Título
Integrating genomic-enabled prediction and high-throughput phenotyping in breeding for climate-resilient bread wheat
Autor
Philomin Juliana
Osval Antonio Montesinos-Lopez
Jose Crossa
Suchismita Mondal
Lorena González Pérez
Jesse Poland
JULIO HUERTA-ESPINO
Leonardo Abdiel Crespo Herrera
Govindan Velu
Susanne Dreisigacker
Paulino Pérez-Rodríguez
Francisco Pinto
Ravi Singh
Nivel de Acceso
Acceso Abierto
Materias
Genomics - (AGROVOC) Phenotypes - (AGROVOC) Breeding - (AGROVOC) Climatic factors - (AGROVOC) Soft wheat - (AGROVOC) Genomic Selection - (AGROVOC) Phenotyping - (AGROVOC) WHEAT - (AGROVOC) CLIMATE CHANGE - (AGROVOC) RESILIENCE - (AGROVOC) AGRICULTURAL SCIENCES AND BIOTECHNOLOGY - (AGROVOC) BREEDING - (AGROVOC) NURSERY - (AGROVOC) CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA - (CTI)
Resumen o descripción
Genomic selection and high-throughput phenotyping (HTP) are promising tools to accelerate breeding gains for high-yielding and climate-resilient wheat varieties. Hence, our objective was to evaluate them for predicting grain yield (GY) in drought-stressed (DS) and late-sown heat-stressed (HS) environments of the International maize and wheat improvement center’s elite yield trial nurseries. We observed that the average genomic prediction accuracies using fivefold cross-validations were 0.50 and 0.51 in the DS and HS environments, respectively. However, when a different nursery/year was used to predict another nursery/year, the average genomic prediction accuracies in the DS and HS environments decreased to 0.18 and 0.23, respectively. While genomic predictions clearly outperformed pedigree-based predictions across nurseries, they were similar to pedigree-based predictions within nurseries due to small family sizes. In populations with some full-sibs in the training population, the genomic and pedigree-based prediction accuracies were on average 0.27 and 0.35 higher than the accuracies in populations with only one progeny per cross, indicating the importance of genetic relatedness between the training and validation populations for good predictions. We also evaluated the item-based collaborative filtering approach for multivariate prediction of GY using the green normalized difference vegetation index from HTP. This approach proved to be the best strategy for across-nursery predictions, with average accuracies of 0.56 and 0.62 in the DS and HS environments, respectively. We conclude that GY is a challenging trait for across-year predictions, but GS and HTP can be integrated in increasing the size of populations screened and evaluating unphenotyped large nurseries for stress–resilience within years.
Fecha de publicación
2019
Tipo de publicación
Artículo
Recurso de información
Formato
application/pdf
Idioma
Inglés
Audiencia
Investigadores
Repositorio Orígen
Repositorio Institucional de Publicaciones Multimedia del CIMMYT
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