Institute of Plant Breeding, Seed Science, and Population Genetics, University of Hohenheim, 70593, Stuttgart, Germany.
Theor Appl Genet. 2011 Jun;123(1):1-10. doi: 10.1007/s00122-011-1561-4. Epub 2011 Mar 9.
With best linear unbiased prediction (BLUP), information from genetically related candidates is combined to obtain more precise estimates of genotypic values of test candidates and thereby increase progress from selection. We developed and applied theory and Monte Carlo simulations implementing BLUP in 2 two-stage maize breeding schemes and various selection strategies. Our objectives were to (1) derive analytical solutions of the mixed model equations under two breeding schemes, (2) determine the optimum allocation of test resources with BLUP under different assumptions regarding the variance component ratios for grain yield in maize, (3) compare the progress from selection using BLUP and conventional phenotypic selection based on mean performance solely of the candidates, and (4) analyze the potential of BLUP for further improving the progress from selection. The breeding schemes involved selection for testcross performance either of DH lines at both stages (DHTC) or of S(1) families at the first stage and DH lines at the second stage (S(1)TC-DHTC). Our analytical solutions allowed much faster calculations of the optimum allocations and superseded matrix inversions to solve the mixed model equations. Compared to conventional phenotypic selection, the progress from selection was slightly higher with BLUP for both optimization criteria, namely the selection gain and the probability to select the best genotypes. The optimum allocation of test resources in S(1)TC-DHTC involved ≥ 10 test locations at both stages, a low number of crosses (≤ 6) each with 100-300 S(1) families at the first stage, and 500-1,000 DH lines at the second stage. In breeding scheme DHTC, the optimum number of test candidates at the first stage was 5-10 times larger, whereas the number of test locations at the first stage and the number of test candidates at the second stage were strongly reduced compared to S(1)TC-DHTC.
采用最佳线性无偏预测（BLUP），将遗传相关候选者的信息结合起来，可以更精确地估计候选者的基因型值，从而提高选择的进展。我们开发并应用了理论和蒙特卡罗模拟，在两个两阶段玉米育种方案和各种选择策略中实施 BLUP。我们的目标是：（1）推导出两个育种方案下混合模型方程的解析解；（2）确定在不同的玉米产量方差分量比假设下，BLUP 下测试资源的最优分配；（3）比较基于候选者仅平均表现的 BLUP 和常规表型选择的选择进展；（4）分析 BLUP 进一步提高选择进展的潜力。这些育种方案涉及选择 DH 系的测验交表现（在两个阶段均进行选择，即 DHTC）或 S(1)系的测验交表现（仅在第一阶段进行选择，然后在第二阶段选择 DH 系，即 S(1)TC-DHTC）。我们的解析解可以更快速地计算最优分配，并且取代了矩阵求逆来求解混合模型方程。与常规表型选择相比，BLUP 对于两种优化标准（即选择增益和选择最佳基因型的概率）的选择进展略高。在 S(1)TC-DHTC 中，最优的测试资源分配涉及在两个阶段都有≥10 个测试位置，每个阶段的交叉数量较少（≤6），每个阶段有 100-300 个 S(1)系，在第二阶段有 500-1000 个 DH 系。在 DHTC 育种方案中，第一阶段的最优测试候选者数量比 S(1)TC-DHTC 大 5-10 倍，而第一阶段的测试位置数量和第二阶段的测试候选者数量与 S(1)TC-DHTC 相比大幅减少。
