- Methodology article
- Open Access
Structural variation in the chicken genome identified by paired-end next-generation DNA sequencing of reduced representation libraries
© Kerstens et al; licensee BioMed Central Ltd. 2011
- Received: 6 May 2010
- Accepted: 3 February 2011
- Published: 3 February 2011
Variation within individual genomes ranges from single nucleotide polymorphisms (SNPs) to kilobase, and even megabase, sized structural variants (SVs), such as deletions, insertions, inversions, and more complex rearrangements. Although much is known about the extent of SVs in humans and mice, species in which they exert significant effects on phenotypes, very little is known about the extent of SVs in the 2.5-times smaller and less repetitive genome of the chicken.
We identified hundreds of shared and divergent SVs in four commercial chicken lines relative to the reference chicken genome. The majority of SVs were found in intronic and intergenic regions, and we also found SVs in the coding regions. To identify the SVs, we combined high-throughput short read paired-end sequencing of genomic reduced representation libraries (RRLs) of pooled samples from 25 individuals and computational mapping of DNA sequences from a reference genome.
We provide a first glimpse of the high abundance of small structural genomic variations in the chicken. Extrapolating our results, we estimate that there are thousands of rearrangements in the chicken genome, the majority of which are located in non-coding regions. We observed that structural variation contributes to genetic differentiation among current domesticated chicken breeds and the Red Jungle Fowl. We expect that, because of their high abundance, SVs might explain phenotypic differences and play a role in the evolution of the chicken genome. Finally, our study exemplifies an efficient and cost-effective approach for identifying structural variation in sequenced genomes.
- Read Pair
- Chicken Genome
- Chicken Breed
- Inversion Breakpoint
- Reduce Representation Library
Structural variation within the genome, including insertions, duplications, deletions, and inversions of up to multiple kilobase pairs, have recently been described in a variety of species, including humans [1–3], mice , rats , silkworms  drosophila , and dogs . These genomic variations were recently found to be widespread, encompassing 5% of the human genome , and are thought to be involved in (co)determining complex phenotypes [10, 11].
The contribution of structural variants (SVs) to complex phenotypes has been measured by association analyses of variance in gene expression levels (traits) and the presence of SVs. SNPs and SVs have been shown to account for 83.6% and 17.7%, respectively, of the total detected genetic variation in gene expression, with only a limited overlap . The effect that SVs have on gene expression is likely underestimated given the much less completeness and accuracy with which SVs could be queried at that time. In humans, SVs have been associated with sporadic and Mendelian diseases, such as Williams-Beuren syndrome, mental retardation, and red-green color blindness. SVs have also been associated with complex human traits, such as autism, schizophrenia, Crohn's disease, and susceptibility to HIV infection . Because of their association with human diseases, the importance of SVs has become increasingly apparent [9, 14, 15]. For most other species, including the major farm animals, chickens, cattle, and pigs, the extent and biological consequences of SVs have remained largely unknown due to the lack of a cost-effective approach for detecting SVs.
Until recently, comparative genomic hybridization (array-CGH) was the most commonly used method for detecting SVs . Fosmid paired-end sequencing, which is a more laborious technique, has been used to detect SVs larger than 8 kb [17, 18]. The inability to resolve smaller SVs using array-CGH results in the over-representation of larger SVs in current databases of structural variation (e.g., http://projects.tcag.ca/variation/). The resolution of array-CGH, though extremely costly, can be improved by using high-resolution whole-genome tiling arrays. Most of these SVs have been identified by methods that do not resolve SV end points at the base pair level. In addition, methods like array-CGH are based on a reference genome that currently does not encompass all SVs within the population and, thus, is limited in scope. Genomic regions that are the result of deletions not present in the reference genome are not captured by the array and not analyzed for SVs.
Next generation sequencing (NGS) technology was recently shown to be a powerful alternative to array-CGH for identifying genomic structural variation [1, 7, 19]. Using paired-end sequencing, SVs can be identified with single base pair resolution. Moreover paired-end sequencing allows for the detection of balanced rearrangements in which there is no gain or loss of a genomic region, such as inversions and translocations, which cannot be identified by array-CGH. Paired-end sequencing and mapping (PEM) involves sequencing the paired ends of fragments of known insert size from a genomic DNA library and computationally mapping DNA reads to a reference genome.
Here, we used PEM on reduced representation libraries (RRLs) of pooled chicken DNA samples. In the chicken genome, only 43 (larger) SVs have been described thus far . These SVs encompass 16 chicken-turkey inter-specific copy number variants (CNV) and 32 chicken-duck inter-specific CNVs, of which five CNVs overlap with inter-specific chicken-turkey CNVs . In chicken, some phenotypes have already been linked to structural variation, including the pea-comb  and late feathering  phenotypes. With PEM of an RRL, we provide a cost-effective approach for exploring the presence of SVs at high resolution within four chicken breeds.
Paired-end sequencing and mapping
Sequencing and mapping results for the four chicken breeds analyzed for structural variation
Brown egg layer
White egg layer
RRL construction simulated by an in silico Alu I digest of the WASHUC2 build of the reference chicken genome
Number of fragments
Sequenced (32 bp reads)
RRL coverage calculated
101 Mb (8%)
18.7 Mb (1.5%)
151 Mb (12%)
30.3 Mb (2.4%)
In each breed, roughly 0.1% of the mapping read pairs had no concordant alignment in the reference genome, referred to as discordant paired-end reads [2, 17], indicating a potential SV. Discordantly mapping read pairs are those whose distance apart is less or greater than expected from the RRL size range or in another relative orientation than expected based on the reference genome (Table 1). Paired reads that mapped to two different chromosomes (up to 0.12%) were excluded from further analysis. Discordantly mapping read pairs of the larger chicken chromosomes (1-15,20 and Z) with similar mapping coordinates and predicting a similar putative SV were clustered in 10,559 clusters. Clusters were classified as having an insert size that was too large (deletions, n = 5135), too small (insertions, n = 5241), or an incorrect orientation of ends (inversion breakpoints, n = 183) with respect to the chicken genome sequence.
Comparison of the mapping quality and distribution between concordantly and discordantly mapping read pairs
Number of mapping read pairs
Average mapping quality
Validation structural polymorphisms
Size in RRL
Discriminating putative SVs from false positives
The results suggest that the presence of concordantly mapping reads partly overlapping the predicted SV region did not correlate with the quality of SV prediction, whereas reference errors in the predicted SV region correlated negatively. Furthermore, the results indicate that putative SVs predicted by a single or a few discordantly mapping read pairs that mapped a slightly different distance than expected were false positives, whereas the majority of putative SVs with greatly deviating mapping distances were confirmed as being true SVs. With this limited number of observations, we formulated a simple but fitting rule to determine SV clusters with a high likelihood of representing a genomic rearrangement from false positives.
Breed-specific and shared SVs
Distribution of predicted SVs
Analyses of putative deletions for their effects on gene annotations
Truncation last exon
Truncation exon 9 or 5' deletion exon 10
5' deletion in last exon
5' deletion in exon 4
Truncation exon 10
Truncation exon 2
Truncation last exon
Putative functional annotations of predicted SVs
% within exons
% CR1 1
% GGLTR 2
% other 3
% TR 4
% dust 5
SVs at base pair resolution and overlap with functional elements
Annotation of confirmed deletions and DNA signatures at breakpoints
CR1-F0, Z-REP, trf, dust
CR1-Y4, dust, trf
CR1-D2, Mariner1, GG, dust
By sampling a portion of the genome from four chicken lines using stringent SV detection constraints, we detected 188 SVs encompassing ~130 kb. Assuming considerable limitation in the detection of classes of SVs by our method, the chicken genome may differ in SVs to a greater extent than in SNPs. Therefore, we counted the total number of nucleotides involved. The majority of SVs identified by our method were small deletions, most of which resulted in a loss of repetitive motifs in intronic regions or a loss of unannotated sequences in intergenic regions. Both insertions mapped to intergenic regions as sequences of a few tens of base pairs and low complexity. We also predicted rearrangements in coding regions, revealed the exact breakpoints on the reference genome for 16 SVs, and confirmed our predictions. To what extent SVs in intronic and intergenic regions contribute to the evolution of the chicken genome or chicken phenotypes remains unclear, especially because the functions of these genomic regions are largely unknown . To date, studies involving the detection and exploitation of genetic variation in chicken encompass large SVs by means of CNVs but do not include smaller SVs. Our study reveals that, given their high frequency, these smaller SVs will need to be incorporated in genotyping because they might explain phenotypic differences. In addition, our data suggest that structural variation has contributed to genetic differentiation among current domesticated chicken breeds and the Red Jungle Fowl, and might have played a role in chicken genome evolution.
RRL-based approach to SV detection
Currently, sequence-based genome-wide surveys of SVs involve the preparation of whole genome fragment libraries in combination with paired-end sequencing. Such approaches require relatively large investments, particularly if multiple individuals from multiple breeds have to be screened. This study demonstrated the potential of massive parallel paired-end sequencing of RRLs constructed from the pooled DNA of multiple individuals. SVs were predicted based on the read pair information from the paired-end sequenced small insert RRL, which was purposely created for SNP detection. The small RRL size allowed for PCR-based confirmation and characterization of the SV at the base pair level of acquired deletions and small insertions with minimal sequencing efforts. Revealing inversion and translocation breakpoints is much more laborious due to the limited information RRL approaches provide. We showed that read pair analysis of a paired-end sequenced RRL is already sufficient for obtaining a first glimpse of SVs in a particular sequenced species. This RRL based strategy put constraints on the quality of the reference genome because assembly errors will result in false positive SV predictions in reference based detection approaches. Uncertainty about the quality of assembly of some of the smaller micro-chromosomes together with computational limits at the time of this study were the reasons why we did not analyze the whole genome for SVs. An enhanced assembly of the chicken reference genome and the increasing computational power allow for improvement in the detection of SVs using our approach. Furthermore the use of multiple RRLs including large and small fragments pools, that are separately tagged and paired-end sequenced together in bulk, will considerably improve SV detection at small increase of cost. More demanding is PEM of a randomly sheared and size-selected whole genome library providing a more complete catalog of rearrangements characterized between a sample and a reference [1, 19]. An even more complete picture including SVs of a larger size and more complex rearrangements will require paired-end sequencing of several libraries of different insert sizes . The detection of all structural variation, which requires whole genome sequencing and de novo assembly, is extremely demanding. However, the identification of (small) deletions and insertions with comparable or shorter length than the standard deviation of paired-end insert sizes requires de novo assemblies, because such SVs cannot reliably be identified by mapping approaches. Moreover, reference-based approaches, included mapping approaches, are biased to the completeness of the reference and, thus, ignore variants in regions that are missing from the reference genome due to structural variation. Finally, de novo assembly has the advantage of resolving SVs to a single base pair level, and inserted sequences can be obtained .
Next generation sequencing
We used a NGS approach to identify genomic rearrangements within four commercial chicken breeds by comparing their genomes to the sequenced chicken genome (Red Jungle Fowl). We excluded several classes of sequence reads from further analysis, including reads that did not show the restriction enzyme tag and those that showed more than one mismatch in the alignment. The first constraint was applied to eliminate false positive insertion predictions due to a breakdown of the RRL resulting in shorter spans of paired-end reads, whereas the second constraint was applied to reduce the number of false predictions due to sequencing errors. However, we realize that by taking these measures we also discard many read pairs because of true nucleotide variation, which occur in one of every 200 bp in the chicken . The inclusion of read pairs with more than one mismatch in the alignment can be considered but has a risk of falsely predicted SVs due to mapping errors, requiring a revalidation of our proposed SV size deviation versus the observed frequency rule (Figure 4). On the other hand, reducing the mapping constraints might reveal additional true SVs potentially hidden in the considerable fraction of read pairs with only one end or no end mapped to the reference when using our mapping constraints. However, this fraction of read pairs with mapping problems might also largely represent sequences of gaps in the genome (estimated to encompass ~100 Mb in total) and, thus, cannot be mapped.
SV distribution across breeds
Theoretically, our approach for identifying SVs allows the prediction of SVs and insight into how a predicted SV is distributed across breeds. We showed that the observed distribution of SVs is a good predictor for the actual distribution of the SV in breeds. Even with limited sampling, predicted SV distributions correlated with the PCR-based genotyping results of pooled samples (Table 3). In general, PCR-based genotyping revealed that predicted SVs are more widely shared in breeds than predicted by our sequencing-based estimation. This situation is caused by limited sampling, and the reduction of target sequence complexity by creating RRLs might have contributed to this difference. Our sampling regimen required enzyme recognition sequences flanking a SV within the size range for the RRL to include a particular SV in the RRL. Breed-specific SNPs in Alu I sites may have caused one or both SV alleles to not be sampled and are, thus, not predicted to be present in that breed, consequently affecting our sequencing-based estimation of SV distribution across breeds. Conversely, our PCR-based genotyping approach with pooled samples was not affected by sampling limitation or Alu I SNPs and revealed the presence of SVs in a breed even at allele frequencies of 0.1 (data not shown).
Because of the difference in the predicted presence of a SV in a breed and the genotyping results, we realize that the 186 SVs with which we estimated breed specificity might not be fully representative. The use of different RRL sizes (150-200 bp in layers and 125-200 in broilers) is reflected in a 1.5-2-fold difference in the SVs detected in broilers and layers. The fairly large percentage of SVs shared in broilers can be interpreted as being due to the effects of selection during line development by commercial companies and is consistent with the results of recent SNP genotyping , but it might be over-estimated in our study due to the difference in RRL construction. The percentage of predicted SVs shared by brown egg layers and broiler 1, however, is an indication that these breeds are more genetically related compared to the other breeds. Recent SNP genotyping results for brown and white egg layers and three broiler lines also indicated that the brown egg layer breed is more closely related to broiler lines than to white egg layers , which is in agreement with our conclusion based on SV distribution.
Abundance, location, and size of SVs in the chicken genome
The reduction in the percentage of the genome covered by sequencing a RRL instead of randomly sampling the whole genome placed high constraints on the detection of SVs. The actual amount of SVs is likely much higher because we only sampled those that are flanked by restriction sites, and such that the intermediate sequence length of the variant was in the size range of the RRL. Large insertions were not expected to be detected because our RRL approach only allows for the detection of up to about 170 bp, the size between the maximum RRL fragment size (~200 bp) minus the mapping size of two completely overlapping reads (32 bp)
Although the larger SVs are most likely under-represented in our data due to the constraints of the applied detection method, we can conclude that the majority of SVs in the chicken genome are smaller than 1 kb (Figure 6). This finding is consistent with human studies  in which SV abundance inversely correlated with SV size. We observed that 99% of the predicted SVs were located in intronic (43%) and intergenic regions (56%), which together comprise ~90% of the chicken genome. As expected, SVs were less abundant in coding regions because, like SNPs, they are more likely to have negative impacts and be eliminated by purifying selection. Moreover the observed lower abundance of SVs in coding region is consistent with the idea that the most common rearrangement mechanism requires substrates, such as microhomology, low copy repeats, and segmental duplications, which are more abundant in non-coding regions [10, 32, 33]. In 3 of 15 sequenced SV breakpoints, we were able to identify signatures in the DNA sequence indicating the mechanism by which SVs are formed. All identified signatures involved microhomology at the breakpoint junction that resulted from either nonhomologous end-joining or replication fork stalling and template switching events . Other SVs did not show a clear sequence signature.
We provided a first glimpse of the abundance and genomic locations of structural variation in the chicken genome by identifying 188, mostly small, rearrangements, some of which were in coding regions, though a majority was located in non-coding regions. Based on the present data, we expect to find thousands of small (<1 kb) and hundreds of larger rearrangements in the whole chicken genome, encompassing more nucleotides than SNPs, and that are putatively involved in phenotypic variation. We observed that structural variation has contributed to genetic differentiation among current domesticated chicken breeds and the Red Jungle Fowl. Finally, we showed that little sequencing effort on a reduced representation of a genome is sufficient for the detection and base pair level annotation of a variety of SVs in a sequenced genome.
SV detection using RRLs of pooled samples and NGS
Individual DNA samples were pooled according to breed and the genome complexity reduced by isolating a fraction of a complete genome digest. The isolated reduced representation library (RRL) was paired-end sequenced using Illumina genome Analyzer technology. The paired-end reads were aligned to the reference chicken genome WASHUC2 build and SVs are identified as significant differences between the mapping distances identified by the paired-end reads and the size range used for constructing the RRLs. Deletions relative to the reference genome were identified by paired ends spanning a genomic region in the reference genome longer than the size in the RRL, whereas insertions were identified by paired ends spanning a shorter genomic region in the reference sequence than expected based on the RRL. Inversion breakpoints were detected by paired ends that mapped in a different relative orientation compared to the reference genome.
Genomic DNA was extracted from 30 μl of blood from 25 unrelated F0 individuals from brown and white egg layer lines and two broiler lines consisting of 13 males and 12 females (Broiler 1) and 25 males (Broiler 2) using a Puregene DNA isolation kit (D-70KA; Gentra Systems, Inc., USA).
The RRLs were prepared by digesting 25 μg of pooled DNA using 1,000 units of the restriction enzyme Alu I in a total volume of 240 μl. The selection of the restriction enzyme was based on the 10-fold reduction of genome complexity in the optimum size range (100-200 bp) of the sequencing technology platform (Genome Analyzer, Illumina). The digested DNA sample was fractionated on a 10% precast polyacrylamide gel (Biorad) at 100 V for 3 h and stained with ethidium bromide. The size fractions were sliced out of the gel and the DNA was mechanically sheared and and eluted over night in 300 μl recovery buffer (8 mM Tris pH 8.0, 0.08 mM EDTA, 1.25 M ammonium acetate. After a 15-min incubation at 65°C, the eluent was purified using a Montage DNA Gel Extraction Device (Millipore Corporation, Bedford, MA) and precipitated with isopropanol. The DNA was washed with ethanol and re-suspended in DNA hydration solution (Gentra Systems, Inc., USA).
We prepared the Genome Analyzer paired-end flow cell according to the manufacturer's protocol.
Five picomole aliquots of the RRLs were processed using the Illumina Cluster Generation Station (Illumina, Inc., USA) following the manufacturer's recommendations. The Illumina GAII Genome Analyzer (Illumina, Inc., USA) was programmed to produce a theoretical fixed read length of 36 bp.
Images from the instrument were processed using the manufacturer's software to generate FASTQ sequence files. Paired reads that had both the RRL restriction tag and a per base phred (Ewing and Green, 1998) quality score of at least 20 were selected using custom Perl scripts and aligned to the chicken genome (WASHUC2) using the MAQ  algorithm v0.7.1 with parameters -1 32 -2 32 -a 220.
Alignment results were analysed according to the MAQ  documentation by using custom perl and bash scripts. Paired reads in which one or both ends were mapped with more than one mismatch or mapped ambiguously on the reference sequence were excluded from analysis, as these would not reliably detect SVs. Discordantly mapping read pairs in which the two ends mapped >220 bp apart were classified as deletions and subsequently clustered based on overlapping mapping positions. SVs longer than 100 kb disrupted clustering and were excluded. Read pairs that mapped within 100 bp of each other were classified as insertions, whereas read pairs that mapped with one of the two ends in the incorrect orientation were classified as inversions. Both insertions and inversions were also clustered based on mapping positions by applying custom made Perl scripts.
Confirmation of identified SVs
For each SV cluster, we recorded the number of reads spanning the rearrangement, regardless of whether a normally mapping pair was observed or whether a sequence gap in the WASHUC2 build was present within the genomic range in which the deletion was predicted. SV clusters were prioritized for validation as follows: (i) an alternative mapping quality score of at least 60, (ii) both reads of a discordantly mapping pair mapped within a single predicted Ensembl exon or gene , and (iii) the genomic sequence flanking the SV allows primer design (Primer3Plus ) within 200 bp. We applied these criteria for selecting candidates distributed over the 220 bp-20 Kbp (deletions) and 32 bp-100 bp (insertions) size ranges. If these criteria yielded more than one candidate, the candidate with the highest alternative mapping quality score was selected.
Primers were designed to span the possible breakpoint by locating them 40-200 bp outside the mapping location of discordantly mapping read pairs. The minimum and maximum aberrant PCR product size was expected to be the sum of the minimum/maximum fragment size in the RLL and required flanking genomic region for primer development. PCR reactions were initially performed on DNA of the Red Jungle Fowl reference animal UCD001 and the pooled samples of all four breeds. For breeds in which the rearrangements were detected, individual samples were genotyped by PCR. The PCR products of homozygous individuals, or samples in which only the aberrantly sized product resulted, were sequenced on a conventional Sanger capillary sequencer and the results compared to the reference sequence using megablast with parameter -F F to identify breakpoints. Both ends of the PCR product on the reference (Red Jungle Fowl) were sequenced and mapped to the reference to ensure that it originated from the expected genomic position.
Confirmed SVs were defined as those for which PCR reactions resulted in a distinct band in the expected size range in at least the breed for which the rearrangement was predicted and with no matching band in the UCD001 reference animal. The PCR results had to be supported by unambiguous sequencing data mapping confirming the rearrangement.
The data from this paper have been submitted to the NCBI Short Read Archive (http://www.ncbi.nlm.nih.gov/Traces/sra/sra.cgi) under accession no. SRA026771.
The SVs identified in this study that have not been confirmed and annotated at the base pair level are available upon request, awaiting a central repository of structural variation in genomes.
We thank Mari Smits and Hendrik-Jan Megens for critically reading the manuscript and their helpful comments. This study was funded by European Union grant FOOD-CT-2004-506416 (Eadgene). Sequencing of the RRLs was funded by Cobb-Vantress Inc, USA and Hendrix Genetics, The Netherlands.
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