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

Chunqing Cao¹, Farhan Bin Mohamed², Mohd Shahrizal Bin Sunar¹

¹Faculty of Computing, Universiti Teknologi Malaysia, 81310 Johor, Bahru, Johor, Malaysia.

|May 27, 2026

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This study introduces the Channel-Weighted Attention Genomic Selection Convolutional Network (CWAGS) for crop breeding. CWAGS improves genomic selection accuracy and provides insights into trait-specific genetic architectures.

Area of Science:

Genomics
Plant Breeding
Machine Learning

Background:

Genomic selection accelerates crop trait improvement but struggles with complex genetic interactions.
Novel deep learning models are needed to enhance genomic selection accuracy and interpretability.

Purpose of the Study:

To introduce the Channel-Weighted Attention Genomic Selection Convolutional Network (CWAGS) for genomic data analysis.
To improve prediction accuracy and biological interpretability in genomic selection.

Main Methods:

Developed CWAGS, a convolutional neural network with channel-weighted attention and depthwise separable convolutions.
Integrated DropPath regularization with residual connections for enhanced generalization.
Applied CWAGS to four benchmark datasets for evaluation.

Keywords:

Channel-weighted attention Crop breeding Deep learning Genomic selection Multi-trait selection

Related Experiment Videos

Main Results:

CWAGS demonstrated improved average accuracy by 1.2%-4.8% over suboptimal models.
Channel attention weights revealed distinct genetic architectures for different traits, offering biological interpretability.
The model provides insights into genotype-phenotype relationships for precision breeding.

Conclusions:

CWAGS balances prediction accuracy and biological interpretability, serving as a reference for precision genomic selection.
The framework enhances crop genetic improvement by increasing breeding efficiency.