Name (abbreviation) | Bayesian | Penalized | Nonparametric |
---|---|---|---|

Least-squares regression (LSR) | |||

Bayesian ridge regression (BRR) or RR-BLUP | X | X | |

BLUP using a genomic relationship matrix (G-BLUP) | X | X | |

Trait-specific BLUP (TA-BLUP) | X | X | |

BayesA | X | ||

BayesB | X | ||

BayesC | X | ||

Bayes SSVS | X | ||

Bayesian LASSO (BL) | X | ||

Double hierarchical generalized linear models (DHGLM) | |||

Least absolute shrinkage and selection operator (LASSO) | X | ||

Partial least-squares regression (PLS) | X | ||

Principal component regression (PCR) | X | ||

Elastic net (EN) | X | ||

Reproducing kernel Hilbert spaces regressions (RKHS) | X | X | X |

Support vector regression (SVR) | X | X | |

Boosting^{a} | NA | NA | NA |

Random forests (RF) | X | ||

Neural networks (NN)^{b} | X | X | X |

The following are early references of the use of the above methods for genomic prediction (references with the original description of some of the methods are also given in earlier sections of this article and in the references given here). LSR, BRR, BayesA, and BayesB, Meuwissen

*et al.*(2001); G-BLUP, VanRaden (2008); TA-BLUP, Zhang*et al.*(2010); BayesC, Habier*et al.*(2011); Bayes SSVS, Calus*et al.*(2008); BL, de los Campos*et al.*(2009); DHGLM, Shen*et al.*(2011); LASSO, Usai*et al.*(2009); PLS and SVR, Moser*et al.*(2009); PCR, Solberg*et al.*(2009); EN, croiseau*et al.*(2011); RKHS, gianola*et al.*(2006); Boosting, González-Recio*et al.*(2010); RF, González-Recio and Forni (2011); and NN, Okut*et al.*(2011).↵a Boosting as an estimation technique could be applied to any method, Bayesian or penalized, parametric or nonparametric.

↵b NN could be implemented in a nonpenalized, penalized, or Bayesian framework.