Abstract
In most crops, genetic and environmental factors interact in complex ways giving rise to substantial genotype-by-environment interactions (G×E). We propose that computer simulations leveraging field trial data, DNA sequences, and historical weather records can be used to tackle the longstanding problem of predicting cultivars’ future performances under largely uncertain weather conditions. We present a computer simulation platform that uses Monte Carlo methods to integrate uncertainty about future weather conditions and model parameters. We use extensive experimental wheat yield data (n = 25,841) to learn G×E patterns and validate, using left-trial-out cross-validation, the predictive performance of the model. Subsequently, we use the fitted model to generate circa 143 million grain yield data points for 28 wheat genotypes in 16 locations in France, over 16 years of historical weather records. The phenotypes generated by the simulation platform have multiple downstream uses; we illustrate this by predicting the distribution of expected yield at 448 cultivar-location combinations and performing means-stability analyses.
Highlights
In most crops, genetic and environmental factors interact in complex ways giving rise to substantial genotype-by-environment interactions (G×E)
Accurate predictions of future performances in target environments require considering the possible weather conditions that may occur within a region and how individual genotypes are expected to react to those conditions
We propose that computer simulations that integrate field trial data, DNA sequences, and historical weather records can be used to address the difficult task of predicting genotype performance and stability using limited years of field testing per genotype
Summary
Genetic and environmental factors interact in complex ways giving rise to substantial genotype-by-environment interactions (G×E). We propose that computer simulations leveraging field trial data, DNA sequences, and historical weather records can be used to tackle the longstanding problem of predicting cultivars’ future performances under largely uncertain weather conditions. We propose that computer simulations that integrate field trial data, DNA sequences, and historical weather records can be used to address the difficult task of predicting genotype performance and stability using limited years of field testing per genotype. Our approach is largely data-driven; we use a G×E model incorporating SNPs and ECs to learn how each cultivar reacted to the environmental conditions. We use these patterns, together with DNA polymorphisms and historical weather records, to simulate the expected performance of specific genotypes at specific locations. The Monte Carlo (MC) method used to simulate phenotypes integrates uncertainty about future weather conditions and model parameters (e.g., SNP or EC effects and their interactions)
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