New Nanoscale Genome Mapping Tool Pinpoints Causal Autoimmunity Variants

More than ten million people have now been genotyped, and the overwhelming majority of the disease-linked variants they carry sit not in the protein-coding blueprint of the genome but in its vast noncoding expanse, which makes up more than 95 percent of human DNA. For decades this has been a frustrating paradox for genetics: genome-wide association studies have catalogued hundreds of thousands of variants associated with common diseases, yet fewer than five percent of disease-associated variants are thought to be functionally active, and the noncoding sequences that dominate the signal remain largely unusable in clinical genetics. A team at the University of Oxford reports in Nature Genetics a technology designed to close that gap, and in doing so they have uncovered a previously unknown mechanism by which a single DNA letter change raises the risk of multiple autoimmune diseases.

The core difficulty is architectural. The noncoding genome is not read linearly: cis-regulatory elements such as enhancers can lie more than a million base pairs away from the genes they control, often buried inside the introns of unrelated genes. Enhancers, bound by clusters of transcription factors, physically loop through three-dimensional space to contact promoters near transcription start sites, and the specificity of these contacts is shaped by CTCF-bound boundary elements operating through cohesin-mediated loop extrusion. Chromatin structure is therefore a key determinant of gene expression, but conventional chromosome conformation capture (3C) methods, which rely on restriction enzymes that cut only at specific sequence motifs, cannot resolve contacts below roughly 500 base pairs and cannot generate single-allele data for most variants. Only about 30 percent of variants lie close enough to a restriction cut site to be studied at all.

The Oxford group, led by James Davies together with Jim Hughes and Thomas Milne, developed a variant-to-function platform called Micro Capture-C variant-to-function, or MCCv, built on their earlier Micro Capture-C method. Instead of restriction enzymes, the technique uses micrococcal nuclease to fragment chromatin, allowing data to be generated from any site of interest. Oligonucleotide probes are designed directly over the variant being tested, and ultra-short-range ligation junctions of less than 800 base pairs define the nanoscale topology at the variant itself, while long-range junctions spanning one kilobase to one megabase define the structure of the whole locus. The result is the highest-resolution 3C data produced to date, capable of resolving chromatin architecture at the level of individual transcription factor binding sites while simultaneously identifying the target genes contacted by each regulatory element.

The single-allele capability is what makes the platform powerful for genetics. In cells that are heterozygous for a variant, the reference and alternate chromosomes can be separated computationally, so each allele effectively serves as an internal control for the other, minimizing experimental noise. In benchmarking experiments on 42 heterozygous gain-of-function enhancer variants in Jurkat cells, inactive reference alleles displayed a regular nucleosomal patterning, whereas active alternate alleles showed distinctive fine-scale contact patterns consistent with transcription factor interactions within nucleosome-depleted regions. The method captured 86.7 percent of reads for allelic analysis, and analysis of the rheumatoid arthritis-associated variant rs2793109 revealed that the disease allele creates a RUNX1 motif with a complex pattern of short-range contacts, while simultaneously showing allele-specific changes in long-range interactions with the promoters of ZEB1 and ZNF438. By phasing the captured variant with other heterozygous SNPs across the locus, the team linked the risk allele to increased ZEB1 expression using intron-rich RNA sequencing.

Applied systematically to immune-mediated inflammatory disease genetics, the platform delivered striking results. Single-cell ATAC-seq analysis showed that CD4-positive T cells, particularly central memory T cells, carry the highest burden of IMID-associated variation. The researchers imputed 3,104 independent haplotypes from 400 studies and tested each variant within them for molecular mechanisms, assigning a potential causal variant and mechanism to 47 percent of haplotypes, including 405 unique enhancer variants. Deep MCCv capture in activated primary CD4-positive T cells then mapped enhancer-promoter contacts with a sensitivity far beyond genome-wide methods: the strongest contact for each variant was supported by a mean of roughly 839 ligation junctions, compared with an average of just 2.5 junctions from Micro-C, and a fivefold better signal-to-noise ratio. At the 1q24.3-25.1 locus, four independent disease signals mapped to four distinct regulatory elements, with the eczema enhancer contacting FASL and the vitiligo, celiac disease and Crohn’s disease enhancers contacting TNFSF14 while skipping the intervening TNFSF18 promoter.

Ranking promoters by contact frequency allowed the team to nominate 251 unique causal genes, and these clustered in key immune pathways and were enriched for tractable drug targets and rare primary immunodeficiency mutations. Notably, only 60.5 percent of variants contacted their nearest gene most strongly, underscoring how misleading nearest-gene assumptions can be. Single-allele analysis of the 405 captured regulatory elements showed that 54.6 percent of heterozygous disease-associated variants had at least one significantly altered contact, and disease variants showed far greater architectural changes than matched controls for cell type, haplotype and disease association. When the researchers paired these contact data with phased RNA sequencing, variants with detectable changes in allelic contact frequency were roughly threefold enriched for allelic imbalance in expression of their nominated genes, with directional concordance between the chromatin and RNA effects at up to 88.2 percent of well-phased pairs.

The centerpiece discovery came from the rs4409785 variant at the 11q21 locus, associated with multiple sclerosis, rheumatoid arthritis and several other autoimmune conditions. Motif analysis showed that the disease-associated allele creates an entirely new CTCF binding site, a neo-CTCF motif, and the reference allele is conserved throughout hominin and primate evolution, confirming that the risk allele is a gain of function. MCCv footprinting revealed a precise 20-base-pair footprint over the CTCF motif only on the alternate allele, and CTCF ChIP-seq and ATAC-seq from heterozygous donors confirmed increased CTCF binding and open chromatin there. To prove causality, the team coupled the platform with genome editing: using the cytosine base editor BE4max in primary CD4-positive T cells from a heterozygous donor, they mutated three critical bases of the CTCF motif while leaving the variant itself intact. A single experiment yielded six separable profiles, and editing the motif on the risk allele reverted both the long-range contacts to convergent downstream CTCF sites and the nanoscale chromatin structure to the inert nucleosomal pattern of the reference chromosome.

The mechanistic consequence was unexpected. Neither allele of rs4409785 contacts any gene promoter directly; instead, the neo-CTCF site forms a subdomain that walls off a T cell-specific super-enhancer lying between the FAM76B and SESN3 genes. Phased MCCv from the SESN3 promoter showed that the risk allele significantly reduces contacts between that super-enhancer and the SESN3 promoter, and phased RNA sequencing linked the variant to a 36.6 percent reduction in SESN3 transcript on the risk chromosome. SESN3 encodes Sestrin 3, the least-characterized member of a family known to inhibit mTOR signaling, a central pathway controlling immune cell activation. In primary human T cells, SESN3 knockout left mTOR signaling unchanged under normal conditions but increased it under amino acid starvation. Using an endogenously tagged cell line and co-immunoprecipitation, the team showed that SESN3 interacts with the GATOR2 complex during starvation and that this interaction is disrupted by tryptophan but not by other amino acids, identifying SESN3 as a tryptophan sensor for the mTOR pathway in T cells.

In vivo work confirmed the physiological relevance. Transplanting genome-edited hematopoietic stem and progenitor cells into mice and inducing experimental autoimmune encephalomyelitis, a model of multiple sclerosis, the researchers observed accelerated onset of disease symptoms and weight loss in animals lacking Sesn3, along with increased infiltrating activated T cells in the central nervous system. Together, the findings demonstrate that a common noncoding variant can promote autoimmunity through a gain-of-function CTCF mechanism that silences a metabolic sensor of immune homeostasis, a mechanism the authors note could only have been fully resolved with high-resolution, allele-specific chromatin mapping. Because genetic evidence is known to increase the probability of clinical trial success roughly 2.6-fold, the team argues that MCCv, which can already capture 405 sites in parallel and could plausibly exceed 1,000 targets per experiment, offers a scalable route from GWAS catalogs to validated causal variants, genes and therapeutic targets.

Subject of Research: Allele-specific nanoscale chromatin architecture mapping to identify causal noncoding regulatory variants in inflammatory disease

Article Title: Single-allele nanoscale mapping of regulatory variants

Article References: Hamley, J. C., Zhang, W., Willmott, D., Chen, L. Y., Sharlandjieva, V., Li, H., Dalgleish, J. L. T., Denny, N., Agarwal, G., Hentges, L., Ozcan, B., Doll, R. M., Wei, Y., Riva, S. G., Venkatesh, S. S., Arachi, M., Agarwal, D., Akkuratov, E. E., Baxter, M., … Davies, J. O. J. (2026). Single-allele nanoscale mapping of regulatory variants. Nature Genetics, 58(10), 2660-2672. https://doi.org/10.1038/s41588-026-02776-8

Image Credits: AI Generated

DOI: 10.1038/s41588-026-02776-8

Keywords: MCCv, Micro Capture-C, noncoding variants, chromatin architecture, CTCF, super-enhancer, SESN3, mTOR signaling, autoimmunity, GWAS, CD4 T cells, genome editing