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16/100

⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. Clinical trials

Topic: Genetics and APOE

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. Percentile-rank rescaling preserved an enrichment factor above one in non-European populations for five of ten traits (Alzheimer disease, breast cancer, body mass index, LDL cholesterol, and systolic blood pressure), traits whose European and target-cohort distributions were shifted but comparable in shape. Three traits (coronary artery disease, height, and schizophrenia) carried distributions that differed in shape rather than location, a pattern traceable to discovery-cohort bias that recalibration could not repair either, and two further traits (type 2 diabetes and educational attainment) showed intermediate behaviour, present but never enriched in one case, and an apparent success that rank-mapping correctly unmasked as artefactual in the other. Because each of these patterns is detectable before any individual-level claim is made, TrustPGS gives clinicians and downstream models a falsifiable, per-trait basis for deciding when a reliability label can be trusted on a new population, rather than a single portability promise that holds or fails silently.

Key findings

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.
Percentile-rank rescaling preserved an enrichment factor above one in non-European populations for five of ten traits (Alzheimer disease, breast cancer, body mass index, LDL cholesterol, and systolic blood pressure), traits whose European and target-cohort distributions were shifted but comparable in shape.
Three traits (coronary artery disease, height, and schizophrenia) carried distributions that differed in shape rather than location, a pattern traceable to discovery-cohort bias that recalibration could not repair either, and two further traits (type 2 diabetes and educational attainment) showed intermediate behaviour, present but never enriched in one case, and an apparent success that rank-mapping correctly unmasked as artefactual in the other.
Because each of these patterns is detectable before any individual-level claim is made, TrustPGS gives clinicians and downstream models a falsifiable, per-trait basis for deciding when a reliability label can be trusted on a new population, rather than a single portability promise that holds or fails silently.

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Study details

Evidence strengthModerate

How this study scored 16/100

Preprint — not yet peer-reviewed. It scored 24 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it. We have no data on times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 15 of the 62 points we could judge it on, which is 24 out of 100.

Component	Points	Why
Journal tier	2 / 10	Tier 4 — not a peer-reviewed journal (bioRxiv)
Journal's citation record	0 / 4	Not a journal article, so the journal's record does not count
Study design	0 / 12	No study design recorded — scores zero, not guessed (not stated in the record — scored 0)
Strength of evidence	3 / 5	Rated “moderate” in our summary
Peer-review validation	4 / 10	Preprint — not yet peer-reviewed. Hosted on a preprint server.
How recent	5 / 5	2026 — within 1 year(s) of the newest study here
Sample size	0 / 4	Participant count not stated — scores zero (not stated in the record — scored 0)
Effect reported	0 / 3	No effect size reported — scores zero (not stated in the record — scored 0)
Studied in people	0 / 3	Not stated whether this was studied in people — scores zero (not stated in the record — scored 0)
Randomised / blinded	0 / 2	No randomisation or blinding stated — scores zero (not stated in the record — scored 0)
Times cited	— / 7	No citation count (not found in OpenAlex) — left out of this score
Citations per year	— / 5	No citation count or no year — left out of this score
Impact for its field and year	— / 6	No field-weighted citation impact (OpenAlex has none yet) — left out of this score
Cited by studies here	— / 6	Not yet checked against this library's citation graph — left out of this score
Still being cited	— / 4	No yearly citation counts — left out of this score
Senior author's track record	— / 4	Senior author's record not available — left out of this score
Institution's track record	— / 3	No institution listed in OpenAlex — left out of this score
Multi-centre collaboration	— / 3	Institutions not listed — left out of this score
Indexed in PubMed	1 / 2	Not in PubMed, but has a DOI
Free to read	0 / 2	Behind a paywall — scores zero
Points	15 / 62	Scored on 12 of 20 parts. We have no data for the rest, so they are left out: 15 of the 62 points we could judge it on is 24 out of 100.
Total	24 / 100	Capped to 16 because this is not a peer-reviewed article.

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Cite this study APA, Vancouver, BibTeX · download .bib or .ris

Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.

APA 7th

Onawole, A. T., Adegoke, R. A., & Amoo, O. (2026). TrustPGS: When can a polygenic score be trusted? A per-individual reliability framework across ancestries. bioRxiv. https://doi.org/10.64898/2026.07.01.735762

Vancouver

Onawole AT, Adegoke RA, Amoo O. TrustPGS: When can a polygenic score be trusted? A per-individual reliability framework across ancestries. bioRxiv. 2026. doi:10.64898/2026.07.01.735762.

BibTeX

@article{onawole2026-45a812c1,
  author  = {Onawole, A. T. and Adegoke, R. A. and Amoo, O.},
  title   = {{TrustPGS: When can a polygenic score be trusted? A per-individual reliability framework across ancestries}},
  journal = {bioRxiv},
  year    = {2026},
  doi     = {10.64898/2026.07.01.735762},
  url     = {https://doi.org/10.64898/2026.07.01.735762}
}

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