Massive Immune Cell Atlas Traces How Genetic Variants Drive Disease From Chromatin to Gene Expression

An international team of researchers has assembled one of the largest single-cell genetic atlases ever built, profiling more than ten million immune cells from 1,108 Finnish individuals to reveal, in unprecedented detail, how disease-associated DNA variants exert their effects on gene regulation. The study, published in Nature, combines simultaneous measurements of chromatin accessibility and gene expression in the same cells, allowing scientists to trace the full molecular cascade that connects a genetic variant to altered immune function. The work offers testable mechanistic explanations for more than half of known immune disease associations, a milestone in the long-standing effort to convert statistical genetic discoveries into biological understanding.

The central challenge the researchers set out to address is a familiar one in human genetics. Most of the thousands of genetic variants identified by genome-wide association studies as risk factors for disease do not lie within protein-coding genes. Instead, they sit in regulatory regions of the genome, the stretches of DNA that control when, where, and how strongly genes are switched on. Decades of work have established this pattern, but translating a regulatory variant into a concrete molecular mechanism has remained difficult, because the field lacked an empirical framework for characterizing the downstream consequences of regulatory variation at population scale.

Single-cell molecular quantitative trait locus mapping, a technique that links genetic variants to gene regulation measured in individual cells, has emerged as a promising approach. However, previous efforts suffered from limited statistical power and, crucially, from the inability to measure chromatin state and gene expression simultaneously in the same cells. Without both layers of information, researchers could observe that a variant was associated with changes in gene expression or with changes in chromatin accessibility, but they could not reliably connect the two steps into a single causal chain running from DNA accessibility through to transcript output.

To overcome these limitations, the consortium performed paired single-nucleus RNA sequencing and single-nucleus assay for transposase-accessible chromatin sequencing on peripheral blood mononuclear cells from FinnGen donors, capturing roughly ten million nuclei. The resulting atlas identified 51,083 cis-expression quantitative trait loci affecting 20,829 genes, 338,100 cis-chromatin accessibility quantitative trait loci affecting 210,584 open chromatin peaks, 119,094 putative causal variants, and 593,765 statistical links between regulatory peaks and the genes they control. These numbers represent a resource of extraordinary depth, spanning the major classes of immune cells found in blood and providing cell-type-resolved maps of genetic regulation.

A key analytical insight came from classifying variants according to whether they completed the full regulatory cascade. Variants that altered chromatin accessibility and were also linked, through a peak-gene connection, to altered gene expression showed twice the rate of colocalization with disease associations compared with variants that affected chromatin alone. In other words, the variants most likely to be genuinely causal for disease are those whose effects can be traced through the entire chain, from open chromatin to enhancer activity to target gene expression. This finding provides a practical filter for prioritizing candidate causal variants in complex disease loci.

To validate these statistical findings experimentally, the team turned to massively parallel reporter assays, a high-throughput technique that tests the regulatory activity of thousands of DNA sequences simultaneously. The assays confirmed the regulatory effects of 10,428 fine-mapped molecular quantitative trait loci, providing direct experimental support for a substantial fraction of the atlas’s predictions and demonstrating that the computational framework captures biologically real regulatory variation rather than statistical artifacts.

Perhaps the most conceptually significant discovery concerns genes under strong evolutionary constraint, those whose sequences change rarely because mutations are harmful. Previous studies had noted a paradox: disease variants preferentially target constrained genes, yet constrained genes appear depleted of detectable expression quantitative trait loci. The new atlas resolves this contradiction by revealing a phenomenon the authors call multilayered regulatory buffering. At constrained genes, chromatin accessibility changes caused by variants occur with normal effect sizes, but their transmission to gene expression is attenuated because these genes are regulated through weaker and more numerous enhancer-gene links. The redundancy of many weak regulatory connections dampens the expression consequence of any single regulatory perturbation.

Crucially, the reporter assay experiments confirmed that this buffering operates downstream of the regulatory element itself. Constraint acts at the interface between chromatin and expression rather than on the intrinsic activity of the cis-regulatory DNA. This distinction matters because it explains why the paradox exists in the first place: standard expression quantitative trait locus studies measure only the final output, and buffering at the chromatin-to-expression step suppresses that output signal even when the underlying chromatin effects are intact. The finding reconciles apparently conflicting observations across the field and suggests that disease risk at constrained genes may accumulate through subtle, distributed effects that individual expression studies systematically miss.

The atlas also delivers concrete mechanistic hypotheses for specific diseases. At autoimmune loci, the researchers traced complete regulatory cascades at TICAM1, an adaptor protein in Toll-like receptor signaling, and RHOH, a small GTPase involved in T cell receptor signaling, both linked to autoimmune hypothyroidism. They similarly dissected Finnish-enriched variants at TNRC18 and IL21R, the latter a receptor for an interleukin with established roles in T cell biology. Beyond blood-related traits, the framework explained associations in other tissues, including loci connected to Alzheimer’s disease such as PILRB and TYROBP, genes involved in microglial signaling, illustrating that regulatory mechanisms mapped in immune cells can illuminate neurological and other complex diseases.

The resource is being made broadly available to the research community. Summary statistics for chromatin accessibility quantitative trait loci, expression quantitative trait loci, and peak-gene links are publicly accessible and browsable through an interactive web interface, and the analysis pipelines, including the CASCADE classification framework and supporting software packages, have been released on GitHub. Individual-level data remain protected under Finnish and European data regulations but can be accessed by approved researchers through established application channels. As the field moves toward functional interpretation of the ever-growing catalog of disease-associated variants, atlases of this kind, which connect genetic variation to regulatory architecture across millions of cells and hundreds of donors, are likely to become foundational references for drug target discovery and precision medicine in immunology and beyond.

Subject of Research: Population-scale single-cell multiomic mapping of regulatory genetic variation in human immune cells

Article Title: Population-scale immune multiome atlas reveals regulatory disease mechanisms

Article References: Kanai, M., Delorey, T. M., Honkanen, J., Rodosthenous, R. S., Juvila, J., Murphy, S., Teixeira-Soldano, I., Hwang, H. S., Karjalainen, J., Halonen, J., Panagiotaropoulou, G., Zhang, Y., McCabe, C., Chen, E., Nanki, K., Yoshida, T., Liu, K., Glean, M., Mehrotra, N., … Xavier, R. J. (2026). Population-scale immune multiome atlas reveals regulatory disease mechanisms. Nature. https://doi.org/10.1038/s41586-026-11078-2

Image Credits: AI Generated

DOI: 10.1038/s41586-026-11078-2

Keywords: single-cell multiomics, chromatin accessibility, gene expression, quantitative trait loci, FinnGen, immune cells, regulatory variants, enhancer-gene links, autoimmune disease, genetic fine-mapping, reporter assays, evolutionary constraint