Genomic Prediction of Additive and Non-additive Effects Using Genetic Markers and Pedigrees.

ABSTRACT: The genetic merit of individuals can be estimated using models with dense markers and pedigree information. Early genomic models accounted only for additive effects. However, the prediction of non-additive effects is important for different forest breeding systems where the whole genotypic value can be captured through clonal propagation. In this study, we evaluated the integration of marker data with pedigree information, in models that included or ignored non-additive effects. We tested the models Reproducing Kernel Hilbert Spaces (RKHS) and BayesA, with additive and additive-dominance frameworks. Model performance was assessed for the traits tree height, diameter at breast height and rust resistance, measured in 923 pine individuals from a structured population of 71 full-sib families.

SUBMITTER: de Almeida Filho JE 

PROVIDER: S-EPMC6686920 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature