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Table 1 Accuracies of prediction of GEV in the methods of genomic selection

From: EM algorithm for Bayesian estimation of genomic breeding values

Methods

 

Data I

Data II

wBSR

p = 0.01

0.699 ± 0.007

(0.950 ± 0.005)

0.843 ± 0.014

(0.961 ± 0.009)

 

p = 0.05

0.730 ± 0.006

(0.947 ± 0.005)

0.857 ± 0.012

(0.871 ± 0.010)

 

p = 0.1

0.743 ± 0.006

(0.940 ± 0.005)

0.848 ± 0.014

(0.882 ± 0.016)

 

p = 0.2

0.755 ± 0.006

(0.924 ± 0.005)

0.820 ± 0.017

(0.795 ± 0.022)

 

p = 0.5

0.760 ± 0.005

(0.868 ± 0.007)

0.665 ± 0.023

(0.507 ± 0.031)

 

p = 1.0

0.697 ± 0.007

(1.080 ± 0.008)

0.840 ± 0.015

(0.914 ± 0.017)

BSR

 

0.748 ± 0.006

(1.100 ± 0.007)

0.838 ± 0.015

(0.885 ± 0.019)

SSVS

p = 0.01

0.718 ± 0.007

(1.033 ± 0.006)

0.887 ± 0.011

(1.002 ± 0.009)

 

p = 0.05

0.747 ± 0.006

(1.036 ± 0.005)

0.874 ± 0.012

(0.942 ± 0.013)

 

p = 0.1

0.762 ± 0.005

(1.027 ± 0.005)

0.846 ± 0.014

(0.865 ± 0.018)

 

p = 0.2

0.772 ± 0.005

(1.008 ± 0.005)

n.d.

 

p = 0.5

0.773 ± 0.005

(0.944 ± 0.005)

n.d.

  1. The means of correlation coefficients between the predicted GBV over 100 and 20 repetitions in Data I and Data II, respectively, are listed with the standard errors. The means of regression coefficients of true on predicted GEV are given with the standard errors in the parenthesis.
  2. wBSR: EM-based modified BSR method proposed in this paper.
  3. BSR: MCMC-based Bayesian shrinkage regression method (BayesA).
  4. SSVS: MCMC-based stochastic search variable selection method (BayesB).
  5. For the parameters ν and S, we set ν = 4.012 and S = 0.002 for BSR and wBSR with p = 1.0 (EM-based BSR) and ν = 4.234 and S = 0.0429 for SSVS and wBSR with p < 1.0.
  6. "n.d." indicates that the analysis was not done.