Granularity of<i>SERPINA1</i>alleles by DNA sequencing in CanCOLD
Bibliographic record
Abstract
DNA sequencing of theSERPINA1gene to detect α1-antitrypsin (AAT) deficiency (AATD) may provide a better appreciation of the individual and cumulative impact of genetic variants on AAT serum levels and COPD phenotypes. AAT serum level and DNA sequencing of the coding regions ofSERPINA1were performed in 1359 participants of the Canadian Cohort Obstructive Lung Disease (CanCOLD) study. Clinical assessment for COPD included questionnaires, pulmonary function testing and computed tomography (CT) imaging. Phenotypes were tested for association withSERPINA1genotypes collated into four groups: normal (MM), mild (MS and MI), intermediate (heterozygote MZ, non-S/non-Z/non-I, compound IS, and homozygote SS) and severe (ZZ and SZ) deficiency. Smoking strata and MZ-only analyses were also performed. 34 genetic variants were identified including 25 missense mutations. Overall, 8.1% of alleles in this Canadian cohort were deficient and 15.5% of 1359 individuals were carriers of at least one deficient allele. Four AATD subjects were identified and had statistically lower diffusion capacity and greater CT-based emphysema. No COPD phenotypes were associated with mild and intermediate AATD in the overall cohort or stratified by smoking status. MZ heterozygotes had similar CT-based emphysema, but lowered diffusion capacity compared with normal and mild deficiency. In this Canadian population-based cohort, comprehensive genetic testing for AATD reveals a variety of deficient alleles affecting 15.5% of subjects. COPD phenotype was demonstrated in severe deficiency and MZ heterozygotes. This study shows the feasibility of implementing a diagnostic test for AATD using DNA sequencing in a large cohort.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".