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FunGen-xQTL Resources

This repository contains 91 datasets developed by the ADSP Functional Genomics Consortium.

Published at: https://statfungen.github.io/xqtl-resources/

Dataset Categories

Study Information (6 datasets)

  • Knight-ADRC study info: The Memory and Aging Project at the Charles F. And Joanne Knight Alzheimer's Disease Research Center (Knight-ADRC at Washington University in St. Louis) collects collects plasma, CSF, fibroblast, neuroimaging clinical and cognition data longitudinally and autopsied brain samples. This clinical information combined with deep molecular phenotyping (i.e. genetic, proteomics, transcriptomics and others) will lead to the identification of novel genetic modifiers, protective variants, molecular biomarkers and the novel targets. Participants were recruited by the Knight-ADRC at Washington University in St. Louis (MO). Knight-ADRC participants have to be at least 65 years old and have no memory problems or mild dementia at the time of enrollment.
  • MAGENTA study info: Multi-Ancestry Genomics, Epigenomics, and Transcriptomics of Alzheimer’s (MAGENTA) Project: Participants include 465 individuals (AA – 113 with AD, 118 cognitively intact controls; NHW – 116 with AD, 118 controls) ascertained by the John P. Hussman Institute for Human Genomics (HIHG) at the University of Miami Miller School of Medicine (Miami, FL), North Carolina A&T State University (Greensboro, NC), and Case Western Reserve University (Cleveland, OH). Participants were ascertained as part of the ADSP Follow-up Study and included both cases (>65 years of age of onset) and controls (>65 years of age at age of exam). All participants were adjudicated by a clinical panel with expertise in AD related disorders and classified as AD according to standard criteria developed by the National Institute of Aging and the Alzheimer’s Association.
  • MiGA study info: Microglia Genomic Atlas from the Netherlands Brain Bank (NBB) and the Neuropathology Brain Bank and Research CoRE at Mount Sinai Hospital. The permission to collect human brain material was obtained from the Ethical Committee of the VU University Medical Center, Amsterdam, The Netherlands, and the Mount Sinai Institutional Review Board. For the Netherlands Brain bank, informed consent for autopsy, the use of brain tissue and accompanied clinical information for research purposes was obtained per donor ante-mortem.
  • ROSMAP study info: Religious Orders Study (ROS) or the Rush Memory and Aging Project (MAP) study: ROS is a longitudinal clinical-pathologic cohort study of aging and Alzheimer's disease (AD) run from Rush University that enrolled individuals from religious communities for longitudinal clinical analysis and brain donation. Participants were enrolled from more than 40 groups of religious orders (nuns, priests, brothers) across the United States. MAP is a longitudinal, epidemiologic clinical-pathologic cohort study of common chronic conditions of aging with an emphasis on decline in cognitive and motor function and risk of Alzheimer’s disease that began in 1997 and is run from Rush University.
  • STARNET: STARNET is an RNA expression study of various disease-relevant tissues obtained from living patients with cardiovascular disease (CVD). The inclusion criterion for patients was eligibility for coronary artery by-pass graft (CABG) surgery.
  • The Mount Sinai Brain Bank (MSBB) study info: This cohort study generated large-scale matched multi-Omics data in AD and control brains for exploring novel molecular underpinnings of AD.

GWAS Summary Statistics (5 datasets)

Omics Data (43 datasets)

QTL Results (36 datasets)

Reference Data (1 datasets)

  • Non-Hispanic White Linkage Disequilibrium Reference Panel: LD matrices calculated from whole genome sequencing data from 16571 non-Hispanic white individuals obtained from the Genome Center for Alzheimer's Disease (GCAD). Correlation matrices were calculated between SNPs within 1361 LD blocks which were obtained from this Github page (generated from 1000 Genomes EUR samples).

Repository Structure

.
├── content/                  # Source markdown files
│   ├── xqtl-data/           # xQTL datasets
│   │   ├── study_info/      # Study descriptions
│   │   ├── gwas/            # GWAS summary statistics
│   │   ├── omics/           # Omics data
│   │   ├── qtl/             # QTL results
│   │   └── reference_data/  # Reference panels
│   └── *.md                 # Documentation pages
├── scripts/                  # Processing scripts
│   └── hugo_generator.py
└── website/                  # Generated Hugo site (git-ignored)

Contributing

To add or update content:

  1. Edit markdown files in content/ directory
  2. Run make or python scripts/hugo_generator.py --serve to preview locally
  3. Submit a pull request

The website/ directory is automatically generated and should not be edited directly.

Building the Site

# Install Hugo (https://gohugo.io/getting-started/installing/)
# Then run:

# Generate and serve locally
python scripts/hugo_generator.py --serve

# Build for production
python scripts/hugo_generator.py --build --minify

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