Comprehensive genomic atlas of plasma proteome in the Japanese population: the Nagahama study
Bibliographic record
Abstract
Abstract Combining the plasma proteome with the genome offers insights into diseases. Here, we characterized the genetic architecture of plasma proteins in 1,823 Japanese individuals. We identified 1,876 protein quantitative trait loci (pQTLs) comprising 1,395 variants associated with 1,254 unique proteins, with 77 pQTLs being specific to the Japanese population. Multi-ancestry fine-mapping identified 475 credible sets shared between Japanese cis -pQTLs and European cis -eQTLs. By integrating both cis - and trans -pQTLs, we identified a Japanese-specific trans -pQTL hotspot in the CD36 gene, associated with 10 proteins and linked to decreased platelet and white blood cell counts. Leveraging Mendelian randomization (MR) integrating pQTLs and Biobank Japan genome-wide association study (GWAS), we identified 42 putative causal relationships between 24 proteins and 20 diseases. This analysis identified 11 proteins (FCRL1, KLB, ADH1B, ADH1C, IL1RL1, IL18R1, DPEP1, HP, MICB, IL6R, LRP11) as potential drug targets. Our findings significantly enhance the understanding of the plasma proteomic landscape in an East Asian population and provide a valuable resource for prioritizing population-specific therapeutic targets.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".