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<p>Equitable hereditary‑cancer genomics requires variant interpretation that performs reliably in under‑represented and admixed populations—not only in well‑sampled European cohorts. Building on Bianco and Planello (2025), this Comment outlines an equity‑by‑design workflow that couples regional allele‑frequency baselines (e.g., GenomeIndia, IndiGenomes, ABraOM) with calibrated functional evidence from saturation genome editing and related multiplex assays, implemented within updated ClinGen/ACMG‑AMP Bayesian point‑based frameworks. The pipeline defines sub‑national AF strata, quantifies AF uncertainty, maps quantitative functional readouts to PS3/BS3 strengths, and integrates AF, functional, in‑silico and clinical signals with transparent scoring while tracking fairness metrics (e.g., VUS rate ratios) and using patient‑centered reporting language. Overall, routinely combining local population priors with decision‑grade functional likelihoods provides a practical, auditable pathway to reduce VUS disparities and strengthen the global validity of clinical genomic interpretation.</p>

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Equity-aware variant interpretation needs local allele frequencies and calibrated functional evidence: comment on Bianco & Planello (2025)

  • 期刊论文

  • 发表日期：

    2026年1月21日

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  • M. Vijayasimha

摘要

Equitable hereditary‑cancer genomics requires variant interpretation that performs reliably in under‑represented and admixed populations—not only in well‑sampled European cohorts. Building on Bianco and Planello (2025), this Comment outlines an equity‑by‑design workflow that couples regional allele‑frequency baselines (e.g., GenomeIndia, IndiGenomes, ABraOM) with calibrated functional evidence from saturation genome editing and related multiplex assays, implemented within updated ClinGen/ACMG‑AMP Bayesian point‑based frameworks. The pipeline defines sub‑national AF strata, quantifies AF uncertainty, maps quantitative functional readouts to PS3/BS3 strengths, and integrates AF, functional, in‑silico and clinical signals with transparent scoring while tracking fairness metrics (e.g., VUS rate ratios) and using patient‑centered reporting language. Overall, routinely combining local population priors with decision‑grade functional likelihoods provides a practical, auditable pathway to reduce VUS disparities and strengthen the global validity of clinical genomic interpretation.