TITLE: Chromatin loops and expression QTL colocalization reveal novel gene targets for T1D-associated GWAS variants in immune cells
AUTHORS: Joaquin  Reyna, Sourya Bhattacharyya, Nikhil Rao, Abhijit Chakraborty, Ferhat Ay

[DESCRIPTION]
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. 

[STEPS]
SECTION: Main Colocalization Pipeline
1. 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
 

SECTION: Main Colocalization Pipeline
2. 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
 

SECTION: Main Colocalization Pipeline
3. 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
 

SECTION: Main Colocalization Pipeline
4. 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.
SECTION: Gene Candidate Prioritization
7. Investigate TF binding sites using the Genome Browser + others
SECTION: Gene Candidate Prioritization
6. Investigate Candidate Genes using the WashU Epigenome Browser


SECTION: Main Colocalization Pipeline
1.2. Filter for SNPs with genome-wide significance (5e-8)
SECTION: Main Colocalization Pipeline
1.1. Remap the GWAS SNP coordinate to GRCh37
 

SECTION: Main Colocalization Pipeline
1.3. 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

SECTION: Main Colocalization Pipeline
5. 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. 
SECTION: Main Colocalization Pipeline
5.1. Extract all SNP-gene pairs that are +/- 500kb from a colocalized SNP 

 

SECTION: Main Colocalization Pipeline
5.2. Intersect loops with SNP-gene pairs
 

SECTION: Main Colocalization Pipeline
5.3. Label each SNP-Gene pair 
For each SNP-Gene pair label whether it is an eQTL, colocalized pair, contains a loop and all metadata. 

SECTION: Gene Candidate Prioritization
6.4. 
Look for genes with several SNPs which overlap important loops
SECTION: Gene Candidate Prioritization
6.1. Generate BED + Index files for 1D tracks
- single-cell ATAC-seq data
- SNP and gene locations


 

 


SECTION: Gene Candidate Prioritization
6.2. Generate BEDPE + Index files for 2D tracks
- loop data for all cell lines 

 

 

SECTION: Gene Candidate Prioritization
6.3. Add ChromHMM tracks from the Public Hubs
SECTION: Gene Candidate Prioritization
7.1. Load JASPAR tracks, query SNP locations, investigate motifs
- JASPAR tracks can be added from the Public Hub
- https://jaspar.genereg.net/
SECTION: Gene Candidate Prioritization
7.2. Query SNPs using ADASTRA website
https://adastra.autosome.ru/zanthar 
SECTION: Gene Candidate Prioritization
8. Confirm expression of candidate genes using the DICE database
https://dice-database.org/