BACKGROUND: Genomic selection serves as an effective approach to accelerate the improvement of agronomic traits in crops. However, as a core technique in modern crop breeding, genomic selection still faces many challenges in capturing complex interactions among genetic variants. This study proposes the Channel-Weighted Attention Genomic Selection Convolutional Network (CWAGS), a novel convolutional neural network specifically designed for genomic data. The major innovations define CWAGS: It employs a channel-weighted attention mechanism that reveals trait-specific genetic architectures through adaptive weight assignment to different genomic features. Second, it enhances computational efficiency through a depthwise separable convolution architecture. And integrates DropPath random depth regularization with residual connections to boost the model's generalization capability across diverse genetic backgrounds. RESULTS: Analysis of channel attention weights demonstrates CWAGS's biological interpretability: different traits exhibit distinct genetic architectures, providing insights into genotype-phenotype relationships. In a comprehensive evaluation with four benchmark datasets, the CWAGS model improved average accuracy by 1.2%-4.8% compared with the suboptimal models. Channel weight attention analysis revealed distinct genetic architectures for yield, quality, and morphological traits, providing a reference for the development of deep learning frameworks for precision genomic selection. CONCLUSIONS: By balancing prediction accuracy, and biological interpretability, CWAGS provides a reference framework for precision genomic selection. This framework facilitates crop genetic improvement through enhanced breeding efficiency.
Cao et al. (2026) studied this question.