Identifying and testing marker-trait associations for growth and phenology in three pine species: Implications for genomic prediction.
Identifying and testing marker-trait associations for growth and phenology in three pine species: Implications for genomic prediction.
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DOI：
10.1111/eva.13345
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发表时间：
2022-03
影响因子：
4.1
通讯作者：
Cavers S
中科院分区：
文献类型：
--
作者：
Perry A;Wachowiak W;Beaton J;Iason G;Cottrell J;Cavers S
在树种，基因组预测提供了潜在的预测成熟的性状值在早期生长阶段，如果强大的标记性状协会可以确定。在这里，我们应用一种新的多物种的方法，从一个新的基因分型阵列的基因型，基于20,795单核苷酸多态性（SNP）从三个密切相关的松树种（樟子松，松钩和松mugo），测试与生长和物候数据从一个共同的花园研究的关联。然后测试使用显著相关的SNP构建的预测模型，并将其应用于欧洲赤杨的独立多点田间试验，并评估预测性状值的能力。118个SNPs与松树的性状有显著相关性。与出芽相关的常见SNPs（MAF > 0.05）仅在参与生长和发育的基因puplant中发现，而与生长和出芽相关的SNPs也位于参与对环境的响应的基因puplant中，并且在较小程度上，生殖。在两个独立站点之一，我们开发的模型在预测值和观察到的高度数据之间产生了高度显著的相关性（YA，高度2020：r = 0.376，p < 0.001）。用我们的萌芽模型估计的预测值与其中一个地点的萌芽持续时间呈弱正相关（GS，2015：r = 0.204，p = 0.034; 2018：r = 0.205，p = 0.034-0.037），与另一个地点的萌芽时间呈负相关（YA：r =-0.202，p = 0.046）。基因组预测导致选择的树木，其平均高度高于每个站点的平均值。我们的研究结果提供了初步的支持预测模型的能力，预测性状值的树木，同时强调需要谨慎地将它们应用到生长在不同环境中的树木。
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 (Pinus sylvestris, Pinus uncinata and Pinus mugo), 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 P. sylvestris 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: r = 0.376, p < 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: r = 0.204, p = 0.034; 2018: r = 0.205, p = 0.034–0.037) and negatively associated with budburst timing at the other (YA: r = −0.202, p = 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.
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