Genome-wide detection of copy number variations using high-density SNP genotyping platforms in Holsteins
© Jiang et al.; licensee BioMed Central Ltd. 2013
Received: 3 November 2012
Accepted: 12 February 2013
Published: 27 February 2013
Copy number variations (CNVs) are widespread in the human or animal genome and are a significant source of genetic variation, which has been demonstrated to play an important role in phenotypic diversity. Advances in technology have allowed for identification of a large number of CNVs in cattle. Comprehensive explore novel CNVs in the bovine genome would provide valuable information for functional analyses of genome structural variation and facilitating follow-up association studies between complex traits and genetic variants.
In this study, we performed a genome-wide CNV detection based on high-density SNP genotyping data of 96 Chinese Holstein cattle. A total of 367 CNV regions (CNVRs) across the genome were identified, which cover 42.74Mb of the cattle genome and correspond to 1.61% of the genome sequence. The length of the CNVRs on autosomes range from 10.76 to 2,806.42 Kb with an average of 96.23 Kb. 218 out of these CNVRs contain 610 annotated genes, which possess a wide spectrum of molecular functions. To confirm these findings, quantitative PCR (qPCR) was performed for 17 CNVRs and 13(76.5%) of them were successfully validated.
Our study demonstrates the high density SNP array can significantly improve the accuracy and sensitivity of CNV calling. Integration of different platforms can enhance the detection of genomic structure variants. Our results provide a significant replenishment for the high resolution map of copy number variation in the bovine genome and valuable information for investigation of genomic structural variation underlying traits of interest in cattle.
KeywordsCopy number variations Cattle BovineHD beadChip Genome variation Quantitative real time PCR
Recent studies have discovered an abundance of copy number variations (CNVs) in human and domestic animal genomes [1–9]. CNV is defined as a variable copy number of DNA segments ranging from 50bp to several megabases (Mb) compared with a reference genome . The initial study from the analysis of the human genome indicated that single-nucleotide polymorphisms (SNPs) are the most important source of genome sequence diversity and the main contributors to phenotypic variation, environmental response and disease susceptibility . However, the first two genome-wide scans of CNVs in the human genome, which are considered as landmark of CNV studies, have showed that CNVs distribute ubiquitously in the genome [2, 11] and are important source of genetic variance . Since then, thousands of novel CNVs were detected in the human genome [13–16]. So far, there are 179,450 CNVs identified in the human genome (Database of genomic variants, DGV: http://dgvbeta.tcag.ca/dgv/app/home?ref=NCBI36/hg18) which cover more than 53% human genome. Besides in human, CNVs have been also identified in many other species, including mouse [17–19], fruit fly , dog , pig [6, 21, 22] and cattle [4, 5, 8, 23–26].
It has been revealed that although CNVs account for a smaller proportion of all variations comparing with SNPs, they involve larger genome region of all variant bases  and can potentially influence phenotypes or lead to diseases by employing a wide variety of mechanisms, such as changing gene dosage, disrupting genes structure [28, 29] and altering gene expression by exposing recessive alleles or indirectly through disturbing the regulation regions of genes . Multiple studies in human have identified that CNVs contribute to phenotypic diversity and complex diseases such as developmental delay, systemic lupus erythematosus, autism and neuroblastoma [31–36]. Phenotype variations caused by CNVs were also observed in domestic animals. For instance, the Pea-comb phenotype in chicken is caused by the duplication of the first intron of the Sox5 gene . The white coat phenotype in pigs is caused by the copy number variation in the KIT gene  and the white and grey coat colour in sheep is caused by the copy number variation in the ASIP gene  . A duplication encompassing the FGF3, FGF4, FGF19 and ORAOV1 genes lead to dorsal hair ridge and susceptibility to dermoid sinus in dogs . It was also reported that CNVs may be associated with many diseases and developmental abnormalities in domestic animals, such as cone-rod dystrophy 3  and startle disease in dogs , osteopetrosis and abortions and stillbirths in cattle [43, 44]. Furthermore, it has been reported that a CNVR located on BTA18 is associated with the index of total merit and protein production, fat production and herd life in Holstein cattle . These demonstrate that CNVs can be considered as promising markers for some traits or diseases in domestic animals.
Currently, there are two main platforms, i.e., comparative genomic hybridization (CGH) arrays [46–48] and SNP arrays [1, 49, 50], which have been extensively used in human and animals for CNV screens. The advantages and disadvantages associated with each platform were compared in [51, 52]. CGH arrays have the highest signal-to-noise ratios, but give relatively low or intermediate resolution in CNV detection. SNP arrays provide high resolution of CNVs and are more convenient for high-throughput analysis and follow-up association studies due to the quantification of allele-specific copy number [51–53]. Therefore, many studies pay more attention to CNV detection based on SNP arrays, particularly along with the increasing availability of high density SNP arrays. In recent years, advances in next-generation sequencing have provided a new platform for more detailed characterization of CNVs in human and animal genomes [3–5, 8, 16]. But it is still too expensive for detecting CNVs in a large-scale population. In addition, methods for CNV detection using sequence data are still limited and more comprehensive algorithms or programs are needed for sequence-based CNV detection with higher resolution and sensitivity.
In the present study, we investigated genome-wide characteristics of CNVs in Chinese Hosltein cattles by using the bovine high-density (770K) SNP arrays. Consequentially, we identified 358 candidate CNV regions on 29 autosomes and 9 candidate CNV regions on the X chromosome. The result is an important complementary to the CNV map in the cattle genome, which provides an important resource for studies of genomic variation in the cattle genome.
Genome-wide detection of CNVs
Gene content of CNVRs
A total of 610 genes within or overlapped with the CNVRs were retrieved from the Ensembl Genes 69 Database http://asia.ensembl.org/biomart/martview/, including 554 protein-coding genes, 20 pseudogenes, 14 miRNA, 12 snRNA, three snoRNA, three rRNA, two miscRNA and two retrotransposed gene (Additional file 1: Table S4). Nearly 60% (218) of the CNVRs encompass one or more annotated genes, while 40% (149) of them without any annotated genes.
After converting the bovine Ensembl gene IDs to their orthologous associated human gene IDs, we found 447 human orthologous genes (Additional file 1: Table S4), of which 374 are included in the Human Database of Genomic Variants http://dgvbeta.tcag.ca/dgv/app/home?ref=NCBI36/hg18. The GO analysis for the 447 genes show that genes of the terms of cognition, environmental response, olfactory receptor activity and neurological system process are dominantly represented in the bovine CNVRs (Additional file 1: Table S5). The KEGG pathway analysis revealed that these genes are mainly represented in the pathway of olfactory transduction (Additional file 1: Table S6).
CNV Validation by qPCR
Quantitative PCR (qPCR) was performed to validate 17 CNVRs chosen from the CNVRs detected in the study. One or two pairs of primers were designed for each CNVR. These CNVRs represent different status of copy number variation (i.e., loss, gain and both) and different CNVR frequencies (varied from 1.17 to 98.86%) (Additional file 1: Table S3). For each CNVR, 14 positive samples (i.e., samples containing CNVR judged by PennCNV) on average were tested. For CNVRs with lower frequencies all positive sample(s) were tested, while for CNVRs with higher frequencies part of positive samples were tested. In addition, a certain number of random negative samples were also tested as negative control in qPCR.
Several algorithms for CNV detection based on SNP array have been developed and implemented in different programs, e.g., PennCNV, GADA, cnvPartition, etc. Each algorithm has its strengths and weaknesses as summarized by Winchester et al. . However, most CNV studies based on SNP array in human and animals used only the PennCNV software (e.g., Bae et al. ; Hou et al. ; Glessner et al. ), although Winchester et al.  recommended using multiple algorithms on a single dataset to produce the most informative and reliable results. In this study, we chose to use only PennCNV for two reasons: 1) some studies indicated that PennCNV is more reliable in detecting CNVs than some other algorithms (e.g., Marenne et al. ); 2) When using multiple algorithms, it is difficult to made a conclusion how many CNVs there should be, if we only accept the CNVs commonly detected by all algorithms, there are certainly a lot of CNVs being missed, on the other hand, if we accept all CNVs detected by different algorithms, there must be a lot of false positive CNVs. Furthermore, in this study, CNV was inferred with a strict criterion (i.e., it must contain ten or more consecutive SNPs) to reduce the risk of high false positive rate due to use only one algorithm.
Comparison between results of the current study and results from other studies
Findings from different studies
Overlapped CNVRs of this study
Total length (Mb)
Percentage of count
Total length (Mb)
Percentage of length
Fadista et al. a
Liu et al. b
Hou et al. 
Bae et al. 
Hou et al. 
Jiang et al. 
Hou et al. 
Bickhart et al. 
Zhan et al. 
Stothard et al. 
Characterization of cattle CNVRs on autosomes based on different platforms
Summary statistics of CNVRs
No. of CNVR
Fadista et al. 
Liu et al. 
SNP-based Studies (54k chip)
Hou et al. 
Bae et al. 
Hou et al. 
Jiang et al. 
SNP-based Studies (HD chip)
Hou et al. *
Bickhart et al. 
Zhan et al. 
Stothard et al. 
In order to confirm these potential CNVRs, we performed quantitative PCR for 17 randomly selected CNVRs and 13 of them (76.5%) were confirmed successfully. The percentage is higher than the results of previous reports in animals [6, 21, 26]. It can be explained that the high density probe of the BovineHD beadChip and the strict CNV definition (i.e., it must contain ten or more consecutive SNPs) were used in this study. Most of the positive samples revealed by PennCNV prediction agreed well with the qPCR experiments. However, there are also a small proportion of false negative samples. The average false negative rate for each CNVR was 20.1%. False negative identification in CNV detection has also been reported in previous studies [6, 21, 22, 61]. It demonstrates that although the strict criteria of our study can minimize the false-positive rate, it also simultaneously resulted in false-negative rate. Besides, some positive samples which are not confirmed may not be really the false positive ones. Because the primers used to confirm the CNVRs may have been designed outside the actual boundaries for some individuals as the CNVRs are the union of CNVs in different animals.
The CNVRs identified in our study cover or overlap with a total of 610 genes, of which 447 are orthologous with corresponding human genes. Most (374) of these orthologous genes are included in the Human Database of Genomic Variants (Additional file 1: Table S4), i.e. they are also related with CNVs in human. Especially, the functions of some genes are enriched in the same GO terms (such as plasma membrane, cognition and sensory perception) and pathways (such as olfactory transduction) as those reported in other CNV studies in cattle and other mammals [1, 17, 25, 26, 55]. We also compared the 367 CNVRs identified in this study with the reported QTL collected in the cattle QTL database (http://www.animalgenome.org/QTLdb/doc/genome_versions#UMD_3.1). Since some QTL have too large confidence interval, we focused on QTL with confidence interval less than 30cM and considered those QTLs with overlapped confidence intervals greater than 50% as the same QTL. In this way, we identified 259 QTL in total. 341 out of the 367 CNVRs harbor or partially overlap with 182 (70%) QTL (Additional file 1: Table S7). These QTL are involved in many traits, such as milk production traits, carcass traits, reproduction traits, and health traits (see Additional file 1: Table S7).
In summary, we identified 367 CNVRs distributed on all of the 29 autosomes and the X chromosome of the bovine genome using the BovineHD beadChip. qPCR was performed for 17 CNVRs to validate the results and 13 (76.5%) of them were confirmed successfully. Six hundred and ten genes are covered by or overlapped with these CNVRs, most of which are also reported to be related with CNVs in the human genome. Compared with the results of CNV studies based on bovine 54K SNP array, CNVs detected in this study have smaller mean size, higher resolution and higher qPCR validation rate, suggesting that CNV detection based on high-density SNP arrays can significantly improve the accuracy and sensitivity of CNV calling. Findings in our study enhance the CNV map in the cattle genome and provide meaningful information for investigation of associations between CNVs and important traits in cattle in further study.
Sample collection and genotyping
The study population consisted of 96 Chinese Holstein cattle with unknown relationship among them, including 86 bulls and 10 cows. The Chinese Holstein originated from crosses of European Holstein-Friesian with Chinese Yellow cattle about 70 yr ago. Since then, continuous introgression of foreign Holstein genes (live bulls, semen, and embryos), mainly from North America, have been conducted. Therefore, the current population has a close relationship with the North American Holstein.
Genomic DNA samples were extracted from blood samples of cows and semen samples of bulls. The blood samples were collected along with the regular quarantine inspection of the farms. The concentration and the purity of genomic DNA were assessed on the Nanovue Spectrophotometer. All samples were genotyped with the Illumina High-Density BovineSNP beadChip containing 777,692 SNPs that uniformly span the bovine genome with an average gap size of 3.43 Kb and a median gap size of 2.68 Kb. All the markers were clustered and genotyped using the BEADSTUDIO software (Illumina). The whole procedure for collection of the blood samples was carried out in strict accordance with the protocol approved by the Animal Welfare Committee of China Agricultural University (Permit number: DK996).
In order to increase the confidence in CNV detection, strict quality control of the genotype data was applied according to the signal-to-noise ratios of each sample. The quality of the final data sets was assessed by the standard deviation of Log R ratio (LRR_SD) and B allele frequency drift (BAF_drift) of each sample. Only those samples with LRR_SD <0.30 and BAF drift <0.01 were included. Finally, 85 (10 cows and 75 bulls) with average call rate of 99.9% out of the 96 samples were remained for CNV detection on autosomal chromosomes and 88 (10 cows and 78 bulls) for CNV detection on the X chromosome.
Identification of cattle CNVs
The PennCNV software  was employed to infer cattle CNVs in this study. This algorithm incorporates multiple information, including total signal intensity (LRR) and allelic intensity ratio (BAF), the population frequency of B allele (PFB) of SNPs, the distance between neighboring SNPs and the pedigree information where available. The LRR and BAF of all SNPs for all samples were exported from the BeadStudio software (Illumina). The PFB file was generated based on the BAF of each SNP. The SNP genomic positions on chromosomes were derived from the bovine UMD3.1 genome sequence assembly . Furthermore, the signal intensity of each SNP which is subject to genomic waves was adjusted for the GC content of the 500Kb genomic region of its both sides using the -gcmodel option of PennCNV. PennCNV was run using the –test option without considering pedigree information since the relationship of the individuals in our study population is unknown. The analysis of the X chromosome and autosomes were separately performed in this study.
Following the CNV studies using high density SNP chip in human [64, 65], we define a CNV as it must contain ten or more consecutive SNPs and a CNV region (CNVR) as the overlapping region covered by the CNVs identified across all samples according to Redon et al. .
Gene contents and functional annotation
Gene contents of the identified CNVRs were retrieved from the Ensembl Genes 69 Database using the BioMart Database http://asia.ensembl.org/biomart/martview/ based on the bovine UMD3.1 sequence assembly. Gene Ontology (GO) analysis  and .Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses  were performed for genes that were completely included within or overlapped with the CNVRs with the DAVID bioinformatics resource  [http://david.abcc.ncifcrf.gov/summary.jsp] to determine their functional enrichment. Before these analyses, these bovine Ensembl gene IDs were converted to their human ortholog Ensembl gene IDs with BioMart since the annotated genes in the cattle genome are limited. We also compared these human ortholog genes with the CNV related genes reported in the Human Database of Genomic Variants (DGV) http://dgvbeta.tcag.ca/dgv/app/home?ref=NCBI36/hg18.
Quantitative real time PCR (qPCR) was used to validate CNVRs detected in the study. The relative comparative threshold cycle (2-≥≥CT) method was used to quantify copy number changes by comparing the ΔCt [cycle threshold (Ct) of target region minus Ct of control region] value of samples to be tested to the ΔCt of a calibrator without CNV [69, 70]. CNVRs were tested by using SYBR Green chemistry as recommended by the manufacturers. We designed the PCR primers using the Primer 3 webtool (http://frodo.wi.mit.edu/primer3/). For each target CNVR, two pairs of primers were designed considering the uncertainty of the CNVR boundaries. Moreover, the In-Silico PCR program from the UCSC browser (http://genome.ucsc.edu/) was used for in silico specificity analysis to ensure the primers only matching the sequence of interest. A serial diluted genomic DNA samples from a common cattle was used as template for creating a standard curve of each primer. Amplification efficiencies of all primers were calculated based on the standard curves. The copy number of each CNVR was compared with a region in the control gene Basic transcription factor 3 (BTF3) as done in previous studies . All PCR primers were designed based on its reference sequence in NCBI. PCR amplifications were performed in a total volume of 20 μL consisting of the following reagents: 1 μL DNA (around 50 ng), 1 μL (20 pM/μL) of both forward primer and reverse primer, 10μL of Master Mix (2×) and water (Roche Applied Science). All RT-PCRs were run in triplicate. PCRs were run as follows: 5min at 95°C followed by 40 cycles at 95°C for 10 sec and 60°C for 10 sec. All PCRs were performed in 96-well clear reaction plates (Roche Applied Science). The average CT value of three replications of each sample was calculated and normalized against the control gene with the assumption that there are two copies of DNA segment in the control region. For each CNVR to be validated, a value from the formula 2×2-≥≥CT was calculated for each individual. For autosomal chromosomes, a value around 2 indicates the individual is in normal status (without CNV), a value around 3 or above indicates it is in gain status, and a value 1 or below indicates it is in loss status. For X chromosome, the judgment for cows is the same as stated above. For bulls, the corresponding values for normal, loss and gain status were around 1, 0 and 2 or above, respectively.
The authors appreciate the financial support provided by the 948 Program of the Ministry of Agriculture of China (2011-G2A(2)), the National Natural Science Foundations of China (31201772), the National High Technology Research and Development Program of China (863 Program 2011AA100302), the Program for Changjiang Scholar and Innovation Research Team in University (IRT1191), and the China Post doctoral Science Foundation (2011M500037).
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