Meta-Analysis of Genome-Wide Association Studies in >80 000 Subjects Identifies Multiple Loci for C-Reactive Protein Levels
Bibliographic record
Abstract
BACKGROUND: C-reactive protein (CRP) is a heritable marker of chronic inflammation that is strongly associated with cardiovascular disease. We sought to identify genetic variants that are associated with CRP levels. METHODS AND RESULTS: We performed a genome-wide association analysis of CRP in 66 185 participants from 15 population-based studies. We sought replication for the genome-wide significant and suggestive loci in a replication panel comprising 16 540 individuals from 10 independent studies. We found 18 genome-wide significant loci, and we provided evidence of replication for 8 of them. Our results confirm 7 previously known loci and introduce 11 novel loci that are implicated in pathways related to the metabolic syndrome (APOC1, HNF1A, LEPR, GCKR, HNF4A, and PTPN2) or the immune system (CRP, IL6R, NLRP3, IL1F10, and IRF1) or that reside in regions previously not known to play a role in chronic inflammation (PPP1R3B, SALL1, PABPC4, ASCL1, RORA, and BCL7B). We found a significant interaction of body mass index with LEPR (P<2.9×10(-6)). A weighted genetic risk score that was developed to summarize the effect of risk alleles was strongly associated with CRP levels and explained ≈5% of the trait variance; however, there was no evidence for these genetic variants explaining the association of CRP with coronary heart disease. CONCLUSIONS: We identified 18 loci that were associated with CRP levels. Our study highlights immune response and metabolic regulatory pathways involved in the regulation of chronic inflammation.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.020 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".