J Integr Plant Biol. ›› 2023, Vol. 65 ›› Issue (1): 117-132.DOI: 10.1111/jipb.13380

• Functional Omics and Systems Biology • Previous Articles     Next Articles

Time series canopy phenotyping enables the identification of genetic variants controlling dynamic phenotypes in soybean

Delin Li1†, Dong Bai1†, Yu Tian1†, Ying-Hui Li1*, Chaosen Zhao2, Qi Wang1,3, Shiyu Guo1,3, Yongzhe Gu1, Xiaoyan Luan4, Ruizhen Wang2, Jinliang Yang5, Malcolm J. Hawkesford6, James C. Schnable5*, Xiuliang Jin1*, Li-Juan Qiu1*   

  1. 1. The National Key Facility for Crop Gene Resources and Genetic ImprovementNFCRI)/Key Laboratory of Crop Gene Resource and Germplasm EnhancementMOA)/Key Laboratory of Soybean BiologyBeijing)MOA), Institute of Crop Science Chinese Academy of Agricultural Sciences Beijing 100081 China;
    2. Crops Research Institute of Jiangxi Academy of Agricultural Sciences Nanchang 330200 China;
    3. College of Agriculture Northeast Agricultural University Harbin 150030 China;
    4. Soybean Research Institute Heilongjiang Academy of Agricultural Sciences Harbin 150086 China;
    5. Department of Agronomy and Horticulture University of Nebraska-Lincoln Lincoln Nebraska 68583 USA;
    6. Plant Sciences Department, Rothamsted Research, West Common, Harpenden Hertfordshire AL5 2JQ UK
    These authors contributed equally to this work.
    *Correspondences: Ying‐Hui Li (liyinghui@caas.cn); James C. Schnable (schnable@unl.edu); Xiuliang Jin (jinxiuliang@caas.cn); Li-Juan Qiu (qiulijuan@caas.cn, Dr. Qiu is fully responsible for the distribution of the materials associated with this article)
  • Received:2022-06-30 Accepted:2022-10-10 Online:2022-10-11 Published:2023-01-01

Abstract: Advances in plant phenotyping technologies are dramatically reducing the marginal costs of collecting multiple phenotypic measurements across several time points. Yet, most current approaches and best statistical practices implemented to link genetic and phenotypic variation in plants have been developed in an era of single-time-point data. Here, we used time-series phenotypic data collected with an unmanned aircraft system for a large panel of soybean (Glycine max (L.) Merr.) varieties to identify previously uncharacterized loci. Specifically, we focused on the dissection of canopy coverage (CC) variation from this rich data set. We also inferred the speed of canopy closure, an additional dimension of CC, from the time-series data, as it may represent an important trait for weed control. Genome-wide association studies (GWASs) identified 35 loci exhibiting dynamic associations with CC across developmental stages. The time-series data enabled the identification of 10 known flowering time and plant height quantitative trait loci (QTLs) detected in previous studies of adult plants and the identification of novel QTLs influencing CC. These novel QTLs were disproportionately likely to act earlier in development, which may explain why they were missed in previous single-time-point studies. Moreover, this time-series data set contributed to the high accuracy of the GWASs, which we evaluated by permutation tests, as evidenced by the repeated identification of loci across multiple time points. Two novel loci showed evidence of adaptive selection during domestication, with different genotypes/haplotypes favored in different geographic regions. In summary, the time-series data, with soybean CC as an example, improved the accuracy and statistical power to dissect the genetic basis of traits and offered a promising opportunity for crop breeding with quantitative growth curves.

Key words: canopy coverage, dynamic regulation, GWAS, soybean, time series, unmanned aircraft system

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