title: Unraveling additive from nonadditive effects using genomic relationship matrices.
abstract: The application of quantitative genetics in plant and animal breeding has largely focused on additive models, which may also capture dominance and epistatic effects. Partitioning genetic variance into its additive and nonadditive components using pedigree-based models (P-genomic best linear unbiased predictor) (P-BLUP) is difficult with most commonly available family structures. However, the availability of dense panels of molecular markers makes possible the use of additive- and dominance-realized genomic relationships for the estimation of variance components and the prediction of genetic values (G-BLUP). We evaluated height data from a multifamily population of the tree species Pinus taeda with a systematic series of models accounting for additive, dominance, and first-order epistatic interactions (additive by additive, dominance by dominance, and additive by dominance), using either pedigree- or marker-based information. We show that, compared with the pedigree, use of realized genomic relationships in marker-based models yields a substantially more precise separation of additive and nonadditive components of genetic variance. We conclude that the marker-based relationship matrices in a model including additive and nonadditive effects performed better, improving breeding value prediction. Moreover, our results suggest that, for tree height in this population, the additive and nonadditive components of genetic variance are similar in magnitude. This novel result improves our current understanding of the genetic control and architecture of a quantitative trait and should be considered when developing breeding strategies.
authors.affiliation: Agronomy Department, University of Florida, Gainesville, Florida 32611 School of Forest Resources and Conservation, University of Florida, Gainesville, Florida 32611 gpeter@ufl.edu p.munoz@ufl.edu.
authors.forename: Patricio R
authors.initials: PR
authors.lastname: Muñoz
authors.affiliation: School of Forest Resources and Conservation, University of Florida, Gainesville, Florida 32611 Genetics and Genomics Graduate Program, University of Florida, Gainesville, Florida 32611.
authors.forename: Marcio F R
authors.initials: MF
authors.lastname: Resende
authors.affiliation: School of Forest Resources and Conservation, University of Florida, Gainesville, Florida 32611.
authors.forename: Salvador A
authors.initials: SA
authors.lastname: Gezan
authors.affiliation: EMBRAPA Forestry, Estrada da Ribeira, Colombo, PR 83411-000 Brazil Department of Forest Engineering, Universidade Federal de Viçosa, Viçosa, MG 36571-000 Brazil.
authors.forename: Marcos Deon Vilela
authors.initials: MD
authors.lastname: Resende
authors.affiliation: Department of Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama 35294.
authors.forename: Gustavo
authors.initials: G
authors.lastname: de Los Campos
authors.affiliation: School of Forest Resources and Conservation, University of Florida, Gainesville, Florida 32611 University of Florida Genetics Institute, University of Florida, Gainesville, Florida 32611.
authors.forename: Matias
authors.initials: M
authors.lastname: Kirst
authors.affiliation: School of Forest Resources and Conservation, University of Florida, Gainesville, Florida 32611.
authors.forename: Dudley
authors.initials: D
authors.lastname: Huber
authors.affiliation: School of Forest Resources and Conservation, University of Florida, Gainesville, Florida 32611 University of Florida Genetics Institute, University of Florida, Gainesville, Florida 32611 gpeter@ufl.edu p.munoz@ufl.edu.
authors.forename: Gary F
authors.initials: GF
authors.lastname: Peter
mesh_terms: D000465:Algorithms; D001947:Breeding; D003433:Crosses, Genetic; D005819:Genetic Markers; D023281:Genomics / Q000379:methods; D040582:Inheritance Patterns; D008957:Models, Genetic; D010375:Pedigree; D041603:Pinus taeda / Q000235:genetics
chemical_list: D005819:Genetic Markers
keywords: G-BLUP; GenPred; Genomic selection; dominance relationship matrix; nonadditive; realized relationship matrices; shared data resource