Scion (New Zealand Forest Research Institute Ltd.), 49 Sala Street, Rotorua, 3010, New Zealand.
BMC Plant Biol. 2020 May 11;20(1):205. doi: 10.1186/s12870-020-02403-6.
Many conifer breeding programs are paying increasing attention to breeding for resistance to needle disease due to the increasing importance of climate change. Phenotyping of traits related to resistance has many biological and temporal constraints that can often confound the ability to achieve reliable phenotypes and consequently, reliable genetic progress. The development of next generation sequencing platforms has also enabled implementation of genomic approaches in species lacking robust reference genomes. Genomic selection is, therefore, a promising strategy to overcome the constraints of needle disease phenotyping.
We found high accuracy in the prediction of genomic breeding values in the disease-related traits that were well characterized, reaching 0.975 for genotyped individuals and 0.587 for non-genotyped individuals. This compared well with pedigree-based accuracies of up to 0.746. Surprisingly, poorly phenotyped disease traits also showed very high accuracy in terms of correlation of predicted genomic breeding values with pedigree-based counterparts. However, this was likely caused by the fact that both were clustered around the population mean, while deviations from the population mean caused by genetic effects did not appear to be well described. Caution should therefore be taken with the interpretation of results in poorly phenotyped traits.
Implementation of genomic selection in this test population of Pinus radiata resulted in a relatively high prediction accuracy of needle loss due to Dothistroma septosporum compared with a pedigree-based approach. Using genomics to avoid biological/temporal constraints where phenotyping is reliable appears promising. Unsurprisingly, reliable phenotyping, resulting in good heritability estimates, is a fundamental requirement for the development of a reliable prediction model. Furthermore, our results are also specific to the single pathogen mating-type that is present in New Zealand, and may change with future incursion of other pathogen varieties. There is no doubt, however, that once a robust genomic prediction model is built, it will be invaluable to not only select for host tolerance, but for other economically important traits simultaneously. This tool will thus future-proof our forests by mitigating the risk of disease outbreaks induced by future changes in climate.
由于气候变化的重要性日益增加，许多针叶树育种计划越来越关注对针病抗性的选育。与抗性相关的性状表型具有许多生物学和时间限制，这常常会干扰获得可靠表型的能力，从而影响可靠的遗传进展。下一代测序平台的发展也使得在缺乏强大参考基因组的物种中实施基因组方法成为可能。因此，基因组选择是克服针病表型限制的一种很有前途的策略。
我们发现，在特征良好的与疾病相关的性状中，基因组育种值的预测准确性非常高，对于已基因型个体达到 0.975，对于非基因型个体达到 0.587。这与最高可达 0.746 的基于系谱的准确性相当。令人惊讶的是，表型较差的疾病性状在预测基因组育种值与基于系谱的对应值之间的相关性方面也表现出非常高的准确性。然而，这很可能是因为两者都聚集在群体平均值周围，而由遗传效应引起的偏离群体平均值的情况似乎没有得到很好的描述。因此，在表型较差的性状中，对结果的解释应谨慎。
在 Pinus radiata 的这个测试群体中实施基因组选择，与基于系谱的方法相比，导致 Dothistroma septosporum 的针损失的预测准确性相对较高。利用基因组学避免表型可靠的生物学/时间限制似乎很有前途。毫不奇怪，可靠的表型，导致良好的遗传力估计，是开发可靠预测模型的基本要求。此外，我们的结果也特定于新西兰存在的单一病原体交配型，并且可能随着其他病原体品种的未来入侵而改变。然而，毫无疑问，一旦建立了稳健的基因组预测模型，它不仅对于选择宿主耐受性，而且对于同时选择其他经济上重要的性状，都将是非常宝贵的。因此，该工具将通过减轻未来气候变化引起的疾病爆发的风险，使我们的森林免受未来的影响。
