Trans-eQTLs Can Be Used to Identify Tissue-Specific Gene Regulatory Networks
Bibliographic record
Abstract
Previous high throughput screening studies indicated trans-eQTLs tend to be tissue specific. In this study, I probed if this feature can be used to find tissue-specific gene regulatory networks. eQTL data for 19,960 genes were obtained from the eQTLGen study. Next, eQTLs that display both cis and trans regulatory effects (P<5e-8) were selected and the association between their corresponding genes were examined by Mendelian randomization. The findings were once more validated using eQTL data from the INTERVAL study. The trans-regulatory impact of 138 genes on 342 genes were detected (P≤5e-8). Majority of the identified gene-pairs aggregated into networks with scale free topology. Examining the function of genes indicated they are involved in immune processes. The hub genes mainly shared transcription regulation activity. On average a gene in the network was under the regulatory control of 34 cis-eQTLs and 6 trans-eQTLs and genes with higher heritability tended to exert higher regulatory impact. This study indicates tissue-specific gene-regulatory networks can be detected by investigating their genomic underpinnings. The identified networks displayed scale-free topology indicating hub genes of a network could be targeted to correct abnormalities at cellular level.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".