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Assessment of long-term trends in genetic mean and variance after the introduction of genomic selection in layers: a simulation study

Authors
  • Pocrnic, Ivan1
  • Obšteter, Jana2
  • Gaynor, R. Chris1
  • Wolc, Anna3, 4
  • Gorjanc, Gregor1
  • 1 The University of Edinburgh, Edinburgh , (United Kingdom)
  • 2 Agricultural Institute of Slovenia, Ljubljana , (Slovenia)
  • 3 Iowa State University, Ames, IA , (United States)
  • 4 Hy-Line International, Dallas Center, IA , (United States)
Type
Published Article
Journal
Frontiers in Genetics
Publisher
Frontiers Media SA
Publication Date
May 10, 2023
Volume
14
Identifiers
DOI: 10.3389/fgene.2023.1168212
Source
Frontiers
Keywords
Disciplines
  • Genetics
  • Original Research
License
Green

Abstract

Nucleus-based breeding programs are characterized by intense selection that results in high genetic gain, which inevitably means reduction of genetic variation in the breeding population. Therefore, genetic variation in such breeding systems is typically managed systematically, for example, by avoiding mating the closest relatives to limit progeny inbreeding. However, intense selection requires maximum effort to make such breeding programs sustainable in the long-term. The objective of this study was to use simulation to evaluate the long-term impact of genomic selection on genetic mean and variance in an intense layer chicken breeding program. We developed a large-scale stochastic simulation of an intense layer chicken breeding program to compare conventional truncation selection to genomic truncation selection optimized with either minimization of progeny inbreeding or full-scale optimal contribution selection. We compared the programs in terms of genetic mean, genic variance, conversion efficiency, rate of inbreeding, effective population size, and accuracy of selection. Our results confirmed that genomic truncation selection has immediate benefits compared to conventional truncation selection in all specified metrics. A simple minimization of progeny inbreeding after genomic truncation selection did not provide any significant improvements. Optimal contribution selection was successful in having better conversion efficiency and effective population size compared to genomic truncation selection, but it must be fine-tuned for balance between loss of genetic variance and genetic gain. In our simulation, we measured this balance using trigonometric penalty degrees between truncation selection and a balanced solution and concluded that the best results were between 45° and 65°. This balance is specific to the breeding program and depends on how much immediate genetic gain a breeding program may risk vs. save for the future. Furthermore, our results show that the persistence of accuracy is better with optimal contribution selection compared to truncation selection. In general, our results show that optimal contribution selection can ensure long-term success in intensive breeding programs using genomic selection.

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