ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2019
Training population selection and use of fixed effects to optimize genomic predictions in a historical USA winter wheat panel.
Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.
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Who cites it
62 citing papers in PubMed, 122 citations in OpenAlex.
- Improving grain yield prediction in Southern US oat germplasm using genomics information and environmental covariates.The plant genome · 2026Article
- Genome-wide association study identifies genomic regions under selection for domestication and agronomic traits in intermediate wheatgrass.The plant genome · 2026Article
- Uncovering the genetic architecture of biomass yield and related traits in Southern US oat germplasm using genome-wide association study.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- GS-Impute: A neural network framework for accurate imputation of low-density markers in across-population genomic selection.Plant communications · 2026Article
- Genomic prediction using a cost-effective mid-density SNP platform in spring wheat.Frontiers in plant science · 2026Article
- Enhancing genomic predictive ability of yield and yield-related traits in spring wheat by integrating major plant adaptation genes as a fixed effect.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Genomic selection with GWAS-identified QTL markers enhances prediction accuracy for quantitative traits in poplar (Populus deltoides).Communications biology · 2025Article
- Environment ensemble models for genomic prediction in common bean (Phaseolus vulgaris L.).The plant genome · 2025Article
- Genomic prediction in Persian walnut: Optimization levers according to genetic architecture of complex traits.The plant genome · 2025Article
- Genomic selection: Essence, applications, and prospects.The plant genome · 2025Review
- Environmental genomic selection to leverage polygenic local adaptation in barley landraces.Communications biology · 2025Article
- Exploiting historical agronomic data to develop genomic prediction strategies for early clonal selection in the Louisiana sugarcane variety development program.The plant genome · 2025Article
- Identification of robust yield quantitative trait loci derived from cultivated emmer for durum wheat improvement.The plant genome · 2025Article
- Genomic strategies to facilitate breeding for increased β-Glucan content in oat (Avena sativa L.).BMC genomics · 2025Article
- Advances in genomic tools for plant breeding: harnessing DNA molecular markers, genomic selection, and genome editing.Biological research · 2024Review
- Enhancing Across-Population Genomic Prediction for Maize Hybrids.Plants (Basel, Switzerland) · 2024Article
- Natural alleles of LEAFY and WAPO1 interact to regulate spikelet number per spike in wheat.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Genomic selection optimization in blueberry: Data-driven methods for marker and training population design.The plant genome · 2024Article
- Molecular mechanisms controlling grain size and weight and their biotechnological breeding applications in maize and other cereal crops.Journal of advanced research · 2024Review
- Ability of Genomic Prediction to Bi-Parent-Derived Breeding Population Using Public Data for Soybean Oil and Protein Content.Plants (Basel, Switzerland) · 2024Article
2 more citing papers are in PubMed but not listed here.
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Authors and funding
12 authors at 7 institutions in 1 country.
Funding
Abstract
key messageThe optimization of training populations and the use of diagnostic markers as fixed effects increase the predictive ability of genomic prediction models in a cooperative wheat breeding panel. Plant breeding programs often have access to a large amount of historical data that is highly unbalanced, particularly across years. This study examined approaches to utilize these data sets as training populations to integrate genomic selection into existing pipelines. We used cross-validation to evaluate predictive ability in an unbalanced data set of 467 winter wheat (Triticum aestivum L.) genotypes evaluated in the Gulf Atlantic Wheat Nursery from 2008 to 2016. We evaluated the impact of different training population sizes and training population selection methods (Random, Clustering, PEVmean and PEVmean1) on predictive ability. We also evaluated inclusion of markers associated with major genes as fixed effects in prediction models for heading date, plant height, and resistance to powdery mildew (caused by Blumeria graminis f. sp. tritici). Increases in predictive ability as the size of the training population increased were more evident for Random and Clustering training population selection methods than for PEVmean and PEVmean1. The selection methods based on minimization of the prediction error variance (PEV) outperformed the Random and Clustering methods across all the population sizes. Major genes added as fixed effects always improved model predictive ability, with the greatest gains coming from combinations of multiple genes. Maximum predictabilities among all prediction methods were 0.64 for grain yield, 0.56 for test weight, 0.71 for heading date, 0.73 for plant height, and 0.60 for powdery mildew resistance. Our results demonstrate the utility of combining unbalanced phenotypic records with genome-wide SNP marker data for predicting the performance of untested genotypes.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.