Understanding the genetic basis of dynamic plant phenotypes has largely been limited because of a lack of space and labor resources needed to record dynamic traits, often destructively, for a large number of genotypes. However, the recent advent of image‐based phenotyping platforms has provided the plant science community with an effective means to nondestructively evaluate morphological, developmental, and physiological processes at regular, frequent intervals for a large number of plants throughout development. The statistical frameworks typically used for genetic analyses (e.g., genome‐wide association mapping, linkage mapping, and genomic prediction) in plant breeding and genetics are not particularly amenable for repeated measurements. Random regression (RR) models are routinely used in animal breeding for the genetic analysis of longitudinal traits and provide a robust framework for modeling trait trajectories and performing genetic analysis simultaneously. We recently used a RR approach for genomic prediction of shoot growth trajectories in rice (Oryza sativa L.) from 33,674 single nucleotide polymorphisms. In this study, we have extended this approach for genetic inference by leveraging genomic breeding values derived from RR models for rice shoot growth during early vegetative development. This approach provides improvements over conventional single time point analyses for discovering loci associated with shoot growth trajectories. The RR approach uncovers persistent as well as time‐specific transient quantitative trait loci. This methodology can be widely applied to understand the genetic architecture of other complex polygenic traits with repeated measurements.

中文翻译：

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利用从随机回归模型获得的育种值对纵向性状进行遗传推断。

对动态植物表型的遗传基础的了解在很大程度上受到限制，原因是缺乏足够的空间和劳动力资源来记录许多基因型的动态性状（通常是破坏性的）。但是，最近基于图像的表型分型平台的出现为植物科学界提供了一种有效的手段，可以在整个开发过程中定期，频繁地无损地评估形态，发育和生理过程。植物育种和遗传学中通常用于遗传分析（例如，全基因组关联图谱，连锁图谱和基因组预测）的统计框架并不是特别适合重复测量。随机回归（RR）模型通常在动物育种中用于纵向性状的遗传分析，并为建模性状轨迹和同时进行遗传分析提供了可靠的框架。最近，我们使用RR方法对水稻芽生长轨迹的基因组预测（来自33,674个单核苷酸多态性的Oryza sativa L. 在这项研究中，我们通过利用从RR模型获得的基因组育种值来促进早期营养发育期间的水稻新芽生长，从而扩展了这种遗传推断方法。该方法相对于用于发现与枝条生长轨迹相关的基因座的常规单时间点分析提供了改进。RR方法揭示了持续的以及特定时间的瞬时定量性状位点。该方法可广泛应用于通过重复测量来了解其他复杂的多基因性状的遗传结构。