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.
journal_name
Geneticsjournal_title
Geneticsauthors
Gianola D,van Kaam JBdoi
10.1534/genetics.107.084285subject
Has Abstractpub_date
2008-04-01 00:00:00pages
2289-303issue
4eissn
0016-6731issn
1943-2631pii
178/4/2289journal_volume
178pub_type
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