Identification of genome-wide copy number variations among diverse pig breeds by array CGH
- Yan Li†1,
- Shuqi Mei†2,
- Xuying Zhang1,
- Xianwen Peng2,
- Gang Liu1,
- Hu Tao1,
- Huayu Wu2,
- Siwen Jiang1,
- Yuanzhu Xiong1 and
- Fenge Li1Email author
© Li et al.; licensee BioMed Central Ltd. 2012
Received: 17 August 2012
Accepted: 19 December 2012
Published: 24 December 2012
Recent studies have shown that copy number variation (CNV) in mammalian genomes contributes to phenotypic diversity, including health and disease status. In domestic pigs, CNV has been catalogued by several reports, but the extent of CNV and the phenotypic effects are far from clear. The goal of this study was to identify CNV regions (CNVRs) in pigs based on array comparative genome hybridization (aCGH).
Here a custom-made tiling oligo-nucleotide array was used with a median probe spacing of 2506 bp for screening 12 pigs including 3 Chinese native pigs (one Chinese Erhualian, one Tongcheng and one Yangxin pig), 5 European pigs (one Large White, one Pietrain, one White Duroc and two Landrace pigs), 2 synthetic pigs (Chinese new line DIV pigs) and 2 crossbred pigs (Landrace × DIV pigs) with a Duroc pig as the reference. Two hundred and fifty-nine CNVRs across chromosomes 1–18 and X were identified, with an average size of 65.07 kb and a median size of 98.74 kb, covering 16.85 Mb or 0.74% of the whole genome. Concerning copy number status, 93 (35.91%) CNVRs were called as gains, 140 (54.05%) were called as losses and the remaining 26 (10.04%) were called as both gains and losses. Of all detected CNVRs, 171 (66.02%) and 34 (13.13%) CNVRs directly overlapped with Sus scrofa duplicated sequences and pig QTLs, respectively. The CNVRs encompassed 372 full length Ensembl transcripts. Two CNVRs identified by aCGH were validated using real-time quantitative PCR (qPCR).
Using 720 K array CGH (aCGH) we described a map of porcine CNVs which facilitated the identification of structural variations for important phenotypes and the assessment of the genetic diversity of pigs.
Genetic and archaeological findings suggest that pig domestication began about 9000–10000 years before present (YBP) at multiple sites across Eurasia, followed by their subsequent spread at a worldwide scale . Historically, Europe and China are two major areas of pig breeding . Over the past centuries, pigs have shown marked differences between these two areas, even if many European pig breeds carry far Eastern haplotypes at high frequencies because of an ancient introgression with Chinese swine . The Chinese pigs differ significantly from European pig breeds such as the Large White for many traits including fatness and ear traits [3–5]. Genetic variation within the gene pool which produce the above different phenotypes are selected for or against by evolution. Microsatellites, single nucleotide polymorphisms (SNPs) were the main measures of genetic variations in pigs, producing a USMARC pig SNP map (http://www.marc.usda.gov/genome/swine/marker_list.html) and the PorcineSNP60 Genotyping BeadChip with 62163 SNP probes . Recently, structural variations including insertions, duplications, deletions, inversions and translocations of DNA have been shown to contribute to the major phenotypic variations . Copy number variation (CNV) is described as a segment of DNA >1 kb that is copy number variable when compared with a reference genome . This variation may either be inherited or caused by de novo mutation [9–12]. It has become apparent that CNVs are genome-wide present in the human genome  and the genome of farm animals including cattle [13–16], avian [17–19], sheep , goat . About a range from 5% to 16% of the human genome was covered by CNVs [22, 23]. CNVs can lead to striking phenotypic consequences as a result of altering gene dosage, disrupting coding sequences, or perturbing long-range gene regulation by position effects [24–26]. These striking phenotypic consequences include some common complex diseases such as autism , schizophrenia , auto-immune Addison's disease .
Recently many efforts have been used to detect pig CNVs. By a custom-made tiling oligonucleotide array, 37 CNV regions (CNVRs) across chromosomes 4, 7, 14, and 17 were identified in 12 unrelated Duroc boars . Comparative genome hybridization (CGH) array was also conducted for chromosomes 7 and 8 in 9 different pig populations including Duroc, Large White, Meishan, Pietrain, Hampshire and Wild Boar . By analyzing data from the Porcine SNP60 BeadChip, 49 CNVRs were identified in 55 animals from an Iberian × Landrace cross (IBMAP)  and 382 CNVRs were identified from three purebred populations (Yorkshire, Landrace and Songliao Black) and one Duroc × Erhualian crossbred population . Up until now, few studies have confirmed the genome-wide presence of CNVs in pigs using array CGH (aCGH) with high-density probes. Here we reported the use of high-resolution oligonucleotide aCGH to identify the CNV regions in 12 individual pigs from different pig populations. This analysis provided a high-resolution map of copy number variations in the pig genome with a median probe spacing of 2506 bp relative to the latest porcine genome assembly (Sscrofa9.2).
Results and discussion
The overview of CNVR library
Using the custom tiling oligonucleotide aCGH approach, Fadista et al.  addressed 37 CNVRs on the Sus scrofa chromosomes (SSCs) 4, 7, 14, and 17 of the preliminary assembly of pig genome among 12 Duroc boars. Ramayo-Caldas et al.  detected 49 CNVRs using the Porcine SNP60 BeadChip data of 55 animals from an Iberian × Landrace cross. Wang et al.  detected 382 CNVRs based on the Porcine SNP60 genotyping data of 474 pigs. Two of the 37 CNVRs (5.41%) detected by Fadista et al. , 8 of the 49 CNVRs (16.32%) detected by Ramayo-Caldas et al. , 24 of the 382 CNVRs (6.28%) detected by Wang et al.  were identical or overlapped with the detected CNVRs in this study (Additional file 2). Totally 39 of the presently detected 259 CNVRs (15.06%) were identical or overlapped with those previously reported pig CNVRs (Additional file 2). The main potential reasons for this less well-overlapping result could be the different genetic backgrounds of pig samples, different platforms and various calling algorithms between the present study and other studies.
Compared with PorcineSNP60 Genotyping BeadChip, the detection power of 720 K aCGH was enhanced by dense marker density, uniform distribution of probes along each chromosome [6, 30]. Hence, some small CNVRs can be detected by aCGH technique, as the minimum CNV lengths were 2.30 kb in our present study, and 2.08 kb in the study of Fadista et al. , whereas the minimum CNV length detected by SNP chip were 5.03 kb and 44.65 kb, respectively [30, 31].
CNVRs chromosome distribution and status
Chromosome distribution of CNVRs in pigs
CNVR Size (bp)
Chr. Size (bp)
Concerning copy number status, 93 (35.91%) CNVRs were called as gains, 140 (54.05%) were called as losses and the remaining 26 (10.04%) were called as both gains and losses. Previously, it has been suggested that deletions are under stronger purifying selection than duplications . If so, deletions should be both less frequent and shorter than duplications . However, when we compared the length of gains with losses in the CNVRs, loss regions had slightly larger sizes than gain regions with the average length of 57.39 kb and 45.86 kb respectively (T-test not statistically significant at p value > 0.05). The possible reason was that the aCGH approach might favor the identification of deletions [14, 15, 21, 28]. As the samples were collected from 9 different populations, the considerable number of CNVRs status displaying in ‘both gains and losses’ might be due to the different genetic origins.
Putative population-specific CNVRs and cluster analysis
Some putative population-specific CNVRs were detected. For example, 6 CNVRs including CNVR_132 were purebred Landrace-specific, and CNVR_145 were purebred DIV-specific. CNVR_100 including KIT gene contained amplifications specifically in 8 pigs with dominant white color and a Pietrain pig with black spots, and CNVR_251 contained gains in pigs without dominant white color such as Yangxin, Erhualian, Tongcheng and Pietrain pigs. However, due to the limited samples used in the present study, the putative population-specific CNVRs need future study. And we also found 3 de novo CNVRs, of which CNVR_IDs 36, 149 were present in 2 Landrace × DIV crossbred pigs but not in their parents, while CNVR_259 were absent in 2 Landrace × DIV crossbred pigs but present in their parents.
Duplicated sequences colocalize with CNVRs in the pig genome
Although the exact interpretation of mechanisms responsible for generating CNVs is still unclear, previous studies have noted a four- to twenty-fold enrichment of CNVs near SDs in the other mammalian genomes [22, 32, 36]. Duplicated sequences are typical segments of DNA which range in size from one to hundreds of kb, share a high level of sequence identity (≥ 90%) and occur at more than one site within the genome . Under the same filter criterion, about 66.02% (171/259) of CNV regions directly overlapping with Sus scrofa duplicated sequences were identified through blasting the CNVR sequence against the Ensembl pig genomic sequences. As our present BLAST results did not retain a CNVR overlapping with a duplicated sequence by less than 1000 bp, so the overlaps of CNVs and their targeted duplicated sequences were under reporting. There were 13.5–25.0% CNVRs mapped to duplicated sequences in the previous reports [28, 37]. The difference may be related to differences in samples. CNVRs overlapping duplicated sequences were significantly different in average size (87.12 kb versus 22.23 kb, t-test p < 0.01) with the CNVRs that did not overlap duplicated sequences, consistent with previous CNV studies reporting a stronger association between duplicated sequences and long CNVRs [9, 11].
Gene contents of pig CNV regions
Enriched GO terms and KEGG pathway associated with the CNV regions (Modified Fisher Exact P-value ≤ 0.05)
Involved genes/ total genes (%)
GO:0007608 ~ sensory perception of smell
GO:0007606 ~ sensory perception of chemical stimulus
GO:0007600 ~ sensory perception
GO:0050890 ~ cognition
GO:0007186 ~ G-protein coupled receptor protein signaling pathway
GO:0050877 ~ neurological system process
GO:0007166 ~ cell surface receptor linked signal transduction
GO:0004984 ~ olfactory receptor activity
GO:0047961 ~ glycine N-acyltransferase activity
GO:0016021 ~ integral to membrane
GO:0031224 ~ intrinsic to membrane
Evolutionary rates of pig monomorphic and CNVR genes compared with human and mouse
Pig CNVRs overlapped with QTL regions
We queried the animal QTL database that held publicly available QTL data on livestock species. Retrieving all the porcine QTLs (http://www.animalgenome.org/cgi-bin/QTLdb/SS/download?file=gbpSS_10.2) within 2 Mb of our CNVRs resulted that 34 CNVRs overlapped with QTLs for several important traits including average daily gain (ADG) (Additional file 4). However, as the pig QTLs are not fully defined, the contribution of these QTL-overlapping CNVRs to complex traits needs further study.
Validation of CNVRs by real-time quantitative (qPCR)
qPCR was performed to validate 2 CNVRs (CNVR_IDs 100 and 215) detected by the aCGH experiment. Thirteen DNA samples including the reference used in aCGH were used for qPCR analysis. CNVR_100 and CNVR_215 were validated (Additional file 5) with the p threshold values 0.05 as the previous reports .
In summary, we described a map of porcine CNVs between breeds by a high-resolution array CGH, which was confirmed to be a very valid method to detect porcine genome-wide CNVs. With a stringent CNV calling criterion, 259 highly reliable CNV regions were reported here among diverse pig breeds. Future studies are required to assess the function of CNVs on pig important phenotypes. Our results facilitated the identification of structural variations for important phenotypes and the assessment of the genetic diversity in pigs.
All animal procedures were performed according to protocols approved by the Biological Studies Animal Care and Use Committee of Hubei Province, PR China. Twelve pigs including one White Duroc pig (♀), one Chinese Yangxin pig (♂), one Chinese Erhualian pig (♀), one Chinese Tongcheng pig (♀), one Large White pig (♀), one Pietrain pig (♂), two Landrace pigs (♂), two DIV pigs (♀) and two Landrace × DIV pigs (♀, ♂) were selected to function as test animals. Chinese Erhualian pigs were a strain of Chinese Taihu pig breed. Synthetic Line DIV was a result of cross of Landrace, Large White, Tongcheng or Taihu pigs. An unrelated female Duroc pig was selected as the common reference. The genomic DNA of 13 pig samples was extracted and purified from semen, whole blood or ear notch.
A 3 × 720 K whole genome tiling aCGH (NCBI GEO accession no. GPL16165) was designed (NimbleGen Systems, http://www.nimblegen.com) from the Sscrofa9.2 release (http://www.sanger.ac.uk/Projects/S_scrofa/), which was the new release at the time of the experiment. The probe design fundamentals were described in the NimbleGen technical note (http://www.nimblegen.com/products/lit/probe_design_2008_06_04.pdf). The probes with length of 50–60 bp were integrated into an array design using ArrayScribeTM, which resulted in a design with a median probe spacing of 2506 bp. Test DNA and reference DNA samples were independently labeled with either Cy3 or Cy5 dyes. Labeled DNA was co-hybridized to the custom-made NimbleGen CGH array (3 × 720 K). The array format included 3 arrays on single slides containing 719,336 probes. The arrays were scanned using a 5 μm scanner, and NimbleScan software (Roche NimbleGen) 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 Cy5-labeled reference sample were computed. Before normalization and segmentation analysis, spatial correction was applied. 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 included in NimbleScan software . CNVRs were called as the segments with at least 5 consecutive probes, a mean |log2 ratio| of >0.50 and detected in two or more animals . Since the CNV calling pipeline requires at least 5 consecutive probes, our theoretical resolution for CNV detection is 10299 bp (median spacing × 4 + median oligo length × 5). As females had two copies of X-linked genes and males only had one copy, male–female aCGH resulted in an excess of female signals for X-linked genes that can be used to calibrate the threshold values and detection methods . aCGH data have been submitted to the GenBank gene expression omnibus database under the accession number GSE41488. The dendrogram were generated by average linkage clustering algorithm of Cluster 3.0 software .
In order to check if the CNVRs overlapped any duplicated sequence, BLAST was used to query the CNVRs sequences against the Sus scrofa genome sequence (Sscrofa9.2). Sequences were retained as duplicated sequences if they had ≥ 1 kb and ≥ 90% identity and occurred at more than one site within the genome.
Gene contents in the identified CNVRs were retrieved from the Sscrofa9.2 assembly using the BioMart (http://www.biomart.org/) . Gene content of pig CNV regions was assessed using Ensembl transcripts. The DAVID functional annotation tool (http://david.abcc.ncifcrf.gov/) was used to perform GO classification and KEGG pathway annotation of CNV mRNAs. Functional annotation terms from the ontologies of "biological processes", "molecular function" and "cellular component" were recorded. Since only a limited number of genes in the pig genome have been annotated, we converted the pig Ensembl transcripts IDs to orthologous mouse and human Ensembl gene IDs by BioMart, then carried out the GO and pathway analyses, as described previously .
All the porcine QTLs data were downloaded from pig QTL database (http://www.animalgenome.org/cgi-bin/QTLdb/SS/download?file=gbpSS_10.2) . The CNVRs were considered to be overlapping pig QTLs if they were within 2 Mb of pig QTLs .
Validation of CNVRs by qPCR
Determination of CNVRs by qPCR was performed using the Roche LightCycler® 480 Detection System and obtained the crossing thresholds (Ct) value following the guidelines of the manufacturer. The primers were designed using the Primer Premier 5 software and were available in the Additional file 6. As previously reported , the copy number of each CNVR was normalized against the Col10 region, a control region in the genome that did not vary in copy number between the pigs. Triplicate wells of reactions (15 μL) contained 7.5 μL SYBR Green Real-time PCR Master Mix, 1 μL of 10–20 ng/μL gDNA, 0.3 μL 5 μM of each primer and 0.1 μL ROX. The cycling conditions consisted of 1 cycle at 95°C for 10 min, followed by 40 cycles at 94°C for 20 sec, 60°C for 20 sec, and 72°C for 20 sec, with fluorescence acquisition at 74°C in single mode. The specific PCR products were confirmed by the results of melting curve analysis and agarose gel electrophoresis. Analysis of resultant crossing thresholds (Ct) was performed using the -ΔΔCt method .
Copy number variation
Polymerase chain reaction
Comparative genome hybridization
Real-time quantitative PCR
Relative quantification value
Quantitative trait locus
Tyrosine-protein kinase Kit
Cytochrome P450 gene family
Single nuclotide polymorphism
Homo sapiens chromosome
Sus scrofa chromosome
The database for annotation, visualization and integrated discovery
kyoto encyclopedia of genes and genomes
locally weighted polynomial regression
We thank the anonymous reviewers for critical reading and discussions of the manuscript. We are grateful to Prof. Alan Archibald (The Roslin Institute) for the suggestions for this study, and to CapitalBio Corporation for the technical assistance with NimbleGen CGH analysis. The authors also acknowledge the farmers for providing pig samples.
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