ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2016
Multiple-trait- and selection indices-genomic predictions for grain yield and protein content in rye for feeding purposes.
Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
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48 citing papers in PubMed.
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- Implementing marker covariates and multi-trait genomic selection models to improve grain milling, appearance, cooking, and edible quality in rice (Oryza sativa L.).The plant genome · 2025Article
- Genomic and phenomic prediction for soybean seed yield, protein, and oil.The plant genome · 2025Article
- Implementing multi-trait genomic selection to improve grain milling quality in oats (Avena sativa L.).The plant genome · 2024Article
- Review of applications of artificial intelligence (AI) methods in crop research.Journal of applied genetics · 2024Review
- Genomic prediction for agronomic traits in a diverse Flax (Linum usitatissimum L.) germplasm collection.Scientific reports · 2024Article
- CropGS-Hub: a comprehensive database of genotype and phenotype resources for genomic prediction in major crops.Nucleic acids research · 2024Article
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- Genomic prediction reveals unexplored variation in grain protein and lysine content across a vast winter wheat genebank collection.Frontiers in plant science · 2023Article
- Prediction of additive, epistatic, and dominance effects using models accounting for incomplete inbreeding in parental lines of hybrid rye and sugar beet.Frontiers in plant science · 2023Article
- Time-series multispectral imaging in soybean for improving biomass and genomic prediction accuracy.The plant genome · 2022Article
- Comparison of single-trait and multi-trait genomic predictions on agronomic and disease resistance traits in spring wheat.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Article
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- Multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in US wheat.BMC genomics · 2022Article
- Multi-Trait Multi-Environment Genomic Prediction for End-Use Quality Traits in Winter Wheat.Frontiers in genetics · 2022Article
- Genomic Prediction Strategies for Dry-Down-Related Traits in Maize.Frontiers in plant science · 2022Article
- Selection of parental lines for plant breedingFrontiers in plant science · 2022Article
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
key messageExploiting the benefits from multiple-trait genomic selection for protein content prediction relying on additional grain yield information within training sets is a realistic genomic selection approach in rye breeding. ABSTRACT: Multiple-trait genomic selection (MTGS) was specially designed to benefit from the information of genetically correlated indicator traits in order to improve genomic prediction accuracies. Two segregating F3:4 rye testcross populations genotyped using diversity array technology markers and evaluated for grain yield (GY) and protein content (PC) were considered. The aims of our study were to explore the benefits of MTGS over single-trait genomic selection (STGS) for GY and PC prediction and to apply GS to predict different selection indices (SIs) for GY and PC improvement. Our results using a two-trait model (2TGS) empirically confirm that the ideal scenario to exploit the benefits of MTGS would be when the predictions of a relatively low heritable target trait with scarce phenotypic records are supported by an intensively phenotyped genetically correlated indicator trait which has higher heritability. This ideal scenario is expected for PC in practice. According to our GS implementation, MTGS can be performed in order to achieve more cycles of selection by unit of time. If the aim is to exclusively improve the prediction accuracy of a scarcely phenotyped trait, 2TGS will be a more accurate approach than a three-trait model which incorporates an additional correlated indicator trait. In general for balanced phenotypic information, we recommend to perform GS considering SIs as single traits, this method being a simple, direct and efficient way of prediction.
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