309 Beyond predictions: managing inbreeding and variability in the genomic era. (7th December 2018)

• Record Type: Journal Article
• Authors: Maltecca, C; Howard, J; Baes, C; Pryce, J
• Is Part Of: Journal of animal science. Volume 96(2018)Supplement 3
• Page Start: 117
• Page End: 118
• Publication Date: 2018-12-07
• Subjects: Genomic selection -- inbreeding -- variability; Livestock -- Periodicals; Livestock; Electronic journals; Periodicals; 636.005
• DOI: 10.1093/jas/sky404.258
• Languages: English
• ISSNs: 0021-8812

Abstract

Abstract: Routine inclusion of genomic information in livestock species has completed the first phase of genomic selection (GS) adoption as a breeding standard; however, the full potential of this tool is far from being realized. Since its introduction, genomic selection has revolutionized the breeding world with the opportunity of using DNA to generate fast and accurate individual predictions. A similar degree of change has yet to be seen in the utilization of genomic information to manage livestock populations. The reasons of success of GS are evident when looking at the tremendous impact it had on accelerating the rate of genetic gain. This has been achieved through substantially reducing the generation interval as candidates can be identified at an early age through the use of genetic markers to develop genomic breeding values with acceptable levels of accuracy. Furthermore, GS has enabled a significant boost in intensity through an increased number of candidates genotyped and available for selection. Yet, GS has not substantially changed the basic mechanisms of animal breeding, since genomic information is only used to effectively rank individuals at an earlier age based on their additive merit. Theory and early simulations suggested that implementation of GS should result in a lower rate of inbreeding per generation. However, experience has shown that sires selected on GEBV have a higher inbreeding than those selected using conventional approaches. GS offers considerable flexibility to boost genetic trends in traits of interest. It also provides an opportunity for more sustainable breeding in terms of fitness and genetic variability. In our work, we illustrate several examples of how GS can be used to better define inbreeding and how this can, in turn, be used to balance long term variability and short term gains for optimal genetic management of livestock populations.