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      Optimized Genetic Testing for Polledness in Multiple Breeds of Cattle

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          Abstract

          Many breeds of modern cattle are naturally horned, and for sound husbandry management reasons the calves frequently undergo procedures to physically remove the horns by disbudding or dehorning. These procedures are however a welfare concern. Selective breeding for polledness – absence of horns – has been effective in some cattle breeds but not in others ( Bos indicus genotypes) due in part to the complex genetics of horn phenotype. To address this problem different approaches to genetic testing which provide accurate early-in-life prediction of horn phenotype have been evaluated, initially using microsatellites (MSAT) and more recently single nucleotide polymorphism (SNP). A direct gene test is not effective given the genetic heterogeneity and large-sized sequence variants associated with polledness in different breeds. The current study investigated 39,943 animals of multiple breeds to assess the accuracy of available poll testing assays. While the standard SNP-based test was an improvement on the earlier MSAT haplotyping method, 1999 (9.69%) out of 20,636 animals tested with this SNP-based assay did not predict a genotype, most commonly associated with the Indicus-influenced breeds. The current study has developed an optimized poll gene test that resolved the vast majority of these 1999 unresolved animals, while the predicted genotypes of those previously resolved remained unchanged. Hence the optimized poll test successfully predicted a genotype in 99.96% of samples assessed. We demonstrated that a robust set of 5 SNPs can effectively determine P C and P F alleles and eliminate the ambiguous and undetermined results of poll gene testing previously identified as an issue in cattle.

          Most cited references32

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          Domestication and early agriculture in the Mediterranean Basin: Origins, diffusion, and impact.

          The past decade has witnessed a quantum leap in our understanding of the origins, diffusion, and impact of early agriculture in the Mediterranean Basin. In large measure these advances are attributable to new methods for documenting domestication in plants and animals. The initial steps toward plant and animal domestication in the Eastern Mediterranean can now be pushed back to the 12th millennium cal B.P. Evidence for herd management and crop cultivation appears at least 1,000 years earlier than the morphological changes traditionally used to document domestication. Different species seem to have been domesticated in different parts of the Fertile Crescent, with genetic analyses detecting multiple domestic lineages for each species. Recent evidence suggests that the expansion of domesticates and agricultural economies across the Mediterranean was accomplished by several waves of seafaring colonists who established coastal farming enclaves around the Mediterranean Basin. This process also involved the adoption of domesticates and domestic technologies by indigenous populations and the local domestication of some endemic species. Human environmental impacts are seen in the complete replacement of endemic island faunas by imported mainland fauna and in today's anthropogenic, but threatened, Mediterranean landscapes where sustainable agricultural practices have helped maintain high biodiversity since the Neolithic.
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            Production of hornless dairy cattle from genome-edited cell lines.

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              On the origin of cattle: How aurochs became cattle and colonized the world

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                Author and article information

                Journal
                G3 (Bethesda)
                Genetics
                G3: Genes, Genomes, Genetics
                G3: Genes, Genomes, Genetics
                G3: Genes, Genomes, Genetics
                G3: Genes|Genomes|Genetics
                Genetics Society of America
                2160-1836
                25 November 2019
                February 2020
                : 10
                : 2
                : 539-544
                Affiliations
                [* ]School of Veterinary Science, University of Queensland, Gatton, QLD 4343, Australia,
                []Department of Agriculture and Fisheries, Rockhampton, QLD 4702, Australia,
                []CSIRO Agriculture and Food, St Lucia, QLD 4067, Australia,
                [§ ]Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St Lucia, QLD 4072, Australia,
                [** ]Bioinformatics and Biostatistics, Neogen GeneSeek, Lincoln, NE, 68504 and
                [†† ]Neogen Australasia, University of Queensland, Gatton, QLD 4343, Australia
                Author notes
                [1 ]Corresponding author: School of Veterinary Science, The University of Queensland, Office 133, SVS Building 8114, Gatton Campus, Gatton, QLD 4343, Australia. E-mail: i.randhawa@ 123456uq.edu.au
                Author information
                http://orcid.org/0000-0001-7224-191X
                http://orcid.org/0000-0003-0783-4748
                http://orcid.org/0000-0002-3536-8265
                http://orcid.org/0000-0002-5606-3970
                Article
                GGG_400866
                10.1534/g3.119.400866
                7003080
                31767638
                1a270960-308b-4bf1-b4aa-8067345b6d31
                Copyright © 2020 Randhawa et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 10 August 2019
                : 24 November 2019
                Page count
                Figures: 0, Tables: 4, Equations: 0, References: 44, Pages: 6
                Categories
                Genomic Prediction

                Genetics
                poll gene,bovine,dehorning,genetic testing,animal welfare,genomic prediction,genpred,shared data resources

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