Zhu Jiashuai, Giri Khageswor, Lin Zibei, Cogan Noel O, Jacobs Joe L, Smith Kevin F
Faculty of Science, The University of Melbourne, Parkville, VIC, Australia.
Agriculture Victoria, AgriBio Centre, Bundoora, VIC, Australia.
Front Plant Sci. 2025 Jun 9;16:1579376. doi: 10.3389/fpls.2025.1579376. eCollection 2025.
Genomic Prediction (GP) considering Genotype by Environment (G×E) interactions was, for the first time, used to assess the environment-specific seasonal performance and genetic potential of perennial ryegrass ( L.) in a regional evaluation system across southeastern Australia. The study analysed the Dry Matter Yield (DMY) of 72 base cultivars and endophyte symbiotic effects using multi-harvest, multi-site trial data, and genomic data in a best linear unbiased prediction framework. Spatial analysis corrected for field heterogeneities, while Leave-One-Out Cross Validation assessed predictive ability. Results identified two distinct mega-environments: mainland Australia (AUM) and Tasmania (TAS), with cultivars showing environment-specific adaptation (Base and Bealey in AUM; Platinum and Avalon in TAS) or broad adaptability (Shogun). The G×E-enhanced GP model demonstrated an overall 24.9% improved predictive accuracy (Lin's Concordance Correlation Coefficient, CCC: 0.542) over the Australian industry-standard best linear unbiased estimation model (CCC: 0.434), with genomic information contributing a 12.7% improvement (CCC: from 0.434 to 0.489) and G×E modelling providing an additional 10.8% increase (CCC: from 0.489 to 0.542). Narrow-sense heritability increased from 0.31 to 0.39 with G×E inclusion, while broad-sense heritability remained high in both mega-environments (AUM: 0.73, TAS: 0.74). These findings support informed cultivar selection for the Australian dairy industry and enable genomics-based parental selection in future breeding programs.
在澳大利亚东南部的区域评估系统中，首次运用了考虑基因型与环境互作（G×E）的基因组预测（GP）来评估多年生黑麦草在特定环境下的季节性表现和遗传潜力。该研究在最佳线性无偏预测框架下，利用多收获期、多地点的试验数据以及基因组数据，分析了72个基础品种的干物质产量（DMY）和内生菌共生效应。空间分析校正了田间的异质性，同时采用留一法交叉验证来评估预测能力。结果确定了两个不同的大环境：澳大利亚大陆（AUM）和塔斯马尼亚（TAS），品种表现出特定环境适应性（AUM的Base和Bealey；TAS的Platinum和Avalon）或广泛适应性（Shogun）。与澳大利亚行业标准的最佳线性无偏估计模型相比（林氏一致性相关系数CCC：0.434），G×E增强的GP模型的预测准确性总体提高了24.9%（CCC：0.542），其中基因组信息贡献了12.7%的提高（CCC：从0.434提高到0.489），G×E建模额外提高了10.8%（CCC：从0.489提高到0.542）。纳入G×E后，狭义遗传力从0.31提高到0.39，而在两个大环境中广义遗传力均保持较高水平（AUM：0.73，TAS：0.74）。这些发现为澳大利亚乳业的品种选择提供了依据，并有助于未来育种计划中基于基因组学的亲本选择。
