Machine learning assisted dynamic phenotypes and genomic variants help understand the ecotype divergence in rapeseed.

Three ecotypes of rapeseed, winter, spring, and semi-winter, have been formed to enable the plant to adapt to different geographic areas. Although several major loci had been found to contribute to the flowering divergence, the genomic footprints and associated dynamic plant architecture in the vegetative growth stage underlying the ecotype divergence remain largely unknown in rapeseed. Here, a set of 41 dynamic i-traits and 30 growth-related traits were obtained by high-throughput phenotyping of 171 diverse rapeseed accessions. Large phenotypic variation and high broad-sense heritability were observed for these i-traits across all developmental stages. Of these, 19 i-traits were identified to contribute to the divergence of three ecotypes using random forest model of machine learning approach, and could serve as biomarkers to predict the ecotype. Furthermore, we analyzed genomic variations of the population, QTL information of all dynamic i-traits, and genomic basis of the ecotype differentiation. It was found that 213, 237, and 184 QTLs responsible for the differentiated i-traits overlapped with the signals of ecotype divergence between winter and spring, winter and semi-winter, and spring and semi-winter, respectively. Of which, there were four common divergent regions between winter and spring/semi-winter and the strongest divergent regions between spring and semi-winter were found to overlap with the dynamic QTLs responsible for the differentiated i-traits at multiple growth stages. Our study provides important insights into the divergence of plant architecture in the vegetative growth stage among the three ecotypes, which was contributed to by the genetic differentiation, and might contribute to environmental adaption and yield improvement.