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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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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das Gracas Dias, Kaio Olimpio [1, 2] ; Gezan, Salvador Alejandro [3] ; Guimaraes, Claudia Teixeira [4] ; Nazarian, Alireza [3] ; da Costa e Silva, Luciano [5] ; Parentoni, Sidney Netto [4] ; de Oliveira Guimaraes, Paulo Evaristo [4] ; Anoni, Carina de Oliveira [1] ; Villela Padua, Jose Maria [2] ; Pinto, Marcos de Oliveira [4] ; Noda, Roberto Willians [4] ; Gomes Ribeiro, Carlos Alexandre [6] ; de Magalhaes, Jurandir Vieira [4] ; Franco Garcia, Antonio Augusto [1] ; de Souza, Joao Candido [2] ; Moreira Guimaraes, Lauro Jose [4] ; Pastina, Maria Marta [4]
Total Authors: 17
[1] Univ Sao Paulo, Escola Super Agr Luiz de Queiroz, Dept Genet, Piracicaba, SP - Brazil
[2] Univ Fed Lavras, Dept Biol, Lavras, MG - Brazil
[3] Univ Florida, Sch Forest Resources & Conservat, Gainesville, FL 32611 - USA
[4] Embrapa Milho & Sorgo, Sete Lagoas, MG - Brazil
[5] SAS Inst Inc, JMP Div, Cary, NC - USA
[6] Univ Fed Vicosa, Dept Biol Geral, Vicosa, MG - Brazil
Total Affiliations: 6
Document type: Journal article
Source: HEREDITY; v. 121, n. 1, p. 24-37, JUL 2018.
Web of Science Citations: 5

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. (AU)

FAPESP's process: 16/12977-7 - Genomic selection implementation in maize using a statistical-genetics model that accounts for genotype-by-environment interaction, additive and non-additive genetic effects
Grantee:Kaio Olimpio das Graças Dias
Support Opportunities: Scholarships in Brazil - Post-Doctoral