Multi-ancestry proteome-phenome-wide Mendelian randomization offers a comprehensive protein-disease atlas and potential therapeutic targets
Bibliographic record
Abstract
Abstract Circulating proteins influence disease risk and are valuable drug targets. To enhance the discovery of protein-phenotype associations and identify potential therapeutic targets across diverse populations, we conducted proteome-phenome-wide Mendelian randomization in three ancestries, followed by comprehensive sensitivity analyses. We tested the potential causal effects of up to 2,265 unique proteins on a curated list of 355 distinct phenotypes, assessing 726,035 protein-phenotype pairs in European, 33,078 in African, and 115,352 in East Asian ancestries. Notably, 119 proteins were instrumentable only in African ancestry and 17 proteins only in East Asian ancestry due to allele frequency differences that are common in these ancestries but rare in European ancestry. We identified 3,949, 56, and 325 unique protein-phenotype pairs in European, African, and East Asian ancestries, respectively, and assessed their druggability using multiple databases. We highlighted the causal role of IL1RL1 in inflammatory bowel diseases, supported by multiple orthogonal lines of evidence. Taken together, this study underscores the importance of multi-ancestry inclusion and offers a comprehensive atlas of protein-phenotype associations across three ancestries, enhancing our understanding of proteins involved in disease etiology and potential therapeutic targets. Results are available at the Common Metabolic Diseases Knowledge Portal ( https://broad.io/protein_mr_atlas ).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".