Large-scale GWAS integrated with image-based phenotyping reveals loci and trait-associated markers for Perilla frutescens seed traits.
4 affiliations
- 1Upland Crop Breeding Division, Rural Development Administration, National Institute of Crop and Food Science, Miryang, 50424, Republic of Korea.
- 2Department of Crop Science, College of Sanghuh Life Science, Konkuk University, Seoul, 05029, Republic of Korea.
- 3Plan Management Division, Rural Development Administration, National Institute of Agricultural Sciences, Jeonju, 54874, Republic of Korea.
- 4Upland Crop Breeding Division, Rural Development Administration, National Institute of Crop and Food Science, Miryang, 50424, Republic of Korea. [email protected].
Abstract
Background
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.Results
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.Conclusions
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.
Related
- Integrated phenotyping reveals coordinated physiological responses to cold, freezing, and recovery in Ramonda resurrection plants
- Integrated Analysis of Grapevine Responses to Winter Pruning Reveals Temperature‐Associated, Polyphenol‐Rich Xylem Occlusions
- Integrated Multi-Omics Analysis Reveals Molecular Signatures Associated with Growth Divergence in Mandarin Fish (Siniperca chuatsi).
- Integrated lncRNA and mRNA Transcriptome Analysis Reveals Candidate Regulatory Networks Associated with Postnatal Longissimus Dorsi Muscle Development in Hanper Sheep.
- Integrated Multiomic Analysis Reveals an EBV‐Associated MIF–SPP1+ Macrophage Axis Linking Tumor Stemness and Immune Dysfunction in Nasopharyngeal Carcinoma
BibTeX
@article{an2026largescale,
title = {{Large-scale GWAS integrated with image-based phenotyping reveals loci and trait-associated markers for Perilla frutescens seed traits.}},
author = {Yeon Ju An and Eunae Yoo and Jung-In Kim and Sang Woo Kim and Soo-Kwon Park and Yo-Han Yoo and Girim Park and Myoung Hui Lee and Seung-Hyun Kim and Eunsoo Lee},
journal = {BMC plant biology},
year = {2026},
month = jul,
doi = {10.1186/s12870-026-09596-2},
url = {https://europepmc.org/article/MED/42902162},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Large-scale GWAS integrated with image-based phenotyping reveals loci and trait-associated markers for Perilla frutescens seed traits.
AU - Yeon Ju An
AU - Eunae Yoo
AU - Jung-In Kim
AU - Sang Woo Kim
AU - Soo-Kwon Park
AU - Yo-Han Yoo
AU - Girim Park
AU - Myoung Hui Lee
AU - Seung-Hyun Kim
AU - Eunsoo Lee
PY - 2026
DA - 2026/07/01
JO - BMC plant biology
DO - 10.1186/s12870-026-09596-2
UR - https://europepmc.org/article/MED/42902162
AB - Background 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.Results 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.Conclusions 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.
SN - 1471-2229
ER -