Queensland Department of Agriculture and Fisheries, Maroochy Research Facility, 47 Mayers Road, Nambour, QLD, 4560, Australia.
Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Maroochy Research Facility, 47 Mayers Road, Nambour, QLD, 4560, Australia.
BMC Genomics. 2021 May 20;22(1):370. doi: 10.1186/s12864-021-07694-z.
Improving yield prediction and selection efficiency is critical for tree breeding. This is vital for macadamia trees with the time from crossing to production of new cultivars being almost a quarter of a century. Genomic selection (GS) is a useful tool in plant breeding, particularly with perennial trees, contributing to an increased rate of genetic gain and reducing the length of the breeding cycle. We investigated the potential of using GS methods to increase genetic gain and accelerate selection efficiency in the Australian macadamia breeding program with comparison to traditional breeding methods. This study evaluated the prediction accuracy of GS in a macadamia breeding population of 295 full-sib progeny from 32 families (29 parents, reciprocals combined), along with a subset of parents. Historical yield data for tree ages 5 to 8 years were used in the study, along with a set of 4113 SNP markers. The traits of focus were average nut yield from tree ages 5 to 8 years and yield stability, measured as the standard deviation of yield over these 4 years. GBLUP GS models were used to obtain genomic estimated breeding values for each genotype, with a five-fold cross-validation method and two techniques: prediction across related populations and prediction across unrelated populations.
Narrow-sense heritability of yield and yield stability was low (h = 0.30 and 0.04, respectively). Prediction accuracy for yield was 0.57 for predictions across related populations and 0.14 when predicted across unrelated populations. Accuracy of prediction of yield stability was high (r = 0.79) for predictions across related populations. Predicted genetic gain of yield using GS in related populations was 474 g/year, more than double that of traditional breeding methods (226 g/year), due to the halving of generation length from 8 to 4 years.
The results of this study indicate that the incorporation of GS for yield into the Australian macadamia breeding program may accelerate genetic gain due to reduction in generation length, though the cost of genotyping appears to be a constraint at present.
提高产量预测和选择效率对树木育种至关重要。对于澳洲坚果树来说,这一点至关重要,因为从杂交到培育出新品种的时间几乎长达四分之一世纪。基因组选择(GS)是植物育种的有用工具,特别是对于多年生树木,有助于提高遗传增益率并缩短育种周期。我们研究了使用 GS 方法来增加遗传增益并提高澳大利亚澳洲坚果育种计划的选择效率的潜力,并与传统育种方法进行了比较。本研究评估了 GS 在一个由 32 个家系(29 个亲本,组合的反交)的 295 个全同胞后代的澳洲坚果育种群体中的预测准确性,其中包括一个亲本子集。该研究使用了树龄 5 至 8 年的历史产量数据,以及一组 4113 个 SNP 标记。重点关注的性状是树龄 5 至 8 年的平均坚果产量和产量稳定性,以这 4 年的产量标准差来衡量。使用 GBLUP GS 模型为每个基因型获得基因组估计育种值,使用五重交叉验证方法和两种技术:跨相关群体的预测和跨不相关群体的预测。
产量和产量稳定性的狭义遗传力较低(h分别为 0.30 和 0.04)。跨相关群体的产量预测准确率为 0.57,跨不相关群体的预测准确率为 0.14。跨相关群体预测产量稳定性的准确率较高(r=0.79)。在相关群体中使用 GS 预测产量的遗传增益为 474g/年,是传统育种方法(226g/年)的两倍多,这是由于世代长度从 8 年缩短至 4 年。
本研究结果表明,由于世代长度缩短,将 GS 用于产量的澳洲坚果育种计划可能会加速遗传增益,尽管目前基因分型的成本似乎是一个限制因素。