Resistance to Pratylenchus brachyurus in Vitis species population through multivariate approaches and mixed models

Authors

DOI:

https://doi.org/10.1590/1678-992x-2017-0387

Keywords:

Vitis genotypes, nematode, root, genetic variability

Abstract

Genetically diverse interspecific hybrids of Vitis were selected for resistance to Pratylenchus brachyurus. Three segregating populations with 57 hybrid crosses were evaluated. The parents included Vitis romanetiiC166-043 × 07355-075, 06354-047 × Cereza and 06354- 047 × Nocera, selected from the germplasm bank at the University of California, Davis, the United States. The experiment was arranged in a randomized block design, with three replications and three plants per plot. Root mass, nematodes per gram of root and reproduction factor were determined and used as quantitative variables; 16 multi categoric descriptors were also evaluated. The traits were analyzed using the Ward-Modified Location Model procedure (WardMLM) for the composition of genotype groups. Genetic parameters and prediction of genetic values by Restricted Maximum Likelihood / Best Linear Unbiased Prediction (REML / BLUP) were assessed. The Ward-MLM classification strategy supported the formation of three homogeneous groups. Group I comprised 13 hybrids; Group II, 26 hybrids; and Group III, 18 hybrids. Groups II and III contained hybrids resistant to P. brachyurus. High broad sense heritability values were found for root mass, reproduction factor and nematodes per gram of root, which provided genetic gain and allowed selection of resistant genotypes available for cloning, since the total genetic variance occurred due to the dominance effects. Of the 57 genotypes assessed, those with the lowest genotypic values for reproduction factor were selected as resistant, including: CH3.2, CH3.23, CH3.8, CH3.37, CH3.38, CH3.41, CH3.36, CH2.1, CH2.7, CH1.1, CH1.3 and CH1.2.

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Published

2019-08-01

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Section

Genetics and Plant Breeding

How to Cite

Resistance to Pratylenchus brachyurus in Vitis species population through multivariate approaches and mixed models. (2019). Scientia Agricola, 76(5), 424-433. https://doi.org/10.1590/1678-992x-2017-0387