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Additional file 1: Supplementary Figure 1. Forest plots for the 5 most significant CpGs in female samples meta-analysis of ADNI and AIBL blood sample datasets. Shown are odds ratios and confidence intervals that describe changes in odds of AD (on the multiplicative scale) associated with a one percent increase in DNA methylation beta values (i.e., increase in beta values by 0.01) after adjusting for covariate variables age at visit, batches and proportions of different blood cell types in each sample. Supplementary Figure 2. Forest plots for the 4 significant CpGs with P < 10-5 in male samples meta-analysis of ADNI and AIBL blood sample datasets. Shown are odds ratios and confidence intervals that describe changes in odds of AD (on the multiplicative scale) associated with a one percent increase in DNA methylation beta values (i.e., increase in beta values by 0.01) after adjusting for covariate variables age at visit, batches, and proportions of different blood cell types in each sample. Supplementary Figure 3. Overlap between DMRs and CpGs in males and females. There was no overlap between significant sex-specific DMRs and significant sex-specific individual CpGs in either (A) males or (B) females. Supplementary Figure 4. Comparison of results for DNA methylation differences in female sample meta-analysis vs. male sample meta- analysis. (A) overlap of AD-associated CpGs (B) overlap of AD-associated DMRs and (C) there was only modest correlation between effect estimates for CpG to AD associations in female meta-analysis vs. those from male meta-analysis. Supplementary Figure 5. Performance of different sex-specific logistic regression models for predicting AD diagnosis in out-of-sample validation. The training and testing datasets included samples from the AIBL and AddNeuroMed datasets, respectively. MRS was computed as the sum of methylation beta values for significant CpGs weighted by their estimated effect sizes from the sex-specific meta-analysis of AIBL and ADNI datasets. In males, significant CpGs for the MRS included 2 CpGs with P < 10-5 identified in the interaction analysis that are also available in the AddNeuroMed dataset; in females, significant CpGs for MRS included 9 CpGs with P < 10-5 identified in AD vs. CN comparison that are also available in AddNeuroMed dataset. Abbreviations: AUC = Area Under ROC curve, AD = Alzheimer's disease, CN = cognitive normal. Supplementary Figure 6. The changes in AD-associated B cell type proportions in females were more pronounced than in males in all three datasets (ADNI, AIBL, AddNeuroMed). Supplementary Figure 7. Comparison of years of education (PTEDUCAT) in cognitive normal (CN) and AD subjects from ADNI dataset. There was not significant association between AD status and years of education in females (P = 0.23) or males (P = 0.12), using Wilcoxon rank-sum test.

DNA methylation (DNAm) plays a crucial role in a number of complex diseases. However, the reliability of DNAm levels measured using Illumina arrays varies across different probes. Previous research primarily assessed probe reliability by comparing duplicate samples between the 450k-450k or 450k-EPIC platforms, with limited investigations on Illumina EPIC arrays. We conducted a comprehensive assessment of the EPIC array probe reliability using 138 duplicated blood DNAm samples generated by the Alzheimer's Disease Neuroimaging Initiative study. We introduced a novel statistical measure, the modified intraclass correlation, to better account for the disagreement in duplicate measurements. We observed higher reliability in probes with average methylation beta values of 0.2 to 0.8, and lower reliability in type I probes or those within the promoter and CpG island regions. Importantly, we found that probe reliability has significant implications in the analyses of Epigenome-wide Association Studies (EWAS). Higher reliability is associated with more consistent effect sizes in different studies, the identification of differentially methylated regions (DMRs) and methylation quantitative trait locus (mQTLs), and significant correlations with downstream gene expression. Moreover, blood DNAm measurements obtained from probes with higher reliability are more likely to show concordance with brain DNAm measurements. Our findings, which provide crucial reliable information for probes on the EPIC array, will serve as a valuable resource for future DNAm studies., Competing Interests: Declaration of Interest Statement The authors report there are no competing interests to declare.

Background Growing evidence has demonstrated that DNA methylation (DNAm) plays an important role in Alzheimer's disease (AD) and that DNAm differences can be detected in the blood of AD subjects. Most studies have correlated blood DNAm with the clinical diagnosis of AD in living individuals. However, as the pathophysiological process of AD can begin many years before the onset of clinical symptoms, there is often disagreement between neuropathology in the brain and clinical phenotypes. Therefore, blood DNAm associated with AD neuropathology, rather than with clinical data, would provide more relevant information on AD pathogenesis. Methods We performed a comprehensive analysis to identify blood DNAm associated with cerebrospinal fluid (CSF) pathological biomarkers for AD. Our study included matched samples of whole blood DNA methylation, CSF Aβ 42 , phosphorylated tau 181 (pTau 181 ), and total tau (tTau) biomarkers data, measured on the same subjects and at the same clinical visits from a total of 202 subjects (123 CN or cognitively normal, 79 AD) in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. To validate our findings, we also examined the association between premortem blood DNAm and postmortem brain neuropathology measured on a group of 69 subjects in the London dataset. Results We identified a number of novel associations between blood DNAm and CSF biomarkers, demonstrating that changes in pathological processes in the CSF are reflected in the blood epigenome. Overall, the CSF biomarker-associated DNAm is relatively distinct in CN and AD subjects, highlighting the importance of analyzing omics data measured on cognitively normal subjects (which includes preclinical AD subjects) to identify diagnostic biomarkers, and considering disease stages in the development and testing of AD treatment strategies. Moreover, our analysis revealed biological processes associated with early brain impairment relevant to AD are marked by DNAm in the blood, and blood DNAm at several CpGs in the DMR on HOXA5 gene are associated with pTau 181 in the CSF, as well as tau-pathology and DNAm in the brain, nominating DNAm at this locus as a promising candidate AD biomarker. Conclusions Our study provides a valuable resource for future mechanistic and biomarker studies of DNAm in AD.