Estimating Breeding Values: Principles and Applications

The concept of breeding value is paramount in animal breeding. It represents an individual’s genetic potential as a parent, reflecting alleles that can be passed to offspring. Accurately estimating breeding values is crucial for selecting animals that will produce desirable progeny traits, leading to genetic improvement. This article explores breeding value estimation principles, applications, and genomic information’s impact.

Understanding Breeding Values

Breeding values are typically estimated based on an individual’s progeny performance deviation from a reference population’s average performance. This deviation is then multiplied by heritability’s square root to obtain the estimated breeding value (EBV). The EBV formula is:

EBV=h2⋅PEBV=h2⋅Pwhere h2h2 is heritability and PP is phenotypic deviation from the mean. Heritability is crucial in breeding value estimation because it represents the proportion of phenotypic variance attributable to genetic variance. Moreover, higher trait heritability allows more accurate breeding value estimation based on phenotypic information.

Accuracy of Breeding Value Estimation

The accuracy of breeding value estimation measures how well the estimated breeding value (EBV) reflects an individual’s true breeding value (TBV). It ranges from 0 to 1, with higher values indicating greater accuracy. The accuracy of EBV is influenced by several factors, such as the amount of information, heritability, and the relationship to selection candidates. For instance, more phenotypic and pedigree information increases EBV accuracy. Furthermore, higher heritability traits tend to have more accurate EBVs. Additionally, information from closer relatives, like offspring and full siblings, contributes more to EBV accuracy than information from more distant relatives. The accuracy of EBV is important because it determines the expected response to selection. In other words, the higher the accuracy, the greater the response to selection based on EBV.

Genomic Selection and Breeding Value Estimation

The advent of genomic selection has revolutionized breeding value estimation. By incorporating genomic information into the estimation process, breeders can achieve higher accuracy and faster genetic progress. Genomic selection involves predicting the breeding values of selection candidates based on their genotypes at thousands of genetic markers distributed across the genome. One of the main advantages of genomic selection is its ability to differentiate the breeding values of full siblings. In conventional breeding value estimation, full siblings have the same expected breeding value based on their pedigree information. However, due to Mendelian sampling, full siblings can have different true breeding values. Genomic selection allows for more accurate estimation of these differences, leading to more efficient selection.

Optimizing Breeding Programs with Genomic Selection

To optimize the design of breeding programs using genomic selection, it is essential to understand selection index theory. This theory helps predict the outcome of performance recording, genetic evaluation, and selection in a breeding program. Several selection schemes can be modeled to compare the efficiency of different strategies, such as conventional selection (CS), genomic selection (GS1), genomic selection (GS2), and genomic selection (GS3). By comparing the expected genetic gain and accuracy of these selection schemes, breeders can optimize their breeding programs to maximize long-term genetic progress.

Practical Considerations in Breeding Value Estimation

When implementing genomic selection, there are several practical considerations to keep in mind. First, incorporating genomic evaluation into a breeding program involves additional costs for sample collection, genotyping, and data processing. It is essential to evaluate the economic feasibility of this investment and plan for the payback period. Second, the accuracy of genomic prediction depends on the size and composition of the reference population. A larger reference population with high-quality phenotypes and genotypes will result in more accurate GEBVs. Finally, it is crucial to validate genomic predictions by assessing the correlation and regression of highly reliable breeding values or adjusted phenotypes on GEBVs. This process ensures the consistency and unbiasedness of the genomic predictions.

Conclusion

In conclusion, breeding value estimation is a fundamental concept in animal breeding that enables breeders to select individuals with superior genetic potential for desired traits. The incorporation of genomic information has significantly improved the accuracy and efficiency of breeding value estimation, leading to faster genetic progress in livestock populations. 

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