In G3 (Bethesda, Md.)
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 towards this goal. Here we predict maize yield using deep neural networks, compare the efficacy of two 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 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 datatype 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 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 phenotypic prediction of complex traits in complex environments and a potential mechanism to better understand the influence of environmental and genetic factors.
Kick Daniel R, Wallace Jason G, Schnable James C, Kolkman Judith M, Alaca Barış, Beissinger Timothy M, Edwards Jode, Ertl David, Flint-Garcia Sherry, Gage Joseph L, Hirsch Candice N, Knoll Joseph E, de Leon Natalia, Lima Dayane C, Moreta Danilo, Singh Maninder P, Thompson Addie, Weldekidan Teclemariam, Washburn Jacob D
2023-Jan-10
Convolutional Neural Network, Deep learning, GEM, Gene-by-Environment Interaction (GxE), Phenotypic Prediction
