Rare variant analyses in large-scale cohorts identified SLC13A1 associated with chronic pain
Bibliographic record
Abstract
ABSTRACT: Chronic pain is a prevalent disease with increasing clinical challenges. Genome-wide association studies in chronic pain patients have identified hundreds of common pathogenic variants, yet they only explained a portion of individual variance of chronic pain. With the advances in next-generation sequencing technologies, it is now feasible to conduct rarer variants studies in large-scale databases. Here, we performed gene-based rare variant analyses in 200,000 human subjects in the UK biobank whole-exome sequencing database for investigating 9 different chronic pain states and validated our findings in 3 other large-scale databases. Our analyses identified the SLC13A1 gene coding for sodium/sulfate symporter associated with chronic back pain and multisite pain at the genome-wide level and with chronic headache, knee, and neck and shoulder pain at the nominal level. Seven loss-of-function rare variants were identified within the gene locus potentially contributing to the development of chronic pain, with 2 of them individually associated with back pain and multisite pain. These 2 rare variants were then tested for replication in 3 other biobanks, and the strongest evidence was found for rs28364172 as an individual contributor. Transcriptional analyses of Slc13a1 in rodents showed substantial regulation of its expression in the dorsal root ganglia and the sciatic nerve in neuropathic pain assays. Our results stress the importance of the SLC13A1 gene in sulfate homeostasis in the nervous system and its critical role in preventing pain states, thus suggesting new therapeutic approaches for treating chronic pain in a personalized manner, especially in people with mutations in the SLC13A1 gene.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".