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Refining penalized ridge regression: a novel method for optimizing the regularization parameter in genomic

Abelardo Montesinos-López1, Osval A Montesinos-López2, Federico Lecumberry3

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Summary

A new method for tuning Ridge regression improves genomic prediction accuracy. This approach enhances breeding value estimation in plant breeding by optimizing the penalization parameter, leading to significant gains in prediction performance.

Keywords:
GenPredShared Data Resourcebreeding valuesgenomic predictionpenalized regressionplant breedingridge regression

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Area of Science:

  • Genetics
  • Quantitative Genetics
  • Bioinformatics

Background:

  • Genomic selection (GS) is vital for estimating breeding values efficiently.
  • Ridge regression is a popular genomic prediction method, but its performance hinges on optimal penalization parameter tuning.

Purpose of the Study:

  • To introduce a novel and more efficient method for selecting the optimal penalization parameter in Ridge regression for genomic prediction.
  • To evaluate the performance of the proposed method against conventional approaches using real-world datasets.

Main Methods:

  • Development of a novel algorithm for optimal penalization parameter selection in Ridge regression.
  • Comparative analysis of the proposed method and the conventional method across 14 real plant breeding datasets.

Main Results:

  • The proposed method outperformed the conventional method in 13 out of 14 datasets.
  • Significant gains in prediction accuracy (Pearson's correlation) of 56.15% were observed across datasets.
  • No significant gains were noted in terms of normalized mean square error.

Conclusions:

  • The novel method for penalization parameter selection shows strong potential for improving Ridge regression in genomic prediction.
  • Adoption of this method can enhance the selection of candidate lines in plant breeding programs.
  • The findings support the utility of advanced parameter tuning for more accurate genomic evaluations.