Near-perfect genome sequencing in medical genetics

Near-perfect genome sequencing (NPGS) concept — convergence of long-read WGS, diploid assembly, pangenome references, and AI-driven variant interpretation, with a Bayesian framework in which genomic completeness becomes interpretive evidence

This perspective proposes the near-perfect genome sequencing (NPGS) concept, framed as the convergence of long-read WGS, diploid genome assembly, pangenome references, and AI-driven variant interpretation. The authors put forward a Bayesian framework in which genomic completeness itself constitutes interpretive evidence for variant classification, with direct implications for variants of uncertain significance. NPGS is positioned as a pillar spanning postnatal, prenatal, and oncological settings, with a staged implementation roadmap toward the one-test paradigm. Real-world challenges — cost, computational demand, equity, and ethics — are also addressed.

Synthesis written by Geno'X. For the full original abstract, please refer to the source publication.

This Nat Genet perspective clearly articulates the strategic direction of diagnostic genomics: the near-complete genome, rather than perpetual panel expansion, as the first-tier test. The idea that genomic completeness is itself Bayesian evidence to reclassify variants of uncertain significance is conceptually strong. This is a framing vision more than new data, but it is useful for guiding laboratory organizational choices.