Genome-wide association and linkage analysis of histidine-rich glycoprotein identifies common variants associated with plasma histidine-rich glycoprotein concentrations
Bibliographic record
Abstract
Background The plasma protein histidine-rich glycoprotein (HRG) interacts with multiple plasma ligands with various roles in coagulation, immunity, and angiogenesis. Through its inhibition of factor XIIa, HRG regulates the contact pathway of blood coagulation. Plasma HRG concentrations are highly heritable and vary widely, which may impact HRG function. Objectives To determine the genetic factors contributing to HRG variability. Methods Plasma HRG concentrations were measured in a healthy sibling cohort of 1152 subjects and a second healthy cohort of 2304 individuals of Irish descent. We performed genome-wide association study and meta-analysis on the European subset of these cohorts. Using the sibling subset of the 2 cohorts ( n = 934 in 460 sibships), we explored linkage patterns to identify additional signals associated with variation in HRG concentrations that may be driven by rare variants. Two HRG missense variants associated with decreased HRG concentrations were expressed in vitro . Results Narrow-sense heritability was estimated at 69%. Meta-analysis identified an association between HRG concentrations and 2 independent signals at the HRG locus. Variants at these chromosome 3 loci collectively explained 45% of the variation in HRG concentrations. In vitro expression of 2 HRG variants associated with decreased HRG concentrations had no impact on HRG secretion. Linkage analysis of HRG concentrations identified 3 further regions contributing to differences in HRG concentrations. Conclusion The results of this genome-wide association study, investigating HRG concentration variation in a healthy population, provide new insights into the genetic control of circulating HRG concentrations and generate data for colocalization and Mendelian randomization studies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".