Plant Breeding, TUM School of Life Sciences, Technical University of Munich, 85354, Freising, Germany.
Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70599, Stuttgart, Germany.
Theor Appl Genet. 2023 Aug 31;136(9):203. doi: 10.1007/s00122-023-04446-3.
Genomic prediction of GCA effects based on model training with full-sib rather than half-sib families yields higher short- and long-term selection gain in reciprocal recurrent genomic selection for hybrid breeding, if SCA effects are important. Reciprocal recurrent genomic selection (RRGS) is a powerful tool for ensuring sustainable selection progress in hybrid breeding. For training the statistical model, one can use half-sib (HS) or full-sib (FS) families produced by inter-population crosses of candidates from the two parent populations. Our objective was to compare HS-RRGS and FS-RRGS for the cumulative selection gain ([Formula: see text]), the genetic, GCA and SCA variances ([Formula: see text],[Formula: see text], [Formula: see text]) of the hybrid population, and prediction accuracy ([Formula: see text]) for GCA effects across cycles. Using SNP data from maize and wheat, we simulated RRGS programs over 10 cycles, each consisting of four sub-cycles with genomic selection of [Formula: see text] out of 950 candidates in each parent population. Scenarios differed for heritability [Formula: see text] and the proportion [Formula: see text] of traits, training set (TS) size ([Formula: see text]), and maize vs. wheat. Curves of [Formula: see text] over selection cycles showed no crossing of both methods. If [Formula: see text] was high, [Formula: see text] was generally higher for FS-RRGS than HS-RRGS due to higher [Formula: see text]. In contrast, HS-RRGS was superior or on par with FS-RRGS, if [Formula: see text] or [Formula: see text] and [Formula: see text] were low. [Formula: see text] showed a steeper increase and higher selection limit for scenarios with low [Formula: see text], high [Formula: see text] and large [Formula: see text]. [Formula: see text] and even more so [Formula: see text] decreased rapidly over cycles for both methods due to the high selection intensity and the role of the Bulmer effect for reducing [Formula: see text]. Since the TS for FS-RRGS can additionally be used for hybrid prediction, we recommend this method for achieving simultaneously the two major goals in hybrid breeding: population improvement and cultivar development.
基于全同胞而不是半同胞家系进行模型训练的 GCA 效应基因组预测,如果 SCA 效应很重要,那么在杂交种杂种优势的正反交轮回基因组选择中会产生更高的短期和长期选择增益。正反交轮回基因组选择(RRGS)是确保杂种优势持续选择进展的有力工具。为了训练统计模型,可以使用通过来自两个亲本种群的候选者的种群间杂交产生的半同胞(HS)或全同胞(FS)家系。我们的目标是比较 HS-RRGS 和 FS-RRGS 对累积选择增益 ([Formula: see text])、杂种群体的遗传、GCA 和 SCA 方差 ([Formula: see text],[Formula: see text], [Formula: see text]) 以及整个循环中 GCA 效应的预测准确性 ([Formula: see text])。使用来自玉米和小麦的 SNP 数据,我们模拟了 10 个周期的 RRGS 程序,每个周期由四个子周期组成,每个亲本群体中有 950 个候选者中的 950 个进行基因组选择。对于遗传率 [Formula: see text] 和性状比例 [Formula: see text]、训练集 (TS) 大小 ([Formula: see text]) 和玉米与小麦的情况,方案有所不同。随着选择周期的增加,[Formula: see text] 的曲线没有交叉两种方法。如果 [Formula: see text] 较高,则由于 [Formula: see text] 较高,FS-RRGS 通常比 HS-RRGS 的 [Formula: see text] 更高。相反,如果 [Formula: see text] 或 [Formula: see text] 且 [Formula: see text] 较低,则 HS-RRGS 优于或与 FS-RRGS 相当。如果方案具有较低的 [Formula: see text]、较高的 [Formula: see text] 和较大的 [Formula: see text],则 [Formula: see text] 显示出更陡峭的增加和更高的选择极限。对于这两种方法,由于选择强度高且 Bulmer 效应降低 [Formula: see text] 的作用,因此 [Formula: see text] 和 [Formula: see text] 在整个循环中都迅速下降。由于 FS-RRGS 的 TS 还可以用于杂种预测,因此我们建议使用这种方法同时实现杂种优势中的两个主要目标:群体改良和品种开发。