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Abstract：
A central goal of biology is to understand how genetic variation produces phenotypic variation, which has been described as a genotype to phenotype (G to P) map. The plant form is continuously shaped by intrinsic developmental and extrinsic environmental inputs, and therefore plant phenomes are highly multivariate and require comprehensive approaches to fully quantify. Yet a common assumption in plant phenotyping efforts is that a few pre-selected measurements can adequately describe the relevant phenome space. Our poor understanding of the genetic basis of root system architecture is at least partially a result of this incongruence. Root systems are complex 3D structures that are most often studied as 2D representations measured with relatively simple univariate traits. In prior work, we showed that persistent homology, a topological data analysis method that does not pre-suppose the salient features of the data, could expand the phenotypic trait space and identify new G to P relations from a commonly used 2D root phenotyping platform. Here we extend the work to entire 3D root system architectures of maize seedlings from a mapping population that was designed to understand the genetic basis of maize-nitrogen relations. Using a panel of 84 univariate traits, persistent homology methods developed for 3D branching, and multivariate vectors of the collective trait space, we found that each method captures distinct information about root system variation as evidenced by the majority of non-overlapping QTL, and hence that root phenotypic trait space is not easily exhausted. The work offers a data-driven method for assessing 3D root structure and highlights the importance of non-canonical phenotypes for more accurate representations of the G to P map.
摘要：
生物学的中心目标是了解遗传变异如何产生表型变异，已被描述为基因型到表型(G到P)图。植物形态由内在发育和外在环境输入不断塑造，因此，植物表型是高度多变量的，需要全面的方法来完全量化。然而，植物表型鉴定工作中的一个常见假设是，一些预先选择的测量可以充分描述相关的表型空间。我们对根系结构的遗传基础了解不足至少部分是这种不一致的结果。根系是复杂的3D结构，通常以相对简单的单变量特征测量的2D表示进行研究。在之前的工作中,我们证明了持续的同源性，一种拓扑数据分析方法，不预先假定数据的显著特征，可以扩展表型性状空间，并从常用的2D根表型平台识别新的G到P关系。在这里，我们将工作扩展到来自作图种群的玉米幼苗的整个3D根系结构，该作图种群旨在了解玉米-氮关系的遗传基础。使用84个单变量性状的面板，为3D分支开发的持续同源方法，和集体特征空间的多元向量，我们发现每种方法都能捕获有关根系变异的不同信息，大多数非重叠QTL证明了这一点，因此，根表型性状空间不容易耗尽。这项工作提供了一种数据驱动的方法来评估3D根结构，并强调了非规范表型对于更准确地表示G到P图的重要性。
