The cell-type–specific genetic architecture of chronic pain in brain and dorsal root ganglia
Bibliographic record
Abstract
Chronic pain is a complex clinical problem comprising multiple conditions that may share a common genetic profile. GWAS have identified many risk loci whose cell-type context remains unclear. Here, we integrated GWAS data on chronic pain with single-cell RNA-Seq (scRNA-Seq) data from human brain and dorsal root ganglia (hDRG) and single-cell chromatin accessibility data from human brain and mouse dorsal horn. Pain-associated variants were enriched in glutamatergic neurons, mainly in the prefrontal cortex, hippocampal CA1-3, and amygdala. In hDRG, the hPEP.TRPV1/A1.2 neuronal subtype showed robust enrichment. Chromatin accessibility analyses revealed variant enrichment in excitatory and inhibitory neocortical neurons in the brain and in midventral neurons and oligodendrocyte precursor cells in the mouse dorsal horn. Gene-level heritability in the brain highlighted roles for kinase activity, GABAergic synapses, axon guidance, and neuron projection development. In hDRG, implicated genes were related to glutamatergic signaling and neuronal projection. In cervical DRG of patients with acute versus chronic pain, scRNA-Seq data from neuronal or non-neuronal cells were enriched for chronic pain-associated genes (e.g., EFNB2, GABBR1, NCAM1, SCN11A). This cell-type-specific genetic architecture of chronic pain across central and PNS circuits provides a foundation for targeted translational research.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".