ighted a large degree of variation in methylation even between MZ twins. This suggests that the maternal in utero environment, including placentation and nutrient supply, may have an important influence on the neonatal epigenome. In a study of DNA methylation in maternal infant pairs Kile et al found variable levels of correlation between methylation patterns of mothers and their offspring, indicative of some level of environmental contribution to this discordance. Many prenatal exposures have been linked to variation in DNA methylation including smoking, depression and under- or over-nutrition. Folate exposure in utero has been implicated in the determination of spinal bone mineral density at age 9 years in the ALSPAC cohort which could in turn plausibly impact upon attained height. However, an assessment of the relationship between maternal folate intake during pregnancy, MTHFR genotype and body fat at age 9 years in this cohort showing no association between these factors MedChemExpress Go 6983 indicates that folate exposure in utero is unlikely to explain the associations observed between cord blood methylation and fat mass in the current study. A further and extremely relevant potential contributor to interindividual variation in methylation at birth is the genetic determination of DNA methylation patterns. Variation in gene expression arising from allele-specific DNA methylation is well documented. Cis acting genetic variation might determine DNA methylation levels and explain some of the interindividual variation in methylation levels at birth. These changes would be expected to be stable over time and could not strictly be considered as `programmed’ events, rather inherited phenomena. The search for determinants of methylation variation at birth should include both genetic and environmental factors at play during the in utero period. The current study has a number of limitations; the Illumina Cancer Panel I array was used to derive quantitative measures of gene-specific DNA methylation. This array only contained a fraction of 
those genes observed to be differentially expressed in our high vs low BMI analysis. Furthermore, it is biased heavily towards tumour suppressor genes, oncogenes, DNA repair genes, cell cycle control, apoptosis and differentiation genes, X-linked and imprinted genes. This bias was overcome to a degree by the PubMed ID:http://www.ncbi.nlm.nih.gov/pubmed/22189475 targeted approach and only using data from those genes implicated in the determination of body composition. Due to the technology employed, the reported associations rely on a few `representative’ CpG sites for each gene interrogated. A more comprehensive analysis of the gene regions of interest is required to gain a detailed understanding of the relationship between DNA methylation, gene regulation and phenotype. As with all studies of this nature, multiple testing limits the robustness of the inferences that can be made about the observed associations. By applying robust statistical approaches we consider that we have minimised the potential for false positives, however further studies are required to establish the true validity of our observations. In addition to replication of the reported observations in other cohorts, further investigation of temporal variation in DNA methylation patterns from birth across childhood would be highly informative, together with examination of their relationship with developmental trajectories of body composition traits. The use of novel approaches for strengthening causal inference could a
