Otimização do tamanho de população sob acasalamento seletivo na seleção assistida por marcadores moleculares
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Revista Brasileira de Zootecnia
Abstract
Foram simulados diferentes tamanhos populacionais para estimar os valores fenotípicos na seleção assistida por marcadores para características quantitativas com valores de herdabilidade de 0,10; 0,40 e 0,70. Procedeu-se à análise de agrupamento com os desempenhos fenotípicos, cuja finalidade foi obter estruturas de classificação entre as amostras visando à otimização na detecção de QTL. O sistema de simulação genética (Genesys) foi utilizado para a simulação de três genomas (cada qual com uma única característica cuja distinção estava no valor da herdabilidade) e das populações base e inicial. Cada população inicial foi submetida à seleção assistida por marcadores por 20 gerações consecutivas, em que os genitores selecionados acasalavam-se seletivamente, entre os melhores e os piores. Essa estratégia seletiva de acasalamento mostrou-se eficiente na redução do número de indivíduos requeridos em uma população para mapeamento de QTL. À medida que aumenta a magnitude da herdabilidade, menores tamanhos populacionais são exigidos para manter similaridades nos incrementos fenotípicos. O emprego de amostras com 300, 250 e 200 ou mais indivíduos para as herdabilidades de 0,10; 0,40 e 0,70, respectivamente, é desnecessário, tendo em vista as inferências equivalentes indicadas pelos métodos de otimização de Tocher e da ligação completa, oriundas do sistema de análises estatísticas e genéticas (SAEG).
Different population sizes were simulated to estimate the phenotypic values in the selection assisted by markers for quantitative characteristics with heritability values of 0.10, 0.40 and 0.70. Cluster analysis with the phenotypic performance was carried out aiming at obtaining classification structure among the samples to optimize QTL detection. The genetic simulation system (Genesys) was used for the simulation of three genomes (each one consisting of a single characteristic whose distinction was the value of heritability), and the base and original populations. Each initial population was submitted to selection assisted by markers for 20 consecutive generations, in which selected parents mated selectively among the best and the worst ones. This selective strategy of mating proved itself to be efficient in reducing the number of individuals required in a population for QTL mapping. As the magnitude of heritability increases, lower population sizes are required to maintain similarities in phenotypic increments. The use of samples with 300, 250 and 200 individuals or more for heritabilities of 0.10, 0.40 and 0.70, respectively, is unnecessary, because of the equivalent inferences indicated by Tocher optmization and complete linkage methods from the system of statistical and genetic analysis (SAEG).
Different population sizes were simulated to estimate the phenotypic values in the selection assisted by markers for quantitative characteristics with heritability values of 0.10, 0.40 and 0.70. Cluster analysis with the phenotypic performance was carried out aiming at obtaining classification structure among the samples to optimize QTL detection. The genetic simulation system (Genesys) was used for the simulation of three genomes (each one consisting of a single characteristic whose distinction was the value of heritability), and the base and original populations. Each initial population was submitted to selection assisted by markers for 20 consecutive generations, in which selected parents mated selectively among the best and the worst ones. This selective strategy of mating proved itself to be efficient in reducing the number of individuals required in a population for QTL mapping. As the magnitude of heritability increases, lower population sizes are required to maintain similarities in phenotypic increments. The use of samples with 300, 250 and 200 individuals or more for heritabilities of 0.10, 0.40 and 0.70, respectively, is unnecessary, because of the equivalent inferences indicated by Tocher optmization and complete linkage methods from the system of statistical and genetic analysis (SAEG).
