2023 journal article

Yield prediction through integration of genetic, environment, and management data through deep learning

G3-GENES GENOMES GENETICS, 13(4).

By: D. Kick *, J. Wallace *, J. Schnable *, J. Kolkman *, B. Alaca *, T. Beissinger *, J. Edwards *, D. Ertl ...

author keywords: phenotypic prediction; gene-by-environment interaction (GxE); GEM; convolutional neural network; deep learning
TL;DR: Deep learning provides a promising avenue for phenotypic prediction of complex traits in complex environments and a potential mechanism to better understand the influence of environmental and genetic factors. (via Semantic Scholar)
UN Sustainable Development Goal Categories
2. Zero Hunger (OpenAlex)
Source: Web Of Science
Added: February 12, 2024

Abstract Accurate prediction of the phenotypic outcomes produced by different combinations of genotypes, environments, and management interventions remains a key goal in biology with direct applications to agriculture, research, and conservation. The past decades have seen an expansion of new methods applied toward this goal. Here we predict maize yield using deep neural networks, compare the efficacy of 2 model development methods, and contextualize model performance using conventional linear and machine learning models. We examine the usefulness of incorporating interactions between disparate data types. We find deep learning and best linear unbiased predictor (BLUP) models with interactions had the best overall performance. BLUP models achieved the lowest average error, but deep learning models performed more consistently with similar average error. Optimizing deep neural network submodules for each data type improved model performance relative to optimizing the whole model for all data types at once. Examining the effect of interactions in the best-performing model revealed that including interactions altered the model's sensitivity to weather and management features, including a reduction of the importance scores for timepoints expected to have a limited physiological basis for influencing yield—those at the extreme end of the season, nearly 200 days post planting. Based on these results, deep learning provides a promising avenue for the phenotypic prediction of complex traits in complex environments and a potential mechanism to better understand the influence of environmental and genetic factors.