Unveiling genetic signatures of immune response in immune-related diseases through single-cell eQTL analysis across diverse conditions
Bibliographic record
Abstract
Deciphering the intricate regulatory mechanisms underlying biological processes holds promise for elucidating how genetic variants contribute to immune-related disorders. We map genetic effects on gene expression (expression quantitative trait locus, eQTL) using single-cell transcriptomes of 152 samples from 38 healthy individuals, covering baseline state and lipopolysaccharide challenge either before or after Bacillus Calmette-Guerin vaccination. Interestingly, we uncover a monocyte eQTL linked to the LCP1, shedding light on inter-individual variations in trained immunity. Furthermore, we elucidate genetic and epigenetic regulatory networks of CD55 and SLFN5. Of note, our results support the pivotal roles of SLFN5 in COVID-19 pathogenesis by incorporating disease-associated loci, chromatin accessibility, and transcription factor binding affinities, aligning with the established functions of SLFN5 in restricting virus replication during viral infection. Our study provides a paradigm to decipher genetic underpinnings of complex traits by integrating single-cell eQTLs with multi-omics data from patients and public databases.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".