Assessing combining abilities, genomic data, and genotype × environment interactions to predict hybrid grain sorghum performance

Abstract Genomic selection in maize (Zea mays L.) has been one factor that has increased the rate of genetic gain when compared with other cereals. However, the technological foundations in maize also exist in other cereal crops that would allow prediction of hybrid performance based on general (GCA...

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Autores principales:	Jales M. O. Fonseca, Patricia E. Klein, Jose Crossa, Angela Pacheco, Paulino Perez‐Rodriguez, Perumal Ramasamy, Robert Klein, William L Rooney
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Plant culture SB1-1110 Genetics QH426-470
Acceso en línea:	https://doaj.org/article/2af6ec11fe2d42038db80e7c7927a858
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spelling	oai:doaj.org-article:2af6ec11fe2d42038db80e7c7927a8582021-12-05T07:50:12ZAssessing combining abilities, genomic data, and genotype × environment interactions to predict hybrid grain sorghum performance1940-337210.1002/tpg2.20127https://doaj.org/article/2af6ec11fe2d42038db80e7c7927a8582021-11-01T00:00:00Zhttps://doi.org/10.1002/tpg2.20127https://doaj.org/toc/1940-3372Abstract Genomic selection in maize (Zea mays L.) has been one factor that has increased the rate of genetic gain when compared with other cereals. However, the technological foundations in maize also exist in other cereal crops that would allow prediction of hybrid performance based on general (GCA) and specific (SCA) combining abilities applied through genomic‐enabled prediction models. Further, the incorporation of genotype × environment (G × E) interaction effects present an opportunity to deploy hybrids to targeted environments. To test these concepts, a factorial mating design of elite yet divergent grain sorghum lines generated hybrids for evaluation. Inbred parents were genotyped, and markers were used to assess population structure and develop the genomic relationship matrix (GRM). Grain yield, height, and days to anthesis were collected for hybrids in replicated trials, and best linear unbiased estimates were used to train classical GCA‐SCA–based and genomic (GB) models under a hierarchical Bayesian framework. To incorporate population structure, GB was fitted using the GRM of both parents and hybrids. For GB models, G × E interaction effects were included by the Hadamard product between GRM and environments. A leave‐one‐out cross‐validation scheme was used to study the prediction capacity of models. Classical and genomic models effectively predicted hybrid performance and prediction accuracy increased by including genomic data. Genomic models effectively partitioned the variation due to GCA, SCA, and their interaction with the environment. A strategy to implement genomic selection for hybrid sorghum [Sorghum bicolor (L.) Moench] breeding is presented herein.Jales M. O. FonsecaPatricia E. KleinJose CrossaAngela PachecoPaulino Perez‐RodriguezPerumal RamasamyRobert KleinWilliam L RooneyWileyarticlePlant cultureSB1-1110GeneticsQH426-470ENThe Plant Genome, Vol 14, Iss 3, Pp n/a-n/a (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Plant culture SB1-1110 Genetics QH426-470
spellingShingle	Plant culture SB1-1110 Genetics QH426-470 Jales M. O. Fonseca Patricia E. Klein Jose Crossa Angela Pacheco Paulino Perez‐Rodriguez Perumal Ramasamy Robert Klein William L Rooney Assessing combining abilities, genomic data, and genotype × environment interactions to predict hybrid grain sorghum performance
description	Abstract Genomic selection in maize (Zea mays L.) has been one factor that has increased the rate of genetic gain when compared with other cereals. However, the technological foundations in maize also exist in other cereal crops that would allow prediction of hybrid performance based on general (GCA) and specific (SCA) combining abilities applied through genomic‐enabled prediction models. Further, the incorporation of genotype × environment (G × E) interaction effects present an opportunity to deploy hybrids to targeted environments. To test these concepts, a factorial mating design of elite yet divergent grain sorghum lines generated hybrids for evaluation. Inbred parents were genotyped, and markers were used to assess population structure and develop the genomic relationship matrix (GRM). Grain yield, height, and days to anthesis were collected for hybrids in replicated trials, and best linear unbiased estimates were used to train classical GCA‐SCA–based and genomic (GB) models under a hierarchical Bayesian framework. To incorporate population structure, GB was fitted using the GRM of both parents and hybrids. For GB models, G × E interaction effects were included by the Hadamard product between GRM and environments. A leave‐one‐out cross‐validation scheme was used to study the prediction capacity of models. Classical and genomic models effectively predicted hybrid performance and prediction accuracy increased by including genomic data. Genomic models effectively partitioned the variation due to GCA, SCA, and their interaction with the environment. A strategy to implement genomic selection for hybrid sorghum [Sorghum bicolor (L.) Moench] breeding is presented herein.
format	article
author	Jales M. O. Fonseca Patricia E. Klein Jose Crossa Angela Pacheco Paulino Perez‐Rodriguez Perumal Ramasamy Robert Klein William L Rooney
author_facet	Jales M. O. Fonseca Patricia E. Klein Jose Crossa Angela Pacheco Paulino Perez‐Rodriguez Perumal Ramasamy Robert Klein William L Rooney
author_sort	Jales M. O. Fonseca
title	Assessing combining abilities, genomic data, and genotype × environment interactions to predict hybrid grain sorghum performance
title_short	Assessing combining abilities, genomic data, and genotype × environment interactions to predict hybrid grain sorghum performance
title_full	Assessing combining abilities, genomic data, and genotype × environment interactions to predict hybrid grain sorghum performance
title_fullStr	Assessing combining abilities, genomic data, and genotype × environment interactions to predict hybrid grain sorghum performance
title_full_unstemmed	Assessing combining abilities, genomic data, and genotype × environment interactions to predict hybrid grain sorghum performance
title_sort	assessing combining abilities, genomic data, and genotype × environment interactions to predict hybrid grain sorghum performance
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/2af6ec11fe2d42038db80e7c7927a858
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Assessing combining abilities, genomic data, and genotype × environment interactions to predict hybrid grain sorghum performance

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