- Research article
- Open Access
Copy number variation in the bovine genome
© Fadista et al; licensee BioMed Central Ltd. 2010
Received: 11 December 2009
Accepted: 6 May 2010
Published: 6 May 2010
Copy number variations (CNVs), which represent a significant source of genetic diversity in mammals, have been shown to be associated with phenotypes of clinical relevance and to be causative of disease. Notwithstanding, little is known about the extent to which CNV contributes to genetic variation in cattle.
We designed and used a set of NimbleGen CGH arrays that tile across the assayable portion of the cattle genome with approximately 6.3 million probes, at a median probe spacing of 301 bp. This study reports the highest resolution map of copy number variation in the cattle genome, with 304 CNV regions (CNVRs) being identified among the genomes of 20 bovine samples from 4 dairy and beef breeds. The CNVRs identified covered 0.68% (22 Mb) of the genome, and ranged in size from 1.7 to 2,031 kb (median size 16.7 kb). About 20% of the CNVs co-localized with segmental duplications, while 30% encompass genes, of which the majority is involved in environmental response. About 10% of the human orthologous of these genes are associated with human disease susceptibility and, hence, may have important phenotypic consequences.
Together, this analysis provides a useful resource for assessment of the impact of CNVs regarding variation in bovine health and production traits.
Cattle, part of the Cetartiodactyl order of eutherian mammals , is an important source of human nutrition worldwide as well as the most studied ruminant model of metabolism, reproduction, and disease . Following the milestone publication of the cattle genome assembly along with annotation of functional elements and variation [2, 3], we are now enabled to search for genomic regions that impact the genetic variation of important phenotypic traits.
Genomic structural variation, including insertions, duplications, deletions, inversions and translocations of DNA, has long been known to be present in animal genomes [4, 5] but had predominantly been assumed to be to rare events and often associated with disease. This notion changed in 2004 when two groups of researchers published the first genome-wide maps of copy number variation in seemingly healthy individuals [6, 7]. Copy number variant (CNV) is described as a segment of DNA > = 1 kb that is copy number variable when compared with a reference genome . Before these landmark studies, it was thought that SNPs were the major source of genetic variation between individuals  but genomic structural genetic variation is now known to cover more base pairs [10–17], and to have a higher per-locus mutation rate than SNPs do .
There are indications that CNVs appear throughout the genome not only in humans, but also in other primates [19–21], rodents [22–30], flies [31, 32], dogs , chickens  and cattle . Nevertheless, other than humans and mice [29, 36–40], little is known about how CNVs contribute to normal phenotypic variation and disease susceptibility. Up until now, relatively few studies have confirmed the presence of CNVs in cattle [35, 41, 42], of which only one study focused on genome-wide detection of CNVs , but at low resolution using version 3 of bovine genome assembly .
Here we report the use of high-resolution oligonucleotide array comparative genomic hybridization (array CGH) to identify 304 CNV regions in 20 animals (14 Holsteins, 2 Red Danish, 3 Simmental and 1 Hereford). With an average probe spacing of 420 bp relative to the latest bovine genome assembly (BT4, 2007) , this analysis provides the highest-resolution map of copy number variation in the cattle genome to date.
The goal of our study was to characterize levels and patterns of copy number variation among bovine animals. Therefore, to assess the bovine CNV landscape, the genomic DNA of 20 bovine samples from two dairy (14 Holsteins, 2 Red Danish) and two beef breeds (3 Simmental, 1 Hereford) were analyzed. Assessment of copy number variation between samples was done using a set of Nimblegen HD2 CGH arrays that tile across the genome with approximately 6.3 million unique oligo probes with a mean probe spacing of 420 bp, using the latest genome assembly (BT4) .
We opted for a dye-swap loop array design, rather than a common reference design, so that each sample was hybridized to two different samples in two different dye orientations. Dye swap is used to compensate for dye bias, while the loop design (known to be more efficient than the reference design [43, 44]) is applied to help assign the CNV gain and loss status more accurately for each sample based on the number of samples with the CNV.
Array CGH evaluation
For evaluation of our array CGH platform, four sex-mismatched arrays and one self-self hybridization (all in dye swaps) were used to assess the false positive rate (see Methods). Probes were interpreted as revealing a copy-number difference if the standard error of the log-intensity ratio was beyond an intensity-ratio threshold. The adequacy of this threshold in detecting copy-number differences was confirmed by conducting sex-mismatched hybridizations, comparing the number of X-linked probes beyond the threshold. From the 88.52 Mb length of chromosome X, 3.21 Mb were greater than the threshold, yielding an estimate of 3.62% for the rate of false positives (FP). The false positive rate (FPR) is conservatively overestimated due to: (1) the assumption that there are no CNVs in the chromosome X of sex-mismatched arrays; (2) calling for FP was done at individual arrays rather than if they were detected in both dye swaps; (3) and because the self-self hybridization array yielded much lower FPR at individual array calling (0.0085%), and zero FPR when calling CNVs detected in both dye swap.
Pattern and frequency of CNV regions
Characteristics of the CNV regions, with sizes in base pairs (bp).
1,716 - 2,031,343
1,737 - 416,858
1,716 - 2,031,343
When comparing the size distribution between the 202 losses and the 102 gains, no significant difference was found (Wilcoxon rank sum test, p-value > 0.05), although we detected significantly more losses than gains (exact binomial test, p-value = 1.01e-08). This bias in detecting more deletions could be due to both biological and technical reasons. One of the main mechanisms responsible for the CNV formation, non-allelic homologous recombination (NAHR), has been shown to generate more deletions than duplications . As also noted by others [10, 31, 47], a technical bias favoring the detection of deletions may be responsible since our CNV detection pipeline have more power to detect a loss (log2(1/2) = -1) than a gain (log2(3/2) = 0.58). With 69% of the CNVRs described within 50 kb in size (figure 4), it should be noted that a significant proportion of the CNVs are near our effective resolution of 1.5 kb. This indicates that the experimental detection of 304 CNVRs may greatly underestimate the actual number of CNVs in the cattle genome, and that a substantial proportion of CNVs could be smaller than 1.5 kb in size. CNVs >2 Mb in size were not detected, which may be a consequence of the number and size of sequence gaps in the current outline of the cattle genome sequence assembly (75 654 gaps spanning 5.8% of the assembly).
When assessing hybridization signals in the unassembled chromosome (ChrUn), it was verified that only the male vs. female hybridizations were detecting CNVs in some regions. Although the number of females in this study is small (n = 2), the findings suggest that these regions may be from the bovine chr Y (Additional file 11, Table S9). It is known that the Bos taurus genome assembly was not only composed by a female animal, but also had a BAC library sequenced from a male animal, from which the corresponding Y chromosome scaffolds were unlabeled and placed in the ChrUn . Consequently, this study highlights regions for future genome assembly improvements.
In accordance with analyses conducted in humans [10, 11], we detected that the GC content of the CNVRs (43.6%) are slightly larger than of the whole genome (41.8%), which supports the notion that CNVs arise more often in GC rich regions.
SDs are associated with CNVs
Segmental duplications (SDs), defined as regions of length > = 1 kb with at least 90% sequence identity , are important elements in the formation of CNVs via non-allelic homologous recombination (NAHR) throughout the mammalian lineage [39, 50, 51]. To test whether the non-random association between CNVs and SDs was preserved in our high-resolution data, the overlap of CNVs with segmental duplications was determined. Segmental duplications were overlapped by 20% (61) of the CNVRs, which implies that CNVs are enriched near segmental duplication (permutation test, p-value < 0.001). It should be noted that the enrichment is increased when testing only the CNVRs bigger than 20 kb, with segmental duplications overlapping 47% of those CNVRs. This is also consistent with previous CNV studies reporting a stronger association between segmental duplications and long CNVRs [10, 45].
Nearly 30% (90) of the CNVRs encompassed 348 full-length genes as annotated in Ensembl  (Additional file 4, Table S2), but contrary to segmental duplications, the enrichment of CNVRs in genic areas is not significant. This indicates that the gene content of the CNVRs does not significantly differ from the whole bovine genome. The fact that none of the 481 ultraconserved elements , nor the 611 new long conserved noncoding sequences in vertebrates , were found in the CNV regions (whereas six of them would be expected by chance, permutation p-value < 0.001), further supports the notion that CNVs are significantly depleted in highly conserved functional elements.
Enriched GO terms associated with the CNV regions (FDR p-value ≤ 0.01).
intrinsic to membrane
integral to membrane
molecular transducer activity
serine-type endopeptidase inhibitor activity
olfactory receptor activity
signal transducer activity
G-protein coupled receptor activity
scavenger receptor activity
transmembrane receptor activity
cell surface receptor linked signal transduction
regulation of biological process
G-protein coupled receptor protein signaling pathway
regulation of cellular process
Evolutionary rates for monomorphic and CNV genes.
Wilcoxon rank-sum test, P value
When examining the human orthologs for the cattle genes  affected by CNV, we studied 167 human ortholog genes of which 84 overlap with the genomic coordinates of previously reported human structural variation, as seen in the Database of Genomic Variants (DGV) . Since it is unlikely that CNVs in the human-cow common ancestor would have been conserved, the overlapping CNVs most probably reflect the existence of orthologous genomic regions of structural instability that are prone to recurrently generate polymorphisms in both species. However, this may also indicate that the CNVs annotated in DGV, being derived using different technological and analytical platforms, have a large variance in CNV resolution which may overestimate CNV sizes . Consequently, even if the annotated CNVs represent true structural variation it is difficult to estimate the actual boundaries of the CNV and subsequently the overlap of DGV CNVs with the CNVs identified here.
CNV affecting genes associated with disease
Querying for copy number variant genes that had an orthologous human gene with OMIM morbid ID reference , revealed that 19 of these genes have been associated in human disease (susceptibility to sarcoidoisis and Alzheimer's disease, myopathy, encephalopathy, ataxia, etc - Additional file 6, Table S4). Likewise, when probing orthologous human genes involved in genome-wide association studies (GWAS) , 12 genes associated with human disease traits were found (Additional file 7, Table S5). We also queried the Animal QTL database  that holds publicly available QTL data on livestock species. Retrieving all the bovine QTLs within 2 Mb of our CNVRs resulted in 110 QTLs, which can hold putative valuable information for some important traits of interest (Additional file 8, Table S6). The database of Online Mendelian Inheritance in Animals (OMIA)  was also queried, and 21 cow phenotypes within 2 Mb of CNVRs were retrieved (Additional file 9, Table S7).
Comparison with other mammalian CNV studies
Comparison between this and other mammalian CNV studies using array CGH.
Validation of the CNVRs
To evaluate the accuracy of the copy number assignments, quantitative real time-PCR was used as described previously . Briefly, two control regions, one site on the X chromosome and one site on an autosome, plus ten potential CNV regions were selected. Six quantitative PCRs immediately confirmed the existence of copy number variation in these regions (Additional file 10, Table S8), whereas primer sets in four regions did not work satisfactorily (see Methods). Primer sets were therefore re-designed within these four CNV regions, and using these new primers the PCR reactions were performed successfully. Thus, the existence of copy number variation in all ten regions was confirmed by quantitative PCR.
The study outlined in this paper yields the highest-resolution analysis of bovine CNVs to date. Using a genome-wide tiling oligo array CGH, the largest number of CNV regions yet reported in cattle (304 CNVRs; average of 47 per genome) have been identified. Almost all (98%) of the CNV regions discovered here are novel relative to previous reports [35, 42], thereby vastly expanding our insight of genome structural variation in cattle. With an effective resolution of 1.5 kb in detecting CNVs, resulting in a median CNV size of 16.7 kb, our data shows that at least 0.68% of the cattle genome can vary in copy number in seemingly healthy animals. This is most probably an underestimate of the true genomic fraction that is tolerant to copy number due to the low number of animals sampled and their close relatedness.
As detected previously, not only in cattle  but also in other species [10, 25, 27, 33], CNVs are strongly associated with segmental duplications (SDs). This SD relation creates a lack of probe coverage in and around duplicated sequences , which significantly hampers the applicability of genome-wide association studies using SNP arrays to tag SDs-driven CNVs.
In addition, our data suggests that smaller CNVs (<50 kb) are much more frequent than larger ones, which is in agreement with other high resolution studies [14, 17, 45]. If this can be extrapolated for the whole cattle genome, the commercially available Illumina 50 k SNP panel (with an average probe spacing of 54 kb ) would not be sufficient to detect the bulk of existing CNVs. Consequently, further characterization of cattle CNVs should be done with similar high-density array CGH or using next-generation sequencing technologies. The latter identifies a more complete size and class ranges of structural variation [68–79].
As previously shown, copy number variants can have an impact on phenotypic variation mainly due to gene-dosage effects , and are often associated with disease susceptibility [38–40, 81]. In this analysis, CNVRs were found to be enriched for genes with functions related to environmental response, such as immune and sensory functions previously noticed in other species [10, 25, 27, 33, 35]. The enrichment aspect is an interesting finding, since variation in immunity related genes have been associated with disease. In particular, genes of the major histocompatibility complex (MHC) http://www.ebi.ac.uk/ipd/mhc/, of which some are included in our dataset, are reported to be responsible for differences in predisposition to diseases like mastitis, dermatophilosis and other tick infections . Concerning the genetics of milk production and lactation, we found none of the 197 unique milk protein genes and the over 6000 mammary-related genes within our CNV regions. This is expected since these genes are known to be highly conserved and evolving more slowly than other genes in the bovine genome .
Many genes and QTLs associated with human and cow diseases were found to be copy number variable or located nearby CNV regions. The fact that some bovine CNVs occurred in regions orthologous to human CNVs, reflect most likely recurrent CNV formation, rather than ancestral CNVs maintained in both species. These regions could be hotspots of CNV genesis due to their fragile structural architecture that prompts frequent rearrangements.
In summary, the data presented here extends and establish the fact that a significant part of cattle genome is copy number variable within and between breeds and that our high-resolution array CGH is a valid method to detect bovine CNVs in a genome-wide manner. With a limited amount of sampled animals and breeds, and a stringent CNV calling criteria, the CNV regions reported here are believed to be highly reliable, but the number might greatly underestimate true number of CNVs in cattle populations. Consequently, future studies are required to assess the functional significance of CNVs and their impact on health and productive efficiency in cattle.
The genomic DNA of 20 bovine samples was obtained from 4 dairy and beef breeds (14 Holsteins, 3 Simmental 2 Red danish and 1 Hereford). The pedigree scheme for the related animals is in Additional file 2, Figure S1. DNA was extracted and purified from blood as described elsewhere , in order to pass Nimblegen quality control requirements. We adhered to our national and institutional guidelines for the ethical use and treatment of animals in experiments.
DNA fragmentation, labeling, hybridization, washing and array imaging were carried out according to the manufacturer's protocol and done as previously described . Briefly, the genomic DNA samples were fragmented by sonication and labeled with fluorescent dyes Cy3 and Cy5. According to the dye swap loop design (Additional file 12, Table S10), samples were co-hybridized with a MAUI hybridization system (BioMicro Systems) to custom-made cattle CGH 2.1 M (HD2) arrays (Roche NimbleGen, Madison, WI). In order to cover the latest bovine genome assembly (bt4) with high density, the custom CGH arrays were planned in 3 designs. Each design covered a specific set of chromosomes with 2.1 million probes, which yielded 420 bp of average probe spacing (301 bp median probe spacing). The probe design fundamentals are described by the array manufacturer and elsewhere .
The arrays were scanned using a 5 μm scanner, and Nimblescan software (Roche Nimblegen, Madison, WI) was used to retrieve fluorescent intensity raw data from the scanned images of the oligonucleotide tiling arrays. For each spot on the array, log2-ratios of the Cy3-labeled test sample versus Cy-5 reference sample were computed. Before normalization and segmentation analysis, spatial correction was applied. Spatial correction reduces some artifacts observed in CGH data from 2.1 M arrays, adjusting position-dependent non-uniformity of signals across the array. Specifically, locally weighted polynomial regression (loess) was used to adjust signal intensities based on X, Y feature position . Normalization was then performed using the q-spline method , followed by segmentation using the CNV calling algorithm segMNT . This algorithm is shown to outperform both DNACopy , which is one of the most widely used CNV calling algorithm in the literature, and StepGram , the algorithm used by Agilent for CGH arrays. The segments with mean log2ratio ≥ |0.4| and at least 5 consecutive probes were retained. From these, a CNV was called if it was detected in both dye swap arrays and detected at least in two different dye swap hybridizations (i.e. in two hybridizations with an animal in common). Since the CNV calling pipeline requires at least 5 consecutive probes before calling a region copy number variant, our theoretical resolution for CNV detection is 1465 bp (median spacing*4 + median oligo length*5).
False positive rate
The false positive rate was calculated based on the 8 sex-mismatched arrays in this study: the length of chromosome × (from all the 8 hybs.) having a log2-ratio with a different signal than it should (given the sex-mismatched hybridization), dividing by the length of chrX multiplied by the number of sex-mismatched arrays (25,694,212/(88,516,663*8) = 3.62%). Since the FPR can be overestimated from sex-mismatched arrays, due to the assumption that no CNV exist in the chrX of sex-mismatched arrays, the FPR was also calculated from the self-self experiment and was determined as the length of sequence that would normally be called a CNV with our CNV calling pipeline. The FPR was determined as being 0.0085%.
Bovine segmental duplication (SD) data was retrieved from . They used two independent approaches to detect segmental duplications: WGAC (whole-genome assembly comparison), which is a BLAST-based analysis of all assembled sequence that detects self alignments (>90% and 1 kb); and WSSD (whole-genome shotgun sequence detection), which is an assembly-independent approach that examines the reference sequence for an increase in WGS read depth-of-coverage. This strategy has been used previously to map SDs in the human  and mouse  genomes. From their global data we choose to filter out those SDs bigger than 94% identity using WGAC if they were not also confirmed by WSSD. The reason for this relates to the fact that the assembly of highly similar duplicated sequences will often be missed, collapsed or mis-assigned [90, 91].
The association of CNVRs with genomic features (SDs, assembly gaps, genes and conserved elements) was tested by randomly permuting the genomic position of each CNVR 10 000 times and determining the sequence content of the resulting region or flanking regions.
Validation with RT-PCR was executed as previously described , with the Applied Biosystems 7900HT Sequence Detection System used for the Taqman assays, and downstream analysis performed with SDS 2.2 software. The full sequence of the CNVR was BLASTN-searched against the bovine genome sequence in order to identify a subregion that was unique and specific to the chromosomal location of the CNVR. PCR primers and probes were designed in this subregion of the CNVR using the ProbeFinder software from Roche Applied Science (Additional file 10, Table S8). Criteria for classifying a "not working" primer involved two parameters: reaction efficiency below 85%, and Pearson correlation of each standard curve below 0.95. Only ten of the original twenty bovine samples were used due to lack of DNA availability. For each target, the relative quantification analysis with a reference female sample was done to calculate estimated copy numbers of each sample.
The full data set and designs from the oligo array CGH experiments have been submitted to GEO  under the accession ID GSE18174.
We thank the Bovine Genome Sequencing and Analysis Consortium for providing access to the Bos Taurus genome sequence assembly. We thank Søren Svendsen and Hanne Jørgensen for technical assistance. This work was conducted as part of the SABRETRAIN Project, funded by the Marie Curie Host Fellowships for Early Stage Research Training, as part of the 6th Framework Programme of the European Commission. We also acknowledge the support from The Danish Food Industry Agency, Danish Ministry of Food, Agriculture and Fisheries as well as support from Viking Genetics.
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