Albert Henry

Understanding the genetic basis of gene expression can shed light on the regulatory mechanisms underlying complex traits and diseases^1,2. Single-cell-resolved measures of RNA levels and single-cell expression quantitative trait loci have revealed genetic regulation that drives cell states and types across diverse tissues³. Here we describe phase 1 of TenK10K, which leverages single-cell RNA sequencing data from more than 5 million cells across 28 immune cell types, along with matched whole-genome sequencing data from 1,925 individuals. This provides power to detect associations for rare and low-frequency genetic variants that have largely been uncharacterized in their impact on cell-specific gene expression. We map the effects of both common and rare variants in a cell-type-specific manner using a recent method that increases power by modelling single cells directly rather than aggregated ‘pseudobulk’ counts. We identify putative common regulatory variants for 83% of all 21,404 genes tested and cumulative rare-variant signals for 47% of those genes. We explore how genetic effects vary across cell types and states, develop a framework to determine the degree of single-cell expression quantitative trait loci cell-type specificity and show that about half of the effects are observed only in one or a few cell types. By integrating our results with functional annotations and disease information, we further characterize the likely molecular modes of action for many disease-variant associations. Finally, we explore the effects that genetic variants have on gene expression across continuous cell states, and effects that vary cell-state abundance directly.

Understanding how genetic variation shapes gene regulation at single-cell resolution is crucial for elucidating complex disease mechanisms¹, but has been limited by the lack of large-scale single-cell multi-omics data^2,3,4. Here we present single-cell assay for transposase-accessible chromatin with sequencing of 3.5 million peripheral blood mononuclear cells from 1,042 donors with matched whole-genome sequencing. Across 28 immune cell types, we tested 440,996 chromatin accessibility peaks and identified 243,225 chromatin accessibility quantitative trait loci (caQTLs) at 100,483 peaks, 60% of them cell-type specific. Rare variant analysis identified 38,161 associated peaks, with predicted transcription factor-binding disruption implicated at individual loci. Colocalization with expression QTLs (eQTLs) from single-cell RNA sequencing data (5.4 million peripheral blood mononuclear cells)⁵ defined 28,834 candidate cis-regulatory elements. Across genome-wide association studies for 16 diseases and 44 blood traits, caQTLs colocalized 4.5–22.6% more signals than eQTLs alone. Cell-type-specific and tissue-specific context, distal effects and multi-causal-variant models raised genome-wide association study–eQTL concordance from approximately 16% to approximately 65%. Cell state further modulates genetic effects, with 27% of over 200,000 caQTL–cell-state interactions lacking a main effect. At the inflammatory bowel disease risk gene ADCY7, accessibility effects varied with genotype and G protein-mediated pathway activity in CD4⁺ T cells. A graph neural network trained on both eQTL and caQTL layers inferred peak–gene links in unpaired multiome data with up to 17.2% higher accuracy than QTL-agnostic data, uncovering 128 additional transcription factor–target pairs (22% increase). Together, these results show that genetic variants shape chromatin accessibility in a cell-type-specific and cell-state-specific manner, influencing immune function and disease risk.

Tandem repeats (TRs)-highly polymorphic, repetitive sequences across the human genome-are important regulators of gene expression but remain underexplored due to challenges in accurate genotyping and analysis¹. Here we generate whole-genome and single-cell RNA sequencing from more than 5.4 million blood-derived cells across 1,925 individuals in two cohorts², and perform meta-analysis to characterize the impact of variation in more than 1.7 million TR loci on immune cell type-specific gene expression. We identify more than 69,000 single-cell expression quantitative trait loci, 30.7% of which are specific to 1 of 28 immune cell types, and reveal dynamic regulatory effects using cell-state inference. Matched single-cell assay for transposase-accessible chromatin with high-throughput sequencing profiles from more than 3.4 million nuclei in 922 individuals³ uncover chromatin accessibility quantitative trait loci for nearly one-third of expression-associated TRs, supporting coordinated effects on cis-regulatory architecture. Fine-mapping implicates 1,490 TRs as candidate causal drivers of gene expression in 6.1% of tested genes, and colocalization analyses highlight more than 200 genes in which TRs likely mediate genetic associations with immune and haematological traits. Together, these results provide a genome-wide, multiomic view of TR-mediated regulation in the human immune system, establishing TRs as key contributors to cell type-specific regulatory variation and complex trait architecture.

Background: Cardiac disease is linked to early cognitive decline and dementia, with shared risk factors such as hypertension, inactivity, and APOE4 genotype. White matter hyperintensities (WMH) may mediate this association. Methods: We investigated associations between 29 cardiac MRI (CMR) measures and 5 regional WMH volumes independently of known CVD risk factors. We analysed 30,961 UK biobank participants without pre-existing cardiovascular disease (CVD) or known dementia. We empirically validated the CMR traits by associating these to incidence CVD using Cox’s regression models. Linear regression models were used to identify association between 6 regional WMH volumes and cognitive tests outcomes (executive function and processing speed), as well as with 29 CMR traits. Results: Twenty-one CMR traits associate with heart failure and atrial fibrillation, 10 with coronary heart disease, and 4 with abdominal aortic aneurysm. Frontal and parietal WMH volumes associate with executive function and processing speed. We identify distinct groups of CMR associations where right heart traits (excluding RVEF) primarily associate with parietal WMH volumes, contrasting with the more widespread associations of left cardiac and aortic traits. Higher values of aortic areas, LV mass and wall thickness associate with higher WMH volumes for all brain regions. Furthermore, increased WMH volumes associate with increased minimum and maximum ascending aortic areas. We additionally observe associations between increased biventricular ESV and increased WMH volumes, with increased EF associating with lower WMH volumes. Conclusions: We show that CMR derived measurements of function and structure associate with regional WMH volumes implicated in executive functioning and processing speed, independently of known cerebrovascular risk factors in people without established disease.

BACKGROUND: Heart failure (HF) and its main subtypes, heart failure with preserved ejection fraction (HFpEF) and heart failure with reduced ejection fraction (HFrEF), impose an enormous health burden on elders. Assessment of the circulating proteome to illuminate pathogenesis could open new opportunities for treatment. METHODS: We conducted a plasma proteomics screen of incident HF and its subtypes in 2 older population-based cohorts, the CHS (Cardiovascular Health Study) and the AGES-RS (Aging, Gene/Environment Susceptibility–Reykjavik Study). The 2 studies used SomaLogic platforms, with 4404 aptamers in common. Multivariable Cox models were fit to evaluate individual-protein associations with HF, HFpEF, and HFrEF separately in each cohort, and study-specific associations were combined by fixed-effects meta-analysis. Replication was performed in the ARIC (Atherosclerosis Risk in Communities) cohort. Two-sample Mendelian randomization of HF and its subtypes, along with colocalization analysis, was performed to support causal inference. RESULTS: Among 8599 participants, 1590 experienced incident HF (536 HFpEF, 471 HFrEF). There were 119 proteins associated with HF, 15 proteins with HFpEF, and 11 proteins with HFrEF, at Bonferroni-corrected significance. Among these, 9 have never previously been identified for cardiovascular diseases, and another 61 represent new associations with incident HF or its subtypes. Of these 70 proteins, 55 of the 66 available replicated externally. Mendelian randomization analysis revealed 7 proteins genetically associated with HF at nominal significance; 2 were separately associated with HFpEF, and another 2 with HFrEF. Seven of these 9 proteins (NPDC1 [neural proliferation differentiation and control protein 1], APOF [apolipoprotein F], LMAN2 [lectin, mannose-binding 2], ADIPOQ [adiponectin], CD14 [cluster of differentiation 14], ARHGAP1 [Rho GTPase-activating protein 1], C9 [complement 9]) showed new, possibly causal associations, although we did not detect evidence for colocalization. CONCLUSIONS: In this large-scale proteomic study involving 3 longitudinal cohorts of older adults, we identified and replicated 55 novel protein markers of HF or its subtypes, and 7 new, possibly causal proteins. These proteins may enhance risk prediction, improve understanding of pathobiology, and help prioritize targets for therapeutic development of these foremost disorders in elders.

Coronary artery disease (CAD) remains a leading cause of morbidity and mortality worldwide, and identifying accessible blood‐based biomarkers is therefore a clinical priority. Given the involvement of oxidative stress and immune cell dysfunction in atherosclerosis, we investigated whether mitochondrial reactive oxygen species (mROS) production in peripheral blood mononuclear cells (PBMCs) is associated with CAD. This exploratory study analyzed PBMCs from 40 BioHEART‐CT participants with or without CT‐defined CAD using MitoSOX‐based flow cytometry. In parallel, single‐cell RNA sequencing (scRNA‐seq) was conducted in the same individuals to investigate differential expression of CCBE1, a recently implicated gene in cardiovascular disease, across PBMC populations. Overall, mROS levels in PBMCs and their major cellular subtypes did not show consistent or meaningful associations with CAD status or with modifiable cardiovascular risk factors. Small, subgroup‐specific signals—such as moderate association between lymphocyte mROS and coronary artery calcium score in males, and a modest inverse association between monocyte mROS and hypertension—were exploratory and not uniform across analyses. scRNA‐seq analysis did not identify a distinct CCBE1 expression signature in PBMCs. These findings indicate that PBMC‐derived mROS is unlikely to serve as a useful cross‐sectional biomarker of CAD in stable populations.

Genome-wide association studies (GWAS) have been instrumental in uncovering the genetic basis of complex traits. When integrated with expression quantitative trait loci (eQTL) mapping, they can help identify effector genes within GWAS loci through gene regulatory mechanisms. Recent single-cell eQTL (sc-eQTL) studies suggest that genetic effects on gene expression are often cell type- and subtype-specific, offering opportunities to uncover context-specific roles of effector genes that traditional GWAS and eQTL studies in bulk tissues may obscure. Here, we leverage sc-eQTL mapping results from the TenK10K phase 1 project to prioritise effector genes across 69 diseases and 31 biomarker traits, from 12,266 eGenes identified in 28 peripheral immune cell types. Using Mendelian randomisation (MR) and colocalisation, we present a catalogue of predicted effector genes and their estimated directional effects on the trait at cell type resolution, spanning 54,245 associations across 5,270 genes and 28 cell types for disease traits, and 348,103 associations across 9,581 genes and 28 cell types for biomarker traits. By quantifying polygenic enrichment at both the single-cell and cell type levels, we identify distinct immune cell contributions to immune-related and systemic conditions. We demonstrate differential polygenic enrichment of Crohn’s disease and COVID-19 amongst dendritic cell subtypes. Integrating clinical drug development data shows that therapeutic compounds targeting the gene-trait associations identified in this study are twice as likely to secure regulatory approval. Using Crohn’s disease as a motivating example, we demonstrate how population-based sc-eQTL data can pinpoint risk loci, effector genes, and cell types, complementing findings from disease-focused tissue samples. Our findings provide a foundational resource for understanding the cell-type-specific genetic architecture of disease and for guiding therapeutic discovery.

Understanding how genetic variation influences gene regulation at the single-cell level is crucial for elucidating the mechanisms underlying complex diseases. However, limited large-scale single-cell multi-omics data have constrained our understanding of the regulatory pathways linking variants to cell type-specific gene expression. Here we present chromatin accessibility profiles from 3.5 million peripheral blood mononuclear cells (PBMCs) across 1,042 donors, generated using single-cell ATAC-seq, including ∼100,000 PBMCs from 90 donors using multiome (RNA+ATAC) sequencing, with matched whole-genome sequencing. We identified 440,996 chromatin peaks across 28 immune cell types and mapped 243,225 chromatin accessibility quantitative trait loci (caQTLs), of which 60% were cell type-specific. Rare variant analysis revealed associations with 27,927 peaks, recapitulating the same cell type-specific regulatory architecture. Colocalization with the eQTLs from scRNA-seq data (5.4 million PBMCs) identified 31,688 candidate cis -regulatory elements; around half (44.40%) show evidence of putative mediating effects via chromatin accessibility. Integrating caQTLs with GWAS summary statistics for 16 diseases and 44 blood traits uncovered 4.5% - 22.6% more colocalized signals compared with using eQTLs alone, many of which have not been reported in prior studies. We show that the limited concordance between GWAS and eQTL signals can be substantially improved by incorporating cell type– and tissue-specific context, distal regulatory effects, models allowing for multiple causal variants, and promoter/enhancer priming, together increasing overlap from ∼20% to ∼60%. In addition, using a graph neural network, we inferred peak-to-gene relationships from unpaired multiome data by integrating caQTL and eQTL signals, achieving up to 80% higher accuracy than with paired multiome data lacking QTL information. This gain translated into improved gene regulatory network inference, enabling the identification of 128 additional transcription factors (TF)–target gene pairs (a 22% increase). Together, these results provide a comprehensive single-cell map of chromatin accessibility and genetic variation in human circulating immune cells, establishing a powerful resource for dissecting cell type-specific regulation and advancing our understanding of genetic risk for complex diseases.

Background: Endothelial dysfunction is an early contributor to atherosclerosis. This study combined CT imaging of coronary artery disease (CAD) and patient-dervied endothelial colony-forming cells (ECFCs) transcriptional profiling to investigate potential mechanisms underlying endothelial dysfunction in atherosclerosis. Methods: Twenty-six individuals with CT-defined CAD and eighteen non-CAD controls were included in the Discovery Cohort for bulk RNA sequencing and transcriptomic analysis of ECFCs. Differential gene expression analysis was performed, and candidate genes were selected based on logFC and p-value. Candidate genes were carried forward for gene expression validation using quantitative real-time PCR (qRT-PCR) in a Validation Cohort. Mitochondrial reactive oxygen species (mROS) production and mitochondrial mass were assessed using multi-colour flow cytometry. Functional validation of the top candidate was conducted in using human umbilical vein endothelial cells (HUVECs) using loss-of-function genetic approach. Expression Quantitative Trait Loci (eQTL)-association analysis was conducted using genotype data from the BioHEART-CT cohort. Results: Pairwise analysis identified six differentially expressed protein-coding genes in CAD ECFCs: CCBE1 (Collagen and Calcium Binding EGF Domain-Containing 1), SPINT2, CRISPLD1, PIEZO2, EPB41L3 , and AC005943.1 . qRT-PCR in the Validation Cohort confirmed significantly higher CCBE1 expression in CAD patients. Individuals with relative CCBE1 fold change expression>10 had a 2.8-fold increase in the log-odds ratio of CT-defined CAD. CAD ECFCs displayed elevated mROS and mitochondrial mass. CCBE1 knockdown in HUVECs reduced mROS and mitochondrial mass without affecting proliferation or permeability, but shifted cells into a metabolically elevated state, marked by increased ATP production, respiration and glycolysis. CCBE1 cis-eQTLs were associated with increased odds of CAD in the BioHEART-CT cohort. Conclusions: CCBE1 expression in ECFCs was higher in patients with CT-defined CAD versus non-CAD. Quantitative assessment of transcript levels supported a causal relationship between greater CCBE1 expression and CAD burden and risk, and functional experiments on CCBE1 knockdown demonstrated improved mitochondrial function in human endothelial cells. GRAPHICAL Novelty and Significance: What is Known?: The endothelium plays a critical role in vascular health and susceptibility to atherosclerosis. Mitochondrial dysfunction has been implicated in atherosclerosis, but its role and mechanism in individual susceptibility to CAD in humans is not known. What New Information Does This Article Contribute?: Novel approach integrating CT imaging with ECFC functional data to link vascular structure with endothelial biology ex vivo . Patients with CT-defined CAD had 3.6-fold higher CCBE1 expression compared to non-CAD within the Validation Cohort. Cis -eQTL-association analysis revealed increased odds of CAD. CCBE1 knockdown improved mitochondrial function in human endothelial cells. Together, these 4 lines of evidence point to a novel and causal role for CCBE1 in human CAD.

Understanding the genetic basis of gene expression can shed light on the regulatory mechanisms underlying complex traits and diseases. Single cell-resolved measures of RNA levels and single-cell expression quantitative trait loci (sc-eQTLs) have revealed genetic regulation that drives sub-tissue cell states and types across diverse human tissues. Here, we describe the first phase of TenK10K, the largest-to-date dataset of matched whole-genome sequencing (WGS) and single-cell RNA-sequencing (scRNA-seq). We leverage scRNA-seq data from over 5 million cells across 28 immune cell types, and matched WGS, from 1,925 individuals, which provides power to detect associations between rare and low-frequency genetic variants that have largely been uncharacterised in their impact on cell-specific gene expression. We map the effects of both common and rare variants in a cell type-specific manner using a recently introduced method that increases power by modelling single cells directly rather than relying on aggregated ‘pseudobulk’ counts. We identify putative common regulatory variants for 83% of all 21,404 genes tested and cumulative rare variant signals for 47% of genes. We explore how genetic effects vary across cell type and state spectra, develop a framework to determine the degree to which sc-eQTLs are cell type-specific, and show that about half of the effects are observed only in one or a few cell types. By integrating our results with functional annotations and disease information, we also further characterise the likely molecular modes of action for many disease-variant associations. Finally, we explore the effects that genetic variants have on gene expression across continuous cell states and functions, and effects that vary cell state abundance directly.