Traceability

Cannabis sativa L. (Cannabaceae) is a flowering herb that has been domesticated and has agronomic value as a source of food, fiber, and medicine. In its chemical composition, the most prominent molecules found are tetrahydrocannabinol (THC) and cannabinol (CBD), with THC having psychotropic activity and used for recreational and spiritual purposes for millennia. On the other hand, the genomic analysis of the plant, allowed the identification of hundreds to millions of different markers, using short repetitive sequences (SSR) and single nucleotide polymorphisms (SNPs), which are frequently used to describe and characterize circulating genomic variants, hybrids and know information about the different geographic origins of the plant. Due to the number of markers obtained, machine learning algorithms are an alternative for the study to develop improved identification of these markers that can differentiate in the plant's biogeography analysis. The purpose of this project is to identify genetic markers using machine learning methodology to identify the distribution of circulating genotypes and geographic origin of C. sativa coming from different regions of Brazil.