Investigating the Genomic Background and Predictive Ability of Genotype-by-environment Interactions in Maize Grain Yield Based on Reaction Norm Models

<p>Genotype-by-environment interaction (GEI) is among the greatest challenges for maize breeding programs. Strong GEI limits both the prediction of genotype performance across variable environmental conditions and the identification of genomic regions associated with grain yield. Incorporating GEI into yield prediction models has been shown to improve prediction accuracy of yield; nevertheless, more work is needed to further understand this complex interaction across populations and environments. The main objectives of this study were to: 1) assess GEI in maize grain yield based on reaction norm models and predict hybrid performance across a gradient of environmental conditions and 2) perform a genome-wide association study (GWAS) and post-GWAS analyses for maize grain yield, using data from 2014 to 2017 of the Genomes 2 Fields initiative hybrid trial. After quality control, 2,126 hybrids with genotypic and phenotypic data were assessed across 86 environments, combination of location and year, although not all hybrids were evaluated in all environments. Heritability was greater in higher-yielding environments due to an increase in genetic variability in these environments in comparison to the low-yielding environments. GWAS was carried out for yield, and the five single nucleotide polymorphisms (SNPs) with the highest magnitude of effect were selected in each environment for follow-up analyses. Many candidate genes in proximity of selected SNPs have been previously reported with roles in stress response. Genomic prediction was performed to assess prediction accuracy of previously tested or untested hybrids in environments from a new growing season. These scenarios were labeled CV-Y, cross validation across years, and CV-YG, cross validation across years with only untested hybrids. Prediction accuracy was 0.34 (CV-Y) and 0.21 (CV-YG) when compared to Best Linear Unbiased Prediction (BLUPs) that did not utilize genotypic or environmental relationships and 0.80 (CV-Y) and 0.60 (CV-YG) when compared to the whole-dataset model that used the genomic relationships and the environmental gradient. These results identify regions of the genome for future selection to improve yield, and a methodology to increase the number of hybrids evaluated across locations of a multi-environment trial through genomic prediction.</p>

<p>Phenotype, genotype, weather, and metadata are provided at Genomes 2 Fields <a href="https://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/GenomesToFields_2014_2017_v1">https://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/GenomesToFields_2014_2017_v1</a>. The Genomes 2 Fields initiative is a large-scale partnership between the public and private sectors to organize a large-scale multi-environment maize trial with locations across North America. The goal of this experiment is to have a dataset with phenotypic, genotypic, and environmental data for many locations to model genotype-by-environment interaction. Hybrid maize yield was the primary focus of our analysis of this study. Experimental design generally was two row plots in a randomized complete block design. Yield was often mechanically harvested from both rows of the plot and standardized to 15% moisture. BLUPF90 is a statistical software package that runs in a command line interface for use in quantitative genetics. Reaction norm models were performed in BLUPF90 in this study for genome-wide association study (GWAS) and genomic prediction. </p>