Saturation Genome Editing (SGE)
Also known as: SGE, saturation genome editing
A CRISPR-based assay that introduces every possible single-nucleotide variant at a genomic locus in its native context, then measures functional impact through cell viability or other selection. The gold standard for clinical variant classification in cancer genes.
Source: Findlay GM et al. 'Accurate classification of BRCA1 variants with saturation genome editing.' Nature 2018;562(7726):217-222. https://doi.org/10.1038/s41586-018-0461-z
Primary reference ↗Saturation genome editing (SGE) uses CRISPR-Cas9 to introduce every possible single-nucleotide change at a genomic locus — in its native chromosomal context, under endogenous regulatory control. Unlike VAMP-seq (which uses plasmid-expressed fusion proteins), SGE measures variant effects in the cell’s own genome, capturing all mechanisms of loss-of-function: structural, catalytic, regulatory, and splicing.
How It Differs from Other DMS Methods
| Feature | SGE | VAMP-seq | Library-based DMS |
|---|---|---|---|
| Expression context | Endogenous locus | Plasmid overexpression | Plasmid/yeast |
| Selection | Cell viability (or other) | Fluorescence sorting | Growth competition |
| Captures splicing defects | Yes | No | No |
| Captures regulatory defects | Yes | No | No |
| Scale | ~4,000 SNVs per exon | ~5,000-10,000 per protein | ~5,000-10,000 per protein |
| Cost/complexity | Highest | Moderate | Moderate |
Why It’s the Gold Standard
SGE is considered the gold standard for clinical variant classification because:
- Endogenous context — variants are tested at their native locus with normal splicing, regulation, and expression levels
- Comprehensive LOF capture — cell viability selection catches loss-of-function from any mechanism, not just protein abundance
- Clinical-grade validation — the Findlay 2018 BRCA1 SGE dataset is used by ClinGen expert panels for variant reclassification
Key Datasets
| Gene | Variants | Selection | Paper |
|---|---|---|---|
| BRCA1 (exons 18-23, BRCT domain) | 1,262 | HAP1 cell viability | Findlay et al. 2018, Nature |
| BRCA1 (exon 2-5, RING domain) | 575 | HAP1 cell viability | Findlay et al. 2018, Nature |
Relationship to ESM-2
ESM-2 predictions on BRCA1 correlate with SGE functional scores: BRCT domain rho = 0.534 (global), rho = 0.772 (non-active-site). This is the second strongest domain correlation in the NeuroAutomata benchmark pipeline, after CYP2C9 heme-binding (rho = 0.811). The SGE cross-reference is significant because SGE is the most comprehensive functional assay available — if ESM-2 correlates with SGE, it’s capturing real functional constraint.