Validating a predictive model of cannabinoid inheritance with feral, clinical, and industrial Cannabis sativa.

Am J Bot. 2020 Oct 25;:

Authors: Wenger JP, Dabney CJ, ElSohly MA, Chandra S, Radwan MM, Majumdar CG, Weiblen GD

Abstract
PREMISE: How genetic variation within a species affects phytochemical composition is a fundamental question in botany. The ratio of two specialized metabolites in Cannabis sativa, tetrahydrocannabinol (THC) and cannabidiol (CBD), can be grouped into three main classes (THC-type, CBD-type, and intermediate type). We tested a genetic model associating these three groups with functional and nonfunctional alleles of the cannabidiolic acid synthase gene (CBDAS).
METHODS: We characterized cannabinoid content and assayed CBDAS genotypes of >300 feral C. sativa plants in Minnesota, United States. We performed a test cross to assess CBDAS inheritance. Twenty clinical cultivars obtained blindly from the National Institute on Drug Abuse and 12 Canadian-certified grain cultivars were also examined.
RESULTS: Frequencies of CBD-type, intermediate-type, and THC-type feral plants were 0.88, 0.11, and 0.01, respectively. Although total cannabinoid content varied substantially, the three groupings were perfectly correlated with CBDAS genotypes. Genotype frequencies observed in the test cross were consistent with codominant Mendelian inheritance of the THC:CBD ratio. Despite significant mean differences in total cannabinoid content, CBDAS genotypes blindly predicted the THC:CBD ratio among clinical cultivars, and the same was true for industrial grain cultivars when plants exhibited >0.5% total cannabinoid content.
CONCLUSIONS: Our results extend the generality of the inheritance model for THC:CBD to diverse C. sativa accessions and demonstrate that CBDAS genotyping can predict the ratio in a variety of practical applications. Cannabinoid profiles and associated CBDAS segregation patterns suggest that feral C. sativa populations are potentially valuable experimental systems and sources of germplasm.

PMID: 33103246 [PubMed – as supplied by publisher]


Source: ncbi

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