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Increasing genomic-enabled prediction accuracy by modeling genotype × environment interactions in kansas wheat

Creator: Jarquín, D.
Creator: Lemes da Silva, C.
Creator: Gaynor, C.
Creator: Poland, J.A.
Creator: Fritz, A.K.
Creator: Howard, R.
Creator: Battenfield, S.D.
Creator: Crossa, J.
Year: 2017
URI: https://hdl.handle.net/10883/21078
Language: English
Publisher: CSSA :
Publisher: Wiley
Copyright: CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose
Type: Article
Place of Publication: Madison, WI (USA)
Pages: 1-15
Issue: 2
Volume: 10
DOI: 10.3835/plantgenome2016.12.0130
Description: Wheat (Triticum aestivum L.) breeding programs test experimental lines in multiple locations over multiple years to get an accurate assessment of grain yield and yield stability. Selections in early generations of the breeding pipeline are based on information from only one or few locations and thus materials are advanced with little knowledge of the genotype × environment interaction (G × E) effects. Later, large trials are conducted in several locations to assess the performance of more advanced lines across environments. Genomic selection (GS) models that include G × E covariates allow us to borrow information not only from related materials, but also from historical and correlated environments to better predict performance within and across specific environments. We used reaction norm models with several cross-validation schemes to demonstrate the increased breeding efficiency of Kansas State University’s hard red winter wheat breeding program. The GS reaction norm models line effect (L) + environment effect (E), L + E + genotype environment (G), and L + E + G + (G × E) effects) showed high accuracy values (>0.4) when predicting the yield performance in untested environments, sites or both. The GS model L + E + G + (G × E) presented the highest prediction ability (r = 0.54) when predicting yield in incomplete field trials for locations with a moderate number of lines. The difficulty of predicting future years (forward prediction) is indicated by the relatively low accuracy (r = 0.171) seen even when environments with 300+ lines were included.
Agrovoc: GENOTYPE ENVIRONMENT INTERACTION
Agrovoc: GENOMES
Agrovoc: WHEAT
ISSN: 1940-3372
Journal: The Plant Genome


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    Genetic Resources including germplasm collections, wild relatives, genotyping, genomics, and IP

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