Nature Genetics

How inflammation is established and resolved within tissues remains a fundamental question in human biology. Psoriasis is a chronic inflammatory skin disease driven by the IL-23/IL-17 pathway, with IL-17 pathway targeting biologics producing more rapid clinical improvement than IL-23 inhibitors. This difference raises the question of how IL-17 and IL-23 inhibition differentially remodel the cellular and transcriptional landscape of psoriatic skin during resolution. Here, we generated a longitudinal spatial atlas of plaque psoriasis using 2 micron high-definition spatial transcriptomics (HD-SPT), profiling 42 matched biopsies from 11 biologic treatment-naive patients before and during IL-17- or IL-23-targeted therapy. By integrating HD-SPT with a single-cell RNA-sequencing (sc-RNAseq) reference atlas, we resolved the cellular composition and spatial organization of psoriatic skin and mapped its remodeling during treatment. We identified a distinct cluster of SPRR2F+ keratinocytes (KCs) that was spatially restricted to the upper stratum granulosum at the transition to the stratum corneum and was strongly associated with inflamed psoriatic epidermis. SPRR2F+ KCs were enriched for antimicrobial and inflammatory barrier genes, including S100A8, S100A9, LCN2, IL36G, and PI3, and their abundance correlated with disease activity during treatment. In psoriasis patient-derived human epidermal KCs (P-HEK), SPRR2F knockdown attenuated inflammatory and antimicrobial transcriptional responses, supporting a functional role for SPRR2F in sustaining the inflammatory KC response in psoriasis. Longitudinal profiling further revealed distinct trajectories of tissue remodeling following IL-17 and IL-23 inhibition. Following IL-17 inhibition, SPRR2F+ KCs and their characteristic transcriptional signature resolved rapidly, whereas IL-23 inhibitors produced more gradual resolution of KC-centered transcriptional changes accompanied by broader immune and stromal remodeling. Together, these findings established the first HD-SPT atlas of a human skin disease and revealed distinct cellular and transcriptional trajectories of psoriatic skin during therapeutic resolution, identifying SPRR2F as a regulator of persistent KC inflammation.

Human centromeres harbor extensive structural variation that has remained largely inaccessible to human genetics because their repetitive -satellite arrays are absent from standard reference and imputation panels. Here we catalog 188,589 centromeric structural variants (SVs) from haplotype-resolved long-read assemblies of 196 Human Pangenome Reference Consortium individuals aligned to the telomere-to-telomere CHM13 reference. Resolving recurrent SVs into monophyletic components along centromeric haplotype phylogenies makes them imputable from pericentromeric SNPs (mean R2CV = 0.407 vs. 0.065 unresolved). In 124 individuals with haplotype-resolved nanopore methylation, we identify 418 same-chromosome SV-CpG associations that form local hypomethylation-enriched domains in the centromeric region, most strongly for deletions. We also show that a truncated 113-bp monomer on chromosome 8 is associated with the position of the CENP-A-associated centromere dip region (CDR) at n = 151, independent of array length. Imputed into 630 individuals with expression and splicing data, centromeric SVs are associated with 141 genes, 14 of them new relative to the SNP-based cis-QTL analysis, including the H3K9 methyltransferase SUV39H1. In 315,204 All of Us participants, we identify 170 associations between 61 SV predictors and 27 clinical phenotypes, including substance-related disorders and breast cancer. Our study brings a previously inaccessible class of variation into population-scale genetics and links centromeric SVs to centromere organization, gene expression, splicing and complex traits.

While polygenic gene-by-environment (GxE) signals are increasingly detected in biobank data, it is often unclear whether they reflect the phenotype's measurement scale or one of two biologically distinct mechanisms: heterogeneous genetic effects across environmental strata (effect heterogeneity) or systematically larger but correlated effects in one stratum (amplification). We introduce REGENA (Robust Estimator of Gene-ENvironment Architectures), a variance-component method that disentangles these contributions at biobank scale by learning a phenotype transformation that minimizes scale-induced GxE, thereby assessing scale-dependence while jointly estimating the cross-environment genetic correlation and environment-specific genetic variances. In simulations, REGENA is well-calibrated and powerful while recovering the transformation underlying scale-dependent GxE. Applied to 43 quantitative traits and seven environmental exposures in 291,273 UK Biobank individuals, REGENA finds that roughly half of trait-environment pairs with significant GxE are scale-dependent. Among the remaining scale-independent trait-environment pairs, GxE heritability accounted for an average of 15% of additive heritability and was driven primarily by effect heterogeneity (accounting for 71% of GxE heritability on average) although there was substantial variation in its relative contribution across trait-environment pairs. Environment-specific effects tend to make a larger contribution at lower allele frequencies, accounting for 6% of genetic variance in low-frequency variants versus 3.9% at common variants (signed test p = 1.7e-24). Modeling these distinctions improves environment-specific polygenic prediction (14% on average), with the largest gains for pairs whose genetic effects are least correlated across environments (p = 4.0e-3; 3.8-fold for testosterone stratified by sex and 2.32-fold for waist-hip ratio stratified by alcohol consumption). Further, environment-stratified GWAS detects 174 loci missed by pooled GWAS with 67 having opposite-signed effects across strata.

Human genome-wide association studies have identified hundreds of thousands of variant trait associations, but interpreting their mechanistic consequences at allele resolution remains a central bottleneck. DNA foundation models pretrained on genomic sequence corpora learn features predictive of regulatory activity, yet their training objectives are dominated by the conserved background common to all individuals, rather than the sparse variant sites at which human genetic variation is concentrated. Here we introduce Mendel, a DNA foundation model trained from scratch on human whole genome sequences using a variant-centric objective that concentrates supervision on allelic events within reference aligned sequence context. On held-out individuals, Mendel attains 0.984 micro-averaged SNV allele-prediction accuracy, compared with 0.311 for Evo 2. Mendel residual difficulty is selectively enriched at cis-eQTL variants, prioritizes fine-mapped causal variants (AUROC 0.731), where Evo 2 residuals performed near chance, and when converted into a cis-expression prior for modelling, improves prediction relative to an unweighted baseline in all 50 GTEx tissues.

Structural variants (SVs), including variable number tandem repeats (VNTRs), are a major source of human genetic variation, but their impact on the proteome remains poorly characterized. Using a long-read assembly-based reference panel, we imputed 54,578 general SVs and 15,826 VNTRs in 54,306 UK Biobank participants to interrogate their effects on the abundances of 2,923 plasma proteins. SVs and VNTRs together explained ~8.0% of variant-based heritability for protein abundance, with 123 proteins driven predominantly (>80%) by SVs. We identified 8,065 independent SV-protein and 4,101 VNTR-protein associations (with 601 highly unlikely to be driven by small genetic variants) that frequently perturb active regulatory elements, topologically associating domain boundaries, and post-transcriptional mechanisms. Integrative analyses with gene expression and complex trait SV association data identified 1,353 protein-trait association pairs, such as a highly pleiotropic insertion in MAN1A2 associated with 86 distinct phenotypes and a 3' UTR deletion in SULT2A1 linked to gallstone disease, illustrating plausible mechanisms whereby SV-induced regulatory perturbations mediate disease susceptibility. This SV-protein atlas bridges a critical gap between large-scale genomic alterations and proteomic variation, providing novel mechanistic insights into human disease biology and therapeutic discovery.

Protein coding variants span the gamut of structural and functional effects on proteins, and they have similarly heterogeneous effects on traits. Their functional effects can be predicted and categorized, particularly using machine learning models, and integrating these predictors benefits gene discovery. However, it remains unclear how well known structural features and predicted functional scores explain the heterogeneous effects of protein coding variants on human complex traits. We introduce the Kernel Allelic Series Model (KASM), which integrates multiple functional annotations to form a kernel describing how allelic effects within a gene covary. We apply it to exome data from 26 traits and 786,871 individuals from UK Biobank and All of Us, integrating 35 annotations describing protein structure and predicting allelic effects. We estimate that rare missense heritability is at least 1.27{+/-}0.09% on average, 1.83{+/-}0.11% under our most flexible model, exceeding pLoF heritability (0.87{+/-}0.05%) and far exceeding missense burden heritability as previously estimated (0.54{+/-}0.04%). AlphaMissense is the single most enriched missense predictor, but a composite score doubles its enrichment. A series of structural features is enriched for heritability, from amino acid chemical features and structural motifs to residue contacts and protein-protein interaction interfaces. Examining pairs of variants, we find that most missense effects are modestly correlated, but an exception is when residues are close in 3D structure but distant in 1D sequence; such pairs also covary across species. Finally, we analyze individual genes and identify gene-specific enrichments, like a 95-fold enrichment among PIEZO1 hydrogen bond residues, that nominate functional hypotheses.

Partitioned-heritability studies have shown which tissues carry the heritability of complex traits, but whether the regulatory layer whose annotations are most informative for heritability within a tissue differs between traits has not been tested, because measured annotations for different layers come from different sets of biosamples. Sequence-to-function models predict the allelic effect of every variant on several regulatory layers with a single model, so that layers can be compared on the same variants within the same tissues. We grouped AlphaGenome Atlas predictions into 12 lineages and six regulatory layers and partitioned the heritability of 11 complex traits with stratified LD score regression. Traits localized largely to the expected lineages, and within these lineages the most informative predicted layer differed between traits (P = 3.6 x 10^-8). Predicted chromatin-accessibility (DNase) effects were the most informative for height and bone mineral density, whereas predicted transcription-initiation (CAGE) effects were the most informative for Alzheimer's disease, most brain traits and cholesterol levels. The difference persisted across sensitivity analyses, and the predicted contrast held for three related traits whose layers had been predicted in advance (P = 2 x 10^-7). Predicted CAGE effects also remained informative after conditioning on fine-mapped eQTLs and measured regulatory annotations. These results identify differences across traits in the heritability information captured by predicted regulatory layers and offer testable hypotheses for investigating the regulatory basis of complex traits.

ReNU syndrome is a severe neurodevelopmental disorder caused by de novo variants in the spliceosomal small nuclear RNA (snRNA) gene RNU4-2. Pathogenic variants cluster in two distinct structural regions of the U4 snRNA produced by RNU4-2, the T-loop and Stem III. Aberrant 5' splice site selection is a molecular hallmark of ReNU syndrome that correlates with phenotypic severity and could be leveraged as a quantitative biomarker to facilitate preclinical and clinical therapeutic development. We analysed RNA-sequencing data from two independent cohorts totalling 30 individuals with ReNU syndrome and 54 controls. We identified 483 alternative splicing events shared across both cohorts, with the highest concordance in 105 alternative 5' splice-site (A5SS) events. Using these 105 A5SS events, we derived a minimal reproducible signature from only six sites. The splicing signature perfectly distinguished 19 n.64_65insT individuals from 54 controls, and produced no false positives when applied to a further 5,984 whole blood controls. In addition, the signature was specific to variants within the U4-2 T-loop, which are associated with greater clinical severity. Further, we find evidence of alternative splicing events that are specific to variants in Stem III. In a patient-derived induced pluripotent stem cell (iPSC) model of the recurrent RNU4-2 n.64_64insT variant, the splicing signature was fully recapitulated across all stages of differentiation into cortical neuron-like cells. Together, these findings define a robust RNA biomarker for T-loop ReNU syndrome for use in therapeutic development, and illustrate a general framework for portable biomarker discovery in spliceopathies.

Neurodevelopmental and psychiatric conditions have long been hypothesised to emerge from changes in the structure and function of the brain. However, the molecular and cellular mechanisms linking brain structure to these conditions remain unknown. Here, using genetic and structural/diffusion neuroimaging data from 59,283 individuals spanning adults, children and neonates, we identify widespread genetic associations between ADHD, anxiety and depression and multiple phenotypes indexing cortical size and subcortical volume. Multivariate genetic analyses demonstrate that these associations are largely driven by a common cortical size genetic factor and ADHD. Mendelian randomisation indicates that genetically predicted reduced cortical size is causal for ADHD and not vice versa. Using single-nucleus RNA sequencing of the developing cortex, we further demonstrate that although cortical size and ADHD are enriched in different cell types - proliferating progenitors and postmitotic neurons, respectively, they converge on neurogenic genetic programmes active during the transition between the two cell types. This programme peaks at mid-gestation and is localised to the outer subventricular and intermediate zones, the regions where progenitor amplification and early neuronal migration drive human cortical expansion, and is enriched for genes that are differentially expressed in human and macaque developing cortex. Separately, the negative genetic correlation between ADHD and cortical size measures are strongest in cortical regions that have expanded most in humans relative to macaques. Together, these findings identify a novel, spatiotemporally restricted molecular mechanism underlying neurogenesis that is enriched for polygenic effects of both cortical size and ADHD.

Rare protein-truncating variants (PTVs) and damaging missense variants in constrained genes are enriched among people with autism spectrum disorder (ASD), schizophrenia and related conditions, but most studies used clinical cohorts. In European-ancestry participants of the Adolescent Brain Cognitive Development Study (ABCD, 3,523 children) and the All of Us Research Program (AoU, 302,421 adults), we summed, separately for rare PTVs and damaging missense variants, the selection coefficients of affected genes into genetic loads (GLs). In ABCD, one additional typical constrained gene with a PTV was associated with 0.22 SD lower cognition and with ASD (odds ratio 1.80). In AoU, it was associated with ASD (1.34), schizophrenia (1.51) and intellectual disability (ID, 2.02). Missense GL showed smaller, concordant associations. Risk rose with gene constraint, to almost ninefold odds of ID in the most constrained genes, and carriers of a constrained PTV (12% of AoU participants) made up 31% of ID, 20% of schizophrenia and 17% of ASD cases. GL was essentially uncorrelated with polygenic scores. In African-ancestry participants, PTV GL retained 71-125% of its European-ancestry effect, versus 23-62% for matched polygenic scores. Constraint-weighted rare variant load contributes measurably to cognitive and neuropsychiatric outcomes in population cohorts.

Genetic variants associated with neurological traits are enriched in enhancers active in neurons. However, there are many types of neurons with varying functions across the brain, making it difficult to pinpoint insights into disease pathology. We set out to investigate whether cell type and brain region specificity of transcriptional enhancers could reveal new insights into the cellular pathology of neurological traits. We performed H3K27ac ChIP-seq on neurons and glia sorted from post-mortem tissue for six brain regions in triplicate and integrated these datasets with existing single-cell ATAC-seq data. While 87% of neuronal enhancers had similar activity across brain regions, we identified 40,049 neuronal regulatory elements that vary in activity across brain regions, which we termed Brain Region Variable Elements (BRVEs). Some BRVEs reflect differences in cell composition, such as a high proportion of medium spiny neurons (MSNs) in the nucleus accumbens, whereas others capture developmental trajectories, including enhancers with robust activity across telencephalic regions that are inactive in the diencephalic hypothalamus. Genetic variants within BRVEs are disproportionately enriched for the heritability of neurological traits, capturing far more heritability than neuronal enhancers with uniform activity across the brain. Additionally, genes linked to BRVEs are also more likely to be implicated in neuropsychiatric disorders by differential expression studies. Finally, we present a new method, GWAS-LOCATE, that leverages variation in enhancer activity across the brain to assign 23,494 GWAS loci to specific cell types and brain regions, including 9.8% linked to MSNs. This includes a BMI risk locus linked to the transcription factor ISL1. ISL1 knockdown in iPSC-derived MSNs revealed differentially expressed genes enriched for BMI heritability, supporting a role for ISL1-mediated MSN pathways with BMI. Collectively, these findings demonstrate that the enhancer specificity across the brain provides a powerful framework for dissecting complex trait biology and revealing cellular pathology.

Genetic studies have identified thousands of variants associated with brain-related traits. However, the majority of these map to non-coding regions and their causal roles and functional consequences are unclear. In this study, we profiled gene expression and chromatin accessibility in ~140,000 individual nuclei from 40 post-mortem adult human brain samples from 11 donors spanning four brain regions (amygdala, hippocampus, hypothalamus and prefrontal cortex). Integrating these data with genome-wide association study statistics allowed us to prioritise specific neural cell populations relevant for complex traits. Concordant with epidemiological evidence, we prioritise similar neuronal cell populations for BMI, schizophrenia, bipolar disorder and age at menarche associated variants. Our paired multiomic data also provides support for putative enhancer-gene relationships relevant to Alzheimer's disease. These data provide a valuable resource to help interpret trait-associated genetic variation and nominate effector transcripts and cellular pathways relevant to brain-related phenotypes.

Childhood apraxia of speech (CAS) is a neurodevelopmental disorder of speech-motor planning and programming, associated with literacy, academic, and psychosocial difficulties. We performed whole-exome sequencing in 11 families comprising 21 affected and 15 unaffected individuals with phenotyping spanning speech, language, cognition, literacy, and motor function. We identified 60 prioritized variant observations representing 22 unique variants across 20 genes with family-based segregation analysis assessed in informative relatives; 5 arose de novo. Our cohort included multiple affected siblings and extended relatives, with several families carrying multiple prioritized variants, suggesting cumulative or oligogenic susceptibility. Speech and literacy impairment co-occurred with oral-motor, feeding, and fine-motor difficulties. BrainSpan co-expression analysis identified MECP2 and ANK2 as the genes with the highest observed connectivity in the sampled postnatal network. Exploratory analysis of the KOLF2.1J perturbation atlas in undifferentiated human induced pluripotent stem cells identified a positive LMO3 expression-response score following NIPBL repression and opposing LMO3 and ANK2 response directions within a cohesin-associated perturbation cluster. These observations motivate testing in human neural cells. Functional enrichment, protein-association, and developmental-expression analyses supported shared biological themes involving chromatin regulation, neuronal signaling, and extracellular-matrix organization. Among six families, multiple affected relatives shared prioritized variants across a unique set of several genes, demonstrating multilocus candidate profiles whose individual and combined effects may contribute to CAS susceptibility. We propose four novel CAS candidate genes: LMO3 and PCDH10, previously linked to human accelerated regions, as well as MYCBP2 and PTPRS. Our work provides multiple testable hypotheses for future variant-specific functional validation in speech-motor development.

Polygenic scores (PGS) are increasingly common in human genetics research, yet their performance and portability vary substantially across traits and populations. We systematically evaluate prediction accuracy and cross-ancestry portability for 3,994 published PGSs across the phenome in the ancestrally diverse CCPM biobank. We identify widespread trait-specific and cross-trait associations, demonstrating both the utility and breadth of existing polygenic predictors. However, predictive effects frequently differ across ancestry groups, highlighting persistent challenges for portability that threaten equity in downstream applications. We further find that accuracy and portability depend strongly on whether the PGS is continuous or thresholded. Finally, through hierarchical mixed modeling, we identify score-development, genome features, and cohort characteristics associated with improved PGS accuracy and portability. These results highlight key factors supporting future development of robust and portable scores for research and clinical translation.

Psoriasis is associated with increased cardiovascular morbidity, but the extent and structure of the shared genetic architecture remain incompletely understood. We integrated large-scale European-ancestry genome-wide summary statistics for psoriasis and seven cardiovascular diseases to quantify genetic overlap, model shared liability and prioritize candidate mechanisms. Psoriasis showed modest genetic correlations with all cardiovascular outcomes, but mixture modelling estimated that more than half of psoriasis-associated non-zero variants were shared with five cardiovascular diseases. Genomic structural equation modelling of a latent psoriasis-CVD factor identified 167 independent risk loci, including 50 not genome-wide significant in any component trait. Multimodal prioritization nominated 473 candidate genes and 90 approved compounds with therapeutic relevance, including shared targets involving PDE4B, GLP1R, KEAP1 and PRDM16. Mediation and Mendelian randomization analyses implicated cardiometabolic traits, although no single trait accounted for more than half of the shared liability. These results prioritize genetic mechanisms and pharmacological hypotheses for psoriasis-associated cardiovascular multimorbidity.

Diverse human cell types establish specialized functions through lineage- and context-specific regulatory programs. Interpreting non-coding genetic risk requires integrated multi-omic reference maps that directly connect regulatory DNA to cellular expression across human tissues. Here we present a single-nucleus multi-omic atlas comprising 459,856 transcriptomic and chromatin accessibility profiles from 21 adult human tissues and four donors, including paired measurements from 160,688 nuclei. The atlas resolves nine cell lineages, 61 broad cell types and 313 subclusters, and identifies 1,085,062 candidate cis-regulatory elements (cCREs), including 161,270 novel elements absent from ENCODE. Regulatory activity was dominated by cell identity but refined by tissue context. Joint profiling enabled 871,177 cCRE-gene associations and revealed lineage-specific regulatory architectures. Cross-tissue accessibility further identified lineage-restricted and constitutively inaccessible chromatin domains, the latter showing preferential hypomethylation across human cancers. Furthermore, we leverage this dataset to train sequence-to-function models to predict chromatin-accessibility effects for 548,656 fine-mapped variants, identifying 18,133 high-effect variants, including 1,120 broadly active variants. Models trained for eight endothelial subtypes further resolve predicted variant effects across vascular beds. Together, this atlas provides a comprehensive cellular and computational framework for interpreting regulatory sequence, context-dependent gene control, and complex trait genetics across the human body.

Host genetic variation influences heterogeneity in infectious disease severity. Genome-wide association studies (GWAS) have identified 69 lead single nucleotide polymorphisms (SNPs) associated with critical COVID-19, but most lack known regulatory mechanisms. To date, the genetic cis-regulatory effects of critical COVID-19 GWAS lead SNPs have not been systematically characterized across acute illness and convalescent phases in a COVID-19 specific cohort. Here, performing severity-stratified, longitudinal cis-expression quantitative trait loci (cis-eQTL) mapping and colocalization with critical COVID-19 GWAS signals, we find that host cis-regulatory effects vary across clinical severity and disease phase. We analyzed peripheral blood mononuclear cells from 1,031 patients in the Immunophenotyping Assessment in a COVID-19 Cohort (IMPACC), a prospective multicenter study of adults hospitalized with COVID-19, profiled across 10 timepoints spanning acute illness and convalescence. We identified 23 GWAS lead SNPs that colocalized with cis-eQTLs at 26 candidate causal genes, with 16 detectable only when severity stratification and longitudinal sampling were incorporated. A colocalized, protective allele (chr19:4,717,660) at the TMIGD2 locus upregulated TMIGD2 expression exclusively during the acute phase in the prolonged-critical/fatal cohort, with no detectable effect in the rapid-recovery cohort. Our results show that host genetic effects on critical COVID-19 can be context-dependent, with cis-regulatory effects varying across clinical severity and disease phase. Severity-stratified, longitudinal cis-eQTL designs have greater sensitivity to identify candidate causal genes for critical COVID-19 GWAS associations than single-timepoint cross-sectional studies.

A polygenic score (PGS) is a unitless weighted sum of allele dosages, interpretable only as a percentile within a reference distribution. Evaluating risk across models requires scoring each individual against a whole reference panel, but current CPU pipelines process targets or models sequentially, making full cross-product calculations prohibitively expensive. We reformulated PGS calculation as a cohort-wide sparse matrix product on a GPU and optimized it via an autonomous coding agent under read-only inputs, cold starts, and strict numerical validation against pgsc_calc. The scorer was "trained" by the agent accessing synthetic data and real 1000 Genomes data. On a synthetic benchmark, the scorer achieved a 10.6-fold speedup during the training course (184.9 s to 17.4 s) and ran 71-fold faster than pgsc_calc. However, on held-out real genomes, the synthetic-trained scorer was 15.3-fold slower than the real-data-trained version and diverged significantly from pgsc_calc (maximum absolute difference of 3.7x10-1 vs. 3.1x10-4). Three replications of the synthetic loop from an identical prompt showed that agent optimization trajectory variance explains 91 to 96% of total wall-clock variance, against 0 to 2.6% for the data sampled and 0.2 to 3.3% for repeated execution. A memory-bounded rebuild then scored all the 3,202 1000 Genome genotypes against all 5,385 models in 6.5 h within a 72 GB memory cap, released here as a frozen scoring manifest with ancestry-stratified reference distributions for the whole PGS Catalog

Nucleosome positioning plays a fundamental role in regulating DNA accessibility and cellular activity, yet the precise rules by which mammalian sequences instruct this organization have remained incomplete. Prior work has established that intrinsic sequence preferences dictate nucleosome architecture in yeast, but in mammals, this sequence code is modulated by chromatin state and cell-type-specific factors. Here, we train a deep-learning model on genome-wide maps from mouse embryonic stem cells to predict nucleosome occupancy directly from the primary DNA sequence. Our model accurately predicts in vivo occupancy and captures shared biological signals while effectively denoising assay-specific technical biases. Through in silico mutagenesis, we identify specific nucleosome positioning regions comprised of short 5-to-20-base-pair motifs concentrated within dense regulatory windows. Furthermore, we demonstrate that repetitive DNA elements play a major role in establishing these highly organized nucleosome arrays on a genomic scale. Specifically, CCCTC-binding factor motifs embedded within short interspersed nuclear elements, zinc-finger-bound motifs within long interspersed nuclear elements, and microsatellite repeats act as potent organizers of nucleosome phasing. Together, our findings extend the concept of sequence-encoded nucleosome architecture to mammalian genomes and reveal that regularly spaced nucleosome islands are shaped by short, recurrent sets of DNA motifs that couple local sequence to chromatin structure.

Our companion sequencing study uncovered 253 genes robustly associated with autism spectrum disorder (ASD), yet the biological programs they impact, and the cellular, developmental, and spatial contexts in which they converge, remain unresolved. Here we systematically contextualize genes associated with ASD across neurodevelopment and cortical areas, integrating developmental single-cell and spatial atlases with gene- and isoform co-expression, regulatory, proteomic, and synaptic networks. Genetic burden concentrates within temporally-resolved neuronal subclasses: newborn excitatory neurons, immature interneurons, and maturing intratelencephalic lineages. Genes associated with ASD converge on three functional programs--gene regulation, neuronal morphogenesis, and synaptic transmembrane signaling machinery--resolved from 28 ASD-associated networks, several of which are directly regulated by ASD genes including MEF2C, SOX11, and FOXP2. These programs are spatially patterned, with risk genes exhibiting an increasing anterior-to-posterior cortical expression gradient, anchored in the primary visual cortex and driven by excitatory neuron gene-regulatory programs. Finally, ASD risk genes associated with more severe developmental phenotypes show broader excitatory neuron enrichment and less interneuron involvement. Together, these findings anchor ASD genetic vulnerability to specific neurodevelopmental lineages, epochs, and molecular substrates, and delineate the features that distinguish ASD from comorbidities with broader developmental impacts.