Approach of Genetic Diversity of Lippia alba (Mill) and Petiveria alliacea L.: Medicinal Plants of Colombia

• Published in: Plant molecular biology reporter
• Main Authors: Tarazona-Pulido, Lina, Rugeles-Silva, Paula Andrea, Cardona Tobar, Karen Melissa, Díaz-Ariza, Lucía Ana, Muñoz Florez, Jaime Eduardo, López-Álvarez, Diana
• Format: Journal Article
• Language: English
• Published: 29.08.2024
• ISSN: 0735-9640; 1572-9818
• DOI: 10.1007/s11105-024-01484-z

Abstract The medicinal Lippia alba and Petiveria alliacea , originating from Central and South America, exhibit a wide range of beneficial properties, including antimicrobial, antifungal, anti-inflammatory, antitumor, analgesic, and antibacterial effects. However, little is known about their population structure and genetic diversity, which may hinder the establishment of their cultivation in different regions of Colombia. In this study, we conducted a comprehensive analysis of the genetic diversity and population structure of 17 samples of L. alba from the departments of Tolima, Valle del Cauca, and Putumayo, as well as 31 samples of P. alliacea from the departments of Cundinamarca, Boyacá, Tolima, and Valle del Cauca. We employed restriction-site associated DNA sequencing (RADseq) with the enzyme PstI. We performed denovo_map and ref_map pipeline for L. alba and identified a total of 17,036 loci and 14,562 SNPs, respectively, revealing a genetic variation of 5.19% (FST of 0.05; p < 0.001) among its populations and two delimited genetic groups. For P. alliacea , in denovo_map our analysis discovered 6395 SNPs, indicating substantial genetic variation of 75% among the studied populations (FST = 0.75; p < 0.001), resulting in the delineation of four genetic groups. Our findings will contribute to providing valuable molecular data on the populations of these medicinal plants and provide evidence of the genetic flow existing among L. alba populations, while P. alliacea populations are more structured. We also performed a FDIST analysis; for L. alba using the results with reference, we identified 37 SNPs coding for proteins in biological, molecular, and cellular processes; and finally, we highlighted the gene SASPL_104284 involved in metabolic processes.