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A Penalized Regression Method for Genomic Prediction Reduces Mismatch between Training and Testing Sets.

Osval A Montesinos-López¹, Cristian Daniel Pulido-Carrillo¹, Abelardo Montesinos-López²

¹Facultad de Telemática, Universidad de Colima, Colima 28040, Mexico.

|August 29, 2024

Summary

Genomic selection accuracy improves by weighting training data. This method down-weights features that differ between training and testing sets, enhancing predictive model performance in plant breeding.

Keywords:

Elastic Net regression Lasso regression Ridge regression genomic selection mismatch weighted regression

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Area of Science:

Agricultural Science
Genetics
Bioinformatics

Background:

Genomic selection (GS) revolutionizes plant breeding by reducing phenotyping costs.
GS accuracy suffers from training-testing set mismatches, limiting predictive model effectiveness.
Existing methods struggle to adapt models to target populations' genetic and environmental variations.

Purpose of the Study:

To develop a novel weighting strategy for genomic selection models.
To enhance the accuracy and efficiency of predictive models in plant breeding.
To mitigate the impact of training-testing set discrepancies on genomic prediction.

Main Methods:

Introduced a binary-Lasso regression approach to estimate feature importance (β coefficients).
Applied inverse β coefficients as weights in Lasso, Ridge, and Elastic Net models (WLasso, WRidge, WElastic Net).
Weighted models prioritize features less discriminatory between training and testing sets, using the glmnet library.

Main Results:

Consistent improvements in predictive accuracy across six diverse datasets.
Demonstrated significant reduction in normalized root mean square error (NRMSE).
Validated the effectiveness of the proposed weighting strategy in enhancing genomic prediction.

Conclusions:

The developed weighting method effectively improves genomic selection accuracy.
This approach offers a practical solution for training-testing set mismatches in plant breeding.
The method's straightforward implementation facilitates wider adoption in genomic prediction.