Chen Dijun, Neumann Kerstin, Friedel Swetlana, Kilian Benjamin, Chen Ming, Altmann Thomas, Klukas Christian
Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), D-06466 Gatersleben, Germany Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, P.R. China.
Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), D-06466 Gatersleben, Germany.
Plant Cell. 2014 Dec;26(12):4636-55. doi: 10.1105/tpc.114.129601. Epub 2014 Dec 11.
Significantly improved crop varieties are urgently needed to feed the rapidly growing human population under changing climates. While genome sequence information and excellent genomic tools are in place for major crop species, the systematic quantification of phenotypic traits or components thereof in a high-throughput fashion remains an enormous challenge. In order to help bridge the genotype to phenotype gap, we developed a comprehensive framework for high-throughput phenotype data analysis in plants, which enables the extraction of an extensive list of phenotypic traits from nondestructive plant imaging over time. As a proof of concept, we investigated the phenotypic components of the drought responses of 18 different barley (Hordeum vulgare) cultivars during vegetative growth. We analyzed dynamic properties of trait expression over growth time based on 54 representative phenotypic features. The data are highly valuable to understand plant development and to further quantify growth and crop performance features. We tested various growth models to predict plant biomass accumulation and identified several relevant parameters that support biological interpretation of plant growth and stress tolerance. These image-based traits and model-derived parameters are promising for subsequent genetic mapping to uncover the genetic basis of complex agronomic traits. Taken together, we anticipate that the analytical framework and analysis results presented here will be useful to advance our views of phenotypic trait components underlying plant development and their responses to environmental cues.
在气候变化的背景下，迫切需要显著改良的作物品种来养活快速增长的人口。虽然主要作物物种已有基因组序列信息和出色的基因组工具，但以高通量方式对表型性状或其组成部分进行系统量化仍然是一项巨大挑战。为了帮助弥合基因型与表型之间的差距，我们开发了一个用于植物高通量表型数据分析的综合框架，该框架能够从随时间的无损植物成像中提取大量表型性状列表。作为概念验证，我们研究了18个不同大麦（Hordeum vulgare）品种营养生长期间干旱响应的表型组成部分。我们基于54个代表性表型特征分析了生长时间内性状表达的动态特性。这些数据对于理解植物发育以及进一步量化生长和作物性能特征具有很高的价值。我们测试了各种生长模型来预测植物生物量积累，并确定了几个支持植物生长和胁迫耐受性生物学解释的相关参数。这些基于图像的性状和模型衍生参数有望用于后续的遗传图谱研究，以揭示复杂农艺性状的遗传基础。综上所述，我们预计本文提出的分析框架和分析结果将有助于推进我们对植物发育潜在表型性状组成部分及其对环境线索响应的认识。
