Genetic markers have emerged as one of the most promising tools for species identification and geographic traceability in biodiversity conservation and international trade of biological products. However, traditional molecular markers rarely have sufficient resolution at lower taxonomic levels, especially for discriminating closely related forest tree species and their populations. In this study, we developed a panel of RNA-Seq based single nucleotide polymorphism (SNP) markers for tracing the geographic origin of an endangered conifer, Cathaya argyrophylla, which is a paleoendemic restricted to four mountain regions in subtropical China. A total of 69 individuals from five populations (DLS, SHS, HP, BMS, and DYS) covering the entire range were used for transcriptome sequencing. Based on these transcriptomic data, we evaluated genetic variation and population structure of C. argyrophylla, and found extremely low nucleotide diversity but strong population differentiation. We also screened 113 population-specific SNP loci, including 96 for BMS, eight for DYS, six for SHS, two for HP, and one for one of the three subpopulations from DLS. According to these geographically diagnostic SNPs, we designed four population-specific molecular barcodes for PCR amplification. To test the utility and efficiency of the four markers in geographic discrimination, double-blind experiment was performed using 157 individuals labelled without any locality information. We found that almost all tested individuals could be successfully assigned to their geographic localities. Our study not only sheds some new light on the genetic profile of C. argyrophylla, but also provides a practical and cost-efficient solution for geographic traceability using transcriptome-derived SNPs.
Keywords: Cathaya; RNA-seq; SNPs; double-blind trial; geographic traceability; population genetics.
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