Bayesian and classical approaches for the estimation of genetic parameters and coefficients of repeatability of acerola quality traits
Abstract
Although acerola (Malpighia emarginata DC.) is a tropical fruit of high interest due to its high ascorbic acid content and attractive sensory attributes, fruit production is characterized by high genetic variability. Additionally, the use of new biometric tools for acerola breeding is scarce. This study aimed to estimate genetic parameters and the coefficient of repeatability, as well as determine the optimal number of fruits for quality trait analyses in different acerola genotypes, using different approaches. Twenty-three (Experiment I) and thirty-five (Experiment II) genotypes were evaluated in a randomized block design with four replicates and three plants per plot. Twenty fruits per plant were harvested and evaluated for the following quality traits: diameter, mass, skin color (lightness, chroma and hue), firmness, soluble solids (SS), titratable acidity (TA), SS/TA ratio, and ascorbic acid content. The genetic parameters and the coefficient of repeatability were estimated for each experiment using classical and Bayesian methods. Both approaches achieved similar results on estimating variance components, genetic parameters and the coefficient of repeatability. Genetic parameters showed favorable conditions for acerola selection. The coefficient of repeatability was high for all acerola quality traits. A total of 17 fruits are required for the effective selection of acerola genotypes with an accuracy of 95%.
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References
Abeywardena, V. (1972). An application of principal components analysis in genetics. Journal of Genetics, 61, 27–51. DOI: https://doi.org/10.1007/BF02984099
Alcoforado, A. T. W., Pedrozo, C. Â., Mayer, M. M., & Lima-Primo, H. E. (2019). Repeatability of morpho-agronomic characters of Theobroma grandiflorum fruits. Revista Brasileira de Fruticultura, 41(2), e142. DOI: https://doi.org/10.1590/0100-29452019142
Alvares, C. A., Stape, J. L., Sentelhas, P. C., Gonçalves, J. L. M., & Sparovek, G. (2013). Köppen’s climate classification map for Brazil. Meteorologische Zeitschrift, 22(6), 711–728. DOI: https://doi.org/10.1127/0941-2948/2013/0507
Andrade Júnior, V. C., Oliveira, A. J. M., Guimarães, A. G., Ferreira, M. A. M., Cavalcanti, V. P., & Fernandes, J. S. C. (2020). Repeatability and heritability of production characters in strawberry fruits. Horticultura Brasileira, 38(1), 89–93. DOI: https://doi.org/10.1590/s0102-053620200114
Association of Official Analytical Chemists [AOAC]. (2016). Official methods of analysis of AOAC International (20th ed.). Rockville, MD: AOAC International.
Azevedo, C. V. G., Val, B. H. P., Araújo, L. C. A., Juhász, A. C. P., Di Mauro, A. O., & Unêda-Trevisoli, S. H. (2021). Genetic parameters of soybean populations obtained from crosses between grain and food genotypes. Acta Scientiarum. Agronomy, 43(1), e46968. DOI: https://doi.org/10.4025/actasciagron.v43i1.46968
Brito, O. G., Andrade Júnior, V. C., Azevedo, A. M., Donato, L. M. S., Silva, L. R., & Ferreira, M. A. M. (2019). Study of repeatability and phenotypical stabilization in kale using frequentist, Bayesian and bootstrap resampling approaches. Acta Scientiarum. Agronomy, 41(1), e42606. DOI: https://doi.org/10.4025/actasciagron.v41i1.42606
Catarina, R. S., Pereira, M. G., Vettorazzi, J. C. F., Cortes, D. F. M., Poltronieri, T. P. S., Azevedo, A. O. N., ... Viana, A. P. (2020). Papaya (Carica papaya L.) S1 family recurrent selection: Opportunities and selection alternatives from the base population. Scientia Horticulturae, 260, 108848. DOI: https://doi.org/10.1016/j.scienta.2019.108848
Cruz, C. D., Regazzi, A. J., & Carneiro, P. C. S. (2012). Modelos biométricos aplicados ao melhoramento genético (4. ed.). Viçosa, MG: Editora UFV.
Delva, L., & Schneider, R. G. (2013). Acerola (Malpighia emarginata DC): production, postharvest handling, nutrition, and biological activity. Food Reviews International, 29(2), 107-126. DOI: https://doi.org/10.1080/87559129.2012.714433
Diel, M. I., Lúcio, A. D., Olivoto, T., Pinheiro, M. V. M., Krysczun, D. K., Sari, B. G., & Schmidt, D. (2020). Repeatability coefficients and number of measurements for evaluating traits in strawberry. Acta Scientiarum. Agronomy, 42(1), e43357. DOI: https://doi.org/10.4025/actasciagron.v42i1.43357
Evangelista, J. S. P. C., Peixoto, M. A., Coelho, I., Alves, R., Resende, M. D. V., Silva, F. F., ... Bhering, L. L. (2022). Genetic evaluation and selection in Jatropha curcas through Frequentist and Bayesian inferences. Bragantia, 81, 1-12. DOI: https://doi.org/10.1590/1678-4499.20210262
Farinelli, D., Portarena, S., Silva, D. F., Traini, C., Silva, G. M., Silva, E. C., ..., Villa, F. (2021). Variability of fruit quality among 103 acerola (Malpighia emarginata D. C.) phenotypes from the subtropical region of Brazil. Agriculture, 11(11), e2722. DOI: https://doi.org/10.3390/agriculture11111078
Ferreira, I. C., Silva, V. P., Vilvert, J. C., Souza, F. F., Freitas, S. T., & Lima, M. S. (2021). Brazilian varieties of acerola (Malpighia emarginata DC.) produced under tropical semi-arid conditions: Bioactive phenolic compounds, sugars, organic acids, and antioxidant capacity. Journal of Food Biochemistry, 45(8), e13829. DOI: https://doi.org/10.1111/jfbc.13829
Ferreira, M. A. R., Vilvert, J. C., Silva, B. O. S., Ferreira, I. C., Souza, F. F., & Freitas, S. T. (2022). Multivariate selection index of acerola genotypes for fresh consumption based on fruit physicochemical attributes. Euphytica, 218(25). DOI: https://doi.org/10.1007/s10681-022-02978-1
Gualberto, N. C., Oliveira, C. S., Nogueira, J. P., Jesus, S., Caroline, H., Araujo, S., ... Narain, N. (2021). Bioactive compounds and antioxidant activities in the agro-industrial residues of acerola (Malpighia emarginata L.), guava (Psidium guajava L.), genipap (Genipa americana L.) and umbu (Spondias tuberosa L.) fruits assisted by ultrasonic or shaker. Food Research International, 147, 110538. DOI: https://doi.org/10.1016/j.foodres.2021.110538
Hadfield, J. D. (2010). MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. Journal of Statistical Software, 33(2), 1–22. DOI: https://doi.org/10.18637/jss.v033.i02
Jesus, O. N., Lima, L. K. S., Souza, P. U., & Girardi, E. A. (2021). Genetic parameters, correlation and repeatability of agronomic characters of yellow passion fruit genotypes in three harvest cycles. Bragantia, 80, e1621. DOI: https://doi.org/10.1590/1678-4499.20200294
Lazaro, A., Boada, M., Villarino, R., & Girbau, D. (2019). Color measurement and analysis of fruit with a battery-less NFC sensor. Sensors, 19(7). DOI: https://doi.org/10.3390/s19071741
Liu, C., Qi, X., Song, L., Li, Y., & Li, M. (2018). Species identification, genetic diversity and population structure of sweet cherry commercial cultivars assessed by SSRs and the gametophytic self-incompatibility locus. Scientia Horticulturae, 237, 28–35. DOI: https://doi.org/10.1016/j.scienta.2018.03.063
Lopes, R., Bruckner, C. H., Cruz, C. D., Lopes, M. T. G., & Freitas, G. B. (2001). Repetibilidade de características do fruto de aceroleira. Pesquisa Agropecuária Brasileira, 36(3), 507–513. DOI: https://doi.org/10.1590/S0100-204X2001000300015
Magalhães, D. S., Rufni, J. C. M., Alburquerque, A. S., Viol, R. E., Fagundes, M. C. P., & Menezes, T. P. (2018). Genetic diversity among accessions of acerola based on the quality of fruits. Comunicata Scientiae, 9(2), 133–141. DOI: https://doi.org/10.14295/CS.v9i2.2961
Malikouski, R. G., Peixoto, M. A., Morais, A. L., Elizeu, A. M., Rocha, J. R. A. S. C., Zucoloto, M., & Bhering, L. L. (2021). Repeatability coefficient estimates and optimum number of harvests in graft/rootstock combinations for ‘tahiti’ acid lime. Acta Scientiarum. Agronomy, 43(1), e51740. DOI: https://doi.org/10.4025/actasciagron.v43i1.51740
Mansour, H., Nordheim, E. V., & Rutledge, J. J. (1981). Estimators of repeatability. Theoretical and Applied Genetics, 60(3), 151–156. DOI: https://doi.org/10.1007/BF00264520
Maranhão Ribeiro, C. M. C., Sousa, T. P. A., Holschuh, H. J., Ribeiro, M. T. J. B., Silva, S. M., & Maciel, M. I. S. (2018). Fruit development and ripening of acerola ‘Okinawa’ cultivar. Acta Horticulturae, 1198, 199–204. DOI: https://doi.org/10.17660/ActaHortic.2018.1198.32
Mariano-Nasser, F. A. D. C., Nasser, M. D., Furlaneto, K. A., Ramos, J. A., Vieites, R. L., & Pagliarini, M. K. (2017). Bioactive compounds in different acerola fruit cultivares. Semina: Ciências Agrárias, 38(4), 2505–2514. DOI: https://doi.org/10.5433/1679-0359.2017v38n4Supl1p2505
Pathare, P. B., Opara, U. L., & Al-Said, F. A.-J. (2013). Colour measurement and analysis in fresh and processed foods: a review. Food and Bioprocess Technology, 6, 36–60. DOI: https://doi.org/10.1007/s11947-012-0867-9
Prakash, A., & Baskaran, R. (2018). Acerola, an untapped functional superfruit: a review on latest frontiers. Journal of Food Science and Technology, 55(9), 3373–3384. DOI: https://doi.org/10.1007/s13197-018-3309-5
Resende, M. D. V., & Alves, R. S. (2020). Linear, generalized, hierarchical, Bayesian and random regression mixed models in genetics/ genomics in plant breeding. Functional Plant Breeding Journal, 3(2), 121–152. DOI: https://doi.org/10.35418/2526-4117/v2n2a1
Ritzinger, R., Kobayashi, A. K., & Oliveira, J. R. P. (2003). A cultura da aceroleira. Cruz das Almas, BA: Embrapa Mandioca e Fruticultura.
Ritzinger, R., Ritzinger, C. H. S. P., Fonseca, N., & Machado, C. F. (2017). Advances in the propagation of acerola. Revista Brasileira de Fruticultura, 40(3), e928. DOI: https://doi.org/10.1590/0100-29452018928
Santos, C. P., Batista, M. C., Saraiva, K. D. C., Roque, A. L. M., Miranda, R. S., Silva, L. M. A., ... Costa, J. H. (2019). Transcriptome analysis of acerola fruit ripening: insights into ascorbate, ethylene, respiration, and softening metabolisms. Plant Molecular Biology, 101(3), 269–296. DOI: https://doi.org/10.1007/s11103-019-00903-0
Santos, H. G., Jacomine, P. K. T., Anjos, L. H. C., Oliveira, V. A., Lumbreras, J. F., Coelho, M. R., ... Cunha, T. J. F. (2018). Brazilian Soil Classification System (5th ed.). Brasília, DF: Embrapa.
Silva, F. A., Viana, A. P., Corrêa, C. C. G., Carvalho, B. M., Sousa, C. M. B., Amaral, B. D., ... Glória, L. S. (2020). Impact of Bayesian inference on the selection of Psidium guajava. Scientific Reports, 10(1). DOI: https://doi.org/10.1038/s41598-020-58850-6
Singh, M., Al-Yassin, A., & Omer, S. O. (2015). Bayesian estimation of genotypes means, precision, and genetic gain due to selection from routinely used barley trials. Crop Science, 55(2), 501–513. DOI: https://doi.org/10.2135/cropsci2014.02.0111
Smith, B. J. (2007). boa: an R package for MCMC output convergence assessment and posterior inference. Journal of Statistical Software, 21(11), 1–37. DOI: https://doi.org/10.18637/jss.v021.i11
Valadares, N. R., Fernandes, A. C. G., Rodrigues, C. H. O., Brito, O. G., Paula Gomes, L. S., Magalhães, J. R., ... Azevedo, A. M. (2022). Bayesian approach to estimate genetic parameters and selection of sweet potato half-sib progenies. Scientia Horticulturae, 294, 110759. DOI: https://doi.org/10.1016/j.scienta.2021.110759
Vasconcelos, U. A. A., Dias, L. A. D. S., Corrêa, T. R., Rosmaninho, L. B. C., Silva, D. R. M., & Zaidan, I. R. (2020). Estimates of genetic parameters and path analysis of crambe: An important oil plant for biofuel production. Acta Scientiarum. Agronomy, 42(1), e42490. DOI: https://doi.org/10.4025/actasciagron.v42i1.42490
Volpato, L., Alves, R. S., Teodoro, P. E., Resende, M. D. V., Nascimento, M., Nascimento, A. C. C., ... Borém, A. (2019). Multi-trait multi-environment models in the genetic selection of segregating soybean progeny. PLoS ONE, 14(4), e0215315. DOI: https://doi.org/10.1371/journal.pone.0215315
Zaouay, F., & Mars, M. (2014). Phenotypic variation and estimation of genetic parameters to improve fruit quality in Tunisian pomegranate (Punica granatum L.) accessions. Journal of Horticultural Science and Biotechnology, 89(2), 221–228. DOI: https://doi.org/10.1080/14620316.2014.11513072
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Funding data
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Grant numbers 88887.606972/2021-00 -
Empresa Brasileira de Pesquisa Agropecuária
Grant numbers 20.18.01.024.00.05.001