Abstract

Deciphering the impact of genetic variants on gene regulation is fundamental to understanding human disease. Although gene regulation often involves long-range interactions, it is unknown to what extent non-coding genetic variants influence distal molecular phenotypes. Here, we integrate chromatin profiling for three histone marks in lymphoblastoid cell lines (LCLs) from 75 sequenced individuals with LCL-specific Hi-C and ChIA-PET-based chromatin contact maps to uncover one of the largest collections of local and distal histone quantitative trait loci (hQTLs). Distal QTLs are enriched within topologically associated domains and exhibit largely concordant variation of chromatin state coordinated by proximal and distal non-coding genetic variants. Histone QTLs are enriched for common variants associated with autoimmune diseases and enable identification of putative target genes of disease-associated variants from genome-wide association studies. These analyses provide insights into how genetic variation can affect human disease phenotypes by coordinated changes in chromatin at interacting regulatory elements.



Visualization

DATA SOURCE


   
Show local QTLs
Show distal QTLs
Show signal tracks
Show HiC (takes several minutes to visualize according to your computer resources)
Show ChiA-PET
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Legend for QTL tracks: The QTL tracks contain all associations passing the p-value threshold corresponding to a 10% FDR. Each association is represented in the "interaction" format, that is, as a link between the QTL SNP (1 base) and the peak. The scores (colors of the links) represent the association effect sizes.


Data overview

Our data are organized in the following directories:

Data processing: Personal genomes, Histone marks, DNaseI-seq, RNAseq, HiC, ChIA-PET

QTL analysis: QTLs

Other analyses: Motif analysis/TF binding, GWAS

Raw data: GEO

Code: Code

Quick start

The most common use of this portal is using the QTL analysis. For that, all you need to download is the QTL directory at http://chromovar3d.stanford.edu/QTLs/

http://chromovar3d.stanford.edu/QTLs/results has the following contents (see READMEs in each of these for more detailed descriptions of the files):

Genomic regions

Data matrices

QTLs