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Reproducing kernel hilbert spaces regression methods for genomic assisted prediction of quantitative traits Gianola D; van Kaam JBGenetics 2008[Apr]; 178 (4): 2289-303Reproducing kernel Hilbert spaces regression procedures for prediction of total genetic value for quantitative traits, which make use of phenotypic and genomic data simultaneously, are discussed from a theoretical perspective. It is argued that a nonparametric treatment may be needed for capturing the multiple and complex interactions potentially arising in whole-genome models, i.e., those based on thousands of single-nucleotide polymorphism (SNP) markers. After a review of reproducing kernel Hilbert spaces regression, it is shown that the statistical specification admits a standard mixed-effects linear model representation, with smoothing parameters treated as variance components. Models for capturing different forms of interaction, e.g., chromosome-specific, are presented. Implementations can be carried out using software for likelihood-based or Bayesian inference.|*Models, Genetic[MESH]|*Quantitative Trait, Heritable[MESH]|Animals[MESH]|Bayes Theorem[MESH]|Chickens/genetics[MESH]|Chromosomes[MESH]|Genome/*genetics[MESH]|Regression Analysis[MESH] |
