Prediction accuracy of genomic selection models for earliness in tomato

ABSTRACT Genomic selection is considered to be an important tool in plant breeding programs. However, its application in the earliness of tomato (Solanum lycopersicum L.) has not been studied. The objective of the present study was to evaluate the prediction performance of six statistical models for...

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Autores principales:	Hernández-Bautista,Aurelio, Lobato-Ortiz,Ricardo, García-Zavala,J. Jesús, Cruz-Izquierdo,Serafín, Chávez-Servia,José Luis, Rocandio-Rodríguez,Mario, Moreno-Ramírez,Yolanda Del Rocío, Hernandez-Leal,Enrique, Hernández-Rodríguez,Martha, Reyes-Lopez,Delfino
Lenguaje:	English
Publicado:	Instituto de Investigaciones Agropecuarias, INIA 2020
Materias:	Genetic gain genomic selection Solanum lycopersicum statistical models
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-58392020000400505
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spelling	oai:scielo:S0718-583920200004005052020-11-22Prediction accuracy of genomic selection models for earliness in tomatoHernández-Bautista,AurelioLobato-Ortiz,RicardoGarcía-Zavala,J. JesúsCruz-Izquierdo,SerafínChávez-Servia,José LuisRocandio-Rodríguez,MarioMoreno-Ramírez,Yolanda Del RocíoHernandez-Leal,EnriqueHernández-Rodríguez,MarthaReyes-Lopez,Delfino Genetic gain genomic selection Solanum lycopersicum statistical models ABSTRACT Genomic selection is considered to be an important tool in plant breeding programs. However, its application in the earliness of tomato (Solanum lycopersicum L.) has not been studied. The objective of the present study was to evaluate the prediction performance of six statistical models for six quantitative characteristics related to earliness in tomato. The study used phenotypic and genotypic data belonging to an F2 population consisting of 172 tomato plants. Simple sequence repeat (SSR) markers were obtained using genotypic information, and the genomic values were estimated by the following six different statistical models: Bayesian Lasso (BL), Bayesian ridge regression (BRR), BayesA, BayesB, BayesCπ, and reproducing kernel Hilbert spaces (RKHS) regression. The correlation values ranged from 0.17 to 0.57. The highest association values were found in days to flowering of the third inflorescence and 1000-seed weight, which were greater than 0.5. In general, all the models performed in a similar manner because only slight differences were observed among the correlation values. Specifically, BL, BayesB, and RKHS exhibited the highest Pearson correlation values for most traits. According to the results, genomic selection could be a useful tool to support tomato breeding focused on earliness.info:eu-repo/semantics/openAccessInstituto de Investigaciones Agropecuarias, INIAChilean journal of agricultural research v.80 n.4 20202020-07-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-58392020000400505en10.4067/S0718-58392020000400505
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	Genetic gain genomic selection Solanum lycopersicum statistical models
spellingShingle	Genetic gain genomic selection Solanum lycopersicum statistical models Hernández-Bautista,Aurelio Lobato-Ortiz,Ricardo García-Zavala,J. Jesús Cruz-Izquierdo,Serafín Chávez-Servia,José Luis Rocandio-Rodríguez,Mario Moreno-Ramírez,Yolanda Del Rocío Hernandez-Leal,Enrique Hernández-Rodríguez,Martha Reyes-Lopez,Delfino Prediction accuracy of genomic selection models for earliness in tomato
description	ABSTRACT Genomic selection is considered to be an important tool in plant breeding programs. However, its application in the earliness of tomato (Solanum lycopersicum L.) has not been studied. The objective of the present study was to evaluate the prediction performance of six statistical models for six quantitative characteristics related to earliness in tomato. The study used phenotypic and genotypic data belonging to an F2 population consisting of 172 tomato plants. Simple sequence repeat (SSR) markers were obtained using genotypic information, and the genomic values were estimated by the following six different statistical models: Bayesian Lasso (BL), Bayesian ridge regression (BRR), BayesA, BayesB, BayesCπ, and reproducing kernel Hilbert spaces (RKHS) regression. The correlation values ranged from 0.17 to 0.57. The highest association values were found in days to flowering of the third inflorescence and 1000-seed weight, which were greater than 0.5. In general, all the models performed in a similar manner because only slight differences were observed among the correlation values. Specifically, BL, BayesB, and RKHS exhibited the highest Pearson correlation values for most traits. According to the results, genomic selection could be a useful tool to support tomato breeding focused on earliness.
author	Hernández-Bautista,Aurelio Lobato-Ortiz,Ricardo García-Zavala,J. Jesús Cruz-Izquierdo,Serafín Chávez-Servia,José Luis Rocandio-Rodríguez,Mario Moreno-Ramírez,Yolanda Del Rocío Hernandez-Leal,Enrique Hernández-Rodríguez,Martha Reyes-Lopez,Delfino
author_facet	Hernández-Bautista,Aurelio Lobato-Ortiz,Ricardo García-Zavala,J. Jesús Cruz-Izquierdo,Serafín Chávez-Servia,José Luis Rocandio-Rodríguez,Mario Moreno-Ramírez,Yolanda Del Rocío Hernandez-Leal,Enrique Hernández-Rodríguez,Martha Reyes-Lopez,Delfino
author_sort	Hernández-Bautista,Aurelio
title	Prediction accuracy of genomic selection models for earliness in tomato
title_short	Prediction accuracy of genomic selection models for earliness in tomato
title_full	Prediction accuracy of genomic selection models for earliness in tomato
title_fullStr	Prediction accuracy of genomic selection models for earliness in tomato
title_full_unstemmed	Prediction accuracy of genomic selection models for earliness in tomato
title_sort	prediction accuracy of genomic selection models for earliness in tomato
publisher	Instituto de Investigaciones Agropecuarias, INIA
publishDate	2020
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-58392020000400505
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Prediction accuracy of genomic selection models for earliness in tomato

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