Genotype recommendations for high performance and stability based on multiple traits selection across a multi-environment in rapeseed

特质 最佳线性无偏预测 油菜籽 选择(遗传算法) 阿米 理论(学习稳定性) 基因-环境相互作用 生物技术 基因型 生物 统计 农学 数学 计算机科学 遗传学 机器学习 基因 程序设计语言
作者
Zhaojie Li,Wei Wu
出处
期刊:European Journal of Agronomy [Elsevier]
卷期号:145: 126787-126787 被引量:5
标识
DOI:10.1016/j.eja.2023.126787
摘要

Breeding programs for rapeseed genotypes with high yield potential and other critically targeted parameters such as high oil content and lodging resistance are important strategies for sustainable rapeseed (Brassica napus) production. However, the selection and recommendation of ideal genotypes with high performance and stability across multiple environments based on multiple traits has always been a difficult task. In this regard, two popular methods–additive main effect and multiplicative interaction (AMMI) and best linear unbiased prediction (BLUP), were adopted for analyzing the genotype × environment interaction. A superiority index (WAASBY) was introduced to integrate the mean performance and stability of the single traits. A multi–trait stability index (MTSI) was applied for genotype recommendation in combination with high performance and stability, based on multiple traits across five site–year environments. In the present multi–environment trials, the dataset (involving 41 recorded parameters assessed in 23 genotypes) was used to illustrate the application of genotype recommendations. The results showed that the reliability of the BLUP model for selecting a single parameter was assured because of the high genotypic accuracy of selection (ranging from 0.81 to 0.97). Genotype recommendations for mean performance and stability based on a single trait are partial and prejudiced, whereas selection based on multiple traits is desirable. The feasibility of MTSI application for genotype recommendations considering multiple traits was further evidenced by multiple analytical and statistical approaches. The MTSI always showed a significant and consistent relationship with WAASBY for all targeted parameters, i.e., seed yield, oil content and lodging resistance (R2 =0.26*–0.80**). Three ideal genotypes (Huayouza 50, Qingyou 3 and Zheyou 51) were selected in consideration of both the mean performance and stability of three targeted parameter, as represented by lower MTSI (1.60–1.69). This study implies that MTSI is an accurate, robust, and easy–to–handle indicator for breeders and agronomists who desire simultaneous genotype selection based on multiple traits to achieve high mean performance and stability across multiple environments.

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