Mapping DNA interaction landscapes in psoriasis susceptibility loci highlights KLF4 as a target gene in 9q31.

BACKGROUND: Genome-wide association studies (GWAS) have uncovered many genetic risk loci for psoriasis, yet many remain uncharacterised in terms of the causal gene and their biological mechanism in disease. This is largely a result of the findings that over 90% of GWAS variants map outside of protein-coding DNA and instead are enriched in cell type- and stimulation-specific gene regulatory regions. RESULTS: Here, we use a disease-focused Capture Hi-C (CHi-C) experiment to link psoriasis-associated variants with their target genes in psoriasis-relevant cell lines (HaCaT keratinocytes and My-La CD8+ T cells). We confirm previously assigned genes, suggest novel candidates and provide evidence for complexity at psoriasis GWAS loci. For one locus, uniquely, we combine further epigenomic evidence to demonstrate how a psoriasis-associated region forms a functional interaction with the distant (> 500 kb) KLF4 gene. This interaction occurs between the gene and active enhancers in HaCaT cells, but not in My-La cells. We go on to investigate this long-distance interaction further with Cas9 fusion protein-mediated chromatin modification (CRISPR activation) coupled with RNA-seq, demonstrating how activation of the psoriasis-associated enhancer upregulates KLF4 and its downstream targets, relevant to skin cells and apoptosis. CONCLUSIONS: This approach utilises multiple functional genomic techniques to follow up GWAS-associated variants implicating relevant cell types and causal genes in each locus; these are vital next steps for the translation of genetic findings into clinical benefit.

Chromatin interactions reveal novel gene targets for drug repositioning in rheumatic diseases.

OBJECTIVES: There is a need to identify effective treatments for rheumatic diseases, and while genetic studies have been successful it is unclear which genes contribute to the disease. Using our existing Capture Hi-C data on three rheumatic diseases, we can identify potential causal genes which are targets for existing drugs and could be repositioned for use in rheumatic diseases. METHODS: High confidence candidate causal genes were identified using Capture Hi-C data from B cells and T cells. These genes were used to interrogate drug target information from DrugBank to identify existing treatments, which could be repositioned to treat these diseases. The approach was refined using Ingenuity Pathway Analysis to identify enriched pathways and therefore further treatments relevant to the disease. RESULTS: Overall, 454 high confidence genes were identified. Of these, 48 were drug targets (108 drugs) and 11 were existing therapies used in the treatment of rheumatic diseases. After pathway analysis refinement, 50 genes remained, 13 of which were drug targets (33 drugs). However considering targets across all enriched pathways, a further 367 drugs were identified for potential repositioning. CONCLUSION: Capture Hi-C has the potential to identify therapies which could be repositioned to treat rheumatic diseases. This was particularly successful for rheumatoid arthritis, where six effective, biologic treatments were identified. This approach may therefore yield new ways to treat patients, enhancing their quality of life and reducing the economic impact on healthcare providers. As additional cell types and other epigenomic data sets are generated, this prospect will improve further.

Chromatin Looping Links Target Genes with Genetic Risk Loci for Dermatological Traits.

Chromatin looping between regulatory elements and gene promoters presents a potential mechanism whereby disease risk variants affect their target genes. In this study, we use H3K27ac HiChIP, a method for assaying the active chromatin interactome in two cell lines: keratinocytes and skin lymphoma-derived CD8+ T cells. We integrate public datasets for a lymphoblastoid cell line and primary CD4+ T cells and identify gene targets at risk loci for skin-related disorders. Interacting genes enrich for pathways of known importance in each trait, such as cytokine response (psoriatic arthritis and psoriasis) and replicative senescence (melanoma). We show examples of how our analysis can inform changes in the current understanding of multiple psoriasis-associated risk loci. For example, the variant rs10794648, which is generally assigned to IFNLR1, was linked to GRHL3, a gene essential in skin repair and development, in our dataset. Our findings, therefore, indicate a renewed importance of skin-related factors in the risk of disease.

Analysis of chromatin organization and gene expression in T cells identifies functional genes for rheumatoid arthritis.

Genome-wide association studies have identified genetic variation contributing to complex disease risk. However, assigning causal genes and mechanisms has been more challenging because disease-associated variants are often found in distal regulatory regions with cell-type specific behaviours. Here, we collect ATAC-seq, Hi-C, Capture Hi-C and nuclear RNA-seq data in stimulated CD4+ T cells over 24 h, to identify functional enhancers regulating gene expression. We characterise changes in DNA interaction and activity dynamics that correlate with changes in gene expression, and find that the strongest correlations are observed within 200 kb of promoters. Using rheumatoid arthritis as an example of T cell mediated disease, we demonstrate interactions of expression quantitative trait loci with target genes, and confirm assigned genes or show complex interactions for 20% of disease associated loci, including FOXO1, which we confirm using CRISPR/Cas9.

Replication of Associations of Genetic Loci Outside the HLA Region With Susceptibility to Anti-Cyclic Citrullinated Peptide-Negative Rheumatoid Arthritis.

OBJECTIVE: Genetic polymorphisms within the HLA region explain only a modest proportion of anti-cyclic citrullinated peptide (anti-CCP)-negative rheumatoid arthritis (RA) heritability. However, few non-HLA markers have been identified so far. This study was undertaken to replicate the associations of anti-CCP-negative RA with non-HLA genetic polymorphisms demonstrated in a previous study. METHODS: The Rheumatoid Arthritis Consortium International densely genotyped 186 autoimmune-related regions in 3,339 anti-CCP-negative RA patients and 15,870 controls across 6 different populations using the Illumina ImmunoChip array. We performed a case-control replication study of the anti-CCP-negative markers with the strongest associations in that discovery study, in an independent cohort of anti-CCP-negative UK RA patients. Individuals from the arcOGEN Consortium and Wellcome Trust Case Control Consortium were used as controls. Genotyping in cases was performed using Sequenom MassArray technology. Genome-wide data from controls were imputed using the 1000 Genomes Phase I integrated variant call set release version 3 as a reference panel. RESULTS: After genotyping and imputation quality control procedures, data were available for 15 non-HLA single-nucleotide polymorphisms in 1,024 cases and 6,348 controls. We confirmed the known markers ANKRD55 (meta-analysis odds ratio [OR] 0.80; P = 2.8 × 10(-13) ) and BLK (OR 1.13; P = 7.0 × 10(-6) ) and identified new and specific markers of anti-CCP-negative RA (prolactin [PRL] [OR 1.13; P = 2.1 × 10(-6) ] and NFIA [OR 0.85; P = 2.5 × 10(-6) ]). Neither of these loci is associated with other common, complex autoimmune diseases. CONCLUSION: Anti-CCP-negative RA and anti-CCP-positive RA are genetically different disease subsets that only partially share susceptibility factors. Genetic polymorphisms located near the PRL and NFIA genes represent examples of genetic susceptibility factors specific for anti-CCP-negative RA.

Identifying Causal Genes at the Multiple Sclerosis Associated Region 6q23 Using Capture Hi-C.

BACKGROUND: The chromosomal region 6q23 has been found to be associated with multiple sclerosis (MS) predisposition through genome wide association studies (GWAS). There are four independent single nucleotide polymorphisms (SNPs) associated with MS in this region, which spans around 2.5 Mb. Most GWAS variants associated with complex traits, including these four MS associated SNPs, are non-coding and their function is currently unknown. However, GWAS variants have been found to be enriched in enhancers and there is evidence that they may be involved in transcriptional regulation of their distant target genes through long range chromatin looping. AIM: The aim of this work is to identify causal disease genes in the 6q23 locus by studying long range chromatin interactions, using the recently developed Capture Hi-C method in human T and B-cell lines. Interactions involving four independent associations unique to MS, tagged by rs11154801, rs17066096, rs7769192 and rs67297943 were analysed using Capture Hi-C Analysis of Genomic Organisation (CHiCAGO). RESULTS: We found that the pattern of chromatin looping interactions in the MS 6q23 associated region is complex. Interactions cluster in two regions, the first involving the rs11154801 region and a second containing the rs17066096, rs7769192 and rs67297943 SNPs. Firstly, SNPs located within the AHI1 gene, tagged by rs11154801, are correlated with expression of AHI1 and interact with its promoter. These SNPs also interact with other potential candidate genes such as SGK1 and BCLAF1. Secondly, the rs17066096, rs7769192 and rs67297943 SNPs interact with each other and with immune-related genes such as IL20RA, IL22RA2, IFNGR1 and TNFAIP3. Finally, the above-mentioned regions interact with each other and therefore, may co-regulate these target genes. CONCLUSION: These results suggest that the four 6q23 variants, independently associated with MS, are involved in the regulation of several genes, including immune genes. These findings could help understand mechanisms of disease and suggest potential novel therapeutic targets.

Capture Hi-C identifies a novel causal gene, IL20RA, in the pan-autoimmune genetic susceptibility region 6q23.

BACKGROUND: The identification of causal genes from genome-wide association studies (GWAS) is the next important step for the translation of genetic findings into biologically meaningful mechanisms of disease and potential therapeutic targets. Using novel chromatin interaction detection techniques and allele specific assays in T and B cell lines, we provide compelling evidence that redefines causal genes at the 6q23 locus, one of the most important loci that confers autoimmunity risk. RESULTS: Although the function of disease-associated non-coding single nucleotide polymorphisms (SNPs) at 6q23 is unknown, the association is generally assigned to TNFAIP3, the closest gene. However, the DNA fragment containing the associated SNPs interacts through chromatin looping not only with TNFAIP3, but also with IL20RA, located 680 kb upstream. The risk allele of the most likely causal SNP, rs6927172, is correlated with both a higher frequency of interactions and increased expression of IL20RA, along with a stronger binding of both the NFκB transcription factor and chromatin marks characteristic of active enhancers in T-cells. CONCLUSIONS: Our results highlight the importance of gene assignment for translating GWAS findings into biologically meaningful mechanisms of disease and potential therapeutic targets; indeed, monoclonal antibody therapy targeting IL-20 is effective in the treatment of rheumatoid arthritis and psoriasis, both with strong GWAS associations to this region.

Capture Hi-C reveals novel candidate genes and complex long-range interactions with related autoimmune risk loci.

Genome-wide association studies have been tremendously successful in identifying genetic variants associated with complex diseases. The majority of association signals are intergenic and evidence is accumulating that a high proportion of signals lie in enhancer regions. We use Capture Hi-C to investigate, for the first time, the interactions between associated variants for four autoimmune diseases and their functional targets in B- and T-cell lines. Here we report numerous looping interactions and provide evidence that only a minority of interactions are common to both B- and T-cell lines, suggesting interactions may be highly cell-type specific; some disease-associated SNPs do not interact with the nearest gene but with more compelling candidate genes (for example, FOXO1, AZI2) often situated several megabases away; and finally, regions associated with different autoimmune diseases interact with each other and the same promoter suggesting common autoimmune gene targets (for example, PTPRC, DEXI and ZFP36L1).