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