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Genetic Variants Associated with Long-Terminal Repeats Can Diagnostically Classify Cannabis Varieties.
Oultram, Jackson M J; Pegler, Joseph L; Symons, Greg M; Bowser, Timothy A; Eamens, Andrew L; Grof, Christopher P L; Korbie, Darren J.
Afiliação
  • Oultram JMJ; Centre for Plant Science, School of Environmental and Life Sciences, College of Engineering, Science and Environment, University of Newcastle, Callaghan, NSW 2308, Australia.
  • Pegler JL; Centre for Plant Science, School of Environmental and Life Sciences, College of Engineering, Science and Environment, University of Newcastle, Callaghan, NSW 2308, Australia.
  • Symons GM; Extractas Bioscience, 160 Birralee Road, Westbury, TAS 7303, Australia.
  • Bowser TA; Impact Science Consulting, 24 Leighton Bay Drive, Metung, VIC 3904, Australia.
  • Eamens AL; School of Health and Behavioural Sciences, University of the Sunshine Coast, Sippy Downs, QLD 4556, Australia.
  • Grof CPL; Centre for Plant Science, School of Environmental and Life Sciences, College of Engineering, Science and Environment, University of Newcastle, Callaghan, NSW 2308, Australia.
  • Korbie DJ; Centre for Personalised Nanomedicine, Australian Institute of Bioengineering and Nanotechnology, The University of Queensland, St. Lucia, QLD 4072, Australia.
Int J Mol Sci ; 23(23)2022 Nov 22.
Article em En | MEDLINE | ID: mdl-36498868
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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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Cannabis / Alucinógenos Tipo de estudo: Diagnostic_studies / Risk_factors_studies Idioma: En Revista: Int J Mol Sci Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Austrália País de publicação: Suíça

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Cannabis / Alucinógenos Tipo de estudo: Diagnostic_studies / Risk_factors_studies Idioma: En Revista: Int J Mol Sci Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Austrália País de publicação: Suíça