Sex-Specific Genetic Determinants of Asthma-COPD Phenotype and COPD in Middle-Aged and Older Canadian Adults: An Analysis of CLSA Data
Bibliographic record
Abstract
The etiology of sex differences in the risk of asthma-COPD phenotype and COPD is still not completely understood. Genetic and environmental risk factors are commonly believed to play an important role. This study aims to identify sex-specific genetic markers associated with asthma-COPD phenotype and COPD using the Canadian Longitudinal Study on Aging (CLSA) Baseline Comprehensive and Genomic data. There were a total of 1,415 COPD cases. Out of them, 504 asthma-COPD phenotype cases were identified. 20,524 participants without a diagnosis of asthma and COPD served as controls. We performed genome-wide SNP-by-sex interaction analysis. SNPs with an interaction p-value < 10−5 were included in a sex-stratified multivariable logistic regression for asthma-COPD phenotype and COPD outcomes. 18 and 28 SNPs had a significant interaction term p-value < 10−5 with sex in the regression analyses of asthma-COPD phenotype and COPD outcomes, respectively. Sex-stratified multivariable analysis of asthma-COPD phenotype showed that 7 SNPs in/near SMYD3, FHIT, ZNF608, RIMBP2, ZNF133, BPIFB1, and S100B loci were significant in males. Sex-stratified multivariable analysis of COPD showed that 8 SNPs in/near MAGI1, COX18, OSTC, ELOVL5, C7orf72 FGF14, and NKAIN4 were significant in males, and 4 SNPs in/near genes CAMTA1, SATB2, PDE10A, and LINC00908 were significant in females. An SNP in the ZPBP gene was associated with COPD in both males and females. Identification of sex-specific loci associated with asthma-COPD phenotype and COPD may offer valuable evidence toward a better understanding of the sex-specific differences in the pathophysiology of the diseases.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".