Topic: Amyloid, tau and blood-test diagnosis
IntroductionPolygenic risk scores (PRS) may improve Alzheimers disease (AD) risk prediction before symptom onset, yet choosing an appropriate model can be challenging. MethodsUsing the standardized GenoPred pipeline, 1,752 PRS models (9 algorithms; 584 configurations; 3 genome-wide association studies) were evaluated and stratified by genetic ancestry and APOE diplotype. PRS models were evaluated using 11,200 clinical or autopsy-confirmed AD cases and 19,321 controls age [≥]65 from the Alzheimers Disease Sequencing Project Release 5. ResultsPRS results were not consistent across methodologies (Spearmans {rho}: -0.49 to 1), with >95% of individuals having PRS in both the top and bottom risk deciles. Top-performing PRS were effective at stratifying AD risk across ancestries (AFR: P=2.04x10- 26; AMR: P=4.57x10-21; EAS: P=6.21x10-40; EUR: P=7.90x10-187). DiscussionPRS parameters should be optimized for each ancestry. Contradictory signals across methodologies underscore the need for carefully choosing suitable PRS methods and fine-tuning algorithmic parameters to ensure accuracy and consistency. Data AvailabilityAccess to the ADSP is controlled by The National Institute on Aging Genetics of Alzheimers Disease (NIAGADS). All scripts used to analyze the data are freely available at https://github.com/jmillerlab/prs_comparisons.
Key findings
- ResultsPRS results were not consistent across methodologies (Spearmans {rho}: -0.49 to 1), with >95% of individuals having PRS in both the top and bottom risk deciles.
Why it matters
This study evaluated 1,752 polygenic risk score models and found results were not consistent across methods, with most individuals falling in both the top and bottom risk deciles depending on the model. It concerns genetic risk prediction methods and does not test any treatment, cure or prevention.
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Study details
- Evidence strengthLow
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 20 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 impact for its field and year, so that was left out: it earned 19 of the 94 points we could judge it on, which is 20 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 2 / 10 | Tier 4 — not a peer-reviewed journal (medRxiv (preprint)) |
| 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 | 1 / 5 | Rated “low” 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 | 1 / 7 | Cited 1 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.5 citations a year over 2 year(s) since 2026 |
| 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 | 1 / 6 | Cited by 1 other studies in this library |
| Still being cited | 0 / 4 | Cited 1 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 1 / 4 | Last-listed author Justin B. Miller: h-index 16 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | University of Kentucky: institution h-index 824 (OpenAlex) |
| Multi-centre collaboration | 0 / 3 | OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 2 / 2 | Free to read |
| Points | 19 / 94 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 19 of the 94 points we could judge it on is 20 out of 100. |
| Total | 20 / 100 | Capped to 16 because this is not a peer-reviewed article. |
What each of these 20 components measures, and what this score is not →
Read the original
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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. The record names 7 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.
APA 7th
Ward, E. L., Nelson, P. T., Katsumata, Y., Fardo, D. W., Jicha, G. A., Alzheimer's Disease Neuroimaging Initiative,, et al. (2026). Alzheimer's Polygenic Risk Scores Are Not Interchangeable: Evidence from 1,752 Models. medRxiv (preprint). https://doi.org/10.64898/2026.09.02.26362060
Vancouver
Ward EL, Nelson PT, Katsumata Y, Fardo DW, Jicha GA, Alzheimer's Disease Neuroimaging Initiative,, et al. Alzheimer's Polygenic Risk Scores Are Not Interchangeable: Evidence from 1,752 Models. medRxiv (preprint). 2026. doi:10.64898/2026.09.02.26362060.
BibTeX
@article{ward2026-3ac879df,
author = {Ward, E. L. and Nelson, P. T. and Katsumata, Y. and Fardo, D. W. and Jicha, G. A. and {Alzheimer's Disease Neuroimaging Initiative,} and Miller, J. B.},
title = {{Alzheimer's Polygenic Risk Scores Are Not Interchangeable: Evidence from 1,752 Models}},
journal = {medRxiv (preprint)},
year = {2026},
doi = {10.64898/2026.09.02.26362060},
url = {https://doi.org/10.64898/2026.09.02.26362060}
}
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