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.

Background: Multiplex tests allow for measurement of allergen-specific IgE responses to multiple allergen extracts and components and have several advantages for large cohort studies. Due to significant methodological differences, test systems are difficult to integrate in meta-analyses/systematic reviews since there is a lack of datasets with direct comparison. We aimed to create models for statistical integration of allergen-specific IgE to peanut/tree nut allergens from three IgE-test platforms. Methods: Plasma from Canadian and Austrian children with peanut/tree nut sensitization and a cohort of sensitized, high-risk, pre-school asthmatics (total n=166) were measured with three R&D multiplex IgE test platforms: Allergy Explorer, ALEX (Macro Array Dx), MeDALL-chip (Mechanisms of Development of Allergy) (Thermo Fisher), and EUROLINE (EUROIMMUN). Skin prick test (n=51) and ImmunoCAP (n=62) results for extracts were available in a subset. Regression models (Multivariate Adaptive Regression Splines, local polynomial regression) were applied if >30% of samples were positive to the allergen. Intra-test correlations between PR-10 and nsLTP allergens were assessed. Results: Using two regression methods, we demonstrated the ability to model allergen-specific relationships with acceptable measures of fit (r2=94-56%) for peanut and tree nut sIgE testing at the extract and component-level, in order from highest to lowest: Ara h 2, Ara h 6, Jug r 1, Ana o 3, Ara h 1, Jug r 2, Cor a 9. Conclusion: Our models support the notion that conversion is reasonably possible between sIgE multiplex platforms for allergen extracts and components and may provide options to aggregate data for future meta-analysis.