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    Copy number variants in the sheep genome detected using multiple approaches

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    15
    Author
    Jenkins, GM; Goddard, ME; Black, MA; Brauning, R; Auvray, B; Dodds, KG; Kijas, JW; Cockett, N; McEwan, JC
    Date
    2016-06-08
    Source Title
    BMC Genomics
    Publisher
    BMC
    University of Melbourne Author/s
    Goddard, Michael
    Affiliation
    Agriculture and Food Systems
    Metadata
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    Document Type
    Journal Article
    Citations
    Jenkins, G. M., Goddard, M. E., Black, M. A., Brauning, R., Auvray, B., Dodds, K. G., Kijas, J. W., Cockett, N. & McEwan, J. C. (2016). Copy number variants in the sheep genome detected using multiple approaches. BMC GENOMICS, 17 (1), https://doi.org/10.1186/s12864-016-2754-7.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/259492
    DOI
    10.1186/s12864-016-2754-7
    Abstract
    BACKGROUND: Copy number variants (CNVs) are a type of polymorphism found to underlie phenotypic variation, both in humans and livestock. Most surveys of CNV in livestock have been conducted in the cattle genome, and often utilise only a single approach for the detection of copy number differences. Here we performed a study of CNV in sheep, using multiple methods to identify and characterise copy number changes. Comprehensive information from small pedigrees (trios) was collected using multiple platforms (array CGH, SNP chip and whole genome sequence data), with these data then analysed via multiple approaches to identify and verify CNVs. RESULTS: In total, 3,488 autosomal CNV regions (CNVRs) were identified in this study, which substantially builds on an initial survey of the sheep genome that identified 135 CNVRs. The average length of the identified CNVRs was 19 kb (range of 1 kb to 3.6 Mb), with shorter CNVRs being more frequent than longer CNVRs. The total length of all CNVRs was 67.6Mbps, which equates to 2.7 % of the sheep autosomes. For individuals this value ranged from 0.24 to 0.55 %, and the majority of CNVRs were identified in single animals. Rather than being uniformly distributed throughout the genome, CNVRs tended to be clustered. Application of three independent approaches for CNVR detection facilitated a comparison of validation rates. CNVs identified on the Roche-NimbleGen 2.1M CGH array generally had low validation rates with lower density arrays, while whole genome sequence data had the highest validation rate (>60 %). CONCLUSIONS: This study represents the first comprehensive survey of the distribution, prevalence and characteristics of CNVR in sheep. Multiple approaches were used to detect CNV regions and it appears that the best method for verifying CNVR on a large scale involves using a combination of detection methodologies. The characteristics of the 3,488 autosomal CNV regions identified in this study are comparable to other CNV regions reported in the literature and provide a valuable and sizeable addition to the small subset of published sheep CNVs.

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