TrustPGS: When can a polygenic score be trusted? A per-individual reliability framework across ancestries

07 Jul 20264 min read0 viewsVerified Feed

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Polygenic scores summarise genetic predisposition to a trait, but a population-level accuracy figure cannot tell a clinician whether a given prediction is reliable for the person in front of them. This gap is most consequential for individuals whose ancestry is under-represented in the discovery cohort, precisely the patients for whom a wrong trust call carries the highest clinical cost.
We present TrustPGS, a framework that tells clinicians and downstream models which individual predictions can be trusted and which cannot, so that polygenic scores can inform clinical decisions rather than being acted on uniformly regardless of how well-supported each prediction is. The framework rests on two axes calibrated on a discovery cohort, the consensus of a Bayesian posterior-sample ensemble and the directional agreement of the top-magnitude linkage-disequilibrium blocks.
We computed SBayesRC posterior-sample scores for ten polygenic traits in the 1000 Genomes Project phase-3 cohort and tested whether the resulting trust labels transfer, without recalibration, to the ancestrally diverse Simons Genome Diversity Project, comparing strict application of the European cutoffs, percentile-rank rescaling, and within-cohort recalibration.

bioRxiv (Biomedical Preprints) published a clinical update in Research Highlights on 07 Jul 2026.

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A per-individual reliability framework across ancestries.

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bioRxiv (Biomedical Preprints)