Genetic diversity in yellow passion fruit populations

Viana, Alexandre Pío; Pereira, Telma Nair Santana; Pereira, Messias Gonzaga; Souza, Margarete Magalhães; Maldonado, J. F.; Amaral, Antônio Teixeira do

doi:10.12702/1984-7033.v06n01a12

Cited by 18 publications

(12 citation statements)

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“…This approach is used to characterize the genetic structure of populations as a selective indicator of promising genotypes, as well as for germplasm conservation (Viana et al, 2006;Oliveira et al, 2007;Barbosa et al, 2011). However, as in most fields of biological sciences, the results of statistical analysis based on traditional algorithms tend to be unsatisfactory, particularly for classification analysis.…”

Section: Introductionmentioning

confidence: 99%

Superiority of artificial neural networks for a genetic classification procedure

et al. 2015

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ABSTRACT. The correct classification of individuals is extremely important for the preservation of genetic variability and for maximization of yield in breeding programs using phenotypic traits and genetic markers. The Fisher and Anderson discriminant functions are commonly used multivariate statistical techniques for these situations, which allow for the allocation of an initially unknown individual to predefined groups. However, for higher levels of similarity, such as those found in backcrossed populations, these methods have proven to be inefficient. Recently, much research has been devoted to developing a new paradigm of computing known as artificial neural networks (ANNs), which can be used to solve many statistical problems, including classification problems. The aim of this study was to evaluate the feasibility of ANNs as an evaluation technique of genetic diversity by comparing their performance with that of traditional methods. The discriminant functions were equally ineffective in discriminating the populations, with error rates of 23-82%, thereby preventing the correct discrimination of individuals between populations. The ANN was effective in classifying populations with low and high differentiation, such as those derived from a genetic design established from backcrosses, even in cases of low differentiation of the data sets. The ANN appears to be a promising technique to solve classification problems, since the number of individuals classified incorrectly by the ANN was always lower than that of the discriminant functions. We envisage the potential relevant application of this improved procedure in the genomic classification of markers to distinguish between breeds and accessions.

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Section: Introductionmentioning

confidence: 99%

Superiority of artificial neural networks for a genetic classification procedure

et al. 2015

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“…This approach was also used to characterize the genetic structure of the sampled plants as criteria and indicators for the selection of promising genotypes for breeding programs, aside from the conservation of germplasm of the sampled species (Arriel et al 2006, Oliveira et al 2006, Viana et al 2006, Oliveira et al 2007). …”

Section: Introductionmentioning

confidence: 99%

Artificial neural network analysis of genetic diversity in Carica papaya L.

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Viana

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et al. 2011

Crop Breed. Appl. Biotechnol.

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“…Para desenvolver esses inoculantes, são necessários estudos prévios de seleção bacteriana e testes de eficiência. Esses estudos podem ser auxiliados pela quantificação da distância entre os tratamentos por métodos multivariados, que são frequentemente usados em estudos de divergência genética nos programas de melhoramento de plantas (Cruz et al, 2004;Viana et al, 2006 …”

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