ArticleThe plant genome2023
Genomic selection performs as effectively as phenotypic selection for increasing seed yield in soybean.
Article in The plant genome, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Comparative evaluation of GWAS-prioritized SNP, SSR, and insertion-type structural variant marker panels for genomic prediction in soybean.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Efficiency under epistasis of haplotype-based genomic selection for pure line breeding.Heredity · 2026Article
- A Quantitative Trait Nucleotide-Based Genomic Selection Strategy for Seed Oil and Protein Content in Soybean.Plants (Basel, Switzerland) · 2026Article
- Integrative machine learning approach for identifying genes associated with quantitative traits: A soybean (Glycine max) yield case study.The plant genome · 2026Article
- The current state of Japanese soybean breeding using DNA marker technology.Breeding science · 2026Article
- Article
- Genomic prediction for grain yield and biotic stress resistance in field pea (Frontiers in plant science · 2026Article
- Phenotypic and genetic dissection of seed shape related traits in soybean (Frontiers in plant science · 2026Article
- Leveraging GWAS-Identified Markers in Combination with Bayesian and Machine Learning Models to Improve Genomic Selection in Soybean.International journal of molecular sciences · 2025Article
- GPS: Harnessing data fusion strategies to improve the accuracy of machine learning-based genomic and phenotypic selection.Plant communications · 2025Article
- Dissecting the genetic and phenotypic basis of salinity tolerance in mungbean: insights from multi-stage phenotyping, GWAS and genomic prediction.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Environment ensemble models for genomic prediction in common bean (Phaseolus vulgaris L.).The plant genome · 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
- Targeted genotyping (90K-SPET) facilitates genome-wide association studies and the prediction of yield-related traits in faba bean (Vicia faba L.).BMC plant biology · 2025Article
- Genomic and phenomic prediction for soybean seed yield, protein, and oil.The plant genome · 2025Article
- Genomic selection shows improved expected genetic gain over phenotypic selection of agronomic traits in allotetraploid white clover.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Harnessing Multi-Omics Strategies and Bioinformatics Innovations for Advancing Soybean Improvement: A Comprehensive Review.Plants (Basel, Switzerland) · 2024Review
- Genomic-inferred cross-selection methods for multi-trait improvement in a recurrent selection breeding program.Plant methods · 2024Article
- Multi-trait multi-environment genomic prediction of preliminary yield trial in pulse crop.The plant genome · 2024Article
- Q&A: Methods for estimating genetic gain in sub-Saharan Africa and achieving improved gains.The plant genome · 2024Article
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5 authors.
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Abstract
Increasing the rate of genetic gain for seed yield remains the primary breeding objective in both public and private soybean [Glycine max (L.) Merr.] breeding programs. Genomic selection (GS) has the potential to accelerate the rate of genetic gain for soybean seed yield. Limited studies to date have validated GS accuracy and directly compared GS with phenotypic selection (PS), and none have been reported in soybean. This study conducted the first empirical validation of GS for increasing seed yield using over 1,500 lines and over 7 yr (2010-2016) of replicated experiments in the University of Nebraska-Lincoln soybean breeding program. The study was designed to capture the varying genetic relatedness of the training population to three validation sets: two large biparental populations (TBP-1 and TBP-2) and a large validation set comprised of 457 preselected advanced lines derived from 45 biparental populations (TMP). We found that prediction accuracy (.54) realized in our validation experiments was comparable with what we obtained from a series of cross-validation experiments (.64). Both GS and PS were more effective for increasing the population mean performance compared with random selection (RS). We found a selection advantage of GS over PS, where higher genetic gain and identification of top-performing lines was maximized at 10-20% selected proportion. Genomic selection led to small increases in genetic similarity when compared with PS and RS presumably because of a significant shift on allelic frequencies toward the extremes, suggesting that it could erode genetic diversity more quickly. Overall, we found that GS can perform as effectively as PS but that measures should be considered to protect against loss of genetic variance when using GS.
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