Crohn's disease is a complex inflammatory bowel disease resulting from an interplay of genetic, microbial and environmental factors. Cell-type-specific contributions to Crohn's disease etiology and genetic risk are incompletely understood. Here we built a comprehensive atlas of cell-type-resolved chromatin accessibility comprising 557,310 candidate cis-regulatory elements (cCREs) in terminal ileum and ascending colon from 23 patients with active and inactive Crohn's disease and 16 healthy controls. We identified cell-type-specific, anatomical location-specific and context-specific cCREs and characterized the regulatory programs underlying inflammatory responses in the intestinal mucosa of patients with Crohn's disease. These cell-type-resolved data confirmed that Crohn's disease heritability is primarily enriched in adaptive immune cells, in particular T cells, and in innate immune cells, including neutrophils. Using fine-mapped, noncoding Crohn's disease variants, we identified 30 variants located within cCREs. Our atlas provides a comprehensive resource to study gene-regulatory effects in Crohn's disease and health and highlights the cellular complexity underlying Crohn's disease risk.

The three-dimensional organization of enhancers and promoters at nucleosome resolution remains poorly resolved, limiting our understanding of the structural basis of transcriptional regulation. Here, we developed a simulation framework that leverages region-capture micro-C contact maps to infer conformational ensembles of megabase-scale regions at nucleosome resolution. A key component of this framework is a micro-C balancing strategy that identifies variation in contact density. The simulations reproduce pairwise distance distributions measured by chromatin tracing and contain packing domains and nucleosome clutches, as observed in imaging studies. The inferred structures reveal a striking departure from the classical view of euchromatin as uniformly open. Instead, euchromatin generally forms condensed domains with comparable densities but smaller sizes than heterochromatin domains. Kilobase-scale regions at promoters and enhancers often protrude from these condensed domains and become highly accessible. This arrangement effectively compartmentalizes regulatory elements from the surrounding chromatin, facilitating protein binding and enhancer-promoter communication.

Our understanding of the genetic architecture of obesity is primarily based on studies of adults, with sparser data from childhood. Here, we conducted age-stratified genetic association studies against objectively measured (n = 62,276) or recalled childhood adiposity-related traits (neff = 599,924), identifying 624 common variants associated with childhood adiposity, with one-third having no concordant association with adult body mass index. Signals linked to the leptin-melanocortin pathway (BSX, GNAS, LEPR and PCSK1) and incretin signaling (GIPR and GLP1R) showed childhood-specific effects on adiposity. Single-nucleus RNA sequencing data identified childhood-specific adiposity-regulating cell populations in the arcuate nucleus and mammillary bodies, indicating neurocircuits that regulate adiposity specifically during childhood. Finally, sequencing in 479,615 individuals uncovered rare protein-coding variation in ADCY3, CALCR, MC4R, MRAP2, POMC and MYH13, exhibiting stronger associations in childhood than in adults. Our findings suggest that childhood provides a more sensitive window for the study of key endocrine and neuropeptide pathways that regulate energy balance.

Medulloblastoma is a biologically heterogeneous childhood cerebellar tumor harboring frequent chromatin-modifying gene alterations. How these alterations promote transcriptional programs governing malignancy remains poorly defined. To address this knowledge gap, we evaluated chromatin states across medulloblastoma subgroups by multi-modal integration of histone modifications with mutational, DNA methylation and transcriptomic profiles. A bivalent/poised enhancer (EnhBiv) state was specifically enriched at the promoters of neurodevelopmental genes in Group 3/4 medulloblastoma. Integrative bioinformatics coupled with chromatin occupancy studies identified aberrant KDM2B binding at EnhBiv-enriched promoters. CRISPR-mediated knockout or acute protein degradation of KDM2B selectively suppressed the growth of medulloblastoma models in vitro and in vivo. Mechanistically, KDM2B promotes sequential recruitment of Polycomb repressive complexes (PRC1/PRC2) and EnhBiv chromatin, thereby repressing neuronal differentiation programs. Collectively, we provide foundational insights into an epigenetic basis of medulloblastoma, nominating KDM2B as a selective dependency in high-risk subgroups that warrants consideration as a candidate therapeutic target.

Human genetic diversity generates an astonishing variety of facial shapes, and craniofacial anomalies rank among the most common birth defects. Identifying the cellular mechanisms that mediate the genome's influence on facial variation remains a challenge. Here we created a multimodal facial atlas across embryonic weeks 6-11, providing single-cell transcriptomics, chromatin accessibility and spatial transcriptomics, all at single-cell resolution. We characterized 56 cell states, mapping mesenchymal subtypes and their gene-enhancer cis-regulatory landscapes in space and time. Gene expression associations with facial traits were strongest in early mesenchymal progenitor cells, gradually becoming more region restricted. Autocorrelation analysis revealed patterning genes that define spatial neighborhoods of mesenchyme, potentially explaining trait specificity of their nearby variants. Enhancers of key pathology-related genes were found to be likely vehicles for generating facial variation in modern human populations. One such enhancer, linked to PAX1 expression, appears to be important for normal skeletal development in mice. Finally, facial effects inferred from the genome-wide association study, cell signaling interaction analysis and validation in mice revealed that peripheral nerves fine-tune maxilla shape during embryonic development. Together, these data offer new insights into the mechanisms underlying human phenotypic individuality and yield a multimodal atlas useful for studying craniofacial development and abnormalities.

Genetic associations shared by multiple traits provide evidence about the biological role of disease associated variants. Here we propose genetic factor analysis (GFA), a multiphenotype analysis method that identifies common patterns of cross-trait associations, the signatures of shared biological processes. GFA overcomes limitations of alternative methods by automatically selecting the number of factors, accounting for sample overlap and allowing factors to be non-orthogonal. We apply GFA to analysis of 22 common risk factors for coronary artery disease and type 2 diabetes to partition the heritability of these traits into 15 pleiotropic components. In addition, we use GFA to obtain a biologically meaningful decomposition of a large set of blood cell composition phenotypes and use these results to increase precision in multivariable Mendelian randomization. In this application, we find that accounting for overlapping samples is critical to obtaining biologically interpretable results.

Understanding plant resistance to insects has lagged owing to a scarcity of cloned resistance genes. Here we report cloning of the greenbug resistance gene Gb3 in wheat (Triticum aestivum L.), initially introgressed into cultivars such as 'TAM112' from Aegilops tauschii. Gb3 encodes an intracellular kinase-pseudokinase protein, featuring a unique MAP3K-like domain. This gene probably originated through fusion, duplication and indel events in the Triticeae tribe, retaining its functionality in several Aegilops species. Transgenic Gb3 expression in the susceptible Fielder wheat enhanced resistance to multiple biotypes, while the Gb3 knockout in TAM112 resulted in susceptibility. Comprehensive analyses indicate that greenbugs preferentially feed on susceptible plants, inducing a prolonged oxidative stress response. Conversely, resistant plants expressing Gb3 mediate greenbug resistance by producing oxylipins and other defensive compounds. Our findings lay the groundwork for investigating plant-insect interactions and provide a crucial genetic resource for developing sustainable aphid resistance in cereal crops.

Genetic risk variants for common diseases are predominantly located in non-coding regulatory regions and modulate gene expression. Although bulk tissue studies have elucidated shared mechanisms of regulatory and disease-associated genetics, the cellular specificity of these mechanisms remains largely unexplored. Here we present a comprehensive, single-nucleus multi-ancestry atlas of genetic regulation of gene expression in the human prefrontal cortex, comprising 5.6 million nuclei from 1,384 donors of diverse ancestries. Through multi-resolution analyses spanning eight major cell classes and 27 subclasses, we identify genetic regulation for 14,258 genes, with 981 showing cell type-specific regulatory effects at the class level and 857 at the subclass level. Colocalization of genetic variants associated with gene regulation and disease traits uncovers novel cell type-specific genes implicated in Alzheimer's disease, schizophrenia and other disorders that were not detectable in bulk tissue analyses. Analysis of dynamic genetic regulation at the single-nucleus level identifies 2,073 genes with regulatory effects that vary across developmental trajectories, inferred from a broad age range of donors. We also uncover 1,655 genes with trans-regulatory effects, revealing distal regulation of gene expression. This high-resolution atlas provides insight into the cell type-specific regulatory architecture of the human brain, and offers novel mechanistic targets for understanding the genetic basis of neuropsychiatric and neurodegenerative diseases.

The widespread adoption of clinical sequencing and large-scale national and international genomics initiatives has transformed genomic medicine. These efforts have enabled new diagnostic tests and computational tools for processing sequencing-derived information, yet interpretation of many variants remains limited. In parallel, over the last two decades, computational structural genomics (CSG) has emerged as a complementary approach that shifts from static genetic annotation to dynamic, mechanistic interpretation by leveraging models of gene products and their organization within biomolecular complexes of protein, DNA, RNA and small molecules. In this Perspective, we highlight how CSG has resulted in a comprehensive and mechanistic understanding of how interindividual genetic variants function, by integrating sequence information, motifs, domains, molecular structures, molecular dynamics simulations, multi-omics data annotations and artificial intelligence-driven tools. These advanced technologies enable prediction of variant effects at atomic resolution and support simultaneous classification according to their mechanisms of dysfunction.

Protein-protein interaction (PPI) networks accurately map environmental perturbations to their molecular consequences in cells, yet the effects of natural genetic variation on PPIs remain poorly understood. Here we measured 61 reporter PPIs across inbred strains of Saccharomyces cerevisiae harboring ~12,000 single-nucleotide polymorphisms and identified protein-interaction quantitative trait loci (piQTLs) linking genomic variation to in vivo PPI strength. trans-acting piQTLs were significantly enriched and exhibited stronger per-variant effect sizes than cis-acting piQTLs. Consistent with the small-world architecture of PPI networks, trans-piQTLs often mapped to genes encoding proteins in close network proximity to reporter PPIs. We identified and validated piQTLs in poorly annotated regions, including noncoding RNAs and 3'UTRs, with effects comparable to coding single-nucleotide polymorphisms. piQTLs also overlapped more with protein abundance QTLs than expression QTLs, highlighting how PPI networks capture post-transcriptional and proteome-level regulation. This study provides a roadmap for dissecting complex traits through PPI network analysis.

Food production must rise while its environmental footprint falls, an imperative sharpened by pressure to use fewer external inputs. Meeting this challenge requires gains from aspects that crop breeding has so far overlooked. One such source is the soil, because every crop transforms the environment in which it grows, shaping the conditions experienced by the next crop. We propose that these legacy effects, expressed through altered soil nutrients, water, structure and microbial communities, could offer new breeding targets. Many traits that modulate these effects vary within major crop species, and this variation is heritable and therefore selectable. We advocate breeding for farming systems to exploit this untapped genetic dimension. We outline the mechanisms underpinning this relationship, the frameworks needed to quantify it and the practical challenges of integrating legacy-aware selection into breeding pipelines. Every crop leaves a legacy, and understanding this provides a pathway to a more sustainable agricultural future.