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Simultaneous Estimation of Spatial and Genetic Effects Using Hierarchical Generalized Linear Models
Spatial modelling is recognized as an important factor for assessment of breeding values in plant trials. 841 Scots pines were analyzed using version 2.0 of the hglm package. Both random additive genetic effects and spatial effects were included in the model. The covariance structures for the genetic and spatial effects were given by the additive relationship matrix and a conditional autoregressive (CAR) model, respectively. The genetic variance decreased by 24% when the spatial effects were included, which confirms the importance of including spatial effects in plant trials. The possibility to fit CAR models in the hglm package is expected to facilitate spatial modelling in genetic studies including animal breeding applications.
Keywords:
Breeding values, Random spatial effects, Spatial correlation
