Genome‐wide copy number profiling using high‐density SNP array in chickens

Phenotypic diversity is a direct consequence resulting mainly from the impact of underlying genetic variation, and recent studies have shown that copy number variation (CNV) is emerging as an important contributor to both phenotypic variability and disease susceptibility. Herein, we performed a genome‐wide CNV scan in 96 chickens from 12 diversified breeds, benefiting from the high‐density Affymetrix 600 K SNP arrays. We identified a total of 231 autosomal CNV regions (CNVRs) encompassing 5.41 Mb of the chicken genome and corresponding to 0.59% of the autosomal sequence. The length of these CNVRs ranged from 2.6 to 586.2 kb with an average of 23.4 kb, including 130 gain, 93 loss and eight both gain and loss events. These CNVRs, especially deletions, had lower GC content and were located particularly in gene deserts. In particular, 102 CNVRs harbored 128 chicken genes, most of which were enriched in immune responses. We obtained 221 autosomal CNVRs after converting probe coordinates to Galgal3, and comparative analysis with previous studies illustrated that 153 of these CNVRs were regarded as novel events. Furthermore, qPCR assays were designed for 11 novel CNVRs, and eight (72.73%) were validated successfully. In this study, we demonstrated that the high‐density 600 K SNP array can capture CNVs with higher efficiency and accuracy and highlighted the necessity of integrating multiple technologies and algorithms. Our findings provide a pioneering exploration of chicken CNVs based on a high‐density SNP array, which contributes to a more comprehensive understanding of genetic variation in the chicken genome and is beneficial to unearthing potential CNVs underlying important traits of chickens.     

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.     

Genome-wide mapping of copy number variations in commercial hybrid pigs using a high-density SNP genotyping array

Copy number variations (CNVs) are important forms of structural variation in human and animals and can be considered as a major genetic component of phenotypic diversity. Here we used the Illumina PorcineSNP60 BeadChip V2 and a DLY [Duroc × (Large White × Landrace)] commercial hybrid population to identify 272 CNVs belonging to 165 CNV regions (CNVRs), of which 66 are new. As CNVRs are specific to origin of population, our DLY-specific data is an important complementary to the existing CNV map in the pig genome. Eight CNVRs were selected for validation by quantitative real-time PCR (qRT-PCR) and the accurate rate was high (87.25%). Gene function analysis suggested that a common CNVR may play an important role in multiple traits, including growth rate and carcass quality.     

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.     

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.     

A genome‐wide association study of copy number variations with umbilical hernia in swine

Umbilical hernia (UH) is one of the most common congenital defects in pigs, leading to considerable economic loss and serious animal welfare problems. To test whether copy number variations (CNVs) contribute to pig UH, we performed a case–control genome‐wide CNV association study on 905 pigs from the Duroc, Landrace and Yorkshire breeds using the Porcine SNP60 BeadChip and penncnv algorithm. We first constructed a genomic map comprising 6193 CNVs that pertain to 737 CNV regions. Then, we identified eight CNVs significantly associated with the risk for UH in the three pig breeds. Six of seven significantly associated CNVs were validated using quantitative real‐time PCR. Notably, a rare CNV (CNV14:13030843–13059455) encompassing the NUGGC gene was strongly associated with UH (permutation‐corrected P = 0.0015) in Duroc pigs. This CNV occurred exclusively in seven Duroc UH‐affected individuals. SNPs surrounding the CNV did not show association signals, indicating that rare CNVs may play an important role in complex pig diseases such as UH. The NUGGC gene has been implicated in human omphalocele and inguinal hernia. Our finding supports that CNVs, including the NUGGC CNV, contribute to the pathogenesis of pig UH.     

Copy number variants in Italian Large White pigs detected using high‐density single nucleotide polymorphisms and their association with back fat thickness

The aim of this study was to identify copy number variants (CNVs) in Italian Large White pigs and test them for association with back fat thickness (BFT). Within a population of 12 000 performance‐tested pigs, two groups of animals with extreme and divergent BFT estimated breeding values (EBVs; 147 with negative and 150 with positive EBVs) were genotyped with the Illumina Porcine SNP60 BeadChip. CNVs were detected with penncnv software. We identified a total of 4146 CNV events in 170 copy number variation regions (CNVRs) located on 15 porcine autosomes. Validation of detected CNVRs was carried out (i) by comparing CNVRs already detected by other studies and (ii) by semiquantitative fluorescent multiplex (SQFM) PCR of a few CNVRs. Most of CNVRs detected in Italian Large White pigs (71.2%) were already reported in other pig breeds/populations, and 82.1% of the CNV events detected by penncnv were confirmed by SQFM PCR. For each CNVR, we compared the occurrence of CNV events between the pigs of the high and low BFT EBV tails. Sixteen regions showed significance at P < 0.10, and seven were significant at P < 0.05 but were not significant after Bonferroni correction (Fisher's exact test). These results indicated that CNVs could explain a limited fraction of the genetic variability of fat deposition in Italian Large White pigs. However, it was interesting to note that one of these CNVRs encompassed the ZPLD1 gene. In humans, a rare CNV event including this gene is associated with obesity. Studies identifying CNVs in pigs could assist in elucidating the genetic mechanisms underlying human obesity.     

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.     

Genome-Wide Identification of Copy Number Variations in Chinese Holstein

Recent studies of mammalian genomes have uncovered the vast extent of copy number variations (CNVs) that contribute to phenotypic diversity. Compared to SNP, a CNV can cover a wider chromosome region, which may potentially incur substantial sequence changes and induce more significant effects on phenotypes. CNV has been becoming an alternative promising genetic marker in the field of genetic analyses. Here we firstly report an account of CNV regions in the cattle genome in Chinese Holstein population. The Illumina Bovine SNP50K Beadchips were used for screening 2047 Holstein individuals. Three different programes (PennCNV, cnvPartition and GADA) were implemented to detect potential CNVs. After a strict CNV calling pipeline, a total of 99 CNV regions were identified in cattle genome. These CNV regions cover 23.24 Mb in total with an average size of 151.69 Kb. 52 out of these CNV regions have frequencies of above 1%. 51 out of these CNV regions completely or partially overlap with 138 cattle genes, which are significantly enriched for specific biological functions, such as signaling pathway, sensory perception response and cellular processes. The results provide valuable information for constructing a more comprehensive CNV map in the cattle genome and offer an important resource for investigation of genome structure and genomic variation underlying traits of interest in cattle.     

Precision-mapping and statistical validation of quantitative trait loci by machine learning

Background

DNA sequence diversity within the human genome may be more greatly affected by copy number variations (CNVs) than single nucleotide polymorphisms (SNPs). Although the importance of CNVs in genome wide association studies (GWAS) is becoming widely accepted, the optimal methods for identifying these variants are still under evaluation. We have previously reported a comprehensive view of CNVs in the HapMap DNA collection using high density 500 K EA (Early Access) SNP genotyping arrays which revealed greater than 1,000 CNVs ranging in size from 1 kb to over 3 Mb. Although the arrays used most commonly for GWAS predominantly interrogate SNPs, CNV identification and detection does not necessarily require the use of DNA probes centered on polymorphic nucleotides and may even be hindered by the dependence on a successful SNP genotyping assay.

Results

In this study, we have designed and evaluated a high density array predicated on the use of non-polymorphic oligonucleotide probes for CNV detection. This approach effectively uncouples copy number detection from SNP genotyping and thus has the potential to significantly improve probe coverage for genome-wide CNV identification. This array, in conjunction with PCR-based, complexity-reduced DNA target, queries over 1.3 M independent NspI restriction enzyme fragments in the 200 bp to 1100 bp size range, which is a several fold increase in marker density as compared to the 500 K EA array. In addition, a novel algorithm was developed and validated to extract CNV regions and boundaries.

Conclusion

Using a well-characterized pair of DNA samples, close to 200 CNVs were identified, of which nearly 50% appear novel yet were independently validated using quantitative PCR. The results indicate that non-polymorphic probes provide a robust approach for CNV identification, and the increasing precision of CNV boundary delineation should allow a more complete analysis of their genomic organization.     

Copy number variation and variant discovery in Bullmastiff dogs

Identification of genomic variants within dogs is important for understanding genetic factors contributing to breed diversity and phenotypic traits. This study aimed to identify sources of variation in the Bullmastiff using high‐density signal intensity and whole‐genome sequence data. Close to 3000 copy number variants (CNVs) were identified in Bullmastiff dogs using Canine HD BeadChip data. When CNVs were collated, 82 CNV regions (CNVRs) were detected, 50% in transcribed regions encompassing 432 genes. Fifty of the CNVRs detected have not been reported in other breeds and represent potential breed‐specific variants. A proportion of the CNVR variants with predicted modifying effects on gene pathways may contribute to breed traits. Approximately 5 million putative variants per dog, inclusive of single nucleotide polymorphisms (SNPs), multi‐nucleotide polymorphisms (MNPs) and insertion and deletions (INDELs), were identified from DNA sequence data on a small number of animals. Identification of genetic variants in the Bullmastiff highlights sources of variation in the breed and molecular markers that will assist in future trait and disease investigations in dogs.     

Distribution,functional impact,and origin mechanisms of copy number variation in the barley genome

Background

There is growing evidence for the prevalence of copy number variation (CNV) and its role in phenotypic variation in many eukaryotic species. Here we use array comparative genomic hybridization to explore the extent of this type of structural variation in domesticated barley cultivars and wild barleys.

Results

A collection of 14 barley genotypes including eight cultivars and six wild barleys were used for comparative genomic hybridization. CNV affects 14.9% of all the sequences that were assessed. Higher levels of CNV diversity are present in the wild accessions relative to cultivated barley. CNVs are enriched near the ends of all chromosomes except 4H, which exhibits the lowest frequency of CNVs. CNV affects 9.5% of the coding sequences represented on the array and the genes affected by CNV are enriched for sequences annotated as disease-resistance proteins and protein kinases. Sequence-based comparisons of CNV between cultivars Barke and Morex provided evidence that DNA repair mechanisms of double-strand breaks via single-stranded annealing and synthesis-dependent strand annealing play an important role in the origin of CNV in barley.

Conclusions

We present the first catalog of CNVs in a diploid Triticeae species, which opens the door for future genome diversity research in a tribe that comprises the economically important cereal species wheat, barley, and rye. Our findings constitute a valuable resource for the identification of CNV affecting genes of agronomic importance. We also identify potential mechanisms that can generate variation in copy number in plant genomes.     

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 199 bp to 2344 kb 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.     

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.     

Copy Number Variation in Thai Population

Copy number variation (CNV) is a major genetic polymorphism contributing to genetic diversity and human evolution. Clinical application of CNVs for diagnostic purposes largely depends on sufficient population CNV data for accurate interpretation. CNVs from general population in currently available databases help classify CNVs of uncertain clinical significance, and benign CNVs. Earlier studies of CNV distribution in several populations worldwide showed that a significant fraction of CNVs are population specific. In this study, we characterized and analyzed CNVs in 3,017 unrelated Thai individuals genotyped with the Illumina Human610, Illumina HumanOmniexpress, or Illumina HapMap550v3 platform. We employed hidden Markov model and circular binary segmentation methods to identify CNVs, extracted 23,458 CNVs consistently identified by both algorithms, and cataloged these high confident CNVs into our publicly available Thai CNV database. Analysis of CNVs in the Thai population identified a median of eight autosomal CNVs per individual. Most CNVs (96.73%) did not overlap with any known chromosomal imbalance syndromes documented in the DECIPHER database. When compared with CNVs in the 11 HapMap3 populations, CNVs found in the Thai population shared several characteristics with CNVs characterized in HapMap3. Common CNVs in Thais had similar frequencies to those in the HapMap3 populations, and all high frequency CNVs (>20%) found in Thai individuals could also be identified in HapMap3. The majorities of CNVs discovered in the Thai population, however, were of low frequency, or uniquely identified in Thais. When performing hierarchical clustering using CNV frequencies, the CNV data were clustered into Africans, Europeans, and Asians, in line with the clustering performed with single nucleotide polymorphism (SNP) data. As CNV data are specific to origin of population, our population-specific reference database will serve as a valuable addition to the existing resources for the investigation of clinical significance of CNVs in Thais and related ethnicities.     

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.     

Identification of Genome-Wide Copy Number Variations among Diverse Pig Breeds Using SNP Genotyping Arrays

Copy number variations (CNVs) are important forms of genetic variation complementary to SNPs, and can be considered as promising markers for some phenotypic and economically important traits or diseases susceptibility in domestic animals. In the present study, we performed a genome-wide CNV identification in 14 individuals selected from diverse populations, including six types of Chinese indigenous breeds, one Asian wild boar population, as well as three modern commercial foreign breeds. We identified 63 CNVRs in total, which covered 9.98 Mb of polymorphic sequence and corresponded to 0.36% of the genome sequence. The length of these CNVRs ranged from 3.20 to 827.21 kb, with an average of 158.37 kb and a median of 97.85 kb. Functional annotation revealed these identified CNVR have important molecular function, and may play an important role in exploring the genetic basis of phenotypic variability and disease susceptibility among pigs. Additionally, to confirm these potential CNVRs, we performed qPCR for 12 randomly selected CNVRs and 8 of them (66.67%) were confirmed successfully. CNVs detected in diverse populations herein are essential complementary to the CNV map in the pig genome, which provide an important resource for studies of genomic variation and the association between various economically important traits and CNVs.     

Genome wide CNV analysis reveals additional variants associated with milk production traits in Holsteins

Background

Milk production is an economically important sector of global agriculture. Much attention has been paid to the identification of quantitative trait loci (QTL) associated with milk, fat, and protein yield and the genetic and molecular mechanisms underlying them. Copy number variation (CNV) is an emerging class of variants which may be associated with complex traits.

Results

In this study, we performed a genome-wide association between CNVs and milk production traits in 26,362 Holstein bulls and cows. A total of 99 candidate CNVs were identified using Illumina BovineSNP50 array data, and association tests for each production trait were performed using a linear regression analysis with PCA correlation. A total of 34 CNVs on 22 chromosomes were significantly associated with at least one milk production trait after false discovery rate (FDR) correction. Some of those CNVs were located within or near known QTL for milk production traits. We further investigated the relationship between associated CNVs with neighboring SNPs. For all 82 combinations of traits and CNVs (less than 400 kb in length), we found 17 cases where CNVs directly overlapped with tag SNPs and 40 cases where CNVs were adjacent to tag SNPs. In 5 cases, CNVs located were in strong linkage disequilibrium with tag SNPs, either within or adjacent to the same haplotype block. There were an additional 20 cases where CNVs did not have a significant association with SNPs, suggesting that the effects of those CNVs were probably not captured by tag SNPs.

Conclusion

We conclude that combining CNV with SNP analyses reveals more genetic variations underlying milk production traits than those revealed by SNPs alone.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-683) contains supplementary material, which is available to authorized users.     

Fine mapping of copy number variations on two cattle genome assemblies using high density SNP array

ABSTRACT: BACKGROUND: Btau_4.0 and UMD3.1 are two distinct cattle reference genome assemblies. In our previous study using the low density BovineSNP50 array, we reported a copy number variation (CNV) analysis on Btau_4.0 with 521 animals of 21 cattle breeds, yielding 682 CNV regions with a total length of 139.8 megabases. RESULTS: In this study using the high density BovineHD SNP array, we performed high resolution CNV analyses on both Btau_4.0 and UMD3.1 with 674 animals of 27 cattle breeds. We first compared CNV results derived from these two different SNP array platforms on Btau_4.0. With two thirds of the animals shared between studies, on Btau_4.0 we identified 3,346 candidate CNV regions representing 142.7 megabases (~4.70%) of the genome. With a similar total length but 5 times more event counts, the average CNVR length of current Btau_4.0 dataset is significantly shorter than the previous one (42.7kb vs. 205 kb). Although subsets of these two results overlapped, 64% (91.6 megabases) of current dataset was not present in the previous study. We also performed similar analyses on UMD3.1 using these BovineHD SNP array results. Approximately 50% more and 20% longer CNVs were called on UMD3.1 as compared to those on Btau_4.0. However, a comparable result of CNVRs (3,438 regions with a total length 146.9 megabases) was obtained. We suspect that these results are due to that UMD3.1's efforts of placing unplaced contigs and removing unmerged alleles. Selected CNVs were further experimentally validated, achieving a 73% PCR validation rate, which is considerably higher than the previous validation rate. About 20-45% of CNV regions overlapped with cattle RefSeq genes and Ensembl genes. Panther and IPA analyses indicated that these genes provide a wide spectrum of biological processes involving immune system, lipid metabolism, cell, organism and system development. CONCLUSION: We present a comprehensive result of cattle CNVs at a higher resolution and sensitivity. We identified over 3,000 candidate CNV regions on both Btau_4.0 and UMD3.1, further compared current datasets with previous results, and examined the impacts of genome assemblies on CNV calling.