Table 2

The comparison of the applied approaches used by participants for estimation of genomic breeding value of quantitative trait.

Approach no.

Authors

Method

Acc.

Reg. Coef.

MSD

Shared (%)

Loss (%)


1

Calus et al.[10]*

BayesA bivariate

0.85

0.84

1.06

0.91

45.4

17

14

2

Calus et al. [10]

BayeaA univariate

0.84

0.83

1.05

0.90

46.9

58

18

3

Calus et al. [10]

BayesC bivariate

0.87

0.89

1.01

0.88

42.4

71

10

4

Calus et al. [10]

BayesC univariate

0.86

0.87

1.01

0.89

44.1

68

12

5

Calus et al. [10]

GBLUP bivariate

0.83

0.81

1.07

0.90

47.8

57

19

6

Calus et al. [10]

GBLUP univariate

0.83

0.80

1.10

0.90

48.9

54

22

7

Calus et al. [10]

Pedigree-BLUP univariate

0.49

0.46

0.88

0.71

66.4

17

79

8

Calus et al. [10]

Pedigree-BLUP bivariate

0.50

0.47

0.88

0.72

66.8

23

62

9

Cleveland et al. [11]

BayesA_all 1

0.85

0.86

1.13

0.96

45.0

70

12

10

Cleveland et al. [11]

BayesA_s12

0.49

0.52

0.94

0.91

63.4

26

63

11

Cleveland et al. [11]

BayesA_s22

0.67

0.66

0.94

0.84

56.5

54

33

12

Coster and Calus[12]

PLSR3

0.76

0.73

9.05

7.31

76.4

16

83

13

Nadaf et al. [13]

BayesB

0.89

0.89

1.04

0.91

41.7

77

8

14

Nadaf et al. [13]

BayesB + Pedigree information

0.88

0.88

1.02

0.90

42.2

71

9

15

Nadaf et al. [13]

GBLUP + Pedigree information

0.81

0.80

1.09

0.92

49.2

56

21

16

Nadaf et al. [13]

GBLUP

0.82

0.80

1.12

0.92

49.1

71

23

17

Ogutu et al. [8]

Boosting

0.47

0.38

0.19

0.15

280.7

29

65

18

Ogutu et al. [8]

Support vector

0.69

0.63

1.54

1.20

48.3

49

36

19

Schulz-Streeck et al. [14]

Ridge regression

0.85

0.84

1.02

0.86

59.6

59

19

20

Schulz-Streeck et al. [14]

Spatial regression

0.83

0.81

1.08

0.88

46.4

63

19

21

Shen et al. [15]

DHGLM4

0.82

0.80

1.03

0.84

49.9

58

15

22

Sun et al. [16]

BayesCpi

0.89

0.89

1.05

0.91

41.6

77

8

23

Zhang et al. [17]

BayesB

0.89

0.89

1.05

0.91

42.0

74

8

24

Zhang et al. [17]

TA–BLUP–sub5

0.89

0.89

1.03

0.90

42.2

73

9

25

Zhang et al. [17]

TA–BLUP–all6

0.89

0.89

1.06

0.92

41.9

72

9

26

Zukowski et al.

GBLUP

0.58

0.59

1.12

0.96

87.0

41

38


* Reference to applied method;1 with use of all markers in analyses; 2 with use of subset of markers in analyses; 3 Partial least squares regression; 4 Double hierarchical generalized linear models; 5 BLUP with trait specific matrix obtained with use of subset of markers; 6 BLUP with trait specific matrix obtained with use of all markers. Acc=accuracies of DGV (Acc.); linear regression coefficients of TBV on DGV; mean square differences (MSD) between TBV and DGV; percentage of IDs shared between the groups of young individuals selected on TBV and EBV (Shared) and percentage of loss of response to selection when 10% are selected based on EBV instead of TBV for quantitative trait (QT)

Pszczola et al. BMC Proceedings 2011 5(Suppl 3):S1   doi:10.1186/1753-6561-5-S3-S1

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