Evaluation of two 13-loci STR multiplex system regarding identification and origin discrimination of Brazilian Cannabis sativa samples

Int J Legal Med. 2020 Sep;134(5):1603-1612. doi: 10.1007/s00414-020-02338-5. Epub 2020 Jun 25.

Abstract

According to the Brazilian Federal Police (BFP), the Brazilian Cannabis sativa illicit market is mainly supplied by drugs originated from Paraguay and Northeastern Brazil (Marijuana Polygon region). These two known routes, the increasing indoor cultivations (supported by online market), and drugs from Uruguay are also in BFP's sight. Forensic tools to aid police intelligence were published in the past years. In genetics, microsatellites have gained attention due to their individualization capability. This study aims to evaluate the effectiveness and efficiency of two STR multiplex systems previously proposed in 94 Cannabis sativa samples seized in Brazil. Principal coordinate analyses (PCoA), forensic parameters, and genetic structure analysis were executed. Both panels were effective in individualizing and origin discriminating all samples, and the system proposed in 2015 demonstrated better results. For this marker set, the probability of identity for a random individual is approximately one in 65 billion; also, the PCoA shows a clear genetic distinction among samples according to its origin. Bayesian inference populational structure analysis indicated a significant genetic diversity among seizure groups, matching with its origin. Overall, the STR multiplex systems were able to achieve its purpose in individualizing and differentiating, according to geographic region, Brazilian Cannabis sp. samples.

Keywords: Cannabis sativa; Forensic genetics; Genetic markers; Individualization; Origin tracking.

MeSH terms

  • Brazil
  • Cannabis / genetics*
  • Drug Trafficking / prevention & control
  • Forensic Genetics
  • Genetic Loci*
  • Genetic Structures
  • Genotype*
  • Humans
  • Law Enforcement / methods
  • Microsatellite Repeats*
  • Multiplex Polymerase Chain Reaction*
  • Principal Component Analysis