Developing growth-associated molecular markers via high-throughput phenotyping in spinach

High-throughput imaging and genomic information can be combined to optimize marker development. Genome-wide association studies identified loci associated with plant growth traits. We identified candidate genes associated with plant growth and development. Despite advances in sequencing for genotyping, the lack of rapid, accurate, and reproducible phenotyping platforms has hampered efforts to use genetic analysis to predict traits of interest. Therefore, the use of high-throughput systems to phenotype traits related to crop growth, yield, quality, and resistance to biotic and abiotic stresses has become a major asset for breeding. Here, we assessed the efficacy of unmanned aircraft system (UAS)-based high-throughput phenotyping to obtain data for molecular marker development for spinach (Spinacia oleracea L.) improvement. We used a UAS equipped with a red-green-blue sensor to capture raw images of 284 spinach accessions throughout the crop cycle. Processed images generated orthomosaic and digital surface models for estimating canopy cover, canopy volume, and excess greenness index models. In addition, we manually recorded the number of days to bolting. Genome-wide association studies against a single-nucleotide polymorphism (SNP) panel obtained by ddRADseq identified 99 SNPs significantly associated with growth parameters. Some of these SNPs are in transcription factor and stress-response genes with possible roles in plant growth and development. The results underscore the utility of combining aerial imaging and genomic data analysis to optimize marker development. This study lays the foundation for the use of UAS-based high-throughput phenotyping for the molecular breeding of spinach.

High-throughput imaging and genomic information can be combined to optimize marker development. Genome-wide association studies identified loci associated with plant growth traits. We identified candidate genes associated with plant growth and development. Despite advances in sequencing for genotyping, the lack of rapid, accurate, and reproducible phenotyping platforms has hampered efforts to use genetic analysis to predict traits of interest. Therefore, the use of high-throughput systems to phenotype traits related to crop growth, yield, quality, and resistance to biotic and abiotic stresses has become a major asset for breeding. Here, we assessed the efficacy of unmanned aircraft system (UAS)-based high-throughput phenotyping to obtain data for molecular marker development for spinach (Spinacia oleracea L.) improvement. We used a UAS equipped with a red-green-blue sensor to capture raw images of 284 spinach accessions throughout the crop cycle. Processed images generated orthomosaic and digital surface models for estimating canopy cover, canopy volume, and excess greenness index models. In addition, we manually recorded the number of days to bolting. Genome-wide association studies against a single-nucleotide polymorphism (SNP) panel obtained by ddRADseq identified 99 SNPs significantly associated with growth parameters. Some of these SNPs are in transcription factor and stress-response genes with possible roles in plant growth and development. The results underscore the utility of combining aerial imaging and genomic data analysis to optimize marker development. This study lays the foundation for the use of UAS-based high-throughput phenotyping for the molecular breeding of spinach.