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Doctoral Thesis
DOI
https://doi.org/10.11606/T.11.2020.tde-12082020-151023
Document
Author
Full name
Tatiana Oliveira Gonçalves de Assis
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
Piracicaba, 2020
Supervisor
Committee
Piedade, Sonia Maria de Stefano (President)
Araújo, Lúcio Borges de
Sarti, Danilo Augusto
Title in English
Generalizations of the AMMI and GGE models to understand the interaction between genotypes and environments and between QTL and environments
Keywords in English
Genotype-by-environment interaction
Multi-environment trials
QTL by environment interaction
QTL detection
Abstract in English
In multi-environmental trials it is common for the genetic characteristics of the cultivars to be influenced by the environments. Thus, the study of tools that allow the analysis of the interaction between genotypes and environments and between QTLs (quantitative trait loci) and environments has gained more and more space among researchers in this area. However, collected data are not always suitable for use with already known models, making it necessary to search for more specific models for certain situations. In this research, we analyzed situations such as data showing hetero- geneity of error variance across environments and also contaminated data, which represent data with outlying observations. In such cases, models already known in the literature, such as the AMMI (additive main-effect and multiplicative interaction) model and the GGE (genotype main-effects + genotype environment interaction) model, are not indicated. Here, we verify the use of the robust AMMI model and weighted AMMI in the detection of QTLs and in the analysis of interactions. We also propose the weighted GGE model and evaluate its effectiveness, comparing it with other models. Two data sets were used. The first data from a simulated pepper (Capsicum annuum L.) back cross population using a crop growth model report genotype to phenotype in a nonlinear way, and the second the doubled-haploid Steptoe × Morex barley (Hordeum vulgare L.) population.
Title in Portuguese
Generalizações dos modelos AMMI e GGE para entender a interação entre genótipos e ambientes e entre QTL e ambientes
Keywords in Portuguese
Detecção de QTL
Ensaios multi-ambientais
Interação genótipo × ambiente
Interação QTL × ambiente
Abstract in Portuguese
Em ensaios multi-ambientais é comum que as características genéticas dos cultivares sejam influenciadas pelos ambientes. Desta forma, o estudo de ferramentas que permitam analisar a interação entre genótipos e ambientes e entre QTLs (locus de característica quatitativa) e ambientes tem ganhado cada vez mais espaço entre os pesquisadores desta área. Porém, nem sempre dados coletados são adequados para serem utilizados com modelos já conhecidos, sendo necessário a busca por modelos mais específicos a certas situações. Nessa pesquisa, analisamos situações como dados que apresentam heterogeneidade de variância dos erros ao longo dos ambientes e também dados contaminados, que representam dados com presença de outliers. Nesses casos, modelos já conhecidos na literatura, como o modelo AMMI (modelo de efeito principal aditivo e interação multiplicativa) e o modelo GGE (modelo de efeito principal de genótipo mais interação genótipo × ambiente), não são indicados. Aqui, verificamos o uso do modelo AMMI robusto e AMMI ponderado na detecção de QTLs e na análise de interações. Também, propomos o modelo GGE ponderado e avaliamos sua eficácia, comparando com outros modelos. Foram usados dois conjunto de dados. O primeiro conjunto de dados são dados simulados de pimentão (textit Capsicum annuum L.) de população cruzada usando modelo de crescimento de culturas para relacionar genótipos a fenótipos de maneira não linear e o segundo, dados de cevada de população duplo haplóide Steptoe × Morex (Hordeum vulgare L.).
 
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Publishing Date
2020-08-13
 
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