Skip to main content

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.