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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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1579376/full |
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| author | Jiashuai Zhu Jiashuai Zhu Khageswor Giri Zibei Lin Noel O. Cogan Noel O. Cogan Joe L. Jacobs Joe L. Jacobs Joe L. Jacobs Kevin F. Smith Kevin F. Smith |
| author_facet | Jiashuai Zhu Jiashuai Zhu Khageswor Giri Zibei Lin Noel O. Cogan Noel O. Cogan Joe L. Jacobs Joe L. Jacobs Joe L. Jacobs Kevin F. Smith Kevin F. Smith |
| author_sort | Jiashuai Zhu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-dc4aea406be647cab4fb642a7a6d8730 |
| institution | OA Journals |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-dc4aea406be647cab4fb642a7a6d87302025-08-20T02:31:12ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-06-011610.3389/fpls.2025.15793761579376Estimation of ryegrass (Lolium) dry matter yield using genomic prediction considering genotype by environment interaction across south-eastern AustraliaJiashuai Zhu0Jiashuai Zhu1Khageswor Giri2Zibei Lin3Noel O. Cogan4Noel O. Cogan5Joe L. Jacobs6Joe L. Jacobs7Joe L. Jacobs8Kevin F. Smith9Kevin F. Smith10Faculty of Science, The University of Melbourne, Parkville, VIC, AustraliaAgriculture Victoria, AgriBio Centre, Bundoora, VIC, AustraliaAgriculture Victoria, AgriBio Centre, Bundoora, VIC, AustraliaAgriculture Victoria, AgriBio Centre, Bundoora, VIC, AustraliaAgriculture Victoria, AgriBio Centre, Bundoora, VIC, AustraliaSchool of Applied Systems Biology, La Trobe University, Bundoora, VIC, AustraliaFaculty of Science, The University of Melbourne, Parkville, VIC, AustraliaSchool of Applied Systems Biology, La Trobe University, Bundoora, VIC, AustraliaAgriculture Victoria, Ellinbank, VIC, AustraliaFaculty of Science, The University of Melbourne, Parkville, VIC, AustraliaAgriculture Victoria, Hamilton, Ellinbank, VIC, AustraliaGenomic 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.https://www.frontiersin.org/articles/10.3389/fpls.2025.1579376/fullregional evaluation systemenvironmental adaptabilitysustainable forage productionmulti-harvest multi-site trialsgenomic selection |
| spellingShingle | Jiashuai Zhu Jiashuai Zhu Khageswor Giri Zibei Lin Noel O. Cogan Noel O. Cogan Joe L. Jacobs Joe L. Jacobs Joe L. Jacobs Kevin F. Smith Kevin F. Smith Estimation of ryegrass (Lolium) dry matter yield using genomic prediction considering genotype by environment interaction across south-eastern Australia Frontiers in Plant Science regional evaluation system environmental adaptability sustainable forage production multi-harvest multi-site trials genomic selection |
| title | Estimation of ryegrass (Lolium) dry matter yield using genomic prediction considering genotype by environment interaction across south-eastern Australia |
| title_full | Estimation of ryegrass (Lolium) dry matter yield using genomic prediction considering genotype by environment interaction across south-eastern Australia |
| title_fullStr | Estimation of ryegrass (Lolium) dry matter yield using genomic prediction considering genotype by environment interaction across south-eastern Australia |
| title_full_unstemmed | Estimation of ryegrass (Lolium) dry matter yield using genomic prediction considering genotype by environment interaction across south-eastern Australia |
| title_short | Estimation of ryegrass (Lolium) dry matter yield using genomic prediction considering genotype by environment interaction across south-eastern Australia |
| title_sort | estimation of ryegrass lolium dry matter yield using genomic prediction considering genotype by environment interaction across south eastern australia |
| topic | regional evaluation system environmental adaptability sustainable forage production multi-harvest multi-site trials genomic selection |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1579376/full |
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