Abstract
Modern whole-genome prediction (WGP) frameworks that focus on multi-environment trials (MET) integrate large-scale genomics, phenomics, and envirotyping data. However, the more complex the statistical model, the longer the computational processing times, which do not always result in accuracy gains. We investigated the use of new kernel methods and modeling structures involving genomics and nongenomic sources of variation in two MET maize data sets. Five WGP models were considered, advancing in complexity from a main-effect additive model (A) to more complex structures, including dominance deviations (D), genotype × environment interaction (AE and DE), and the reaction-norm model using environmental covariables (W) and their interaction with A and D (AW + DW). A combination of those models built with three different kernel methods, Gaussian kernel (GK), Deep kernel (DK), and the benchmark genomic best linear-unbiased predictor (GBLUP/GB), was tested under three prediction scenarios: newly developed hybrids (CV1), sparse MET conditions (CV2), and new environments (CV0). GK and DK outperformed GB in prediction accuracy and reduction of computation time (~up to 20%) under all model–kernel scenarios. GK was more efficient in capturing the variation due to A + AE and D + DE effects and translated it into accuracy gains (~up to 85% compared with GB). DK provided more consistent predictions, even for more complex structures such as W + AW + DW. Our results suggest that DK and GK are more efficient in translating model complexity into accuracy, and more suitable for including dominance and reaction-norm effects in a biologically accurate and faster way.
Highlights
Utilizing the best linear-unbiased prediction (BLUP) has been useful for predicting the performance of unobserved maize hybrids utilizing pedigree or molecular marker relationships of all crosses (Bernardo 1994, 1996)
Based on the results presented in the previous section, we selected six model–kernel method combinations to be jointly evaluated in terms of their capacity to predict novel
We presented the first report on (1) the joint modeling of additive and dominance effects with reactionnorm variation, (2) the modeling of these effects performed by Gaussian Kernel and Deep Kernel, and (3) their comparison with benchmark GBLUP-based modeling
Summary
Utilizing the best linear-unbiased prediction (BLUP) has been useful for predicting the performance of unobserved maize hybrids utilizing pedigree or molecular marker relationships of all crosses (Bernardo 1994, 1996). As the genotypes differ in terms of their allelic constitution, the number of copies of an allele (additivity) and intra-allelic interactions (dominance) are expected to have different degrees of influence on how genotypes respond to environmental variations and how meaningful AW and DW interactions are For this reason, efforts have focused on a more in-depth search for the genomic causes that are linked to the ecophysiological responses of cultivation, either through genomic association studies (Li et al 2018) or by genomic prediction considering reaction-norm kernels (Jarquín et al 2014; Morais Júnior et al 2018) or whole-genome × envirotyping-based factorial regression models (Ly et al 2018; Millet et al 2019). Dense environmental covariables collected in all the environments considered in the two data sets
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