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
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