Subset | E-BLUP | -metric | Kernel | RKHS | BR |
---|---|---|---|---|---|

First | 0.03 | 0.26 | 0.05 | 0.27^{a} | 0.13 |

Second | 0.18 | 0.25 | 0.28 | 0.37 | 0.12 |

Third | 0.18 | −0.13 | 0.06 | −0.01 | 0.17 |

Fourth | −0.04 | 0.12 | 0.13 | 0.28 | 0.15 |

Fifth | 0.17 | 0.06 | 0.23 | 0.15 | 0.25 |

Global | 0.10 | 0.08 | 0.14 | 0.20 | 0.16 |

E-BLUP, Bayesian linear model without genomic information; -metric, linear regression on SNPs based on the -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).

↵

*a*Higher values indicate more accurate predictions. The highest correlation for each set is in italics.