Modeling Genotype × Environment Interaction for Genomic Selection with Unbalanced Data from a Wheat Breeding Program

Abstract

Genomic selection (GS) has successfully been used in plant breeding to improve selection efficiency and reduce breeding time and cost. However, there is not a clear strategy on how to incorporate genotype × environment interaction (GEI) to GS models. Increased prediction accuracy could be achieved using mixed models to exploit GEI by borrowing information from other environments. The objective of this work was to compare strategies to exploit GEI in GS using mixed models. Specifically, we compared strategies to predict new genotypes by borrowing information from other environments modeling the correlation matrix across environments and to design sets of environments aiming for low GEI to predict genomic performance in new environments. We evaluated 1477 advanced wheat (Triticum aestivum L.) lines for yield in 35 location–year combinations genotyped with genotyping‐by‐sequencing (GBS). Mixed models were used to obtain either overall or by‐environment predictions for different sets of environments. Overall accuracy was high (0.5). Borrowing information from relatives evaluated in multiple environments and modeling the correlation matrix across environments was the best strategy to predict new genotypes. On the other hand, the best strategy for predicting the performance of genotypes in new environments was either to predict across locations for single years or to predict within defined mega‐environments (MEs) for any year or location. In summary, higher predictive ability was obtained by characterizing and by modeling GEI in the GS context.

Genomic assisted selection for enhancing line breeding: merging genomic and phenotypic selection in winter wheat breeding programs with preliminary yield trials

“…The prediction of individual trials or locations across years is an especially difficult task (Dawson et al 2013) and we observed a large variation in prediction accuracy for this undertaking in our study (Fig S3), fitting the results of other studies with autogamous crops (Heslot et al 2014; Lado et al 2016). Once multi-environment trials are being conducted, more options open up for enhancing the selection of variety parents like imputing untested lines in tested locations (Burgueño et al 2011; Jarquín et al 2014; Crossa et al 2016; Lopez-Cruz et al 2015) or enhancing the reliability of breeding values by a relationship matrix (Bauer et al 2006; Oakey et al 2007b; Bauer et al 2009; Müller et al 2015).…”

Multienvironment and Multitrait Genomic Selection Models in Unbalanced Early‐Generation Wheat Yield Trials

“…Lado et al (2016) identified mega-environments in a large, unbalanced, multienvironment dataset prior to performing GS by using multiplicative models including the additive main and multiplicative interactive (AMMI) model Zobel et al, 1988) and the GGE model (Yan et al, 2000). As the number of environments increases, so too does the number of parameters that must be estimated during model fitting.…”

“…Subsequent studies have further investigated potential gains in GS accuracy enabled by the incorporation of GEI information. Lado et al (2016) evaluated GS for grain yield in wheat (Triticum aestivum L.) in a total of 35 environments. Jarquín et al (2014) introduced a reaction-norm model modeling GEI as functions of markers and environmental covariates.…”

“…Heading date (HD) was recorded as the Julian date at which 50% of plant tillers within a plot had extruded heads from the boot. Lado et al (2016) studied the use of GS models incorporating GEI data to predict grain yield in highly unbalanced datasets, although they used a relatively large number of environments. After plants reached maturity, a 0.914-m cutting of all aboveground plant material was taken from one of the three inner rows of each plot and placed inside a paper bag.…”

Genotype by environment interaction in sunflower (Helianthus annus L.) to optimize trial network efficiency

“…Even so, there is a gap between the yields obtained by farmers and those obtained in experimental stations using the best management practices (Fischer & Edmeades, 2010). On the other hand, the characterization of genotype by environment interaction (GEI) is necessary to understand the adaptation of cultivars and identification of superior cultivars (van Eeuwijk et al, 1996;de León et al, 2016;Lado et al, 2016). The study of evaluation trials networks efficiency and the determination of relevant management variables that limit yield expression in sunflower are crucial to improve the selection efficiency of superior cultivars (de la Vega et al, 2001).…”