Effective SSR markers for assessing population structure, genetic diversity, and volatile compounds in Atractylodis Rhizoma

Abstract

In this study, we aimed to develop SSR molecular markers for assessing the population structure and genetic diversity of Atractylodes lancea (AL) and Atractylodes chinensis (AC), with a focus on their volatile compounds. By utilizing transcriptome data from AL, we successfully developed 29 pairs of highly polymorphic SSR primers, generating 264 distinct alleles. Cluster analysis revealed three distinct populations: Mao Mountain, Dabie Mountain, and samples from other locations and primers with high discriminatory power were selected. Additionally, a representative core subset of 106 samples was selected from the original germplasm, capable of representing over 99 % of the genetic variation. This core subset provides an efficient resource for future studies on population genetics and trait association. The Support Vector Machine (SVM)-based method achieved an accuracy of 98.8 % in distinguishing Atractylodis Rhizoma (AR) varieties. GC-MS screening identified 24 potential differential metabolites between the two species, with correlation analysis showing significant association. This study successfully developed SSR molecular markers for analyzing genetic diversity in AL and AC and established a reliable method for identifying AR medicinal varieties. These findings provide a solid foundation for future quality control of AR and the selection of superior germplasm.