Genetic Predictors of Fibrin D-Dimer Levels in Healthy Adults
Bibliographic record
Abstract
BACKGROUND: Fibrin fragment D-dimer, one of several peptides produced when crosslinked fibrin is degraded by plasmin, is the most widely used clinical marker of activated blood coagulation. To identity genetic loci influencing D-dimer levels, we performed the first large-scale, genome-wide association search. METHODS AND RESULTS: A genome-wide investigation of the genomic correlates of plasma D-dimer levels was conducted among 21 052 European-ancestry adults. Plasma levels of D-dimer were measured independently in each of 13 cohorts. Each study analyzed the association between ≈2.6 million genotyped and imputed variants across the 22 autosomal chromosomes and natural-log–transformed D-dimer levels using linear regression in additive genetic models adjusted for age and sex. Among all variants, 74 exceeded the genome-wide significance threshold and marked 3 regions. At 1p22, rs12029080 (P=6.4×10(-52)) was 46.0 kb upstream from F3, coagulation factor III (tissue factor). At 1q24, rs6687813 (P=2.4×10(-14)) was 79.7 kb downstream of F5, coagulation factor V. At 4q32, rs13109457 (P=2.9×10(-18)) was located between 2 fibrinogen genes: 10.4 kb downstream from FGG and 3.0 kb upstream from FGA. Variants were associated with a 0.099-, 0.096-, and 0.061-unit difference, respectively, in natural-log–transformed D-dimer and together accounted for 1.8% of the total variance. When adjusted for nonsynonymous substitutions in F5 and FGA loci known to be associated with D-dimer levels, there was no evidence of an additional association at either locus. CONCLUSIONS: Three genes were associated with fibrin D-dimer levels. Of these 3, the F3 association was the strongest, and has not been previously reported.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".