ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2012
Genomic prediction of hybrid performance in maize with models incorporating dominance and population specific marker effects.
Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 96 papers, 1 of them a synthesis that pooled it.
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Who cites it
96 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
- From family trials to genomic mate allocation: statistical and genomic strategies to accelerate sugarcane genetic improvement.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Genomic prediction of agronomic traits in a switchgrass (Panicum virgatum L.) half-sib progeny panel evaluated across multiple environments.The plant genome · 2026Article
- Unveiling the interactions between design optimization, modeling strategies, and spatial analysis in genomic selection.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Progeny-based genomic selection reveals untapped genetic potential in an underutilized medicinal plant,Molecular breeding : new strategies in plant improvement · 2026Article
- Comparing statistical 'phenomic prediction' models for remote-sensing-based phenotyping of maize susceptibility to common rust.Plant phenomics (Washington, D.C.) · 2025Article
- Multifactor Analysis of a Genome-Wide Selection System inPlants (Basel, Switzerland) · 2025Article
- Multi-trait/environment sparse genomic prediction using the SFSI R-package.The plant genome · 2025Article
- Maximizing the accuracy of genetic variance estimation and using a novel generalized effective sample size to improve simulations.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Optimizing the selection of quantitative traits in plant breeding using simulation.Frontiers in plant science · 2025Review
- Genomic prediction of synthetic hexaploid wheat upon tetraploid durum and diploid Aegilops parental pools.The plant genome · 2024Article
- Maximizing efficiency in sunflower breeding through historical data optimization.Plant methods · 2024Article
- Genomic prediction in multi-environment trials in maize using statistical and machine learning methods.Scientific reports · 2024Article
- Improving hybrid rice breeding programs via stochastic simulations: number of parents, number of hybrids, tester update, and genomic prediction of hybrid performance.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- Simultaneous improvement of grain yield and grain protein concentration in durum wheat by using association tests and weighted GBLUP.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- Leveraging data from the Genomes-to-Fields Initiative to investigate genotype-by-environment interactions in maize in North America.Nature communications · 2023Article
- Multivariate Genomic Hybrid Prediction with Kernels and Parental Information.International journal of molecular sciences · 2023Article
- Use of genomic prediction to screen sorghum B-lines in hybrid testcrosses.The plant genome · 2023Article
- Genomic prediction in hybrid breeding: II. Reciprocal recurrent genomic selection with full-sib and half-sib families.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- Genomic prediction in hybrid breeding: I. Optimizing the training set design.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
36 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying high performing hybrids is an essential part of every maize breeding program. Genomic prediction of maize hybrid performance allows to identify promising hybrids, when they themselves or other hybrids produced from their parents were not tested in field trials. Using simulations, we investigated the effects of marker density (10, 1, 0.3 marker per mega base pair, Mbp(-1)), convergent or divergent parental populations, number of parents tested in other combinations (2, 1, 0), genetic model (including population-specific and/or dominance marker effects or not), and estimation method (GBLUP or BayesB) on the prediction accuracy. We based our simulations on marker genotypes of Central European flint and dent inbred lines from an ongoing maize breeding program. To simulate convergent or divergent parent populations, we generated phenotypes by assigning QTL to markers with similar or very different allele frequencies in both pools, respectively. Prediction accuracies increased with marker density and number of parents tested and were higher under divergent compared with convergent parental populations. Modeling marker effects as population-specific slightly improved prediction accuracy under lower marker densities (1 and 0.3 Mbp(-1)). This indicated that modeling marker effects as population-specific will be most beneficial under low linkage disequilibrium. Incorporating dominance effects improved prediction accuracies considerably for convergent parent populations, where dominance results in major contributions of SCA effects to the genetic variance among inter-population hybrids. While the general trends regarding the effects of the aforementioned influence factors on prediction accuracy were similar for GBLUP and BayesB, the latter method produced significantly higher accuracies for models incorporating dominance.
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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.