Cell Genomics

Human immune responses remain poorly characterized across Indigenous South American (ISA) populations, limiting our understanding of human immune variation. Here, we generated a single-cell transcriptional atlas of 1.4 million peripheral blood mononuclear cells from 345 individuals representing 13 South American populations, profiled at baseline and following stimulation with lipopolysaccharide, influenza A virus, or Trypanosoma cruzi. Genetic ancestry was associated with the expression of a large fraction of the transcriptome, often only after stimulation, and explained more variation than age or sex. On average, cis-eQTLs accounted for one-quarter of these ancestry-associated differences. Thousands of regulatory variants were common in ISA populations but rare or absent in European reference datasets. Integrating eQTLs with GWAS revealed stimulation-specific disease-associated regulatory variants and an enrichment of ancestry-associated expression differences among genes with colocalizing signals. Our findings highlight the importance of studying diverse populations across immune contexts to understand the genetic architecture of human immune responses.

Pregnancy involves profound changes to maternal immunity throughout gestation to simultaneously tolerate the fetus and protect both mother and baby from infection. Dysregulation of this process can lead to pregnancy complications and adverse outcomes. However, how the maternal peripheral immune system adapts throughout an uncomplicated pregnancy remains poorly understood, limiting our ability to identify and interpret alterations associated with pregnancy complications. Here, we longitudinally profiled maternal whole-blood transcriptomes of 1,305 nulliparous women at 12, 20, 28 and 36 weeks of gestation (n=3,776 samples) from the Pregnancy Outcome Prediction Study 2 (POPs2). Differential expression analysis revealed that the maternal immune system underwent its most pronounced remodeling in early pregnancy (between 12 and 20 weeks gestation). Co-expressed gene modules demonstrated immune-dominant programs were highly active in early stages, shifting towards increased cellular and metabolic activity in later pregnancy, reflecting maternal adaptation to increasing physiological demands. Through cis-eQTL mapping we found that genetic regulatory effects increasingly diverged from the non-pregnant state as pregnancy advanced. Colocalising with GWAS revealed putative mechanisms underlying genetic predisposition to pregnancy complications, with 58% of these colocalizations detectable only in the context of pregnancy. Finally, we identified eQTL that exhibit significant interactions with gestational age, implicating dynamic regulation of innate immune and metabolic pathways. These findings show that pregnancy is not only a state of changing gene expression, but also a dynamic regulatory environment that reshapes the effects of genetic variation. By characterizing these adaptations across gestation, this work provides a reference for interpreting maternal immune variation and genetic risk across gestation.

Sex differences in human health are typically modeled as binary comparisons between males (XY) and females (XX). However, sex-linked molecular traits vary continuously and are shaped by environmental factors. Here, we introduce the Proteomic Sex Index (PSI), a continuous, plasma proteomics-based measure that quantifies the degree to which an individual's proteome aligns with their chromosomal sex, with higher PSI values indicating profiles less typical for their sex. Leveraging data from 51,310 individuals from six demographically heterogeneous cohorts, we show that PSI captures broad, biologically meaningful variation in sex-enriched traits and diseases. Individuals with proteomic profiles shifted away from their chromosomal sex exhibited trait values less characteristic of that sex. For example, males with higher PSI were shorter, had lower grip strength, and showed reduced severity of COVID-19 (OR=0.59, p=3.0x10-7). Analyses of individuals undergoing gender-affirming hormone therapy showed that, although PSI correlated with circulating sex hormones, it also captured additional variation independent of hormone levels. PSI is partially heritable and genetically influenced. Individuals with Klinefelter syndrome (XXY) had markedly elevated PSI (OR=13.7, p=1.1x10-13), and genome-wide association studies of PSI identified sex-biased cis-pQTLs with evidence of causal links to sex-specific disease risk. PSI provides a nuanced, individualized measure of sex-related biology that enables the study of within-sex molecular variation and its links to health and disease.

Motivation: Interpreting noncoding pharmacogenomic variants requires identifying the regulatory program that may be perturbed and the direction of the effect, information not supplied by population frequency or general regulatory annotations. Results: We developed AetherXeno, an allele-resolved atlas that scores all substitutions across 8,691 experimentally supported elements for PXR, FXR and AhR and connects 17.27 million variants to evidence matched by exact allele. Across the six paired comparisons, the gain in Spearman correlation ranged from 0.039 to 0.334. Every regional bootstrap interval excluded zero. Homology exclusion preserved these findings, while motif core and liver expression enrichment supported biological organization of the score landscape. The browser, programmatic interface and bulk files expose the same versioned records from individual variants to cohort analyses. Availability and implementation: The web resource is available at https://aetherxeno.org and the versioned data at doi:10.5281/zenodo.20753827.

Most genes produce multiple non-coding and coding RNA transcripts with potentially divergent biological functions, yet their contribution to cancer remains largely unexplored. Here, we built an integrated, transcript-resolution RNA sequencing atlas of prostate cancer spanning 1,140 clinical samples from 10 independent studies, covering the full range of disease progression from normal tissue to primary, hormone-sensitive, and castration-resistant disease. Cross-study integration removes technical batch effects while retaining the biological heterogeneity that distinguishes these disease states, a property we validate against long-read sequencing and leverage to place newly incorporated external cohorts within this disease-progression context. We describe transcript patterns of oncogenic and tumor-suppressive long non-coding RNAs along disease progression and identify 41 protein-coding genes with significant isoform switches in cancer-relevant pathways, including WNT2B, RNF43, RELA, BRCA1, ROCK2, and TRIM11. Finally, we provide the Prostate Cancer Atlas (www.prostatecanceratlas.org), an interactive web resource that lets researchers contextualize their own transcriptomic data against this atlas.

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.

Intelligence is often considered to be fundamentally different from other personality traits. Twin studies show that intelligence and the Big Five personality traits are partly influenced by the same genetic factors, but it remains unclear to what degree shared genes, SNPs, and biological pathways influence them. To address these questions, we analyzed the genetic associations of all six traits. First, we calculated their genetic correlations using LD score regression. We then quantified polygenic overlap using a bivariate causal mixture model, finding that approximately 90% of the genetic variants influencing the Big Five personality traits also impact intelligence. Finally, we performed gene prioritization and gene set enrichment analysis on each trait, once again finding substantial overlap. At each level, intelligence was as similar to the Big Five traits as they were to one another. Genetically and biologically, intelligence fits in with the rest of personality.

Human fetal haematopoiesis requires developmental potential to be encoded before lineage identity becomes transcriptionally apparent, but whether three-dimensional genome architecture provides such information is unknown. Here we develop ChromE-seq, a scalable single-cell assay that jointly profiles chromatin conformation and transcription, and integrate it with chromatin accessibility and DNA methylation to generate a multimodal atlas of ~400,000 haematopoietic cells from disomic and trisomy 21 (T21) human fetal liver. Chromatin contacts frequently preceded lineage-specific transcription or persisted after transcription declined, revealing 3D chromatin priming and memory. Unexpectedly, B-lineage and erythroid differentiation followed opposing architectural trajectories, with progressive loss and gain of chromatin connectivity, respectively. T21 disproportionately disrupted B-lineage architecture, redistributed interactions towards shorter genomic distances and weakened an EBF1-centred regulatory circuit. Deletion of a T21-sensitive EBF1 loop anchor reproduced the CLP-to-B-progenitor bottleneck observed in T21. These findings establish 3D genome architecture as an organizing layer of human fetal haematopoiesis that links developmental potential with lineage-specific vulnerability to genomic perturbation.

Large population-based cohorts enable investigation of disease risk and precision prevention, but often lack genome-wide genetic data. We generated and validated new genomic data within the Australian 45 and Up Study, one of the largest Southern Hemisphere cohorts (recruited 2005-2009) with extensive longitudinal and linked health data. In 2022-2023, 30,541 participants were invited (random sub-cohort and all individuals with history of four most common invasive cancers: breast, prostate, melanoma, colorectal), with in-depth mapping of participants' characteristics for people who consented to provide a DNA sample and those who did not. Low-coverage whole-genome sequencing data (0.4-6.3x) followed by imputation yielded ~79 million variants for n=7,408 individuals; data for n=6,827 passed stringent quality control. Genotype concordance was high between duplicates (n=85) and with dense genotyping arrays (n=188). Most unrelated participants with high-quality data had inferred European genetic ancestry (n=6,631, 98%), with n=141 (2%) of inferred non-European or admixed ancestry. As proof-of-principle integration of new genomic data with extensive existing linked health records, polygenic risk scores (PGS) for four most common cancers showed predictive performance broadly consistent with previous studies, including association between PGS and earlier age at prostate cancer diagnosis, and no meaningful differences in cancer stage at diagnosis by PGS. This new resource has substantially enhanced the 45 and Up Study, supporting investigations of disease aetiology, risk stratification, and precision health in Australia and globally.

High-accuracy, high-resolution benchmark data are fundamental to improving the accuracy and generalizability of sequencing technologies and associated algorithms. Most existing benchmarks for DNA sequencing rely on mapping to a single linear reference, omitting inaccessible and highly repetitive complex genomic regions. Here, we present the Genome in a Bottle (GIAB) Consortium's HG002 v5.0q benchmark set, a more comprehensive variant benchmark derived from a fully phased, highly curated, and accurate diploid assembly of HG002. Our approach leverages recent advances in long-read sequencing and assembly to provide confident small- and structural-variant calls across previously inaccessible regions, including segmental duplications, homopolymers, tandem repeats, and complex immune loci. The v5.0 benchmark significantly expands the callable genome relative to v4.2.1, adding x200 Mbp of sequence and increasing variant detection by 18 % for small variants and 3-fold for structural variants compared to previous benchmarks. The benchmark regions include more variants in repetitive regions, including 1.2 x segmental duplications, 1.8 x tandem repeats, and 2.0 x homopolymers, compared to v4.2.1. Separate and combined small- and structural-variant benchmarks are available for three versions of the human reference genome (GRCh37, GRCh38, and CHM13). We validate the reliability of this benchmark set by comparing multiple sequencing technologies and variant calling pipelines and curating differences. The v5.0q benchmark provides a critical resource for the development and validation of next-generation sequencing technologies and analysis methods.

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.

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.

Advances in high-throughput proteomics technologies have enabled the assessment of dynamic health states across biobank-scale cohorts. Disease prediction models built on these data have higher accuracy than baseline clinical models for a broad range of diseases, and provide avenues to understand disease pathogenesis. However, the generalizability of these prediction models across cohorts has yet to be established at scale. Here, we train models for 15 diseases in the UK Biobank (UKB; n=53,026) based on Olink proteomics data, and find high accuracy for disease prediction (mean AUC=0.74; range 0.56-0.89). These models have improved accuracy over models using only clinical factors (mean {Delta}AUC = 0.03), and do not depend on model architecture, with simpler models (e.g. L2) performing as well as complex models (e.g. transformer). We assess generalizability of the UKB-trained models in two external cohorts: FinnGen (FG; n=5,865) and All of Us (AoU; n=7,405), spanning two Olink platforms. We find that proteomics-based prevalent (classification) and incident (prediction over next 5 years) disease models largely generalize across cohorts, but performance varies across diseases. Specifically, 10 of 14 prevalent and 13 of 15 incident disease models show no significant decrease in performance across any biobank. Adjusting for demographic, ancestry, and technical covariates, we demonstrate that differences in phenotyping quality are likely the major drivers of variability across cohorts. These results indicate that proteomic risk models can be powerful and generalizable predictors of disease across multiple cohorts.

Multiple long-term conditions (MLTC), the coexistence of two or more long-term conditions (LTCs) in an individual, is an increasingly prevalent and complex clinical challenge. We aimed to uncover opportunities for therapeutic intervention in MLTC and highlight potential adverse effects using genetics to identify shared biological pathways across conditions. Across 72 heritable LTCs common in adults aged [≥]65, we analysed genome-wide association study (GWAS) summary statistics from meta-analyses incorporating up to three data sources (UK Biobank, FinnGen, and condition-specific consortia) from genetically European-ancestry participants. Across 843 condition pairs sharing genome-wide significant variants, we performed statistical colocalisation to identify shared causal genetic variants. Variants showing evidence of causality for [≥]3 LTCs were prioritised for investigation of likely causal genes, biological pathways, and druggability. Findings were replicated in Our Future Health (OFH, N=550,000). Colocalisation analyses identified 281 genetic variants causal for specific LTC pairs and 85 variants causal for [≥]3 LTCs. These 85 variants were located in 34 genomic regions with strong evidence of shared causality across multiple conditions. Thirteen of the 34 regions fully replicated in OFH, with others having partial replication. Eight variants showed concordant directions of effects across conditions, suggesting potential opportunities for drug development; results include a variant in GDF7 causal for diverticular disease, gastro-oesophageal reflux disease, and prostate cancer, suggesting multisystemic mechanisms and a novel intervention target for this cluster of conditions. We identified six potential drug repurposing opportunities involving clinically targeted proteins, including LPA and IRF5 for cardiovascular and immune-mediated inflammatory conditions, respectively. Fifteen variants showed discordant effects across phenotypes, suggesting possible adverse consequences of pharmacological intervention; for example, a variant in GIPR reduced risk of obesity and sleep apnoea whilst increasing risk of breast cancer. These findings reveal shared mechanisms underlying MLTC and highlight opportunities for therapeutic development and repurposing, whilst also identifying possible adverse effects.

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.

Somatic mosaicism contributes to genomic variation, yet postzygotic structural variants remain under-characterized. We performed long- and short-read WGS from multiple individuals (n=47 normal tissues; n=168 samples) and identified mosaic structural variants in all individuals and germ layers, impacting a median 285.2 kb/genome. Nearly half of breakpoints were independently validated, with tissue distributions reflecting both early and late developmental origins. Most mosaic variants were repeat-mediated and 8.3% overlapped functional elements, an enrichment compared to germline variants. To extend these analyses in samples where long-read sequencing is infeasible, we measured repeat alterations from short-read sequencing, recapitulating mosaic tissue-specific differences. We characterized tumor- and tissue- specific variation in repeats across 15 cancer types and found tumor-related repeat variation to be similar in scale to that of normal mosaic variation. Tracking repeat changes in cell-free DNA provided a noninvasive approach for tumor monitoring. Our analyses revealed widespread repeat-driven structural variation in health and disease.

For most diseases, whether PRS improves prediction beyond clinical data remains unknown, as existing evidence is concentrated in a handful of conditions with established risk models. Using 900,000 participants from UK Biobank and FinnGen, we developed and validated models for predicting 150 diseases and all-cause mortality. Genetic information provided the largest predictive gains in metabolic, cardiovascular, autoimmune, and neurological disease, but added little for genitourinary and respiratory conditions. All significant PRS-clinical risk interactions were negative, with predictive power gains from genetic data larger at lower clinical risk, suggesting that PRS may reveal susceptibility not yet apparent clinically. For example, in atherosclerotic cardiovascular disease, PRS expanded the clinically identified high-risk subgroup by 40%, and this reclassified subgroup had 4.9-fold higher incidence during the follow-up than those remaining low-risk. Our interpretable clinical-genetic models generalized across three biobanks and are publicly available through PrognosIQ.

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.

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.

Despite long-standing epidemiological associations, the mechanism linking folate availability to gestational neural tube defects remains unclear, partly because measuring and interpreting metabolic activity in dynamic biological systems remains challenging. Here, we apply a deep-learning-based graph-guided variational autoencoder (MeRN; Metabolic Representation Network) to infer single-cell metabolic activity and states from scRNA-seq data of mouse embryogenesis. By analyzing folate-deficient embryogenesis from E7.0 to E9.0, we identify a transient state within the nascent neural lineage that is acutely sensitive to folate availability, leading to an interconnected disruption between key bioenergetic pathways and de novo purine biosynthesis. Moreover, metabolically induced growth defects lead to permanent morphological disruptions along the dorsal-ventral axis, which we confirm by generating whole-embryo fate maps using a prime-editing-based lineage recorder (PEtracer). Collectively, our results establish a highly scalable framework for interpreting dynamic changes in embryonic metabolism and elucidating the mechanistic bases underlying environmentally linked congenital disorders.