We modeled differential methylation between pre- (N=35) and post-natal (N=300) non-psychiatric controls using linear modeling approaches. After normalization, probes on the sex chromosomes were dropped (which are more difficult to accurately normalize), as were probes annotated with single nucleotide polymorphisms (SNPs) at the target CpG or single base extension (SBE) site according to dbSNP142 with minor allele frequency > 1%, leaving 456,513 autosomal probes for age-associated DNAm analysis. All three approaches – single CpG, DMR, and block – utilized the below linear model: pij=αi+βiFetalj+∑k=14γjknegPCjk+εij where pij is the proportion methylation for probe i and subject j, Fetalj in a binary variable indicating if the j’th sample is pre- or post-natal, and negPCj are the negative control principal components estimated from the microarray background probes. Therefore αi represents the mean methylation proportion/level in the postnatal samples, and βi is the difference in the fetal samples. For CpG-level analyses, we fit the above linear model with the limma R/Bioconductor package46 to obtain mean differences, moderated t-statistics and corresponding p-values, which we adjusted by the number of tests (i.e. Bonferroni correction47) to conservatively
