Genetics of cluster headache
Bibliographic record
Abstract
Background Cluster headache is the most severe primary headache disorder. A genetic basis has long been suggested by family and twin studies; however, little is understood about the genetic variants that contribute to cluster headache susceptibility. Methods We conducted a literature search of the MEDLINE database using the PubMed search engine to identify all human genetic studies for cluster headache. In this article we provide a review of those genetic studies, along with an overview of the pathophysiology of cluster headache and a brief review of migraine genetics, which have both been significant drivers of cluster headache candidate gene selection. Results The investigation of cluster headache genetic etiology has been dominated by candidate gene studies. Candidate selection has largely been driven by the pathophysiology, such as the striking rhythmic nature of the attacks, which spurred close examination of the circadian rhythm genes CLOCK and HCRTR2. More recently, unbiased genetic approaches such as genome-wide association studies (GWAS) have yielded new genetic avenues of interest including ADCYAP1R1 and MME. Conclusions The majority of candidate genes studied for cluster headache suffer from poor reproducibility. Broader genetic interrogation through larger unbiased GWAS, exome, and whole genome studies may provide more robust candidates, and in turn provide a clearer understanding of the causes of cluster headache.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".