ArticleFrontiers in plant science2016
Implementation of Genomic Prediction in Lolium perenne (L.) Breeding Populations.
Article in Frontiers in plant science, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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38 citing papers in PubMed.
- Harnessing exotic germplasm for red clover improvement: Tracking genomic introgression and deploying genomic selection.The plant genome · 2026Article
- Genetic and genomic resources for turfgrasses: status, applications, and prospects.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Genomic selection shows improved expected genetic gain over phenotypic selection of agronomic traits in allotetraploid white clover.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Machine learning solutions for integrating partially overlapping genetic datasets and modelling host-endophyte effects in ryegrass (Frontiers in plant science · 2025Article
- Isolation and transformation of perennial ryegrass (Frontiers in plant science · 2025Article
- Estimation of ryegrass (Frontiers in plant science · 2025Article
- Genomic prediction of resistance toEvolutionary applications · 2024Article
- Comparison of machine learning methods for genomic prediction of selected Arabidopsis thaliana traits.PloS one · 2024Article
- Including marker x environment interactions improves genomic prediction in red clover (Frontiers in plant science · 2024Article
- 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
- Genome-wide family prediction unveils molecular mechanisms underlying the regulation of agronomic traits inFrontiers in plant science · 2023Article
- Article
- Genomic Prediction of Complex Traits in Forage Plants Species: Perennial Grasses Case.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Sparse bayesian learning for genomic selection in yeast.Frontiers in bioinformatics · 2022Article
- Prediction of heterosis in the recent rapeseed (Brassica napus) polyploid by pairing parental nucleotide sequences.PLoS genetics · 2021Article
- Deterministic and stochastic modelling of impacts from genomic selection and phenomics on genetic gain for perennial ryegrass dry matter yield.Scientific reports · 2021Article
- Characterization and practical use of self-compatibility in outcrossing grass species.Annals of botany · 2021Review
- Review
- Article
- Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance.Scientific reports · 2020Article
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12 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Perennial ryegrass (Lolium perenne L.) is one of the most widely grown forage grasses in temperate agriculture. In order to maintain and increase its usage as forage in livestock agriculture, there is a continued need for improvement in biomass yield, quality, disease resistance, and seed yield. Genetic gain for traits such as biomass yield has been relatively modest. This has been attributed to its long breeding cycle, and the necessity to use population based breeding methods. Thanks to recent advances in genotyping techniques there is increasing interest in genomic selection from which genomically estimated breeding values are derived. In this paper we compare the classical RRBLUP model with state-of-the-art machine learning techniques that should yield themselves easily to use in GS and demonstrate their application to predicting quantitative traits in a breeding population of L. perenne. Prediction accuracies varied from 0 to 0.59 depending on trait, prediction model and composition of the training population. The BLUP model produced the highest prediction accuracies for most traits and training populations. Forage quality traits had the highest accuracies compared to yield related traits. There appeared to be no clear pattern to the effect of the training population composition on the prediction accuracies. The heritability of the forage quality traits was generally higher than for the yield related traits, and could partly explain the difference in accuracy. Some population structure was evident in the breeding populations, and probably contributed to the varying effects of training population on the predictions. The average linkage disequilibrium between adjacent markers ranged from 0.121 to 0.215. Higher marker density and larger training population closely related with the test population are likely to improve the prediction accuracy.
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