Topic: Brain proteins, microglia and lab models
Examples include subpopulations of CD4+ T cells associated with inflammatory bowel disease, characterized by enrichment for a multi-cytokine phenotype and overlap with the naive NF-kB-activated, central memory, and effector memory CD4+ T subtypes, and subpopulations of microglia associated with Alzheimers disease, characterized by depletion of homeostatic programs and localization to the midtemporal gyrus, dorsolateral prefrontal cortex, and medial entorhinal cortex. Separately, disease relationships defined by scDRS-FM score correlations across cells revealed similarities beyond genetic correlations and capture convergence in pathway activity. Overall, scDRS-FM provides a principled and powerful framework for fine-mapping disease-relevant cellular contexts from GWAS and scRNA-seq data.
Key findings
- Examples include subpopulations of CD4+ T cells associated with inflammatory bowel disease, characterized by enrichment for a multi-cytokine phenotype and overlap with the naive NF-kB-activated, central memory, and effector memory CD4+ T subtypes, and subpopulations of microglia associated with Alzh
- Separately, disease relationships defined by scDRS-FM score correlations across cells revealed similarities beyond genetic correlations and capture convergence in pathway activity.
- Overall, scDRS-FM provides a principled and powerful framework for fine-mapping disease-relevant cellular contexts from GWAS and scRNA-seq data.
Why it matters
This computational method identified subpopulations of microglia associated with Alzheimer's disease, characterized by depletion of homeostatic programs and localization to specific brain regions. It is a research framework for fine-mapping disease-relevant cell types from genetic and single-cell data, not a treatment or cure.
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Study details
- Evidence strengthLow
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 22 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 21 of the 94 points we could judge it on, which is 22 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 | 0 / 7 | Cited 0 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.0 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 | 0 / 6 | No other study in this library cites it |
| Still being cited | 0 / 4 | Cited 0 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 1 / 4 | Last-listed author Martin Jinye Zhang: h-index 18 (OpenAlex, whole career) |
| Institution's track record | 3 / 3 | University of Washington: institution h-index 3,540 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 4 institution(s) in 1 country |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 2 / 2 | Free to read |
| Points | 21 / 94 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 21 of the 94 points we could judge it on is 22 out of 100. |
| Total | 22 / 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. Check the citation against the original before you use it.
APA 7th
Turcan, A., Hou, K., Lin, K. Z., Pfenning, A., Sakaue, S., & Zhang, M. J. (2026). Conditional polygenic enrichment distinguishes causal from tagging disease-critical cell populations in single-cell RNA-seq. medRxiv (preprint). https://doi.org/10.64898/2026.08.20.26360914
Vancouver
Turcan A, Hou K, Lin KZ, Pfenning A, Sakaue S, Zhang MJ. Conditional polygenic enrichment distinguishes causal from tagging disease-critical cell populations in single-cell RNA-seq. medRxiv (preprint). 2026. doi:10.64898/2026.08.20.26360914.
BibTeX
@article{turcan2026-c0254966,
author = {Turcan, A. and Hou, K. and Lin, K. Z. and Pfenning, A. and Sakaue, S. and Zhang, M. J.},
title = {{Conditional polygenic enrichment distinguishes causal from tagging disease-critical cell populations in single-cell RNA-seq}},
journal = {medRxiv (preprint)},
year = {2026},
doi = {10.64898/2026.08.20.26360914},
url = {https://doi.org/10.64898/2026.08.20.26360914}
}