A family-aware All of Us benchmark for polygenic prediction and genomic machine learning
Ren G, Wu K, Ren C, Nyiha I, Wu K, Yang J.. medRxiv, 2026-10-26
Topic: Genetics and APOE
Polygenic risk scores (PRSs) have improved substantially in predictive performance, yet results remain difficult to compare across studies that differ in cohorts, phenotype definitions, and evaluation procedures. Comparison to machine-learning methods is harder still, since their performance depends strongly on how data are partitioned and whether related individuals leak across splits. Here, we introduce a family-aware benchmark for polygenic prediction built on version 9 of the All of Us Research Program (AoU). The benchmark assigns entire family components to fixed discovery, calibration, and test partitions, reserves first-degree relatives for within-family evaluation, and separates models trained only on AoU discovery data from those using external GWAS summary statistics. This enables direct comparison between conventional PRS pipelines and machine-learning models trained under identical data budgets. We report results for 14 binary disease traits. Our strongest scores explain 5.7–24.9% of liability-scale variance, with particularly strong prediction for Alzheimer’s disease, type 2 diabetes, prostate cancer, glaucoma, and inflammatory bowel disease, and match or exceed published commercial and academic scores for most traits. Across 12 traits with interpretable within-family estimates of within-sibship-to-population effect ratio, 11 confidence intervals included a ratio of 1. Type 2 diabetes showed an attenuation ratio of 0.86 [0.76, 0.97].
Key findings
- Polygenic risk scores (PRSs) have improved substantially in predictive performance, yet results remain difficult to compare across studies that differ in cohorts, phenotype definitions, and evaluation procedures.
- We report results for 14 binary disease traits.
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Study details
- Effect size24.9%
- Evidence strengthLow
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Preprint — not yet peer-reviewed.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, 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 (medRxiv) |
| 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 | 2 / 3 | Effect reported as a number, without an interval or test |
| 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. |
What each of these 20 components measures, and what this score is not →
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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
Ren, G., Wu, K., Ren, C., Nyiha, I., & Yang, J. (2026). A family-aware All of Us benchmark for polygenic prediction and genomic machine learning. medRxiv. https://doi.org/10.64898/2026.10.22.26366229
Vancouver
Ren G, Wu K, Ren C, Nyiha I, Yang J. A family-aware All of Us benchmark for polygenic prediction and genomic machine learning. medRxiv. 2026. doi:10.64898/2026.10.22.26366229.
BibTeX
@article{ren2026-fa292dde,
author = {Ren, G. and Wu, K. and Ren, C. and Nyiha, I. and Yang, J.},
title = {{A family-aware All of Us benchmark for polygenic prediction and genomic machine learning}},
journal = {medRxiv},
year = {2026},
doi = {10.64898/2026.10.22.26366229},
url = {https://doi.org/10.64898/2026.10.22.26366229}
}
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