Simulation-based future-oriented strategy can optimize decision making in a breeding program
Simulation-based future-oriented strategy can optimize decision making in a breeding program
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基于模拟的面向未来的策略可以优化育种计划中的决策
DOI：
10.1101/2023.01.25.525616
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Iwata Hiroyoshi
中科院分区：
文献类型：
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作者：
Hamazaki Kosuke;Iwata Hiroyoshi
基因组选择等新兴技术已应用于现代动植物育种，以提高品种发布的速度和效率。然而，育种需要关于亲本选择和交配对的决定，这显著影响育种方案的最终遗传增益。选择合适的亲本和交配对，在保持遗传多样性的同时增加遗传增益，仍然是育种者面临的迫切需要。本研究旨在通过结合面向未来的模拟和数值黑箱优化来确定最佳的子代分配策略，以改进亲本和交配对的选择。本研究重点关注后代的优化分配，将育种过程视为一个黑箱函数，其输入是与后代分配策略相关的一组参数，输出是育种方案的最终遗传增益。根据softmax函数将后代分配到每个交配对进行参数化，其输入是分配的多个特征的加权和，包括后代的预期遗传方差和选择标准，例如不同类型的育种值，以最佳地平衡遗传增益和遗传多样性。然后通过面向未来的育种模拟，通过称为StoSOO的黑箱优化算法优化加权参数。模拟研究，以评估我们的新方法的潜力表明，育种策略的基础上优化的重量达到了近10%的遗传增益比，在短短四代内的所有交配对的后代的平等分配。结果表明，考虑后代期望遗传方差的育种策略能够在整个育种过程中保持遗传多样性，最终遗传增益高于不考虑后代期望遗传方差的育种策略，通过对亲本和交配对的选择进行优化，可以显著提高品种选育的速度和效率。此外，通过改变模拟设置，我们面向未来的优化框架的后代分配策略可以很容易地实施到一般的育种方案，有助于加速植物和动物育种的高效率。
Emerging technologies such as genomic selection have been applied to modern plant and animal breeding to increase the speed and efficiency of variety release. However, breeding requires decisions regarding parent selection and mating pairs, which significantly impact the ultimate genetic gain of a breeding scheme. The selection of appropriate parents and mating pairs to increase genetic gain while maintaining genetic diversity is still an urgent need that breeders are facing. This study aimed to determine the best progeny allocation strategies by combining future-oriented simulations and numerical black-box optimization for an improved selection of parents and mating pairs. In this study, we focused on optimizing the allocation of progenies, and the breeding process was regarded as a black-box function whose input is a set of parameters related to the progeny allocation strategies and whose output is the ultimate genetic gain of breeding schemes. The allocation of progenies to each mating pair was parameterized according to a softmax function, whose input is a weighted sum of multiple features for the allocation, including expected genetic variance of progenies and selection criteria such as different types of breeding values, to balance genetic gains and genetic diversity optimally. The weighting parameters were then optimized by the black-box optimization algorithm called StoSOO via future-oriented breeding simulations. Simulation studies to evaluate the potential of our novel method revealed that the breeding strategy based on optimized weights attained almost 10% higher genetic gain than that with an equal allocation of progenies to all mating pairs within just four generations. Among the optimized strategies, those considering the expected genetic variance of progenies could maintain the genetic diversity throughout the breeding process, leading to a higher ultimate genetic gain than those without considering it. These results suggest that our novel method can significantly improve the speed and efficiency of variety development through optimized decisions regarding the selection of parents and mating pairs. In addition, by changing simulation settings, our future-oriented optimization framework for progeny allocation strategies can be easily implemented into general breeding schemes, contributing to accelerated plant and animal breeding with high efficiency.