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Catalog of Promoter Interactions Links Non-Coding Variants, Gene Promoters

NEW YORK (GenomeWeb) – By cataloging promoter interactions, a team of UK researchers uncovered long-range interactions that might play a key part in regulating disease.

Researchers led by the Babraham Institute's Peter Fraser used promoter capture Hi-C (PCHi-C) to generate a catalog of the genomic regions with which some 31,000 promoters interact. As they reported in Cell this week, the researchers found that promoter interactions were cell-type specific and that interacting regions were enriched for expression quantitative trait loci (eQTLs). In addition, by linking SNPs identified through genome-wide association studies to PCHi-C interaction data, the researchers prioritized a number of candidate genes for susceptibility to common diseases.

"By identifying which parts of the genome connect with which genes, we have discovered hundreds of thousands of regions that are necessary to switch genes on and off," Fraser said in a statement. "Small changes to the DNA sequence of these distal regulatory regions can interfere with the normal control of genes, leading to a greater chance of developing a specific disease."

This paper is one of a suite of 41 articles appearing in Cell and other journals from the International Human Epigenome Consortium that provide epigenetic datasets or tools along with glimpses into the role of epigenetics in various conditions such as cancer, immune conditions, and autism.

In this paper, Fraser and his colleagues turned to PCHi-C, a version of the Hi-C method to uncover long-range interactions within the genome that they said could better identify promoter interactions. In PCHi-C, sequence capture is used to pull down fragments that are enriched for such promoter interactions. The researchers applied the approach to 17 human primary blood cell types to generate a catalog of the interactions of 31, 253 annotated promoters.

Overall, the researchers reported that they uncovered 698,187 high-confidence unique promoter interactions. About 10 percent of these were promoter-to-promoter interactions, while the remaining 90 percent were interactions between promoters and other parts of the genome.

A principal components analysis of the interactions revealed close clustering of biological replicates and separating of the individual cell types, suggesting that the promoter interactions are cell-type specific. Some interactions were shared among cells belonging to the same lineage, the researchers said.

Regions of the genome that interacted with promoters were enriched for accessible chromatin and for histone marks like H3K27ac and H3K4me1 that are associated with active enhancers, the researchers added.

By integrating PCHi-C and eQTL information, Fraser and his colleagues found that the regions of the genome that interact with promoters are enriched for eQTLs — and specifically for eQTLs that regulate the same gene that that region is connected to.

For instance, when they examined eQTLs from a large meta-analysis of whole blood, they uncovered 1,214 lead cis-eQTLs that could be traced to promoter-interacting regions that physically touched the eQTL target gene promoter.

They also found that some eQTLs at promoter-interacting regions influenced more than one gene. For instance, the researchers traced the eQTL SNP rs71636780 to a promoter-interacting region of two genes, ARID1A and ZDHHC18, within monocytes. Variants of this SNP had opposing effects on the expression of the two genes.

This, the researchers said, underscores a regulatory role for promoter interactions and provides a possible link between non-coding regulatory variants and target genes.

When Fraser and his colleagues combined their PCHi-C data with summary statistics from 31 genome-wide association studies that examined autoimmune diseases, blood cell traits, and metabolic and other traits, they also found an enrichment of GWAS variants at promoter-interacting regions in relevant cell types.

Based on this, they developed a Bayesian strategy that used their promoter interaction data to prioritize putative disease-associated genes. In this way, the researchers prioritized 2,604 candidate genes for follow up.

"Mapping the genome's regulatory interactions establishes the missing link between a genetic change at one part of the genome with the gene it ultimately affects," co-author Mikhail Spivakov, also at the Babraham Institute, said. "While the results currently look promising, it will take many more years of work and rigorous testing before new treatments become available as a result of this fundamental research."