Detalhes bibliográficos
| Ano de defesa: |
2023 |
| Autor(a) principal: |
Lobo, Antonio Lucas Aguiar |
| Orientador(a): |
Não Informado pela instituição |
| Banca de defesa: |
Não Informado pela instituição |
| Tipo de documento: |
Dissertação
|
| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
Não Informado pela instituição
|
| Programa de Pós-Graduação: |
Não Informado pela instituição
|
| Departamento: |
Não Informado pela instituição
|
| País: |
Não Informado pela instituição
|
| Palavras-chave em Português: |
|
| Link de acesso: |
http://repositorio.ufc.br/handle/riufc/74678
|
Resumo: |
Abiotic stresses cause a huge negative impact on agricultural production, mainly the water deficit. An efficient way to understand the effects of stress on genotypes is through reaction norms. Reaction norm is a regression that shows the possible phenotypes that a given genotype can express under different environmental conditions. As it is a regression, the reaction norm can be decomposed into linear (intercept) and angular (slope) coefficients. These components, obtained as a function of an environmental gradient, provide information on the average performance over the planned environments and the responsibility for environmental improvement. Therefore, average performance is associated with the predictability/stability of a genotype against an environmental gradient and, thus, related to tolerance, while responsiveness is linked to phenotypic plasticity, that is, responsiveness to environmental increments. Thus, the use of this information (components of the reaction norms) instead of the use of raw phenotypic data in the performance of genetic association studies (GWAS), should make it possible to know genomic regions that are more influenced by environmental variables in terms of average performance and responsiveness to water stress. To test this hypothesis, a public genetic diversity panel consisting of 360 tropical maize lines was used, evaluated in eight environments, four under conditions of ideal water supply (WW) and four under conditions of water stress (WS). The root systems of the lines were phenotyped via image capture and processing using the RhizoVision Explorer® software, as well as shoot characters such as plant height, stem diameter, SPAD index and dry mass. At first, analyzes of phenotypic data were performed to extract adjusted means (BLUEs) via mixed linear model adjustment. Subsequently, reaction norms were obtained by genotype-by-genotype regression, considering the BLUEs of each strain in each environment as a response variable and the gradient with information on the environmental variable water supply as a predictor variable. The values of each strain referring to the components of reaction norms, intercept and slope were extracted. Thus, with this information, association analyzes (GWAS) were performed for each character. Afterwards, the candidate genes were annotated, with their potential effects and physiological relationships with water deficit tolerance via the MaizeGDB database. Significant SNPs were identified in practically all maize karyotypes, with the exception of chromosomes 6 and 7 in slope analysis and in chromosome 2 for intercept and slope. The SNPs that appeared in both conditions for more than one trait suggest the occurrence of a pleiotropic effect, as is the case of the Zm00001d048702 gene. A total of 25 significant SNPs were identified, these being for all characters evaluated in WW and WS. Of these, 15 SNPs for average performance, 15 for responsiveness and 5 common to both components (intercept and slope). The genes and/or genomic regions identified here reveal physiological responses and direct or indirect molecular mechanisms related to water deficit tolerance. This information will make it possible to carry out more assertive selections and subsidize breeding programs that aim to implement genomic selection, genomic editing (such as CRISPR) or genotyping technologies such as KASPTM (Kompetitive allele specific PCR) that aim to obtain cultivars intended for water stress conditions with cost reduction in the evaluation process |
