ArticleHeredity2018
Improving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials.
Article in Heredity, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 1 of them a synthesis that pooled it.
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Who cites it
43 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
- Multienvironment multilocus association mapping reveals genomic regions for drought-related traits in tropical maize.G3 (Bethesda, Md.) · 2026Article
- Dissecting genetic variance structure and evaluating genomic prediction models for single-cross hybrids derived from Stiff Stalk and Non-Stiff Stalk maize heterotic groups.G3 (Bethesda, Md.) · 2026Article
- Coformer: a deep learning-based framework for cross-environment and multi-year cotton phenotype prediction and interpretation.Plant methods · 2026Article
- Genomic prediction of stalk lodging resistance and the associated intermediate phenotypes in maize using whole-genome resequence and multi-environmental data.The plant genome · 2025Article
- Article
- Genomic selection of maize test-cross hybrids leveraged by marker sampling.The plant genome · 2025Article
- Hybrid Prediction in Horticulture Crop Breeding: Progress and Challenges.Plants (Basel, Switzerland) · 2024Review
- GIS-FA: an approach to integrating thematic maps, factor-analytic, and envirotyping for cultivar targeting.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Genomic prediction in multi-environment trials in maize using statistical and machine learning methods.Scientific reports · 2024Article
- Genomic dissection of additive and non-additive genetic effects and genomic prediction in an open-pollinated family test of Japanese larch.BMC genomics · 2024Article
- Optimizing drought tolerance in cassava through genomic selection.Frontiers in plant science · 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
- Salinity stress tolerance prediction for biomass-related traits in maize (Zea mays L.) using genome-wide markers.The plant genome · 2023Article
- Identifying QTLs involved in hybrid performance and heterotic group complementarity: new GWAS models applied to factorial and admixed diallel maize hybrid panels.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 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
- Use of genomic prediction to screen sorghum B-lines in hybrid testcrosses.The plant genome · 2023Article
- Genomic studies of the additive and dominant genetic control on production traits of Euterpe edulis fruits.Scientific reports · 2023Article
- Enviromic-based kernels may optimize resource allocation with multi-trait multi-environment genomic prediction for tropical Maize.BMC plant biology · 2023Article
- Increasing genomic prediction accuracy for unphenotyped full-sib families by modeling additive and dominance effects with large datasets in white spruce.Frontiers in plant science · 2023Article
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17 authors.
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Abstract
Breeding for drought tolerance is a challenging task that requires costly, extensive, and precise phenotyping. Genomic selection (GS) can be used to maximize selection efficiency and the genetic gains in maize (Zea mays L.) breeding programs for drought tolerance. Here, we evaluated the accuracy of genomic selection (GS) using additive (A) and additive + dominance (AD) models to predict the performance of untested maize single-cross hybrids for drought tolerance in multi-environment trials. Phenotypic data of five drought tolerance traits were measured in 308 hybrids along eight trials under water-stressed (WS) and well-watered (WW) conditions over two years and two locations in Brazil. Hybrids' genotypes were inferred based on their parents' genotypes (inbred lines) using single-nucleotide polymorphism markers obtained via genotyping-by-sequencing. GS analyses were performed using genomic best linear unbiased prediction by fitting a factor analytic (FA) multiplicative mixed model. Two cross-validation (CV) schemes were tested: CV1 and CV2. The FA framework allowed for investigating the stability of additive and dominance effects across environments, as well as the additive-by-environment and the dominance-by-environment interactions, with interesting applications for parental and hybrid selection. Results showed differences in the predictive accuracy between A and AD models, using both CV1 and CV2, for the five traits in both water conditions. For grain yield (GY) under WS and using CV1, the AD model doubled the predictive accuracy in comparison to the A model. Through CV2, GS models benefit from borrowing information of correlated trials, resulting in an increase of 40% and 9% in the predictive accuracy of GY under WS for A and AD models, respectively. These results highlight the importance of multi-environment trial analyses using GS models that incorporate additive and dominance effects for genomic predictions of GY under drought in maize single-cross hybrids.
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