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Researchers Map Overlap Between Causal SNPs and Regulatory Elements for Autoimmune Conditions

NEW YORK (GenomeWeb) – Researchers from the Broad Institute, Massachusetts General Hospital, Yale School of Medicine, and elsewhere have used a combination of genetic, gene expression, and epigenetic marker information to map the causal variants contributing to 21 inflammatory autoimmune conditions and begin unraveling their regulatory effects.

Their study, appearing online today in Nature, suggests that roughly 90 percent of the causal variants identified in these conditions so far fall in non-coding parts of the genome, affecting enhancers and other regulatory sites.

The team determined that candidate causal variants often fell in and around binding sites used by transcription factors with immune cell-related functions, for example, or master immune differentiation regulators. It also saw over-representation of causal variants at enhancer binding sites associated with immune cell activation after a stimulus.

The study's authors noted that "only [10 to 20 percent of causal variants] directly alter recognizable transcription factor binding motifs."

Rather, they wrote, "most non-coding risk variants, including those that alter gene expression, affect non-canonical sequence determinants not well-explained by current gene regulatory models."

A growing collection of suspected disease risk variants has been detected through genome-wide association studies of inflammatory immune diseases and other conditions, the researchers explained. But between SNPs falling in linkage disequilibrium blocks and difficulties in deciphering the roles of variants in non-protein-coding parts of the genome, it is often tough to track down causal variants and to untangle their biological effects.

"[E]ven assuming the causal variant can be identified, interpretation is limited by incomplete knowledge of non-coding regulatory elements, their mechanisms of action, and the cellular states and processes in which they function," the researchers wrote.

To delve into this further, the researchers started by establishing a fine-mapping algorithm, which they used to narrow on the causal variants with high-density Immunochip data collected for 23,605 healthy individuals and 14,277 individuals with multiple sclerosis.

From there, they expanded the fine-mapping algorithm to assess available data for 21 autoimmune conditions, including Crohn's disease, ulcerative colitis, allergy, rheumatoid arthritis, and other diseases, teasing apart 636 signals identified in past GWAS to find 4,950 suspected causative SNPs.

Using RNA sequencing and chromatin immunoprecipitation sequencing experiments that targeted half a dozen histone marks, meanwhile, the team put together its own regulatory maps for 10 immune cell types, with the goal of understanding the immune consequences of the causal variants implicated from the GWAS data.

That epigenetic data — combined with existing data on 56 cell types from the ENCODE consortium and the National Institutes of Health Epigenomics Project — made it possible for researchers to first map genome-wide regulatory patterns across cell types and then cluster cell types with comparable regulatory features.

When the team layered candidate causal SNPs onto these immune cell regulatory maps, it found an over-representation of apparent autoimmune causal variants at sites coinciding with enhancer regions in B cells and T cells, particularly T cells stimulated by an immune trigger.

Based on their results, the researchers estimated that some 60 percent of apparent candidate SNPs for the autoimmune conditions overlapped with immune enhancer sequences, while 8 percent more fell at gene promoter sites.

Causal SNPs tended to overlap with dozens of transcription factors with immune cell-related functions, for instance, though the precise combination of transcription factor binding sites involved varied depending on the autoimmune condition considered.

A similar approach showed promise for untangling cell type-specific effects for causal SNPs from 18 other diseases or traits such as Alzheimer's disease and migraine headache risk, the study's authors noted.

They cautioned that "[m]uch work remains to be done to characterize SNPs whose causality can be firmly established through genotyping and to facilitate efforts to resolve GWAS signals that remain refractory to fine mapping due to haplotype structure," the researchers wrote.

"Understanding their regulatory mechanisms could have broad implications for autoimmune disease biology and treatment," the researchers concluded, "given genetic links to immune regulators … and implied transcriptional and epigenetic aberrations, all of which are candidates for therapeutic intervention."

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