Development and validation of a minimal SNP genotyping panel for the differentiation of Cannabis sativa cultivars - Alex Cull; David L. Joly (BMC Genomics)
Abstract Background Due to its previously illicit nature, Cannabis sativa had not fully reaped the benefits of recent innovations in genomics and plant sciences. However, Canada’s legalization of C. sativa and products derived from its flower in 2018 triggered significant new demand for robust genotyping tools to assist breeders in meeting consumer demands. Early molecular marker-based research on C. sativa focused on screening for plant sex and chemotype, and more recent research has sought to use molecular markers to target traits of agronomic interest, to study populations and to differentiate between C. sativa cultivars. Results In this study, we have conducted whole genome sequencing of 32 cultivars, mined the sequencing data for SNPs, developed a reduced SNP genotyping panel to discriminate between sequenced cultivars, then validated the 20-SNP panel using DNA from the sequenced cultivars and tested the assays on commercially available dried flower. The assay conversion rate was higher in DNA extracted from fresh plant material than in DNA extracted from dried flower samples. However, called genotypes were internally consistent, highlighting discrepancies between genotypes detected using sequencing data and observed using genotyping assays. The primary contributions of this work are to clearly document the process used to develop minimal SNP genotyping panels, the feasibility of using such panels to differentiate between C. sativa cultivars, and outline improvements and goals for future iterations of PCR-based, minimal SNP panels to enable efficient development genotyping tools to identify and screen C. sativa cultivars. Conclusions Our key recommendations are to increase sampling density to account for intra-cultivar variability; leverage higher read length paired-end short-read technology; conduct in-depth pre- and post-processing of reads, mapping, and variant calling data; integrate trait-associated loci to develop multi-purpose panels; and use iterative approaches for in vitro validation to ensure that only the most discriminant and performant SNPs are retained.