DataSheet_1_AI-assisted selection of mating pairs through simulation-based optimized progeny allocation strategies in plant breeding.zip

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.

诸如基因组选择（genomic selection）这类新兴技术已应用于现代动植物育种，以提升品种审定释放的速度与效率。然而，育种工作需围绕亲本选择与交配组合制定决策，这些决策将显著影响育种方案的最终遗传增益。筛选适配亲本与交配组合，在提升遗传增益的同时维持遗传多样性，仍是育种从业者面临的迫切需求。本研究旨在结合面向未来的育种模拟与数值黑箱优化方法，确定最优后代分配策略，以优化亲本与交配组合的选择。本研究聚焦于后代分配的优化问题，将育种过程视为一项黑箱函数：其输入为与后代分配策略相关的参数集，输出为育种方案的最终遗传增益。各交配组合的后代分配量通过Softmax函数（softmax function）进行参数化，该函数的输入为分配相关多特征的加权和，涵盖后代预期遗传方差以及各类育种值等选择标准，以实现遗传增益与遗传多样性的最优平衡。随后，研究通过名为StoSOO的黑箱优化算法，结合面向未来的育种模拟，对上述加权参数进行优化。为评估所提新方法的潜力而开展的模拟研究表明，基于优化权重的育种策略仅需4个世代，即可实现相较于所有交配组合均等分配后代方案近10%的遗传增益。在各类优化策略中，考虑后代预期遗传方差的策略可在整个育种周期内维持遗传多样性，最终获得的遗传增益高于未纳入该考量因素的策略。上述结果表明，本研究提出的新方法可通过优化亲本与交配组合的选择决策，显著提升品种开发的速度与效率。此外，通过调整模拟设置，本研究所提出的面向未来的后代分配策略优化框架可轻松适配通用育种方案，助力高效快速的动植物育种工作。