ArticleG3 (Bethesda, Md.)2013
Accuracy of across-environment genome-wide prediction in maize nested association mapping populations.
Article in G3 (Bethesda, Md.), 2013. 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.
- 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
- Application of NGS Technology, Association Mapping, and Physical Mapping Technologies to Identify Candidate Genes Associated with Maize (International journal of molecular sciences · 2026Article
- Genomic prediction for grain yield and biotic stress resistance in field pea (Frontiers in plant science · 2026Article
- Using Genome-Wide Association Studies to Reveal DArTseq and SNP Loci Associated with Agronomic Traits and Yield in Maize.Current issues in molecular biology · 2025Article
- Article
- The Use of DArTseq Technology to Identify Markers Related to the Heterosis Effects in Selected Traits in Maize.Current issues in molecular biology · 2023Article
- Assessment of genomic prediction reliability and optimization of experimental designs in multi-environment trials.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Article
- From gene banks to farmer's fields: using genomic selection to identify donors for a breeding program in rice to close the yield gap on smallholder farms.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2021Article
- Genome-wide association study reveals the genetic architecture of root hair length in maize.BMC genomics · 2021Article
- Genetic dissection of Striga hermonthica (Del.) Benth. resistance via genome-wide association and genomic prediction in tropical maize germplasm.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2021Article
- Genomic Prediction with Genotype by Environment Interaction Analysis for Kernel Zinc Concentration in Tropical Maize Germplasm.G3 (Bethesda, Md.) · 2020Article
- Identification of Markers Associated with Yield Traits and Morphological Features in Maize (Plants (Basel, Switzerland) · 2019Article
- Genomic Selection for Yield and Seed Composition Traits Within an Applied Soybean Breeding Program.G3 (Bethesda, Md.) · 2019Article
- Selection of trait-specific markers and multi-environment models improve genomic predictive ability in rice.PloS one · 2019Article
- Genome-Wide Analysis of Grain Yield Stability and Environmental Interactions in a Multiparental Soybean Population.G3 (Bethesda, Md.) · 2018Article
- Genomic Selection Outperforms Marker Assisted Selection for Grain Yield and Physiological Traits in a Maize Doubled Haploid Population Across Water Treatments.Frontiers in plant science · 2018Article
- Predicting rice hybrid performance using univariate and multivariate GBLUP models based on North Carolina mating design II.Heredity · 2017Article
- Genomic Selection in Multi-environment Crop Trials.G3 (Bethesda, Md.) · 2016Article
- Accuracy of Genomic Prediction in Switchgrass (Panicum virgatum L.) Improved by Accounting for Linkage Disequilibrium.G3 (Bethesda, Md.) · 2016Article
- Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Most of previous empirical studies with genome-wide prediction were focused on within-environment prediction based on a single-environment (SE) model. In this study, we evaluated accuracy improvements of across-environment prediction by using genetic and residual covariance across correlated environments. Predictions with a multienvironment (ME) model were evaluated for two corn polygenic leaf structure traits, leaf length and leaf width, based on within-population (WP) and across-population (AP) experiments using a large maize nested association mapping data set consisting of 25 populations of recombinant inbred-lines. To make our study more applicable to plant breeding, two cross-validation schemes were used by evaluating accuracies of (CV1) predicting unobserved phenotypes of untested lines and (CV2) predicting unobserved phenotypes of lines that have been evaluated in some environments but not others. We concluded that (1) genome-wide prediction provided greater prediction accuracies than traditional quantitative trait loci-based prediction in both WP and AP and provided more advantages over quantitative trait loci -based prediction for WP than for AP. (2) Prediction accuracy with ME was significantly greater than that attained by SE in CV1 and CV2, and gains with ME over SE were greater in CV2 than in CV1. These gains were also greater in WP than in AP in both CV1 and CV2. (3) Gains with ME over SE attributed to genetic correlation between environments, with little effect from residual correlation. Impacts of marker density on predictions also were investigated in this study.
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