Phytochemical and genetic marker (SCoT and CBDP) based study of genetic diversity and population structure in natural populations of Cannabis sativa L.: A high-value sustainable biodiversity of North-Indian Himalaya

Abstract

Cannabis sativa is a versatile, sustainable cash crop grown in vast agro-climatic zones of India. The present study used comprehensive datasets involving phytochemical and molecular genotyping to characterize forty-two wild cannabis accessions collected from fourteen geographical locations of the North Indian Himalayas. Two gene-targeted dominant molecular markers, i.e., start codon targeted (SCoT) polymorphism and CAAT-box derived polymorphism (CBDP), were used for molecular genotyping to evaluate genetic assortment and interrelatedness among and within wild inhabitants of C.sativa. Multiple t-tests indicated significant differences among the studied cannabis populations for the phytochemical traits. Five major cannabinoids were identified in Indian Cannabis sativa germplasm, and most of the populations were of drug/marijuana types. The considerable diversity in drug types was observed at the chemical and genetic levels. Twenty-six SCoT and twenty-one CBDP markers generated a sum of 134, and 80 alleles averaged with 3.08 and 6.65 alleles per primer, respectively. The average polymorphic information content (PIC) and resolving power per primer indicated both SCoT and CBDP as ideal markers system for genetic diversity study in Cannabis sativa. The population diversity analysis based on SCoT, CBDP, and cumulative marker dataset revealed a high genetic differentiation (Gst>0.15), and a low estimated gene flow (Nm<1.0) between the studied populations. The highest gene diversity (H) and Shannon diversity Index (I) were observed for the population of Matiyal location (Pop3) using SCoT and cumulative marker dataset. However, Population 7 (Pop7) of location Kanatal was found to be highly diverse using the CBDP marker dataset. The multivariate analyses, i.e., Neighbour-joining tree based phenetic analysis, Principal Coordinate Analysis (PCoA), and Structure, revealed that there was no individual geographical location based grouping among the cannabis populations using both markers data set individually and their cumulative datasets. However, the allocation of accessions in common groups corresponding to their geographical locations was maximum in the case of Analysis with a cumulative marker data set. Analysis of molecular variance (AMOVA) also demonstrated a maximum percentage of molecular variation at the intra-population level (77–86%) rather than the inter-population level (14–23%). Altogether, our data suggest that both SCoT and CBDP markers are the ideal markers to study the genetic relationship among geographically different populations of C.sativa, and populations identified as highly diverse can be utilized as parents in Indian cannabis breeding programs.