Scion (New Zealand Forest Research Institute), Rotorua, New Zealand.
Agriculture Victoria, AgriBio Centre, DEDJTR, Bundoora, Victoria, Australia.
PLoS One. 2018 Dec 10;13(12):e0208232. doi: 10.1371/journal.pone.0208232. eCollection 2018.
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.
基因组选择是一种在动植物育种中已被证实的技术,可以加速遗传增益,但尚未在林木育种中得到充分实现。本文通过随机模拟,研究了基因组选择(GS)相对于正向选择(FS)在典型针叶树育种计划中的潜在优势。还考虑了加速选择材料部署的方法,包括在成熟种子园或原始品种上进行高位嫁接,在档案中增加克隆的重复用于杂交,以及胚胎发生和克隆繁殖。研究发现,当训练群体规模较大(800 个克隆对 3000 个克隆)或遗传力较高(0.2 对 0.5)时,每代的遗传增益会显著增加。当大的训练群体(3000 个克隆)和高遗传力性状(0.5)相结合时,获得的最大遗传增益为 24%。基因组育种值(GEBVs)的准确性随着训练群体中克隆数量的增加、性状的遗传力和 SNP 标记的密度而增加。模拟 GEBVs 的计算精度和单位时间的遗传增益表明,训练群体中 2000 个克隆是针叶树有效基因组选择的最小规模。与正向选择相比,当训练群体中有 2000 个克隆和 60K SNP 面板时,可预期胸径(DBH)每年增加 1.58 毫米,木材密度每年增加 2.44 千克/立方米。经过一代(9 年),DBH 和木材密度的年遗传增益分别相当于 14.23 毫米和 21.97 千克/立方米。部署所选个体的克隆总是比部署后代/幼苗产生更高的额外遗传增益。从基因组选择中选择遗传材料并进行高位嫁接以早期克隆似乎是最佳选择。将基因组选择应用于针叶树育种计划,并结合高位嫁接和胚胎发生等部署工具,是加速遗传增益向森林传递的有力工具。