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Table 1 Accuracy of predictions of the six models

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

Model Pearson correlation Root mean squared error
  5-fold cross-validation TGV TBV TGV TBV
  Mean Min Max     
Elastic Net 0.5071 0.4486 0.5308 0.9233 0.8659 2.2276 3.4618
Lasso 0.5062 0.4466 0.5293 0.9240 0.8705 2.1642 3.5478
Adaptive Lasso 0.4951 0.4454 0.5152 0.9195 0.8759 2.0757 3.9911
RR 0.4717 0.4050 0.5037 0.8246 0.8213 2.9046 3.3767
RR-BLUP 0.4628 0.3905 0.4951 0.8455 0.8315 2.9894 3.6487
Adaptive Elastic Net 0.4285 0.4013 0.4667 0.8968 0.8112 2.3404 4.2325
  1. Pearson correlation between GEBVs and (1) the observed values from the 5-fold cross-validation, (2) the true expectation of the phenotypes of the 1000 non-phenotyped candidates (TGV), (3) the true expectation of the phenotypes of the progenies of the 1000 non-phenotyped candidates (TBV); and the root mean squared error with respect to TGV and TBV.
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BMC Proceedings

ISSN: 1753-6561

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