Center for Quantitative Genetics and Genomics, Aarhus University, Blichers Alle 20, 8830 Tjele, Denmark.
Animal Breeding and Genomics Group, Wageningen University and Research, 6700 AH Wageningen, the Netherlands.
J Dairy Sci. 2021 Jul;104(7):8122-8134. doi: 10.3168/jds.2020-19823. Epub 2021 Apr 30.
National and international across-population selection is often recommended and fairly common in the current breeding practice of dairy cattle, with the primary aims to increase genetic gain and genetic variability. The aim of this study was to test the hypothesis that the strategy of truncation selection of sires across populations [i.e., competitive gene flow strategy (CGF)] may not necessarily maximize genetic gain in the long term in the presence of genotype-by-environment interaction (G×E). Two alternative strategies used to be compared with CGF were forced gene flow (FGF) strategies, with 10 or 50% of domestic dams forced to be mated with foreign sires (FGF10%, FGF50%). Two equal-size populations (N = 1,000) that were selected for the same breeding goal trait (h = 0.3) under G×E correlation (r) of either 0.9 or 0.8 were simulated to test these 3 different strategies. Each population first experienced either 5 or 20 differentiation generations (G), then 15 migration generations. Discrete generations were simulated for simplicity. Each population performed a within-population conventional breeding program during differentiation generations and the 3 across-population sire selection strategies based on joint genomic prediction during migration generations. The 4 Gr combinations defined 4 different levels of differentiation degree between the 2 populations at the start of migration. The true rate of inbreeding over the last 10 migration generations in each scenario was constrained at 0.01 to provide a fair basis for comparison of genetic gain across scenarios. Results showed that CGF maximized the genetic gain after 15 migration generations in 5_0.9 combination only, the case of the lowest differentiation degree, with a superiority of 0.4% (0.04 genetic SD units) over the suboptimal strategy. While in 5_0.8, 20_0.9, and 20_0.8 combinations, 2 FGF strategies had a superiority in genetic gain of 2.3 to 12.5% (0.21-1.07 genetic SD units) over CGF after 15 migration generations, especially FGF50%. The superiority of FGF strategies over CGF was that they alleviated inbreeding, introduced new genetic variance in the early migration period, and improved accuracy in the entire migration period. Therefore, we concluded that CGF does not necessarily maximize the genetic gain of across-population genomic breeding programs given moderate G×E. The across-population selection strategy remains to be optimized to maximize genetic gain.
国内外跨群体选择通常被推荐，并且在奶牛的当前育种实践中相当常见，其主要目的是增加遗传增益和遗传变异性。本研究的目的是检验这样一种假设，即通过跨群体截断选择（即竞争基因流策略，CGF）的策略可能不一定在存在基因型与环境互作（G×E）的情况下从长远来看最大化遗传增益。与 CGF 相比，曾使用两种替代策略，即强制基因流（FGF）策略，其中 10%或 50%的国内母畜被迫与国外父畜交配（FGF10%、FGF50%）。模拟了两个相等大小的群体（N=1000），在 G×E 相关系数（r）为 0.9 或 0.8 的情况下，选择相同的育种目标性状（h=0.3），以测试这 3 种不同的策略。每个群体首先经历 5 或 20 个分化世代（G），然后经历 15 个迁移世代。为了简单起见，模拟了离散世代。在分化世代期间，每个群体执行一个群体内常规育种计划，并在迁移世代中基于联合基因组预测执行 3 个跨群体父系选择策略。4 个 Gr 组合定义了在迁移开始时两个群体之间的 4 个不同分化程度。在每个场景中，最后 10 个迁移世代中的实际近交率被限制在 0.01，以提供跨场景遗传增益比较的公平基础。结果表明，仅在分化程度最低的 5_0.9 组合中，CGF 在 15 次迁移世代后最大化了遗传增益，其优势为 0.4%（0.04 个遗传标准差单位），优于次优策略。而在 5_0.8、20_0.9 和 20_0.8 组合中，2 种 FGF 策略在 15 次迁移世代后具有 2.3%至 12.5%（0.21-1.07 个遗传标准差单位）的遗传增益优势，特别是 FGF50%。FGF 策略优于 CGF 的优势在于它们减轻了近交，在早期迁移期引入了新的遗传方差，并在整个迁移期提高了准确性。因此，我们得出结论，在适度的 G×E 下，CGF 不一定最大化跨群体基因组育种计划的遗传增益。跨群体选择策略仍需优化以最大化遗传增益。
