Modeling Epistasis in Genomic Selection.
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ABSTRACT:
SUBMITTER: Jiang Y
PROVIDER: S-EPMC4596682 | biostudies-literature | 2015 Oct
REPOSITORIES: biostudies-literature
Publications
Modeling epistasis in genomic selection is impeded by a high computational load. The extended genomic best linear unbiased prediction (EG-BLUP) with an epistatic relationship matrix and the reproducing kernel Hilbert space regression (RKHS) are two attractive approaches that reduce the computational load. In this study, we proved the equivalence of EG-BLUP and genomic selection approaches, explicitly modeling epistatic effects. Moreover, we have shown why the RKHS model based on a Gaussian kerne ...[more]
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