title: Locally epistatic genomic relationship matrices for genomic association and prediction.
abstract: In plant and animal breeding studies a distinction is made between the genetic value (additive plus epistatic genetic effects) and the breeding value (additive genetic effects) of an individual since it is expected that some of the epistatic genetic effects will be lost due to recombination. In this article, we argue that the breeder can take advantage of the epistatic marker effects in regions of low recombination. The models introduced here aim to estimate local epistatic line heritability by using genetic map information and combining local additive and epistatic effects. To this end, we have used semiparametric mixed models with multiple local genomic relationship matrices with hierarchical designs. Elastic-net postprocessing was used to introduce sparsity. Our models produce good predictive performance along with useful explanatory information.
authors.affiliation: Department of Plant Breeding and Genetics, Cornell University, Ithaca, New York 14853 da346@cornell.edu.
authors.forename: Deniz
authors.identifier: http://orcid.org/0000-0003-0553-6798
authors.initials: D
authors.lastname: Akdemir
authors.affiliation: Department of Plant Breeding and Genetics, Cornell University, Ithaca, New York 14853.
authors.forename: Jean-Luc
authors.initials: JL
authors.lastname: Jannink
mesh_terms: D000818:Animals; D001947:Breeding; D004843:Epistasis, Genetic; D016678:Genome; D008957:Models, Genetic; D010944:Plants / Q000235:genetics; D011995:Recombination, Genetic
keywords: GenPred; epistasis; genomic selection; mixed models; shared data resource