title: Reproducing kernel hilbert spaces regression methods for genomic assisted prediction of quantitative traits.
abstract: Reproducing 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.
authors.affiliation: Department of Animal Sciences, University of Wisconsin, Madison, Wisconsin 53706, USA.
authors.forename: Daniel
authors.initials: D
authors.lastname: Gianola
authors.forename: Johannes B C H M
authors.initials: JB
authors.lastname: van Kaam
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D002645:Chickens / Q000235:genetics; D002875:Chromosomes; D016678:Genome / Q000235:genetics*; D008957:Models, Genetic; D019655:Quantitative Trait, Heritable; D012044:Regression Analysis