Simulating deployment of genetic gain in a radiata pine breeding program with genomic selection

Abstract

Genomic selection (GS) is currently being used in the New Zealand radiata pine (Pinus radiata D. Don) breeding program to accelerate genetic gain. GS also has the potential to accelerate the deployment of genetic gain to the production forest through early selection. The increased rate of genetic gain in the breeding cycle will need to be transferred more quickly to realise that gain in the deployment population. GS selections will have lower accuracies than selections based on phenotypic data as currently practised; however, it is unknown how this will affect the genetic gain from GS-based deployment. Moreover, census size and turnover rate need to be optimised to cope with the influx of new marker-based selected material into a commercial orchard. We utilised a stochastic simulation approach to investigate these concepts, comparing three deployment scenarios: half-sib open-pollinated orchards (OP), full-sib control-pollinated orchards (CP) and clonal deployment through somatic embryogenesis. When accounting for time, genomic selection in OP, CP and clonal deployment pathways increased genetic gain by 9.5%, 15.9% and 44.6% respectively compared to phenotypic selection. The optimal orchard scenario would be genomic-selected control-pollination with a low census size (n = 40, males and females combined), low female turnover (5%) and a high male turnover (15–25%). This scheme balances high genetic gain with high seed yield while moderating the rate of inbreeding.

Stone Pine (Pinus pinea L.) High-Added-Value Genetics: An Overview

“…For P. flexilis, the authors suggested the use of exome sequencing to select disease-resistant plants [80]. Finally, for P. radiata, the acceleration of genetic gains has been proposed to genetically improve seed orchards [81].…”