Effect of Human Genetic Variability on Gene Expression in Dorsal Root Ganglia and Association with Pain Phenotypes
Bibliographic record
Abstract
Dorsal root ganglia (DRG) relay sensory information to the brain, giving rise to the perception of pain, disorders of which are prevalent and burdensome. Here, we mapped expression quantitative trait loci (eQTLs) in a collection of human DRGs. DRG eQTLs were enriched within untranslated regions of coding genes of low abundance, with some overlapping with other brain regions and blood cell cis-eQTLs. We confirm functionality of identified eQTLs through their significant enrichment within open chromatin and highly deleterious SNPs, particularly at the exon level, suggesting substantial contribution of eQTLs to alternative splicing regulation. We illustrate pain-related genetic association results explained by DRG eQTLs, with the strongest evidence for contribution of the human leukocyte antigen (HLA) locus, confirmed using a mouse inflammatory pain model. Finally, we show that DRG eQTLs are found among hits in numerous genome-wide association studies, suggesting that this dataset will help address pain components of non-pain disorders.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".