Diot Julien, Iwata Hiroyoshi
Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan.
Front Plant Sci. 2023 Jan 11;13:1050198. doi: 10.3389/fpls.2022.1050198. eCollection 2022.
Advances in genotyping technologies have provided breeders with access to the genotypic values of several thousand genetic markers in their breeding materials. Combined with phenotypic data, this information facilitates genomic selection. Although genomic selection can benefit breeders, it does not guarantee efficient genetic improvement. Indeed, multiple components of breeding schemes may affect the efficiency of genetic improvement and controlling all components may not be possible. In this study, we propose a new application of Bayesian optimisation for optimizing breeding schemes under specific constraints using computer simulation.
Breeding schemes are simulated according to nine different parameters. Five of those parameters are considered constraints, and 4 can be optimised. Two optimisation methods are used to optimise those parameters, Bayesian optimisation and random optimisation.
The results show that Bayesian optimisation indeed finds breeding scheme parametrisations that provide good breeding improvement with regard to the entire parameter space and outperforms random optimisation. Moreover, the results also show that the optimised parameter distributions differ according to breeder constraints.
This study is one of the first to apply Bayesian optimisation to the design of breeding schemes while considering constraints. The presented approach has some limitations and should be considered as a first proof of concept that demonstrates the potential of Bayesian optimisation when applied to breeding schemes. Determining a general "rule of thumb" for breeding optimisation may be difficult and considering the specific constraints of each breeding campaign is important for finding an optimal breeding scheme.
基因分型技术的进步使育种者能够获取其育种材料中数千个遗传标记的基因型值。结合表型数据，这些信息有助于进行基因组选择。虽然基因组选择可以使育种者受益，但它并不能保证有效的遗传改良。实际上，育种方案的多个组成部分可能会影响遗传改良的效率，而且控制所有组成部分可能是不可能的。在本研究中，我们提出了贝叶斯优化的一种新应用，即使用计算机模拟在特定约束条件下优化育种方案。
根据九个不同参数模拟育种方案。其中五个参数被视为约束条件，四个参数可以进行优化。使用两种优化方法来优化这些参数，即贝叶斯优化和随机优化。
结果表明，贝叶斯优化确实找到了在整个参数空间内能够实现良好育种改良的育种方案参数化，并且优于随机优化。此外，结果还表明，优化后的参数分布因育种者的约束条件而异。
本研究是最早将贝叶斯优化应用于考虑约束条件的育种方案设计的研究之一。所提出的方法有一些局限性，应被视为一个初步的概念验证，展示了贝叶斯优化应用于育种方案时的潜力。确定育种优化的一般“经验法则”可能很困难，考虑每个育种活动的具体约束条件对于找到最优育种方案很重要。
