Alzheimers / Parkinsons Colocalization Analysis

Analysis performed by Mike Gloudemans

Updated 12/16/2019

Summary

These project contains the scripts required to perform the colocalization analyses described in the paper.

The top-level project directory should contain the folders bin, data, output, tmp, and scripts. All scripts should be run from this top-level directory, or they'll be unable to locate the required files.

Components within scripts folder

scripts/pipe.sh

The full workflow for all colocalization-related analyses.

scripts/auxiliary

A few other miscellaneous scripts for analyses that may be of interest but were not explicitly described in the paper.

scripts/config

Config files for selection of SNP lists and for the colocalization tests themselves.

scripts/enumerate_candidate_snps

Scripts for assembling a broad list of candidate SNPs and loci for further follow-up using the other data sources described in the paper.

Also, scripts for annotating these candidates with GWAS, colocalization, and ATAC-seq peak overlaps.

And, importantly, QC checks for the above steps!

scripts/enumerate_candidate_snps/ld_annotations

Selection of "LD buddies" near GWAS SNPs.

scripts/munge_gwas

Scripts for formatting GWAS files consistently.

scripts/test_mapt_haplotypes

Scripts for differential testing of expression between the two possible MAPT haplotypes.

Required Tools

The following analyses were performed using other publicly available tools. To fully complete the analyses, you will have to install or link these tools within the bin subfolder, or modify the pipe.sh script to include the paths to the directories where these tools are installed.

• The tools for downloading and munging the publicly available GWAS summary statistics are available at https://github.com/mikegloudemans/gwas-download/.
• The colocalization pipeline is available in a basic form at https://bitbucket.org/mgloud/production_coloc_pipeline/src. An extended and hopefully easier-to-use pipeline with a greater variety of options and analyses, closer to what was used for this paper, will soon be available at https://github.com/mikegloudemans/ensemble_coloc_pipeline.
• Graphical visualization of colocalizations was performed using LocusCompare (Liu et al. 2019)
• The analysis performed in this paper uses an integration of the publicly available tools FINEMAP (Benner et al. 2016) and eCAVIAR (Hormozdiari et al. 2016)

Required Data

This project makes uses of a variety of data tables, some already publicly available and some internally generated. I'm currently exploring ways to just link all or most of the required data files here as a single download. Until then, please contact me directly (see Contact section below) and I'll share the relevant files directly, ASAP.

90% of the analysis, including the core colocalization analysis can be completed using just the following data files:

Getting started

• An hg38-formatted version of the 1000 Genomes VCF is required for computing allele frequencies in a reference population.
• GWAS summary statistics are publicly available; consistently-formatted versions of these and other GWAS can be downloaded directly.
• GTEx v8 brain eQTL association statistics can be downloaded from the GTEx Portal. Some minor pre-processing will be required to run these scripts; this process is described here.

All required data

To run all the scripts listed here, the data folder technically needs to contain all of the following files:

• data/1KG: 1KG VCF for hg38, publicly available for download as described above.
• data/atacseq: Single-cell and bulk ATAC-seq peaks. Availability TBA.
• data/dbsnp: Downloadable from dbSNP; versions for hg19 and hg38 will be needed. We recommend version 150 because this is what we used.
• data/eqtls: GTEx eQTLs for v8, downloadable from GTEx Portal as described above.
• data/gencode: Gencode annotations
• data/gtex: GTEx folder containing raw RNA-seq counts, for the MAPT analysis
• data/gwas: All GWAS, downloadable as described above. If already re-formatted for colocalization, place them in a formatted subdirectory; if not, place them in a raw subdirectory.
• data/gwas_top_hits:
• data/hgnc_conversion: Mapping of Ensembl gene IDs to HGNC names, obtained through Biomart.
• data/indexed-dbsnp: Sorted and tabixed version of dbsnp for fast access
• data/ld: Pairwise LD scores from 1K Genomes Phase 1, used for selecting candidate SNP lists.
• data/mapt: List of SNPs that almost always differ between the two possible MAPT haplotypes, used for labeling GTEx samples by haplotype status

Contact

For any questions about this pipeline, obtaining specific data files, or any colocalization-related analyses for this project, please contact Mike Gloudemans (mgloud@stanford.edu). I'll be glad to help you get these analyses up and running!