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Cited by 122Genetic and phenotypic traits in livestockGenetics and Plant BreedingGenetic Mapping and Diversity in Plants and Animals

Improving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials

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Dias, Kaio Olímpio Das Graças, Gezan, Salvador Alejandro, Guimarães, Claudia Teixeira, Nazarian, Alireza, da Costa e Silva, Luciano, Parentoni, Sidney Netto, de Oliveira Guimarães, Paulo Evaristo, de Oliveira Anoni, Carina +9 more·2018·Heredity
DOI 10.1038/s41437-018-0053-6PMC PMC5997769
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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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  • The inclusion of dominance effects in traditional WGP-MET resulted in increased predictive accuracy in models based on GBLUP compared with other methods.

    “As reported in other studies in plants, the inclusion of dominance effects in traditional WGP–MET resulted in increased predictive accuracy in models based on GBLUP compared with other methods.”

    From Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials (2021) · cites this paper as Dias et al. (2018)

    The Statistical analyses section (page not provided) says the study used GBLUP for additive-only and additive-plus-dominance MET models. The Accuracy of MET-GS models for drought tolerance-related traits section reports improved accuracy from adding dominance for several maize traits, but little improvement for others. The study does not establish that its dominance-inclusive GBLUP model outperformed other prediction methods; its direct comparison is between additive-only and additive-plus-dominance models. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

  • GBLUP models containing A+D effects doubled the predictive capacity for grain yield in maize under diverse environmental conditions, such as environments with limited water availability.

    “Dias et al. (2018) demonstrated that GBLUP models containing A+D effects doubled the predictive capacity for grain yield in maize under diverse environmental conditions, such as environments with limited water availability (i.e., drought-stress screening trials).”

    From Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials (2021) · cites this paper as Dias et al. (2018)

    The Abstract reports that the additive-plus-dominance GBLUP model doubled predictive accuracy for maize grain yield under water-stressed conditions, specifically using the CV1 validation scheme. The Accuracy of MET-GS models section reports a smaller, approximately 28% improvement under well-watered conditions, so the doubling does not extend across the diverse conditions implied by the claim. Page numbers are unavailable in our copy. (LLM, conf=0.90)

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Authors
Dias, K. O. D. G., Gezan, S. A., Guimarães, C. T., Nazarian, A., da Costa e Silva, L., Parentoni, S. N., de Oliveira Guimarães, P. E., de Oliveira Anoni, C., Pádua, J. M. V., de Oliveira Pinto, M., Noda, R. W., Ribeiro, C. A. G., de Magalhães, J. V., Garcia, A. A. F., de Souza, J. C., Guimarães, L. J. M., & Pastina, M. M.
Affiliations
  • Universidade Federal de Lavras (BR)
  • University of Florida (US)
  • Embrapa Milho e Sorgo (BR)
  • Universidade Federal de Viçosa (BR)
ORCID
Venue
Heredity
Publisher
Springer Science and Business Media LLC
Published
2018-02-23
Volume
121
Issue
1
Pages
24-37
ISSN
  • 0018-067X (ISSN-L)
  • 1365-2540
DOI
10.1038/s41437-018-0053-6
PMC
PMC5997769
OpenAlex
W2792329433
Cited by
115
References
65
Citations by year
2026: 8 · 2025: 7 · 2024: 13 · 2023: 16 · 2022: 21 · 2021: 23 · 2020: 16 · 2019: 9 · 2018: 2
FWCI
7.67
Type
journal article
Language
en
Open access
Licence
www.springer.com/tdm (tdm)
Locations
  • Heredity (publishedVersion)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • Fundação de Amparo à Pesquisa do Estado de São Paulo (2016/12977‐7)
  • Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (23038, program PREMIO 2045/2014)
  • Empresa Brasileira de Pesquisa Agropecuária
  • Conselho Nacional de Desenvolvimento Científico e Tecnológico
  • Fundação de Amparo à Pesquisa do Estado de Minas Gerais
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Genetics and Plant Breeding
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
Keywords
genomic selection, drought tolerance, maize, additive and dominance effects, multi-environment trials, prediction accuracy, single-cross hybrids, cross-validation, gene-environment interaction, SNP markers, genotyping-by-sequencing
MeSH terms
Plant Breeding, Adaptation, Biological, Algorithms, Environment, Genetic Markers, Genotype, Models, Genetic, Phenotype, Selection, Genetic, Stress, Physiological, Reproducibility of Results, Genome, Plant, Quantitative Trait, Heritable, Polymorphism, Single Nucleotide, Genomics, Droughts, Gene-Environment Interaction

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