Pleiotropy, the phenomenon in which one gene or genetic variant affects multiple characteristics, has long been both a blessing and a headache for researchers. On one hand, shared genetic associations provide valuable clues about the biological pathways that connect diseases; a variant that raises both cholesterol and coronary artery disease risk, for example, points toward lipid metabolism as a common mechanism. On the other hand, detecting and interpreting these shared patterns is statistically treacherous. Genome-wide association studies, known as GWAS, typically analyze one trait at a time, and the summary statistics they produce are noisy, correlated with one another, and often derived from studies whose participants overlap substantially. Existing multivariate methods struggle with these complications, frequently requiring analysts to guess how many underlying factors exist or to assume, unrealistically, that those factors are statistically independent of one another.

GFA tackles these problems head-on with a Bayesian modeling framework rooted in empirical Bayes matrix factorization, an approach previously developed by Wang and Stephens. Conceptually, the method treats the matrix of genetic effect estimates across many traits as a composite signal that can be decomposed into a small number of pleiotropic factors, each representing a pattern of cross-trait genetic associations, plus trait-specific residual effects. Crucially, the model does not force the factors to be orthogonal, meaning it can capture overlapping biological processes that simpler dimension-reduction techniques such as principal component analysis would blur together. The method also automatically determines how many factors are needed to explain the data, sparing researchers an arbitrary and potentially consequential choice, and it explicitly models the sampling correlations that arise when GWAS datasets share participants.

To demonstrate the power of the approach, the researchers applied GFA to a clinically urgent question: the shared genetic architecture of coronary artery disease and type 2 diabetes, two of the leading causes of death worldwide, along with 22 common risk factors that include lipid measures, blood pressure traits, body size measurements and markers of inflammation. The analysis partitioned the heritability of these traits into 15 distinct pleiotropic components. Each component tells a different story. Some factors capture broad metabolic influences that push risk of both diseases in the same direction, while others reveal more nuanced patterns, such as effects that raise one risk factor while lowering another, or influences that act on diabetes but not on heart disease. This kind of decomposition transforms a wall of association statistics into a structured map of biological processes, allowing researchers to see which combinations of traits travel together genetically and which diverge.

The value of that map extends beyond description. When genetic risk for a disease can be split into interpretable components, each component becomes a hypothesis about mechanism. A factor that loads heavily on inflammation markers and on coronary artery disease, for instance, suggests that inflammatory pathways contribute to cardiovascular risk independently of cholesterol. A factor dominated by adiposity traits with effects on both diseases points to shared metabolic consequences of body fat distribution. Researchers can then prioritize these pathways for experimental follow-up, design better polygenic risk scores that reflect distinct biological axes rather than a single blended score, and identify subtypes of disease that may respond differently to treatment. The approach echoes and extends earlier soft-clustering strategies used to classify type 2 diabetes genetic loci, but does so in a fully probabilistic framework that quantifies uncertainty at every step.

The team then turned to a second, very different application: the composition of blood cells. Dozens of traits describe the relative abundance of different immune cell types in circulating blood, from red cell characteristics to the proportions of various lymphocyte and myeloid populations, and these traits are strongly genetically correlated with one another and with common diseases. Applying GFA to this battery of phenotypes yielded a biologically meaningful decomposition in which individual factors corresponded to coherent groups of related cell types. Rather than treating each blood trait as an isolated variable, the factors capture the underlying developmental and regulatory programs that shape the immune system’s cellular makeup, providing a compressed and interpretable representation of immune genetics.

This blood cell decomposition was not merely an exercise in description. The researchers used the estimated factors as instruments in multivariable Mendelian randomization, a technique that uses genetic variants as natural experiments to probe whether an exposure causally influences a disease outcome. Multivariable Mendelian randomization is notoriously sensitive to weak instruments and to correlations among the exposures being tested, and the tightly correlated blood cell traits make the analysis especially fragile. By replacing the raw traits with GFA factors, the researchers increased the precision of the causal estimates and obtained cleaner, more interpretable results about which immune cell characteristics influence disease risk.

Perhaps the most striking technical lesson from the study concerns a problem that is easy to overlook: sample overlap. Large GWAS consortia frequently recruit from the same population biobanks, so the summary statistics for supposedly different traits may be computed in many of the same individuals. This induces correlations among the estimation errors that, if ignored, can masquerade as genuine shared genetic signal or distort factor estimates in ways that render them biologically meaningless. The authors found that accounting for overlapping samples was critical to obtaining interpretable results in their Mendelian randomization application. GFA builds this correction directly into its model, estimating the error correlations from the data rather than assuming them away, which distinguishes it from many earlier approaches to cross-trait analysis.

The practical accessibility of the method is another notable feature. GFA runs on GWAS summary statistics rather than individual-level genotype data, which means it can be applied to the hundreds of publicly available GWAS results without requiring access to protected participant records. The method is implemented in an open-source R package, and the authors have deposited all the code and data needed to replicate every analysis in the paper through public repositories, an unusually thorough commitment to reproducibility. The work was supported in part by the National Human Genome Research Institute and the National Institute of Allergy and Infectious Diseases.

As biobanks swell to millions of participants and GWAS results accumulate for thousands of phenotypes, methods like GFA address a pressing need: turning scattered single-trait associations into a coherent picture of how genetic variation shapes human biology. By automatically identifying the number of shared factors, allowing those factors to overlap, and correcting for the messy realities of shared study populations, the method offers a rigorous statistical foundation for the emerging field of phenome-wide genetics. If the patterns it uncovers hold up across broader collections of traits and ancestries, the hidden architecture of pleiotropy may soon become far less hidden, and the biological threads connecting heart disease, diabetes, immune traits and beyond will be that much easier to trace.

Subject of Research: A statistical method for identifying shared polygenic factors of genetic pleiotropy across human traits using GWAS summary statistics

Article Title: Genetic factor analysis for characterizing phenome-wide patterns of genetic pleiotropy

Article References: Morrison, J., Willwerscheid, J., Sylvertooth, D., He, X., & Stephens, M. (2026). Genetic factor analysis for characterizing phenome-wide patterns of genetic pleiotropy. Nature Genetics. https://doi.org/10.1038/s41588-026-02753-1

Image Credits: AI Generated

DOI: 10.1038/s41588-026-02753-1

Keywords: genetic pleiotropy, genetic factor analysis, GWAS, polygenic factors, coronary artery disease, type 2 diabetes, Mendelian randomization, blood cell traits, Bayesian statistics, matrix factorization, heritability, Nature Genetics

The research team drew on 245,388 whole-genome sequences from the All of Us Research Program, one of the largest and most ancestrally diverse biomedical data resources ever assembled in the United States, and combined them with data from the UK Biobank, the dominant resource in European-ancestry genomics. From these combined resources, the investigators developed multiancestry polygenic risk scores for 32 traits and diseases, ranging from quantitative blood biomarkers to common chronic conditions. Their central question was deceptively simple: when building a risk score for a given population, is it better to use the largest possible pooled dataset, or to match the training data to the ancestry of the people being predicted?

The answer, the study shows, depends on context. For many traits, the sheer diversity of the All of Us cohort improved prediction accuracy, particularly for participants from populations that have historically been under-represented in genetic research, including people of African, Hispanic and Indigenous American ancestry. This finding matters because the performance gap between ancestry groups has been a major obstacle to equitable clinical deployment of polygenic scores. When the training data include more individuals who share ancestry segments with the target population, the score captures more of the variants and effect sizes relevant to that population, and prediction improves.

But the study also delivered a cautionary result. Maximizing sample size by meta-analyzing the All of Us and UK Biobank data was not universally optimal. For traits with lower polygenicity, meaning traits influenced by a smaller number of variants with larger effects, training exclusively on All of Us data performed best for participants of African ancestry. The explanation lies in ancestry-enriched genetic effects: variants or effect sizes that are specific to, or much more common in, particular ancestry groups. When such effects are averaged together with data from a very different population, the pooled score can dilute the very signals that matter most for the target group. In other words, more data is not always better data if the additional data come from a population whose genetic architecture differs meaningfully from the people being predicted.

To quantify these effects, the researchers examined how individual-level prediction accuracy decayed as a function of ancestry divergence between the discovery genome-wide association study and the target individual. Consistent with theoretical expectations and prior work, accuracy declined roughly linearly with increasing genetic distance from the discovery population. However, this decay was substantially attenuated when the training data themselves were multiancestry. A score trained across multiple ancestry groups retained more of its predictive power as individuals became more distant from any single discovery population, suggesting that multiancestry training acts as a form of insurance against the portability problem that has plagued single-ancestry scores.

Methodologically, the study was rigorous and comprehensive. The team used REGENIE for genome-wide association analyses, METAL for meta-analysis, and the Bayesian shrinkage frameworks PRS-CS and PRS-CSx for score construction, the latter designed specifically for cross-population prediction. They estimated SNP-based heritability and polygenicity with SBayesS and GCTB, quantified heritability with LD Score Regression, and assessed cross-ancestry genetic correlations with Popcorn. This combination allowed them to connect observed differences in score performance to underlying properties of trait architecture, such as heritability, polygenicity and the degree to which effect sizes are shared or divergent across ancestries.

One of the most striking illustrations of ancestry-enriched effects came from blood panel traits. For several hematological measures, multiancestry meta-analysis scores showed improved accuracy at the individual level in African ancestry participants, driven by variants that are far more common in African-ancestry populations than in European-ancestry populations. A classic example in human genetics is the regulatory variant in the Duffy antigen receptor for chemokines gene, which strongly influences neutrophil counts and is nearly fixed in many African-ancestry populations but rare elsewhere. Variants of this kind are invisible to predominantly European discovery studies, yet they can carry substantial predictive weight. Including diverse populations in discovery is therefore not merely a matter of fairness; it uncovers biology that would otherwise be missed entirely.

The findings arrive at a moment when polygenic risk scores are beginning to move into clinical settings, with several chronic disease scores already being evaluated for implementation in diverse US populations. Earlier work, including a widely cited 2019 analysis by Martin and colleagues, warned that clinical use of current scores could exacerbate health disparities because their accuracy is so uneven across ancestry groups. The new study provides a practical roadmap for mitigating that risk. It suggests that institutions building clinical scores should evaluate multiple training strategies per trait, considering the trait’s genetic architecture, the ancestry composition of the intended population, and the availability of ancestrally matched discovery data, rather than defaulting to the largest available meta-analysis.

The study also underscores the strategic value of programs like All of Us, which was designed from the outset to reflect the diversity of the United States, with more than half of its participants coming from under-represented racial and ethnic backgrounds. The results demonstrate concretely that this design choice pays scientific dividends: the diversity of the cohort is not just an ethical feature but a source of predictive power that a homogeneous biobank of equivalent size could not replicate. At the same time, the authors note that individual prediction accuracy still declines with ancestry divergence, and that even the best multiancestry strategies leave gaps for populations that remain thinly sampled, such as Indigenous and Pacific Islander groups. Continued investment in globally representative genomic resources remains essential.

Looking forward, the study’s framework, with its systematic comparison of single-ancestry, meta-analyzed and cross-population score construction across dozens of traits, offers a template for future work. The authors have released their PRS-CS and PRS-CSx weights, analysis code and figure-generation scripts via Zenodo, enabling other researchers to reproduce and extend the findings. As polygenic prediction edges closer to routine clinical use, the message of this research is clear: equitable genetic risk prediction will not emerge automatically from bigger data. It will require deliberate attention to who is included in discovery studies, how their genetic architecture differs across traits, and which training strategy genuinely serves each population best. Diversity, in genomics as in so many domains, is not just a matter of representation but of scientific and clinical performance.

Subject of Research: Multiancestry polygenic risk score development and evaluation using the All of Us Research Program and UK Biobank

Article Title: All of Us diversity and scale yield context-dependent improvements in polygenic prediction

Article References: Tsuo, K., Shi, Z., Ge, T., Mandla, R., Hou, K., Ding, Y., Pasaniuc, B., Wang, Y., & Martin, A. R. (2026). All of Us diversity and scale yield context-dependent improvements in polygenic prediction. Nature Genetics. https://doi.org/10.1038/s41588-026-02734-4

Image Credits: AI Generated

DOI: 10.1038/s41588-026-02734-4

Keywords: polygenic risk scores, All of Us Research Program, UK Biobank, genetic diversity, genome-wide association studies, ancestry-enriched effects, health disparities, precision medicine, multiancestry meta-analysis, genetic architecture, Nature Genetics, biobanks

Myeloproliferative neoplasms, which include polycythemia vera, essential thrombocythemia, and primary myelofibrosis, are blood cancers in which a single mutated hematopoietic stem cell expands and crowds out its healthy competitors. The result is excessive production of red blood cells, platelets, or other myeloid lineages, along with a heightened risk of thrombosis and, in some patients, transformation to acute leukemia. Interferon-α has been used therapeutically in these diseases since the 1980s, and modern pegylated formulations can achieve molecular remissions in a substantial fraction of patients. Yet the drug’s mechanism of action at the level of individual stem cells and their clonal descendants has remained incompletely understood.

To dissect this mechanism, the researchers employed single-cell multiomics, a suite of techniques that measures gene expression and other molecular features in thousands to millions of individual cells simultaneously. By profiling hematopoietic stem and progenitor cells isolated from patients with myeloproliferative neoplasms, they were able to trace how mutant and wild-type clones respond to interferon-α exposure at single-cell resolution. This approach is critical because bulk measurements average across heterogeneous cell populations and can mask the clonal dynamics that ultimately determine treatment response.

The study’s central discovery is that interferon-α perturbs clonal competition by reshaping blood development along two complementary axes. The first axis involves lymphoid differentiation. Rather than simply poisoning the malignant clones, interferon-α augments the capacity of hematopoietic stem and progenitor cells to generate lymphoid lineages, the family of blood cells that includes lymphocytes and other immune cells. Because myeloproliferative neoplasm clones are typically skewed toward myeloid output, promoting lymphoid differentiation has the net effect of normalizing the distorted lineage balance that defines these diseases. Blood counts fall toward normal not because cells are killed indiscriminately, but because the developmental trajectory of the stem cell pool is redirected.

The second axis involves inflammatory myeloid differentiation. Interferon-α modulates the clonal dynamics of the disease by driving myeloid cells through inflammatory differentiation states. Inflammatory myelopoiesis, the emergency production of myeloid cells in response to infection or tissue damage, is normally a transient process. In the context of myeloproliferative neoplasms, interferon-α appears to co-opt these inflammatory programs in ways that alter the fitness and behavior of competing clones. Cells that pass through interferon-stimulated inflammatory states may lose their proliferative advantage, slowing the expansion of the mutant clone relative to residual normal hematopoiesis.

Together, these two mechanisms reframe how interferon therapy should be understood. Type 1 interferons are best known as the body’s first line of antiviral defense, released by infected cells to warn neighbors and place them in an antiviral state. But interferon signaling also acts as a regulator of hematopoiesis, the lifelong process by which hematopoietic stem cells replenish all blood lineages. The new data indicate that this immunological signaling molecule functions as an ecological force within the bone marrow, changing which clones thrive and which decline. Clonal competition, the Darwinian struggle among stem cells carrying different genetic and epigenetic states, is thus not only governed by cell-intrinsic mutations but also by inflammatory cues from the environment.

This perspective carries significant implications for personalized medicine in myeloproliferative neoplasms. Treatment response to interferon-α varies widely among patients, and the reasons have been obscure. If the drug’s efficacy depends on reshaping clonal architecture through lymphoid and inflammatory differentiation programs, then the baseline developmental and inflammatory state of a patient’s hematopoietic system may predict response. Single-cell profiling could, in principle, identify which patients harbor clones susceptible to interferon-mediated redirection and which carry clones that resist these pressures, guiding therapeutic selection before months of treatment.

The findings also resonate with a broader theme in modern hematology: the recognition that inflammation shapes clonal hematopoiesis throughout life. Age-related clonal hematopoiesis, in which mutant clones expand in otherwise healthy individuals, is accelerated by inflammatory conditions, and inflammatory cytokines can favor the expansion of clones carrying mutations in genes such as TET2 and DNMT3A. The new study extends this logic to therapeutic interferon signaling, showing that a clinically administered cytokine can deliberately manipulate the same clonal competition that inflammation naturally influences. In effect, interferon-α therapy converts an ecological principle into a treatment strategy.

Technically, the power of the single-cell multiomics approach lies in its ability to resolve fates that would otherwise be invisible. By capturing transcriptomic profiles of individual hematopoietic stem and progenitor cells, researchers can identify rare subpopulations, quantify lineage priming, and detect interferon-stimulated gene expression programs at the level of single cells. When combined with clonal tracking, this reveals whether lymphoid-skewed or inflammatory cells derive from mutant or wild-type ancestors, and how interferon exposure shifts the contributions of each. Such resolution is essential for distinguishing a true change in stem cell behavior from a passive consequence of cell death or selective survival.

For patients, the study provides reassurance that interferon-α’s benefits rest on comprehensible biology rather than blunt cytotoxicity. Normalizing blood counts by restoring balanced lineage output, rather than by depleting the marrow, suggests a therapeutic modality that works with the regenerative machinery of hematopoiesis instead of against it. It also raises the possibility of combination strategies designed to amplify the lymphoid-promoting effects of interferon or to potentiate the inflammatory states that disadvantage malignant clones, potentially lowering drug doses and reducing the side effects that have historically limited interferon therapy.

Looking forward, the work opens several avenues of investigation. Researchers will want to determine which downstream interferon signaling components mediate the lymphoid differentiation boost, how inflammatory myeloid states translate into altered clonal fitness, and whether similar mechanisms operate in other hematologic malignancies treated with interferons. Longitudinal single-cell studies tracking individual patients before, during, and after therapy could reveal the kinetics of clonal reshaping and identify the molecular signatures of durable molecular remission. More broadly, the study positions type 1 interferon not merely as an antiviral cytokine but as a master regulator of developmental competition within human tissues, a concept likely to influence how clinicians and scientists think about inflammation, stem cells, and cancer therapy in the years ahead.

Subject of Research: How interferon-α reshapes human blood development and clonal competition in myeloproliferative neoplasms

Article Title: Type 1 interferon perturbates clonal competition by reshaping human blood development

Article References: Lama, C., Isakov, D., Rosenberg, S., Quijada-Álamo, M., Saurty-Seerunghen, M. S., Moein, S., Nozais, M., Abera, T.-A., Sakaguchi, O., Totwani, M., Freed, G., Zaydon, L., Poon, C.-L., Parghi, N., Kubas-Meyer, A., Xie, A. X., Omar, M., Choi, D., Castillo-Tokumori, F., … Nam, A. S. (2026). Type 1 interferon perturbates clonal competition by reshaping human blood development. Nature Genetics. https://doi.org/10.1038/s41588-026-02751-3

Image Credits: AI Generated

DOI: 10.1038/s41588-026-02751-3

Keywords: type 1 interferon, interferon-alpha, clonal competition, hematopoietic stem cells, myeloproliferative neoplasms, single-cell multiomics, lymphoid differentiation, inflammatory myeloid differentiation, blood counts, hematopoiesis, clonal hematopoiesis, Nature Genetics

The study, published in Nature Genetics, describes a clinically annotated, population-scale single-cell atlas built from bone marrow samples of 341 patients, spanning the full continuum of the disease from precursor conditions through newly diagnosed myeloma to relapsed and refractory disease after multiple lines of therapy. Using single-cell RNA sequencing enriched for plasma cells and the surrounding CD45-positive immune compartment, the researchers captured the transcriptomes of tens of thousands of individual cells, allowing them to dissect the malignant compartment cell by cell rather than averaging signals across bulk tumor tissue, which has historically masked the very heterogeneity that drives treatment failure.

Technically, the effort was formidable. Samples were processed using the MARS-seq platform and an updated version, MARS-seq2.0, with rigorous quality control on mitochondrial content, unique molecular identifier counts and detected genes. Cells were annotated through a computational pipeline combining scVI latent-space integration, uniform manifold approximation and projection embeddings, and inferCNV-based inference of copy number alterations to distinguish malignant plasma cells from their normal counterparts. The team then applied non-negative matrix factorization to decompose malignant gene expression into recurrent transcriptional programs, validated for stability through hundreds of bootstrapped iterations. This dual-layer analytical strategy allowed the investigators to separate two largely independent axes of tumor biology: what kind of myeloma a patient has, and how fast that myeloma is growing.

The first axis yielded five recurrent malignant transcriptional archetypes, designated MM1 through MM5, each corresponding to a stable pattern of gene expression anchored in distinct biological pathways. These archetypes align with known myeloma biology, including immunoglobulin heavy chain translocations such as t(11;14) with its cyclin D and BCL-2 dependencies, t(4;14) with NSD2 dysregulation, MAF and MAFB associated programs, and features reflecting unfolded protein response burden and bone marrow niche interactions. Crucially, the archetypes were not merely descriptive. They correlated with genomic features, therapeutic sensitivity patterns and clinical outcomes, and the team demonstrated that the classification could be ported to independent bulk RNA datasets, including the Blueprint cohort and the Multiple Myeloma Research Foundation’s CoMMpass cohort of treatment-naive patients, confirming that the single-cell-defined signatures retain prognostic power even when measured on standard clinical platforms.

The second axis, orthogonal to the archetypes, is a proliferative program. By scoring single-cell proliferation signatures and characterizing plasmablastic cells, the rapidly dividing precursors of antibody-secreting plasma cells, the researchers quantified the fraction of malignant cells actively cycling in each patient’s marrow. Proliferation has long been recognized as a poor prognostic marker in myeloma, measured historically by crude methods such as plasma cell labeling indices. The new work refines this concept at single-cell resolution, showing that the proportion of proliferating malignant plasma cells stratifies patients within every archetype, revealing intra-archetypal heterogeneity that earlier bulk approaches could not detect. In relapsed and refractory patients, higher proliferative fractions predicted shorter progression-free survival, and the effect persisted in multivariate Cox regression models adjusting for cytogenetic risk, age and prior treatment lines.

Combining the two axes produced an improved risk stratifier that outperformed existing molecular subtyping schemes. Patients could be placed into joint archetype-proliferation subgroups with meaningfully distinct progression-free and overall survival, and the framework added prognostic information beyond standard clinical variables including high-risk cytogenetics and chromosome 1p deletion. The validation in CoMMpass, one of the largest longitudinally followed myeloma cohorts in the world, demonstrated robustness and portability across sequencing platforms, an essential prerequisite for clinical translation. In principle, a myeloma patient’s tumor could one day be assigned to an archetype and proliferation state from a routine biopsy, guiding intensity of upfront therapy and informing decisions about transplantation, novel agents or early escalation.

Perhaps the most clinically electrifying result, however, came from the atlas’s use as a target-discovery engine. The team built a computational pipeline that ranked every protein-coding gene by a composite score integrating malignant enrichment, specificity for malignant plasma cells relative to normal plasma cells, and restriction across healthy tissues, the latter being critical to minimize off-tumor toxicity for any future immunotherapy. This screen surfaced FCRL2, an Fc receptor-like molecule with established roles in B cell biology, as a surface target expressed by malignant plasma cells but largely restricted to the B cell lineage elsewhere in the body. The atlas approach meant the researchers could verify not just that myeloma cells express FCRL2, but that expression is preserved across archetypes and proliferation states, addressing the antigen escape problem that plagues current myeloma immunotherapies.

The translational proof followed swiftly. The researchers engineered chimeric antigen receptor T cells directed against FCRL2 and tested them against myeloma cell lines with varying levels of target expression. In vitro, FCRL2-redirected CAR-T cells killed antigen-positive myeloma cells in an antigen-specific manner, with luciferase-based co-culture assays showing progressive suppression of tumor cell growth compared to non-transduced controls, and detailed immunophenotyping confirming proper CAR expression and memory-phenotype differentiation of the engineered cells. In mouse models, FCRL2-targeted CAR-T cells conferred a significant survival benefit. Given that existing myeloma immunotherapies targeting BCMA and GPRC5D eventually fail through antigen loss and relapse, a third lineage-restricted target backed by a genome-wide, single-cell-verified prioritization pipeline offers a credible path toward combination or sequential immunotherapy strategies.

For patients, the near-term significance is prognostic rather than therapeutic: an archetype and proliferation score could refine risk assessment well before relapse, when treatment decisions matter most. For the field, the study establishes a template for how population-scale single-cell atlases can move beyond description into actionable classification and target nomination. All of the underlying data, including the full scRNA-seq dataset deposited in the Gene Expression Omnibus and the analysis code released openly by the Amit lab, are publicly available, ensuring that other groups can interrogate, extend and challenge the framework. As single-cell sequencing costs fall and clinical grade assays mature, the line between research atlases and routine diagnostics grows thinner, and this myeloma atlas may be remembered as a turning point where that line was crossed for a historically intractable cancer.

Subject of Research: Single-cell transcriptomic atlas of multiple myeloma defining malignant archetypes, proliferative states, and the immunotherapy target FCRL2

Article Title: A single-cell atlas of multiple myeloma defines malignant archetypes and proliferative states

Article References: Zada, M., Kurilovich, A., Shapira, N., Wang, S.-Y., Sharet-Eshed, R., Kfir-Erenfeld, S., Schlossberg, M., Zorde, E., Asherie, N., Gur, C., Chalan, P., Shalita, R., Ben Yehuda, M., Zwicky, P., von Locquenghien, M., Ingelfinger, F., Mazuz, K., David, E., Gurevich-Shapiro, A., … Amit, I. (2026). A single-cell atlas of multiple myeloma defines malignant archetypes and proliferative states. Nature Genetics, 58(9), 2254-2269. https://doi.org/10.1038/s41588-026-02725-5

Image Credits: AI Generated

DOI: 10.1038/s41588-026-02725-5

Keywords: multiple myeloma, single-cell RNA sequencing, transcriptional archetypes, proliferation, FCRL2, CAR-T cell therapy, risk stratification, plasma cells, Nature Genetics, precision medicine, CoMMpass cohort, target discovery

The research, conducted by an international team of investigators, presents a comprehensive comparative resource for adult human and mouse adrenal glands built on two complementary technologies: single-cell transcriptomics, which profiles the gene activity of thousands of individual cells, and spatial transcriptomics, which maps where those gene-expression programs reside within the intact architecture of the organ. By combining these approaches, the authors have produced what is effectively a high-resolution atlas of the adrenal gland in two species, capturing not only which cell types exist but also how their molecular identities and locations differ between human and mouse.

The significance of this resource lies in what it corrects. The adrenal cortex, the outer layer of the gland, is organized into zones that produce different classes of steroid hormones. In the classical textbook model, mineralocorticoids such as aldosterone are produced in the outermost zone, glucocorticoids such as cortisol in the middle zone, and androgen precursors in the innermost zone. This zonal model was largely established through work in rodents. The new single-cell and spatial data confirm that while broad zonal logic exists in both species, the underlying cellular states, the repertoire of expressed enzymes and the dynamics of cell differentiation diverge substantially between human and mouse, meaning that findings in mice cannot be assumed to apply directly to human adrenal physiology.

One of the study’s central findings concerns steroidogenic states. Steroid-producing cells do not represent a single, fixed identity; instead, they occupy a spectrum of molecular states defined by which steroidogenic enzymes they express, at what levels, and in what combinations. The comparative analysis shows that these states are shaped by species-specific programs. Human adrenal cells display enzymatic configurations and regulatory signatures that differ from their mouse counterparts in ways that affect how hormone synthesis is partitioned across the gland. For researchers studying disorders such as congenital adrenal hyperplasia, adrenal insufficiency, Cushing’s syndrome and primary aldosteronism, this is a critical caveat: therapeutic strategies validated in mouse models may engage cellular programs that human adrenal tissue either lacks or deploys differently.

The second major theme of the study is tissue turnover. The adrenal cortex is one of the most dynamic organs in the body, with its steroid-producing cells continuously replaced throughout life. The prevailing model, again derived largely from rodent studies, holds that a cap of progenitor cells near the outer surface of the gland continuously generates new steroidogenic cells that migrate inward, mature, perform their hormonal duties and eventually die, a conveyor-belt-like process of renewal. The new human data reveal that this turnover process, while conceptually conserved, proceeds with species-specific characteristics. The cellular sources of renewal, the differentiation trajectories and the pace of cell replacement differ between human and mouse, underscoring that the mouse conveyor-belt model is an approximation rather than a faithful replica of human adrenal maintenance.

Technically, the study’s strength comes from its dual-method design. Single-cell RNA sequencing excels at resolving cellular heterogeneity, distinguishing rare cell populations and reconstructing differentiation trajectories from the gene-expression fingerprints of individual cells. But dissociating an organ into single cells destroys spatial context, which is essential in an organ as architecturally organized as the adrenal gland. Spatial transcriptomics restores that context by measuring gene activity in intact tissue sections, allowing the researchers to verify that the cell states identified in the single-cell data occupy coherent anatomical positions. By applying both methods in parallel to human and mouse adrenal glands, the team could cross-validate cell-type assignments and map species differences with confidence, producing a resource designed to serve as a reference standard for the field.

The comparative framing also carries implications for regenerative medicine and drug development. Efforts to grow functional adrenal tissue in the laboratory, whether from stem cells or organoid cultures, depend on knowing which molecular programs must be activated to generate authentic steroid-producing cells. If those programs are species-specific, protocols optimized against mouse reference data may steer human cells toward the wrong developmental endpoints. Conversely, a human adrenal atlas provides a benchmark against which laboratory-grown adrenal cells can be quality-controlled, accelerating the path toward cell-based therapies for patients whose adrenal glands no longer function.

The resource is also expected to inform cancer research. Adrenocortical carcinoma is a rare but aggressive malignancy with limited treatment options, and its cellular origins remain incompletely understood. A detailed map of normal adrenal cell states in humans, including the progenitor populations and differentiation intermediates that tumors may hijack, offers researchers a framework for identifying which programs are reactivated in cancer and for designing therapies that target tumor-specific vulnerabilities while sparing normal steroid production.

Beyond disease, the study speaks to a broader lesson in modern biology: organ-level conclusions drawn from one species do not automatically transfer to another, even for organs as structurally similar as human and mouse adrenals. Evolution has conserved the gland’s essential function, the synthesis of life-sustaining steroids, but has implemented that function through partly divergent cellular and molecular means. As single-cell atlases accumulate across tissues and species, this theme recurs, and the adrenal gland now stands as a particularly clear example because its physiology is so directly tied to clinically essential hormones.

The authors have made their comparative dataset available as a community resource, allowing endocrinologists, developmental biologists and computational scientists to query cell-type markers, explore spatial gene-expression patterns and test their own hypotheses against the data. In an era when mouse models remain indispensable but increasingly scrutinized, resources of this kind provide the translational bridge that the field needs: a way to know precisely where the mouse is a faithful model of human biology, and where it is not. For the adrenal gland, at least, the answer is now written cell by cell, and it is more species-specific than anyone had fully appreciated.

Subject of Research: Comparative single-cell and spatial transcriptomic analysis of adult human and mouse adrenal glands

Article Title: Human and mouse adrenal glands are characterized by species-specific steroidogenic states and tissue turnover

Article References: Kastriti, M. E., Maksimov, D., Krupinova, J., Utkina, M., Beltsevich, D., Roslyakova, A., Glazova, O., Kaziakhmedova, S., Ryabova, A., Kuznetsova, A., Shcherbakova, A., Bondarenko, E., Antysheva, Z., Albert, E., Urusova, L., Lapshina, A., Chevais, A., Avsievich, E., Trofimov, V., … Adameyko, I. (2026). Human and mouse adrenal glands are characterized by species-specific steroidogenic states and tissue turnover. Nature Genetics, 58(9), 2270-2283. https://doi.org/10.1038/s41588-026-02737-1

Image Credits: AI Generated

DOI: 10.1038/s41588-026-02737-1

Keywords: adrenal gland, single-cell transcriptomics, spatial transcriptomics, steroidogenesis, tissue turnover, human-mouse comparison, adrenal cortex, hormone production, cell atlas, Nature Genetics, endocrinology, regenerative medicine

Copy number variants, or CNVs, are deletions or duplications of stretches of DNA that can span anywhere from a few hundred bases to millions of bases and can remove, add, or disrupt entire genes. Unlike single-nucleotide variants, which change a single DNA letter, CNVs reshape the genome’s structural landscape, and when they occur in genes critical to brain development they can have outsized effects on neurodevelopmental and psychiatric outcomes. Several recurrent CNVs, such as deletions at the 22q11.2 locus, have been known for years to dramatically elevate schizophrenia risk, but nearly all of that knowledge was built on cohorts of predominantly European descent. Whether the same structural variants, or entirely different ones, contribute to schizophrenia in East Asian populations, which make up a substantial fraction of the world’s population and carry distinct patterns of genomic variation, remained an open and important question.

The new study addressed that question by assembling and analyzing genome-wide data from individuals of East Asian ancestry, comparing the burden of rare copy number variants in people diagnosed with schizophrenia against unaffected controls. The analytic strategy relied on high-quality genotyping arrays and sequencing-based calls that allow researchers to detect deletions and duplications across the genome, followed by careful filtering to remove likely artifacts and annotation of each variant against gene content, known disease loci, and measures of a gene’s intolerance to loss-of-function variation. Burden tests, which ask whether cases collectively carry more large, rare, gene-disrupting CNVs than controls, form the statistical backbone of this kind of work, and the study applied them with the sample sizes needed to detect effects that individual variants alone would be too rare to reveal.

The results confirmed that the fundamental burden signal holds across ancestries. People with schizophrenia in East Asian cohorts carried a significant excess of rare CNVs, particularly those that are large, that remove or duplicate many genes, and that overlap genes previously implicated in neurodevelopmental disorders. This is precisely the pattern observed in European studies, and its replication in an East Asian setting carries real weight: it suggests that the structural-variant contribution to schizophrenia is not an artifact of any one population’s genomic history or ascertainment, but a genuine and broadly shared feature of the disorder’s genetic architecture. For clinicians and researchers in East Asia, it also validates the use of CNV screening in psychiatric care and research contexts far beyond the populations in which those tools were originally developed.

Beyond confirming the overall burden, the analyses pinpointed specific rare copy number variants associated with schizophrenia in East Asian populations, contributing new population-specific resolution to a catalog of risk loci that has been heavily Eurocentric. Some of these signals overlap with CNV loci already known from European studies, reinforcing their status as reproducible schizophrenia risk factors, while the East Asian data add power and detail to their characterization. Because the frequencies of specific structural variants differ across populations, owing to drift, demographic history, and selection, mapping them in East Asian genomes is essential for building risk models and genetic counseling frameworks that actually fit the populations being served.

The most ambitious component of the work, however, was its meta-analysis. By combining East Asian results with those from large European ancestry studies, the investigators boosted statistical power well beyond what either cohort could achieve alone and searched for CNV loci associated with schizophrenia across ancestries. This trans-ancestry approach identified additional risk loci that no single population had the numbers to confirm on its own. The logic is straightforward: if a rare variant’s effect is genuine, pooling evidence across populations with different linkage disequilibrium patterns and different variant spectrums reduces confounding and sharpens the signal. Structural variants, which are often individually very rare and recently arisen, benefit especially from this strategy because their pathogenicity is less dependent on population-specific genetic background than that of common variants.

A striking unifying theme emerged from the annotation of these loci. The genes disrupted by the associated CNVs were significantly enriched for those that are intolerant to loss-of-function variants, meaning that in population sequencing databases, damaging mutations in these genes appear far less often than expected by chance. Genes under strong purifying selection in this way are typically those in which gene dosage matters: losing one copy, or gaining an extra one, perturbs biological systems enough to be selected against. In the brain, dosage-sensitive genes cluster in pathways governing synaptic function, neuronal development, and signaling. The finding that schizophrenia-associated CNVs converge on loss-of-function intolerant genes ties the disorder’s structural-variant risk to the same dosage-sensitive neurodevelopmental biology implicated by de novo mutations in autism, developmental delay, and congenital anomalies, reinforcing a picture of overlapping genetic mechanisms across neuropsychiatric conditions.

The implications run in several directions at once. Scientifically, the study helps close a long-standing gap in psychiatric genetics, demonstrating that rare structural variation is a universal contributor to schizophrenia risk and supplying East Asian-specific loci that will refine global catalogs of disease genes. Methodologically, it shows the value of building genomic resources in understudied populations and then integrating them through meta-analysis rather than extrapolating from European data. Clinically, dosage-sensitive CNV loci identified across ancestries could inform the emerging practice of returning secondary findings to psychiatric patients, since carriers of known pathogenic CNVs may benefit from surveillance for associated medical comorbidities. And for drug discovery, each new risk locus is a pointer toward biology, with dosage-sensitive genes offering mechanistic hypotheses about synaptic and developmental processes that go awry in psychosis.

The study also arrives amid a broader recalibration of how the field thinks about the genetics of schizophrenia. Genome-wide association studies have catalogued hundreds of common variant loci that collectively explain a large share of heritability but individually contribute tiny effects, while rare, high-impact variants such as large CNVs explain a smaller but more mechanistically tractable slice of risk. Rare structural variants, particularly those spanning multiple loss-of-function intolerant genes, are among the strongest known genetic risk factors for the disorder, and the demonstration that this risk architecture replicates in East Asian populations strengthens confidence that findings from these variants will translate broadly. The remaining challenges are considerable: sample sizes for rare variant discovery in non-European populations still lag far behind those in Europe, detection and comparison of CNVs across platforms and ancestries remains technically demanding, and translating locus discovery into biological understanding requires functional work well beyond association statistics.

Still, the trajectory is clear. Schizophrenia genetics has moved from single candidate genes to genome-wide surveys, from one continent to many, and from catalogs of associations to mechanistic principles such as dosage sensitivity and loss-of-function intolerance that bind risk loci into coherent biological stories. By showing that East Asian populations carry the same excess of rare, gene-disrupting copy number variants, and by using trans-ancestry pooling to surface additional risk loci enriched in genes that evolution refuses to let break, this work takes a significant step toward a genetic account of schizophrenia that genuinely fits the global population it affects. It is a reminder that the path to understanding a universal human illness must, by necessity, run through all of humanity’s genomes.

Subject of Research: Rare copy number variants associated with schizophrenia in East Asian populations

Article Title: Contribution of copy number variants to schizophrenia in East Asian populations

Article References: Chen, Y., Feng, Q., Lam, M., Yu, M., Sun, Y., Huai, C., Jana, B., Fu, J., Liao, C., Ye, R., Kim, S., Tubbs, J. D., Shanta, O., Thiruvahindrapuram, B., Jen, Y., Zhao, G., Wang, J., Stanley Global Asia Initiatives, Schwab, S. G., … Huang, H. (2026). Contribution of copy number variants to schizophrenia in East Asian populations. Nature Genetics. https://doi.org/10.1038/s41588-026-02732-6

Image Credits: AI Generated

DOI: 10.1038/s41588-026-02732-6

Keywords: schizophrenia, copy number variants, East Asian populations, genomics, rare variants, meta-analysis, genetic risk loci, loss-of-function intolerance, Nature Genetics, psychiatric genetics, population genetics, neurodevelopmental genes

The study set out to answer a deceptively simple question: what happens to the somatic mutations already present in normal tissue when a patient undergoes treatment for cancer? Chemotherapy and radiotherapy are designed to kill rapidly dividing malignant cells, but they also expose the surrounding normal tissue to powerful DNA-damaging agents and growth pressures. The researchers reasoned that these pressures should act as a selective filter, favoring any normal cells whose preexisting mutations happen to confer survival advantages under treatment conditions.

Using deep sequencing of normal esophageal epithelium, the team compared the mutational landscapes of patients who had received cancer therapy with those who had not. The analysis focused on clonal expansions — the visible footprints left behind when a single mutant cell divides into a visible population of descendants. In untreated individuals, the dominant clones were largely shaped by age-related selection, with mutations in genes such as NOTCH1 frequently outcompeting wild-type tissue simply by conferring a growth advantage in the aging esophagus.

After cancer treatment, however, the picture changed markedly. The spectrum and composition of mutant clones in normal tissue were measurably altered, with certain mutations rising to prominence precisely because they helped their host cells withstand the assault of therapy. The data indicate that treatment does not simply create these mutants de novo in most cases; rather, it selects for mutants that were already present at low frequencies before therapy began. In evolutionary terms, cancer therapy acts as a strong selective sweep applied to a pre-populated landscape of somatic variation.

One of the most consequential findings concerns mutations in genes that are themselves the targets of existing drugs — so-called druggable mutants. The study reports that some of these treatment-resilient clones carry alterations that would, in a tumor setting, justify targeted therapy. The paradox is uncomfortable: a treatment intended to eliminate cancer can enrich, in the surrounding normal tissue, mutant lineages that bear the hallmarks of drug resistance and survival resilience. These enriched normal clones persist after therapy, potentially reshaping the long-term biology of the organ.

Technically, the work relied on high-depth targeted sequencing and mutational signature analysis, approaches that allow researchers to distinguish mutations caused by therapy-induced DNA damage from those that predate treatment. Mutational signatures — characteristic patterns of base changes left by distinct mutational processes such as platinum chemotherapy or radiation — served as a molecular timestamp. By reading these signatures, the team could show that many of the clones enriched after therapy carried mutations acquired years earlier, which then expanded under the new selective conditions created by treatment.

The findings speak to a broader concept in modern oncology and somatic genetics: the idea of cancer therapy as an evolutionary force acting on the whole organism, not merely on the tumor. Normal tissues across the body accumulate mutations steadily with age, and the esophagus is exceptional in the sheer density of mutant clones it harbors. When cytotoxic therapy sweeps through the body, it does not distinguish cleanly between malignant growth and advantaged normal lineages. Cells in normal tissue that can survive the insult, repair the damage, or proliferate afterward will predictably come to occupy more of the tissue.

This reframing has practical implications for how clinicians think about the late effects of cancer treatment. Long-term survivors of chemotherapy and radiotherapy are known to face elevated risks of second cancers in and near the treatment field. The new results suggest a mechanistic route for part of that risk: therapy-driven expansion of mutant clones in normal tissue may enlarge the population of cells standing ready to acquire the remaining mutations needed for full malignant transformation. A larger target population, in principle, raises the probability that transformation events will occur during the decades of life that follow successful treatment.

The study also adds nuance to debates about surveillance and prevention. If druggable mutants can be enriched in normal tissue by therapy, then monitoring the clonal composition of normal epithelium after treatment could, in future, help stratify patients by their reservoir of treatment-resilient clones. Conversely, the observation raises questions about whether certain therapy regimens could be tailored to minimize the selection of high-risk clones in critical organs. Such applications remain speculative, but the study establishes the principle that clonal dynamics in normal tissue are a measurable and modifiable consequence of cancer care.

For the field of somatic evolution, the work reinforces a lesson that has been building for years: the boundary between normal and cancerous tissue is not a simple genetic divide but a continuum shaped by ongoing selection. The esophagus of a treated cancer patient is not the same organ, in evolutionary terms, as the esophagus of an untreated person of the same age. Therapy rewrites the competitive hierarchy among resident mutants, and the winners of that rewritten contest carry scars — and sometimes survival advantages — that could shape the patient’s health for decades to come. Understanding and eventually managing this hidden evolution may become an integral part of cancer survivorship.

The concept underlying this study has an instructive parallel in the blood. Clonal hematopoiesis, the age-related expansion of mutant blood cell lineages, was shown in recent years to be accelerated by chemotherapy, with certain cytotoxic agents favoring clones carrying mutations in DNA-damage response genes such as TP53 and PPM1D. The new esophageal findings extend this principle to an epithelial organ, suggesting that therapy-driven selection of preexisting somatic mutants may be a general feature of how cytotoxic treatment interacts with aging tissues throughout the body. What differs between tissues is which genes matter: in the esophagus, the selective landscape appears dominated by lineages whose advantages lie in survival and repopulation rather than in a single canonical chemotherapy-resistance pathway.

The evolutionary logic at work is a familiar one to population biologists. Standing genetic variation within a population allows rapid adaptation when the environment shifts, because the favorable variants need not wait for new mutations to arise. The esophagus supplies abundant standing variation: sequencing studies of normal esophageal epithelium have found that by the seventh decade of life, a large fraction of the lining is occupied by mutant clones, many carrying mutations in genes under strong positive selection such as NOTCH1, PIK3CA, and TP53. Against this backdrop, a course of chemotherapy or radiotherapy functions as an environmental catastrophe of precisely the kind that reshuffles competitive hierarchies. Clones that were minor participants before treatment can emerge as dominant occupants of the tissue afterward, not because they acquired new advantages during therapy, but because the advantages they already possessed suddenly became decisive.

The distinction between selection and induction is central to interpreting the results, and the mutational signature evidence is what makes the distinction possible. Platinum-based chemotherapy, for example, leaves a recognizable imprint of specific base substitutions, while ionizing radiation produces characteristic patterns of small deletions and structural changes. If treatment were primarily creating new mutant clones, the enriched lineages should carry therapy-associated signatures in the very mutations driving their expansion. Instead, the study’s reading of these molecular timestamps indicates that the driver mutations in enriched clones largely predate exposure, with therapy-associated damage appearing only as secondary background. This ordering matters for risk assessment: the reservoir of potentially selectable mutants is established decades before treatment, during the ordinary accumulation of somatic mutations with age, which means the composition of that reservoir at the time of diagnosis may already shape the evolutionary consequences of whatever therapy follows.

The enrichment of druggable mutants in normal tissue deserves particular attention. In oncology, the term druggable usually signals an opportunity: a mutation in a kinase or other signaling protein that a targeted inhibitor can attack. But the same alterations, when present in expanded normal clones, complicate that picture. A normal lineage carrying an activating mutation in a growth-promoting pathway has, by definition, a proliferative or survival edge, and the study indicates that some such lineages are precisely the ones favored under treatment. Whether these clones represent a meaningful precursor state for later malignancy, or remain benign passengers indefinitely, is a question the study raises but cannot fully resolve. Longitudinal sampling of survivors will be needed to determine how stable these treatment-enriched populations are, and whether their persistence correlates with clinically meaningful outcomes.

There is also a methodological lesson embedded in the work. Much of what is known about the somatic genetics of cancer treatment comes from sequencing tumors before and after therapy, an approach that necessarily views evolution through the lens of the malignant population. Sequencing the adjacent normal tissue offers a complementary view of the same selective event from the perspective of the bystanders. The two views can diverge in informative ways, because the pressures experienced by normal epithelium in a treated field differ from those experienced by a tumor with its own evolving defenses. Building a complete picture of therapy as an evolutionary force will likely require attending to both.

Finally, the findings arrive at a moment when the population of long-term cancer survivors is growing steadily worldwide. As more people live decades beyond curative treatment, the late biological consequences of therapy become a public health question in their own right. This study does not settle those questions, but it demonstrates that the somatic evolution of normal tissue is a measurable consequence of cancer care, and therefore a legitimate target for monitoring, modeling, and eventually perhaps intervention.

Subject of Research: How cancer treatment changes the selection of preexisting somatic mutations in normal esophageal tissue

Article Title: Cancer treatment alters mutant selection in normal esophagus

Article References: Fowler, J. C., Arbore, G., Sood, R. K., Abnizova, I., Albarello, L., Pickering, O., Murai, K., Banerjee, U., Brunon, S., Ong, S. H., Cossu, A., Elmore, U., Puccetti, F., Fernandez-Antoran, D., Tonon, G., Dellabona, P., Rosati, R., Hill, S. L., Underwood, T., … Jones, P. H. (2026). Cancer treatment alters mutant selection in normal esophagus. Nature Genetics. https://doi.org/10.1038/s41588-026-02738-0

Image Credits: AI Generated

DOI: 10.1038/s41588-026-02738-0

Keywords: esophagus, somatic mutations, clonal evolution, cancer treatment, chemotherapy, radiotherapy, mutational signatures, drug resistance, NOTCH1, normal tissue, Nature Genetics, second cancers