1. Introduction

We collated results from recent epigenome-wide association studies in aging and AD and developed MIAMI-AD, an integrative database of blood DNAm across sex, aging, and AD. Researchers can search for CpGs, regions, or genes of their interests to benefit from recent scientific advances. In the following, we use studies to refer to research publications and datasets to refer to the resulting summary statistics from the studies.

Several queries are provided:

(1) Genome-wide Query enables selection of CpGs based on significance threshold or location; it provides:

  • Manhattan plot of a the entire genome for the chosen study
  • Selected CpGs (at user-specified significance threshold or location) in the chosen study

(2) Gene Query provides an overview of CpGs associated with a specific gene or region including:

  • Manhattan plot of CpGs located within genes
  • Annotations (UCSC Genes, ENSEMBL genes, and transcripts, chromatin states, CpG islands)
  • Download summary statistics (odds ratio, effect estimate, pValues) of CpGs and DMRs in recent AD and aging studies.

(3) CpG Query provides detailed information on specific CpGs including:

  • Forest plots from a meta-analysis
  • Annotations (relation to CpG island, UCSC reference gene group, mQTL information)
  • Meta-analysis results (odds ratio, direction in individual datasets, confidence interval)
  • Individual study results (odds ratio, pValue)
  • Brain-blood correlations

(4) Epigenetic Clocks Query enables

  • Selection of CpGs in multiple epigenetic clocks
  • Determination of shared and unique CpGs in multiple epigenetic clocks

2. Citation

If you find MIAMI-AD helpful, please cite our paper:

Lukacsovich D et al. (2023) MIAMI-AD (Methylation in Aging and Methylation in AD): an integrative atlas of DNA methylation across sex, aging, and Alzheimer's disease. bioRxiv

3. Included Studies

MIAMI-AD included studies that met two main criteria: (1) having more than 100 total subjects and (2) conducting a genome-wide study of more than 100k CpGs. For each study, we included as many CpGs as possible, either CpGs that passed quality control or CpGs listed in supplementary tables.

Please see HERE for details on the studies included in MIAMI-AD.

4. Terms of Use

If you use MIAMI-AD, you agree to:

  • You will cite our paper above in any publication where you have used results from MIAMI-AD.
  • You will also cite the original study papers in which the results were obtained.

5. Acknowledgements

This project is supported by funding from NIH grants RF1AG061127, RF1NS128145, and R01AG062634.


MIAMI-AD ( https://miami-ad.org ) is a web application developed using the Shiny platform 1-3 . The web interface is organized into four main sections, Genome-wide Query, Gene Query, CpG Query, and Epigenetic Clock Query . Each of these sections is further divided into three subsections,

  1. Datasets: Here, users can select the studies or epigenetic clocks they wish to examine. The other subtabs remain hidden until a selection is made in this section.
  2. Display Data: This section shows tables of summary statistics tailored to the chosen datasets and relevant parameters from the main tabs. Users also have the option to download these tables as Excel files.
  3. Display Plots: Visual representations of the data can be found here, including Manhattan plots, Venn diagrams, forest plots, and genomic annotations, annotation from the UCSC track hub, and computed chromatin states from the NIH Roadmap Epigenomics project 4

The source code for MIAMI-AD is available at https://github.com/TransBioInfoLab/MIAMI.AD .

The Genome-wide Query tool

Designed to allow users to select CpGs across all chromosomes based on a significance threshold, this tool is valuable for comparing association results from one or more studies. Figure 1 illustrates the workflow of the Genome-wide Query:

  1. First, the user can choose one or more phenotypes of interest from options such as 'AD Neuropathology' , 'Aging' , 'AD Biomarkers' , 'Dementia Clinical Diagnosis' , 'Mild Cognitive Impairment (MCI)' , or 'Sex' , available in the left panel.
  2. On the right panel, under the Dataset tab, a table displays the studies and datasets associated with the selected phenotypes. Here, we refer to 'studies' as research publications and 'datasets' as the resulting summary statistics from these studies.
  3. Next, the user can select datasets of interest by checking the boxes in the rightmost column. The Display Data tab then presents the CpGs that meet the search criteria. It includes annotations on location and associated genes, along with summary statistics such as the direction of the association, test statistic values, raw P values, and multiple comparison-adjusted P values.
  4. Additionally, the Display Plot tab generates visualizations, such as Venn diagrams to illustrate the numbers of overlapping and unique CpGs across multiple studies, as well as Manhattan plots for genome-wide representation of the data.

The Gene Query tool

This tool provides detailed information on DNAm differences within a specific gene or genomic region.

  1. The interface for input is similar to the Genome-wide Query Tool, except that on the left panel, the user additionally specifies the name of the gene (or region) they want to explore and selects the desired annotation tracks such as UCSC gene, ENSEMBL genes, ENSEMBL transcripts, chromatin states, and CpG islands ( Figure 2 )
  2. Upon completing the inputs, the Display Data tab on the right panel presents a summary of statistics for CpGs located within a specified range of + 2 kb around the gene of interest. This summary includes information on the direction of association and the values of the statistical measures (e.g., odds ratio, t-statistic, and the corresponding P values). In addition, under “Multi-omics Resources”, links to GWAS catalog 5 , NIAGADS GenomicsDB 6 , and the Agora brain gene expression 7 databases are presented, providing users a more comprehensive view on the genomic region or gene.
  3. The Display Plot tab generates visualizations, including a mini-Manhattan plot of the CpGs found within the gene, along with the selected annotation tracks such as CpG island, gene transcripts, and computed chromatin states. These plots offer a clear and concise way to interpret the data and understand the relationships between DNAm and the phenotype in the gene or region of interest.

The CpG Query tool

This tool offers information about specific CpGs of interest ( Figure 3 ) To use this tool,

  1. The user begins by inputting the desired phenotypes on the left panel and providing a list of CpGs they want to explore. In the right panel, the user selects the relevant studies from the available Datasets.
  2. In the Display Data tab, the tool provides not only summary statistics for the selected CpGs but also valuable annotations for each CpG. These annotations include CpG location and genes associated with the CpG. Under “Multi-omics and Multi-tissue Resources”, the tool provides links to mQTL (methylation quantitative trait loci) information obtained from a recent large-scale meta-analysis 8 , correlation of blood DNAm with brain DNAm at the particular CpG 9 , GWAS catalog 5 , NIAGADS GenomicsDB 6 , and the Agora brain gene expression 7 databases. Additionally, the tool includes the correlation of blood DNAm with brain DNAm at the particular CpG 9 , offering insights into potential cross-tissue relationships. Furthermore, MIAMI-AD searches among recently published epigenetic clocks and identifies matches if a selected CpG is a component of any epigenetic clock.
  3. Under the Display Plot tab, the tool generates forest plots to illustrate the association of the selected CpGs with the specified phenotypes across different datasets. This visualization aids in understanding the relationship between DNAm at CpG sites and the various phenotypes of interest.

The Epigenetic Clock Query tool

This tool serves the purpose of comparing with and selecting CpGs that have been utilized in constructing blood-based epigenetic clocks ( Figure 4 ). It incorporates the widely described pan-tissue clock by Horvath (2013) 7 , as well as the recently developed DunedinPACE clock, which is associated with the risk of dementia onset 8 , among others. To use the Epigenetic Clock tool,

  1. Users start by choosing the specific epigenetic clocks of interest from the Dataset category.
  2. Subsequently, upon navigating to the Display Data tab, the tool provides a list of the CpGs that are incorporated into one or more of the selected clocks, accompanied by their respective coefficients within each clock.
  3. In the Display Plot section, users can view the number of CpGs shared across multiple clocks, as well as those unique to individual clocks, as visualized by Venn diagrams.


Lukacsovich D et al. (2023) MIAMI-AD (Methylation in Aging and Methylation in AD): an integrative atlas of DNA methylation across sex, aging, and Alzheimer's disease. medRxiv

Please also cite the original study papers in which the results were obtained.


  1. Chang, W. et al. shiny: Web Application Framework for R. R package version 1.7.4, https://CRAN.R-project.org/package=shiny (2022).
  2. Attali, D. shinyjs: Easily Improve the User Experience of Your Shiny Apps in Seconds. R pakcage version 2.1.0 , https://CRAN.R-project.org/package=shinyjs (2021).
  3. Perrier, V., Meyer, F. & Granjon, D. shinyWidgets: Custom Inputs Widgets for Shiny R package version 0.7.6 , https://CRAN.R-project.org/package=shinyWidgets (2023).
  4. Zhou, X. et al. Epigenomic annotation of genetic variants using the Roadmap Epigenome Browser. Nat Biotechnol 33 , 345-6 (2015).
  5. Sollis, E. et al. The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource. Nucleic Acids Res 51 , D977-D985 (2023).
  6. Greenfest-Allen, E. et al. NIAGADS Alzheimer's GenomicsDB: A resource for exploring Alzheimer's disease genetic and genomic knowledge Alzheimers Dement 20 , 1123-1136 (2024).
  7. Sage Bionetworks. Agora: Discover Alzheimer's Disease Genes. https://agora.adknowledgeportal.org/ (2024).
  8. Min, J.L. et al. Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation. Nat Genet 53 , 1311-1321 (2021).
  9. Hannon, E., Lunnon, K., Schalkwyk, L. & Mill, J. Interindividual methylomic variation across blood, cortex, and cerebellum: implications for epigenetic studies of neurological and neuropsychiatric phenotypes. Epigenetics 10 1024-32 (2015).
  10. Horvath, S. DNA methylation age of human tissues and cell types. Genome Biol 14 , R115 (2013).
  11. Sugden, K. et al. Association of Pace of Aging Measured by Blood-Based DNA Methylation With Age-Related Cognitive Impairment and Dementia. Neurology 99 , e1402-e1413 (2022).

Genome-wide Query

To select a different set of CpGs

- to change significance level: double click on the numbers in the Threshold column

Selected Datasets


To peform pathway analysis of selected CpGs,
Download Tables
and use

Selected CpGs


Selected Datasets


Venn Diagram

Generating Venn Diagram...

Miami Plots

Generating Manhattan Plots...

Gene Query

Selected Datasets




Multi-omics Resources


CpGs in Gene or Genomic Region


Selected Datasets


CpG Statistics Plots

Plotting CpG Statistics...

Genome Track Plots

Plotting Tracks...
Plotting Track Legends...

CpG Query

Selected Datasets




Multi-omics and Multi-tissue Resources


Individual Datasets


CpG in Epigenetic Clocks


Selected Datasets


Forest Plot

Generating Forest Plot...

Epigenetic Clock Query

Selected Epigenetic Clocks


Variable Definitions


Minimum number of epigenetics clocks that include the CpG


Selected Epigenetic Clocks


Venn Diagram

Generating Venn Diagram...

Download Data

Contact & Contribute


For questions, comments, or to contribute to MIAMI-AD, please contact us at:

Lily Wang ( lily.wang@miami.edu ) or David Lukacsovich ( david.lukacsovich@miami.edu )


To contribute your publication's data to MIAMI-AD, please send the Metadata of publication and CpG Summary Statistics and/or DMR Summary Statistics files below to the contact information. Since data files can be large, please upload them to an online storage site (e.g., Dropbox, Google Drive, or Box), and share the download link with us.

If your study includes results for multiple phenotypes or subgroups (e.g., you analyzed male and female samples separately), then please save the results as separate metadata and summary statistics files.

Published studies in MAIMI-AD meet three criteria:

  1. having more than 100 total samples from human subjects
  2. conducting a genome-wide study of more than 100k CpGs
  3. utilizing Illumina 450k, EPIC, or EPICv2 arrays
Paper Metadata

Please provide the following information about your publication:

  • Title - Paper's title
  • PMID - Your paper's PMID
  • Author - First author of your paper
  • Year - The year of the paper's publication
  • Description - A brief description of the data
  • Tissue - The tissue that DNA methylation is measured on
  • Phenotype - The phenotype of the study (e.g., AD biomarkers, AD Neuropathology, Aging, Dementia Clinical Diagnosis, MCI, or Sex)
  • Sex_Specific - Specify if the study-result is sex-specific, and if so whether it is specific to a given gender, or if it measures sex-effects
  • Statistics - Statistics in the Summary Statistics dataset (see below) (e.g., OR for AD, estimate for age effect)
  • Full_EWAS - Specify if summary statistics include all (or most) CpGs, or just a subset from an array (omit if submitting only DMR data)
CpG Summary Statistics

Please provide summary statistics in a (preferably compressed) text file (e.g., .csv, .csv.gz, .tsv.gz), with the following columns:

  • CpG - Names of the CpG probes
  • sample_group - The cohorts that raw data was collected from. For meta-analysis, separate the cohorts with a space and a plus sign (+). For example: AIBL + ADNI. Otherwise, please avoid using plus (+) or semi-colon (;) in cohort names.
  • estimate - Estimated effect size
  • std_err - Standard error of the estimated effect size
  • pvalue - Nominal P-value
  • fdr - Multiple comparison adjusted P-value


DMR Summary Statistics

Please provide summary statistics in a text file (e.g., .csv, .tsv), with the following columns:

  • DMR - Genomic region of the DMRs in the format CHR:START-END. If this is provided, there is no need to provide chr , start , or end .
  • chr - The chromosome the DMR is located on. If region is provided, this does not need to be provided.
  • start - The genomic start position of the DMR. If region is provided, this does not need to be provided.
  • end - The genomic end position of the DMR. If region is provided, this does not need to be provided.
  • sample_group - The cohorts that raw data was collected from. For meta-analysis, separate the cohorts with a space and a plus sign (+). For example: AIBL + ADNI. Otherwise, please avoid using plus (+) or semi-colon (;) in cohort names.
  • nProbes - The number of CpG probes in the DMR
  • pvalue - Nominal P-value
  • adjusted.P - Multiple comparison adjusted P-value
  • direction - Whether the DMR is hyper- or hypo- methylated