Trans-eQTLs Can Be Used to Identify Tissue-Specific Gene Regulatory Networks
Bibliographic record
Abstract
Previous high-throughput screening studies have indicated that trans-eQTLs tend to be tissue-specific. This study investigates whether this feature can be used to identify tissue-specific gene regulatory networks. eQTL data for 19,960 genes were obtained from the eQTLGen study. Next, eQTLs displaying both cis- and trans-regulatory effects (p < 5 × 10−8) were selected, and the association between their corresponding genes was examined by Mendelian randomization. The findings were further validated using eQTL data from the INTERVAL study. The trans-regulatory impact of 138 genes on 342 genes was detected (p < 5 × 10−8). The majority of the identified gene-pairs were aggregated into networks with scale-free topology. An examination of the function of genes indicates they are involved in immune processes. The hub genes primarily shared transcription regulation activity and were associated with blood cell traits. The hub gene, DDAH2, impacted several metabolic and autoimmune disorders. On average, a gene in the network was under the regulatory control of 34 cis-eQTLs and 6 trans-eQTLs, and genes with higher heritabilities tended to exert higher regulatory impacts. This study reports tissue-specific gene regulatory networks can be detected by investigating their genomic underpinnings. The identified networks displayed scale-free topology, indicating that hub genes within a network could be targeted to correct abnormalities.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".