Sex Differences in the Genetic Basis of Morning Serum Cortisol Levels: Genome-Wide Screen Identifies Two Novel Loci Specific to Women
Bibliographic record
Abstract
CONTEXT: Relatively little is known about the influence of specific genes on cortisol levels, particularly morning cortisol levels. OBJECTIVE: The objective of this study was to identify quantitative trait loci associated with morning serum cortisol levels. DESIGN: We carried out a genome screen for morning serum cortisol using linkage and association methods tailored for use in large pedigrees. We conducted these analyses both in the whole sample and partitioned by sex. SETTING: This study was conducted on nine communal Hutterite farms in South Dakota. PARTICIPANTS: The Hutterites are a young founder population who practice a communal, farming lifestyle in the western United States and in Canada. Hutterites (n = 504, 53% female) aged 11-89 yr from a single pedigree participated in this study. MAIN OUTCOME MEASURES: The main outcome measures were markers significantly linked or associated with variation in morning serum cortisol levels. RESULTS: One genome-wide significant association was identified in the whole sample on 11p (D11S1981, P = 0.000092). Results of sex-partitioned analyses indicated that this association was restricted to females (females, P = 0.000084; males, P = 0.20). The 146-bp allele at this locus accounted for 7% of the variance in morning cortisol values in females, and females homozygous for the allele had an 89% increase in morning cortisol levels compared with female noncarriers. A second genome-wide significant association in females was identified on 14q (D14S74, P = 0.000091). CONCLUSIONS: Our results suggest that the genetic determinants of morning cortisol levels may be different for men and women and that loci on 11p and 14q influence morning cortisol levels in women.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".