Interactions with early-life exposures modulate polygenic risk of wheeze and asthma in preschool-aged children
Bibliographic record
Abstract
Background: Asthma is a multifactorial disease with numerous associated genetic and environmental risk factors, however, gene-environment interactions are poorly understood in modulating disease risk. This study determines the polygenic effects of multiple genetic loci and interactions with environmental exposures during early infancy on risk of recurrent wheeze and asthma in pre-school aged children. Methods: We conducted genome-wide association studies (GWAS) and applied a thresholding method to calculate genetic risk scores (GRS) of recurrent wheeze and asthma in 2835 children of the CHILD Cohort Study. Recurrent wheeze was defined as two or more episodes in one year between ages 2-5 years and asthma was diagnosed at age 5 years. In addition, we tested for interaction effects between the GRS and environmental exposures on these respiratory outcomes. Results: GWAS identified associations with known asthma loci on chromosome 17q12 - 17q21 (p < 5e-8). GRS analysis determined that the weighted addition of alleles at four childhood-asthma loci correlated with more than 2-fold higher prevalence of recurrent wheeze (p =1.5e-08) and asthma (p = 9.4e-08) between high vs. low GRS groups. In addition, the GRS interacts with breastfeeding (p = 0.02) and traffic air pollution (NO2; p < 0.01) during the first year of life to modulate risk of recurrent wheeze and childhood-onset asthma. Conclusions: This study reports polygenic effects of multiple genetic loci, which interact with early-life exposures, to determine risk of respiratory outcomes during early childhood. Thus, asthma risk may be determined early in infancy when exposures may modulate genetic risk.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".