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Path correlation and Bayesian analysis on popping expansion components in popcorn hybrids
Correlación de ruta y análisis Bayesiano sobre componentes de expansión en híbridos de maíz pipoca
DOI:
https://doi.org/10.15446/agron.colomb.v38n1.80462Keywords:
Zea mays L. var everta, Bayesian networks, direct and indirect effect, correlation (en)Zea mays L. var. everta, redes bayesianas, efecto directo e indirecto, correlación (es)
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Knowing the cause and effect among two or more traits can help to increase the selection accuracy of superior genotypes. The main objective of this study was to evaluate the cause and effect relationship between expansion volume and kernel size in popcorn hybrids using path analysis and Bayesian network. A total of 41 popcorn hybrids were evaluated through a randomized complete block design (RCBD) with two replicates in the city of Jaboticabal, Brazil. The assessed traits were grain length (GL),
grain thickness (GT), grain width (GW), caryopsis roundness index (CRI), mass of 50 grains (MG), and expansion volume (EV). Measurements were performed on individual grains, using three 50-grain samples from each plot. Pearson’s correlation coefficient, path analysis and Bayesian network were estimated. A negative correlation was detected among EV and the traits, except for GT. Path analysis indicated that MG has a direct and positive effect on EV and the negative correlation observed is mainly due to the indirect effects by GL and GT. Bayesian networks did not detect a direct association between kernel size and expansion volume while indicating that GT is the only trait that can affect popcorn flake size.
El conocimiento sobre la causa y el efecto entre dos o más rasgos puede ayudar a aumentar la precisión de la selección de genotipos superiores. El objetivo principal de este estudio fue evaluar la relación de causa y efecto entre el volumen de expansión y el tamaño del grano en los híbridos de palomitas de maíz utilizando análisis de ruta y red bayesiana. Se evaluó un total de 41 híbridos de palomitas de maíz siguiendo un diseño de bloques
completos al azar (DBCA) con dos repeticiones en la ciudad de Jaboticabal, Brasil. Los rasgos evaluados fueron la longitud del grano (LG), el grosor del grano (GG), el ancho del grano (AG), índice de redondez de la cariopsis (IRC), la masa de 50 granos (MG) y el volumen de expansión (VE). Las mediciones se realizaron en granos individuales, utilizando tres muestras de 50 granos de cada parcela. Se estimaron el coeficiente de correlación de Pearson, el análisis de ruta y la red bayesiana. Se detectó una correlación negativa entre VE y los rasgos, a excepción de GT. El análisis de ruta indicó que MG tiene un efecto directo y positivo sobre VE y la correlación negativa
observada se debe principalmente a los efectos indirectos de LG y GG Las redes bayesianas no detectaron una asociación directa entre el tamaño del núcleo y el volumen de expansión, mientras que indicaban que GG es el único rasgo que puede afectar el tamaño de las hojuelas de palomitas de maíz.
References
Aliferis, C.F., A. Statnikov, I. Tsamardinos, S. Mani, and X.D. Koutsoukos. 2010. Local causal and Markov blanket induction for causal discovery and feature selection for classification part I: algorithms and empirical evaluation. J. Mach. Learn. Res. 11(2010), 171-234.
Amaral, C.B., G.H.F. Oliveira, and G.V. Môro. 2019. Bayesian network: a simplified approach for environmental similarity studies in maize. Crop Breed. Appl. Biot. 19(1), 70-76. Doi: 10.1590/1984-70332019v19n1a10
Cabral, P.D.S., A.T. de Amaral Junior, I.L. de J. Freitas, R.M. Ribeiro, and T.R. da C. Silva. 2016. Relação de causa e efeito de caracteres quantitativos sobre a capacidade de expansão do grão em milho-pipoca. Rev. Ciênc. Agron. 47(1), 108-117. Doi: 10.5935/1806-6690.20160013
Carpentieri-Pípolo, V., H.W. Takahashi, R.M. Endo, M.R. Petek, and A.L. Seifert. 2002. Correlações entre caracteres quantitativos em milho pipoca. Hortic. Bras. 20(4), 551-554. Doi: 10.1590/ S0102-05362002000400008
Coimbra, J.L.M., G. Benin, E.A. Vieira, A.C. de Oliveira, F.I.F. Carvalho, A.F. Guidolin, and A.P. Soares. 2005. Conseqüências da multicolinearidade sobre a análise de trilha em canola. Ciênc. Rural 35(2), 347-352. Doi: 10.1590/S0103-84782005000200015
Cruz, C.D. 2013. Genes: a software package for analysis in experimental statistics and quantitative genetics. Acta Scient. Agron. 35(3), 271-276. Doi: 10.4025/actasciagron.v35i3.21251
Cruz, C.D. and A.J. Regazzi. 1997. Modelos biométricos aplicados ao melhoramento genético. UFV, Viçosa, Brazil.
Cruz, C.D., A.J. Regazzi, and P.C.S. Carneiro. 2012. Modelos biométricos aplicados ao melhoramento genético. UFV, Viçosa, Brazil.
Cruz, J.C. (ed.). 2010. Cultivo do milho. Embrapa Milho e Sorgo. (Sistema de produção 1). Sete Lagoas - MG. URL: https://www.spo.cnptia.embrapa.br/listasptema?p_p_id=listaspportemaportlet_WAR_sistemasdeproducaolf6_1ga1ceportlet&p_p_lifecycle=0&p_p_state=normal&p_p_mode=view&p_p_col_id=column-2&p_p_col_count=1p_r_p_619796851_temaId=1712&_listaspportemaportlet_WAR_sistemasdeproducaolf6_1ga1ceportlet_redirect=%2Ftemas-publicados (accessed 16 March 2020).
Daros, M., A.T. do Amaral Júnior, M.G. Pereira, F.S. Santos, C.A. Scapim, S. de P. Freitas Júnior, R.F. Daher, and M.R. Ávila. 2004. Correlations among agronomic traits in two recurrent selection cycles in popcorn. Ciênc. Rural 34(5), 1389-1394. Doi: 10.1590/S0103-84782004000500010
Embrapa. 2006. Sistema brasileiro de classificação de solos. Embrapa, Brasilia.
Felipe, V.P., M.A. Silva, B.D. Valente, and G.J. Rosa. 2015. Using multiple regression, Bayesian networks and artificial neural networks for prediction of total egg production in European quails based on earlier expressed phenotypes. Poult. Scien. 94(4), 772-780. Doi: 10.3382/ps/pev031
Lyerly, P.J. 1942. Some genetic and morphologic characters affecting the popping expansion of popcorn. J. Amer. Soc. Agron. 34, 986-995. Doi: 10.2134/agronj1942.00021962003400110003x
Margaritis, D. 2003. Learning Bayesian network model structure from data. PhD thesis, Carnegie Mellon University, Pittsburgh, USA.
Marques, R.L. and I. Dutra, 2002. Redes Bayesianas: o que são, para que servem, algoritmos e exemplos de aplicações. Coppe Sistemas, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.
Mohsenin, N.N. 1970. Physical properties of plant and animal materials: structure, physical characteristics and mechanical properties. Gordon and Breach, New York, USA. Doi: 10.1002/food.19870310724
Pearl, J. 2000. Causality: models, reasoning and inference. Cambridge University Press, New York, USA. Doi: 10.1017/S0266466603004109
Pordesimo, L.O., R.C. Anantheswaran, A.M. Fleiscaman, Y.E. Lin, and M.A. Hanna. 1990. Physical properties as indicators of popping characteristics of microwave popcorn. J. Food Sci. 55(5), 1352-1355. Doi: 10.1111/j.1365-2621.1990.tb03934.x
Ribeiro, R.M., A.T. do Amaral Júnior, G.F. Pena, M. Vivas, R.N. Kurosawa, and L.S.A. Gonçalves. 2016. Effect of recurrent selection on the variability of the UENF-14 popcorn population. Crop Breed. Appl. Biotechnol. 16(2), 123-131. Doi: 10.1590/1984-70332016v16n2a19
Scutari, M. 2009. Learning Bayesian networks with the bnlearn R package. CRC Press, Florida, USA. Doi: 10.18637/jss.v035.i03
Souza, T.V. 2003. Aspectos estatísticos da análise de trilha (Path analysis) aplicada em experimentos agrícolas. MSc thesis, Universidade Federal de Lavras, Lavras, Brazil.
Soylu, S. and A. Tekkanat. 2006. Interactions amongst kernel properties and expansion volume in various popcorn genotypes. J. Food Eng. 80(1), 336-341. Doi: 10.1016/j.jfoodeng.2006.06.001
Vencovsky, R. and P. Barriga. 1992. Genética biométrica no fitomelhoramento. Sociedade Brasileira de Genética, Ribeirão Preto, Brazil.
Yu, J., V.A. Smith, P.P. Wang, A.J. Hartemink, and E.D. Jarvis. 2004. Advances to Bayesian network inference for generating causal networks from observational biological data. Bioinformatics 20(18), 3594-3603. Doi: 10.1093/bioinformatics/bth448
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