Improving genomic prediction in wheat with random regression models with genotype-specific phenology-driven environmental covariates.
Dhakal R, Sniadower G, Silva P, Lado B, Sandro P
Climate Adaptation
Faster breeding of heat- and drought-tolerant wheat means the loaves, pasta, and flatbreads that anchor diets across six continents have a better shot at staying affordable as summers grow more extreme.
Researchers wanted to find a better way to predict how well wheat plants would grow across many different locations and weather conditions without having to physically test every variety everywhere. They pulled in weather data matched to specific moments in each plant's life — when it's growing leaves, flowering, and filling its grain — and fed that into prediction models. The result was models that were 50 to 100 percent more accurate at forecasting performance in new, untested locations, meaning breeders can make better choices with less time and fewer field trials.
Key Findings
Random regression models using just 3 environmental covariates outperformed standard genomic prediction models by 50–100% in two cross-validation scenarios.
The new models improved predictions in more than 89% of environments when forecasting performance in locations that had never been phenotyped.
Seven key weather variables tied to vegetative, reproductive, and grain-filling growth phases were enough to capture most of the genotype-by-environment interaction across 71 environments over 10 years.
chevron_right Technical Summary
Scientists developed a smarter way to predict which wheat varieties will perform best under different climate conditions, using weather data tied to specific growth stages. The new models were up to twice as accurate as standard methods, which could accelerate breeding of climate-resilient wheat.
Abstract Preview
Wheat (Triticum aestivum L.), a crucial cereal crop for global food security, faces growing challenges from climate change. Future production requires varieties that are resilient to environmental ...
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