Título

Single-step genomic and pedigree genotype x environment interaction models for predicting wheat lines in international environments

Autor

Paulino Pérez-Rodríguez

Jose Crossa

Jessica Rutkoski

Ravi Singh

Gustavo de los Campos

Juan Burgueño

Susanne Dreisigacker

Nivel de Acceso

Acceso Abierto

Licencia

http://creativecommons.org/licenses/by/4.0

Materias

Genomics - (AGROVOC) Breeding methods - (AGROVOC) Genetic improvement - (AGROVOC) Wheats - (AGROVOC) CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA - (CTI)

Resumen o descripción

Genomic prediction models have been commonly used in plant breeding but only in reduced datasets comprising a few hundred genotyped individuals. However, pedigree information for an entire breeding population is frequently available, as are historical data on the performance of a large number of selection candidates. The single-step method extends the genomic relationship information from genotyped individuals to pedigree information from a larger number of phenotyped individuals in order to combine relationship information on all members of the breeding population. Furthermore, genomic prediction models that incorporate genotype × environment interactions (G × E) have produced substantial increases in prediction accuracy compared with single-environment genomic prediction models. Our main objective was to show how to use single-step genomic and pedigree models to assess the prediction accuracy of 58,798 CIMMYT wheat (Triticum aestivum L.) lines evaluated in several simulated environments in Ciudad Obregon, Mexico, and to predict the grain yield performance of some of them in several sites in South Asia (India, Pakistan, and Bangladesh) using a reaction norm model that incorporated G × E. Another objective was to describe the statistical and computational challenges encountered when developing the pedigree and single-step models in such large datasets. Results indicate that the genomic prediction accuracy achieved by models using pedigree only, markers only, or both pedigree and markers to predict various environments in India, Pakistan, and Bangladesh is higher (0.25–0.38) than prediction accuracy of models that use only phenotypic prediction (0.20) or do not include the G × E term.

Fecha de publicación

2017

Tipo de publicación

Artículo

Recurso de información

http://hdl.handle.net/10883/19136

Formato

application/pdf

Idioma

Inglés

Audiencia

Investigadores

Repositorio Orígen

Repositorio Institucional de Publicaciones Multimedia del CIMMYT

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