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Genomic selection using regularized linear regression models: ridge regression, lasso, elastic net and their

Joseph O Ogutu1, Torben Schulz-Streeck, Hans-Peter Piepho

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Genomic selection (GS) accurately predicts breeding values using various regression methods. Lasso-type methods demonstrated higher accuracy than ridge regression for genomic prediction.

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Area of Science:

  • Quantitative genetics
  • Genomics
  • Animal and plant breeding

Background:

  • Genomic selection (GS) uses genome-wide molecular markers to estimate breeding values.
  • Accurate prediction of genomic estimated breeding values (GEBV) is crucial for genetic improvement.
  • Various statistical methods exist for GEBV prediction, necessitating comparative evaluation.

Purpose of the Study:

  • To comparatively evaluate the predictive performance of six regularized linear regression methods for GEBV prediction.
  • To identify the most efficient and accurate methods for predicting GEBV using dense SNP markers.

Main Methods:

  • Six regularized linear regression methods (ridge regression, ridge regression BLUP, lasso, adaptive lasso, elastic net, adaptive elastic net) were applied.
  • Models were trained on 2000 individuals and used to predict GEBV for 1000 unphenotyped individuals.
  • Predictive accuracy was assessed using root mean squared error and Pearson correlation against true genomic value, true breeding value, and simulated phenotypes via cross-validation.

Main Results:

  • Lasso-type methods (lasso, adaptive lasso, elastic net, adaptive elastic net) showed similar accuracies and outperformed ridge regression and ridge regression BLUP.
  • Ridge regression BLUP performed better than basic ridge regression.
  • Accuracy decreased across all models when predicting true breeding values, with adaptive elastic net being most affected.

Conclusions:

  • All six evaluated models achieved relatively high prediction accuracies on the simulated dataset.
  • Lasso-type methods generally provided higher prediction accuracy compared to ridge regression and ridge regression BLUP.