A genome‐wide association study (GWAS) of the human plasma proteome
Bibliographic record
Abstract
High‐abundance plasma proteins are involved in disease‐associated pathways and are useful biomarkers of disease states and dietary exposures. The concentrations of these proteins vary across individuals, but it is unclear whether the observed differences are related to inter‐individual genetic variation. The objective was to explore associations between genome‐wide genetic variants and 54 plasma proteins belonging to disease‐associated pathways in healthy young adults (n=488) from the Toronto Nutrigenomics and Health Study. We report the first GWAS to be conducted on the human plasma proteome. Protein concentrations were measured by a multiple reaction monitoring HPLC‐MS/MS assay. Linear regressions with an additive mode of inheritance were used to explore the associations between genetic variants and plasma proteins. We identified 40 variants in 12 genes that were significantly ( p < 1.13×10 −9 ) associated with the proteins analyzed. Variants in genes that encode α 1B ‐glycoprotein, gelsolin isoform 1, histidine‐rich glycoprotein, and α 2 ‐HS‐glycoprotein were each associated with circulating levels of their respective protein. These results suggest that common genetic variants modify circulating levels of plasma proteins involved in actin scavenging, inflammation, and other immune responses. Research support from the Advanced Foods and Materials Network.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".