Influence of gene-by-sex interaction on time-to-asthma onset: a large-scale genome-wide meta-analysis
Bibliographic record
Abstract
Background: Asthma is a complex disease with sex-specific differences in prevalence, clinical and biological features. Asthma is more prevalent in males during childhood, while it becomes more frequent in females in adolescence and adulthood. The mechanisms behind these sex-specific differences are not well understood and may involve hormonal changes together with differential genetic predisposition. Objective: Our goal was to identify genetic variants interacting with sex that influence time-to-asthma onset (TAO). Methods: We conducted a large-scale meta-analysis of nine genome-wide interaction studies (GEWIS) of TAO (totaling 7,104 men and 6,970 females of European ancestry) using survival analysis methods applied to pediatric and adult asthmatic and non-asthmatic subjects. Results: We detected three independent loci showing SNP×Sex interaction at the 10-5 level. The most significant association with TAO was female-specific in an intergenic region at 5q32 (Pfemale = 9.1x10-8 versus Pmale=0.56). The other two associations were male-specific: within SORCS2 intron 2 at 4q16 (Pmale=1.3x10-7 versus Pfemale=0.15) and within DGKB intron 1 at 7p21 (Pmale= 3.9x10-7 versus Pfemale=0.23). Functional annotations indicated co-localization of these genetic variants with epigenetic marks and DNA regulatory elements in fibroblasts, lung or blood. Conclusion: By testing gene-by-sex interactions, we identified novel loci influencing asthma risk in a sex-specific manner. Candidate genes in these loci are involved in inflammatory process and immune cell regulation. Further replication of these findings are ongoing.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.037 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".