Perry Annika, Wachowiak Witold, Beaton Joan, Iason Glenn, Cottrell Joan, Cavers Stephen
UK Centre for Ecology & Hydrology Edinburgh Penicuik UK.
Institute of Environmental Biology Faculty of Biology Adam Mickiewicz University in Poznań Poznań Poland.
Evol Appl. 2022 Feb 10;15(2):330-348. doi: 10.1111/eva.13345. eCollection 2022 Feb.
In tree species, genomic prediction offers the potential to forecast mature trait values in early growth stages, if robust marker-trait associations can be identified. Here we apply a novel multispecies approach using genotypes from a new genotyping array, based on 20,795 single nucleotide polymorphisms (SNPs) from three closely related pine species (, and ), to test for associations with growth and phenology data from a common garden study. Predictive models constructed using significantly associated SNPs were then tested and applied to an independent multisite field trial of . and the capability to predict trait values was evaluated. One hundred and eighteen SNPs showed significant associations with the traits in the pine species. Common SNPs (MAF > 0.05) associated with bud set were only found in genes putatively involved in growth and development, whereas those associated with growth and budburst were also located in genes putatively involved in response to environment and, to a lesser extent, reproduction. At one of the two independent sites, the model we developed produced highly significant correlations between predicted values and observed height data (YA, height 2020: = 0.376, < 0.001). Predicted values estimated with our budburst model were weakly but positively correlated with duration of budburst at one of the sites (GS, 2015: = 0.204, = 0.034; 2018: = 0.205, = 0.034-0.037) and negatively associated with budburst timing at the other (YA: = -0.202, = 0.046). Genomic prediction resulted in the selection of sets of trees whose mean height was taller than the average for each site. Our results provide tentative support for the capability of prediction models to forecast trait values in trees, while highlighting the need for caution in applying them to trees grown in different environments.
在树种中，如果能够识别出稳健的标记-性状关联，基因组预测就有可能在早期生长阶段预测成熟性状值。在这里，我们应用一种新颖的多物种方法，使用来自新基因分型阵列的基因型，该阵列基于三个密切相关的松树物种（、和）的20,795个单核苷酸多态性（SNP），来测试与来自共同园圃研究的生长和物候数据的关联。然后，使用显著相关的SNP构建预测模型，并将其应用于的独立多地点田间试验。并评估了预测性状值的能力。118个SNP与松树物种的性状显示出显著关联。与芽形成相关的常见SNP（MAF>0.05）仅在推测参与生长和发育的基因中发现，而与生长和芽萌发相关的SNP也位于推测参与环境响应以及在较小程度上参与繁殖的基因中。在两个独立地点之一，我们开发的模型在预测值与观测到的高度数据之间产生了高度显著的相关性（YA，2020年高度：=0.376，<0.001）。我们的芽萌发模型估计的预测值与其中一个地点的芽萌发持续时间呈弱但正相关（GS，2015年：=0.204，=0.034；2018年：=0.205，=0.034 - 0.037），而与另一个地点的芽萌发时间呈负相关（YA：= - 0.202，=0.046）。基因组预测导致选择了平均高度高于每个地点平均值的树木群体。我们的结果为预测模型预测树木性状值的能力提供了初步支持，同时强调在将其应用于生长在不同环境中的树木时需要谨慎。
