Use este identificador para citar ou linkar para este item:
http://repositorio.ufla.br/jspui/handle/1/48404
Título: | Genomic prediction and genome-wide association study: an application of quantitative genetics in plant breeding programs |
Título(s) alternativo(s): | Predição genômica e estudo de associação genômica: uma aplicação da genética quantitativa em programas de melhoramento de plantas |
Autores: | Von Pinho, Renzo Garcia Beissinger, Timothy Mathes Pádua, José Maria Villela Resende, Marcela Pedroso Mendes Fritsche-Neto, Roberto |
Palavras-chave: | Milho - Melhoramento genético Predição genômica Seleção de pais Mapeamento associativo Milho - Podridão de espiga Milho - Doenças e pragas Zea mays L. Maize - Diseases and pests Maize - Genetic improvement Genomic prediction Parent selection Association mapping Maize - Ear rot |
Data do documento: | 25-Out-2021 |
Editor: | Universidade Federal de Lavras |
Citação: | DE JONG, G. Genomic prediction and genome-wide association study: an application of quantitative genetics in plant breeding programs. 2021. 83 p. Tese (Doutorado em Genética e Melhoramento de Plantas) – Universidade Federal de Lavras, Lavras, 2021. |
Resumo: | The development of new tools and advances in high throughput genomic technologies have facilitated genomic selection the identification of sources of variation, especially of complex traits. Therefore, the availability of abundant and cheap markers made it possible to exploit the marker information in breeding programs. The most common tools used in breeding programs that exploit the dense marker coverage are genomic prediction and genome-wide association studies. In the genomic prediction, marker parameters are estimated from a training dataset with genotyped and phenotyped individuals. Subsequently, the trained model is used to predict performance for individuals that are only genotyped. On the other hand, genome-wide association studies test marker-trait associations that may be responsible for the causal variation of interest. We investigated the performance of different genomic prediction models to select parents in the early stage of a hybrid breeding program using estimated general combining ability and their impact on selection accuracy and long-term genetic gain. We evaluated the performance of five genomic prediction models under different SNP marker densities or QTL genotypes using stochastic simulations of an entire hybrid breeding program. We also investigated the ability of univariate and multivariate GWAS identifying markers linked to loci that contribute to resistance to Diplodia ear rot or Fusarium ear rot or both diseases in maize inbred lines. We evaluated the univariate and multivariate approaches using a maize diverse panel evaluated for three different traits. |
URI: | http://repositorio.ufla.br/jspui/handle/1/48404 |
Aparece nas coleções: | Genética e Melhoramento de Plantas - Doutorado (Teses) |
Arquivos associados a este item:
Arquivo | Descrição | Tamanho | Formato | |
---|---|---|---|---|
TESE_Genomic prediction and genome-wide association study an application of quantitative genetics in plant breeding programs.pdf | 3,12 MB | Adobe PDF | Visualizar/Abrir |
Os itens no repositório estão protegidos por copyright, com todos os direitos reservados, salvo quando é indicado o contrário.