ArticleG3 (Bethesda, Md.)2016
Assessing Predictive Properties of Genome-Wide Selection in Soybeans.
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 48 papers.
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Who cites it
48 citing papers in PubMed.
- Association mapping and genome prediction reveal a complex genetic architecture for resistance to black and frosty pod rot in cacao (Theobroma cacao L.) populations.The plant genome · 2026Article
- Integrative machine learning approach for identifying genes associated with quantitative traits: A soybean (Glycine max) yield case study.The plant genome · 2026Article
- Harnessing genomic prediction in Brassica napus through a nested association mapping population.The plant genome · 2025Article
- Genomic selection: Essence, applications, and prospects.The plant genome · 2025Review
- Article
- Improving genomic prediction of vitamin C content in spinach using GWAS-derived markers.BMC genomics · 2025Article
- Identification of QTL for branch traits in soybean (Frontiers in genetics · 2025Article
- Genome and GWAS analyses for soybean cyst nematode resistance in USDA world-wide common bean (Frontiers in plant science · 2025Article
- Testing source elevation versus genotype as predictors of sugar pine performance in a post-fire restoration planting.Ecosphere (Washington, D.C) · 2024Article
- Selective Genotyping and Phenotyping for Optimization of Genomic Prediction Models for Populations with Different Diversity.Plants (Basel, Switzerland) · 2024Article
- Genomic selection of soybean (Glycine max) for genetic improvement of yield and seed composition in a breeding context.The plant genome · 2023Article
- Integrating genome-wide association study into genomic selection for the prediction of agronomic traits in rice (Molecular breeding : new strategies in plant improvement · 2023Article
- Genome-Wide Association Study and Genomic Prediction of Fusarium Wilt Resistance in Common Bean Core Collection.International journal of molecular sciences · 2023Article
- Molecular breeding for improvement of photothermal adaptability in soybean.Molecular breeding : new strategies in plant improvement · 2023Article
- Genomic selection performs as effectively as phenotypic selection for increasing seed yield in soybean.The plant genome · 2023Article
- Improving predictive ability in sparse testing designs in soybean populations.Frontiers in genetics · 2023Article
- A divide-and-conquer approach for genomic prediction in rubber tree using machine learning.Scientific reports · 2022Article
- A new approach fits multivariate genomic prediction models efficiently.Genetics, selection, evolution : GSE · 2022Article
- Article
- Physiological breeding for yield improvement in soybean: solar radiation interception-conversion, and harvest index.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Article
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Authors and funding
3 authors.
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Abstract
Many economically important traits in plant breeding have low heritability or are difficult to measure. For these traits, genomic selection has attractive features and may boost genetic gains. Our goal was to evaluate alternative scenarios to implement genomic selection for yield components in soybean (Glycine max L. merr). We used a nested association panel with cross validation to evaluate the impacts of training population size, genotyping density, and prediction model on the accuracy of genomic prediction. Our results indicate that training population size was the factor most relevant to improvement in genome-wide prediction, with greatest improvement observed in training sets up to 2000 individuals. We discuss assumptions that influence the choice of the prediction model. Although alternative models had minor impacts on prediction accuracy, the most robust prediction model was the combination of reproducing kernel Hilbert space regression and BayesB. Higher genotyping density marginally improved accuracy. Our study finds that breeding programs seeking efficient genomic selection in soybeans would best allocate resources by investing in a representative training set.
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