Developing Growth-Associated Molecular Markers Via High-Throughput Phenotyping in Spinach

Area of Science:

• Plant Science
• Genomics
• Agricultural Technology

Background:

• High-throughput phenotyping is crucial for genetic analysis in crop improvement.
• Limitations in current phenotyping platforms hinder the use of genomic data for trait prediction.
• Unmanned aircraft systems (UAS) offer a promising solution for rapid and accurate phenotyping.

Purpose of the Study:

• To evaluate the effectiveness of UAS-based high-throughput phenotyping for molecular marker development in spinach (Spinacia oleracea L.).
• To integrate aerial imaging data with genomic information for spinach breeding.
• To identify genetic loci associated with spinach growth and development traits.

Main Methods:

• Utilized a UAS with a red-green-blue sensor to capture images of 284 spinach accessions.
• Processed images to create orthomosaic and digital surface models for estimating canopy cover, volume, and greenness.
• Conducted genome-wide association studies (GWAS) using a SNP panel derived from ddRADseq.
• Manually recorded days to bolting for each accession.

Main Results:

• Identified 99 single-nucleotide polymorphisms (SNPs) significantly associated with spinach growth parameters through GWAS.
• Detected candidate genes, including transcription factors and stress-response genes, linked to plant growth and development.
• Demonstrated the correlation between UAS-derived phenotyping data and genetic markers.

Conclusions:

• Combining UAS-based high-throughput phenotyping with genomic data analysis is effective for optimizing molecular marker development in spinach.
• This approach provides a foundation for advancing molecular breeding strategies in spinach.
• UAS technology significantly enhances the ability to phenotype crops for breeding purposes.