Topic: Brain proteins, microglia and lab models
Multi-trait QTL (xQTL) colocalization has shown great promises in identifying causal variants with shared genetic etiology across multiple molecular modalities, contexts, and complex diseases. However, the lack of scalable and efficient methods to integrate large-scale multi-omics data limits deeper insights into xQTL regulation. Here, we propose ColocBoost, a multi-task learning colocalization method that can scale to hundreds of traits, while accounting for multiple causal variants within a genomic region of interest. ColocBoost employs a specialized gradient boosting framework that can adaptively couple colocalized traits while performing causal variant selection, thereby enhancing the detection of weaker shared signals compared to existing pairwise and multi-trait colocalization methods. We applied ColocBoost genome-wide to 17 gene-level single-nucleus and bulk xQTL data from the aging brain cortex of ROSMAP individuals (average N = 595), encompassing 6 cell types, 3 brain regions and 3 molecular modalities (expression, splicing, and protein abundance). Across molecular xQTLs, ColocBoost identified 16,503 distinct colocalization events, exhibiting 10.7({+/-}0.74)-fold enrichment for heritability across 57 complex diseases/traits and showing strong concordance with element-gene pairs validated by CRISPR screening assays. When colocalized against Alzheimers disease (AD) GWAS, ColocBoost identified up to 2.5-fold more distinct colocalized loci, explaining twice the AD disease heritability compared to fine-mapping without xQTL integration. This improvement is largely attributable to ColocBoosts enhanced sensitivity in detecting gene-distal colocalizations, as supported by strong concordance with known enhancer-gene links, highlighting its ability to identify biologically plausible AD susceptibility loci with underlying regulatory mechanisms. Notably, several genes including BLNK and CTSH showed sub-threshold associations in GWAS, but were identified through multi-omics colocalizations which provide new functional support for their involvement in AD pathogenesis.
Key findings
- ColocBoost employs a specialized gradient boosting framework that can adaptively couple colocalized traits while performing causal variant selection, thereby enhancing the detection of weaker shared signals compared to existing pairwise and multi-trait colocalization methods.
- When colocalized against Alzheimers disease (AD) GWAS, ColocBoost identified up to 2.5-fold more distinct colocalized loci, explaining twice the AD disease heritability compared to fine-mapping without xQTL integration.
Why it matters
This study introduced a statistical method for integrating multi-omics data and applied it to aging brain tissue, identifying more colocalized loci and explaining more Alzheimer's disease heritability than fine-mapping alone. It is a genetics methodology study and does not test any treatment, cure or prevention.
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Study details
- Participants595
- Evidence strengthLow
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 34 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for study design, effect reported 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 32 of the 94 points we could judge it on, which is 34 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 | 2 / 4 | 595 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 2 / 7 | Cited 8 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 4.0 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 | 1 / 4 | Cited 8 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Gao T. Wang: h-index 35 (OpenAlex, whole career) |
| Institution's track record | 3 / 3 | Massachusetts Institute of Technology: institution h-index 2,557 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 7 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 | 32 / 94 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 32 of the 94 points we could judge it on is 34 out of 100. |
| Total | 34 / 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's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
APA 7th
Cao, X., Sun, H., Feng, R., Mazumder, R., Najar, C. F. B. A., Li, Y. I., et al. (2026). Integrative multi-omics QTL colocalization maps regulatory architecture in aging human brain. medRxiv (preprint). https://doi.org/10.1101/2025.04.17.25326042
Vancouver
Cao X, Sun H, Feng R, Mazumder R, Najar CFBA, Li YI, et al. Integrative multi-omics QTL colocalization maps regulatory architecture in aging human brain. medRxiv (preprint). 2026. doi:10.1101/2025.04.17.25326042.
BibTeX
@article{cao2026-517396ca,
author = {Cao, X. and Sun, H. and Feng, R. and Mazumder, R. and Najar, C. F. B. A. and Li, Y. I. and De Jager, P. L. and Bennett and others},
title = {{Integrative multi-omics QTL colocalization maps regulatory architecture in aging human brain}},
journal = {medRxiv (preprint)},
year = {2026},
doi = {10.1101/2025.04.17.25326042},
url = {https://doi.org/10.1101/2025.04.17.25326042}
}
Studies this one cites
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