Table 2

Results from comparison to simulated breeding values (TBV)

Analysis

Method

Var

Acc

MSEP

Rank

Regr

0

TBV

25.346

1

0

1

1

1

Cle_Bayes.A

26.490

0.916

4.369

0.749

0.896

2

Cle_Lasso

32.909

0.916

5.337

0.716

0.804

3

Cle_Student.t

30.554

0.945

3.322

0.791

0.860

4

Pon_GEBV21

18.638

0.901

4.794

0.726

1.051

5

Sch_EBV600

20.354

0.889

5.303

0.700

0.992

6

Muc_SNPL

23.204

0.751

12.101

0.578

0.785

7

Vee_14.SNP

20.166

0.930

3.460

0.764

1.043

8

Vee_BLUP

10.763

0.647

14.728

0.485

0.993

9

Vee_IBD

19.009

0.931

3.475

0.767

1.075

10

Vee_IBS2

18.344

0.932

3.504

0.816

1.095

11

Vee_IBS5

19.579

0.929

3.517

0.781

1.057

12

Vee_SNP1

24.975

0.912

4.433

0.719

0.919

13

Vee_SNP2

17.974

0.925

3.828

0.793

1.098

14

Vee_SNP3

17.928

0.927

3.744

0.779

1.102

15

Ver_Bayes.BLUP

20.721

0.885

5.479

0.691

0.979

16

Ver_BayesA

13.783

0.857

7.092

0.696

1.162

17

Ver_BayesA.B

17.124

0.889

5.435

0.730

1.081

18

Ver_BayesC

17.914

0.861

6.561

0.710

1.024

19

Pon_GEBV1

19.726

0.897

4.971

0.709

1.016

20

best_case

34.256

0.985

1.572

0.935

0.847


1After the QTLMAS workshop an error in the implemented software was found by the participant. The adjusted implementation yielded an accuracy of 0.947.

Bastiaansen et al. BMC Proceedings 2010 4(Suppl 1):S1   doi:10.1186/1753-6561-4-S1-S1

Open Data