PREPRINT NOTICE: This study has NOT been peer reviewed. It is posted on bioRxiv and has not been accepted by a journal. Findings should not be used to guide clinical decisions.

Researchers at the University of Washington have used saturation genome editing (SGE) to measure the functional impact of thousands of variants in two hereditary cancer-risk genes, RAD51D and XRCC2, according to a preprint posted June 13, 2026, on bioRxiv (DOI 10.64898/2026.06.12.731983). The corresponding author is Lea Starita (Department of Genome Sciences, University of Washington).

What is SGE? Saturation genome editing is a laboratory technique that systematically introduces every possible single-nucleotide variant in a target gene into cells and measures the effect on cell fitness — enabling functional classification of variants at scale. This study was conducted entirely in cell lines (preclinical); results have not been validated in patient cohorts.

Scale and accuracy:

  • RAD51D: 5,412 variants measured for cellular fitness; fitness scores discriminated pathogenic from benign variants with AUC = 0.994 (near-perfect)
  • XRCC2: 3,743 variants measured for cellular fitness; AUC = 1.000 (perfect discrimination in this dataset)
  • Additionally, 2,876 RAD51D variants and 2,069 XRCC2 variants were assessed for effects on RNA expression

RNA splicing finding: Integration of RNA data revealed that 24% of RAD51D loss-of-function missense variants act through RNA-mediated mechanisms (primarily aberrant splicing), compared to only 5% in XRCC2 — a mechanistic insight with potential implications for future variant classification.

Why this matters: RAD51D and XRCC2 encode proteins involved in homologous recombination DNA repair. Most variants in these genes are classified as variants of uncertain significance (VUS), limiting clinical utility. This SGE dataset could, after peer review and regulatory consideration, support reclassification of thousands of VUS.

Critical caveats: This is a preprint, not peer reviewed. The study was performed in cell lines, not patients. SGE is a research tool, not a currently available clinical test. Peer review and independent replication are required before these datasets can inform clinical variant classification guidelines.