Human pain genetics database: a resource dedicated to human pain genetics research
Bibliographic record
Abstract
The Human Pain Genetics Database (HPGDB) is a comprehensive variant-focused inventory of genetic contributors to human pain. After curation, the HPGDB currently includes 294 studies reporting associations between 434 distinct genetic variants and various pain phenotypes. Variants were then submitted to a comprehensive analysis. First, they were validated in an independent high-powered replication cohort by testing the association of each variant with 10 different pain phenotypes (n = 1320-26,973). One hundred fifty-five variants replicated successfully (false discovery rate 20%) in at least one pain phenotype, and the association P values of the HPGDB variants were significantly lower compared with those of random controls. Among the 155 replicated variants, 21 had been included in the HPGDB because of their association with analgesia-related and 13 with nociception-related phenotypes, confirming analgesia and nociception as pathways of vulnerability for pain phenotypes. Furthermore, many genetic variants were associated with multiple pain phenotypes, and the strength of their association correlated between many pairs of phenotypes. These genetic variants explained a considerable amount of the variance between different pairs of pain phenotypes, indicating a shared genetic basis among pain phenotypes. In addition, we found that HPGDB variants show many pleiotropic associations, indicating that genetic pathophysiological mechanisms are also shared among painful and nonpainful conditions. Finally, we demonstrated that the HPGDB data set is significantly enriched for functional variants that modify gene expression, are deleterious, and colocalize with open chromatin regions. As such, the HPGDB provides a validated data set that represents a valuable resource for researchers in the human pain field.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.122 | 0.049 |
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".