Linking genetic markers and crop model parameters using neural networks to enhance genomic prediction of integrative traits

• Published in: Frontiers in plant science Vol. 15; p. 1393965
• Main Authors: Larue, Florian, Rouan, Lauriane, Pot, David, Rami, Jean-François, Luquet, Delphine, Beurier, Grégory
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers 30-07-2024; Frontiers Media S.A
• Subjects: Agricultural sciences; CGM-WGP; Computer Science; convolutional neural networks; crop growth model; Genetics; genomic prediction; Life Sciences; Modeling and Simulation; Neural and Evolutionary Computing; Plant Science; Plants genetics; Sciences and technics of agriculture; sorghum; CGM-WGP; crop growth model; sorghum; convolutional neural networks; genomic prediction
• ISSN: 1664-462X; 1664-462X
• DOI: 10.3389/fpls.2024.1393965

Predicting the performance (yield or other integrative traits) of cultivated plants is complex because it involves not only estimating the genetic value of the candidates to selection, the interactions between the genotype and the environment (GxE) but also the epistatic interactions between genomic regions for a given trait, and the interactions between the traits contributing to the integrative trait. Classical Genomic Prediction (GP) models mostly account for additive effects and are not suitable to estimate non-additive effects such as epistasis. Therefore, the use of machine learning and deep learning methods has been previously proposed to model those non-linear effects.
In this study, we propose a type of Artificial Neural Network (ANN) called Convolutional Neural Network (CNN) and compare it to two classical GP regression methods for their ability to predict an integrative trait of sorghum: aboveground fresh weight accumulation. We also suggest that the use of a crop growth model (CGM) can enhance predictions of integrative traits by decomposing them into more heritable intermediate traits.
The results show that CNN outperformed both LASSO and Bayes C methods in accuracy, suggesting that CNN are better suited to predict integrative traits. Furthermore, the predictive ability of the combined CGM-GP approach surpassed that of GP without the CGM integration, irrespective of the regression method used.
These results are consistent with recent works aiming to develop Genome-to-Phenotype models and advocate for the use of non-linear prediction methods, and the use of combined CGM-GP to enhance the prediction of crop performances.