ArticleBMC genetics2019
Accuracy of genomic selection for grain yield and agronomic traits in soft red winter wheat.
Article in BMC genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- 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
- Improving hybrid breeding efficiency in winter oilseed rape under sparse multi-environment testing.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Genomic selection for seed yield enhances flax breeding efficiency.Molecular breeding : new strategies in plant improvement · 2026Article
- Next-Generation Genotyping: Innovations Driving Plant Genomic Improvement.Life (Basel, Switzerland) · 2026Review
- Effectiveness of low-density high-throughput marker platform and easy-to-measure traits for genomic prediction of biomass yield in oat (Avena sativa L.).The plant genome · 2026Article
- Integrating genomic predictions into an applied Central European wheat breeding program.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- The illusion of progress: why most introgressions from wild Triticeae fail to improve wheat agronomically.Frontiers in plant science · 2026Review
- KBeagle: An Adaptive Strategy and Tool for Improving Imputation Accuracy and Computation Time.International journal of molecular sciences · 2025Article
- Low density marker-based effectiveness and efficiency of early-generation genomic selection relative to phenotype-based selection in dolichos bean (Lablab purpureus L. Sweet).The plant genome · 2025Article
- Identification of robust yield quantitative trait loci derived from cultivated emmer for durum wheat improvement.The plant genome · 2025Article
- Assessment of the potential for genomic selection to improve resistance to fusarium stalk rot in maize.Frontiers in plant science · 2025Article
- ShinyGS-a graphical toolkit with a serial of genetic and machine learning models for genomic selection: application, benchmarking, and recommendations.Frontiers in plant science · 2024Article
- Ridge regression and deep learning models for genome-wide selection of complex traits in New Mexican Chile peppers.BMC genomic data · 2023Article
- Utilization of a publicly available diversity panel in genomic prediction of Fusarium head blight resistance traits in wheat.The plant genome · 2023Article
- Identification of stable QTL controlling multiple yield components in a durum × cultivated emmer wheat population under field and greenhouse conditions.G3 (Bethesda, Md.) · 2023Article
- 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
- Genome-wide association mapping and genomic prediction for pre‑harvest sprouting resistance, low α-amylase and seed color in Iranian bread wheat.BMC plant biology · 2022Article
- Genetic characterization of carrot root shape and size using genome-wide association analysis and genomic-estimated breeding values.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Article
- Genetic analysis of morphological traits in spring wheat from the Northeast of China by a genome-wide association study.Frontiers in genetics · 2022Article
- Genomic Selection: A Tool for Accelerating the Efficiency of Molecular Breeding for Development of Climate-Resilient Crops.Frontiers in genetics · 2022Review
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4 authors.
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Abstract
backgroundGenomic selection has the potential to increase genetic gains by using molecular markers as predictors of breeding values of individuals. This study evaluated the accuracy of predictions for grain yield, heading date, plant height, and yield components in soft red winter wheat under different prediction scenarios. Response to selection for grain yield was also compared across different selection strategies- phenotypic, marker-based, genomic, combination of phenotypic and genomic, and random selections.
resultsGenomic selection was implemented through a ridge regression best linear unbiased prediction model in two scenarios- cross-validations and independent predictions. Accuracy for cross-validations was assessed using a diverse panel under different marker number, training population size, relatedness between training and validation populations, and inclusion of fixed effect in the model. The population in the first scenario was then trained and used to predict grain yield of biparental populations for independent validations. Using subsets of significant markers from association mapping increased accuracy by 64-70% for grain yield but resulted in lower accuracy for traits with high heritability such as plant height. Increasing size of training population resulted in an increase in accuracy, with maximum values reached when ~ 60% of the lines were used as a training panel. Predictions using related subpopulations also resulted in higher accuracies. Inclusion of major growth habit genes as fixed effect in the model caused increase in grain yield accuracy under a cross-validation procedure. Independent predictions resulted in accuracy ranging between - 0.14 and 0.43, dependent on the grouping of site-year data for the training and validation populations. Genomic selection was "superior" to marker-based selection in terms of response to selection for yield. Supplementing phenotypic with genomic selection resulted in approximately 10% gain in response compared to using phenotypic selection alone.
conclusionsOur results showed the effects of different factors on accuracy for yield and agronomic traits. Among the factors studied, training population size and relatedness between training and validation population had the greatest impact on accuracy. Ultimately, combining phenotypic with genomic selection would be relevant for accelerating genetic gains for yield in winter wheat.
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