Epigenome-wide contributions to individual differences in childhood phenotypes: a GREML approach

BACKGROUND: DNA methylation is an epigenetic mechanism involved in human development. Numerous epigenome-wide association studies (EWAS) have investigated the associations of DNA methylation at single CpG sites with childhood outcomes. However, the overall contribution of DNA methylation across the genome (R(2)(Methylation)) towards childhood phenotypes is unknown. An estimate of R(2)(Methylation) would provide context regarding the importance of DNA methylation explaining variance in health outcomes. We therefore estimated the variance explained by epigenome-wide cord blood methylation (R(2)(Methylation)) for five childhood phenotypes: gestational age, birth weight, and body mass index (BMI), IQ and ADHD symptoms at school age. We adapted a genome-based restricted maximum likelihood (GREML) approach with cross-validation (CV) to DNA methylation data and applied it in two population-based birth cohorts: ALSPAC (n = 775) and Generation R (n = 1382). RESULTS: Using information from > 470,000 autosomal probes we estimated that DNA methylation at birth explains 32% (SD(CV) = 0.06) of gestational age variance and 5% (SD(CV) = 0.02) of birth weight variance. The R(2)(Methylation) estimates for BMI, IQ and ADHD symptoms at school age estimates were near 0% across almost all cross-validation iterations. CONCLUSIONS: The results suggest that cord blood methylation explains a moderate degree of variance in gestational age and birth weight, in line with the success of previous EWAS in identifying numerous CpG sites associated with these phenotypes. In contrast, we could not obtain a reliable estimate for school-age BMI, IQ and ADHD symptoms. This may reflect a null bias due to insufficient sample size to detect variance explained in more weakly associated phenotypes, although the true R(2)(Methylation) for these phenotypes is likely below that of gestational age and birth weight when using DNA methylation at birth.