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Different strategies for estimating genetic parameters for collar rot resistance characteristics in Passiflora spp

The use of several strategies for estimating genetic parameters in passion fruit (Passiflora spp.) segregating populations has important implications for understanding the nature of variances, especially the traits of resistance to collar rot. The objective of this study was to estimate genetic para...

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Bibliographic Details
Published in:Chilean journal of agricultural research 2021-07, Vol.81 (3), p.281-290
Main Authors: Marostega, Thalita N, Preisigke, Sandra C, Chimello, Antonio M, Silva, Geovani C, Gilio, Thiago A.S, Araujo, Kelly L, Barelli, Marco A.A, Neves, Leonarda G
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Language:English
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Summary:The use of several strategies for estimating genetic parameters in passion fruit (Passiflora spp.) segregating populations has important implications for understanding the nature of variances, especially the traits of resistance to collar rot. The objective of this study was to estimate genetic parameters and predict gains with the selection of genotypes resistant to collar rot in a population of interspecific passion fruit hybrids. For the analysis of restricted maximum likelihood/best linear unbiased prediction (REML/BLUP), 40 interspecific hybrids obtained by crossing resistant wild species with Passiflora edulis Sims genotypes were analyzed. The Comstock and Robinson Design II methodology used two male parents (P. nitida Kunth and P. quadrangularis L.) and two female parents of P. edulis. The evaluations took place for 33 d and 10 variables of resistance to collar rot were characterized. The expression of most variables was due to the genetic effect. Dominance effects predominated. High estimates of heritability based on clone average and selective accuracy were observed, which provides favorable gains in reducing the severity of collar rot by selecting the genotypes 115-5, 115-4, 115-7, 115-6, 115-3, 115-1, 128, 115-9, 115-4. Key words: Comstock Robinson Design II, Fusarium solani, sour passion fruit, genetic resistance, mixed models.
ISSN:0718-5839
0718-5820
0718-5839
DOI:10.4067/S0718-58392021000300281