Implementing genomic selection in sour passion fruit population

Viana, Alexandre Pío; Silva, Fernando Higino de Lima e; Glória, Leonardo Siqueira; Ribeiro, Rodrigo Moreira; Krause, Willian; Boechat, Marcela Santana Bastos

doi:10.1007/s10681-017-2020-3

Cited by 13 publications

(9 citation statements)

References 25 publications

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“…Studies that seek the best models for different species are important to direct breeding programs. For example, for another perennial plant ( Passiflora edulis ), it was observed that the BayesC model was the best model for several traits evaluated in this species 17 . This model assumes a common variance for all effects of markers but also assumes that some markers do not affect 28 .…”

Section: Discussionmentioning

confidence: 95%

Bayesian ridge regression shows the best fit for SSR markers in Psidium guajava among Bayesian models

Silva

Viana

Corrêa

et al. 2021

Sci Rep

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Markers are an important tool in plant breeding, which can improve conventional phenotypic breeding, generating more accurate information outcoming better decision making. This study aimed to apply and compare the fit of different Bayesian models BRR, BayesA, BayesB, BayesB (setting the value from very low to $$\pi$$ π = $${10}^{-5}$$ 10 - 5 ), BayesC and Bayesian Lasso (LASSO) for predictions of the genomic genetic values of productivity and quality traits of a guava population. The models were fitted for traits fruit mass, pulp mass, soluble solids content, fruit number, and production per plant in the genomic prediction with SSR markers, obtained through the CTAB extraction method with 200 primers. The Bayesian ridge regression model showed the best results for all traits and was chosen to predict the individual’s genomic values according to the cross-validation data. A good stabilization of the Markov and Monte Carlo chains was observed with the mean values close to the observed phenotypic means. Heritabilities showed good predictive accuracy. The model showed strong correlations between some traits, allowing indirect selection.

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Section: Discussionmentioning

confidence: 95%

Bayesian ridge regression shows the best fit for SSR markers in Psidium guajava among Bayesian models

Silva

Viana

Corrêa

et al. 2021

Sci Rep

Self Cite

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show abstract

“…Studies that seek the best models for different species are important to direct breeding programs. For example, for another perennial plant (Passiflora edulis), it was observed that the BayesC model was the best model for several variables evaluated in this species 25 . This model assumes a common variance for all effects of markers but also assumes that some markers do not affect 11 .…”

Section: Resultsmentioning

confidence: 96%

“…The heritability coefficient influences the prediction of genomic genetic values, predictive capacity, and association analysis across the genome. With greater heritability of phenotype, there are improvements in the identification of individuals to be used as parents in the next generations, also favoring the identification of regions associated with a characteristic of interest 25 .…”

Section: Discussionmentioning

confidence: 99%

Bayesian Ridge Regression Shows the Best Fit for Ssr Markers in Psidium Guajava Among Bayesian Models

Silva

Viana

Corrêa

et al. 2021

Preprint

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Markers are an important tool in plant breeding, which can improve conventional phenotypic breeding, generating more accurate information outcoming better decision making. This study aimed to apply and compare the fit of different Bayesian models BRR, BayesA, BayesB, BayesB (setting the value from very low to Π = 10-5 ) and BayesC and Bayesian Lasso (LASSO) for predictions of the genomic genetic values of productivity and quality traits of a guava population. A randomized block design with two replications was used. Seventeen full-sib families were evaluated. Fruit mass, pulp mass, soluble solids content, number of fruits, and production per plant were measured. These variables were used in the genomic prediction with SSR markers, obtained through the CTAB extraction method with 200 primers.The Bayesian ridge regression model showed the best results for all variables and was chosen to predict the individuals' genomic values according to the cross-validation data.A good stabilization of the Markov and Monte Carlo chains was observed with the mean values corresponding to the observed phenotypic means. Heritabilities showed good predictive accuracy. The model showed strong correlations between some variables, allowing indirect selection.

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“…Despite the great potential and the prospects for higher consumption and utilization of P. alata as a fruit crop, production remains low and unstable, mainly due to the lack of improved varieties. In passion fruit, like other fruit crops, the main purpose of breeding is to meet the demand for quality (Cavalcante et al, 2017;Pereira et al, 2017;Viana et al, 2017). According to Jung et al (2007), sweet passion fruits should weigh 200 to 300 g, be oval in shape, free from apical softening, with a firm skin, rich pulp yield (over 30%), high sugar content and significant yield.…”

Section: Discussionmentioning

confidence: 99%

“…According to Martins et al (2003), DF and LF are strongly correlated with each other and with TS, but no correlation with YP were found, indicating that larger fruits in P. alata populations do not necessarily have higher pulp content. For P. edulis, there are several reports of negative correlations between TS and WF, WP, DF, LF, number of fruits and Yield (Moraes et al, 2005;Oliveira et al, 2011;Silva et al, 2017;Viana et al, 2017) enabling breeders to successfully select against TS.…”

Section: Discussionmentioning

confidence: 99%

Improving yield and fruit quality traits in sweet passion fruit: evidence for genotype by environment interaction and cross-compatibility in selected genotypes

Chavarría-Perez

Giordani

Dias

et al. 2019

Preprint

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Breeding for yield and fruit quality traits in passion fruits is complex due to the polygenic nature of these traits and the existence of genetic correlations among them. Therefore, studies focused on crop management practices and breeding using modern quantitative genetic approaches are still needed, especially for Passiflora alata, an understudied crop, popular known as the sweet passion fruit. It is assumed to be a self-incompatible species and is highly appreciated for its typical aroma and flavor characteristics. With the aim of estimating the genetic and phenotypic parameters related to fruit traits, our group has already investigated a sweet passion fruit progeny consisting of 100 individuals from which 30 genotypes were selected. In this study, we reevaluated these superior genotypes in three environmental conditions. The results of the multi-environment trial analysis indicate that the genotypes do not behave consistently across these environments, and this was taken into account when selecting genotypes. Pairwise genetic correlations among the fruit traits were evaluated, and different genotype rankings obtained depending on the trait and environment, providing further evidence of genotype by environment interaction. Finally, we used a multiplicative selection index to select 20% of genotypes for weight of pulp and against thickness and weight of the fruit skin. The response to selection was positive for all traits except soluble solids, and the superior (six) genotypes were ranked. The consensus is that open-pollinated populations can be used as commercial varieties in crop species that are sensitive to inbreeding depression or within breeding programs that are not well developed. For these reasons, we performed a complete diallel cross for evaluating the capability of the selected genotypes to be fertilized and set fruits. Most of the crosses were compatible, resulting in over 50% fruit set. It is worth noting that all genotypes produce fruits if used as females, which is essential to guarantee yields in commercial orchards.

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Implementing genomic selection in sour passion fruit population

Cited by 13 publications

References 25 publications

Bayesian ridge regression shows the best fit for SSR markers in Psidium guajava among Bayesian models

Bayesian ridge regression shows the best fit for SSR markers in Psidium guajava among Bayesian models

Bayesian Ridge Regression Shows the Best Fit for Ssr Markers in Psidium Guajava Among Bayesian Models

Improving yield and fruit quality traits in sweet passion fruit: evidence for genotype by environment interaction and cross-compatibility in selected genotypes

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