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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识别和测试三种松树生长和物候的标记性状关联：对基因组预测的影响
DOI：
10.1101/2020.12.22.423987
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Perry A
中科院分区：
文献类型：
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作者：
Perry A
识别复杂性状下的加性遗传变异对于具有经济和/或生态价值的物种是重要的。特别是，DNA标记可以与性状变异，它们可以用来开发模型来预测表型作为未来的选择和保护计划的基础。在这里，SNPs与生长（高度和年生长量）和物候（萌芽和芽集）被确定在三个密切相关的松树物种，包括樟子松（苏格兰松）。采用混合线性模型（MLM）和多位点混合模型（MLMM）方法，使用基因分型阵列筛选编码区的20，795个SNP与性状变异的关联：位于111个位点的113个SNP与性状显著相关，其中大多数与萌芽或生长增量相关。森林植物。鉴定为与出芽显著相关的常见SNPs（MAF > 0.05）发现于仅参与生长和发育的基因puplant中，而与生长和出芽相关的SNPs位于参与生长和发育、对环境的响应以及在较小程度上参与生殖的基因puplant中。预测值估计使用模型的增长有非常显着的相关性与表型量化的aP。在苏格兰的两个地点（YA和GS）建立了森林群落共同环境试验，但只在其中一个地点（YA，2020年的高度：r = 0.376，p < 0.001）。用萌芽模型估计的预测值与其中一个田间点的萌芽持续时间（GS，2018年的持续时间：r = 0.242，p = 0.012）呈弱但显著相关，与另一个田间点的萌芽时间呈负相关（YA，第六阶段：r =-0.216，p = 0.033）。基因组预测使用模型的增长是更成功的比随机选择的方法选择高大的树木在这两个网站。这项研究提供了初步的支持，发展的预测模型的特点是感兴趣的林业和自然资源保护主义者，同时强调需要谨慎，当他们在不同的环境中生长的树木。
Identifying the additive genetic variation underlying complex traits is important for species of economic and/or ecological value. In particular, where DNA markers can be associated with trait variation they can be used to develop models to predict phenotypes as the basis of future selection and conservation programmes. Here, SNPs associated with growth (height and annual increment) and phenology (budburst and bud set) were identified in three closely related pine species includingPinus sylvestris(Scots pine). A genotyping array was used to screen 20,795 SNPs from coding regions for their association with trait variation using mixed linear model (MLM) and multilocus mixed model (MLMM) approaches: 113 SNPs located at 111 loci were significantly associated with the traits, with the majority associated with either budburst or growth increment inP. sylvestris. Common SNPs (MAF > 0.05) identified as significantly associated with bud set were found in genes putatively involved in only growth and development, whereas SNPs associated with growth and budburst were located in genes putatively involved in growth and development, response to environment and, to a lesser extent, reproduction. Predicted values estimated using the model for growth had highly significant correlations with phenotypes quantified in aP. sylvestriscommon environment experiment established at two sites in Scotland (YA and GS), but only at one of the sites (YA, height at 2020: r = 0.376,p< 0.001). Predicted values estimated with the model for budburst were found to be weakly but significantly correlated with duration of budburst at one of the field sites (GS, duration at 2018: r = 0.242,p= 0.012) and negatively associated with timing of budburst at the other (YA, stage six: r = −0.216,p= 0.033). Genomic prediction using the model for growth was more successful than random selection as a method of selecting tall trees at both sites. This study provides tentative support for the development of prediction models for traits that are of interest to both foresters and conservationists, while highlighting the need for caution when applying them to trees growing in different environments.