1. Animal-breeding schemes using genomic information need breeding plans designed to maximise long-term genetic gains
- Author
-
Mark Henryon, Anders Christian Sørensen, and P. Berg
- Subjects
Animal breeding ,Exploit ,Computer science ,Theoretical models ,WIDE SELECTION ,GeneralLiterature_MISCELLANEOUS ,OVERLAPPING GENERATIONS ,CONSERVATION PROGRAMS ,Inbreeding ,Livestock breeding ,DYNAMIC SELECTION ,Hardware_MEMORYSTRUCTURES ,General Veterinary ,ComputingMilieux_THECOMPUTINGPROFESSION ,Genomic selection ,business.industry ,Environmental resource management ,CONTRIBUTION SELECTION ,Breeding plans ,INBREEDING COEFFICIENTS ,veterinary(all) ,Decision framework ,Term (time) ,Risk analysis (engineering) ,DATA SETS ,PREDEFINED RATE ,GENOTYPING STRATEGIES ,ComputingMilieux_COMPUTERSANDSOCIETY ,Genomic information ,Animal Science and Zoology ,DAIRY-CATTLE ,business - Abstract
We argue that animal-breeding schemes need well-designed breeding plans to maximise long-term genetic gains from genomic information. Genomic information has been implemented in livestock breeding schemes with ad hoc breeding plans, suggesting that the potential benefits of genomic information are not being fully exploited. Breeding schemes need well-designed breeding plans to exploit the benefits of genomic information for two reasons. First, there are several components of breeding schemes with genomic information that impact on long-term genetic gains. Second, these components interact, which implies that breeding schemes need to optimise components simultaneously in order to maximise long-term genetic gains. Designing breeding plans that optimise components simultaneously is a complex task. In more cases than not, breeding schemes, their components, and interactions between these components do not allow optimum breeding plans to be designed by mere reasoning. We recommend using decision frameworks to design breeding plans for schemes that use genomic information: testing sound hypotheses by designing and executing controlled experiments using decision tools, such as mathematical-statistical models. These decision frameworks enable us to design optimum breeding plans by providing an objective and theoretical basis to make and validate breeding decisions, enabling us to understand the underlying mechanisms of breeding schemes with genomic information, and allowing us to test the practical implementation of breeding decisions against theoretical models. Genomic information is an exciting prospect for animal breeding, and there is clearly an important role for breeding plans that maximise long-term genetic gains in breeding schemes using genomic information. (C) 2014 Elsevier B.V. All rights reserved.
- Published
- 2014
- Full Text
- View/download PDF