Genomic selection in tree breeding: testing accuracy of prediction models including dominance effect

Abstract

Background The concept of Marker Assisted Selection (MAS) is rapidly evolving in animal and plant breeding. With the advent of high throughput molecular technology, numerous molecular markers distributed throughout the whole genome can be produced to characterize many genetic entries involving new perspectives in methodology of selection. An important research activity has begun in the animal world given the first theoretical framework for a methodology called genomic selection (GS) [1]. Several statistical approaches have been proposed for the prediction of genomic breeding values and numerous results are available that validates the interest of this method in animal breeding. In plants the GS is still limited to very advanced model species involved in genetic improvement and especially from scenario-based simulation [2,3]. In tree breeding the GS could significantly reduce the cost of genetic improvement schemes by limiting the size and number of field experiments; and facilitating the early selection at the nursery stage [4]. If most of the studies on GS have addressed the prediction of breeding value, taking into account the gene additive effects, there is still a lack of analyses dealing with the total genetic value (genotypic value) including both additive and dominance effects. This aspect is important in plant and especially in tree breeding where the goal of some programs is the production of clones or elite families. The aim of this study is to investigate the performance of GS in the context of tree breeding when the selection is based on genotypic value. The proposed approach allows taking into account both additive and dominance effect [5] for each marker in the statistical model. Six scenarios are simulated to test the reliability of the GS in the frame of recurrent selection scheme.