ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2018
Exploitation of data from breeding programs supports rapid implementation of genomic selection for key agronomic traits in perennial ryegrass.
Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Genomic prediction of single cross families of perennial ryegrass under two nitrogen managements.The plant genome · 2026Article
- Genomic selection in timothy (Phleum pratense L.): a comprehensive evaluation of prediction models, multi-trait strategies, and forward validation across Norwegian environments.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Harnessing exotic germplasm for red clover improvement: Tracking genomic introgression and deploying genomic selection.The plant genome · 2026Article
- Integrating genomic prediction into crop DUS testing: new approaches in support of reference collection management and distinctness assessment.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Genomic insights intoFrontiers in plant science · 2026Article
- Genomic prediction of agronomic traits in perennial ryegrass (Lolium perenne L.) and genotype x environment interactions at the limit of the species distribution.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Disentangling soybean GxE effects in an integrated genomic prediction and machine learning-GWAS workflow.Plant methods · 2025Article
- Estimation of ryegrass (Frontiers in plant science · 2025Article
- Machine learning solutions for integrating partially overlapping genetic datasets and modelling host-endophyte effects in ryegrass (Frontiers in plant science · 2025Article
- Including marker x environment interactions improves genomic prediction in red clover (Frontiers in plant science · 2024Article
- Genetic architecture of inter-specific and -generic grass hybrids by network analysis on multi-omics data.BMC genomics · 2023Article
- Developing an integrated genomic selection approach beyond biomass for varietal protection and nutritive traits in perennial ryegrass (Lolium perenne L.).TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- Above-Ground Biomass Estimation in Oats Using UAV Remote Sensing and Machine Learning.Sensors (Basel, Switzerland) · 2022Article
- Genomic Prediction of Complex Traits in Forage Plants Species: Perennial Grasses Case.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Within and combined season prediction models for perennial ryegrass biomass yield using ground- and air-based sensor data.Frontiers in plant science · 2022Article
- Genomic prediction in family bulks using different traits and cross-validations in pine.G3 (Bethesda, Md.) · 2021Article
- Genomic prediction and genomic heritability of grain yield and its related traits in a safflower genebank collection.The plant genome · 2021Article
- The Impact of Alkaloid-ProducingToxins · 2021Review
- Article
- Why and How to Switch to Genomic Selection: Lessons From Plant and Animal Breeding Experience.Frontiers in genetics · 2021Article
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10 authors.
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Abstract
key messageExploitation of data from a ryegrass breeding program has enabled rapid development and implementation of genomic selection for sward-based biomass yield with a twofold-to-threefold increase in genetic gain. Genomic selection, which uses genome-wide sequence polymorphism data and quantitative genetics techniques to predict plant performance, has large potential for the improvement in pasture plants. Major factors influencing the accuracy of genomic selection include the size of reference populations, trait heritability values and the genetic diversity of breeding populations. Global diversity of the important forage species perennial ryegrass is high and so would require a large reference population in order to achieve moderate accuracies of genomic selection. However, diversity of germplasm within a breeding program is likely to be lower. In addition, de novo construction and characterisation of reference populations are a logistically complex process. Consequently, historical phenotypic records for seasonal biomass yield and heading date over a 18-year period within a commercial perennial ryegrass breeding program have been accessed, and target populations have been characterised with a high-density transcriptome-based genotyping-by-sequencing assay. Ability to predict observed phenotypic performance in each successive year was assessed by using all synthetic populations from previous years as a reference population. Moderate and high accuracies were achieved for the two traits, respectively, consistent with broad-sense heritability values. The present study represents the first demonstration and validation of genomic selection for seasonal biomass yield within a diverse commercial breeding program across multiple years. These results, supported by previous simulation studies, demonstrate the ability to predict sward-based phenotypic performance early in the process of individual plant selection, so shortening the breeding cycle, increasing the rate of genetic gain and allowing rapid adoption in ryegrass improvement programs.
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