Abstract
Genomic selection is a proven technology in animal and plant breeding to accelerate genetic gain, but as yet is to be fully realised in forest tree breeding. This paper examines, through stochastic simulation, the potential benefits of genomic selection (GS) over forward selection (FS) in a typical conifer breeding program. Methods of speeding the deployment of selected material were also considered, including top-grafting onto mature seed orchard ortets, using additional replicates of clones in archives for crossing, and embryogenesis and clonal propagation. Genetic gain per generation was found to increase considerably when the size of the training population was larger (800 c.f. 3000 clones), or when the heritability was higher (0.2 c.f. 0.5). The largest genetic gain, of 24% was achieved where large training populations (3000 clones) and high heritability traits (0.5) were combined. The accuracy of genomic breeding values (GEBVs) increased with the increase in the number of clones in the training population, the heritability of the trait and the density of the SNP markers. Calculated accuracies of simulated GEBVs and genetic gain per unit of time suggested that 2000 clones in the training population is the minimum size for effective genomic selection for conifers. Compared with forward selection, genomic selection with 2000 clones in the training population, and a 60K SNP panel, an increase of 1.58 mm per year in diameter-at-breast-height (DBH) and 2.44 kg/m3 per year for wood density can be expected. After one generation (9-years), this would be equivalent to 14.23 mm and 21.97 kg/m3 for DBH and wood density respectively. Deploying clones of the selected individuals always resulted in higher additional genetic gain than deploying progeny/seedlings. Deploying genetic material selected from genomic selection with top-grafting for early coning appeared to be the best option. Application of genomic selection to conifer breeding programs, combined with deployment tools such as top-grafting and embryogenesis are powerful tools to speed the delivery of genetic gain to the forest.
Highlights
Many animal and plant breeders use genomic selection routinely [1, 2, 3]
The accuracy of genomic breeding values (GEBVs) in genomic selection was close to the accuracy of forward selection (FS) for both traits with either low or high heritabilities when the training population size was 3000
The simulated accuracy achieved from genomic selection was lower or equivalent to that achieved in forward selection (Tables 3 and 4), which reflected the population size and the number of markers used in most genomic selection projects in conifers [17, 57,58,59]
Summary
Many animal and plant breeders use genomic selection routinely [1, 2, 3]. In forestry, genomic selection is yet to be fully realised. The benefits of genomic selection in forestry are potentially very large through shortening the long generation intervals, speeding the delivery of genetic gain, and reducing the reliance on field testing [4,5,6]. Benefits include the estimation of both the additive genetic variance and the non-additive genetic variance [7], which potentially gives further benefits. Partitioning the non-additive genetic variance can add to the accuracy of breeding value estimates due to overall greater accuracy in the variance partitioning process. Trees have the ability to be vegetatively propagated (cloned), so that nonadditive genetic variance can be captured by deploying copies of outstanding genotypes [8, 9]
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