Genomic prediction of grain yield and drought-adaptation capacity in sorghum is enhanced by multi-trait analysis
- Autores
- Velazco, Julio Gabriel; Jordan, David R.; Mace, Emma S.; Hunt, Colleen H.; Malosetti, Marcos; Van Eeuwijk, Fred A.
- Año de publicación
- 2019
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Grain yield and stay-green drought adaptation trait are important targets of selection in grain sorghum breeding for broad adaptation to a range of environments. Genomic prediction for these traits may be enhanced by joint multi-trait analysis. The objectives of this study were to assess the capacity of multi-trait models to improve genomic prediction of parental breeding values for grain yield and stay-green in sorghum by using information from correlated auxiliary traits, and to determine the combinations of traits that optimize predictive results in specific scenarios. The dataset included phenotypic performance of 2645 testcross hybrids across 26 environments as well as genomic and pedigree information on their female parental lines. The traits considered were grain yield (GY), stay-green (SG), plant height (PH), and flowering time (FT). We evaluated the improvement in predictive performance of multi-trait G-BLUP models relative to single-trait G-BLUP. The use of a blended kinship matrix exploiting pedigree and genomic information was also explored to optimize multi-trait predictions. Predictive ability for GY increased up to 16% when PH information on the training population was exploited through multi-trait genomic analysis. For SG prediction, full advantage from multi-trait G-BLUP was obtained only when GY information was also available on the predicted lines per se, with predictive ability improvements of up to 19%. Predictive ability, unbiasedness and accuracy of predictions from conventional multi-trait G-BLUP were further optimized by using a combined pedigree-genomic relationship matrix. Results of this study suggest that multi-trait genomic evaluation combining routinely measured traits may be used to improve prediction of crop productivity and drought adaptability in grain sorghum.
EEA Pergamino
Fil: Velazco, Julio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Sección Forrajeras; Argentina. Wageningen University and Research . Biometris – Mathematical and Statistical Methods; Holanda
Fil: Jordan, David R. The University of Queensland. Hermitage Research Facility. Queensland Alliance for Agriculture and Food Innovation; Australia
Fil: Mace, Emma S. The University of Queensland. Hermitage Research Facility. Queensland Alliance for Agriculture and Food Innovation; Australia. Hermitage Research Facility. Department of Agriculture and Fisheries; Australia
Fil: Hunt, Colleen H. The University of Queensland. Hermitage Research Facility. Queensland Alliance for Agriculture and Food Innovation; Australia. Hermitage Research Facility. Department of Agriculture and Fisheries; Australia
Fil: Malosetti, Marcos. Wageningen University and Research . Biometris – Mathematical and Statistical Methods; Holanda
Fil: Eeuwijk, Fred A. van. Wageningen University and Research . Biometris – Mathematical and Statistical Methods; Holanda - Fuente
- Frontiers in Plant Science 10 : 12 p. (July 2019)
- Materia
-
Sorgos
Rendimiento
Genómica
Análisis
Adaptabilidad
Sequía
Sorghum Grain
Yields
Genomics
Analysis
Adaptability
Drought - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/4.0/
- Repositorio
- Institución
- Instituto Nacional de Tecnología Agropecuaria
- OAI Identificador
- oai:localhost:20.500.12123/6077
Ver los metadatos del registro completo
id |
INTADig_303d8262944464b3aa08b41d57779960 |
---|---|
oai_identifier_str |
oai:localhost:20.500.12123/6077 |
network_acronym_str |
INTADig |
repository_id_str |
l |
network_name_str |
INTA Digital (INTA) |
spelling |
Genomic prediction of grain yield and drought-adaptation capacity in sorghum is enhanced by multi-trait analysisVelazco, Julio GabrielJordan, David R.Mace, Emma S.Hunt, Colleen H.Malosetti, MarcosVan Eeuwijk, Fred A.SorgosRendimientoGenómicaAnálisisAdaptabilidadSequíaSorghum GrainYieldsGenomicsAnalysisAdaptabilityDroughtGrain yield and stay-green drought adaptation trait are important targets of selection in grain sorghum breeding for broad adaptation to a range of environments. Genomic prediction for these traits may be enhanced by joint multi-trait analysis. The objectives of this study were to assess the capacity of multi-trait models to improve genomic prediction of parental breeding values for grain yield and stay-green in sorghum by using information from correlated auxiliary traits, and to determine the combinations of traits that optimize predictive results in specific scenarios. The dataset included phenotypic performance of 2645 testcross hybrids across 26 environments as well as genomic and pedigree information on their female parental lines. The traits considered were grain yield (GY), stay-green (SG), plant height (PH), and flowering time (FT). We evaluated the improvement in predictive performance of multi-trait G-BLUP models relative to single-trait G-BLUP. The use of a blended kinship matrix exploiting pedigree and genomic information was also explored to optimize multi-trait predictions. Predictive ability for GY increased up to 16% when PH information on the training population was exploited through multi-trait genomic analysis. For SG prediction, full advantage from multi-trait G-BLUP was obtained only when GY information was also available on the predicted lines per se, with predictive ability improvements of up to 19%. Predictive ability, unbiasedness and accuracy of predictions from conventional multi-trait G-BLUP were further optimized by using a combined pedigree-genomic relationship matrix. Results of this study suggest that multi-trait genomic evaluation combining routinely measured traits may be used to improve prediction of crop productivity and drought adaptability in grain sorghum.EEA PergaminoFil: Velazco, Julio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Sección Forrajeras; Argentina. Wageningen University and Research . Biometris – Mathematical and Statistical Methods; HolandaFil: Jordan, David R. The University of Queensland. Hermitage Research Facility. Queensland Alliance for Agriculture and Food Innovation; AustraliaFil: Mace, Emma S. The University of Queensland. Hermitage Research Facility. Queensland Alliance for Agriculture and Food Innovation; Australia. Hermitage Research Facility. Department of Agriculture and Fisheries; AustraliaFil: Hunt, Colleen H. The University of Queensland. Hermitage Research Facility. Queensland Alliance for Agriculture and Food Innovation; Australia. Hermitage Research Facility. Department of Agriculture and Fisheries; AustraliaFil: Malosetti, Marcos. Wageningen University and Research . Biometris – Mathematical and Statistical Methods; HolandaFil: Eeuwijk, Fred A. van. Wageningen University and Research . Biometris – Mathematical and Statistical Methods; HolandaSwedish University of Agricultural Sciences2019-10-10T10:40:07Z2019-10-10T10:40:07Z2019-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttps://www.frontiersin.org/articles/10.3389/fpls.2019.00997/fullhttp://hdl.handle.net/20.500.12123/60771664-462Xhttps://doi.org/10.3389/fpls.2019.00997Frontiers in Plant Science 10 : 12 p. (July 2019)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2025-09-29T13:44:46Zoai:localhost:20.500.12123/6077instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-09-29 13:44:46.972INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse |
dc.title.none.fl_str_mv |
Genomic prediction of grain yield and drought-adaptation capacity in sorghum is enhanced by multi-trait analysis |
title |
Genomic prediction of grain yield and drought-adaptation capacity in sorghum is enhanced by multi-trait analysis |
spellingShingle |
Genomic prediction of grain yield and drought-adaptation capacity in sorghum is enhanced by multi-trait analysis Velazco, Julio Gabriel Sorgos Rendimiento Genómica Análisis Adaptabilidad Sequía Sorghum Grain Yields Genomics Analysis Adaptability Drought |
title_short |
Genomic prediction of grain yield and drought-adaptation capacity in sorghum is enhanced by multi-trait analysis |
title_full |
Genomic prediction of grain yield and drought-adaptation capacity in sorghum is enhanced by multi-trait analysis |
title_fullStr |
Genomic prediction of grain yield and drought-adaptation capacity in sorghum is enhanced by multi-trait analysis |
title_full_unstemmed |
Genomic prediction of grain yield and drought-adaptation capacity in sorghum is enhanced by multi-trait analysis |
title_sort |
Genomic prediction of grain yield and drought-adaptation capacity in sorghum is enhanced by multi-trait analysis |
dc.creator.none.fl_str_mv |
Velazco, Julio Gabriel Jordan, David R. Mace, Emma S. Hunt, Colleen H. Malosetti, Marcos Van Eeuwijk, Fred A. |
author |
Velazco, Julio Gabriel |
author_facet |
Velazco, Julio Gabriel Jordan, David R. Mace, Emma S. Hunt, Colleen H. Malosetti, Marcos Van Eeuwijk, Fred A. |
author_role |
author |
author2 |
Jordan, David R. Mace, Emma S. Hunt, Colleen H. Malosetti, Marcos Van Eeuwijk, Fred A. |
author2_role |
author author author author author |
dc.subject.none.fl_str_mv |
Sorgos Rendimiento Genómica Análisis Adaptabilidad Sequía Sorghum Grain Yields Genomics Analysis Adaptability Drought |
topic |
Sorgos Rendimiento Genómica Análisis Adaptabilidad Sequía Sorghum Grain Yields Genomics Analysis Adaptability Drought |
dc.description.none.fl_txt_mv |
Grain yield and stay-green drought adaptation trait are important targets of selection in grain sorghum breeding for broad adaptation to a range of environments. Genomic prediction for these traits may be enhanced by joint multi-trait analysis. The objectives of this study were to assess the capacity of multi-trait models to improve genomic prediction of parental breeding values for grain yield and stay-green in sorghum by using information from correlated auxiliary traits, and to determine the combinations of traits that optimize predictive results in specific scenarios. The dataset included phenotypic performance of 2645 testcross hybrids across 26 environments as well as genomic and pedigree information on their female parental lines. The traits considered were grain yield (GY), stay-green (SG), plant height (PH), and flowering time (FT). We evaluated the improvement in predictive performance of multi-trait G-BLUP models relative to single-trait G-BLUP. The use of a blended kinship matrix exploiting pedigree and genomic information was also explored to optimize multi-trait predictions. Predictive ability for GY increased up to 16% when PH information on the training population was exploited through multi-trait genomic analysis. For SG prediction, full advantage from multi-trait G-BLUP was obtained only when GY information was also available on the predicted lines per se, with predictive ability improvements of up to 19%. Predictive ability, unbiasedness and accuracy of predictions from conventional multi-trait G-BLUP were further optimized by using a combined pedigree-genomic relationship matrix. Results of this study suggest that multi-trait genomic evaluation combining routinely measured traits may be used to improve prediction of crop productivity and drought adaptability in grain sorghum. EEA Pergamino Fil: Velazco, Julio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Sección Forrajeras; Argentina. Wageningen University and Research . Biometris – Mathematical and Statistical Methods; Holanda Fil: Jordan, David R. The University of Queensland. Hermitage Research Facility. Queensland Alliance for Agriculture and Food Innovation; Australia Fil: Mace, Emma S. The University of Queensland. Hermitage Research Facility. Queensland Alliance for Agriculture and Food Innovation; Australia. Hermitage Research Facility. Department of Agriculture and Fisheries; Australia Fil: Hunt, Colleen H. The University of Queensland. Hermitage Research Facility. Queensland Alliance for Agriculture and Food Innovation; Australia. Hermitage Research Facility. Department of Agriculture and Fisheries; Australia Fil: Malosetti, Marcos. Wageningen University and Research . Biometris – Mathematical and Statistical Methods; Holanda Fil: Eeuwijk, Fred A. van. Wageningen University and Research . Biometris – Mathematical and Statistical Methods; Holanda |
description |
Grain yield and stay-green drought adaptation trait are important targets of selection in grain sorghum breeding for broad adaptation to a range of environments. Genomic prediction for these traits may be enhanced by joint multi-trait analysis. The objectives of this study were to assess the capacity of multi-trait models to improve genomic prediction of parental breeding values for grain yield and stay-green in sorghum by using information from correlated auxiliary traits, and to determine the combinations of traits that optimize predictive results in specific scenarios. The dataset included phenotypic performance of 2645 testcross hybrids across 26 environments as well as genomic and pedigree information on their female parental lines. The traits considered were grain yield (GY), stay-green (SG), plant height (PH), and flowering time (FT). We evaluated the improvement in predictive performance of multi-trait G-BLUP models relative to single-trait G-BLUP. The use of a blended kinship matrix exploiting pedigree and genomic information was also explored to optimize multi-trait predictions. Predictive ability for GY increased up to 16% when PH information on the training population was exploited through multi-trait genomic analysis. For SG prediction, full advantage from multi-trait G-BLUP was obtained only when GY information was also available on the predicted lines per se, with predictive ability improvements of up to 19%. Predictive ability, unbiasedness and accuracy of predictions from conventional multi-trait G-BLUP were further optimized by using a combined pedigree-genomic relationship matrix. Results of this study suggest that multi-trait genomic evaluation combining routinely measured traits may be used to improve prediction of crop productivity and drought adaptability in grain sorghum. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-10-10T10:40:07Z 2019-10-10T10:40:07Z 2019-07 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
https://www.frontiersin.org/articles/10.3389/fpls.2019.00997/full http://hdl.handle.net/20.500.12123/6077 1664-462X https://doi.org/10.3389/fpls.2019.00997 |
url |
https://www.frontiersin.org/articles/10.3389/fpls.2019.00997/full http://hdl.handle.net/20.500.12123/6077 https://doi.org/10.3389/fpls.2019.00997 |
identifier_str_mv |
1664-462X |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Swedish University of Agricultural Sciences |
publisher.none.fl_str_mv |
Swedish University of Agricultural Sciences |
dc.source.none.fl_str_mv |
Frontiers in Plant Science 10 : 12 p. (July 2019) reponame:INTA Digital (INTA) instname:Instituto Nacional de Tecnología Agropecuaria |
reponame_str |
INTA Digital (INTA) |
collection |
INTA Digital (INTA) |
instname_str |
Instituto Nacional de Tecnología Agropecuaria |
repository.name.fl_str_mv |
INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria |
repository.mail.fl_str_mv |
tripaldi.nicolas@inta.gob.ar |
_version_ |
1844619138035613696 |
score |
12.559606 |