Lapierre, É., Monthony, A. S. & Torkamaneh, D. Genomics-based taxonomy to clarify cannabis classification. Genome 66, 202–211 (2023).
Google Scholar
Clarke, R. & Merlin, M. Cannabis: Evolution and Ethnobotany. (Univ of California Press, 2016).
Hurgobin, B. et al. Recent advances in Cannabis sativa genomics research. New Phytol. 230, 73–89 (2021).
Google Scholar
Cox, C. The Canadian Cannabis Act legalizes and regulates recreational cannabis use in 2018. Health Policy New York. 122, 205–209 (2018).
Google Scholar
Welling, M. T. et al. A belated green revolution for Cannabis: Virtual genetic resources to fast-track cultivar development. Front. Plant Sci. 7, 205761 (2016).
Google Scholar
Martínez, V. et al. Cannabidiol and other non-psychoactive cannabinoids for prevention and treatment of gastrointestinal disorders: useful nutraceuticals?. Int. J. Mol. Sci. 21, 3067 (2020).
Google Scholar
Torkamaneh, D. & Jones, A. M. P. Cannabis, the multibillion dollar plant that no genebank wanted. Genome 65, 1–5 (2021).
Google Scholar
Impact on the Canadian economy. (2023). Available at: https://ised-isde.canada.ca/site/competition-bureau-canada/en/how-we-foster-competition/education-and-outreach/planting-seeds-competition. (Accessed: 5th October 2023)
Hesami, M. et al. Recent advances in cannabis biotechnology. Ind. Crops Prod. 158, 113026 (2020).
Google Scholar
Hanuš, L. O., Meyer, S. M., Muñoz, E., Taglialatela-Scafati, O. & Appendino, G. Phytocannabinoids: A unified critical inventory. Nat. Prod. Rep. 33, 1357–1392 (2016).
Google Scholar
Booth, J. K. & Bohlmann, J. Terpenes in Cannabis sativa—From plant genome to humans. Plant Sci. 284, 67–72 (2019).
Google Scholar
Kovalchuk, I. et al. The genomics of cannabis and its close relatives. Annu. Rev. Plant Biol. 71, 713–739 (2020).
Google Scholar
Grassa, C. J. et al. A new Cannabis genome assembly associates elevated cannabidiol (CBD) with hemp introgressed into marijuana. New Phytol. 230, 1665–1679 (2021).
Google Scholar
Laverty, K. U. et al. A physical and genetic map of Cannabis sativa identifies extensive rearrangements at the THC/CBD acid synthase loci. Genome Res. 29, 146–156 (2019).
Google Scholar
Grassa, C. J. et al. A complete Cannabis chromosome assembly and adaptive admixture for elevated cannabidiol (CBD) content. bioRxiv. https://doi.org/10.1101/458083 (2018).
Google Scholar
Gao, S. et al. A high-quality reference genome of wild Cannabis sativa. Hortic. Res. 7, 73. https://doi.org/10.1038/s41438-020-0295-3 (2020).
Maoz, T. Making cannabis history in 2020. (2020). Available at: https://nrgene.com/making-cannabis-history-in-2020/. (Accessed: 6th October 2023)
Morrell, P. L., Buckler, E. S. & Ross-Ibarra, J. Crop genomics: Advances and applications. Nat. Rev. Genet. 13, 85–96 (2012).
Google Scholar
Schwabe, A. L. & McGlaughlin, M. E. Genetic tools weed out misconceptions of strain reliability in Cannabis sativa: Implications for a budding industry. J. Cannabis Res. 1, 1–16 (2019).
Google Scholar
Metzker, M. L. Sequencing technologies the next generation. Nat. Rev. Genet. 11, 31–46 (2010).
Google Scholar
Wang, J. & Zhang, Z. GAPIT version 3: Boosting power and accuracy for genomic association and prediction. Genom. Proteom. Bioinform. 19, 629–640 (2021).
Google Scholar
Yin, L. et al. rMVP: A memory-efficient, visualization-enhanced, and parallel-accelerated tool for genome-wide association study. Genom. Proteom. Bioinform. 19, 619–628 (2021).
Google Scholar
Torkamaneh, D. & Belzile, F. Genome-Wide Association Studies. 2481, (Springer US, 2022).
Bakker, E., Holloway, A., K Waterman – US Patent App. 17/665, 500 & 2023, U. Autoflowering Markers. Google Patents (2021).
Welling, M. T. et al. An extreme-phenotype genome-wide association study identifies candidate cannabinoid pathway genes in Cannabis. Sci. Rep. 10, 1–14 (2020).
Google Scholar
Song, K., Li, L. & Zhang, G. Coverage recommendation for genotyping analysis of highly heterologous species using next-generation sequencing technology. Sci. Rep. 6, 1–7 (2016).
Scariolo, F. et al. Genotyping analysis by rad-seq reads is useful to assess the genetic identity and relationships of breeding lines in lavender species aimed at managing plant variety protection. Genes (Basel). 12, 1656 (2021).
Google Scholar
Sonah, H. et al. An improved genotyping by sequencing (GBS) approach offering increased versatility and efficiency of SNP discovery and genotyping. PLoS One 8, 1–9 (2013).
Google Scholar
Torkamaneh, D., Laroche, J., Boyle, B., Hyten, D. L. & Belzile, F. A bumper crop of SNPs in soybean through high-density genotyping-by-sequencing (HD-GBS). Plant Biotechnol. J. 19, 860–862 (2021).
Google Scholar
Poland, J. A. & Rife, T. W. Genotyping‐by‐sequencing for plant breeding and genetics. Plant Genome 5. https://doi.org/10.3835/plantgenome2012.05.0005 (2012).
Sun, J. et al. Genome-wide association study of salt tolerance at the germination stage in hemp. Euphytica 219, 1–16 (2023).
Google Scholar
Petit, J. et al. Elucidating the genetic architecture of fiber quality in hemp (Cannabis sativa L.) using a genome-wide association study. Front. Genet. 11, 566314. https://doi.org/10.3389/fgene.2020.566314 (2020).
Petit, J., Salentijn, E. M. J., Paulo, M. J., Denneboom, C. & Trindade, L. M. Genetic architecture of flowering time and sex determination in hemp (Cannabis sativa L): A genome-wide association study. Front. Plant Sci. 11, 569958 (2020).
Google Scholar
Watts, S. et al. Cannabis labelling is associated with genetic variation in terpene synthase genes. Nat. Plants. 7, 1330–1334 (2021).
Google Scholar
Collard, B. C. Y. & Mackill, D. J. Marker-assisted selection: An approach for precision plant breeding in the twenty-first century. Philos. Trans. R. Soc. B Biol. Sci. 363, 557–572 (2008).
Google Scholar
Lapierre, É., de Ronne, M., Boulanger, R. & Torkamaneh, D. Comprehensive phenotypic characterization of diverse drug-type cannabis varieties from the Canadian Legal Market. Plants. 12, 3756 (2023).
Google Scholar
Piluzza, G., Delogu, G., Cabras, A., Marceddu, S. & Bullitta, S. Differentiation between fiber and drug types of hemp (Cannabis sativa L) from a collection of wild and domesticated accessions. Genet. Resour. Crop Evol. 60, 2331–2342 (2013).
Google Scholar
Jannink, J. L., Lorenz, A. J. & Iwata, H. Genomic selection in plant breeding: From theory to practice. Briefings Funct. Genom. Proteom. 9, 166–177 (2010).
Google Scholar
Huang, M., Liu, X., Zhou, Y., Summers, R. M. & Zhang, Z. BLINK: A package for the next level of genome-wide association studies with both individuals and markers in the millions. Gigascience 8, 1–12 (2019).
Google Scholar
R Core Team. R: The R Project for Statistical Computing. (2021). Available at: https://www.r-project.org/. (Accessed: 10th June 2023)
Aboul-Maaty, N.A.-F. & Oraby, H.A.-S. Extraction of high-quality genomic DNA from different plant orders applying a modified CTAB-based method. Bull. Natl. Res. Cent. 43, 1–10 (2019).
Google Scholar
Torkamaneh, D., Laroche, J. & Belzile, F. Fast-gbs v2.0: An analysis toolkit for genotyping-by-sequencing data. Genome 63, 577–581 (2020).
Google Scholar
Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).
Google Scholar
Browning, B. L. & Browning, S. R. Genotype imputation with millions of reference samples. Am. J. Hum. Genet. 98, 116–126 (2016).
Google Scholar
Danecek, P. et al. Twelve years of SAMtools and BCFtools. Gigascience 10, 1–4 (2021).
Google Scholar
Bradbury, P. J. et al. TASSEL: Software for association mapping of complex traits in diverse samples. Bioinformatics 23, 2633–2635 (2007).
Google Scholar
Quinlan, A. R. & Hall, I. M. BEDTools: A flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
Google Scholar
NCBI Cannabis sativa Annotation Release 100. (2019). Available at: https://www.ncbi.nlm.nih.gov/genome/annotation_euk/Cannabis_sativa/100/. (Accessed: 11th October 2023)
Nei, M. & Li, W. H. Mathematical model for studying genetic variation in terms of restriction endonucleases. Proc. Natl. Acad. Sci. 76, 5269–5273 (1979).
Google Scholar
Purcell, S. et al. PLINK: A tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).
Google Scholar
Remington, D. L. et al. Structure of linkage disequilibrium and phenotypic associations in the maize genome. Proc. Natl. Acad. Sci. USA. 98, 11479–11484 (2001).
Google Scholar
Raj, A., Stephens, M. & Pritchard, J. K. FastSTRUCTURE: Variational inference of population structure in large SNP data sets. Genetics 197, 573–589 (2014).
Google Scholar
Jombart, T., Devillard, S. & Balloux, F. Discriminant analysis of principal components: A new method for the analysis of genetically structured populations. BMC Genet. 11, 1–15 (2010).
Google Scholar
Emms, D. M. & Kelly, S. OrthoFinder: Phylogenetic orthology inference for comparative genomics. Genome Biol. 20, 1–14 (2019).
Google Scholar
Cheng, C. Y. et al. Araport11: A complete reannotation of the Arabidopsis thaliana reference genome. Plant J. 89, 789–804 (2017).
Google Scholar
Torkamaneh, D. & Belzile, F. Scanning and filling: Ultra-dense SNP genotyping combining genotyping-by-sequencing, SNP array and whole-genome resequencing data. PLoS One 10, e0131533 (2015).
Google Scholar
Torkamaneh, D. et al. NanoGBS: A miniaturized procedure for GBS library preparation. Front. Genet. 11, 1–8 (2020).
Google Scholar
Zhang, J. et al. Genome-wide association study for flowering time, maturity dates and plant height in early maturing soybean (Glycine max) germplasm. BMC Genom. 16, 1–11 (2015).
Viana, J. P. G. et al. Impact of multiple selective breeding programs on genetic diversity in soybean germplasm. Theor. Appl. Genet. 135, 1591–1602 (2022).
Google Scholar
Liu, X., Geng, X., Zhang, H., Shen, H. & Yang, W. Association and genetic identification of loci for four fruit traits in tomato using InDel markers. Front. Plant Sci. 8, 275267 (2017).
Ren, G. et al. Large-scale whole-genome resequencing unravels the domestication history of Cannabis sativa. Sci. Adv. 7, 2286–2302 (2021).
Google Scholar
Torkamaneh, D. et al. Soybean (Glycine max) Haplotype Map (GmHapMap): A universal resource for soybean translational and functional genomics. Plant Biotechnol. J. 19, 324–334 (2021).
Google Scholar
Wang, W. et al. Genomic variation in 3010 diverse accessions of Asian cultivated rice. Nature. 557, 43–49 (2018).
Google Scholar
Chia, J. M. et al. Maize HapMap2 identifies extant variation from a genome in flux. Nat. Genet. 44, 803–807 (2012).
Google Scholar
Sawler, J. et al. The genetic structure of marijuana and hemp. PLoS One 10, e0133292 (2015).
Google Scholar
Lynch, R. C. et al. Genomic and chemical diversity in Cannabis. CRC. Crit. Rev. Plant Sci. 35, 349–363 (2016).
Google Scholar
Soorni, A., Fatahi, R., Haak, D. C., Salami, S. A. & Bombarely, A. Assessment of genetic diversity and population structure in Iranian Cannabis Germplasm. Sci. Rep. 7, 1–10 (2017).
Google Scholar
Zhang, J. et al. Genetic diversity and population structure of cannabis based on the genome-wide development of simple sequence repeat markers. Front. Genet. 11, 543438 (2020).
Clarke, R. C. & Merlin, M. D. Cannabis domestication, breeding history, present-day genetic diversity, and future prospects. CRC. Crit. Rev. Plant Sci. 35, 293–327 (2016).
Google Scholar
Lye, Z. N. & Purugganan, M. D. Copy number variation in domestication. Trends Plant Sci. 24, 352–365 (2019).
Google Scholar
Springer, N. M. Epigenetics and crop improvement. Trends Genet. 29, 241–247 (2013).
Google Scholar
Gill, R. A. et al. On the role of transposable elements in the regulation of gene expression and subgenomic interactions in crop genomes. CRC. Crit. Rev. Plant Sci. 40, 157–189 (2021).
Google Scholar
Alonge, M. et al. Major impacts of widespread structural variation on gene expression and crop improvement in tomato. (2020). https://doi.org/10.1016/j.cell.2020.05.021
Liseron-Monfils, C. & Ware, D. Revealing gene regulation and associations through biological networks. Curr. Plant Biol. 3–4, 30–39 (2015).
Google Scholar
Smýkal, P., Nelson, M. N., Berger, J. D. & Von Wettberg, E. J. B. The impact of genetic changes during crop domestication. Agronomy. 8, 119 (2018).
Google Scholar
Schwabe, A. L., Hansen, C. J., Hyslop, R. M. & McGlaughlin, M. E. Comparative genetic structure of cannabis sativa including federally produced, wild collected, and cultivated samples. Front. Plant Sci. 12, 675770 (2021).
Google Scholar
de Ronne, M. et al. Mapping of partial resistance to Phytophthora sojae in soybean PIs using whole-genome sequencing reveals a major QTL. Plant Genome 15, e20184 (2022).
Google Scholar
Zhong, H. et al. Uncovering the genetic mechanisms regulating panicle architecture in rice with GPWAS and GWAS. BMC Genom. 22, 1–13 (2021).
Cui, Z. et al. Denser markers and advanced statistical method identified more genetic loci associated with husk traits in maize. Sci. Rep. 10, (2020).
Kaler, A. S., Gillman, J. D., Beissinger, T. & Purcell, L. C. Comparing different statistical models and multiple testing corrections for association mapping in soybean and maize. Front. Plant Sci. 10, 1794. https://doi.org/10.3389/fpls.2019.01794 (2020).
Google Scholar
Izquierdo, P., Kelly, J. D., Beebe, S. E. & Cichy, K. Combination of meta-analysis of QTL and GWAS to uncover the genetic architecture of seed yield and seed yield components in common bean. Plant Genome 16, e20328 (2023).
Google Scholar