Multiomics analysis for dementia


Determine biomarkers for Alzheimer's and other neurodegenerative diseases using comprehensive analysis of multi-omics data including genomics, epigenomics, metabolomics, proteomics, etc.


Large cohorts with phenotype data on dementia are available through the following resources (incomplete table of data types):

  1. The Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium.
  2. The Trans-Omics for Precision Medicine (TOPMed) program. The TOPMed Omics Survey in 2017 includes Framingham Heart Study (FHS) details, which are described clearer in 2016. SABRe is a substudy of FHS and has mRNA and miRNA data. We obtained these data in 2019. On 2021-04-23, Magdalena Sevilla presented a collection of FHS and other omics datasets and some methods for analyzing them.
  3. Exome Sequencing Project (ESP), richly-phenotyped for heart, lung and blood disorders.
  4. Alzheimer's Disease Sequencing Project (ADSP), WES and WGS of thousands of AD and control samples.
  5. Allen Brain Atlases has many different downloadable data types, but from a few mouse and human samples. RNA-Seq data of hundreds of dementia cases.
  6. The Image and Data Archive (IDA) contains mostly image data on 43,290 subjects from 119 studies.
  7. The Alzheimer's Disease Neuroimaging Initiative (ADNI) has genetic and imaging data of 1-3 K samples, which are available through IDA.
  8. Several studies share their data through Accelerating Medicines Partnership - Alzheimer's Disease (AMP-AD) including:
    1. The Adult Changes in Thought (ACT) measured ~4K proteins in ~50 brains and performed WGCNA. Also, proteome data in BLSA (~100 brains), Banner (~200), and UPPpilot (~200).
    2. CHDWB and HBTRC, each has a gene expression profile from ~500 brains, MC-CAA (75), MCADGS (~100 RNA-Seq and GWAS samples).
    3. The Mount Sinai Brain Bank (MSBB) includes proteome and gene expression profiles from hundreds of samples.
    4. The MayoRNAseq includes WGS and RNA-Seq data from 275 Cerebellum (CBE) and 276 Temporal cortex (TCX) samples.
  9. The Religious Orders Study and Memory and Aging Project (ROSMAP) is the richest dataset in AMP-AD in terms of variety of data types, which include gene expression, genome, proteome, metabolome, and image data. Also, DNA methylation, miRNA, WGS, WES, and even H3K9Ac are available for ~1K samples. The dataset is very well documented, and was used to identify xQTL by Philip De Jager.
  10. Mathys, Hansruedi, et al. “Single-cell transcriptomic analysis of Alzheimer’s disease.” Nature (2019).
    Radek's summary: “Showing ~80K single cell transcriptomic analysis on brain tissues from ~50 patients with Alzheimer's disease. Subpopulations of glia and neurons expressed different gene signatures associated with AD. These enormous cataloging data may help to further pinpoint pathways associated with AD.”, and his plan. Habil's presentation in Biggs journal club on 2 July 2019.
  11. The Brain eQTL Almanac (Braineac) generated by UK Brain Expression Consortium (UKBEC) “comprises of genomic and transcriptome data of 134 brains from individuals free of neurodegenerative disorders. Up to 12 brain regions were extracted per brain in parallel for mRNA quantification.”
  12. Omics data were generated in Rhineland Study including DNA methylation from ~2K blood samples. Aslam Imtiaz presented these data in the NeuroCHARGE call on 2019-11-07.
  13. scREAD: A Single-Cell RNA-Seq Database for Alzheimer’s Disease (pdf). It covers 73 datasets from 15 studies, 10 brain regions, 713640 cells.Useful for: a) listing available datasets, b) easy preliminary DE analysis across cell types and disease vs. control conditions.


Dr. Sudha Seshadri, the Founding Director of The Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, Dr. Claudia Satizabal, Dr. Radek Dobrowolski, Dr. Qitao Ran, and Dr. Miranda Orr from Wake Forest University.


  1. Network analysis on proteome data of Fremingham cohort.
  2. DE analysis on RNA-Seq data of 5xFAD and Gpx4Tg mouse models for AD. Four biological replicates in each of the four conditions were generated in Ran's Lab.
  3. Identify senescent cells and their characteristics in human brain.
  4. Assess the effect of lowering expression of CD33 in microglia on AD phenotypes through analysis of single cell RNA-Seq data.
  1. An interactive timeline of Alzheimer's disease by AlzForum.
  2. Satizabal, Claudia L., et al. “Genetic architecture of subcortical brain structures in 38,851 individuals.” Nature genetics 51.11 (2019): 1624-1636.
    They identified a set of genes that is “significantly enriched for Drosophila orthologs associated with neurodevelopmental phenotypes”.
  3. Yamazaki, Yu., et al. “Apolipoprotein E and Alzheimer disease: pathobiology and targeting strategies.” Nat Rev Neurol (2019): 501–518.
  4. Ferreira, Daniel., et al. Biological subtypes of Alzheimer disease: A systematic review and meta-analysis Neurology (2020):94:1-13.
  5. Sey, Nancy YA, et al. A computational tool (H-MAGMA) for improved prediction of brain-disorder risk genes by incorporating brain chromatin interaction profiles. Nature Neuroscience, 2020.
  6. Borghesan, M., et al. “A Senescence-Centric View of Aging: Implications for Longevity and Disease.” Trends in Cell Biology (2020). The review paper suggested by Christi.