Reproducing kernel hilbert spaces regression methods for genomic assisted prediction of quantitative traits

Genetics. 2008 Apr;178(4):2289-303. doi: 10.1534/genetics.107.084285.


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.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Animals
  • Bayes Theorem
  • Chickens / genetics
  • Chromosomes
  • Genome / genetics*
  • Models, Genetic*
  • Quantitative Trait, Heritable*
  • Regression Analysis