Pocrnic Ivan, Obšteter Jana, Gaynor R Chris, Wolc Anna, Gorjanc Gregor
The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Edinburgh, United Kingdom.
Agricultural Institute of Slovenia, Ljubljana, Slovenia.
Front Genet. 2023 May 10;14:1168212. doi: 10.3389/fgene.2023.1168212. eCollection 2023.
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.
基于核心群的育种计划的特点是高强度选择，这会带来高遗传增益，但不可避免地意味着育种群体中遗传变异的减少。因此，此类育种系统中的遗传变异通常会得到系统管理，例如，通过避免近亲交配来限制后代近亲繁殖。然而，高强度选择需要付出最大努力，以使此类育种计划能够长期持续。本研究的目的是通过模拟评估基因组选择对蛋鸡高强度育种计划中遗传均值和方差的长期影响。我们开发了一个蛋鸡高强度育种计划的大规模随机模拟，以比较传统的截断选择与通过最小化后代近亲繁殖或全规模最优贡献选择进行优化的基因组截断选择。我们从遗传均值、基因方差、转化效率、近亲繁殖率、有效种群大小和选择准确性等方面对这些计划进行了比较。我们的结果证实，与传统截断选择相比，基因组截断选择在所有指定指标上都有直接优势。在基因组截断选择后简单地最小化后代近亲繁殖并没有带来任何显著改善。与基因组截断选择相比，最优贡献选择在转化效率和有效种群大小方面更成功，但必须对其进行微调，以平衡遗传方差的损失和遗传增益。在我们的模拟中，我们使用截断选择与平衡解决方案之间的三角惩罚度来衡量这种平衡，并得出最佳结果在45°至65°之间。这种平衡特定于育种计划，取决于育种计划为了未来可能冒多大的直接遗传增益风险以及保留多少。此外，我们的结果表明，与截断选择相比，最优贡献选择的准确性持续性更好。总体而言，我们的结果表明，最优贡献选择能够确保在使用基因组选择的集约化育种计划中取得长期成功。
