Genetic variation in cis-regulatory domains suggests cell type-specific regulatory mechanisms in immunity

Abstract

Studying the interplay between genetic variation, epigenetic changes, and regulation of gene expression is crucial to understand the modification of cellular states in various conditions, including immune diseases. In this study, we characterize the cell-specificity in three key cells of the human immune system by building cis maps of regulatory regions with coordinated activity (CRDs) from ChIP-seq peaks and methylation data. We find that only 33% of CRD-gene associations are shared between cell types, revealing how similarly located regulatory regions provide cell-specific modulation of gene activity. We emphasize important biological mechanisms, as most of our associations are enriched in cell-specific transcription factor binding sites, blood-traits, and immune disease-associated loci. Notably, we show that CRD-QTLs aid in interpreting GWAS findings and help prioritize variants for testing functional hypotheses within human complex diseases. Additionally, we map trans CRD regulatory associations, and among 207 trans-eQTLs discovered, 46 overlap with the QTLGen Consortium meta-analysis in whole blood, showing that mapping functional regulatory units using population genomics allows discovering important mechanisms in the regulation of gene expression in immune cells. Finally, we constitute a comprehensive resource describing multi-omics changes to gain a greater understanding of cell-type specific regulatory mechanisms of immunity.

Fig. 1. CRDs emerging from the inter-individual correlation between chromatin peaks for Monocytes, Neutrophils and T-cells.

CRDs represent the coordinated activity between nearby regulatory elements (promoters and enhancers). Chromosome 5 is represented with a zoom of a region spanning 1000 chromatin peaks. hCRDs are outlined by black triangles, and the hematopoietic lineage is represented on the left. Significant gene-CRD associations (5% FDR) are represented in red. For each cell- type, expressed genes are colored in grey and significantly associated genes are colored in yellow.

Fig. 2. Associations of CRDs and hCRD-QTLs enrichment in autoimmune diseases and blood traits.

a Schematic of CRD associations, including CRD-QTLs, CRD-gene, and CRD-CRD, with the number of associations found within the maximum sample size available for each cell type and epigenomic mark. b Quantile-quantile (Q-Q) plots and genomic inflation factor (λ metric) for hCRD-QTLs across 3 autoimmune diseases: Type 1 diabetes (DT1), rheumatoid arthritis (RA), multiple sclerosis (MS) and Type 2 diabetes (DT2) as negative control. c Odds ratios and standard errors of the effect size for the enrichment in hCRD-QTLs in monocytes(MON), neutrophils (NEU), T-cells (TCL) for eight blood cell count traits: Basophil count (BC), Eosinophil count (EC), Lymphocyte count (LC), Monocyte count (MC), Neutrophil count (NC), Platelet count (PC), Red blood cell count (RBC), and White blood cell count (WBC).

Fig. 3. PCHi-C analysis shows that significant associations involving large genomic distances take place in close physical proximity.

a Fraction of neutrophil chromatin peak pairs on the same chromosome supported by PCHi-C data (CHiCAGO score ≥ 5) at significantly associated (pink) and non-associated (blue) pairs of chromatin peaks within bins of increasing distance between peaks. b Fraction of hCRD-gene and mCRD-gene associations supported by PCHi-C data (CHiCAGO score ≥ 5) at increasing CRD-gene distances. c Fraction of hCRD-gene associations and mCRD-gene associations supported by PCHi-C data (mean CHiCAGO score ≥ 5) for pairs of co-expressed genes (5%FDR) that associate with the same CRD. The fraction is measured at bins of increasing distance between co-expressed genes.