Genetic and Epigenetic Determinants of Tissue Factor Pathway Inhibitor Plasma Levels
Bibliographic record
Abstract
Tissue factor pathway inhibitor (TFPI) regulates the intravascular formation of blood clots, and low TFPI plasma levels increase the risk of both arterial and venous thrombosis. TFPI plasma levels are also heritable, but few genes have been implicated. The objectives of my thesis were to identify genetic variants associated with TFPI plasma levels, and to integrate epigenetic data to better understand the molecular mechanisms by which these variants affect TFPI plasma levels. I started with a Human Genome Epidemiology (HuGE) review of genetic variants associated with TFPI plasma levels (manuscript 1). I meta-analyzed associations between four TFPI variants and TFPI plasma levels, and confirmed an association with rs5940 and rs7586970. I next conducted a linkage analysis of TFPI plasma levels in 251 individuals from the F5L Family Study (manuscript 2) and replicated a linkage region on chromosome 2q identified in the only genome-wide study of TFPI plasma levels reported to date. I meta-analyzed SNPs in the region across three European ancestry study samples, and implicated rs62187992 in the chromosome 2q linkage result, and in the risk of venous thromboembolism in the INVENT Collaboration dataset. Public genomic resources supported three-dimensional looping of the rs62187992 region to the promoter of IKZF2, whereas nearby whole blood DNA methylation did not mediate the association between rs62187992 and TFPI plasma levels in either the F5L Family or MARTHA studies. I last conducted a genome-wide association scan (GWAS) of TFPI plasma levels in the F5L Family Study (manuscript 3). I found that TFPI plasma level-associated SNPs were enriched in promoters and enhancers in vascular endothelial cells, a cell type that expresses TFPI, and I leveraged this enrichment in a prioritized GWAS via stratified false discovery rate control. While I did not identify novel SNPs, I nonetheless outlined a strategy for incorporating epigenetic data into prioritized GWAS. This thesis is the most comprehensive assessment of genetic risk factors for TFPI plasma levels, and integrates epigenetic data to understand biological mechanisms - a first step towards new therapies that will reduce the public health burden of thrombosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".