Wang Zhenyu, Hao Jiongyu, Shi Xiaofan, Wang Qiaoqiao, Zhang Wuping, Li Fuzhong, Mur Luis A J, Han Yuanhuai, Hou Siyu, Han Jiwan, Sun Zhaoxia
College of Agricultural, Shanxi Agricultural University, Taigu, Shanxi, 030801, China.
College of Software, Shanxi Agricultural University, Taigu, Shanxi, 030801, China.
Plant Methods. 2024 Nov 5;20(1):168. doi: 10.1186/s13007-024-01295-z.
Foxtail millet [Setaria italica (L.) Beauv] is a C graminoid crop cultivated mainly in the arid and semiarid regions of China for more than 7000 years. Its grain highly nutritious and is rich in starch, protein, essential vitamins such as carotenoids, folate, and minerals. To expand the utilisation of foxtail millet, efficient and precise methods for dynamic phenotyping of its growth stages are needed. Traditional foxtail millet monitoring methods have high labour costs and are inefficient and inaccurate, impeding the precise evaluation of foxtail millet genotypic variation.
This study introduces a high-throughput imaging system (HIS) with advanced image processing techniques to enhance monitoring efficiency and data quality. The HIS can accurately extract a range of key growth feature parameters, such as plant height (PH), convex hull area (CHA), side projected area (SPA) and colour distribution, from foxtail millet images. Compared with traditional manual measurements, this HIS improved data quality and phenotyping of the key foxtail millet growth traits. High-throughput phenotyping combined with a genome-wide association study (GWAS) revealed genetic loci associated with dynamic growth traits, particularly plant height (PH), in foxtail millet. The loci were linked to genes involved in the gibberellic acid (GA) synthesis pathway related to PH.
The HIS developed in this study enables the efficient and dynamic monitoring of foxtail millet phenotypic traits. It significantly improves the quality of data obtained for phenotyping key growth traits. The integration of high-throughput phenotyping with GWAS provides new insights into the genetic underpinnings of dynamic growth traits, particularly plant height, by identifying associated genetic loci in the GA synthesis pathway. This methodological advancement opens new avenues for the precise phenotyping and exploration of genetic resources in foxtail millet, potentially enhancing its utilisation.
谷子[Setaria italica (L.) Beauv]是一种C4禾本科作物,在中国干旱和半干旱地区种植已有7000多年。其籽粒营养丰富,富含淀粉、蛋白质、类胡萝卜素、叶酸等必需维生素以及矿物质。为扩大谷子的利用,需要高效、精确的方法对其生长阶段进行动态表型分析。传统的谷子监测方法劳动成本高,效率低且不准确,阻碍了对谷子基因型变异的精确评估。
本研究引入了一种具有先进图像处理技术的高通量成像系统(HIS),以提高监测效率和数据质量。该HIS能够从谷子图像中准确提取一系列关键生长特征参数,如株高(PH)、凸包面积(CHA)、侧面投影面积(SPA)和颜色分布。与传统的人工测量相比,该HIS提高了数据质量和谷子关键生长性状的表型分析。高通量表型分析结合全基因组关联研究(GWAS)揭示了与谷子动态生长性状,特别是株高(PH)相关的遗传位点。这些位点与参与与株高相关的赤霉素(GA)合成途径的基因相连。
本研究开发的HIS能够对谷子表型性状进行高效动态监测。它显著提高了关键生长性状表型分析所获得的数据质量。高通量表型分析与GWAS的整合通过识别GA合成途径中的相关遗传位点,为动态生长性状,特别是株高的遗传基础提供了新的见解。这一方法学进展为谷子的精确表型分析和遗传资源探索开辟了新途径,有可能提高其利用率。