Departmento de Biologia Geral, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brasil.
Centro de Seringueira e Sistemas Agroflorestais, Instituto Agronômico (IAC), São Paulo, Brasil.
PLoS One. 2022 May 3;17(5):e0259607. doi: 10.1371/journal.pone.0259607. eCollection 2022.
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: h2 = 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.
洪水灌溉水稻繁殖的最大挑战是鉴定具有优异基因型的品种,这些品种除了具有优异的适应目标环境的能力外,还具有高产、特定谷物品质、抗非生物和生物胁迫的特点。因此,本研究的目的是提出一种多性状和多环境贝叶斯模型,以估计洪水灌溉水稻的遗传参数。为此,评估了属于洪水灌溉水稻育种计划的 25 个水稻基因型。在 2017/2018 农业年度评估了谷物产量和开花。所有实验均采用随机区组设计,设 3 次重复。使用马尔可夫链蒙特卡罗算法估计遗传参数和遗传值。开花的贝叶斯可信度区间高度遗传:h2 = 0.039-0.80 和 0.02-0.91,分别为环境 1 和 2。两个环境中,性状间遗传相关性均显著不为零(环境 1:-0.80 至 0.74;环境 2:-0.82 至 0.86)。在简化模型中,开花的 CVe 和 CVg 之间的关系较高(CVg/CVe = 5.83 和 13.98,环境 1 和 2 分别)。对于完整模型,该性状的相对变异指数估计为:CVe = 4.28 和 4.21,环境 1 和 2 分别。总之,多性状和多环境贝叶斯模型允许对洪水灌溉水稻的遗传参数进行可靠估计。贝叶斯分析为遗传参数提供了稳健的推断。因此,我们建议将该模型用于洪水灌溉水稻基因型的遗传评估及其在其他作物中的推广。遗传参数的精确估计为贝叶斯方法在洪水灌溉水稻遗传改良中的建模问题解决方面带来了新的视角。