Multi-trait and multi-environment Bayesian analysis to predict the G x E interaction in flood-irrigated rice

被引:5
|
作者
da Silva Junior, Antonio Carlos [1 ]
Sant'Anna, Isabela de Castro [2 ]
Silva Siqueira, Michele Jorge [1 ]
Cruz, Cosme Damiao [1 ]
Azevedo, Camila Ferreira [3 ]
Nascimento, Moyses [3 ]
Soares, Plinio Cesar [4 ]
机构
[1] Univ Fed Vicosa, Dept Biol Geral, Vicosa, MG, Brazil
[2] Inst Agron IAC, Ctr Seringueira & Sistemas Agroflorestais, Sao Paulo, Brazil
[3] Univ Fed Vicosa, Dept Estat, Vicosa, MG, Brazil
[4] Empresa Pesquisa Agr Minas Gerais EPAMIG, Vicosa, MG, Brazil
来源
PLOS ONE | 2022年 / 17卷 / 05期
基金
巴西圣保罗研究基金会;
关键词
GENOMIC SELECTION; GENETIC-PARAMETERS; MODELS; GENOTYPE;
D O I
10.1371/journal.pone.0259607
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The biggest challenge for the reproduction of flood-irrigated rice is to identify superior genotypes that present development of high-yielding varieties with specific grain qualities, resistance to abiotic and biotic stresses in addition to superior adaptation to the target environment. Thus, the objectives of this study were to propose a multi-trait and multi-environment Bayesian model to estimate genetic parameters for the flood-irrigated rice crop. To this end, twenty-five rice genotypes belonging to the flood-irrigated rice breeding program were evaluated. Grain yield and flowering were evaluated in the agricultural year 2017/2018. The experimental design used in all experiments was a randomized block design with three replications. The Markov Chain Monte Carlo algorithm was used to estimate genetic parameters and genetic values. The flowering is highly heritable by the Bayesian credibility interval: h(2) = 0.039-0.80, and 0.02-0.91, environment 1 and 2, respectively. The genetic correlation between traits was significantly different from zero in the two environments (environment 1: -0.80 to 0.74; environment 2: -0.82 to 0.86. The relationship of CVe and CVg higher for flowering in the reduced model (CVg/CVe = 5.83 and 13.98, environments 1 and 2, respectively). For the complete model, this trait presented an estimate of the relative variation index of: CVe = 4.28 and 4.21, environments 1 and 2, respectively. In summary, the multi-trait and multi-environment Bayesian model allowed a reliable estimate of the genetic parameter of flood-irrigated rice. Bayesian analyzes provide robust inference of genetic parameters. Therefore, we recommend this model for genetic evaluation of flood-irrigated rice genotypes, and their generalization, in other crops. Precise estimates of genetic parameters bring new perspectives on the application of Bayesian methods to solve modeling problems in the genetic improvement of flood-irrigated rice.
引用
收藏
页数:13
相关论文
共 50 条
  • [1] A Genomic Bayesian Multi-trait and Multi-environment Model
    Montesinos-Lopez, Osval A.
    Montesinos-Lopez, Abelardo
    Crossa, Jose
    Toledo, Fernando H.
    Perez-Hernandez, Oscar
    Eskridge, Kent M.
    Rutkoski, Jessica
    G3-GENES GENOMES GENETICS, 2016, 6 (09): : 2725 - 2744
  • [2] Updating knowledge in estimating the genetics parameters: Multi-trait and Multi-Environment Bayesian analysis in rice
    Azevedo, Camila Ferreira
    Valiati Barreto, Cynthia Aparecida
    Suela, Matheus Massariol
    Nascimento, Moyses
    da Silva Junior, Antonio Carlos
    Campana Nascimento, Ana Carolina
    Cruz, Cosme Damiao
    Soraes, Plinio Cesar
    SCIENTIA AGRICOLA, 2023, 80
  • [3] Multi-trait multi-environment Bayesian model reveals G x E interaction for nitrogen use efficiency components in tropical maize
    Torres, Livia Gomes
    Rodrigues, Mateus Cupertino
    Lima, Nathan Lamounier
    Horta Trindade, Tatiane Freitas
    Fonseca e Silva, Fabyano
    Azevedo, Camila Ferreira
    DeLima, Rodrigo Oliveira
    PLOS ONE, 2018, 13 (06):
  • [4] An R Package for Bayesian Analysis of Multi-environment and Multi-trait Multi-environment Data for Genome-Based Prediction
    Montesinos-Lopez, Osval A.
    Montesinos-Lopez, Abelardo
    Javier Luna-Vazquez, Francisco
    Toledo, Fernando H.
    Perez-Rodriguez, Paulino
    Lillemo, Morten
    Crossa, Jose
    G3-GENES GENOMES GENETICS, 2019, 9 (05): : 1355 - 1369
  • [5] Multi-trait multi-environment diallel analyses for maize breeding
    Coelho, Igor Ferreira
    Alves, Rodrigo Silva
    Rocha, Joao Romero do Amaral Santos de Carvalho
    Peixoto, Marco Antonio
    Teodoro, Larissa Pereira Ribeiro
    Teodoro, Paulo Eduardo
    Pinto, Jefferson Fernando Naves
    dos Reis, Edesio Fialho
    Bhering, Leonardo Lopes
    EUPHYTICA, 2020, 216 (09)
  • [6] Multi-trait multi-environment diallel analyses for maize breeding
    Igor Ferreira Coelho
    Rodrigo Silva Alves
    João Romero do Amaral Santos de Carvalho Rocha
    Marco Antônio Peixoto
    Larissa Pereira Ribeiro Teodoro
    Paulo Eduardo Teodoro
    Jefferson Fernando Naves Pinto
    Edésio Fialho dos Reis
    Leonardo Lopes Bhering
    Euphytica, 2020, 216
  • [7] Multi-Trait and Multi-Environment QTL Analyses for Resistance to Wheat Diseases
    Sukhwinder-Singh
    Hernandez, Mateo V.
    Crossa, Jose
    Singh, Pawan K.
    Bains, Navtej S.
    Singh, Kuldeep
    Sharma, Indu
    PLOS ONE, 2012, 7 (06):
  • [8] Multi-trait multi-environment models in the genetic selection of segregating soybean progeny
    Volpato, Leonardo
    Alves, Rodrigo Silva
    Teodoro, Paulo Eduardo
    Vilela de Resende, Marcos Deon
    Nascimento, Moyses
    Campana Nascimento, Ana Carolina
    Ludke, Willian Hytalo
    da Silva, Felipe Lopes
    Borem, Aluizio
    PLOS ONE, 2019, 14 (04):
  • [9] A Bayesian Genomic Multi-output Regressor Stacking Model for Predicting Multi-trait Multi-environment Plant Breeding Data
    Montesinos-Lopez, Osval A.
    Montesinos-Lopez, Abelardo
    Crossa, Jose
    Cuevas, Jaime
    Montesinos-Lopez, Jose C.
    Salas Gutierrez, Zitlalli
    Lillemo, Morten
    Philomin, Juliana
    Singh, Ravi
    G3-GENES GENOMES GENETICS, 2019, 9 (10): : 3381 - 3393
  • [10] Multi-trait multi-environment genomic prediction of preliminary yield trial in pulse crop
    Saludares, Rica Amor
    Atanda, Sikiru Adeniyi
    Piche, Lisa
    Worral, Hannah
    Dariva, Francoise
    McPhee, Kevin
    Bandillo, Nonoy
    PLANT GENOME, 2024,