Abstract
BackgroundDetermining an animal’s genetic merit using genomic information can improve estimated breeding value (EBV) accuracy; however, the magnitude of the accuracy improvement must be large enough to recover the costs associated with implementing genome-enabled selection. One way to reduce costs is to genotype nucleus herd selection candidates using a low-density chip and to use high-density chip genotyping for animals that are used as parents in the nucleus breeding herd. The objective of this study was to develop a tool to estimate the cost structure associated with incorporating genome-enabled selection into multi-level commercial breeding programs.ResultsFor the purpose of this deterministic study, it was assumed that a commercial pig is created from a terminal line sire and a dam that is a cross between two maternal lines. It was also assumed that all male and female selection candidates from the 1000 sow maternal line nucleus herds were genotyped at low density and all animals used for breeding at high density. With the assumptions used in this analysis, it was estimated that genome-enabled selection costs for a maternal line would be approximately US$0.082 per weaned pig in the commercial production system. A total of US$0.164 per weaned pig is needed to incorporate genome-enabled selection into the two maternal lines. Similarly, for a 600 sow terminal line nucleus herd and genotyping only male selection candidates with the low-density panel, the cost per weaned pig in the commercial herd was estimated to be US$0.044. This means that US$0.21 per weaned pig produced at the commercial level and sired by boars obtained from the nucleus herd breeding program needs to be added to the genetic merit value in order to break even on the additional cost required when genome-enabled selection is used in both maternal lines and the terminal line.ConclusionsBy modifying the input values, such as herd size and genotyping strategy, a flexible spreadsheet tool developed from this work can be used to estimate the additional costs associated with genome-enabled selection. This tool will aid breeders in estimating the economic viability of incorporating genome-enabled selection into their specific breeding program.
Highlights
Determining an animal’s genetic merit using genomic information can improve estimated breeding value (EBV) accuracy; the magnitude of the accuracy improvement must be large enough to recover the costs associated with implementing genome-enabled selection
Traditional BLUP selection relies on phenotypic information measured directly on selection candidates and their relatives to predict the genetic merit for all animals
Traits that are lowly heritable, difficult to measure, sex-limited, measured later in life, or measured after slaughter, have the greatest potential for accuracy improvement when using genome-enabled breeding value estimation compared to traits that can be directly measured on all the candidates before selection
Summary
Determining an animal’s genetic merit using genomic information can improve estimated breeding value (EBV) accuracy; the magnitude of the accuracy improvement must be large enough to recover the costs associated with implementing genome-enabled selection. The objective of this study was to develop a tool to estimate the cost structure associated with incorporating genome-enabled selection into multi-level commercial breeding programs. When breeders incorporate the variation from genomic information into a selection strategy, it is known as genomeenabled selection. This information is used to enhance traditional breeding value estimation. Estimating an animal’s genetic merit at the molecular level may improve estimated breeding value (EBV) accuracy [2]. Traits that are lowly heritable, difficult to measure, sex-limited, measured later in life, or measured after slaughter, have the greatest potential for accuracy improvement when using genome-enabled breeding value estimation compared to traits that can be directly measured on all the candidates before selection
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