Modeling genotype-by-environment interaction, additive and dominance effects into the genomic prediction framework for drought tolerance in maize

• Ano de defesa: 2016
• Autor(a) principal: Dias, Kaio Olímpio das Graças
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Universidade Federal de Lavras; Programa de Pós-Graduação em Genética e Melhoramento de Plantas; UFLA; brasil; Departamento de Biologia
• Palavras-chave em Português: Milho – Melhoramento genético; Resistência a seca; Genótipo; Corn – Breeding; Resistence of dehydration; Genotypes; Genética Vegetal
• Link de acesso: http://repositorio.ufla.br/jspui/handle/1/12109

Drought is one of the major causes of severe yield losses worldwide, and it is considered as an important limiting factor for maize production in tropical areas. Maize breeding for drought tolerance is usually difficult, time consuming and expensive, since the hybrids need to be evaluated in several environments. In this context, an accurate prediction of the performance of untested hybrids in one or more environments is essential to maximize genetic gains. The main goal of this study was to evaluate the accuracy of genomic selection to predict the performance of untested maize single-cross hybrids for drought tolerance, using a statistical-genetics model that account for genotype-by-environment interaction, additive and dominance effects. Phenotypic data of five drought tolerance traits were measured in 308 single-cross hybrids in eight trials, comprising water-stressed (WS) and well-watered (WW) conditions over two years and two locations, in Brazil. The genotypes of the hybrids were inferred based on the genotypes of their parents (inbred lines), using SNP (Single Nucleotide Polymorphism) data obtained via GBS (genotyping-by-sequencing). Genomic selection analysis was done using GBLUP (Genomic Best Linear Unbiased Prediction) by fitting a factor analytic multiplicative mixed model. Our results showed differences in the predictive accuracy between additive (A) and additive+dominant (AD) models for the five traits in both water conditions. However, these differences were more evident under WS conditions. For grain yield (GY), the AD model had a predictive accuracy two times bigger than the A model. Using factor analytic mixed models, including additive and dominance effects, it was possible to investigate the stability of the additive and dominance effects across environments, as well as, the additive and dominance-by-environment interaction, with interesting applications for parental and hybrid selection. In addition,combining WW and WS trials increased the prediction accuracy of untested hybrids in WW and/or WW conditions. These results contribute to a better understanding about the genetic architecture of important traits related to drought tolerance in maize, and highlight the importance of dominance effects for grain yield genomic prediction in single-cross hybrids under both water regimes.The models applied in this study can be easily extended to other crops for which the genotypes are measured in multiple environments and the dominance effects exhibit an important role for heterosis.