ArticleG3 (Bethesda, Md.)2012
Effectiveness of genomic prediction of maize hybrid performance in different breeding populations and environments.
Article in G3 (Bethesda, Md.), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 130 papers, 1 of them a synthesis that pooled it.
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Who cites it
130 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Optimizing Genomic-Enabled Prediction in Small-Scale Maize Hybrid Breeding Programs: A Roadmap Review.Frontiers in plant science · 2021Pooled it
- Genomic selection validated across two generations of loblolly pine breeding.G3 (Bethesda, Md.) · 2026Article
- Genetic diversity and population structure of temperate maize (Zea mays L.) inbred lines from a public breeding program in Argentina.BMC plant biology · 2026Article
- Optimizing genomic predictions in maize using a diversity panel and a multiparental population.The plant genome · 2026Article
- Integrating genomic predictions into an applied Central European wheat breeding program.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Predicted protein 3D structures provide essential insights into the genetic architecture underlying phenotypic diversity in maize.Genome research · 2026Article
- Accounting for population structure in genomic prediction of strawberry sweetness at a global scale.Scientific reports · 2025Article
- Harnessing big data for enhanced genome-wide prediction in winter wheat breeding.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Improving genomic prediction for plant disease using environmental covariates.Plant methods · 2025Article
- Transferability of genomic prediction models across market segments in potato and the effect of selection.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Assessing the utility of genomic selection to breed for durable Ascochyta blight resistance in chickpea.The plant genome · 2025Article
- Genomic selection of maize test-cross hybrids leveraged by marker sampling.The plant genome · 2025Article
- Environmental data provide marginal benefit for predicting climate adaptation.PLoS genetics · 2025Article
- Genomic Prediction in a Self-Fertilized Progenies ofPlants (Basel, Switzerland) · 2025Article
- Optimization of sparse phenotyping strategy in multi-environmental trials in maize.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Enhancing Across-Population Genomic Prediction for Maize Hybrids.Plants (Basel, Switzerland) · 2024Article
- A Dominance Hypothesis Argument for Historical Genetic Gains and the Fixation of Heterosis in Octoploid Strawberry.Genetics · 2024Article
- Investigating genomic prediction strategies for grain carotenoid traits in a tropical/subtropical maize panel.G3 (Bethesda, Md.) · 2024Article
- GbyE: an integrated tool for genome widely association study and genome selection based on genetic by environmental interaction.BMC genomics · 2024Article
- Multienvironment genomic prediction in tetraploid potato.G3 (Bethesda, Md.) · 2024Article
70 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genomic prediction is expected to considerably increase genetic gains by increasing selection intensity and accelerating the breeding cycle. In this study, marker effects estimated in 255 diverse maize (Zea mays L.) hybrids were used to predict grain yield, anthesis date, and anthesis-silking interval within the diversity panel and testcross progenies of 30 F(2)-derived lines from each of five populations. Although up to 25% of the genetic variance could be explained by cross validation within the diversity panel, the prediction of testcross performance of F(2)-derived lines using marker effects estimated in the diversity panel was on average zero. Hybrids in the diversity panel could be grouped into eight breeding populations differing in mean performance. When performance was predicted separately for each breeding population on the basis of marker effects estimated in the other populations, predictive ability was low (i.e., 0.12 for grain yield). These results suggest that prediction resulted mostly from differences in mean performance of the breeding populations and less from the relationship between the training and validation sets or linkage disequilibrium with causal variants underlying the predicted traits. Potential uses for genomic prediction in maize hybrid breeding are discussed emphasizing the need of (1) a clear definition of the breeding scenario in which genomic prediction should be applied (i.e., prediction among or within populations), (2) a detailed analysis of the population structure before performing cross validation, and (3) larger training sets with strong genetic relationship to the validation set.
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