Discovery Of Copy Number Variants Research Articles

Sequenza: allele-specific copy number and mutation profiles from tumor sequencing data

BackgroundExome or whole-genome deep sequencing of tumor DNA along with paired normal DNA can potentially provide a detailed picture of the somatic mutations that characterize the tumor. However, analysis of such sequence data can be complicated by the presence of normal cells in the tumor specimen, by intratumor heterogeneity, and by the sheer size of the raw data. In particular, determination of copy number variations from exome sequencing data alone has proven difficult; thus, single nucleotide polymorphism (SNP) arrays have often been used for this task. Recently, algorithms to estimate absolute, but not allele-specific, copy number profiles from tumor sequencing data have been described. Materials and methodsWe developed Sequenza, a software package that uses paired tumor-normal DNA sequencing data to estimate tumor cellularity and ploidy, and to calculate allele-specific copy number profiles and mutation profiles. We applied Sequenza, as well as two previously published algorithms, to exome sequence data from 30 tumors from The Cancer Genome Atlas. We assessed the performance of these algorithms by comparing their results with those generated using matched SNP arrays and processed by the allele-specific copy number analysis of tumors (ASCAT) algorithm. ResultsComparison between Sequenza/exome and SNP/ASCAT revealed strong correlation in cellularity (Pearson's r = 0.90) and ploidy estimates (r = 0.42, or r = 0.94 after manual inspecting alternative solutions). This performance was noticeably superior to previously published algorithms. In addition, in artificial data simulating normal-tumor admixtures, Sequenza detected the correct ploidy in samples with tumor content as low as 30%. ConclusionsThe agreement between Sequenza and SNP array-based copy number profiles suggests that exome sequencing alone is sufficient not only for identifying small scale mutations but also for estimating cellularity and inferring DNA copy number aberrations.

Identification and Validation of Copy Number Variants in Italian Brown Swiss Dairy Cattle Using Illumina Bovine SNP50 Beadchip®

The determination of copy number variation (CNV) is very important for the evaluation of genomic traits in several species because they are a major source for the genetic variation, influencing gene expression, phenotypic variation, adaptation and the development of diseases. The aim of this study was to obtain a CNV genome map using the Illumina Bovine SNP50 BeadChip data of 651 bulls of the Italian Brown Swiss breed. PennCNV and SVS7 (Golden Helix) software were used for the detection of the CNVs and Copy Number Variation Regions (CNVRs). A total of 5,099 and 1,289 CNVs were identified with PennCNV and SVS7 software, respectively. These were grouped at the population level into 1101 (220 losses, 774 gains, 107 complex) and 277 (185 losses, 56 gains and 36 complex) CNVR. Ten of the selected CNVR were experimentally validated with a qPCR experiment. The GO and pathway analyses were conducted and they identified genes (false discovery rate corrected) in the CNVR related to biological processes cellular component, molecular function and metabolic pathways. Among those, we found the FCGR2B , PPARα , KATNAL1 , DNAJC15 , PTK2 , TG , STAT family , NPM1 , GATA2 , LMF1 , ECHS1 genes, already known in literature because of their association with various traits in cattle. Although there is variability in the CNVRs detection across methods and platforms, this study allowed the identification of CNVRs in Italian Brown Swiss, overlapping those already detected in other breeds and finding additional ones, thus producing new knowledge for association studies with traits of interest in cattle.

Genome-wide identification of copy number variations in Holstein cattle from Baja California, Mexico, using high-density SNP genotyping arrays.

Copy number variations (CNVs) are an important source of genomic structural variation, and can be used as markers to investigate phenotypic and economic traits. CNVs also have functional effects on gene expression and can contribute to disease susceptibility in mammals. Currently, single nucleotide polymorphism genotyping arrays (SNP chips) are the technology of choice for identifying CNV variations. Microarray technologies have recently been used to study the bovine genome. The objective of the present study was to develop CNVs in Holstein cows from the Northwest of Mexico using the Affymetrix Axiom Genome-Wide BOS 1 Array, which assays 648,315 SNPs and provides a wide coverage for genome-wide studies. We applied the two most widely used algorithms for the discovery of CNVs (PennCNV and QuantiSNP) and found 56 CNV regions (CNVRs) representing 0.33% of the bovine genome (8.46 Mb). These CNVRs ranged from 1.5 to 970.8 kb with an average length of 151 kb. They involved 103 genes and showed a 28% overlap with CNVRs already reported. Of the 56 CNVRs found, 20 were novel. In this study we present the first genomic analysis of CNVs in Mexican cattle using high-density SNP data. Our results provide a new reference basis for future genomic variation and association studies between CNVs and phenotypes, especially in Mexican cattle.

Enhanced detection of DNA sequences using end-point PCR amplification and online gel electrophoresis (GE)-ICP-MS: determination of gene copy number variations.

The design and evaluation of analytical methods that permit quantitative analysis of specific DNA sequences is exponentially increasing. For this purpose, highly sensitive methodologies usually based on labeling protocols with fluorescent dyes or nanoparticles are often explored. Here, the possibility of label-free signal amplification using end-point polymerase chain reaction (PCR) are exploited using on-column agarose gel electrophoresis as separation and inductively coupled plasma-mass spectrometry (ICP-MS) for the detection of phosphorus in amplified DNA sequences. The calibration of the separation system with a DNA ladder permits direct estimation of the size of the amplified gene fragment after PCR. With this knowledge, and considering the compound-independent quantification capabilities exhibited by ICP-MS for phosphorus (it is only dependent on the number of P atoms per molecule), the correlation of the P-peak area of the amplified gene fragment, with respect to the gene copy numbers (in the starting DNA), is then established. Such a relationship would permit the determination of copy number variations (CNVs) in genomic DNA using ICP-MS measurements. The method detection limit, in terms of the required amount of starting DNA, is ∼6 ng (or 1000 cells if 100% extraction efficiency is expected). The suitability of the proposed label-free amplification strategy is applied to CNVs monitoring in cells exposed to a chemical agent capable of deletion induction, such as cisplatin.

Copy number variation in the horse genome.

We constructed a 400K WG tiling oligoarray for the horse and applied it for the discovery of copy number variations (CNVs) in 38 normal horses of 16 diverse breeds, and the Przewalski horse. Probes on the array represented 18,763 autosomal and X-linked genes, and intergenic, sub-telomeric and chrY sequences. We identified 258 CNV regions (CNVRs) across all autosomes, chrX and chrUn, but not in chrY. CNVs comprised 1.3% of the horse genome with chr12 being most enriched. American Miniature horses had the highest and American Quarter Horses the lowest number of CNVs in relation to Thoroughbred reference. The Przewalski horse was similar to native ponies and draft breeds. The majority of CNVRs involved genes, while 20% were located in intergenic regions. Similar to previous studies in horses and other mammals, molecular functions of CNV-associated genes were predominantly in sensory perception, immunity and reproduction. The findings were integrated with previous studies to generate a composite genome-wide dataset of 1476 CNVRs. Of these, 301 CNVRs were shared between studies, while 1174 were novel and require further validation. Integrated data revealed that to date, 41 out of over 400 breeds of the domestic horse have been analyzed for CNVs, of which 11 new breeds were added in this study. Finally, the composite CNV dataset was applied in a pilot study for the discovery of CNVs in 6 horses with XY disorders of sexual development. A homozygous deletion involving AKR1C gene cluster in chr29 in two affected horses was considered possibly causative because of the known role of AKR1C genes in testicular androgen synthesis and sexual development. While the findings improve and integrate the knowledge of CNVs in horses, they also show that for effective discovery of variants of biomedical importance, more breeds and individuals need to be analyzed using comparable methodological approaches.

Structural Architecture of SNP Effects on Complex Traits

Despite the discovery of copy-number variation (CNV) across the genome nearly 10 years ago, current SNP-based analysis methodologies continue to collapse the homozygous (i.e., A/A), hemizygous (i.e., A/0), and duplicative (i.e., A/A/A) genotype states, treating the genotype variable as irreducible or unaltered by other colocalizing forms of genetic (e.g., structural) variation. Our understanding of common, genome-wide CNVs suggests that the canonical genotype construct might belie the enormous complexity of the genome. Here we present multiple analyses of several phenotypes and provide methods supporting a conceptual shift that embraces the structural dimension of genotype. We comprehensively investigate the impact of the structural dimension of genotype on (1) GWAS methods, (2) interpretation of rare LOF variants, (3) characterization of genomic architecture, and (4) implications for mapping loci involved in complex disease. Taken together, these results argue for the inclusion of a structural dimension and suggest that some portion of the "missing" heritability might be recovered through integration of the structural dimension of SNP effects on complex traits.

CnvCapSeq: detecting copy number variation in long-range targeted resequencing data.

Targeted resequencing technologies have allowed for efficient and cost-effective detection of genomic variants in specific regions of interest. Although capture sequencing has been primarily used for investigating single nucleotide variants and indels, it has the potential to elucidate a broader spectrum of genetic variation, including copy number variants (CNVs). Various methods exist for detecting CNV in whole-genome and exome sequencing datasets. However, no algorithms have been specifically designed for contiguous target sequencing, despite its increasing importance in clinical and research applications. We have developed cnvCapSeq, a novel method for accurate and sensitive CNV discovery and genotyping in long-range targeted resequencing. cnvCapSeq was benchmarked using a simulated contiguous capture sequencing dataset comprising 21 genomic loci of various lengths. cnvCapSeq was shown to outperform the best existing exome CNV method by a wide margin both in terms of sensitivity (92.0 versus 48.3%) and specificity (99.8 versus 70.5%). We also applied cnvCapSeq to a real capture sequencing cohort comprising a contiguous 358 kb region that contains the Complement Factor H gene cluster. In this dataset, cnvCapSeq identified 41 samples with CNV, including two with duplications, with a genotyping accuracy of 99%, as ascertained by quantitative real-time PCR.

Identification of copy number variants from exome sequence data.

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

Poster #S22 THE ANTI-ANHEDONIC PROPERTIES OF LURASIDONE IN THE CHRONIC MILD STRESS MODEL ARE ASSOCIATED WITH SYNAPTIC AND NEUROPLASTIC CHANGES IN THE RAT PREFRONTAL CORTEX

Despite the discovery of copy-number variation (CNV) across the genome nearly 10 years ago, current SNP-based analysis methodologies continue to collapse the homozygous (i.e., A/A), hemizygous (i.e., A/0), and duplicative (i.e., A/A/A) genotype states, treating the genotype variable as irreducible or unaltered by other colocalizing forms of genetic (e.g., structural) variation. Our understanding of common, genome-wide CNVs suggests that the canonical genotype construct might belie the enormous complexity of the genome. Here we present multiple analyses of several phenotypes and provide methods supporting a conceptual shift that embraces the structural dimension of genotype. We comprehensively investigate the impact of the structural dimension of genotype on (1) GWAS methods, (2) interpretation of rare LOF variants, (3) characterization of genomic architecture, and (4) implications for mapping loci involved in complex disease. Taken together, these results argue for the inclusion of a structural dimension and suggest that some portion of the “missing” heritability might be recovered through integration of the structural dimension of SNP effects on complex traits.

Copy number variation in Fayoumi and Leghorn chickens analyzed using array comparative genomic hybridization

Copy number variation refers to regions along chromosomes that harbor a type of structural variation, such as duplications or deletions. Copy number variants (CNVs) play a role in many important traits as well as in genetic diversity. Previous analyses of chickens using array comparative genomic hybridizations or single-nucleotide polymorphism chip assays have been performed on various breeds and genetic lines to discover CNVs. In this study, we assessed individuals from two highly inbred (inbreeding coefficiency > 99.99%) lines, Leghorn G-B2 and Fayoumi M15.2, to discover novel CNVs in chickens. These lines have been previously studied for disease resistance, and to our knowledge, this represents the first global assessment of CNVs in the Fayoumi breed. Genomic DNA from individuals was examined using the Agilent chicken 244 K comparative genomic hybridization array and quantitative PCR. We identified a total of 273 CNVs overall, with 112 CNVs being novel and not previously reported. Quantitative PCR using the standard curve method validated a subset of our array data. Through enrichment analysis of genes within CNV regions, we observed multiple chromosomes, terms and pathways that were significantly enriched, largely dealing with the major histocompatibility complex and immune responsiveness. Using an additional round of computational and statistical analysis with a different bioinformatic pipeline, we identified 43 CNVs among these as high-confidence regions, 14 of which were found to be novel. We further compared and contrasted individuals of the two inbred lines to discover regions that have a significant difference in copy number between lines. A total of 40 regions had significant deletions or duplications between the lines. Gene Ontology analysis of genomic regions containing CNVs between lines also was performed. This between-line candidate CNV list will be useful in studies with these two unique genetic lines, which may harbor variations that underlie quantitative trait loci for disease resistance and other important traits. Through the global discovery of novel CNVs in chicken, these data also provide resources for further genetic and functional genomics studies.

PSCC: Sensitive and Reliable Population-Scale Copy Number Variation Detection Method Based on Low Coverage Sequencing

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.

Outlier-Based Identification of Copy Number Variations Using Targeted Resequencing in a Small Cohort of Patients with Tetralogy of Fallot

Copy number variations (CNVs) are one of the main sources of variability in the human genome. Many CNVs are associated with various diseases including cardiovascular disease. In addition to hybridization-based methods, next-generation sequencing (NGS) technologies are increasingly used for CNV discovery. However, respective computational methods applicable to NGS data are still limited. We developed a novel CNV calling method based on outlier detection applicable to small cohorts, which is of particular interest for the discovery of individual CNVs within families, de novo CNVs in trios and/or small cohorts of specific phenotypes like rare diseases. Approximately 7,000 rare diseases are currently known, which collectively affect ∼6% of the population. For our method, we applied the Dixon’s Q test to detect outliers and used a Hidden Markov Model for their assessment. The method can be used for data obtained by exome and targeted resequencing. We evaluated our outlier- based method in comparison to the CNV calling tool CoNIFER using eight HapMap exome samples and subsequently applied both methods to targeted resequencing data of patients with Tetralogy of Fallot (TOF), the most common cyanotic congenital heart disease. In both the HapMap samples and the TOF cases, our method is superior to CoNIFER, such that it identifies more true positive CNVs. Called CNVs in TOF cases were validated by qPCR and HapMap CNVs were confirmed with available array-CGH data. In the TOF patients, we found four copy number gains affecting three genes, of which two are important regulators of heart development (NOTCH1, ISL1) and one is located in a region associated with cardiac malformations (PRODH at 22q11). In summary, we present a novel CNV calling method based on outlier detection, which will be of particular interest for the analysis of de novo or individual CNVs in trios or cohorts up to 30 individuals, respectively.

Genome-wide analysis of the role of copy-number variation in pancreatic cancer risk

Although family history is a risk factor for pancreatic adenocarcinoma, much of the genetic etiology of this disease remains unknown. While genome-wide association studies have identified some common single nucleotide polymorphisms (SNPs) associated with pancreatic cancer risk, these SNPs do not explain all the heritability of this disease. We hypothesized that copy number variation (CNVs) in the genome may play a role in genetic predisposition to pancreatic adenocarcinoma. Here, we report a genome-wide analysis of CNVs in a small hospital-based, European ancestry cohort of pancreatic cancer cases and controls. Germline CNV discovery was performed using the Illumina Human CNV370 platform in 223 pancreatic cancer cases (both sporadic and familial) and 169 controls. Following stringent quality control, we asked if global CNV burden was a risk factor for pancreatic cancer. Finally, we performed in silico CNV genotyping and association testing to discover novel CNV risk loci. When we examined the global CNV burden, we found no strong evidence that CNV burden plays a role in pancreatic cancer risk either overall or specifically in individuals with a family history of the disease. Similarly, we saw no significant evidence that any particular CNV is associated with pancreatic cancer risk. Taken together, these data suggest that CNVs do not contribute substantially to the genetic etiology of pancreatic cancer, though the results are tempered by small sample size and large experimental variability inherent in array-based CNV studies.

Chromosome microarrays in diagnostic testing: interpreting the genomic data.

DNA-based Chromosome MicroArrays (CMAs) are now well established as diagnostic tools in clinical genetics laboratories. Over the last decade, the primary application of CMAs has been the genome-wide detection of a particular class of mutation known as copy number variants (CNVs). Since 2010, CMA testing has been recommended as a first-tier test for detection of CNVs associated with intellectual disability, autism spectrum disorders, and/or multiple congenital anomalies…in the post-natal setting. CNVs are now regarded as pathogenic in 14-18 % of patients referred for these (and related) disorders.Through consideration of clinical examples, and several microarray platforms, we attempt to provide an appreciation of microarray diagnostics, from the initial inspection of the microarray data, to the composing of the patient report. In CMA data interpretation, a major challenge comes from the high frequency of clinically irrelevant CNVs observed within "patient" and "normal" populations. As might be predicted, the more common and clinically insignificant CNVs tend to be the smaller ones <100 kb in length, involving few or no known genes. However, this relationship is not at all straightforward: CNV length and gene content are only very imperfect indicators of CNV pathogenicity. Presently, there are no reliable means of separating, a priori, the benign from the pathological CNV classes.This chapter also considers sources of technical "noise" within CMA data sets. Some level of noise is inevitable in diagnostic genomics, given the very large number of data points generated in any one test. Noise further limits CMA resolution, and some miscalling of CNVs is unavoidable. In this, there is no ideal solution, but various strategies for handling noise are available. Even without solutions, consideration of these diagnostic problems per se is informative, as they afford critical insights into the biological and technical underpinnings of CNV discovery. These are indispensable to any clinician or scientist practising within the field of genome diagnostics.

Genome-wide copy number variant discovery in dogs using the CanineHD genotyping array

BackgroundSubstantial contribution to phenotypic diversity is accounted for by copy number variants (CNV). In human, as well as other species, the effect of CNVs range from benign to directly disease-causing which motivates the continued investigations of CNVs. Previous canine genome-wide screenings for CNVs have been performed using high-resolution comparative genomic hybridisation arrays which have contributed with a detailed catalogue of CNVs. Here, we present the first CNV investigation in dogs based on the recently reported CanineHD 170 K genotyping array. The hitherto largest dataset in canine CNV discovery was assessed, 351 dogs from 30 different breeds, enabling identification of novel CNVs and a thorough characterisation of breed-specific CNVs.ResultsA stringent procedure identified 72 CNV regions with the smallest size of 38 kb and of the 72 CNV regions, 38 overlapped 148 annotated genes. A total of 29 novel CNV regions were found containing 44 genes. Furthermore, 15 breed specific CNV regions were identified of which 14 were novel and some of them overlapped putative disease susceptibility genes. In addition, the human ortholog of 23 canine copy number variable genes identified herein has been previously suggested to be dosage-sensitive in human.ConclusionsThe present study evaluated the performance of the CanineHD in detecting CNVs and extends the current catalogue of canine CNV regions with several dozens of novel CNV regions. These novel CNV regions, which harbour candidate genes that possibly contribute to phenotypic variation in dogs or to disease-susceptibility, are a rich resource for future investigations.

Abstract P6-04-02: Defining clinically relevant molecular subtypes in lobular breast cancer within the RATHER consortium

Abstract Background: The Rational Therapy for Breast Cancer (RATHER) Consortium aims to identify novel kinase targets for therapy in poor-prognosis subtypes of breast cancer for which there are currently no targeted therapies available. In this project, the focus is on invasive lobular carcinomas (ILC), which represent 10% of breast tumors. The main strength of the RATHER project is the unique combination of comprehensive molecular data together with detailed clinical information, which enables translation of state-of-the art genomics analyses to the clinic. Our main goal is to identify and validate novel kinase targets for breast cancer therapy in a comprehensive way using large-scale and complementary genomics data (DNA and RNA sequence, copy number variation, gene expression and protein expression). Integrated analysis of these molecular data is performed to define molecular subtypes of ILC with differential clinical outcome. Methods: One hundred and fifty ILC samples (fresh frozen) with &amp;gt;5 years follow-up were collected from two institutes (Cambridge, Netherlands Cancer Institute). All samples were processed following one standard operating protocol to isolate RNA, DNA and protein of high quality. We used a five-pronged approach to identify and validate novel kinase targets for therapy in ILC, namely i) direct re-sequencing of the kinome of 150 ILC breast tumors, ii) determination of abundance and activation status of kinases in these tumors by reverse phase protein lysate array (RPPA) technology, iii) determination of copy number variation (CNV) by genome-wide SNP arrays, iv) mRNA quantitation of both genome and kinome using DNA microarrays and v) RNAseq of a subset of ILC tumors. ILC are compared to triple negative, which allows us to highlight differences and/or similarities between these subtypes and provide clues for therapeutic targets. Results: Data from these independent genome-scale technologies were integrated, yielding a prioritized list of potential kinase targets for therapy in ILC breast cancer. Deep sequencing of the kinome has revealed somatic mutations characteristic of ILC, which are currently being validated via mass spectrometry-based genotyping technology and their possible effects confirmed with gene expression, protein expression and phosphorylation changes. In addition, on a subset of the ILC samples, RNA sequencing was performed to confirm expression of particular mutants. Known gene mutations in ILC such as loss of CDH1 were confirmed. Moreover, the PI3K pathway is found to be frequently altered (50% of the samples). Gene expression analysis, as well as integrative analysis of CNV and gene expression data revealed subsets of ILCs that significantly regulate alternate biological processes and show differential clinical outcome. Such biological subsets are currently being validated with clinical and follow-up data. Updated results will be presented at the meeting. Conclusion: The RATHER project aims to deliver proof-of-concept for novel therapeutic interventions, together with companion molecular diagnostic assays for patient stratification, for up to 10% of breast cancer patients, where current treatment options are unsatisfactory. Ongoing validation of a number of potential targets proves to be promising. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P6-04-02.

High-Resolution SNP Microarray Investigation of Copy Number Variations on Chromosome 18 in a Control Cohort

Copy number variations (CNVs) as described in the healthy population are purported to contribute significantly to genetic heterogeneity. Recent studies have described CNVs using lymphoblastoid cell lines or by application of specifically developed algorithms to interrogate previously described data. However, the full extent of CNVs remains unclear. Using high-density SNP array, we have undertaken a comprehensive investigation of chromosome 18 for CNV discovery and characterisation of distribution and association with chromosome architecture. We identified 399 CNVs, of which loss represents 98%, 58% are less than 2.5 kb in size and 71% are intergenic. Intronic deletions account for the majority of copy number changes with gene involvement. Furthermore, one-third of CNVs do not have putative breakpoints within repetitive sequences. We conclude that replicative processes, mediated either by repetitive elements or microhomology, account for the majority of CNVs in the healthy population. Genomic instability involving the formation of a non-B structure is demonstrated in one region.

Massive screening of copy number population-scale variation in Bos taurus genome

BackgroundCopy number variations (CNVs) represent a significant source of genomic structural variation. Their length ranges from approximately one hundred to millions of base pair. Genome-wide screenings have clarified that CNVs are a ubiquitous phenomenon affecting essentially the whole genome. Although Bos taurus is one of the most important domestic animal species worldwide and one of the most studied ruminant models for metabolism, reproduction, and disease, relatively few studies have investigated CNVs in cattle and little is known about how CNVs contribute to normal phenotypic variation and to disease susceptibility in this species, compared to humans and other model organisms.ResultsHere we characterize and compare CNV profiles in 2654 animals from five dairy and beef Bos taurus breeds, using the Illumina BovineSNP50 genotyping array (54001 SNP probes). In this study we applied the two most commonly used algorithms for CNV discovery (QuantiSNP and PennCNV) and identified 4830 unique candidate CNVs belonging to 326 regions. These regions overlap with 5789 known genes, 76.7% of which are significantly co-localized with segmental duplications (SD).ConclusionsThis large scale screening significantly contributes to the enrichment of the Bos taurus CNV map, demonstrates the ubiquity, great diversity and complexity of this type of genomic variation and sets the basis for testing the influence of CNVs on Bos taurus complex functional and production traits.

On the Structural Plasticity of the Human Genome: Chromosomal Inversions Revisited

With the aid of novel and powerful molecular biology techniques, recent years have witnessed a dramatic increase in the number of studies reporting the involvement of complex structural variants in several genomic disorders. In fact, with the discovery of Copy Number Variants (CNVs) and other forms of unbalanced structural variation, much attention has been directed to the detection and characterization of such rearrangements, as well as the identification of the mechanisms involved in their formation. However, it has long been appreciated that chromosomes can undergo other forms of structural changes - balanced rearrangements - that do not involve quantitative variation of genetic material. Indeed, a particular subtype of balanced rearrangement – inversions – was recently found to be far more common than had been predicted from traditional cytogenetics. Chromosomal inversions alter the orientation of a specific genomic sequence and, unless involving breaks in coding or regulatory regions (and, disregarding complex trans effects, in their close vicinity), appear to be phenotypically silent. Such a surprising finding, which is difficult to reconcile with the classical interpretation of inversions as a mechanism causing subfertility (and ultimately reproductive isolation), motivated a new series of theoretical and empirical studies dedicated to understand their role in human genome evolution and to explore their possible association to complex genetic disorders. With this review, we attempt to describe the latest methodological improvements to inversions detection at a genome wide level, while exploring some of the possible implications of inversion rearrangements on the evolution of the human genome.

Clinical impact of copy number variation analysis using high-resolution microarray technologies: advantages, limitations and concerns

Copy number variation (CNV) analysis has had a major impact on the field of medical genetics, providing a mechanism to identify disease-causing genomic alterations in an unprecedented number of diseases and phenotypes. CNV analysis is now routinely used in the clinical diagnostic laboratory, and has led to a significant increase in the detection of chromosomal abnormalities. These findings are used for prenatal decision making, clinical management and genetic counseling. Although a powerful tool to identify genomic alterations, CNV analysis may also result in the detection of genomic alterations that have unknown clinical significance or reveal unintended information. This highlights the importance of informed consent and genetic counseling for clinical CNV analysis. This review examines the advantages and limitations of CNV discovery in the clinical diagnostic laboratory, as well as the impact on the clinician and family.