School of Life Sciences, University of Warwick, Coventry CV4 7AL, United Kingdom; email:
Annu Rev Genet. 2020 Nov 23;54:417-437. doi: 10.1146/annurev-genet-022620-094553. Epub 2020 Sep 4.
A transition from qualitative to quantitative descriptors of morphology has been facilitated through the growing field of morphometrics, representing the conversion of shapes and patterns into numbers. The analysis of plant form at the macromorphological scale using morphometric approaches quantifies what is commonly referred to as a phenotype. Quantitative phenotypic analysis of individuals with contrasting genotypes in turn provides a means to establish links between genes and shapes. The path from a gene to a morphological phenotype is, however, not direct, with instructive information progressing both across multiple scales of biological complexity and through nonintuitive feedback, such as mechanical signals. In this review, we explore morphometric approaches used to perform whole-plant phenotyping and quantitative approaches in capture processes in the mesoscales, which bridge the gaps between genes and shapes in plants. Quantitative frameworks involving both the computational simulation and the discretization of data into networks provide a putative path to predicting emergent shape from underlying genetic programs.
通过形态计量学这一不断发展的领域，已经可以实现从形态学的定性描述到定量描述的转变，即将形状和模式转化为数字。使用形态计量学方法对宏观形态尺度上的植物形态进行分析，对通常所说的表型进行量化。对具有不同基因型的个体进行定量表型分析，反过来又为建立基因与形状之间的联系提供了一种手段。然而，从一个基因到一个形态表型的路径并不是直接的，指导性信息不仅跨越多个生物复杂性尺度传递，而且还通过机械信号等非直观反馈传递。在这篇综述中，我们探讨了用于进行全植物表型分析的形态计量学方法，以及在中尺度上捕获过程中使用的定量方法，这些方法在植物中的基因和形状之间架起了桥梁。涉及计算模拟和数据离散化为网络的定量框架为从潜在的遗传程序中预测新兴形状提供了一个可能的途径。
