Coformer: a deep learning-based framework for cross-environment and multi-year cotton phenotype prediction and interpretation.

Abstract

Accurate prediction of key agronomic traits in cotton is crucial for advancing its genetic improvement and enabling breeding-by-design. However, when using high-dimensional genomic data (e.g., massive SNP markers) for prediction, traditional models often suffer from overfitting and poor generalization. To address this, we propose an innovative deep learning model-Coformer. Coformer is a hybrid Transformer-autoencoder model: a self-attention Transformer encoder captures long-range SNP dependencies and compresses high-dimensional genotypes into a compact latent representation, which is then decoded by a supervised prediction head to the target phenotype. Through rigorous evaluation on a combined multi-environment, multi-year dataset, Coformer maintains outstanding predictive robustness even without explicitly modeling environmental factors. The model integrates a normalization module and a linear projection layer to enable adaptive input processing and end-to-end training, effectively improving generalization across varying data dimensionalities. Importantly, Coformer is interpretable: it can precisely pinpoint key genetic loci that influence target traits, providing a solid theoretical basis for deeper insight into the genetic underpinnings of phenotypes and for implementing precision breeding. In addition, to accelerate application and dissemination, we have concurrently developed a browser-based Cotton Phenotype Prediction System (CPPS) that seamlessly integrates the full workflow of "data preparation-model training-result interpretation" into a unified graphical interface. The system supports batch processing and result visualization, substantially lowering the barrier for non-specialists and offering an efficient, user-friendly solution to bridge genomics research and breeding practice.

Plant phenotyping relevance

遺伝子型データから綿の農業形質を予測する深層学習手法を開発・多環境多年度で評価し、予測・解釈ワークフローのソフトウェアも実装しているため、形質推定法が研究の中心である。

Code and data availability

The paper's cotton genotype–phenotype dataset (1,202 samples, 7,779 SNPs; traits ZG, GZS, GZG) and the Coformer/CPPS code are not deposited publicly. The Data availability statement only offers data from the corresponding author upon reasonable request, and no public repository, code URL, or supplement link appears in

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