Registro fonte: Genome-wide selection in soybeans and optimization of phenotyping for grain yield

Genome-wide selection in soybeans and optimization of phenotyping for grain yield

In a breeding program, several factors influence the selection of cultivars, mainly due to the high number of genotypes under evaluation and the reduced experimental capacity in the initial phases of the program. In this context, the present study was divided into four parts. The first one aimed to...

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Autor principal:	Matei, Gilvani
Formato:	Tese
Idioma:	Inglês
Publicado em:	Universidade Tecnológica Federal do Paraná 2018
Assuntos:	Plantas - Melhoramento genético Marcadores genéticos Produtividade agrícola Plant breeding Genetic markers Agricultural productivity CNPQ::CIENCIAS AGRARIAS::AGRONOMIA::FITOTECNIA::MELHORAMENTO VEGETAL Fitotecnia
Acesso em linha:	http://repositorio.utfpr.edu.br/jspui/handle/1/3162
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spelling	riut-1-31622018-05-21T16:12:36Z Genome-wide selection in soybeans and optimization of phenotyping for grain yield Seleção genômica ampla em soja e otimização da fenotipagem para produtividade de grãos Matei, Gilvani Benin, Giovani http://lattes.cnpq.br/8634180310157308 Perea, Graciela Maria Salas Godoi, Cláudio Roberto Cardoso de Toledo, Jose Francisco Ferraz de Shannon, Grover Benin, Giovani Plantas - Melhoramento genético Marcadores genéticos Produtividade agrícola Plant breeding Genetic markers Agricultural productivity CNPQ::CIENCIAS AGRARIAS::AGRONOMIA::FITOTECNIA::MELHORAMENTO VEGETAL Fitotecnia In a breeding program, several factors influence the selection of cultivars, mainly due to the high number of genotypes under evaluation and the reduced experimental capacity in the initial phases of the program. In this context, the present study was divided into four parts. The first one aimed to identify the core locations for evaluation and selection of soybean genotypes in the macro-regions 1 (M1) and 2 (M2), in generations with low seed availability. The data set consisted of 22 soybean genotypes grown in 23 sites for three years. The GGL + GGE and G analyses versus the GE analysis were used. The locations Chapada-RS and Maracaju-MS were the most representative sites and discriminant macro-regions 1 and 2, respectively. Identification of the core location is fundamental to evaluation, since it is where the number of test sites can be summarized to a single site by soybean growing macro-region. The second study aimed to evaluate the experimental accuracy of different statistical methods used to analyze the assays with large numbers of soybean genotypes. The grain yield data from 324 soybean genotypes, evaluated in six replicates, were used. The data were analyzed by using the randomized block design, triple lattice design, and Papadakis method. The experimental accuracy indicators of the Papadakis method were more favorable when compared to those of the randomized block and triple lattice designs. Two replicates could be used when analyzing the data without reducing experimental accuracy: a randomized complete block design or the Papadakis method. In the third study, the productive performance, adaptability, and stability of modern soybean cultivars were evaluated in multi-environment assays. A total of 46 cultivars were evaluated in eight environments, in the adaptation micro-regions 102, 201, and 202, during the 2014/2015 harvest. Genotype × complex environment interactions occurred with changes in the ranking of cultivars between the sites. Among the genotypes evaluated, the cultivar NA 5909 RG, parental to the RILs in the genome-wide selection (GWS) assay, was considered to be among the genotypes with higher mean productivities, and it also showed high adaptability and stability. The fourth study had three objectives: to evaluate the accuracy of genomic selection in soybean, to identify the effect of intra-population structure on the accuracy of genomic selection, and to compare the efficiencies of the phenotypic and genomic selections in soybean. The BayesB model with cross validation was used for analyzing the phenotype data from the 324 soybean genotypes. The accuracy of GS for phenotypic characters with genotypic data of 5403 SNP molecular markers was also evaluated. The results indicated that the genotypic accuracy was similar, irrespective of consideration of the population structure. It was observed that the population structure did not significantly affect the accuracy of the models for the traits evaluated. It was verified that with this methodology it is possible to halve the selection time and increase the selection efficiency by 123% for grain yield. Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Em um programa de melhoramento genético varios fatores influenciam na seleção de cultivares, basicamente pelo elevado número de genótipos em avaliação e pela reduzida capacidade experimental em fases iniciais do programa. Nesse contexto, o presente trabalho foi dividido em quatro partes. O primeiro estudo objetivou identificar locais chaves para avaliação e seleção de genótipos de soja nas nas macrorregiões 1 (M1) e 2 (M2), em gerações com pouca disponibilidade de semente. O conjunto de dados consistiu em 22 genótipos de soja cultivados em 23 locais por 3 anos. As análises GGL + GGE e G vs. GE foram usadas. As localidades Chapada-RS e Maracaju-MS foram os locais mais representativos e discriminantes macrorregiões 1 e 2, respectivamente. A identificação das localidades chave é fundamental para a avaliação, onde o número de locais de ensaio pode se resumir a um único local por macrorregião sojícola. O segundo estudo teve como objetivo avaliar a precisão experimental de diferentes métodos de análise estatística para ensaios com elevado número de genótipos de soja. Foram usados dados de produtividade de grãos de 324 genótipos de soja, avaliados em 6 repetições. Os dados foram analisados considerando os delineamentos de blocos ao acaso, látice triplo e uso do método de Papadakis. Os indicadores de precisão experimental do método de Papadakis são mais favoráveis, quando comparados com os delineamentos de blocos ao acaso e látice triplo. Pode-se usar duas repetições e analisar os dados, usando o delineamento de blocos completamente casualizados ou método Papadakis, sem redução da precisão experimental. No terceiro estudo foi avaliado o desempenho produtivo, a adaptabilidade e a estabilidade de cultivares modernas de soja, em ensaios multiambientes. Foram avaliados 46 cultivares em oito ambientes, nas microrregiões de adaptação 102, 201 e 202, na safra 2014/2015. Ocorreu interação genótipo x ambiente complexa, com alterações do ranqueamento de cultivares entre os locais. Dentre os genótipos avaliados a cultivar NA 5909 RG, parental das RILs no ensaio GWS, esteve presente entre genótipos de maiores médias produtivas, apresentando também elevada adaptabilidade e estabilidade. O quarto estudo teve três objetivos: avaliar a precisão da SG na soja; identificar o efeito da estrutura intrapopulação na precisão da seleção genômica; e, comparar a eficiência da seleção fenotípica e genômica na soja. Foi utilizado o modelo BayesB com validação cruzada para dados fenótipicos e genótipicos de 324 genótipos de soja. Avaliou-se a precisão do GS para caracteres fenotípicos com dados genotípicos de 5403 marcadores SNPs. Os resultados indicaram que a precisão genotípica foi semelhante, considerando, ou não, a estrutura da população. Se observou que a estrutura da população não afetou significativamente a precisão dos modelos para os caracteres avaliados. Constatou-se que com esta metodologia torna-se possível reduzir pela metade o tempo de seleção e aumentar a eficiência de seleção em 123% para produtividade de grãos. 2018-05-21T16:12:36Z 2018-05-21T16:12:36Z 2017-12-12 doctoralThesis MATEI, Gilvani. Genome-wide selection in soybeans and optimization of phenotyping for grain yield. 2017. 101 f. Tese (Doutorado em Agronomia) - Universidade Tecnológica Federal do Paraná, Pato Branco, 2017. http://repositorio.utfpr.edu.br/jspui/handle/1/3162 eng openAccess application/pdf Universidade Tecnológica Federal do Paraná Pato Branco Brasil Programa de Pós-Graduação em Agronomia UTFPR
institution	Universidade Tecnológica Federal do Paraná
collection	RIUT
language	Inglês
topic	Plantas - Melhoramento genético Marcadores genéticos Produtividade agrícola Plant breeding Genetic markers Agricultural productivity CNPQ::CIENCIAS AGRARIAS::AGRONOMIA::FITOTECNIA::MELHORAMENTO VEGETAL Fitotecnia
spellingShingle	Plantas - Melhoramento genético Marcadores genéticos Produtividade agrícola Plant breeding Genetic markers Agricultural productivity CNPQ::CIENCIAS AGRARIAS::AGRONOMIA::FITOTECNIA::MELHORAMENTO VEGETAL Fitotecnia Matei, Gilvani Genome-wide selection in soybeans and optimization of phenotyping for grain yield
description	In a breeding program, several factors influence the selection of cultivars, mainly due to the high number of genotypes under evaluation and the reduced experimental capacity in the initial phases of the program. In this context, the present study was divided into four parts. The first one aimed to identify the core locations for evaluation and selection of soybean genotypes in the macro-regions 1 (M1) and 2 (M2), in generations with low seed availability. The data set consisted of 22 soybean genotypes grown in 23 sites for three years. The GGL + GGE and G analyses versus the GE analysis were used. The locations Chapada-RS and Maracaju-MS were the most representative sites and discriminant macro-regions 1 and 2, respectively. Identification of the core location is fundamental to evaluation, since it is where the number of test sites can be summarized to a single site by soybean growing macro-region. The second study aimed to evaluate the experimental accuracy of different statistical methods used to analyze the assays with large numbers of soybean genotypes. The grain yield data from 324 soybean genotypes, evaluated in six replicates, were used. The data were analyzed by using the randomized block design, triple lattice design, and Papadakis method. The experimental accuracy indicators of the Papadakis method were more favorable when compared to those of the randomized block and triple lattice designs. Two replicates could be used when analyzing the data without reducing experimental accuracy: a randomized complete block design or the Papadakis method. In the third study, the productive performance, adaptability, and stability of modern soybean cultivars were evaluated in multi-environment assays. A total of 46 cultivars were evaluated in eight environments, in the adaptation micro-regions 102, 201, and 202, during the 2014/2015 harvest. Genotype × complex environment interactions occurred with changes in the ranking of cultivars between the sites. Among the genotypes evaluated, the cultivar NA 5909 RG, parental to the RILs in the genome-wide selection (GWS) assay, was considered to be among the genotypes with higher mean productivities, and it also showed high adaptability and stability. The fourth study had three objectives: to evaluate the accuracy of genomic selection in soybean, to identify the effect of intra-population structure on the accuracy of genomic selection, and to compare the efficiencies of the phenotypic and genomic selections in soybean. The BayesB model with cross validation was used for analyzing the phenotype data from the 324 soybean genotypes. The accuracy of GS for phenotypic characters with genotypic data of 5403 SNP molecular markers was also evaluated. The results indicated that the genotypic accuracy was similar, irrespective of consideration of the population structure. It was observed that the population structure did not significantly affect the accuracy of the models for the traits evaluated. It was verified that with this methodology it is possible to halve the selection time and increase the selection efficiency by 123% for grain yield.
format	Tese
author	Matei, Gilvani
author_sort	Matei, Gilvani
title	Genome-wide selection in soybeans and optimization of phenotyping for grain yield
title_short	Genome-wide selection in soybeans and optimization of phenotyping for grain yield
title_full	Genome-wide selection in soybeans and optimization of phenotyping for grain yield
title_fullStr	Genome-wide selection in soybeans and optimization of phenotyping for grain yield
title_full_unstemmed	Genome-wide selection in soybeans and optimization of phenotyping for grain yield
title_sort	genome-wide selection in soybeans and optimization of phenotyping for grain yield
publisher	Universidade Tecnológica Federal do Paraná
publishDate	2018
citation	MATEI, Gilvani. Genome-wide selection in soybeans and optimization of phenotyping for grain yield. 2017. 101 f. Tese (Doutorado em Agronomia) - Universidade Tecnológica Federal do Paraná, Pato Branco, 2017.
url	http://repositorio.utfpr.edu.br/jspui/handle/1/3162
_version_	1805316603052556288
score	10,814766

Genome-wide selection in soybeans and optimization of phenotyping for grain yield

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