The importance of environment on respiratory genotype/phenotype relationships in the Inuit
Bibliographic record
Abstract
To cite this article: Candelaria PV, Backer V, Khoo S‐K, Bizzintino JA, Hayden CM, Baynam G, Laing IA, Zhang G, Porsbjerg C, Goldblatt J, LeSouëf PN, The Greenlandic Study Population Group. The importance of environment on respiratory genotype/phenotype relationships in the Inuit. Allergy 2010; 65: 229–237. Abstract Background: Genetic and environmental influences and their interactions are central to asthma pathogenesis. This study aimed to investigate the effects of different macro‐environments on asthma genotype–phenotype associations in two geographically separated populations with common ancestry. Methods: To accomplish this, two unselected populations of Inuit were recruited, one living in Greenland (n = 618) and the other in Denmark (n = 739). Subjects were genotyped for CD14 C‐159T, SCGB1A1 A38G, ADRB2 Arg16Gly and Gln27Glu. The resulting genetic data were analysed for relationships with asthma‐related parameters including lung function, ever asthma, atopy, rhinitis and dermatitis. Results: The results showed contrasting magnitude and direction of genetic associations between the two geographically separate Inuit populations. In Greenland, the ADRB2 16Arg allele was associated with male‐specific lower lung function, but in Denmark the same allele was associated with male‐specific higher lung function. This allele was also associated with higher incidence of ever asthma in Denmark but not in Greenland. The SCGB1A1 38A allele was associated with lower rhinitis prevalence in Greenland but not in Denmark. Conclusions: These associations suggest that environment interacts with candidate asthma genes to modulate asthma pathogenesis in the Inuit.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".