ArticleThe plant genome2021
Training set design in genomic prediction with multiple biparental families.
Article in The plant genome, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Enhancing alfalfa breeding through genomic prediction with exotic germplasm resources.BMC plant biology · 2026Article
- Diversity of cereal rye (Secale cereale) germplasm in the Southeast United States.The plant genome · 2025Article
- Leveraging Automated Machine Learning for Environmental Data-Driven Genetic Analysis and Genomic Prediction in Maize Hybrids.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Within-family genomic selection in strawberry: Optimization of marker density, trial design, and training set composition.The plant genome · 2025Article
- Advances in Soybean Genetic Improvement.Plants (Basel, Switzerland) · 2024Review
- Near-infrared reflectance spectroscopy phenomic prediction can perform similarly to genomic prediction of maize agronomic traits across environments.The plant genome · 2024Article
- Phenomic Selection for Hybrid Rapeseed Breeding.Plant phenomics (Washington, D.C.) · 2024Article
- Genomic prediction of seed nutritional traits in biparental families of oat (Avena sativa).The plant genome · 2023Article
- Genomic selection of soybean (Glycine max) for genetic improvement of yield and seed composition in a breeding context.The plant genome · 2023Article
- Genomic prediction of optimal cross combinations to accelerate genetic improvement of soybean (Frontiers in plant science · 2023Article
- Genomic selection for spot blotch in bread wheat breeding panels, full-sibs and half-sibs and index-based selection for spot blotch, heading and plant height.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Article
- Unraveling the potential of phenomic selection within and among diverse breeding material of maize (Zea mays L.).G3 (Bethesda, Md.) · 2022Article
- The performance of phenomic selection depends on the genetic architecture of the target trait.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Article
- Training set design in genomic prediction with multiple biparental families.The plant genome · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genomic selection is a powerful tool to reduce the cycle length and enhance the genetic gain of complex traits in plant breeding. However, questions remain about the optimum design and composition of the training set. In this study, we used 944 soybean [Glycine max (L.) Merr.] recombinant inbred lines from eight families derived through a partial-diallel mating design among five parental lines. The cross-validated prediction accuracies for the six traits seed yield, 1,000-seed weight, protein yield, plant height, protein content, and oil content were high, ranging from 0.79 to 0.87. We investigated among-family predictions, making use of the special mating design with different degrees of relatedness among families. Generally, the prediction accuracy decreased from full-sibs to half-sib families to unrelated families. However, half-sib and unrelated families also showed substantial variation in their prediction accuracy for a given family, which appeared to be caused at least in part by the shared segregation of quantitative trait loci in both the training and prediction sets. Combining several half-sib families in composite training sets generally led to an increase in the prediction accuracy compared with the best family alone. The prediction accuracy increased with the size of the training set, but for comparable prediction accuracy, substantially more half-sibs were required than full-sibs. Collectively, our results highlight the potential of genomic selection for soybean breeding and, in a broader context, illustrate the importance of the targeted design of the training set.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.