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dc.contributor.authorChen, Frances L.
dc.contributor.authorZimmermann, Madeline
dc.contributor.authorHekman, Jessica P.
dc.contributor.authorLord, Kathryn A.
dc.contributor.authorLogan, Brittney
dc.contributor.authorRussenberger, Jane
dc.contributor.authorLeighton, Eldin A.
dc.contributor.authorKarlsson, Elinor K.
dc.date2022-08-11T08:08:28.000
dc.date.accessioned2022-08-23T15:56:14Z
dc.date.available2022-08-23T15:56:14Z
dc.date.issued2021-09-06
dc.date.submitted2021-12-20
dc.identifier.citation<p>Chen FL, Zimmermann M, Hekman JP, Lord KA, Logan B, Russenberger J, Leighton EA, Karlsson EK. Advancing Genetic Selection and Behavioral Genomics of Working Dogs Through Collaborative Science. Front Vet Sci. 2021 Sep 6;8:662429. doi: 10.3389/fvets.2021.662429. PMID: 34552971; PMCID: PMC8450581. <a href="https://doi.org/10.3389/fvets.2021.662429">Link to article on publisher's site</a></p>
dc.identifier.issn2297-1769 (Linking)
dc.identifier.doi10.3389/fvets.2021.662429
dc.identifier.pmid34552971
dc.identifier.urihttp://hdl.handle.net/20.500.14038/29923
dc.description.abstractThe ancient partnership between people and dogs is struggling to meet modern day needs, with demand exceeding our capacity to safely breed high-performing and healthy dogs. New statistical genetic approaches and genomic technology have the potential to revolutionize dog breeding, by transitioning from problematic phenotypic selection to methods that can preserve genetic diversity while increasing the proportion of successful dogs. To fully utilize this technology will require ultra large datasets, with hundreds of thousands of dogs. Today, dog breeders struggle to apply even the tools available now, stymied by the need for sophisticated data storage infrastructure and expertise in statistical genetics. Here, we review recent advances in animal breeding, and how a new approach to dog breeding would address the needs of working dog breeders today while also providing them with a path to realizing the next generation of technology. We provide a step-by-step guide for dog breeders to start implementing estimated breeding value selection in their programs now, and we describe how genotyping and DNA sequencing data, as it becomes more widely available, can be integrated into this approach. Finally, we call for data sharing among dog breeding programs as a path to achieving a future that can benefit all dogs, and their human partners too.
dc.language.isoen_US
dc.relation<p><a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&list_uids=34552971&dopt=Abstract">Link to Article in PubMed</a></p>
dc.rightsCopyright © 2021 Chen, Zimmermann, Hekman, Lord, Logan, Russenberger, Leighton and Karlsson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectEBV
dc.subjectbehavior
dc.subjectdog breeding
dc.subjectgenetic selection
dc.subjectgenomics
dc.subjectguide dog
dc.subjectheritability
dc.subjectworking dog
dc.subjectEcology and Evolutionary Biology
dc.subjectIntegrative Biology
dc.subjectVeterinary Medicine
dc.titleAdvancing Genetic Selection and Behavioral Genomics of Working Dogs Through Collaborative Science
dc.typeJournal Article
dc.source.journaltitleFrontiers in veterinary science
dc.source.volume8
dc.identifier.legacyfulltexthttps://escholarship.umassmed.edu/cgi/viewcontent.cgi?article=3159&amp;context=faculty_pubs&amp;unstamped=1
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/faculty_pubs/2126
dc.identifier.contextkey26908865
refterms.dateFOA2022-08-23T15:56:14Z
html.description.abstract<p>The ancient partnership between people and dogs is struggling to meet modern day needs, with demand exceeding our capacity to safely breed high-performing and healthy dogs. New statistical genetic approaches and genomic technology have the potential to revolutionize dog breeding, by transitioning from problematic phenotypic selection to methods that can preserve genetic diversity while increasing the proportion of successful dogs. To fully utilize this technology will require ultra large datasets, with hundreds of thousands of dogs. Today, dog breeders struggle to apply even the tools available now, stymied by the need for sophisticated data storage infrastructure and expertise in statistical genetics. Here, we review recent advances in animal breeding, and how a new approach to dog breeding would address the needs of working dog breeders today while also providing them with a path to realizing the next generation of technology. We provide a step-by-step guide for dog breeders to start implementing estimated breeding value selection in their programs now, and we describe how genotyping and DNA sequencing data, as it becomes more widely available, can be integrated into this approach. Finally, we call for data sharing among dog breeding programs as a path to achieving a future that can benefit all dogs, and their human partners too.</p>
dc.identifier.submissionpathfaculty_pubs/2126
dc.contributor.departmentProgram in Molecular Medicine
dc.contributor.departmentProgram in Bioinformatics and Integrative Biology
dc.source.pages662429


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Copyright © 2021 Chen, Zimmermann, Hekman, Lord, Logan, Russenberger, Leighton and Karlsson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Except where otherwise noted, this item's license is described as Copyright © 2021 Chen, Zimmermann, Hekman, Lord, Logan, Russenberger, Leighton and Karlsson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.