Genetic Variants Associated with Long-Terminal Repeats Can Diagnostically Classify Cannabis Varieties

Jackson M J Oultram; Joseph L Pegler; Greg M Symons; Timothy A Bowser; Andrew L Eamens; Christopher P L Grof; Darren J Korbie

doi:10.3390/ijms232314531

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Genetic Variants Associated with Long-Terminal Repeats Can Diagnostically Classify Cannabis Varieties

Journal article

Open access

Peer reviewed

Genetic Variants Associated with Long-Terminal Repeats Can Diagnostically Classify Cannabis Varieties

Jackson M J Oultram, Joseph L Pegler, Greg M Symons, Timothy A Bowser, Andrew L Eamens, Christopher P L Grof and Darren J Korbie

International Journal of Molecular Sciences, Vol.23(23), pp.1-16

2022

DOI: https://doi.org/10.3390/ijms232314531

PMCID: PMC9735643

PMID: 36498868

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Abstract

Cannabinoid Receptor Agonists

Cannabis - chemistry

Cannabis - genetics

Genetic Variation

Hallucinogens

Phylogeny

Polymorphism, Single Nucleotide

Sequence Analysis, DNA

Terminal Repeat Sequences

Cannabis sativa (Cannabis) has recently been legalized in multiple countries globally for either its recreational or medicinal use. This, in turn, has led to a marked increase in the number of Cannabis varieties available for use in either market. However, little information currently exists on the genetic distinction between adopted varieties. Such fundamental knowledge is of considerable value and underpins the accelerated development of both a nascent pharmaceutical industry and the commercial recreational market. Therefore, in this study, we sought to assess genetic diversity across 10 Cannabis varieties by undertaking a reduced representation shotgun sequencing approach on 83 individual plants to identify variations which could be used to resolve the genetic structure of the assessed population. Such an approach also allowed for the identification of the genetic features putatively associated with the production of secondary metabolites in Cannabis. Initial analysis identified 3608 variants across the assessed population with phylogenetic analysis of this data subsequently enabling the confident grouping of each variety into distinct subpopulations. Within our dataset, the most diagnostically informative single nucleotide polymorphisms (SNPs) were determined to be associated with the long-terminal repeat (LTRs) class of retroelements, with 172 such SNPs used to fully resolve the genetic structure of the assessed population. These 172 SNPs could be used to design a targeted resequencing panel, which we propose could be used to rapidly screen different Cannabis plants to determine genetic relationships, as well as to provide a more robust, scientific classification of Cannabis varieties as the field moves into the pharmaceutical sphere.

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Title: Genetic Variants Associated with Long-Terminal Repeats Can Diagnostically Classify Cannabis Varieties
Authors: Jackson M J Oultram (Author) - University of Newcastle Australia
Joseph L Pegler (Author) - University of Newcastle Australia
Greg M Symons (Author) - Extractas Bioscience
Timothy A Bowser (Author) - Impact Science Consulting
Andrew L Eamens (Author) - University of the Sunshine Coast, Queensland, School of Health and Behavioural Sciences - Legacy
Christopher P L Grof (Author) - University of Newcastle Australia
Darren J Korbie (Corresponding Author) - The University of Queensland
Publication details: International Journal of Molecular Sciences, Vol.23(23), pp.1-16
Publisher: MDPI AG
Date published: 2022
DOI: 10.3390/ijms232314531
ISSN: 1422-0067
PMID: 36498868; PMC9735643
Copyright note: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Grant note: G2000855 / CannaPacific Pty Ltd., Australia
Organisation Unit: School of Health - Biomedicine; School of Business and Creative Industries; School of Health and Behavioural Sciences - Legacy
Language: English
Record Identifier: 99695698702621
Output Type: Journal article

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Web Of Science research areas: Biochemistry & Molecular Biology; Chemistry, Multidisciplinary

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