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The challenge with selection on measures of phenotype is that they include both genetic and environmental effects, even if weights are adjusted and/or ratios (which limit comparisons to within contemporary groups) are utilized.  When selection decisions are made on these metrics, selection emphasis is also placed on nongenetic factors, which reduces the efficacy of selection and reduces genetic progress.  Superiority of selection using EPDs (or breeding values) as compared to phenotypes has been demonstrated<ref>Gall, GAE and Y Bakar.  2002.  Application of mixed-model techniques to fish breed improvement:  analysis of breeding-value selection to increase 98-day body weight in tilapia.  Aquaculture.  212(1-4):93-113.</ref><ref>Kuhlers, DL and BW Kennedy.  1992.  Effect of culling on selection response using phenotypic selection or best linear unbiased prediction of breeding values in small, closed herds of swine.  J Anim Sci.  70(8):2338-2348.</ref><ref>Belonsky, GM and BW Kennedy.  1988.  Selection on individual phenotype and best linear unbiased predictor of breeding values in a closed swine herd.  J. Anim Sci.  66:1124-1131.</ref><ref>Hagger, C. 1991.  Effects of selecting on phenotype, on index, or on breeding values, on expected response, genetic relationships, and accuracy of breeding values in an experiment.  J Anim Breed Genet.  108:102-110.</ref>.   
The challenge with selection on measures of phenotype is that they include both genetic and environmental effects, even if weights are adjusted and/or ratios (which limit comparisons to within contemporary groups) are utilized.  When selection decisions are made on these metrics, selection emphasis is also placed on nongenetic factors, which reduces the efficacy of selection and reduces genetic progress.  Superiority of selection using EPDs (or breeding values) as compared to phenotypes has been demonstrated<ref>Gall, GAE and Y Bakar.  2002.  Application of mixed-model techniques to fish breed improvement:  analysis of breeding-value selection to increase 98-day body weight in tilapia.  Aquaculture.  212(1-4):93-113.</ref><ref>Kuhlers, DL and BW Kennedy.  1992.  Effect of culling on selection response using phenotypic selection or best linear unbiased prediction of breeding values in small, closed herds of swine.  J Anim Sci.  70(8):2338-2348.</ref><ref>Belonsky, GM and BW Kennedy.  1988.  Selection on individual phenotype and best linear unbiased predictor of breeding values in a closed swine herd.  J. Anim Sci.  66:1124-1131.</ref><ref>Hagger, C. 1991.  Effects of selecting on phenotype, on index, or on breeding values, on expected response, genetic relationships, and accuracy of breeding values in an experiment.  J Anim Breed Genet.  108:102-110.</ref>.   
EPDs also simplify selection decisions.  Selection using phenotypes can involve the individual’s own phenotype as well as phenotypes on relatives (including progeny, parents, and siblings, as an example).  With Genetic Evaluation, all of this information is combined and weighted appropriately in a single value, the EPD, which simplifies selection.  This same value is even more relevant in the genomics era, because genomic testing provides another source of information for selection.   
EPDs also simplify selection decisions.  Selection using phenotypes can involve the individual’s own phenotype as well as phenotypes on relatives (including progeny, parents, and siblings, as an example).  With [[Genetic Evaluation]], all of this information is combined and weighted appropriately in a single value, the EPD, which simplifies selection.  This same value is even more relevant in the genomics era, because genomic testing provides another source of information for selection.   


The Beef Improvement Federation recommends using genomically-enhanced EPDs (see [[Single-step Genomic BLUP]] and [[Single-step Hybrid Marker Effects Models]]), as opposed to using disjoined marker scores and EPDs separately, as the best method for utilizing genomic data for selection<ref>Muir, WM.  2007.  Comparison of genomic and traditional BLUP-estimated breeding value accuracy and selection response under alternative trait and genomic parameters. J. Anim Brdg Genet.  124(6):342-355.</ref>.  Genetic Evaluation methodologies are always evolving and improving, but all of these methods incorporate all available data on an animal into EPD prediction, including genomic data, and weight it appropriately so that there is a single metric for genetic selection that represents the best estimate of that animal’s genetic merit using all available data.   
The Beef Improvement Federation recommends using genomically-enhanced EPDs (see [[Single-step Genomic BLUP]] and [[Single-step Hybrid Marker Effects Models]]), as opposed to using disjoined marker scores and EPDs separately, as the best method for utilizing genomic data for selection<ref>Muir, WM.  2007.  Comparison of genomic and traditional BLUP-estimated breeding value accuracy and selection response under alternative trait and genomic parameters. J. Anim Brdg Genet.  124(6):342-355.</ref>.  Genetic Evaluation methodologies are always evolving and improving, but all of these methods incorporate all available data on an animal into EPD prediction, including genomic data, and weight it appropriately so that there is a single metric for genetic selection that represents the best estimate of that animal’s genetic merit using all available data.   

Revision as of 04:49, 17 December 2019

The estimation of the value of an animal as a parent for the next generation, or Expected Progeny Difference (EPD), which is simply half of the animal's breeding value, was a major advancement in the ability to select animals to fit production goals. Prior to the development of EPDs the primary method for genetic improvement was some form of subjective visual appraisal[1]. Since the development of methodology to implement Genetic Evaluation in the beef industry (launched in the 1970s)[1], EPDs have been the gold standard for genetic selection. Regardless of their associated accuracy value, they are the best selection tool that producers have to improve genetic merit in a single trait, though indices incorporate EPD information and are the best tools for multiple-trait selection. Nevertheless, there is often confusion surrounding the best tools and information on which to make selection decisions. Phenotypes for quantitative traits are a combination of influences from both genetics (additive, dominance, epistatic) and the environment (permanent and temporary). Alternatively, we can write this as an equation as follows:

P=μ+G+E

where P represents phenotype, μ represents the average phenotypic value for all animals in the population, G is the genotypic value of the individual for the trait and E represents the environmental effect on the animal’s performance[2]. If we expand the equation to define genetic and environmental effects on the phenotype, we can write the equation as follows:

P=μ+A+D+I+EP+ET+GxE

where P and μ are as previously defined, A represents additive genetic effects, D represents dominance, I represents epistasis, EP represents permanent environmental effects, ET represents temporary environmental effects, and GxE represents interactions between genotype and environment[2][3].

EPDs are a function of the additive genetic merit of an individual and reflect its value as a parent. It is important to remember that environmental influences are not heritable, and the only genetic influence that is known to be stably inherited is additive genetic variation, though dominance can be managed through crossbreeding systems. EPDs and indices are the best tools for genetic selection and do reflect average progeny performance[4][5].

The challenge with selection on measures of phenotype is that they include both genetic and environmental effects, even if weights are adjusted and/or ratios (which limit comparisons to within contemporary groups) are utilized. When selection decisions are made on these metrics, selection emphasis is also placed on nongenetic factors, which reduces the efficacy of selection and reduces genetic progress. Superiority of selection using EPDs (or breeding values) as compared to phenotypes has been demonstrated[6][7][8][9]. EPDs also simplify selection decisions. Selection using phenotypes can involve the individual’s own phenotype as well as phenotypes on relatives (including progeny, parents, and siblings, as an example). With Genetic Evaluation, all of this information is combined and weighted appropriately in a single value, the EPD, which simplifies selection. This same value is even more relevant in the genomics era, because genomic testing provides another source of information for selection.

The Beef Improvement Federation recommends using genomically-enhanced EPDs (see Single-step Genomic BLUP and Single-step Hybrid Marker Effects Models), as opposed to using disjoined marker scores and EPDs separately, as the best method for utilizing genomic data for selection[10]. Genetic Evaluation methodologies are always evolving and improving, but all of these methods incorporate all available data on an animal into EPD prediction, including genomic data, and weight it appropriately so that there is a single metric for genetic selection that represents the best estimate of that animal’s genetic merit using all available data.

Interim EPDs

Most beef breed organizations and companies have transitioned to routinely perform their genetic evaluation. Often these routine analyses are performed as frequently as weekly. Prior to this change, many organizations performed genetic evaluations either annually or semi-annually, and thus interim EPD[11] computations were done so that breeders could have at least an early estimate of genetic merit for selection decisions. As noted above with more routine runs of the genetic evaluation, interim EPDs now exist for shorter periods in an animal's life.

References:


  1. ↑ 1.0 1.1 Golden, BL, DJ Garrick, and LL Benyshek. 2009. Milestones in beef cattle genetic evaluation. J Anim Sci. 87(E. Suppl.):E3-E10.
  2. ↑ 2.0 2.1 Bourdon, RM. 2000. Understanding Animal Breeding. Second edition. Prentice Hall, Upper Saddle River, NJ.
  3. ↑ Pierce, BA. 2016. Genetics Essentials. Third edition. MacMillan, New York, New York.
  4. ↑ Thrift, FA and TA Thrift. 2006. Review: Expected versus realized progeny differences for various beef cattle traits. Prof Anim Sci. 22:413-423.
  5. ↑ Kuehn, LA and RM Thallman. 2017. Across-breed EPD tables for the year 2017 adjusted to breed differences for birth year of 2015. Proceedings of the Beef Improvement Federation Annual Meeting and Research Symposium. Pages 112-144.
  6. ↑ Gall, GAE and Y Bakar. 2002. Application of mixed-model techniques to fish breed improvement: analysis of breeding-value selection to increase 98-day body weight in tilapia. Aquaculture. 212(1-4):93-113.
  7. ↑ Kuhlers, DL and BW Kennedy. 1992. Effect of culling on selection response using phenotypic selection or best linear unbiased prediction of breeding values in small, closed herds of swine. J Anim Sci. 70(8):2338-2348.
  8. ↑ Belonsky, GM and BW Kennedy. 1988. Selection on individual phenotype and best linear unbiased predictor of breeding values in a closed swine herd. J. Anim Sci. 66:1124-1131.
  9. ↑ Hagger, C. 1991. Effects of selecting on phenotype, on index, or on breeding values, on expected response, genetic relationships, and accuracy of breeding values in an experiment. J Anim Breed Genet. 108:102-110.
  10. ↑ Muir, WM. 2007. Comparison of genomic and traditional BLUP-estimated breeding value accuracy and selection response under alternative trait and genomic parameters. J. Anim Brdg Genet. 124(6):342-355.
  11. ↑ Wilson, D. E. and R. L. Willham. 1988. Interim Expected Progeny Differences for Young Animals Not Included in National Cattle Evaluations. Journal of Animal Science, Volume 66, Issue 3, March 1988, Pages 618–625, https://doi.org/10.2527/jas1988.663618x
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