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EBMGP: a deep learning model for genomic prediction based on Elastic Net feature selection and bidirectional encoder
Summary
A new deep learning framework, EBMGP, improves genomic prediction accuracy by treating SNPs like language. This method enhances early selection in breeding programs, accelerating genetic gains.
Area of Science:
- Genomics
- Bioinformatics
- Machine Learning
Background:
- Genomic estimated breeding values accelerate breeding programs.
- Deep learning (DL) approaches are increasingly used for genomic prediction (GP).
Purpose of the Study:
- Introduce a novel DL framework, EBMGP, for enhanced genomic prediction.
- Improve predictive accuracy and reduce computational burden in GP.
Main Methods:
- EBMGP uses Elastic Net for feature selection.
- Employs bidirectional encoder representations from transformers embeddings and multi-head attention pooling.
- Models SNPs as 'words' and adjacent SNP groups as 'sentences' to capture genetic interactions.
Main Results:
- EBMGP demonstrated superior predictive performance across diverse plant and animal datasets.
- Outperformed competing models in 13 out of 16 tasks, with accuracy gains of 0.74-9.55% over the second-best model.
- Achieved marked improvement compared to the one-hot representation.
Conclusions:
- EBMGP offers a robust and effective approach for genomic prediction.
- Highlights the potential of DL, particularly transformer-based methods, in life sciences applications.
- Accelerates genetic gains through enhanced early selection.
