Background Season of birth influences allergy risk, however the biological mechanisms underlying this observation are unclear. The environment affects DNA methylation, with potentially long-lasting effects on gene expression and disease. This study examined whether DNA methylation could underlie the association between season of birth and allergy. Methods In a subset of 18-year-old participants from the Isle of Wight (IoW) birth cohort (n=367), the risks of birth season on allergic outcomes were estimated. Whole blood epigenome-wide DNA methylation was measured, and season-associated CpGs detected using a training-and-testing-based technique. Validation examined the 8-year-old Prevention and Incidence of Asthma and Mite Allergy (PIAMA) cohort. The relationships between DNA methylation, season of birth and allergy were examined. CpGs were analysed in IoW third generation cohort newborns. Results Autumn birth increased risk of eczema, relative to spring birth. Methylation at 92 CpGs showed association with season of birth in the epigenome-wide association study. In validation significantly more CpGs had the same directionality than expected by chance, and four were statistically significant. Season-associated methylation was enriched among networks relating to development, the cell cycle, and apoptosis. Twenty CpGs were nominally associated with allergic outcomes. Two CpGs were marginally on the causal pathway to allergy. Season-associated methylation was largely absent in newborns, suggesting it arises postnatally. Conclusions This study demonstrates that DNA methylation in adulthood is associated with season of birth, supporting the hypothesis that DNA methylation could mechanistically underlie the effect of season of birth on allergy, though other mechanisms are also likely to be involved.

FLG-LOF mutations are a significant risk factor for later childhood asthma and rhinitis. However, the pathway to asthma is only through early childhood eczema while a direct effect was observed for childhood rhinitis.

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We propose a Bayesian variable selection method in semi-parametric models with applications to genetic and epigenetic data (e.g., single nucleotide polymorphisms and DNA methylation, respectively). The data are individually standardized to reduce heterogeneity and facilitate simultaneous selection of categorical (single nucleotide polymorphisms) and continuous (DNA methylation) variables. The Gaussian reproducing kernel is applied to the transformed data to evaluate joint effect of the variables, which may include complex interactions between, e.g., single nucleotide polymorphisms and DNA methylation. Indicator variables are introduced to the model for the purpose of variable selection. The method is demonstrated and evaluated using simulations under different scenarios. We apply the method to identify informative DNA methylation sites and single nucleotide polymorphisms in a set of genes based on their joint effect on allergic sensitization. The selected single nucleotide polymorphisms and methylation sites have the potential to serve as early markers for allergy prediction, and consequently benefit medical and clinical research to prevent allergy before its manifestation.

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