Increasing Genomic‐Enabled Prediction Accuracy by Modeling Genotype × Environment Interactions in Kansas Wheat. Issue 2 (1st July 2017)

• Record Type: Journal Article
• Authors: Jarquín, Diego; Lemes da Silva, Cristiano; Gaynor, R. Chris; Poland, Jesse; Fritz, Allan; Howard, Reka; Battenfield, Sarah; Crossa, Jose
• Is Part Of: œplant genome. Volume 10:Issue 2(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-07-01
• Subjects: Plant genomes -- Periodicals; Plant genome mapping -- Periodicals; 572.862
• DOI: 10.3835/plantgenome2016.12.0130
• Languages: English
• ISSNs: 1940-3372

Abstract

Abstract : 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.