Whole Genome Sequencing Isn't the Right Tool for Every Question

Whole genome sequencing (WGS) is a powerful approach. There's no question about that.

The cannabis genome is large, roughly 800 megabases, or around 800 million base pairs depending on the variety. WGS attempts to read all of it. That level of coverage is genuinely necessary for certain applications. If you are assembling a reference genome from scratch, detecting structural variants, or characterizing novel variation in an uncharacterized population, you need the whole picture. In those contexts, WGS is the right tool.

But for strain identification and genetic relatedness, using WGS is like using a cannon to kill a fly.

These questions don't require complete genomic reconstruction. They require comparison.

When the goal is determining whether two plants are the same cultivar, whether one is derived from another, or whether a meaningful genetic relationship exists between samples, what matters are markers. Specific, informative positions in the genome that vary between individuals and can be consistently compared across samples.

Markers are the currency of identity.

And for that, we don't need the whole genome.

This is exactly what reduced representation sequencing was designed to do. Approaches like RADseq use restriction enzymes to cut the genome at specific, reproducible locations, and then sequence only those fragments. Instead of sequencing everything, you sample the genome in a consistent and repeatable way.

The result is tens of thousands of SNP markers per individual.

SNPs, or single nucleotide polymorphisms, are positions where individuals differ by a single base pair. They are stable, heritable, and distributed across the genome, appearing in both genic and intergenic regions.

That distribution matters.

Intergenic SNPs, those located outside of protein-coding regions, are often closer to neutral. On average, they experience weaker selection than sites within genes, making them particularly useful as stable identity markers. Because restriction enzymes cut based on sequence recognition rather than biological function, there is no intentional bias toward coding regions or trait-associated loci. The sampling is broad and consistent across the genome.

This produces a dataset that is highly reproducible and well suited for identity and relatedness analysis.

For these applications, tens of thousands of SNPs are not a compromise on rigor. They are more than sufficient. In fact, this is the same general class of high-density SNP data used in forensic science and kinship analysis.

That distinction matters outside of a purely scientific context.

When an NDA is violated, a licensing agreement is ignored, or a proprietary cultivar is distributed without authorization, the question becomes legal as well as biological. Can you demonstrate that two samples are genetically the same, or that one is derived from another?

DNA fingerprinting built on high-density SNP data provides exactly that kind of evidence. It creates a multilocus genetic profile that can be used to support documentation-backed intellectual property and contractual agreements.

Reduced representation sequencing also has a practical advantage. Cost.

Whole genome sequencing can run from hundreds to thousands of dollars per sample depending on depth and platform. Reduced representation approaches bring that cost down substantially, making genomic documentation accessible to a much broader range of participants.

That accessibility is not a minor detail. It determines who can realistically use these tools.

If genetic identity systems are going to function as real infrastructure, they need to be usable not just by large commercial operations, but by independent breeders and smaller cultivators who have historically had the least access to formal protection mechanisms.

WGS has its place. When you need complete genomic information, it is the right tool.

But for identity, relatedness, and documentation, reduced representation sequencing provides exactly what is needed: dense, reproducible, and cost-effective genetic markers that can answer the questions that actually matter.