Aging is the strongest risk factor for Alzheimer’s disease (AD), yet the role of age-associated DNA methylation (DNAm) changes in blood and their relevance to AD remains poorly understood. We performed a meta-analysis of blood DNAm samples from 475 dementia-free subjects aged over 65 years across two independent cohorts, the Framingham Heart Study (FHS) at Exam 9 and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We adjusted for sex and immune cell-type proportions and corrected batch effects and genomic inflation. Integrative analyses included pathway enrichment, mQTL analysis, colocalization with Alzheimer’s disease and related dementia (ADRD) GWAS summary statistics, brain-blood DNAm correlations, and comparison to independent AD methylation studies. We identified 3758 CpGs and 556 differentially methylated regions (DMRs) consistently associated with chronological age in both cohorts at a 5

Abstract Background Aging is the strongest risk factor for Alzheimer’s disease (AD), but the molecular connections between aging and AD remain unclear. DNA methylation (DNAm) is implicated in both processes. Methods We conducted a meta-analysis of DNAm in prefrontal cortex from two independent postmortem cohorts: the Religious Orders Study and Memory and Aging Project (ROSMAP) and Brains for Dementia Research (BDR). Age-associated CpG sites were identified using cohort-specific linear models adjusted for neuronal proportion, sex, and batch, followed by meta-analysis. We computed epigenetic age acceleration in brain samples as delta-age (DNAmAge − chronological age), and compared clinically diagnosed AD with cognitively unimpaired participants. Functional analyses included genomic feature enrichment, pathway analysis, brain-blood DNAm correlation, and colocalization with genome-wide association study (GWAS) loci. Prognostic relevance of age-associated CpGs was tested in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset using Cox proportional hazards models for disease progression. Results We identified 3264 CpG sites associated with aging; most were hypermethylated and enriched in promoters and CpG islands, and involved genes related to immune regulation and metabolism. Comparison with AD neuropathology-associated methylation showed substantial overlap, with nearly all shared CpGs and regions showing concordant directional changes. Cortical epigenetic age acceleration was higher in ROSMAP participants with clinical AD than in cognitively unimpaired individuals after covariates adjustment, and this association persisted when the cortical clock was restricted to the aging-associated CpGs identified here, suggesting that acceleration in AD is attributable to age-related CpGs. Several CpGs showed significant brain-blood methylation correlations or were linked to AD GWAS risk loci through colocalization analyses. In ADNI, among 33 candidate CpGs selected for concordant aging- and AD-associated changes in cortex and significant brain-blood methylation correlations, baseline methylation at one CpG (cg10752406 in AZU1 promoter) was associated with progression at a 5% false discovery rate after covariate adjustment. Conclusions Aging-associated DNAm changes in prefrontal cortex overlap with AD neuropathology-related changes and are involved in accelerated epigenetic aging in clinical AD. Our study provides valuable insights into the epigenetic landscape of aging and its implications for AD.

Aging is a major risk factor for Alzheimer's disease (AD), but the molecular processes linking aging to AD remain unclear. Epigenetic modifications, particularly DNA methylation (DNAm), play a crucial role in understanding aging and AD. We studied brain DNA methylation (DNAm) changes in normal aging versus AD in late life. We performed a comprehensive meta-analysis of two large cohorts of postmortem prefrontal cortex samples from subjects over 65 years old. Our analysis adjusted estimated cell-type proportions (i.e., the proportion of neurons), sex, and batch effects, and corrected for inflation and multiple testing. We identified numerous DNAm differences consistently associated with aging in both cohorts, highlighting key genes such as ELOVL2 , ISM1 , and KLF14 , which are implicated in various aging processes. These DNAm differences are predominantly hypermethylated, enriched in promoter regions, and associated with genes involved in immune processes and metabolic functions. Our results also revealed significant overlaps between aging-associated DNAm differences and those involved in AD, supporting the hypothesis that aging and AD are interconnected at the molecular level. Intriguingly, nearly all DNAm differences significantly associated with both age (at death) and AD Braak stage showed concordant effect sizes in the same direction. Our study provides valuable insights into the aging-associated epigenetic landscape and its potential implications for AD. As aging and AD are intertwined, targeting age-related epigenetic modifications may offer new therapeutic strategies for AD.

Objective:Alzheimer disease (AD) is a neurodegenerative disorder leading to cognitive decline. Despite growing recognition of sex differences in epidemiology, symptomatology, and clinical outcomes of AD, the molecular mechanisms underlying these variations remain poorly defined. We performed transcriptome association studies of AD aiming to identify sex-specific and sex-dependent transcriptomic profiles that could provide insights into the molecular mechanisms underlying sex differences in AD pathogenesis. Methods:We conducted a meta-analysis of bulk-RNAseq data derived from human postmortem brain studies. Specifically, we analyzed gene expression differences between individuals diagnosed with AD and non-cognitively impaired (NCI) individuals across two key brain regions: the prefrontal cortex and the temporal lobe. We performed stratified differential expression analyses separately in males and females, alongside combined analyses across sexes. Additionally, we assessed the data in relation to known AD genes, proteomic studies, and drug repurposing opportunities. Results:Beyond the genes commonly dysregulated across both sexes, our meta-analyses identified multiple differentially expressed genes (DEGs) between AD and NCI that are either altered in only one sex or show different effects between sexes. Some genes are known AD genes from genetic studies, but others are novel. Correlation with proteomic data suggests that these transcriptional differences have functional significance, potentially contributing to the biological mechanisms underlying sex differences observed in AD. Finally, we identify drug compounds that are potential candidates for treatment. Interpretation:Our findings enhance our understanding of sex-related differences in disease etiology and progression, and underscore the importance of incorporating sex as a critical variable in transcriptomic studies of AD. These insights help pave the way for more precise, personalized medicine approaches that account for sex-specific molecular mechanisms.

Cognitive resilience (CR) contributes to the variability in risk for developing and progressing in Alzheimer's disease (AD) among individuals. Beyond genetics, recent studies highlight the critical role of lifestyle factors in enhancing CR and delaying cognitive decline. DNA methylation (DNAm), an epigenetic mechanism influenced by both genetic and environmental factors, including CR-related lifestyle factors, offers a promising pathway for understanding the biology of CR. We studied DNAm changes associated with the Resilience Index (RI), a composite measure of lifestyle factors, using blood samples from the Healthy Brain Initiative (HBI) cohort. After corrections for multiple comparisons, our analysis identified 19 CpGs and 24 differentially methylated regions significantly associated with the RI, adjusting for covariates age, sex, APOE ε4, and immune cell composition. The RI-associated methylation changes are significantly enriched in pathways related to lipid metabolism, synaptic plasticity, and neuroinflammation, and highlight the connection between cardiovascular health and cognitive function. By identifying RI-associated DNAm, our study provided an alternative approach to discovering future targets and treatment strategies for AD, complementary to the traditional approach of identifying disease-associated variants directly. Furthermore, we developed a Methylation-based Resilience Score (MRS) that successfully predicted future cognitive decline in an external dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI), even after accounting for age, sex, APOE ε4, years of education, baseline diagnosis, and baseline MMSE score. Our findings are particularly relevant for a better understanding of epigenetic architecture underlying cognitive resilience. Importantly, the significant association between baseline MRS and future cognitive decline demonstrated that DNAm could be a predictive marker for AD, laying the foundation for future studies on personalized AD prevention.

Sex is an important factor that contributes to both clinical and biological heterogeneity in Alzheimer’s disease (AD), but the regulatory mechanisms underlying sex differences in AD are still not well understood. DNA methylation (DNAm) is an epigenetic modification that regulates gene transcription and is known to be involved in AD. However, due to analytical and biological complexity, few previous DNAm studies analyzed the X chromosome, where many genes influencing cognitive abilities and immune functions are located. We performed a sex-specific X chromosome-wide analysis of the DNAm data generated by the longitudinal Alzheimer’s Disease Neuroimaging Initiative (ADNI) study. We used mixed effects logistic regression models with AD status as the outcome, adjusted for age, sex, batch, and immune cell-type proportions, random subject effects, and corrected for inflation. Our analysis included 632 female DNAm samples (188 cases, 444 controls) and 652 male DNAm samples (239 cases, 413 controls), measured on blood samples of 179 and 219 independent subjects with ages older than 65 years. Given our modest sample size, we considered CpGs with suggestive significance at the prespecified significance threshold of P < 1×10 -5 . In females, we identified 2 significant CpGs (cg04150893 and cg16580361), mapped to the intergenic region and gene body of the HMGN5 gene. No significant CpGs were identified in male samples. Interestingly, blood DNAm at cg16580361 is significantly associated with DNAm in the prefrontal cortex (Blood Brain DNA methylation Comparison Tool: r = 0.599, P = 1.74×10 -8 ). Consistent with our observed hypermethylation at cg16580361 in ADNI data (OR = 1.11, P = 6.14×10 -6 ), the HMGN5 gene is also significantly upregulated in the frontal cortex of female AD subjects (Agora database https://agora.adknowledgeportal.org : OR = 2 0.227 = 1.17, adjusted P = 5.7×10 -6 ). The HMGN5 gene is involved in the metabolism of the brain antioxidant glutathione. Decreased levels of glutathione have been implicated in both AD onset and progression. Our analysis of the X chromosome in the ADNI study dataset nominated cg16580361 located on the HMGN5 as a plausible biomarker for AD. Future studies that validate our findings in larger and more diverse community-based cohorts are needed.

The Genome Center for Alzheimer’s Disease (GCAD) coordinates the integration and meta-analysis of all available Alzheimer’s disease (AD) relevant whole genome sequencing (WGS) data to facilitate the goal of identifying AD risk or protective genetic variants and eventual therapeutic targets. The WGS datasets are generated via the collaboration of scientists from the Alzheimer’s Disease Sequencing Project (ADSP) and GCAD. To minimize data heterogeneity introduced by different sequencing protocols and machines, GCAD processes all samples using identical pipelines. The raw sequencing data are first mapped to GRCh38/hg38 and variants (SNVs and indels) are called using GATK. Additionally, compact VCF and GDS formatted files are generated to facilitate researchers who want to use smaller pVCFs. SNVs and indels are annotated using the ADSP annotation pipeline. Lastly, structural variants (SV) are called using Smoove and Manta and joint genotyped using GraphTyper2. The dataset (ADSP Release 5, R5, 2024) includes ∼60,000 genomes from >50 diverse cohorts with 4 major ancestries: 47% Non-Hispanic White, 29% Hispanic or Latino, 16% Black or African American and 8% Asian. Data are deeply sequenced (average genome coverage: >30x). CRAMs, gVCFs from GATK, and SV VCFs of a subset of the R5 samples (n = 36,361) were deposited into NIAGADS Data Sharing Service (DSS) ( https://dss.niagads.org/ ) for public distribution in 2022, and similarly, the new samples in R5 will be released after the joint call is complete. In addition, joint-genotype VCFs on SNVs, indels, and SVs will be available. These will undergo full quality control and annotation process. The ADSP and GCAD generate high quality genotype and SV calls. Currently the project is processing ∼60,000 WGS samples sequenced primarily through the ADSP Follow-Up Study, which will contain a more ancestrally diverse set of populations. We anticipate this 2024 release will continue to benefit the research community studying AD genetics.

As dementia cases continue to rise, effective prevention strategies are urgently needed. However, objective biomarkers that directly reflect lifestyle factors remain limited. Life’s Essential 8 (LE8) is a composite of modifiable cardiovascular health metrics, and lower LE8 has been consistently associated with increased risk of dementia. In this study, we aimed to identify DNA methylation biomarkers associated with LE8 scores and investigate their relevance for dementia risk. We performed an epigenome-wide association study of 273 stroke-free, self-identified Hispanic adults aged 40 and older from the Northern Manhattan Study (NOMAS), a community-based urban cohort study. DNA methylation (DNAm) was assessed using Illumina MethylationEPIC arrays. Robust linear models identified CpGs associated with LE8 score, a composite score on eight health metrics including diet quality, physical activity, nicotine exposure, sleep health, body mass index, blood lipids, blood glucose, and blood pressure. Differentially methylated regions were identified by combining P-values in sliding windows while accounting for spatial correlations across the genome. We also performed functional annotation, pathway analyses, and integrative analyses with gene expression, genetic variants, brain-blood correlations, and comparisons with previous dementia studies to identify the most biologically meaningful DNAm sites. After adjusting for age, sex, APOE ε4, immune cell composition, and ancestry, we found 11 CpGs with suggestive evidence of association with LE8 (P-value < 1 × 10–5) and 37 differentially methylated regions that passed multiple-testing correction. These LE8-associated loci mapped to genes and pathways that support vascular integrity and regulate inflammation, key biological processes relevant to both cardiovascular disease and dementia. Integrative analyses highlighted several CpGs in the HOXA5 gene promoter with converging evidence supporting their potential as dementia biomarkers, including strong blood–brain DNAm correlations, association with gene expression and genetic variants, and prior associations with Alzheimer’s disease neuropathology. Our comparison with published results showed that a number of LE8-associated DNA methylation sites are associated with dementia, highlighting the possible connection between cardiovascular health and dementia risk and pointing to potential actionable targets for dementia prevention. Moreover, DNAm biomarkers have clinical potential as objective measures to identify individuals at elevated risk, stratify participants based on biologically informed risk profiles, and monitor epigenetic responses to lifestyle interventions in dementia prevention trials. Future studies in larger and more diverse cohorts are needed to validate and refine these methylation biomarkers for clinical applications.

Aging is the strongest risk factor for Alzheimer's disease (AD), yet the role of age-associated DNA methylation (DNAm) changes in blood and their relevance to AD remains poorly understood. In this study, we performed a meta-analysis of blood DNAm samples from 475 dementia-free subjects aged over 65 years across two independent cohorts, the Framingham Heart Study (FHS) at Exam 9 and the Alzheimer's Disease Neuroimaging Initiative (ADNI). After adjusting for age, sex, and immune cell type proportions, and correcting for batch effects and genomic inflation, we identified 3758 CpGs and 556 differentially methylated regions (DMRs) consistently associated with aging in both cohorts at a 5% false discovery rate. Our pathway enrichment analyses highlighted immune response, metabolic regulation, and synaptic plasticity, all of which are key biological processes implicated in AD. Moreover, our colocalization analysis revealed 32 genomic regions where shared genetic variants influenced both DNAm and dementia risk. Adjusting for age and other covariate variables, we found roughly one-third of aging-associated CpGs are also associated with AD or AD neuropathology in independent studies external to the ADNI and FHS datasets. Finally, we prioritized 9 aging-associated CpGs, located in promoter regions of PDE1B, ELOVL2, PODXL2, and other genomic regions, that showed strong positive blood-to-brain methylation concordance, as well as association with AD or AD neuropathology in independent studies, after adjusting for age and other covariates. Our findings provided insights into the functional overlap between the aging processes and AD, and nominated promising blood-based biomarkers for future AD research.

INTRODUCTION:Distinguishing between molecular changes that precede dementia onset and those resulting from the disease is challenging with cross-sectional studies. METHODS:We studied blood DNA methylation (DNAm) differences and incident dementia in two large longitudinal cohorts: the Offspring cohort of the Framingham Heart Study (FHS) and the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We analyzed blood DNAm samples from > 1000 cognitively unimpaired subjects. RESULTS:Meta-analysis identified 44 CpGs and 44 differentially methylated regions consistently associated with time to dementia in both cohorts. Our integrative analysis identified early processes in dementia, such as immune responses and metabolic dysfunction. Furthermore, we developed a methylation-based risk score, which successfully predicted future cognitive decline in an independent validation set, even after accounting for age, sex, apolipoprotein E ε4, years of education, baseline diagnosis, and baseline Mini-Mental State Examination score. DISCUSSION:DNAm offers a promising source as a biomarker for dementia risk assessment. HIGHLIGHTS:Blood DNA methylation (DNAm) differences at individual CpGs and differentially methylated regions are significantly associated with incident dementia. Pathway analysis revealed DNAm differences associated with incident dementia are significantly enriched in biological pathways involved in immune responses and metabolic processes. Out-of-sample validation analysis demonstrated that a methylation-based risk score successfully predicted future cognitive decline in an independent dataset, even after accounting for age, sex, apolipoprotein E ε4, years of education, baseline diagnosis, and baseline Mini-Mental State Examination score.

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 v1.0 arrays. We conducted a comprehensive assessment of the EPIC v1.0 array probe reliability using 69 blood DNA samples, each measured twice, generated by the Alzheimer's Disease Neuroimaging Initiative study. 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 reliability information for probes on the EPIC v1.0 array, will serve as a valuable resource for future DNAm studies.

IntroductionHispanic/Latino populations are underrepresented in Alzheimer Disease (AD) genetic studies. Puerto Ricans (PR), a three-way admixed (European, African, and Amerindian) population is the second-largest Hispanic group in the continental US. We aimed to conduct a genome-wide association study (GWAS) and comprehensive analyses to identify novel AD susceptibility loci and characterize known AD genetic risk loci in the PR population.Materials and methodsOur study included Whole Genome Sequencing (WGS) and phenotype data from 648 PR individuals (345 AD, 303 cognitively unimpaired). We used a generalized linear-mixed model adjusting for sex, age, population substructure, and genetic relationship matrix. To infer local ancestry, we merged the dataset with the HGDP/1000G reference panel. Subsequently, we conducted univariate admixture mapping (AM) analysis.ResultsWe identified suggestive signals within the SLC38A1 and SCN8A genes on chromosome 12q13. This region overlaps with an area of linkage of AD in previous studies (12q13) in independent data sets further supporting. Univariate African AM analysis identified one suggestive ancestral block (p = 7.2×10−6) located in the same region. The ancestry-aware approach showed that this region has both European and African ancestral backgrounds and both contributing to the risk in this region. We also replicated 11 different known AD loci -including APOE- identified in mostly European studies, which is likely due to the high European background of the PR population.ConclusionPR GWAS and AM analysis identified a suggestive AD risk locus on chromosome 12, which includes the SLC38A1 and SCN8A genes. Our findings demonstrate the importance of designing GWAS and ancestry-aware approaches and including underrepresented populations in genetic studies of AD.

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.

The Genome Center for Alzheimer’s Disease (GCAD) coordinates the integration of all available Alzheimer’s disease (AD) relevant whole genome sequencing (WGS) data with the goal of identifying AD risk or protective genetic variants and eventual therapeutic targets. The WGS datasets are generated through collaboration between investigators from the Alzheimer’s Disease Sequencing Project (ADSP) and GCAD. With the goal of minimizing data heterogeneity, introduced by different sequencing protocols and assays, GCAD processes all samples using standardized pipelines and performs quality control (QC)/quality assurance (QA) checks. Raw sequencing data (FASTQs or BAMs) were aligned to GRCh38/hg38 by BWA, and variant calling and joint genotyping on single nucleotide variants (SNVs), insertions and deletions (indels), were done by GATK. Structural variants (SVs) were called per sample using the Smoove, Manta, and Strelka packages. Preliminary QA checks including sex check, contamination, and genotype concordance were performed followed by QC per ADSP protocol to evaluate the quality of samples and variants. To facilitate access and usage of massive joint-genotype called VCF files, a compact version for storing variant info and sample genotypes only was released first. We dropped 275 (0.7%) samples of poor coverage (<20×), and we flagged 219 (0.6%) samples that were of borderline quality. As a result, the dataset (ADSP Release 4, 2022) includes 36,361 genomes from 40 diverse cohorts with 4 major ancestries: 16,573 Non-Hispanic Whites, 11,358 Hispanics; 5,422 African Americans; and 2,802 Asians. Data are deeply sequenced (average genome coverage: 40x). All samples’ CRAMs and gVCFs from GATK were deposited into NIAGADS Data Sharing Service (DSS) ( https://dss.niagads.org/ ) for public distribution. Joint-genotyped called VCFs are undergoing a full QC/annotation process and will be made available. This joint-genotyped called VCF contains >362M bi-allelic variants, >58M multi-allelic variants, with 95% of variants remaining after QC. SV calling is ongoing and data will be ready prior to the conference. The ADSP and GCAD generate high quality SNVs, indels and SV calls. Currently GCAD is preparing the next release of ∼60,000 more ancestrally-diverse WGS samples sequenced primarily through the ADSP Follow-Up Study, which we anticipate will be released in 2023 to greatly benefit the AD genetics community.

Genome-wide association studies (GWAS) for AD have identified numerous associated loci but have been focused only on the autosomes. We sought to identify novel AD-associated genes on X-Chromosome (X-Chr). We evaluated the association of AD with X-Chr variants in GWAS datasets assembled by the Alzheimer Disease Genetics Consortium including 26,322 non-Hispanic White (NHW) individuals using logistic regression models with covariates for age, sex and principal components of ancestry. The number of independent SNPs in X-Chr was calculated by linkage disequilibrium pruning yielding a study-wide significance threshold of P<7.58×10 −7 . Genes near SNPs surpassing this threshold were further evaluated by expression quantitative trait locus (eQTL) in the BRAINEAC database and differential gene expression analyses using RNA-seq data obtained from autopsied brains contained (GEO: GSE44772 and GSE33000). We also tested association of AD with missense or splicing variants with minor allele count (MAC)≥10 in the same genes using GENESIS and whole exome sequencing (WES) data obtained by the Alzheimer Disease Sequencing Project (ADSP) for 11,172 NHWs. We identified novel associations with variants near NLGN4X (rs34056759; minor allele frequency [MAF] = 0.15, OR = 1.15, P = 5.14×10 −7 ) and PTCHD1 (rs5970663; MAF = 0.30, OR = 1.13, P = 8.26×10 −7 ). The rs5970663 risk allele C was significantly associated with increased expression of PTCHD1 in the medulla region, and PTCHD1 expression was significantly higher in AD cases than in controls in the prefrontal cortex (P<2.1×10 −8 ) and visual cortex (P<2.3×10 −4 ). Rs34056759 was not associated with NLGN4X expression, but NLGN4X expression was significantly lower in AD cases compared to controls in the prefrontal cortex (P<8.7×10 −7 ). A rare PTCHD1 variant (rs201933353; MAF = 0.001) was nominally associated with AD among 6,318 women (OR = 3.72, P = 0.06). We identified novel associations of AD with X-Chr variants near NLGN4X and PTCHD1, loci that were previously associated with autism spectrum disorder. We plan to replicate, fine-map, and annotate the candidate loci identified in this study using large whole genome sequencing ADSP datasets.

Sex has increasingly been recognized as an important factor contributing to the heterogeneity in Alzheimer’s disease (AD). We performed a sex-specific meta-analysis of two large epigenome-wide association studies (ADNI, AIBL) in the blood, with a total of 633 and 651 samples in females and males, respecitively. For each dataset, we adjusted covariate variables age, sex, batch, and immune cell-type proportions. The Inverse-variance fixed-effects meta-analysis model was then used to prioritize the most consistent DNAm differences across datasets. In female samples, 2 CpGs, mapped to the PRRC2A and RPS8 genes, reached the 5% false discovery rate (FDR). No CpGs reached 5% FDR in male samples analysis. At a more relaxed significance threshold of P < 1×10 −5 , an additional 22 CpGs and 5 CpGs were identified in female samples and male samples, respectively. For these 29 AD-associated CpGs, the odds ratios ranged from 0.843 to 1.335 in females and 0.993 to 1.058 in males. The majority of these CpGs were hypermethylated in AD subjects (23 CpGs), located outside CpG islands or shores (27 CpGs), or in distal regions located greater than 2k bp from the TSS (27 CpGs). Only 2 of these 29 CpGs were located in gene promoters at SLC5A8 and C16orf89. At 5% Sidak corrected P-value, our DMR analysis using comb-p software identified 49 distinct differentially methylated regions (DMRs) in female and male samples each. The median numbers of CpGs in these DMRs are 5 CpGs in females and 4 CpGs in males. Among the top 10 most significant DMRs, about half of them (6 in females, 5 in males) were hypermethylated in AD. In females, 5 out of the 10 DMRs were mapped to promoter regions of the NNAT, CCDC169-SOHLH2, ARHGEF15, LPAR5, and ZNF595 genes. In males, 4 out of the 10 DMRs were mapped to promoter regions of the MCCC1, PM20D1, PRR19, TTC23, and LRRC28 genes. As sex is a strong factor underlying phenotypic variability in AD, the results of our study are particularly relevant for a better understanding AD pathophysiology. They also provide a valuable resource for sex-specific biomarkers and drug targets in AD.

Abstract Background Sex is increasingly recognized as a significant factor contributing to the biological and clinical heterogeneity in AD. There is also growing evidence for the prominent role of DNA methylation (DNAm) in Alzheimer’s disease (AD). Methods We studied sex-specific DNA methylation differences in the blood samples of AD subjects compared to cognitively normal subjects, by performing sex-specific meta-analyses of two large blood-based epigenome-wide association studies (ADNI and AIBL), which included DNA methylation data for a total of 1284 whole blood samples (632 females and 652 males). Within each dataset, we used two complementary analytical strategies, a sex-stratified analysis that examined methylation to AD associations in male and female samples separately, and a methylation-by-sex interaction analysis that compared the magnitude of these associations between different sexes. After adjusting for age, estimated immune cell type proportions, batch effects, and correcting for inflation, the inverse-variance fixed-effects meta-analysis model was used to identify the most consistent DNAm differences across datasets. In addition, we also evaluated the performance of the sex-specific methylation-based risk prediction models for AD diagnosis using an independent external dataset. Results In the sex-stratified analysis, we identified 2 CpGs, mapped to the PRRC2A and RPS8 genes, significantly associated with AD in females at a 5% false discovery rate, and an additional 25 significant CpGs (21 in females, 4 in males) at P-value < 1×10−5. In methylation-by-sex interaction analysis, we identified 5 significant CpGs at P-value < 10−5. Out-of-sample validations using the AddNeuroMed dataset showed in females, the best logistic prediction model included age, estimated immune cell-type proportions, and methylation risk scores (MRS) computed from 9 of the 23 CpGs identified in AD vs. CN analysis that are also available in AddNeuroMed dataset (AUC = 0.74, 95% CI: 0.65–0.83). In males, the best logistic prediction model included only age and MRS computed from 2 of the 5 CpGs identified in methylation-by-sex interaction analysis that are also available in the AddNeuroMed dataset (AUC = 0.70, 95% CI: 0.56–0.82). Conclusions Overall, our results show that the DNA methylation differences in AD are largely distinct between males and females. Our best-performing sex-specific methylation-based prediction model in females performed better than that for males and additionally included estimated cell-type proportions. The significant discriminatory classification of AD samples with our methylation-based prediction models demonstrates that sex-specific DNA methylation could be a predictive biomarker for AD. As sex is a strong factor underlying phenotypic variability in AD, the results of our study are particularly relevant for a better understanding of the epigenetic architecture that underlie AD and for promoting precision medicine in AD.

To better understand DNA methylation in Alzheimer’s disease (AD) from both mechanistic and biomarker perspectives, we performed an epigenome-wide meta-analysis of blood DNA methylation in two large independent blood-based studies in AD, the ADNI and AIBL studies, and identified 5 CpGs, mapped to the SPIDR , CDH6 genes, and intergenic regions, that are significantly associated with AD diagnosis. A cross-tissue analysis that combined these blood DNA methylation datasets with four brain methylation datasets prioritized 97 CpGs and 10 genomic regions that are significantly associated with both AD neuropathology and AD diagnosis. An out-of-sample validation using the AddNeuroMed dataset showed the best performing logistic regression model includes age, sex, immune cell type proportions, and methylation risk score based on prioritized CpGs in cross-tissue analysis (AUC = 0.696, 95% CI: 0.616 − 0.770, P- value = 2.78 × 10 −5 ). Our study offers new insights into epigenetics in AD and provides a valuable resource for future AD biomarker discovery.

In the largest Alzheimer disease (AD) genome-wide association studies to date for African-Americans (AA; Reitz et al. JAMA 2013; Kunkle at al. JAMA Neurol 2021) we previously identified in addition to APOE several novel susceptibility loci, including ABCA7, API5, RBFOX1 and IGF1R . We followed up these analyses with an increased sample size (2,913 cases, 5,802 controls) employing the African Genome Resource (AGR) panel. Single-variant association analysis was conducted adjusting for age, sex, principal components and subsequently APOE , applying logistic regression for case-control and general estimating equations for family-based datasets. Within-study results were meta-analyzed using METAL. Gene-based and pathway analyses were conducted via MAGMA. In addition to the previously reported AA risk loci, we identified three novel signals reaching genome-wide significance at chromosomes 3p24 ( TOP2B ; P = 4.7×10 −8 ), 3q26 ( NCEH1 ; P = 3.7×10 −8 ) and 9p23 ( MPDZ ; P = 1.7×10 −8 ), and seventeen novel loci reaching suggestive significance at p ≤ 9×10 −7 . NCEH1 modulates cholesterol metabolism and is neuroprotective against α-synuclein toxicity; MPDZ encodes a scaffolding protein involved in cytoskeleton remodeling. Top2B encodes a DNA topoisomerase involved in DNA transcription. Gene-based analyses identified SLC39A3 (P = 2.9×10 −6 ), involved in zinc transport, as a novel candidate gene. Pathway analyses support the notion that besides immunity, synaptic function, transcription/DNA repair, lipid processing, and intracellular trafficking, which overlap with the major AD-associated pathways in non-Hispanic Whites, also renal function is involved in AD etiology in African Americans. We identified several novel candidate loci for AD in AA. While the major pathways involved in Alzheimer disease etiology in African American individuals are similar to those in non-Hispanic White individuals, the disease-associated loci within these pathways differ. Identification of a significant number of loci at suggestive significance indicates that future studies with further increased sample size will be valuable to identify additional disease-associated loci in this ethnic group.

ABSTRACT We performed a meta-analysis of two large independent blood-based Alzheimer’s disease (AD) epigenome-wide association studies, the ADNI and AIBL studies, and identified 5 CpGs, mapped to the SPIDR, CDH6 genes, and intergenic regions, that were significantly associated with AD diagnosis. A cross-tissue analysis that combined these blood DNA methylation datasets with four additional methylation datasets prioritized 97 CpGs and 10 genomic regions that are significantly associated with both AD neuropathology and AD diagnosis. Our integrative analysis revealed expressions levels of 13 genes and 10 pathways were significantly associated with the AD-associated methylation differences in both brain and blood, many are involved in the immune responses in AD, such as the CD79A, LY86, SP100, CD163, CD200 , and MS4A1 genes and the neutrophil degranulation, antigen processing and presentation, interferon signaling pathways. An out-of-sample validation using the AddNeuroMed dataset showed the best performing logistic regression model included age, sex, cell types and methylation risk score based on prioritized CpGs from cross-tissue analysis (AUC = 0.696, 95% CI: 0.616 - 0.770, P- value = 2.78 × 10 −5 ). Our study provides a valuable resource for future mechanistic and biomarker studies in AD.