Interactions gène-gène et gène-environnement dans les études génétiques de maladies multifactorielles : application à l’asthme et l’atopie
Bibliographic record
Abstract
Asthma results from multiple genetic and environmental factors and from interactions between these factors. The global aim of this thesis was to propose gene-gene and gene-environment interaction strategies of analysis to identify new genes associated with the risk of asthma and atopy. To identify new genes underlying atopy, we have proposed a gene-gene interaction strategy of analysis. This strategy integrates a genome-wide association study (GWAS) to which a statistical filtering of the results is applied, and then a selection of the genes most likely to interact using a text mining method applied to the scientific literature from PubMed. The tests of interactions between genetic variants are applied to the selected gene pairs. These analyzes, conducted in three family studies (n = 3,244), identified an interaction between two genes (ADGRV1 and DNAH5) involved in ciliary mobility, an emerging mechanism in asthma. Our second goal was to identify new genes and gene-environment interactions that influence time-to-asthma onset. A meta-analysis of GWAS of the time-to-asthma onset, conducted in nine studies (n = 19,348), identified a new locus associated with the risk of asthma (16q12) and confirmed four more. Five of these nine studies included environmental factor data on early-life tobacco smoke (ELTS) exposure. We conducted a genome-environment-wide interaction analysis of ELTS exposure on time-to-asthma onset in childhood in the five studies (n = 8,273), using survival analysis methods. The results of all five studies were meta-analyzed and followed by functional annotations. We identified four genes with biologically relevant functions related to tobacco smoke exposure.
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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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".