ArticleThe plant genome2019
Training Population Optimization for Genomic Selection.
Article in The plant genome, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed, 88 citations in OpenAlex.
- Improving grain yield prediction in Southern US oat germplasm using genomics information and environmental covariates.The plant genome · 2026Article
- Finlay-Wilkinson random regression for yield and yield stability prediction in cereals.The plant genome · 2026Article
- Characterization of oat chromosome rearrangements in advanced breeding lines and implications for genetic improvement.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- A benchmarking of genomic selection models for predicting grain-yield related traits using haplotype-based and genome-wide association study-based markers in rice.The plant genome · 2026Article
- A Quantitative Trait Nucleotide-Based Genomic Selection Strategy for Seed Oil and Protein Content in Soybean.Plants (Basel, Switzerland) · 2026Article
- Effectiveness of low-density high-throughput marker platform and easy-to-measure traits for genomic prediction of biomass yield in oat (Avena sativa L.).The plant genome · 2026Article
- Optimized protocol for high-throughput vernalization with speed breeding in winter wheat.Plant methods · 2025Article
- Assessment of genomic prediction capabilities of transcriptome data in a barley multi-parent RIL population.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Experimental evaluation of effectiveness of genomic selection for resistance to northern corn leaf blight in maize.Journal of applied genetics · 2025Article
- Harnessing big data for enhanced genome-wide prediction in winter wheat breeding.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Article
- 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
- Genomic selection: Essence, applications, and prospects.The plant genome · 2025Review
- Improving wheat grain yield genomic prediction accuracy using historical data.G3 (Bethesda, Md.) · 2025Article
- Use of simulation to optimize a sweet corn breeding program: implementing genomic selection and doubled haploid technology.G3 (Bethesda, Md.) · 2024Article
- Simulations of multiple breeding strategy scenarios in common bean for assessing genomic selection accuracy and model updating.The plant genome · 2024Article
- A comparison of design algorithms for choosing the training population in genomic models.Frontiers in genetics · 2024Article
- Genomic prediction of seed nutritional traits in biparental families of oat (Avena sativa).The plant genome · 2023Article
- Optimization of Multi-Generation Multi-location Genomic Prediction Models for Recurrent Genomic Selection in an Upland Rice Population.Rice (New York, N.Y.) · 2023Article
- Utilization of a publicly available diversity panel in genomic prediction of Fusarium head blight resistance traits in wheat.The plant genome · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
core ideasTraining populations can be optimized for specific testing populations. Optimized training populations are smaller, more related, and more predictive. Stratified sampling with a relationship matrix weighted by marker effect is optimal. The effectiveness of genomic selection in breeding programs depends on the phenotypic quality and depth, the prediction model, the number and type of molecular markers, and the size and composition of the training population (TR). Furthermore, population structure and diversity have a key role in the composition of the optimal training sets. Our goal was to compare strategies for optimizing the TR for specific testing populations (TE). A total of 1353 wheat (Triticum aestivum L.) and 644 rice (Oryza sativa L.) advanced lines were evaluated for grain yield in multiple environments. Several within-TR optimization strategies were compared to identify groups of individuals with increased predictive ability. Additionally, optimization strategies to choose individuals from the TR with higher predictive ability for a specific TE were compared. There is a benefit in considering both the population structure and the relationship between the TR and the TE when designing an optimal TR for genomic selection. A weighted relationship matrix with stratified sampling is the best strategy for forward predictions of quantitative traits in populations several generations apart.
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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.