Two-Level Meta-Analysis of Genetic and Epigenetic Markers of Asthma in Preschool Children

<b>Background</b>: Genetic variants within the 17q21 locus and epigenetic modifications regulating immune function have been associated with childhood asthma, yet reported effect sizes vary across studies due to methodological heterogeneity and differences in study design. <b>Objectives</b>: To systematically synthesize evidence on genetic and epigenetic markers associated with childhood asthma using a two-level random-effects meta-analysis integrating published meta-analyses and independent cohort studies. <b>Methods</b>: PubMed/MEDLINE and Embase were searched for studies published in English between 2011 and 2024. Eligible studies included pediatric populations with asthma or wheeze phenotypes assessing predefined genetic (ORMDL3, GSDMB) or epigenetic (AHRR, FOXP3, CpG loci) markers and reporting odds ratios (ORs) or sufficient data for their derivation. Risk of bias was assessed using established quality criteria for observational studies. Quantitative synthesis was performed using a two-level random-effects model with restricted maximum likelihood estimation. <b>Results</b>: Six studies comprising 51,235 children met the inclusion criteria. The overall pooled estimate demonstrated a significant association between molecular markers and childhood asthma (pooled OR = 1.45; 95% confidence interval (CI) 1.30-1.61). Subgroup analyses showed comparable effects for meta-analytic data (OR = 1.39; 95% CI 1.24-1.56) and cohort studies (OR = 1.47; 95% CI 1.31-1.64). Genetic markers yielded a pooled OR of 1.38 (95% CI 1.21-1.56), while epigenetic markers showed a pooled OR of 1.48 (95% CI 1.27-1.73). Heterogeneity in asthma definitions, methylation platforms, and limited representation of non-European populations may affect generalizability. <b>Conclusions</b>: This systematic review and two-level meta-analysis provides robust evidence that both genetic and epigenetic variations contribute to childhood asthma susceptibility and supports integrative multi-omic approaches for early-life risk stratification.