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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Sustainable Perennial Crops Laboratory » Research » Publications at this Location » Publication #422303
Research Project: Development of Pathogen- and Plant-Based Genetic Tools and Disease Mitigation Methods for Tropical Perennial Crops

Location: Sustainable Perennial Crops Laboratory

Title: Integrative analysis of seed morphology, geographic origin, and genetic structure in Medicago with implications for breeding and conservation

Author
item LIM, SEUNGHYUN - Orise Fellow
item Park, Sunchung
item Baek, Insuck
item BOTKIN, JACOB - University Of Minnesota
item Jang, Jae Hee
item HONG, SEOK MIN - Ulsan National Institute Of Science And Technology (UNIST)
item Irish, Brian
item Kim, Moon
item Meinhardt, Lyndel
item Curtin, Shaun
item Ahn, Ezekiel

Submitted to: BMC Plant Biology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/25/2025
Publication Date: 3/3/2025
Citation: Lim, S., Park, S., Baek, I., Botkin, J., Jang, J., Hong, S., Irish, B.M., Kim, M.S., Meinhardt, L.W., Curtin, S.J., Ahn, E.J. 2025. Integrative analysis of seed morphology, geographic origin, and genetic structure in Medicago with implications for breeding and conservation. BMC Plant Biology. 25:274. https://doi.org/10.1186/s12870-025-06304-4.
DOI: https://doi.org/10.1186/s12870-025-06304-4

Interpretive Summary: Alfalfa (Medicago spp.)is an essential crop for livestock feed around the world, making it important to understand the factors that affect its growth and yield for sustainable agriculture and food production. This study examined a large collection of alfalfa seeds to explore the connections between their physical traits, genetic makeup, and geographic origins. We employed machine learning to analyze and categorize the seeds based on these factors. This study revealed that Medicago seeds from different regions possess unique characteristics, likely due to their adaptation to local environments. Furthermore, we identified specific genes associated with their originated regions, which could potentially be used as a marker to identify their origin. This information can assist breeders in selecting and improving alfalfa varieties tailored to specific regions, resulting in better yields and more sustainable agricultural practices.

Technical Abstract: Alfalfa (Medicago spp.) exhibits significant variation in seed morphology, which is potentially linked to both genetic and environmental factors. Understanding the relationship between seed traits, genetic diversity, and geographic origin is crucial for effective breeding and germplasm conservation. This study used machine learning and genome-wide association studies (GWAS) to analyze seed morphology, geographic origin, and genetic structure in a diverse collection of 318 alfalfa accessions. Machine learning models successfully classified accessions based on seed morphology and geographic origin, revealing distinct groups corresponding to different regions. GWAS identified significant genetic loci associated with geographic origin. The study also pinpointed candidate genes, including those potentially involved in stress response, genome stability, and seed development. This research provides valuable insights into the genetic basis of seed morphology and geographic adaptation in alfalfa, which can be used for breeding and germplasm conservation. The integrated use of machine learning and GWAS offers a powerful approach for analyzing complex phenotypic and genotypic data in crop species, benefiting researchers and breeders.