Genome-Wide Association Study of Distressing Premenstrual Symptoms in Two Nordic Populations
Bibliographic record
Abstract
ABSTRACT Background Premenstrual disorders (PMDs) are characterized by affective and physical symptoms before menses, likely due to abnormal sensitivity to normal hormone fluctuations. While sizable heritability has been indicated in twin studies, there are no genome-wide association studies (GWAS) to inform the genetic architecture of PMDs. Methods We conducted a GWAS of 17,511 women with distressing premenstrual symptoms (DPS) and 54,789 women controls of European ancestry from two Nordic population-based cohorts. DPS were assessed using questionnaire or identified as a clinical diagnosis of PMDs in the nationwide healthcare registers. GWAS was performed in each study before meta-analysis, analyses of single nucleotide polymorphism (SNP)-based heritability (h 2 ) and genetic correlations to psychosocial and gynecological phenotypes, as well as blood levels of gonadal steroids. Results In the meta-analysis, one locus at 12p13.3 (rs758170, CACNA1C , P=1.53×10 −8 , OR=0.93, 95% CI 0.90-0.95) was associated with DPS. The SNP-based heritability was estimated 0.072 (SE=0.01, P=2.46 ×10 −12 ). Statistically significant genetic correlations (rg) were found between DPS and all major psychiatric disorders, with the strongest correlation with major depression (rg=0.62, CI 0.49-0.74, P=3.04×10 −22 ). Weaker correlations were noted to gynecological conditions such as endometriosis (rg=0.17, CI 0.01-0.32, P=0.029), while gonadal steroid hormone levels in blood were uncorrelated. Conclusion This study provides the first direct insights into the genetic architecture of PMDs by identifying a SNP associated with DPS and genetic correlations to other conditions. If confirmed in larger independent populations, these findings may advance our understanding of the underlying mechanisms of PMDs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".