To date, there have been studies demonstrating the use of genome-enabled selection within the overall swine breeding selection program. However, little or no scientific literature conveying the related economic considerations for genomic selection has been published. This is an important missing piece of knowledge. As technology improves, the cost of genotyping will continue to decrease, and it is likely that more swine breeders will have the ability to take advantage of genome-enabled selection. This research will allow producers to assess the costs associated with genome-enabled selection to determine the potential for a sufficient return on investment from using genome-enabled selection in a breeding program.
The objective of this study was to develop a tool to determine the cost structure associated with incorporating genome-enabled selection into commercial breeding programs. Assuming 1000 sow nucleus populations for both maternal line genetic programs and genotyping all male and female selection candidates at low density and all animals used for breeding at high density, it was determined that genome-enabled selection costs would be approximately $0.08 per weaned pig in the commercial production system assuming that the boars produced in the nucleus are used at capacity. For a 600 sow terminal line nucleus herd and genotyping only male selection candidates at a low density, the cost per weaned pig in the commercial herd was determined to be $0.05. This means that $0.21 per weaned pig from boars produced in the nucleus would need to be added to the genetic merit value for each market pig in order to break even on the added expense when genome-enabled selection is utilized at the nucleus herd of the breeding pyramid.
Using genomic information to determine an animal’s genetic merit at the molecular level can improve estimated breeding value (EBV) accuracy when compared to an EBV based solely on phenotypic records. However, genome-enabled selection is expensive and the increased genetic improvement rate must be large enough to offset the costs associated with incorporating genome-enabled selection into a breeding program. A flexible spreadsheet tool developed from this work can be utilized to estimate the returns needed to recover additional costs associated with genome-enabled selection by modifying the input values such as herd size and genotyping strategy to represent the specific design of any breeding and production system.