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Genetic Evaluation: Difference between revisions
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Predicting genetic merit for breeding animals is one of the oldest practices that mankind has used to improve food and fiber production. Identifying animals for [[Selection and Mating | selection and mating]] has evolved from visual appraisal to sophisticated analytical models for predicting [[Glossary#A | additive genetic]] merit of animals. Additive genetic merit is the effect of genes that are passed from parent to offspring that can be used to make genetic progress through selection. | |||
In North America, the standard for identifying genetic merit of breeding animals is [[Expected Progeny Difference | expected progeny differences (EPDs)]]. | |||
With very few ''ad hoc'' exceptions, EPDs are produced for North American beef cattle using models based on [[Best Linear Unbiased Prediction]]. Consequently, [[BIF recommends the use of EPD]] when available. | |||
While not all [[Economically Relevant Traits | economically relevant traits]] in all situations and in all North American breed registries have EPDs available, the number of [[Traits | traits and trait components]] that have EPDs has increased dramatically. | |||
Nearly all the major North American beef cattle breed organizations have migrated to weekly genetic evaluations, eliminating the need for [[Expected Progeny Difference#Interim EPDs | interim EPDs]]. | |||
Most of the improvements in the technologies used in genetic evaluation have been motivated by an opportunity to increase [[Accuracy | accuracy of prediction]] and reduce [[Prediction Bias | bias]]. For example, the advent of [[Genotyping | genomic information]] to enhance the [[Accuracy | accuracy]] of prediction has resulted in EPDs for most traits being produced using either [[Single-step Genomic BLUP]] or [[Single-step Hybrid Marker Effects Models]]. The BIF has developed an extensive set of recommendations for the inclusion of [[Genomic Evaluation Guidelines | genomic data in genetic evaluations]]. | |||
[[ | |||
In commercial cattle production, EPDs for [[Economically Relevant Traits | economically relevant traits]] should be combined with appropriate selection tools such as [[Selection Index | selection indices]] to make optimal genetic progress toward achieving [[Breeding Objectives | breeding objectives]]. It must be remembered that EPDs are just tools to make selection decisions to make genetic progress and manage certain genetic risks. | |||
In some special situations in seedstock production breeders may need to make selection decisions using EPDs that are not [[Economically Relevant Traits | economically relevant traits]] in commercial settings in order to enhance the marketability of their breed or breeding animals. For example, if a breed has a perceived defect that is limiting that breed organizations' members from expanding their market for selling germplasm, then selection to improve that characteristic should be included in the seedstock breeder's [[Breeding Objectives | breeding objectives]]. | |||
Critical to genetic evaluation is having high-quality estimates of [[Variance Components | variance components]]. Knowing the heritabilities and correlations of the traits and performing [[Multiple Trait Evaluation | Multiple-Trait Evaluation]] enhances the accuracy of prediction and reduces [[Prediction Bias | bias]] from effects such as incomplete reporting. Equally critical is understanding the [[Connectedness | connectedness]] of the data in a particular data set. Disconnected data can lead to invalid comparisons. | |||
Latest revision as of 17:19, 12 April 2021
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Predicting genetic merit for breeding animals is one of the oldest practices that mankind has used to improve food and fiber production. Identifying animals for selection and mating has evolved from visual appraisal to sophisticated analytical models for predicting additive genetic merit of animals. Additive genetic merit is the effect of genes that are passed from parent to offspring that can be used to make genetic progress through selection.
In North America, the standard for identifying genetic merit of breeding animals is expected progeny differences (EPDs). With very few ad hoc exceptions, EPDs are produced for North American beef cattle using models based on Best Linear Unbiased Prediction. Consequently, BIF recommends the use of EPD when available.
While not all economically relevant traits in all situations and in all North American breed registries have EPDs available, the number of traits and trait components that have EPDs has increased dramatically. Nearly all the major North American beef cattle breed organizations have migrated to weekly genetic evaluations, eliminating the need for interim EPDs.
Most of the improvements in the technologies used in genetic evaluation have been motivated by an opportunity to increase accuracy of prediction and reduce bias. For example, the advent of genomic information to enhance the accuracy of prediction has resulted in EPDs for most traits being produced using either Single-step Genomic BLUP or Single-step Hybrid Marker Effects Models. The BIF has developed an extensive set of recommendations for the inclusion of genomic data in genetic evaluations.
In commercial cattle production, EPDs for economically relevant traits should be combined with appropriate selection tools such as selection indices to make optimal genetic progress toward achieving breeding objectives. It must be remembered that EPDs are just tools to make selection decisions to make genetic progress and manage certain genetic risks.
In some special situations in seedstock production breeders may need to make selection decisions using EPDs that are not economically relevant traits in commercial settings in order to enhance the marketability of their breed or breeding animals. For example, if a breed has a perceived defect that is limiting that breed organizations' members from expanding their market for selling germplasm, then selection to improve that characteristic should be included in the seedstock breeder's breeding objectives.
Critical to genetic evaluation is having high-quality estimates of variance components. Knowing the heritabilities and correlations of the traits and performing Multiple-Trait Evaluation enhances the accuracy of prediction and reduces bias from effects such as incomplete reporting. Equally critical is understanding the connectedness of the data in a particular data set. Disconnected data can lead to invalid comparisons.
