Cropformer: An interpretable deep learning framework for crop genomic prediction.

Abstract

Machine learning and deep learning are extensively employed in genomic selection (GS) to expedite the identification of superior genotypes and accelerate breeding cycles. However, a significant challenge with current data-driven deep learning models in GS lies in their low robustness and poor interpretability. To address these challenges, we developed Cropformer, a deep learning framework for predicting crop phenotypes and exploring downstream tasks. This framework combines convolutional neural networks with multiple self-attention mechanisms to improve accuracy. The ability of Cropformer to predict complex phenotypic traits was extensively evaluated on more than 20 traits across five major crops: maize, rice, wheat, foxtail millet, and tomato. Evaluation results show that Cropformer outperforms other GS methods in both precision and robustness, achieving up to a 7.5% improvement in prediction accuracy compared to the runner-up model. Additionally, Cropformer enhances the analysis and mining of genes associated with traits. We identified numerous single nucleotide polymorphisms (SNPs) with potential effects on maize phenotypic traits and revealed key genetic variations underlying these differences. Cropformer represents a significant advancement in predictive performance and gene identification, providing a powerful general tool for improving genomic design in crop breeding. Cropformer is freely accessible at https://cgris.net/cropformer.

Plant phenotyping relevance

作物の表現型形質を予測する深層学習フレームワークを開発・評価しており、計算的な形質推定が研究の中心である。

Code and data availability

The paper's authors publicly released the Cropformer analysis code on GitHub, and the paper's phenotypic/genotypic analysis datasets (wheat, foxtail millet, tomato, rice, maize) are publicly available at author-cited URLs, directly reproducing this paper's genomic-prediction measurements and analysis.