Comparison of genomic prediction models for general combining ability in early stages of hybrid breeding programs
Comparison of genomic prediction models for general combining ability in early stages of hybrid breeding programs
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杂交育种项目早期一般配合力基因组预测模型的比较
DOI：
10.1002/csc2.21105
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发表时间：
2023
期刊：
影响因子：
2.3
通讯作者：
De Jong G
中科院分区：
文献类型：
--
作者：
De Jong G
本研究评估了基因组预测模型对在杂交育种计划中选择自交系作为亲本的影响。杂交育种计划中的新亲本通常是根据测交的一般配合力（GCA）从早期产量试验中选择的。基因组研究主要集中于预测育种后期的杂交表现，而很大程度上忽略了自交系作为后续育种周期亲本的选择。在这里，我们对玉米 (Zea maysL.) 杂交育种计划进行了 20 年的随机模拟，以评估基因组预测模型的性能，该模型用于根据预测的 GCA 选择亲本。根据两种不同单核苷酸多态性（SNP）标记密度和真实数量性状基因座基因型下所获得的遗传增益和杂种优势评估了五种基因组预测模型。结果表明，使用高密度 SNP 标记比低密度 SNP 标记产生更多的遗传增益和杂种优势。基因组预测模型的相对性能因标记场景而异。对于遗传增益，我们观察到低标记密度的模型之间的差异大于高标记密度的模型之间的差异。对于杂种优势，我们观察到相反的情况，高标记密度模型之间的差异大于低标记密度模型之间的差异。总体而言，适合每个杂种优势库和优势效应的平均或加性效应的模型提供了更好的拟合，因此在杂交育种计划中提供了更高的遗传增益。
This study evaluates the impact of genomic prediction models on selecting inbred lines as parents in hybrid breeding programs. New parents in a hybrid breeding program are typically selected from early‐stage yield trials based on general combining ability (GCA) from testcrosses. Genomic studies have largely focused on predicting hybrid performance in the late stages of the breeding pipeline and largely ignored the selection of inbred lines as parents of the subsequent breeding cycles. Here, we used stochastic simulations of a maize (Zea maysL.) hybrid breeding program for 20 years to evaluate the performance of genomic prediction models for selecting parents based on their predicted GCA. Five genomic prediction models were evaluated in terms of achieved genetic gain and heterosis under two different single nucleotide polymorphism (SNP) marker densities and the true quantitative trait loci genotypes. The results show that using high‐density SNP markers generated more genetic gain and heterosis than the low‐density SNP markers. The relative performance of genomic prediction models differed across marker scenarios. For genetic gain, we observed more differences between the models at low than high marker density. For heterosis, we observed the opposite, more differences between the models at high than low marker density. Overall, models that fitted the average or additive effects specific to each heterotic pool and dominance effects provide a better fit and hence higher genetic gain in hybrid breeding programs.