In recent years, there has been a significant increase in the number of documented cannabis strains and types. However, the genetic information needed to support claims of uniqueness for these strains is currently lacking. To address this issue, a team of researchers from the University of Northern Colorado conducted a study on the reliability of cannabis strains, which is available on the pre-print server BioRxiv.
Traditionally, cannabis has been categorized based on the percentage of Δ9-tetrahydrocannabinol (THC) present in the product. Strains with low THC content are classified as hemp-type, while those with moderate to high THC are considered drug-type. Drug-type strains are further divided into Sativa, Indica, and Hybrid types. Recently, drug-type strains with high cannabidiol (CBD) levels have gained attention for their medical properties.
The conventional nomenclature system primarily focuses on the physical characteristics of plants. Tall plants with narrow leaflets are associated with Sativa strains, while shorter plants with broader leaflets indicate Indica strains. Hybrid strains display a combination of these traits. It is also believed that different strain types have different effects on the human body, with Sativa strains producing invigorating and stimulating effects, and Indica strains being effective for pain management and providing a more relaxing “high.”
Each strain type encompasses numerous different strains, each reportedly having unique effects and popularity among users.
To evaluate the reliability of existing cannabis strain types, researchers Anna Schwabe and Mitchell McGlaughlin obtained samples from various dispensaries in Colorado, California, and Washington. The study included 122 samples from 30 different strains. The researchers used microsatellite genetic analysis to identify shared genetic features among these samples that could indicate strain type. Statistical analysis was also performed to determine the genetic relatedness of the samples.
The study revealed two distinct genotypes among the samples, but these genotypes did not align strongly with the reported proportions of Sativa and Indica phenotypes expected from the strains. If the Sativa and Indica phenotypes were genetically distinct, genetic analysis should have categorized 100% Sativa strains into one genotype and 100% Indica strains into another. However, this was not the case. For example, the strain “Durban Poison,” listed as a 100% Sativa strain, only had an average assignment of 86% to genotype 1. Similarly, the 90% Sativa strain “Sour Diesel” had an average assignment of only 14% to genotype 1. The same inconsistencies were observed among labeled Indica strains.
These findings indicate that the observed genotypes do not consistently correlate with the conventional strain types. This lack of genetic evidence supporting existing strain distinctions can potentially mislead consumers about the cannabis products they are using. While this may be disappointing for recreational users, it is especially concerning for medical patients who rely on specific strains to alleviate specific symptoms.
Years of selective hybridization and breeding have blurred the once-clear genetic link between strain types and genotypes. Consequently, there is a lack of consistency in cannabis strain names.
Ideally, samples sold under a single strain name should be clones or closely related to each other. The study found that only 15 out of the 30 strains tested were first-order or higher relatives among the samples within a strain. Some strains showed low levels of relatedness, but their small sample sizes make it difficult to assess their reliability accurately. Other strains had unrelated genetics or contained multiple outliers.
Moreover, many strains share similar names and may even be variations of the same plant. For example, the strains Golden Goat and Sweet Island Skunk were found to be more related than some pairs of samples sold under the Sour Diesel name. Genetic analysis also revealed that Larry OG and Tahoe OG were genetically identical despite being sold under different names.
The presence of inconsistent, mislabeled, and misidentified strains in the market highlights the need for genetic testing and proper regulation. The current system allows for overlap between unrelated strains due to the lack of genetics-based verification. To ensure clearer and more useful naming of cannabis strains, changes in nomenclature and regulation are necessary. Additionally, a broader study with a larger sample database could help identify the exact causes of these inconsistencies and aid in the development of a more effective naming system.
In conclusion, the reliability of cannabis strain types is questionable due to the lack of consistent genetic evidence supporting existing distinctions. This poses potential risks for consumers, especially medical patients. The inconsistencies in strain names highlight the need for genetic testing and regulation to ensure accurate labeling and avoid confusion in the market.
