CWAGS: multi-trait genomic selection using channel weighted attention convolutional network

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.

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.

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.