome. These variants include copy number variations (CNVs), inversions, insertions, deletions and other complex rearrangements of DNA sequences. This paper briefly reviews the commonly used technologies to discover both genomic structural variants and their potential influences. Particularly, we highlight the array-based, PCR-based and sequencing-based assays, including array-based comparative genomic hybridization (aCGH), representational oligonucleotide microarray analysis (ROMA), multiplex amplifiable probe hybridization (MAPH), multiplex ligation-dependent probe amplification (MLPA), paired-end mapping (PEM), and next-generation DNA sequencing technologies. Furthermore, we discuss the limitations and challenges of current assays and give advices on how to make the database of genomic variations more reliable. Supported by the National High Technology Research and Development Program of China (Grant No. 2006AA020704).     

Identification and functional characterization of copy number variations in diverse chicken breeds

Background

The detection and functional characterization of genomic structural variations are important for understanding the landscape of genetic variation in the chicken. A recently recognized aspect of genomic structural variation, called copy number variation (CNV), is gaining interest in chicken genomic studies. The aim of the present study was to investigate the pattern and functional characterization of CNVs in five characteristic chicken breeds, which will be important for future studies associating phenotype with chicken genome architecture.

Results

Using a commercial 385 K array-based comparative genomic hybridization (aCGH) genome array, we performed CNV discovery using 10 chicken samples from four local Chinese breeds and the French breed Houdan chicken. The female Anka broiler was used as a reference. A total of 281 copy number variation regions (CNVR) were identified, covering 12.8 Mb of polymorphic sequences or 1.07% of the entire chicken genome. The functional annotation of CNVRs indicated that these regions completely or partially overlapped with 231 genes and 1032 quantitative traits loci, suggesting these CNVs have important functions and might be promising resources for exploring differences among various breeds. In addition, we employed quantitative PCR (qPCR) to further validate several copy number variable genes, such as prolactin receptor, endothelin 3 (EDN3), suppressor of cytokine signaling 2, CD8a molecule, with important functions, and the results suggested that EDN3 might be a molecular marker for the selection of dark skin color in poultry production. Moreover, we also identified a new CNVR (chr24: 3484617–3512275), encoding the sortilin-related receptor gene, with copy number changes in only black-bone chicken.

Conclusions

Here, we report a genome-wide analysis of the CNVs in five chicken breeds using aCGH. The association between EDN3 and melanoblast proliferation was further confirmed using qPCR. These results provide additional information for understanding genomic variation and related phenotypic characteristics.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-934) contains supplementary material, which is available to authorized users.     

Genome-Wide Detection of Copy Number Variations among Diverse Horse Breeds by Array CGH

Recent studies have found that copy number variations (CNVs) are widespread in human and animal genomes. CNVs are a significant source of genetic variation, and have been shown to be associated with phenotypic diversity. However, the effect of CNVs on genetic variation in horses is not well understood. In the present study, CNVs in 6 different breeds of mare horses, Mongolia horse, Abaga horse, Hequ horse and Kazakh horse (all plateau breeds) and Debao pony and Thoroughbred, were determined using aCGH. In total, seven hundred CNVs were identified ranging in size from 6.1 Kb to 0.57 Mb across all autosomes, with an average size of 43.08 Kb and a median size of 15.11 Kb. By merging overlapping CNVs, we found a total of three hundred and fifty-three CNV regions (CNVRs). The length of the CNVRs ranged from 6.1 Kb to 1.45 Mb with average and median sizes of 38.49 Kb and 13.1 Kb. Collectively, 13.59 Mb of copy number variation was identified among the horses investigated and accounted for approximately 0.61% of the horse genome sequence. Five hundred and eighteen annotated genes were affected by CNVs, which corresponded to about 2.26% of all horse genes. Through the gene ontology (GO), genetic pathway analysis and comparison of CNV genes among different breeds, we found evidence that CNVs involving 7 genes may be related to the adaptation to severe environment of these plateau horses. This study is the first report of copy number variations in Chinese horses, which indicates that CNVs are ubiquitous in the horse genome and influence many biological processes of the horse. These results will be helpful not only in mapping the horse whole-genome CNVs, but also to further research for the adaption to the high altitude severe environment for plateau horses.     

Accurate Distinction of Pathogenic from Benign CNVs in Mental Retardation

Copy number variants (CNVs) have recently been recognized as a common form of genomic variation in humans. Hundreds of CNVs can be detected in any individual genome using genomic microarrays or whole genome sequencing technology, but their phenotypic consequences are still poorly understood. Rare CNVs have been reported as a frequent cause of neurological disorders such as mental retardation (MR), schizophrenia and autism, prompting widespread implementation of CNV screening in diagnostics. In previous studies we have shown that, in contrast to benign CNVs, MR-associated CNVs are significantly enriched in genes whose mouse orthologues, when disrupted, result in a nervous system phenotype. In this study we developed and validated a novel computational method for differentiating between benign and MR-associated CNVs using structural and functional genomic features to annotate each CNV. In total 13 genomic features were included in the final version of a Naïve Bayesian Tree classifier, with LINE density and mouse knock-out phenotypes contributing most to the classifier''s accuracy. After demonstrating that our method (called GECCO) perfectly classifies CNVs causing known MR-associated syndromes, we show that it achieves high accuracy (94%) and negative predictive value (99%) on a blinded test set of more than 1,200 CNVs from a large cohort of individuals with MR. These results indicate that this classification method will be of value for objectively prioritizing CNVs in clinical research and diagnostics.     

Microarray-based maps of copy-number variant regions in European and sub-Saharan populations

The genetic basis of phenotypic variation can be partially explained by the presence of copy-number variations (CNVs). Currently available methods for CNV assessment include high-density single-nucleotide polymorphism (SNP) microarrays that have become an indispensable tool in genome-wide association studies (GWAS). However, insufficient concordance rates between different CNV assessment methods call for cautious interpretation of results from CNV-based genetic association studies. Here we provide a cross-population, microarray-based map of copy-number variant regions (CNVRs) to enable reliable interpretation of CNV association findings. We used the Affymetrix Genome-Wide Human SNP Array 6.0 to scan the genomes of 1167 individuals from two ethnically distinct populations (Europe, N=717; Rwanda, N=450). Three different CNV-finding algorithms were tested and compared for sensitivity, specificity, and feasibility. Two algorithms were subsequently used to construct CNVR maps, which were also validated by processing subsamples with additional microarray platforms (Illumina 1M-Duo BeadChip, Nimblegen 385K aCGH array) and by comparing our data with publicly available information. Both algorithms detected a total of 42669 CNVs, 74% of which clustered in 385 CNVRs of a cross-population map. These CNVRs overlap with 862 annotated genes and account for approximately 3.3% of the haploid human genome.We created comprehensive cross-populational CNVR-maps. They represent an extendable framework that can leverage the detection of common CNVs and additionally assist in interpreting CNV-based association studies.     

Epigenetic silencing of genomic structural variations

A great amount of copy number variations (CNVs) are identified in the human genome. Most of them are neutral; nevertheless, the role of CNVs in the pathogenesis of hereditary diseases is still significant. Especially, this is important for neuropsychiatric disorders, such as intellectual disability and autism. When analyzing the CNV-associated diseases, the controversial question is to distinguish the pathogenic CNVs among common polymorphic variants and to predict the disease risk in other children of the family. Unfortunately, the mechanisms of phenotypic expression and incomplete penetrance of CNVs remain largely unknown. Currently, incomplete penetrance and variable expressivity of CNVs are attributed mainly to allelic interaction of different genetic variations. However, epigenetic mechanisms of gene expression regulation in the context of structural variation of the genome are poorly explored. It is possible that epigenetic modifications of the genome regions with CNVs may underlie the understanding of ways of phenotypic manifestations of structural variations in the human genome.