Associations between genetic loci and increased susceptibility to autoimmune disease have been well characterized, however, translating this knowledge into mechanistic insight and patient benefit remains a challenge. While improvements in the precision, completeness and accuracy of our genetic understanding of autoimmune diseases will undoubtedly be helpful, meeting this challenge will require two interlinked problems to be addressed: first which of the highly correlated variants at an individual locus is responsible for increased disease risk, and second what are the downstream effects of this variant. Given that the majority of loci are thought to affect non-coding regulatory elements, the second question is often reframed as what are the target gene(s) and pathways affected by causal variants. Currently, these questions are being addressed using a wide variety of novel techniques and datasets. In many cases, these approaches are complementary and it is likely that the most accurate picture will be generated by consolidating information relating to transcription, regulatory activity, chromatin accessibility, chromatin conformation and readouts from functional experiments, such as genome editing and reporter assays. It is clear that it will be necessary to gather this information from disease relevant cell types and conditions and that by doing so our understanding of disease etiology will be improved. This review is focused on the field of autoimmune disease functional genomics with a particular focus on the most exciting and significant research to be published within the last couple of years.
Genetic studies, including genome-wide association studies, have identified many common variants that are associated with autoimmune diseases. Strikingly, in addition to being frequently observed in healthy individuals, a number of these variants are shared across diseases with diverse clinical presentations. This highlights the potential for improved autoimmune disease understanding which could be achieved by characterising the mechanism by which variants lead to increased risk of disease. Of particular interest is the potential for identifying novel drug targets or of repositioning drugs currently used in other diseases. The majority of autoimmune disease variants do not alter coding regions and it is often difficult to generate a plausible hypothetical mechanism by which variants affect disease-relevant genes and pathways. Given the interest in this area, considerable effort has been invested in developing and applying appropriate methodologies. Two of the most important technologies in this space include both low- and high-throughput genomic perturbation using the CRISPR/Cas9 system and massively parallel reporter assays. In this review, we introduce the field of autoimmune disease functional genomics and use numerous examples to demonstrate the recent and potential future impact of these technologies.
Background/Aims We recently performed the largest juvenile idiopathic arthritis (JIA) genome-wide association study (GWAS) to date. Disease-associated loci contain multiple single nucleotide polymorphism (SNPs), and the majority map to non-coding enhancers, making it challenging to define causal variants and genes. Functional genomics datasets in disease-relevant tissues have been shown to be essential for the functional interpretation of GWAS loci. In particular, capture Hi-C (CHi-C) has been successful in detecting chromosomal interactions linking GWAS loci to their target genes. However, such datasets are lacking in JIA. The aim of this study is to bridge this gap and advance the knowledge of the biological mechanisms that underpin susceptibility to JIA, by integrating GWAS with public epigenomics datasets and in-house generated CHi-C from JIA patients. We focus on CD4+ T-cells, which have been shown to be one of the most relevant cell types in JIA. In addition, we use CRISPR-Cas9 to validate the regulatory effect of prioritised variants on their predicted target genes. Methods Credible SNP sets for the top JIA risk loci (P < 5x10-6) were annotated using EpiMap data. Low input whole genome promoter CHi-C (PCHi-C) was performed on CD4+ T-cells isolated from blood from 3 JIA oligoarthritis patients, and data was analysed using CHiCAGO. We employed CRISPR activation (CRISPRa) and CRISPR interference (CRISPRi) in Jurkats to assess whether prioritized JIA variants are capable of regulating the expression of the interacting genes. Results 614 SNPs (out of 735) were found to overlap active enhancers in CD4+ T-cells, and were prioritized for further analysis. We identified numerous significant chromatin interactions in 19 out of 44 non-MHC JIA associated loci, linking JIA SNPs mapping to T-cell enhancers to a total of 61 target genes and revealing potential novel disease pathways. A JIA-associated locus on chromosome 3 contains 39 SNPs. It maps to an intergenic region and the causal gene/s are unclear. Our PCHi-C data revealed that this JIA locus presents chromatin interactions with the promoters of several genes, such as CCRL2, CCR2, CCR3 and CCR5. Two variants were selected for further analysis: rs79815064, which had the highest posterior probability, and rs8005404, the only variant within a CD4+ T-cell enhancer linked to surrounding gene activity. When both SNPs were targeted with CRISPRa and CRISPRi, we observed an increased and decreased expression, respectively, of CCRL2, CCR2, CCR3 and CCR5, confirming their role in disease. These genes belong to the chemokine receptor family and are important regulators of the inflammatory response. Conclusion Our work shows how functional genomics can help identify biological mechanisms by which GWAS variants increase risk of JIA, which in turn will benefit patients through personalised medicine and the identification of therapeutic targets. Disclosure A. Frantzeskos: None. V. Malysheva: None. C. Shi: None. J. Ding: None. J. Bowes: None. W. Thomson: None. S. Eyre: None. M. Spivakov: Shareholder/stock ownership; M.S. is co-founder and shareholder of Enhanc3D Genomics Ltd. G. Orozco: None.
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