Dec 02, 2021

Public workspaceChromatin loops and expression QTL colocalization reveal novel gene targets for T1D-associated GWAS variants in immune cells

This protocol is a draft, published without a DOI.
Chromatin loops and expression QTL colocalization reveal novel gene targets for T1D-associated GWAS variants in immune cells
  • Joaquin Reyna1,2,
  • Sourya Bhattacharyya1,
  • Nikhil Rao1,3,
  • Abhijit Chakraborty1,
  • Ferhat Ay1,4
  • 1La Jolla Institute for Immunology, La Jolla, CA, USA.;
  • 2Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA.;
  • 3Department of Bioengineering, University of California San Diego, La Jolla, CA, USA.;
  • 4Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.
Joaquin Reyna
Open access
External link: https://github.com/joreynajr/dchallenge
Protocol Citation: Joaquin Reyna, Sourya Bhattacharyya, Nikhil Rao, Abhijit Chakraborty, Ferhat Ay 2021. Chromatin loops and expression QTL colocalization reveal novel gene targets for T1D-associated GWAS variants in immune cells. protocols.io https://protocols.io/view/chromatin-loops-and-expression-qtl-colocalization-bzy7p7zn
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License,  which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: In development
We are still developing and optimizing this protocol
Created: November 10, 2021
Last Modified: December 02, 2021
Protocol Integer ID: 55039
Keywords: Type 1 Diabetes, HiChIP, HiC, Colocalization, eQTL, GWAS
Disclaimer
This protocol was made as part of the DChallenge and gives a high level explanation of our steps.
Abstract
Type 1 diabetes (T1D) is a disease characterized by the destruction of β cell populations in the pancreas. Immune cells, and specifically T cells, have been implicated to play a key role in destroying insulin producing β cells by infiltrating the pancreas. To better understand the role of immune regulation in T1D, we colocalized the gene expression quantitative trait loci (eQTL) signals from 18 different immune cell populations (15 from DICE, 3 from BLUEPRINT) with T1D GWAS signals to gather non-coding variants that are likely causal for both the gene expression and the disease association. We further overlapped these variants with chromatin loops mapped in a subset of these immune cell populations to identify potential target genes of the significant non-coding SNPs. Aside from well-studied genes such asBACH2, UBASH3A, PTPN22 and SIRPG, we identified AP003774.1, a long non-coding RNA, that is looping to a ~15kb away regulatory element overlapping a colocalized SNP (rs479777) in various T cell subsets. The looped region overlaps the promoter of another gene (promoter-promoter loop), CCDC88B, however, the eQTL association for this SNP is specific to AP003774.1and is remarkably strong for resting T cell subsets, NK cells and naïve B cells. The same SNP creates strong binding sites for multiple important transcription factors in donors with the non-reference allele leading to higher expression of AP003774.1. We hypothesize that the overexpression of AP003774.1lncRNA mediated through specific non-coding variants in different immune cell populations play a role in immune-related aspects of T1D.
Image Attribution
https://thenounproject.com/term/weaving/
Main Colocalization Pipeline
Main Colocalization Pipeline
Download the GWAS summary statistics (uses GRCh38 coordinates)
Download GWAS summary statistics from the GWAS catalogue.
Download link: http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90014001-GCST90015000/GCST90014023/GCST90014023_buildGRCh38.tsv
CITATION
Chiou, J., Geusz, R.J., Okino, ML. et al (2021). Interpreting type 1 diabetes risk with genetics and single-cell epigenomics. Nature.
LINK
https://doi.org/10.1038/s41586-021-03552-w

Remap the GWAS SNP coordinate to GRCh37
Command
liftover
liftOver -bedPlus=3 -tab <in bed> <chain file> <out lift file> <unmapped>

Filter for SNPs with genome-wide significance (5e-8)
Reformat the file into the required format for colocalization
Estimate standard error of the SNP using beta and MAF values. Standard error is beta/z-score

Download the eQTL summary statistics (uses GRCh37 coordinates)
Many eQTL studies have already been completed with summary statistics. For our project we downloaded preprocessed data from the Mu et al., 2021 which contains Blueprint and DICE eQTL results.
Download link: https://zenodo.org/record/4480206#.YXcophrMJyw
CITATION
Mu, Z., Wei, W., Fair, B. et al (2021). The impact of cell type and context-dependent regulatory variants on human immune traits. Genome Biol.
LINK
https://doi.org/10.1186/s13059-021-02334-x

Run colocalization between the T1D GWAS and all eQTL studies
The coloc package that we used can be referenced here: https://cran.r-project.org/web/packages/coloc/index.html
Command
Colocalization scripts generated by Sourya Bhattacharyya.
Rscript Colocalization_Analysis_GWAS.R <in GWAS> <output directory> <in eQTL>

Intersect colocalized SNPs with HiChIP data
Obtain HiChIP-seq data for several cell types. In our case, we obtained data from CD4+ T-cells, CD8+ T-cells, classical monocytes, non-classical monocytes, naive B cells, natural killer, T follicular helper, Th1, Th17, Th2 and TH1/17, TREGMEM, and TREGNAIVE cells.
Generate a Master table of SNP-gene pairs with loops
For this master table we focused on colocalized SNPs, their colocalized eGenes and other genes as well.
Extract all SNP-gene pairs that are +/- 500kb from a colocalized SNP

Command
Intersect two lists of genetic loci.
bedtools intersect <bed1> <bed2> > <output bed>

Intersect loops with SNP-gene pairs
Command
Intersect two lists of genetic loci pairs.
bedtools pairtopair <bedpe1> <bedpe2> > <output>

Label each SNP-Gene pair
For each SNP-Gene pair label whether it is an eQTL, colocalized pair, contains a loop and all metadata.

Gene Candidate Prioritization
Gene Candidate Prioritization
Investigate Candidate Genes using the WashU Epigenome Browser


Generate BED + Index files for 1D tracks
- single-cell ATAC-seq data
- SNP and gene locations


Command
Compress bed file with bgzip.
bgzip <input> <output>

Command
Create an index for compressed bed file.
tabix <bed.gz>


Generate BEDPE + Index files for 2D tracks
- loop data for all cell lines

Command
bgzip
bgzip <input> <output>

Command
tabix
tabix <bed.gz>

Add ChromHMM tracks from the Public Hubs

Look for genes with several SNPs which overlap important loops
Investigate TF binding sites using the Genome Browser + others
Load JASPAR tracks, query SNP locations, investigate motifs
- JASPAR tracks can be added from the Public Hub
- https://jaspar.genereg.net/
Query SNPs using ADASTRA website
https://adastra.autosome.ru/zanthar
Confirm expression of candidate genes using the DICE database
https://dice-database.org/
Citations
Step 1
Chiou, J., Geusz, R.J., Okino, ML. et al. Interpreting type 1 diabetes risk with genetics and single-cell epigenomics
https://doi.org/10.1038/s41586-021-03552-w
Step 2
Mu, Z., Wei, W., Fair, B. et al. The impact of cell type and context-dependent regulatory variants on human immune traits
https://doi.org/10.1186/s13059-021-02334-x