TABLE 3

Spearman (above diagonal) and Pearson correlations (below diagonal) between estimates of sire effects from the five different methods

E-BLUPEmbedded Image-metricKernelRKHSBR
E-BLUP0.440.770.840.91
Embedded Image-metric0.480.330.360.46
Kernel0.660.270.930.76
RKHS0.840.360.790.85
BR0.920.500.580.80
  • Standard errors <0.0001. E-BLUP, Bayesian linear model without genomic information; Embedded Image-metric, linear regression on SNPs based on the Embedded Image-metric model; kernel, nonparametric kernel regression with SNPs within sire treated as a genomic combination; RKHS, reproducing kernel Hilbert spaces regression with SNPs within sire treated as a genomic combination; BR, Bayesian regression on 1000 SNPs based on Xu (2003).