Quantitative Life Science Initiative, Center for Plant Science Innovation, Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska 68588.
Department of Applied Statistics and Operations Research, Bowling Green State University, Bowling Green, Ohio 43403.
Plant Physiol. 2020 Aug;183(4):1898-1909. doi: 10.1104/pp.20.00277. Epub 2020 May 27.
The phenotypes of plants develop over time and change in response to the environment. New engineering and computer vision technologies track these phenotypic changes. Identifying the genetic loci regulating differences in the pattern of phenotypic change remains challenging. This study used functional principal component analysis (FPCA) to achieve this aim. Time series phenotype data were collected from a sorghum () diversity panel using a number of technologies including conventional color photography and hyperspectral imaging. This imaging lasted for 37 d and centered on reproductive transition. A new higher density marker set was generated for the same population. Several genes known to control trait variation in sorghum have been previously cloned and characterized. These genes were not confidently identified in genome-wide association analyses at single time points. However, FPCA successfully identified the same known and characterized genes. FPCA analyses partitioned the role these genes play in controlling phenotypes. Partitioning was consistent with the known molecular function of the individual cloned genes. These data demonstrate that FPCA-based genome-wide association studies can enable robust time series mapping analyses in a wide range of contexts. Moreover, time series analysis can increase the accuracy and power of quantitative genetic analyses.
植物的表型随着时间的推移而发展，并对环境变化做出响应。新的工程和计算机视觉技术可以跟踪这些表型变化。然而，确定调节表型变化模式差异的遗传基因座仍然具有挑战性。本研究使用功能主成分分析（FPCA）来实现这一目标。利用包括传统彩色摄影和高光谱成像在内的多种技术，从高粱（ Sorghum bicolor ）多样性群体中收集了时间序列表型数据。这项成像研究持续了 37 天，集中在生殖转变时期。为同一群体生成了新的更高密度标记集。先前已经克隆并表征了一些已知控制高粱性状变异的基因。然而，在单个时间点的全基因组关联分析中，这些基因并未被准确识别。但是，FPCA 成功地识别出了相同的已知和已表征的基因。FPCA 分析将这些基因在控制表型方面的作用进行了划分。划分结果与单个克隆基因的已知分子功能一致。这些数据表明，基于 FPCA 的全基因组关联研究可以在广泛的背景下实现稳健的时间序列映射分析。此外，时间序列分析可以提高数量遗传分析的准确性和功效。
