ArticleG3 (Bethesda, Md.)2016
Optimizing Training Population Data and Validation of Genomic Selection for Economic Traits in Soft Winter Wheat.
Article in G3 (Bethesda, Md.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 68 citations in OpenAlex.
- Genomic selection of maize test-cross hybrids leveraged by marker sampling.The plant genome · 2025Article
- Selective Genotyping and Phenotyping for Optimization of Genomic Prediction Models for Populations with Different Diversity.Plants (Basel, Switzerland) · 2024Article
- Leveraging trait and QTL covariates to improve genomic prediction of resistance to Fusarium head blight in Central European winter wheat.Frontiers in plant science · 2024Article
- Utilization of a publicly available diversity panel in genomic prediction of Fusarium head blight resistance traits in wheat.The plant genome · 2023Article
- Improved multi-trait prediction of wheat end-product quality traits by integrating NIR-predicted phenotypes.Frontiers in plant science · 2023Article
- Performance of Bayesian and BLUP alphabets for genomic prediction: analysis, comparison and results.Heredity · 2022Article
- Machine learning models outperform deep learning models, provide interpretation and facilitate feature selection for soybean trait prediction.BMC plant biology · 2022Article
- Genomic prediction of preliminary yield trials in chickpea: Effect of functional annotation of SNPs and environment.The plant genome · 2022Article
- GPTransformer: A Transformer-Based Deep Learning Method for Predicting Fusarium Related Traits in Barley.Frontiers in plant science · 2021Article
- Testing methods and statistical models of genomic prediction for quantitative disease resistance to Phytophthora sojae in soybean [Glycine max (L.) Merr] germplasm collections.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2020Article
- Genetic gains with rapid-cycle genomic selection for combined drought and waterlogging tolerance in tropical maize (Zea mays L.).The plant genome · 2020Article
- The value of early-stage phenotyping for wheat breeding in the age of genomic selection.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2020Article
- Germplasms, genetics and genomics for better control of disastrous wheat Fusarium head blight.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2020Review
- Training Population Design With the Use of Regional Fusarium Head Blight Nurseries to Predict Independent Breeding Lines for FHB Traits.Frontiers in plant science · 2020Article
- Accuracy of genomic selection for grain yield and agronomic traits in soft red winter wheat.BMC genetics · 2019Article
- Combining grain yield, protein content and protein quality by multi-trait genomic selection in bread wheat.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2019Article
- High-throughput phenotyping platforms enhance genomic selection for wheat grain yield across populations and cycles in early stage.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2019Article
- Training population selection and use of fixed effects to optimize genomic predictions in a historical USA winter wheat panel.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2019Article
- Prospects and Challenges of Applied Genomic Selection-A New Paradigm in Breeding for Grain Yield in Bread Wheat.The plant genome · 2018Article
- Investigating Drought Tolerance in Chickpea Using Genome-Wide Association Mapping and Genomic Selection Based on Whole-Genome Resequencing Data.Frontiers in plant science · 2018Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genomic selection (GS) is a breeding tool that estimates breeding values (GEBVs) of individuals based solely on marker data by using a model built using phenotypic and marker data from a training population (TP). The effectiveness of GS increases as the correlation of GEBVs and phenotypes (accuracy) increases. Using phenotypic and genotypic data from a TP of 470 soft winter wheat lines, we assessed the accuracy of GS for grain yield, Fusarium Head Blight (FHB) resistance, softness equivalence (SE), and flour yield (FY). Four TP data sampling schemes were tested: (1) use all TP data, (2) use subsets of TP lines with low genotype-by-environment interaction, (3) use subsets of markers significantly associated with quantitative trait loci (QTL), and (4) a combination of 2 and 3. We also correlated the phenotypes of relatives of the TP to their GEBVs calculated from TP data. The GS accuracy within the TP using all TP data ranged from 0.35 (FHB) to 0.62 (FY). On average, the accuracy of GS from using subsets of data increased by 54% relative to using all TP data. Using subsets of markers selected for significant association with the target trait had the greatest impact on GS accuracy. Between-environment prediction accuracy was also increased by using data subsets. The accuracy of GS when predicting the phenotypes of TP relatives ranged from 0.00 to 0.85. These results suggest that GS could be useful for these traits and GS accuracy can be greatly improved by using subsets of TP data.
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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.