Genome-wide Copy Number Variation Detection Research Articles

Genome-wide copy number variation detection in a large cohort of diverse horse breeds by whole-genome sequencing

Genome-wide copy number variation detection in a large cohort of diverse horse breeds by whole-genome sequencing

A novel scatterplot-based method to detect copy number variation (CNV).

Objective: Most methods to detect copy number variation (CNV) have high false positive rates, especially for small CNVs and in real-life samples from clinical studies. In this study, we explored a novel scatterplot-based method to detect CNVs in microarray samples. Methods: Illumina SNP microarray data from 13,254 individuals were analyzed with scatterplots and by PennCNV. The data were analyzed without the prior exclusion of low-quality samples. For CNV scatterplot visualization, the median signal intensity of all SNPs located within a CNV region was plotted against the median signal intensity of the flanking genomic region. Since CNV causes loss or gain of signal intensities, carriers of different CNV alleles pop up in clusters. Moreover, SNPs within a deletion are not heterozygous, whereas heterozygous SNPs within a duplication show typical 1:2 signal distribution between the alleles. Scatterplot-based CNV calls were compared with standard results of PennCNV analysis. All discordant calls as well as a random selection of 100 concordant calls were individually analyzed by visual inspection after noise-reduction. Results: An algorithm for the automated scatterplot visualization of CNVs was developed and used to analyze six known CNV regions. Use of scatterplots and PennCNV yielded 1019 concordant and 108 discordant CNV calls. All concordant calls were evaluated as true CNV-findings. Among the 108 discordant calls, 7 were false positive findings by the scatterplot method, 80 were PennCNV false positives, and 21 were true CNVs detected by the scatterplot method, but missed by PennCNV (i.e., false negative findings). Conclusion: CNV visualization by scatterplots allows for a reliable and rapid detection of CNVs in large studies. This novel method may thus be used both to confirm the results of genome-wide CNV detection software and to identify known CNVs in hitherto untyped samples.

Genome-wide characterization of copy number variations in the host genome in genetic resistance to Marek\u2019s disease using next generation sequencing

BackgroundMarek’s disease (MD) is a highly neoplastic disease primarily affecting chickens, and remains as a chronic infectious disease that threatens the poultry industry. Copy number variation (CNV) has been examined in many species and is recognized as a major source of genetic variation that directly contributes to phenotypic variation such as resistance to infectious diseases. Two highly inbred chicken lines, 63 (MD-resistant) and 72 (MD-susceptible), as well as their F1 generation and six recombinant congenic strains (RCSs) with varied susceptibility to MD, are considered as ideal models to identify the complex mechanisms of genetic and molecular resistance to MD.ResultsIn the present study, to unravel the potential genetic mechanisms underlying resistance to MD, we performed a genome-wide CNV detection using next generation sequencing on the inbred chicken lines with the assistance of CNVnator. As a result, a total of 1649 CNV regions (CNVRs) were successfully identified after merging all the nine datasets, of which 90 CNVRs were overlapped across all the chicken lines. Within these shared regions, 1360 harbored genes were identified. In addition, 55 and 44 CNVRs with 62 and 57 harbored genes were specifically identified in line 63 and 72, respectively. Bioinformatics analysis showed that the nearby genes were significantly enriched in 36 GO terms and 6 KEGG pathways including JAK/STAT signaling pathway. Ten CNVRs (nine deletions and one duplication) involved in 10 disease-related genes were selected for validation by using quantitative real-time PCR (qPCR), all of which were successfully confirmed. Finally, qPCR was also used to validate two deletion events in line 72 that were definitely normal in line 63. One high-confidence gene, IRF2 was identified as the most promising candidate gene underlying resistance and susceptibility to MD in view of its function and overlaps with data from previous study.ConclusionsOur findings provide valuable insights for understanding the genetic mechanism of resistance to MD and the identified gene and pathway could be considered as the subject of further functional characterization.

Genome-Wide Detection of CNVs and Association With Body Weight in Sheep Based on 600K SNP Arrays.

Copy number variations (CNVs) are important genomic structural variations and can give rise to significant phenotypic diversity. Herein, we used high-density 600K SNP arrays to detect CNVs in two synthetic lines of sheep (DS and SHH) and in Hu sheep (a local Chinese breed). A total of 919 CNV regions (CNVRs) were detected with a total length of 48.17 Mb, accounting for 1.96% of the sheep genome. These CNVRs consisted of 730 gains, 102 losses, and 87 complex CNVRs. These CNVRs were significantly enriched in the segmental duplication (SD) region. A CNVR-based cluster analysis of the three breeds revealed that the DS and SHH breeds share a close genetic relationship. Functional analysis revealed that some genes in these CNVRs were also significantly enriched in the olfactory transduction pathway (oas04740), including members of the OR gene family such as OR6C76, OR4Q2, and OR4K14. Using association analyses and previous gene annotations, we determined that a subset of identified genes was likely to be associated with body weight, including FOXF2, MAPK12, MAP3K11, STRBP, and C14orf132. Together, these results offer valuable information that will guide future efforts to explore the genetic basis for body weight in sheep.

Genomic structural diversity in Criollo Argentino horses: Analysis of copy number variations

Copy number variation (CNV) has been proved to be widespread in human, animal and plant genomes. Together with single nucleotide polymorphisms (SNPs), CVNs play a key role in genetic diversity. In this study, genome-wide detection of CNVs was performed based on SNP data from 24 Criollo Argentino horses genotyped with the GGP Equine70k array. Overall, 165 CNVs meeting stringent quality control criteria were identified and then aggregated into 87 CNV regions (CNVRs), representing a horse genome coverage of 13.69 Mb. Functional analysis of CNVRs allowed the identification of 337 genes implicated in a wide range of biological functions such as signal transducer activity (olfactory receptors), receptor activities and binding. Furthermore, enrichment analysis showed that the most represented protein classes (over 25%) were immunoglobulin receptor subfamily, immunoglobulins and major histocompatibility complex antigen (beta-2-microglobulin). To the best of our knowledge, this is the first report of CNV in Criollo Argentino horses.

Genome-wide detection of copy-number variations in local cattle breeds

The aim of the present study was to identify copy-number variations (CNVs) in Cinisara (CIN) and Modicana (MOD) cattle breeds on the basis of signal intensity (logR ratio) and B allele frequency of each marker, using Illumina’s BovineSNP50K Genotyping BeadChip. The CNVs were detected with the PennCNV and SVS 8.7.0 software and were aggregated into CNV regions (CNVRs). PennCNV identified 487 CNVs in CIN that aggregated into 86 CNVRs, and 424 CNVs in MOD that aggregated into 81 CNVRs. SVS identified a total of 207 CNVs in CIN that aggregated into 39 CNVRs, and 181 CNVs in MOD that aggregated into 41 CNVRs. The CNVRs identified with the two softwares contained 29 common CNVRs in CIN and 17 common CNVRs in MOD. Only a small number of CNVRs identified in the present study have been identified elsewhere, probably because of the limitations of the array used. In total, 178 and 208 genes were found within the CNVRs of CIN and MOD respectively. Gene Ontology and KEGG pathway analyses showed that several of these genes are involved in milk production, reproduction and behaviour, the immune response, and resistance/susceptibility to infectious diseases. Our results have provided significant information for the construction of more-complete CNV maps of the bovine genome and offer an important resource for the investigation of genomic changes and traits of interest in the CIN and MOD cattle breeds. Our results will also be valuable for future studies and constitute a preliminary report of the CNV distribution resources in local cattle genomes.

Genome‐wide detection ofCNVs associated with beak deformity in chickens using high‐density 600KSNParrays

Beak deformity (crossed beaks) is found in several indigenous chicken breeds including Beijing-You studied here. Birds with deformed beaks have reduced feed intake and poor production performance. Recently, copy number variation (CNV) has been examined in many species and is recognized as a source of genetic variation, especially for disease phenotypes. In this study, to unravel the genetic mechanisms underlying beak deformity, we performed genome-wide CNV detection using Affymetrix chicken high-density 600K data on 48 deformed-beak and 48 normal birds using penncnv. As a result, two and eight CNV regions (CNVRs) covering 0.32 and 2.45Mb respectively on autosomes were identified in deformed-beak and normal birds respectively. Further RT-qPCR studies validated nine of the 10 CNVRs. The ratios of six CNVRs were significantly different between deformed-beak and normal birds (P<0.01). Within these six regions, three and 21 known genes were identified in deformed-beak and normal birds respectively. Bioinformatics analysis showed that these genes were enriched in six GO terms and one KEGG pathway. Five candidate genes in the CNVRs were further validated using RT-qPCR. The expression of LRIG2 (leucine rich repeats and immunoglobulin like domains 2) was lower in birds with deformed beaks (P<0.01). Therefore, the LRIG2 gene could be considered a key factor in view of its known functions and its potential roles in beak deformity. Overall, our results will be helpful for future investigations of the genomic structural variations underlying beak deformity in chickens.

Comprehensive performance comparison of high-resolution array platforms for genome-wide Copy Number Variation (CNV) analysis in humans

BackgroundHigh-resolution microarray technology is routinely used in basic research and clinical practice to efficiently detect copy number variants (CNVs) across the entire human genome. A new generation of arrays combining high probe densities with optimized designs will comprise essential tools for genome analysis in the coming years. We systematically compared the genome-wide CNV detection power of all 17 available array designs from the Affymetrix, Agilent, and Illumina platforms by hybridizing the well-characterized genome of 1000 Genomes Project subject NA12878 to all arrays, and performing data analysis using both manufacturer-recommended and platform-independent software. We benchmarked the resulting CNV call sets from each array using a gold standard set of CNVs for this genome derived from 1000 Genomes Project whole genome sequencing data.ResultsThe arrays tested comprise both SNP and aCGH platforms with varying designs and contain between ~0.5 to ~4.6 million probes. Across the arrays CNV detection varied widely in number of CNV calls (4–489), CNV size range (~40 bp to ~8 Mbp), and percentage of non-validated CNVs (0–86%). We discovered strikingly strong effects of specific array design principles on performance. For example, some SNP array designs with the largest numbers of probes and extensive exonic coverage produced a considerable number of CNV calls that could not be validated, compared to designs with probe numbers that are sometimes an order of magnitude smaller. This effect was only partially ameliorated using different analysis software and optimizing data analysis parameters.ConclusionsHigh-resolution microarrays will continue to be used as reliable, cost- and time-efficient tools for CNV analysis. However, different applications tolerate different limitations in CNV detection. Our study quantified how these arrays differ in total number and size range of detected CNVs as well as sensitivity, and determined how each array balances these attributes. This analysis will inform appropriate array selection for future CNV studies, and allow better assessment of the CNV-analytical power of both published and ongoing array-based genomics studies. Furthermore, our findings emphasize the importance of concurrent use of multiple analysis algorithms and independent experimental validation in array-based CNV detection studies.

Mapping of genome-wide copy number variations in the Iranian indigenous cattle using a dense SNP data set

The objective of this study was to present the first map of the copy number variations (CNVs) in Iranian indigenous cattle based on a high-density single nucleotide polymorphism (SNP) dataset. A total of 90 individuals were genotyped using the Illumina BovineHD BeadChip containing 777 962 SNPs. The QuantiSNP algorithm was used to perform a genome-wide CNV detection across autosomal genome. After merging the overlapping CNV, a total of 221 CNV regions were identified encompassing 36.4 Mb or 1.44% of the bovine autosomal genome. The length of the CNV regions ranged from 3.5 to 2252.8 Kb with an average of 163.8 Kb. These regions included 147 loss (66.52%) and 74 gain (33.48%) events containing a total of 637 annotated Ensembl genes. Gene ontology analysis revealed that most of genes in the CNV regions were involved in environmental responses, disease susceptibility and immune system functions. Furthermore, 543 of these genes corresponded to the human orthologous genes, which involved in a wide range of biological functions. Altogether, 73% of the 221 CNV regions overlapped either completely or partially with those previously reported in other cattle studies. Moreover, novel CNV regions involved several quantitative trait loci (QTL)-related to adaptative traits of Iranian indigenous cattle. These results provided a basis to conduct future studies on association between CNV regions and phenotypic variations in the Iranian indigenous cattle.

Identification of copy number variations in three Chinese horse breeds using 70K single nucleotide polymorphism BeadChip array.

Copy number variation (CNV), an essential form of genetic variation, has been increasingly recognized as one promising genetic marker in the analysis of animal genomes. Here, we used the Equine 70K single nucleotide polymorphism genotyping array for the genome-wide detection of CNVs in 96 horses from three diverse Chinese breeds: Debao pony (DB), Mongolian horse (MG) and Yili horse (YL). A total of 287 CNVs were determined and merged into 122 CNV regions (CNVRs) ranging from 199bp to 2344kb in size and distributed in a heterogeneous manner on chromosomes. These CNVRs were integrated with seven existing reports to generate a composite genome-wide dataset of 1558 equine CNVRs, revealing 69 (56.6%) novel CNVRs. The majority (69.7%) of the 122 CNVRs overlapped with 438 genes, whereas 30.3% were located in intergenic regions. Most of these genes were associated with common CNVRs, which were shared by divergent horse breeds. As many as 60, 42 and 91 genes overlapping with the breed-specific ss were identified in DB, MG and YL respectively. Among these genes, FGF11, SPEM1, PPARG, CIDEB, HIVEP1 and GALR may have potential relevance to breed-specific traits. These findings provide valuable information for understanding the equine genome and facilitating association studies of economically important traits with equine CNVRs in the future.

Genome-Wide Detection of CNVs and Their Association with Meat Tenderness in Nelore Cattle

Brazil is one of the largest beef producers and exporters in the world with the Nelore breed representing the vast majority of Brazilian cattle (Bos taurus indicus). Despite the great adaptability of the Nelore breed to tropical climate, meat tenderness (MT) remains to be improved. Several factors including genetic composition can influence MT. In this article, we report a genome-wide analysis of copy number variation (CNV) inferred from Illumina® High Density SNP-chip data for a Nelore population of 723 males. We detected >2,600 CNV regions (CNVRs) representing ≈6.5% of the genome. Comparing our results with previous studies revealed an overlap in ≈1400 CNVRs (>50%). A total of 1,155 CNVRs (43.6%) overlapped 2,750 genes. They were enriched for processes involving guanosine triphosphate (GTP), previously reported to influence skeletal muscle physiology and morphology. Nelore CNVRs also overlapped QTLs for MT reported in other breeds (8.9%, 236 CNVRs) and from a previous study with this population (4.1%, 109 CNVRs). Two CNVRs were also proximal to glutathione metabolism genes that were previously associated with MT. Genome-wide association study of CN state with estimated breeding values derived from meat shear force identified 6 regions, including a region on BTA3 that contains genes of the cAMP and cGMP pathway. Ten CNVRs that overlapped regions associated with MT were successfully validated by qPCR. Our results represent the first comprehensive CNV study in Bos taurus indicus cattle and identify regions in which copy number changes are potentially of importance for the MT phenotype.

Genome-wide detection of CNVs in Chinese indigenous sheep with different types of tails using ovine high-density 600K SNP arrays.

Chinese indigenous sheep can be classified into three types based on tail morphology: fat-tailed, fat-rumped, and thin-tailed sheep, of which the typical breeds are large-tailed Han sheep, Altay sheep, and Tibetan sheep, respectively. To unravel the genetic mechanisms underlying the phenotypic differences among Chinese indigenous sheep with tails of three different types, we used ovine high-density 600K SNP arrays to detect genome-wide copy number variation (CNV). In large-tailed Han sheep, Altay sheep, and Tibetan sheep, 371, 301, and 66 CNV regions (CNVRs) with lengths of 71.35 Mb, 51.65 Mb, and 10.56 Mb, respectively, were identified on autosomal chromosomes. Ten CNVRs were randomly chosen for confirmation, of which eight were successfully validated. The detected CNVRs harboured 3130 genes, including genes associated with fat deposition, such as PPARA, RXRA, KLF11, ADD1, FASN, PPP1CA, PDGFA, and PEX6. Moreover, multilevel bioinformatics analyses of the detected candidate genes were significantly enriched for involvement in fat deposition, GTPase regulator, and peptide receptor activities. This is the first high-resolution sheep CNV map for Chinese indigenous sheep breeds with three types of tails. Our results provide valuable information that will support investigations of genomic structural variation underlying traits of interest in sheep.

28: Copy Number Variation Detection Using Next-Generation Sequencing

Copy number variation (CNV) is defined as losses or gains of a DNA fragment between one kilobase to several megabases and contributes to the pathogenesis of multiple genetic disorders. CNV detection has relied on G-banding for large CNVs, array comparative genomic hybridization (aCGH) for genome-wide CNV detection, and Multiplex Ligation-dependent Probe Amplification (MLPA) and fluorescent in situ hybridization (FISH) for identification of CNVs in specific targeted areas. Clinical …

A genome-wide scan for copy number variations using high-density single nucleotide polymorphism array in Simmental cattle.

Copy number variations (CNVs) have recently been identified as promising sources of genetic variation, complementary to single nucleotide polymorphisms (SNPs). As a result, detection of CNVs has attracted a great deal of attention. In this study, we performed genome-wide CNV detection using Illumina Bovine HD BeadChip (770k) data on 792 Simmental cattle. A total of 263 CNV regions (CNVRs) were identified, which included 137 losses, 102 gains and 24 regions classified as both loss and gain, covering 35.48 Mb (1.41%) of the bovine genome. The length of these CNVRs ranged from 10.18 kb to 1.76 Mb, with an average length of 134.78 kb and a median length of 61.95 kb. In 136 of these regions, a total of 313 genes were identified related to biological functions such as transmembrane activity and olfactory transduction activity. To validate the results, we performed quantitative PCR to detect nine randomly selected CNVRs and successfully confirmed seven (77.6%) of them. Our results present a map of cattle CNVs derived from high-density SNP data, which expands the current CNV map of the cattle genome and provides useful information for investigation of genomic structural variation in cattle.

A genome-wide detection of copy number variation using SNP genotyping arrays in Beijing-You chickens.

Copy number variation (CNV) has been recently examined in many species and is recognized as being a source of genetic variability, especially for disease-related phenotypes. In this study, the PennCNV software, a genome-wide CNV detection system based on the 60 K SNP BeadChip was used on a total sample size of 1,310 Beijing-You chickens (a Chinese local breed). After quality control, 137 high confidence CNVRs covering 27.31 Mb of the chicken genome and corresponding to 2.61 % of the whole chicken genome. Within these regions, 131 known genes or coding sequences were involved. Q-PCR was applied to verify some of the genes related to disease development. Results showed that copy number of genes such as, phosphatidylinositol-5-phosphate 4-kinase II alpha, PHD finger protein 14, RHACD8 (a CD8α- like messenger RNA), MHC B-G, zinc finger protein, sarcosine dehydrogenase and ficolin 2 varied between individual chickens, which also supports the reliability of chip-detection of the CNVs. As one source of genomic variation, CNVs may provide new insight into the relationship between the genome and phenotypic characteristics.

Analysis of genome-wide copy number variations in Chinese indigenous and western pig breeds by 60 K SNP genotyping arrays.

Copy number variations (CNVs) represent a substantial source of structural variants in mammals and contribute to both normal phenotypic variability and disease susceptibility. Although low-resolution CNV maps are produced in many domestic animals, and several reports have been published about the CNVs of porcine genome, the differences between Chinese and western pigs still remain to be elucidated. In this study, we used Porcine SNP60 BeadChip and PennCNV algorithm to perform a genome-wide CNV detection in 302 individuals from six Chinese indigenous breeds (Tongcheng, Laiwu, Luchuan, Bama, Wuzhishan and Ningxiang pigs), three western breeds (Yorkshire, Landrace and Duroc) and one hybrid (Tongcheng×Duroc). A total of 348 CNV Regions (CNVRs) across genome were identified, covering 150.49 Mb of the pig genome or 6.14% of the autosomal genome sequence. In these CNVRs, 213 CNVRs were found to exist only in the six Chinese indigenous breeds, and 60 CNVRs only in the three western breeds. The characters of CNVs in four Chinese normal size breeds (Luchuan, Tongcheng and Laiwu pigs) and two minipig breeds (Bama and Wuzhishan pigs) were also analyzed in this study. Functional annotation suggested that these CNVRs possess a great variety of molecular function and may play important roles in phenotypic and production traits between Chinese and western breeds. Our results are important complementary to the CNV map in pig genome, which provide new information about the diversity of Chinese and western pig breeds, and facilitate further research on porcine genome CNVs.

Functional analysis—Make or break for cancer predictability

Copy number variations (CNVs) encompass a variety of genetic alterations including deletions and amplifications and cluster in regions of the human genome with intrinsic instability. Small-sized CNVs can act as initial genetic changes giving rise to larger CNVs such as acquired somatic copy number aberrations (CNAs) promoting cancer formation. Previous studies provided evidence for CNVs as an underlying cause of elevated breast cancer risk when targeting breast cancer susceptibility genes and of accelerated breast cancer progression when targeting oncogenes. With the development of novel techniques for genome-wide detection of CNVs at increasingly higher resolution, it became possible to qualitatively and quantitatively analyse manifestation of DNA damage resulting from defects in any of the large variety of DNA double-strand break (DSB) repair mechanisms. Breast carcinogenesis, particularly in familial cases, has been linked with a defect in the homologous recombination (HR) pathway, which in turn switches damage removal towards alternative, more error-prone DSB repair pathways such as microhomology-mediated non-homologous end joining (mmNHEJ). Indeed, increased error-prone DSB repair activities were detected in peripheral blood lymphocytes from individuals with familial breast cancer risk independently of specific gene mutations. Intriguingly, sequence analysis of breakpoint regions revealed that the majority of genome aberrations found in breast cancer specimens are formed by mmNHEJ. Detection of pathway-specific error-prone DSB repair activities by functional testing was proposed to serve as biomarker for hereditary breast cancer risk and responsiveness to therapies targeting HR dysfunction. Identification of specific error-prone DSB repair mechanisms underlying CNAs and ultimately mammary tumour formation highlights potential targets for future breast cancer prevention regimens.

CNV Workshop: an integrated platform for high-throughput copy number variation discovery and clinical diagnostics

BackgroundRecent studies have shown that copy number variations (CNVs) are frequent in higher eukaryotes and associated with a substantial portion of inherited and acquired risk for various human diseases. The increasing availability of high-resolution genome surveillance platforms provides opportunity for rapidly assessing research and clinical samples for CNV content, as well as for determining the potential pathogenicity of identified variants. However, few informatics tools for accurate and efficient CNV detection and assessment currently exist.ResultsWe developed a suite of software tools and resources (CNV Workshop) for automated, genome-wide CNV detection from a variety of SNP array platforms. CNV Workshop includes three major components: detection, annotation, and presentation of structural variants from genome array data. CNV detection utilizes a robust and genotype-specific extension of the Circular Binary Segmentation algorithm, and the use of additional detection algorithms is supported. Predicted CNVs are captured in a MySQL database that supports cohort-based projects and incorporates a secure user authentication layer and user/admin roles. To assist with determination of pathogenicity, detected CNVs are also annotated automatically for gene content, known disease loci, and gene-based literature references. Results are easily queried, sorted, filtered, and visualized via a web-based presentation layer that includes a GBrowse-based graphical representation of CNV content and relevant public data, integration with the UCSC Genome Browser, and tabular displays of genomic attributes for each CNV.ConclusionsTo our knowledge, CNV Workshop represents the first cohesive and convenient platform for detection, annotation, and assessment of the biological and clinical significance of structural variants. CNV Workshop has been successfully utilized for assessment of genomic variation in healthy individuals and disease cohorts and is an ideal platform for coordinating multiple associated projects.Availability and ImplementationAvailable on the web at: http://sourceforge.net/projects/cnv