Large-scale GWAS integrated with image-based phenotyping reveals loci and trait-associated markers for Perilla frutescens seed traits

Abstract

Perilla ( Perilla frutescens ) is an important oilseed crop in East Asia with high nutritional and economic value. Seed size and seed coat color are key agronomic traits influencing yield, oil quality, and market preference. However, genetic studies in perilla remain limited by small population sizes and low-throughput phenotyping, restricting their application in breeding. A large-scale genome-wide association study (GWAS) was conducted by integrating high-throughput image-based phenotyping in a panel of 493 perilla accessions. Measurements of seed morphology, including area, perimeter, and length, and color traits (RGB components) were obtained. Genotyping-by-sequencing generated high-quality single-nucleotide polymorphism (SNP) datasets, and GWAS was performed using four statistical models (general linear model, mixed linear model, FarmCPU, and BLINK). A total of 44 significant trait-SNP associations were identified, corresponding to 20 unique SNPs, as several SNPs (including those on chromosomes 6, 10, 15, and 17) were associated with multiple correlated traits. Linkage disequilibrium-based analysis showed candidate genes involved in phenylpropanoid metabolism and carbohydrate pathways. Predicted protein-altering variants, including non-synonymous and stop-gained mutations, were detected in key genes. Derived cleaved amplified polymorphic sequence markers developed near peak SNPs distinguished phenotypic differences between allelic groups, demonstrating their effectiveness for trait differentiation. This study represents the first large-scale GWAS integrating image-based phenotyping for seed traits in perilla and provides candidate dCAPS markers with potential applicability to marker-assisted selection, pending validation in independent breeding populations. These findings offer valuable genetic resources and a practical framework for molecular breeding and crop improvement in perilla.

Plant phenotyping relevance

大規模GWASに統合された高スループット画像ベース表現型解析が中心で、種子形態・色形質の抽出方法を実質的に適用しているため。

Code and data availability

The article describes image-based seed phenotyping and GWAS for 493 Perilla accessions, but no public phenotype dataset, seed images, analysis code, or trained models are deposited. The only public deposit mentioned is a whole-genome resequencing BioProject (PRJNA1276257), which is molecular omics data, not a phenotyp/

No evidence-backed public reproduction asset is currently recorded.