Co-Innovation Center for Sustainable Forestry in South China, College of Forestry, Nanjing Forestry University, Nanjing, Jiangsu Province, China.
School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China.
PLoS One. 2021 Oct 28;16(10):e0259278. doi: 10.1371/journal.pone.0259278. eCollection 2021.
Leaf morphology exhibits tremendous diversity between and within species, and is likely related to adaptation to environmental factors. Most poplar species are of great economic and ecological values and their leaf morphology can be a good predictor for wood productivity and environment adaptation. It is important to understand the genetic mechanism behind variation in leaf shape. Although some initial efforts have been made to identify quantitative trait loci (QTLs) for poplar leaf traits, more effort needs to be expended to unravel the polygenic architecture of the complex traits of leaf shape. Here, we performed a genome-wide association analysis (GWAS) of poplar leaf shape traits in a randomized complete block design with clones from F1 hybrids of Populus deltoides and Populus simonii. A total of 35 SNPs were identified as significantly associated with the multiple traits of a moderate number of regular polar radii between the leaf centroid and its edge points, which could represent the leaf shape, based on a multivariate linear mixed model. In contrast, the univariate linear mixed model was applied as single leaf traits for GWAS, leading to genomic inflation; thus, no significant SNPs were detected for leaf length, measures of leaf width, leaf area, or the ratio of leaf length to leaf width under genomic control. Investigation of the candidate genes showed that most flanking regions of the significant leaf shape-associated SNPs harbored genes that were related to leaf growth and development and to the regulation of leaf morphology. The combined use of the traditional experimental design and the multivariate linear mixed model could greatly improve the power in GWAS because the multiple trait data from a large number of individuals with replicates of clones were incorporated into the statistical model. The results of this study will enhance the understanding of the genetic mechanism of leaf shape variation in Populus. In addition, a moderate number of regular leaf polar radii can largely represent the leaf shape and can be used for GWAS of such a complicated trait in Populus, instead of the higher-dimensional regular radius data that were previously considered to well represent leaf shape.
叶片形态在物种间和种内表现出巨大的多样性,可能与适应环境因素有关。大多数杨树物种具有巨大的经济和生态价值,其叶片形态可以很好地预测木材生产力和环境适应性。了解叶片形状变异的遗传机制非常重要。尽管已经初步努力鉴定杨树叶片性状的数量性状位点 (QTL),但需要进一步努力来揭示叶片形状这一复杂性状的多基因结构。在这里,我们在随机完全区组设计中对来自 Populus deltoides 和 Populus simonii F1 杂种的无性系的杨树叶片形状性状进行了全基因组关联分析 (GWAS)。基于多元线性混合模型,共鉴定出 35 个 SNP 与叶片中心点和边缘点之间的多个规则极半径的多个性状显著相关,这些性状可以代表叶片形状。相比之下,单叶性状的单变量线性混合模型应用于 GWAS 会导致基因组膨胀,因此在基因组控制下,没有检测到叶片长度、叶片宽度测量值、叶片面积或叶片长度与宽度之比的显著 SNP。候选基因的研究表明,显著叶片形状相关 SNP 的侧翼区域大多包含与叶片生长和发育以及叶片形态调控相关的基因。传统实验设计与多元线性混合模型的结合使用可以大大提高 GWAS 的功效,因为大量个体的多个性状数据和无性系的重复数据被纳入统计模型。本研究的结果将增强对杨树叶片形状变异遗传机制的理解。此外,数量适中的规则叶片极半径可以很大程度上代表叶片形状,并可用于杨树等复杂性状的 GWAS,而不是以前认为能很好地代表叶片形状的高维规则半径数据。