Estimation of ryegrass (Lolium) dry matter yield using genomic prediction considering genotype by environment interaction across south-eastern Australia
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 (Lolium perenne L.) in a regional evaluation system across southeastern Australia. The stud...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1579376/full |
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| Summary: | 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 (Lolium perenne 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. |
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| ISSN: | 1664-462X |