Multi-environment Genomic Selection in Rice Elite Breeding Lines

Abstract

Background Assessing the performance of elite lines in target environments is essential for breeding programs to select the most relevant genotypes. One of the main complexities in this task resides in accounting for the genotype by environment interactions. Genomic prediction models that integrate information from multi-environment trials and environmental covariates can be efficient tools in this context. The objective of this study was to assess the predictive ability of different genomic prediction models to optimize the use of multi-environment information. We used 111 elite breeding lines representing the diversity of the international rice research institute breeding program for irrigated ecosystems. The lines were evaluated for three traits (days to flowering, plant height, and grain yield) in 15 environments in Asia and Africa and genotyped with 882 SNP markers. We evaluated the efficiency of genomic prediction to predict untested environments using seven multi-environment models and three cross-validation scenarios. Results The elite lines were found to belong to the indica group and more specifically the indica-1B subgroup which gathered improved material originating from the Green Revolution. Phenotypic correlations between environments were high for days to flowering and plant height (33% and 54% of pairwise correlation greater than 0.5) but low for grain yield (lower than 0.2 in most cases). Clustering analyses based on environmental covariates separated Asia’s and Africa's environments into different clusters or subclusters. The predictive abilities ranged from 0.06 to 0.79 for days to flowering, 0.25–0.88 for plant height, and − 0.29–0.62 for grain yield. We found that models integrating genotype-by-environment interaction effects did not perform significantly better than models integrating only main effects (genotypes and environment or environmental covariates). The different cross-validation scenarios showed that, in most cases, the use of all available environments gave better results than a subset. Conclusion Multi-environment genomic prediction models with main effects were sufficient for accurate phenotypic prediction of elite lines in targeted environments. These results will help refine the testing strategy to update the genomic prediction models to improve predictive ability.

Genetic gains in IRRI’s rice salinity breeding and elite panel development as a future breeding resource

“…Selection intensity ( i ) and accuracy ( r ) have been improved by integrating the genomic selection in the breeding program, selecting and advancing the lines based on the breeding values rather than phenotypic BLUPs (Heffner et al 2009 ; Jannink et al 2010 ; Desta and Ortiz 2014 ; Crossa et al 2017 ; Chung and Liao 2020 , 2021; Dreisigacker et al 2023b ). Replicated experimental designs with robust analysis based on mixed-model approaches accounting for spatial trends and G x E interactions have been incorporated into the program to improve heritability and selection accuracy (Voss-Fels et al 2018 ; Cooper et al 2020 ; Xu et al 2020 ; Cooper et al 2021 ; Xu et al 2022 ; Cooper et al 2023 ; Nguyen et al 2023 ). In the future, the salinity breeding program is targeting to move to a 2-year breeding cycle by integrating genomic selection with speed breeding and early recycling of the genotypes (Jighly et al 2019 ).…”

High-dimensional multi-omics measured in controlled conditions are useful for maize platform and field trait predictions

“…Literature indicates that including environmental covariates is not always advantageous. For instance, Monteverde et al (2019); Nguyen et al (2023) reported that adding environmental covariates resulted in worse predictions than GBLUP in rice while predicting new environments, which could be attributed to higher inter-environmental differences. In the studies predicting new genotypes, Jarquín et al (2014); Malosetti et al (2016) and Rincent et al (2019) observed little to no improvement with a GxW model.…”

“…For instance, Monteverde et al (2019); Nguyen et al (2023) reported that adding environmental covariates resulted in worse predictions than GBLUP in rice while predicting new environments, which could be attributed to higher inter-environmental differences. In the studies predicting new genotypes, Jarquín et al ( 2014…”

Stability of grain zinc concentrations across lowland rice environments favors zinc biofortification breeding

“…Furthermore, it has been shown that agronomic management and environmental factors influence Zn uptake, translocation, and loading into grains and that seasonal effects possibly linked to water supply and soil redox state further affect [Zn] in grains ( Goloran et al., 2019 ; Inabangan-Asilo et al., 2019 ). For grain yield, extensive genotype × environment interactions (GEI) are common in rice ( Huang et al., 2021 ; Nguyen et al., 2023 ), and this necessitates multi-environment trials (METs) during variety development. Since GEI is only important when it causes significant changes in genotypic rankings in different environments, it is important for the efficacy of a breeding program to obtain estimates of the strength of GEI effects relative to genotype (G) and environment (E) effects.…”