Chromatin landscapes and genetic risk in systemic lupus

Abstract

BackgroundSystemic lupus erythematosus (SLE) is a multi-system, complex disease in which the environment interacts with inherited genes to produce broad phenotypes with inter-individual variability. Of 46 single nucleotide polymorphisms (SNPs) shown to confer genetic risk for SLE in recent genome-wide association studies, 30 lie within noncoding regions of the human genome. We therefore sought to identify and describe the functional elements (aside from genes) located within these regions of interest.MethodsWe used chromatin immunoprecipitation followed by sequencing to identify epigenetic marks associated with enhancer function in adult neutrophils to determine whether enhancer-associated histone marks were enriched within the linkage disequilibrium (LD) blocks encompassing the 46 SNPs of interest. We also interrogated available data in Roadmap Epigenomics for CD4+ T cells and CD19+ B cells to identify these same elements in lymphoid cells.ResultsAll three cell types demonstrated enrichment of enhancer-associated histone marks compared with genomic background within LD blocks encoded by SLE-associated SNPs. In addition, within the promoter regions of these LD blocks, all three cell types demonstrated enrichment for transcription factor binding sites above genomic background. In CD19+ B cells, all but one of the LD blocks of interest were also enriched for enhancer-associated histone marks.ConclusionsMuch of the genetic risk for SLE lies within or near genomic regions of disease-relevant cells that are enriched for epigenetic marks associated with enhancer function. Elucidating the specific roles of these noncoding elements within these cell-type-specific genomes will be crucial to our understanding of SLE pathogenesis.Electronic supplementary materialThe online version of this article (doi:10.1186/s13075-016-1169-9) contains supplementary material, which is available to authorized users.

Overall Downregulation of mRNAs and Enrichment of H3K4me3 Change Near Genome-Wide Association Study Signals in Systemic Lupus Erythematosus: Cell-Specific Effects

“…These data have implicated T cells (CD4 > CD8), B cells, and monocytes in SLE. One study utilizing neutrophil ChIP-seq data found enrichment of genes with potential ETS TF binding near SLE GWAS SNPs ( 27 ). To our knowledge, there are no previous studies that have used disease-related changes in RNA and H3K4me3 to analyze GWAS variants.…”

“…95 SNP with GWAS p -values less than 1E-8 were selected from a study of 1,311 SLE patients ( 26 ). Additionally, a set of 46 SNPs ( 27 ) curated from two published studies was used ( 28 , 29 ) and this is referred to as LD46 and a set of 25 SNPs curated from a meta-analysis of 7,219 SLE cases was used and referred to as META25 ( 28 ). All three sets of SNPs were used to generate a joint set of 143 SNPs used for analyzing RNA-seq data.…”

Thinking BIG rheumatology: how to make functional genomics data work for you

“…When these SNPs are associated with changes in gene expression, termed eQTLs, they can be used to implicate cell types that are likely to play a role in disease, such as T-cell genes in RA and B cells in lupus [ 86 , 87 ], although the underlying mechanism is not always clear. To uncover the molecular link between SNPs and disease, two studies which used data from genome-wide association studies (GWAS) and chromatin assays of multiple cell types found were able to implicate high-confidence causal SNPs that have the potential to disrupt regulatory elements [ 88 – 90 ]. But these studies, limited as they are by the sensitivity of GWAS and generic chromatin profiles from unrelated donors, are just the beginning.…”

Broadening our understanding of the genetics of Juvenile Idiopathic Arthritis (JIA): Interrogation of three dimensional chromatin structures and genetic regulatory elements within JIA-associated risk loci

“…(1) most of the tag SNPs that identify risk regions are located in non-coding regions of the genome [ 6 ]; and (2) these regions are highly enriched for non-coding elements such as enhancers [ 7 , 8 ]. The reverse is also true: if one maps enhancer elements in specific cell types, those mapped regions are highly enriched in GWAS-identified SNPs for diseases that affect those particular cells or tissues [ 9 ].…”