Multivariate Bayesian analysis for genetic evaluation and selection of Eucalyptus in multiple environment trials

IF 1.2 4区 农林科学 Q2 AGRICULTURE, MULTIDISCIPLINARY
F. M. Ferreira, J. S. P. C. Evangelista, Saulo F. S. Chaves, R. S. Alves, D. Silva, Renan Garcia Malikouski, M. Resende, L. L. Bhering, G. A. Santos
{"title":"Multivariate Bayesian analysis for genetic evaluation and selection of Eucalyptus in multiple environment trials","authors":"F. M. Ferreira, J. S. P. C. Evangelista, Saulo F. S. Chaves, R. S. Alves, D. Silva, Renan Garcia Malikouski, M. Resende, L. L. Bhering, G. A. Santos","doi":"10.1590/1678-4499.20210347","DOIUrl":null,"url":null,"abstract":"Forest plantations are strong allies in preserving natural resources, providing social and economic benefits. The plantations carried out in the coming years will be vital to meet the growing demand for forest products. To ensure the continuity of genetic progress and the good results achieved with the improvement of forest species, statistical methods that accurately selects superior genotypes are desirable. Multi-trait multi-environment trials are preferred over single-trait single-environment trials, since they can exploit the covariance between traits and environments, increasing the analysis’s prediction power. The Bayesian multi-trait multi-environments approach (BMTME) combines the cited advantages with the parsimony of Bayesian statistics promoting a more informative data analysis. Thus, the aims of this study were to estimate genetic parameters, evaluate genetic variability, and select eucalyptus clones through BMTME models. To this end, a data set with 215 eucalyptus clones evaluated in four environments for diameter at breast height and Pilodyn penetration was used. The Markov Chain Monte Carlo algorithm was applied to estimate the variance components and genetic parameters and to predict the genotypic values. The Smith-Hazel index was used to simultaneously achieve gains with selection for both traits. The BMTME approach provided high accuracies, being a good strategy to the evaluation of multiple environmental trials of Eucalyptus for breeding purposes.","PeriodicalId":9260,"journal":{"name":"Bragantia","volume":null,"pages":null},"PeriodicalIF":1.2000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bragantia","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1590/1678-4499.20210347","RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 1

Abstract

Forest plantations are strong allies in preserving natural resources, providing social and economic benefits. The plantations carried out in the coming years will be vital to meet the growing demand for forest products. To ensure the continuity of genetic progress and the good results achieved with the improvement of forest species, statistical methods that accurately selects superior genotypes are desirable. Multi-trait multi-environment trials are preferred over single-trait single-environment trials, since they can exploit the covariance between traits and environments, increasing the analysis’s prediction power. The Bayesian multi-trait multi-environments approach (BMTME) combines the cited advantages with the parsimony of Bayesian statistics promoting a more informative data analysis. Thus, the aims of this study were to estimate genetic parameters, evaluate genetic variability, and select eucalyptus clones through BMTME models. To this end, a data set with 215 eucalyptus clones evaluated in four environments for diameter at breast height and Pilodyn penetration was used. The Markov Chain Monte Carlo algorithm was applied to estimate the variance components and genetic parameters and to predict the genotypic values. The Smith-Hazel index was used to simultaneously achieve gains with selection for both traits. The BMTME approach provided high accuracies, being a good strategy to the evaluation of multiple environmental trials of Eucalyptus for breeding purposes.
多环境试验中桉树遗传评价与选择的多元贝叶斯分析
人工林是保护自然资源、提供社会和经济效益的强大盟友。未来几年进行的造林对于满足对林产品日益增长的需求至关重要。为了保证遗传进步的连续性和森林物种改良取得的良好结果,需要精确选择优良基因型的统计方法。多性状多环境试验优于单性状单环境试验,因为它们可以利用性状与环境之间的协方差,提高分析的预测能力。贝叶斯多特征多环境方法(BMTME)将上述优点与贝叶斯统计的简洁性相结合,促进了更翔实的数据分析。因此,本研究的目的是通过BMTME模型估计遗传参数,评估遗传变异,并选择桉树无性系。为此,使用了215个桉树无性系在四种环境下对胸围直径和Pilodyn穿透度进行评估的数据集。采用马尔可夫链蒙特卡罗算法估计方差分量和遗传参数,预测基因型值。Smith-Hazel指数用于同时获得两个性状的选择增益。BMTME方法具有较高的准确性,是评价桉树育种多环境试验的良好策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Bragantia
Bragantia AGRICULTURE, MULTIDISCIPLINARY-
CiteScore
2.40
自引率
8.30%
发文量
33
审稿时长
4 weeks
期刊介绍: Bragantia é uma revista de ciências agronômicas editada pelo Instituto Agronômico da Agência Paulista de Tecnologia dos Agronegócios, da Secretaria de Agricultura e Abastecimento do Estado de São Paulo, com o objetivo de publicar trabalhos científicos originais que contribuam para o desenvolvimento das ciências agronômicas. A revista é publicada desde 1941, tornando-se semestral em 1984, quadrimestral em 2001 e trimestral em 2005. É filiada à Associação Brasileira de Editores Científicos (ABEC).
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信