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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".