Project description:Background The goat (Capra hircus) represents one of the most important farm animal species. It is reared in all continents with an estimated world population of about 800 million of animals. Despite its importance, studies on the goat genome are still in their infancy compared to those in other farm animal species. Comparative mapping between cattle and goat showed only a few rearrangements in agreement with the similarity of chromosome banding. We carried out a cross species cattle-goat array comparative genome hybridization (aCGH) experiment in order to identify copy number variations (CNVs) in the goat genome analysing animals of different breeds (Saanen, Camosciata delle Alpi, Girgentana, and Murciano-Granadina) using a tiling oligonucleotide array with ~385,000 probes designed on the bovine genome. Results We identified a total of 161 CNVs (an average of 17.9 CNVs per goat), with the largest number in the Saanen breed and the lowest in the Camosciata delle Alpi goat. By aggregating overlapping CNVs identified in different animals we determined CNV regions (CNVRs): on the whole, we identified 127 CNVRs covering about 11.47 Mb of the virtual goat genome referred to the bovine genome (0.435% of the latter genome). These 127 CNVRs included 86 loss and 41 gain and ranged from about 24 kb to about 1.07 Mb with a mean and median equal to 90,292 bp and 49,530 bp, respectively. To evaluate whether the identified goat CNVRs overlap with those reported in the cattle genome, we compared our results with those obtained in four independent cattle experiments. Overlapping between goat and cattle CNVRs was highly significant (P<0.0001) suggesting that several chromosome regions might contain recurrent interspecies CNVRs. Genes with environmental functions were over-represented in goat CNVRs as reported in other mammals. Conclusions We describe a first map of goat CNVRs. This provides information on a comparative basis with the cattle genome by identifying putative recurrent interspecies CNVs between these two ruminant species. Several goat CNVs affect genes with important biological functions. Further studies are needed to evaluate the functional relevance of these CNVs and their effects on behavior, production, and disease resistance traits in goats.

Project description:Background The goat (Capra hircus) represents one of the most important farm animal species. It is reared in all continents with an estimated world population of about 800 million of animals. Despite its importance, studies on the goat genome are still in their infancy compared to those in other farm animal species. Comparative mapping between cattle and goat showed only a few rearrangements in agreement with the similarity of chromosome banding. We carried out a cross species cattle-goat array comparative genome hybridization (aCGH) experiment in order to identify copy number variations (CNVs) in the goat genome analysing animals of different breeds (Saanen, Camosciata delle Alpi, Girgentana, and Murciano-Granadina) using a tiling oligonucleotide array with ~385,000 probes designed on the bovine genome. Results We identified a total of 161 CNVs (an average of 17.9 CNVs per goat), with the largest number in the Saanen breed and the lowest in the Camosciata delle Alpi goat. By aggregating overlapping CNVs identified in different animals we determined CNV regions (CNVRs): on the whole, we identified 127 CNVRs covering about 11.47 Mb of the virtual goat genome referred to the bovine genome (0.435% of the latter genome). These 127 CNVRs included 86 loss and 41 gain and ranged from about 24 kb to about 1.07 Mb with a mean and median equal to 90,292 bp and 49,530 bp, respectively. To evaluate whether the identified goat CNVRs overlap with those reported in the cattle genome, we compared our results with those obtained in four independent cattle experiments. Overlapping between goat and cattle CNVRs was highly significant (P<0.0001) suggesting that several chromosome regions might contain recurrent interspecies CNVRs. Genes with environmental functions were over-represented in goat CNVRs as reported in other mammals. Conclusions We describe a first map of goat CNVRs. This provides information on a comparative basis with the cattle genome by identifying putative recurrent interspecies CNVs between these two ruminant species. Several goat CNVs affect genes with important biological functions. Further studies are needed to evaluate the functional relevance of these CNVs and their effects on behavior, production, and disease resistance traits in goats. We analysed CNVs in the goat genome by means of a cross-species aCGH experiment using the Roche NimbleGen platform (Roche NimbleGen Inc., Madison, WI; http://www.nimblegen.com) based on custom tiling arrays designed on the bovine (Bos taurus) genome, Btau_4.0 version, including a fraction of BTA13 of the University of Maryland (UMD) Bos taurus v. 2.0 assembly (ftp://ftp.cbcb.umd.edu/pub/data/Bos_taurus/Bos_taurus_UMD_2.0/). Arrays contained ~385,000 probes on a single slide to provide an evenly distributed coverage with an average interval of ~6 kb for the Btau_4.0 genome. The BTA13 of the UMD v. 2.0 assembly was included from nucleotide position 48 M bp to nucleotide position 78 M bp (4,673 oligonucleotides and average spacing of ~6 kb). This chromosome region was analysed as internal control because it contains the ASIP gene, not assembled in the BTA13 of the Btau_4.0 version Goat genomic DNA was extracted from blood of 2 Camosciata delle Alpi, 3 Girgentana, 3 Saanen, 1 black and 1 brown Murciano-Granadina goats using the Wizard® Genomic DNA Purification kit (Promega Corporation, Madison, WI). All analysed animals were females. Reference DNA sample of one (C1) Camosciata delle Alpi goat was labeled with Cy5 and co-hybridised with the other test DNA samples labelled with Cy3 on 9 different arrays. A self hybridisation (reference labelled by both Cy5 and Cy3) was carried out in another array. Hybridization and array scanning were performed by Roche NimbleGen as previously described. Data normalization was conducted using the normalize.qsline method from the Bioconductor package in R. Then data were analysed for each hybridization using normalized log2 ratios using the CGHweb server (http://compbio.med.harvard.edu/CGHweb/) that includes multiple algorithms.

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