Unithiol

All computations and statistical analyses were performed using R 3.0.2⁴² and Bioconductor 2.13^{43 (link)}. Signal intensities were imported into R using the methylumi package⁴⁴ as a methylumi object. Initial quality control checks were performed using functions in the methylumi package to assess concordance between reported and genotyped gender. Non-CpG SNP probes on the array were also used to confirm that all four brain regions and matched bloods were sourced from the same individual in the London Cohort and two brain regions in the Mount Sinai cohort where expected. Data was pre-processed in the R package wateRmelon using the dasen function as previously described^{11 (link)}. Array data for each of the tissues was normalized separately and initial analyses were performed separately by tissue. The effects of age and sex were regressed out before subsequent analysis. For identification of DMPs specifically altered with respect to neuropathological measures of AD, we performed a quantitative analysis where samples were analyzed using linear regression models in respect to Braak stage (London N = 117, Mount Sinai N = 144) and amyloid burden (Mount Sinai N = 144). We used a two-level strategy for avoiding spurious signals due to SNPs rather than DNA methylation differences. Probes with common (MAF > 5%) SNPs in the CG or single base extension position or probes that are nonspecific or mismapped were flagged and disregarded in the evaluation of our results^{45 (link)}. In order to also clean up rarer SNPs whilst discarding minimum data, within each tissue, and for each probe, we discarded beta values lying more than four times the interquartile range from the mean; these extreme outliers are generally the result of polymorphisms. Data was analyzed separately in each brain region using linear regression with probes ranked according to P value, and Q-Q plots assessed to check for P value inflation (see Supplementary Fig. S5 for example). To identify differentially methylated regions (DMRs), we identified spatially correlated P values within our data using the Python module comb-p^{18 (link)} to group ≥4 spatially correlated CpGs within a 500bp sliding window. The CETS package in R¹⁷ was used to check whether our top-ranked DMPs were mediated by the effect of differential neuronal cell proportions across samples. To identify probes with consistent associations between Braak stage and methylation across the three cortical regions, we employed a meta-analysis of EC, STG and PFC. P values from the individual region results for each site were generated using Fisher’s method and (as a way of controlling for the covariance of the samples which come from the same individuals) Brown’s method. Raw data has been deposited in GEO under accession number GSE43414.