Population-scale single-cell RNA-seq profiling across dopaminergic neuron differentiation

Scalable eQTL mapping using single-nucleus RNA-sequencing of recombined gametes from a small number of individuals

Phenotypic differences between individuals of a species are often caused by differences in gene expression, which are in turn caused by genetic variation. Expression quantitative trait locus (eQTL) analysis is a methodology by which we can identify such causal variants. Scaling eQTL analysis is costly due to the expense of generating mapping populations, and the collection of matched transcriptomic and genomic information. We developed a rapid eQTL analysis approach using single-cell/nucleus RNA sequencing of gametes from a small number of heterozygous individuals. Patterns of inherited polymorphisms are used to infer the recombinant genomes of thousands of individual gametes and identify how different haplotypes correlate with variation in gene expression. Applied to Arabidopsis pollen nuclei, our approach uncovers both cis- and trans-eQTLs, ultimately mapping variation in a master regulator of sperm cell development that affects the expression of hundreds of genes. This establishes snRNA-sequencing as a powerful, cost-effective method for the mapping of meiotic recombination, addressing the scalability challenges of eQTL analysis and enabling eQTL mapping in specific cell-types.

Spatiotemporal proteomic and transcriptomic landscape of DAT+ dopaminergic neurons development and function

Dopaminergic (DA) neurons expressing the dopamine transporter (DAT) play vital roles in physiology and the regulation of mental and neurological disorders. This study investigates spatiotemporal proteomic and transcriptomic changes in DAT+ DA neurons from key brain regions-the nucleus accumbens (NAc), substantia nigra (SNc), and ventral tegmental area (VTA)-at postnatal milestones: day 7 (P7), day 30 (P30), and day 60 (P60). Using advanced multi-omics techniques, including ultrasensitive trace sample proteomics and SMART-seq2, we reveal distinct molecular profiles within DA neuron populations, reflecting their developmental progression and functional roles. Immunofluorescence mapping validates these findings and underscores the dynamic molecular architecture of DA neurons. Aldh1a1 expression, a key enzyme for retinoic acid production, progressively increases over time, reflecting its involvement in neuronal development and specialized functions. This study provides valuable insights into the development and function of DAT+ DA neurons.

AAV vectors trigger DNA damage response-dependent pro-inflammatory signalling in human iPSC-derived CNS models and mouse brain

Adeno-associated viral (AAV) vector-based gene therapy is gaining foothold as treatment for genetic neurological diseases with encouraging clinical results. Nonetheless, dose-dependent adverse events have emerged in recent clinical trials through mechanisms that remain unclear. We have modelled here the impact of AAV transduction in cell models of the human central nervous system (CNS), taking advantage of induced pluripotent stem cells. Our work uncovers vector-induced innate immune mechanisms that contribute to cell death. While empty AAV capsids were well tolerated, the AAV genome triggered p53-dependent DNA damage responses across CNS cell types followed by the induction of inflammatory responses. In addition, transgene expression led to MAVS-dependent activation of type I interferon responses. Formation of DNA damage foci in neurons and gliosis were confirmed in murine striatum upon intraparenchymal AAV injection. Transduction-induced cell death and gliosis could be prevented by inhibiting p53 or by acting downstream on STING- or IL-1R-mediated responses. Together, our work identifies innate immune mechanisms of vector sensing in the CNS that can potentially contribute to AAV-associated neurotoxicity.

Exploring human brain development and disease using assembloids

How the human brain develops and what goes awry in neurological disorders represent two long-lasting questions in neuroscience. Owing to the limited access to primary human brain tissue, insights into these questions have been largely gained through animal models. However, there are fundamental differences between developing mouse and human brain, and neural organoids derived from human pluripotent stem cells (hPSCs) have recently emerged as a robust experimental system that mimics self-organizing and multicellular features of early human brain development. Controlled integration of multiple organoids into assembloids has begun to unravel principles of cell-cell interactions. Moreover, patient-derived or genetically engineered hPSCs provide opportunities to investigate phenotypic correlates of neurodevelopmental disorders and to develop therapeutic hypotheses. Here, we outline the advances in technologies that facilitate studies by using assembloids and summarize their applications in brain development and disease modeling. Lastly, we discuss the major roadblocks of the current system and potential solutions.

distQTL: Distribution Quantitative Trait Loci Identification by Population-Scale Single-Cell Data

Mapping expression quantitative trait loci (eQTLs) is a powerful method to study how genetic variation influences gene expression. Traditional bulk eQTL methods rely on averaged gene expression across a possibly heterogeneous mixture of cells, which can obscure underlying regulatory heterogeneity. Single-cell eQTL methods circumvent the averaging artifacts, providing an immense opportunity to interrogate transcriptional regulation at a much finer resolution. Recent developments in metric space regression methods allow the use of full empirical distributions as response objects instead of simple summary statistics such as mean. Here, we leverage Fréchet regression to identify distribution QTLs (distQTLs) using population-scale single-cell RNA sequencing data. We apply distQTL to the OneK1K cohort, consisting of scRNA-seq data of peripheral blood mononuclear cells from 982 donors, and compare results to various eQTL approaches based on summary statistics and mixed effects modeling. We demonstrate the superior performance of distQTL across different gene expression contexts compared to other methods and benchmark our results against findings from the Genotype-Tissue Expression Project. Finally, we orthogonally validate calls from distQTL using cell-type-specific epigenomic profiles.

Mapping dynamic regulation of gene expression using single-cell transcriptomics and application to complex disease genetics

Single-cell transcriptome data can provide insights into how genetic variation influences biological processes involved in human physiology and disease. However, the identification of gene-level associations in distinct cell types faces several challenges, including the limited reference resources from population-scale studies, data sparsity in single-cell RNA sequencing, and the complex cell state pattern of expression within individual cell types. Here, we develop genetic models of cell-type-specific and cell-state-adjusted gene expression in mid-brain neurons undergoing differentiation from induced pluripotent stem cells. The resulting framework quantifies the dynamics of the genetic regulation of gene expression and estimates its cell-type specificity. As an application, we show that the approach detects known and new genes associated with schizophrenia and enables insights into context-dependent disease mechanisms. We provide a genomic resource from a phenome-wide application of our models to more than 1,500 phenotypes from the UK Biobank. Using longitudinal, genetically determined expression, we implement a predictive causality framework, evaluating the prediction of future values of a target gene expression using prior values of a putative regulatory gene. Collectively, the results of this work demonstrate the insights that can be gained into the molecular underpinnings of disease by quantifying the genetic control of gene expression at single-cell resolution.

Single-cell eQTL mapping reveals cell-type-specific genes associated with the risk of gastric cancer

Most expression quantitative trait locus (eQTL) analyses have been conducted in heterogeneous gastric tissues, limiting understanding of cell-type-specific regulatory mechanisms. Here, we employed a pooled multiplexing strategy to profile 399,683 gastric cells from 203 Chinese individuals using single-cell RNA sequencing (scRNA-seq). We identified 19 distinct gastric cell types and performed eQTL analyses, uncovering 8,498 independent eQTLs, with a considerable fraction (81%, 6,909/8,498) exhibiting cell-type-specific effects. Integration of these eQTLs with genome-wide association studies for gastric cancer (GC) revealed four co-localization signals in specific cell types. Genetically predicted cell-type-specific gene expression identified 15 genes associated with GC risk, including the upregulation of MUC1 exclusively in parietal cells, linked to decreased GC risk. Our findings highlight substantial heterogeneity in the genetic regulation of gene expression across gastric cell types and provide critical cell-type-specific annotations of genetic variants associated with GC risk, offering new molecular insights underlying GC.

Dissecting cardiovascular disease-associated noncoding genetic variants using human iPSC models

Advancements in genomics have revealed hundreds of loci associated with cardiovascular diseases, highlighting the role genetic variants play in disease pathogenesis. Notably, most variants lie within noncoding genomic regions that modulate transcription factor binding, chromatin accessibility, and thereby the expression levels and cell type specificity of gene transcripts. Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) have emerged as a powerful tool to delineate the pathogenicity of such variants and elucidate the underlying transcriptional mechanisms. Our review discusses the basics of noncoding variant-mediated pathogenesis, the methodologies utilized, and how hiPSC-based heart models can be leveraged to dissect the mechanisms of noncoding variants.

CellBouncer, A Unified Toolkit for Single-Cell Demultiplexing and Ambient RNA Analysis, Reveals Hominid Mitochondrial Incompatibilities

Pooled processing, in which cells from multiple sources are cultured or captured together, is an increasingly popular strategy for droplet-based single cell sequencing studies. This design allows efficient scaling of experiments, isolation of cell-intrinsic differences, and mitigation of batch effects. We present CellBouncer, a computational toolkit for demultiplexing and analyzing single-cell sequencing data from pooled experiments. We demonstrate that CellBouncer can separate and quantify multi-species and multi-individual cell mixtures, identify unknown mitochondrial haplotypes in cells, assign treatments from lipid-conjugated barcodes or CRISPR sgRNAs, and infer pool composition, outperforming existing methods. We also introduce methods to quantify ambient RNA contamination per cell, infer individual donors' contributions to the ambient RNA pool, and determine a consensus doublet rate harmonized across data types. Applying these tools to tetraploid composite cells, we identify a competitive advantage of human over chimpanzee mitochondria across 10 cell fusion lines and provide evidence for inter-mitochondrial incompatibility and mito-nuclear incompatibility between species.

Transcriptome-wide association studies at cell state level using single-cell eQTL data

Transcriptome-wide association studies (TWAS) have been widely used to prioritize relevant genes for diseases. Current methods for TWAS test gene-disease associations at bulk tissue or cell-type-specific pseudobulk level, which do not account for the heterogeneity within cell types. We present TWiST, a statistical method for TWAS analysis at cell state resolution using single-cell expression quantitative trait loci (eQTL) data. Our method uses pseudotime to represent cell states and models the effect of gene expression on trait as a continuous pseudotemporal curve. Therefore, it allows flexible hypothesis testing of global, dynamic, and nonlinear effects. Through simulation studies and real data analysis, we demonstrated that TWiST leads to significantly improved power compared to pseudobulk methods that ignores heterogeneity due to cell states. Application to the OneK1K study identified hundreds of genes with dynamic effects on autoimmune diseases along the trajectory of immune cell differentiation. TWiST presents great promise to understand disease genetics using single-cell eQTL studies.

Single-cell eQTL mapping in yeast reveals a tradeoff between growth and reproduction

Expression quantitative trait loci (eQTLs) provide a key bridge between noncoding DNA sequence variants and organismal traits. The effects of eQTLs can differ among tissues, cell types, and cellular states, but these differences are obscured by gene expression measurements in bulk populations. We developed a one-pot approach to map eQTLs in Saccharomyces cerevisiae by single-cell RNA sequencing (scRNA-seq) and applied it to over 100,000 single cells from three crosses. We used scRNA-seq data to genotype each cell, measure gene expression, and classify the cells by cell-cycle stage. We mapped thousands of local and distant eQTLs and identified interactions between eQTL effects and cell-cycle stages. We took advantage of single-cell expression information to identify hundreds of genes with allele-specific effects on expression noise. We used cell-cycle stage classification to map 20 loci that influence cell-cycle progression. One of these loci influenced the expression of genes involved in the mating response. We showed that the effects of this locus arise from a common variant (W82R) in the gene GPA1, which encodes a signaling protein that negatively regulates the mating pathway. The 82R allele increases mating efficiency at the cost of slower cell-cycle progression and is associated with a higher rate of outcrossing in nature. Our results provide a more granular picture of the effects of genetic variants on gene expression and downstream traits.

Missing regulatory effects on complex traits: Contribution of distal variants

Most genetic effects on complex traits lie in non-coding regions, yet many show no regulatory activity in standard gene expression assays. In this issue of Cell Genomics, Arthur et al.1 add early development-like cell types and chromatin assays, showing that distal variants missed in expression assays partly explain this discrepancy.

Multiomic QTL mapping reveals phenotypic complexity of GWAS loci and prioritizes putative causal variants

Most GWAS loci are presumed to affect gene regulation; however, only ∼43% colocalize with expression quantitative trait loci (eQTLs). To address this colocalization gap, we map eQTLs, chromatin accessibility QTLs (caQTLs), and histone acetylation QTLs (haQTLs) using molecular samples from three early developmental-like tissues. Through colocalization, we annotate 10.4% (n = 540) of GWAS loci in 15 traits by QTL phenotype, temporal specificity, and complexity. We show that integration of chromatin QTLs results in a 2.3-fold higher annotation rate of GWAS loci because they capture distal GWAS loci missed by eQTLs, and that 5.4% (n = 13) of GWAS colocalizing eQTLs are early developmental specific. Finally, we utilize the iPSCORE multiomic QTLs to prioritize putative causal variants overlapping transcription factor motifs to elucidate the potential genetic underpinnings of 296 GWAS-QTL colocalizations.

Rapidly evolved genomic regions shape individual language abilities in present-day humans

1Minor genetic changes have produced profound differences in cognitive abilities between humans and our closest relatives, particularly in language. Despite decades of research, ranging from single-gene studies to broader evolutionary analyses[1, 2, 3, 4, 5], key questions about the genomic foundations of human language have persisted, including which sequences are involved, how they evolved, and whether similar changes occur in other vocal learning species. Here we provide the first evidence directly linking rapidly evolved genomic regions to language abilities in contemporary humans. Through extensive analysis of 65 million years of evolutionary events in over 30,000 individuals, we demonstrate that Human Ancestor Quickly Evolved Regions (HAQERs)[5] - sequences that rapidly accumulated mutations after the human-chimpanzee split - specifically influence language but not general cognition. These regions evolved to shape language development by altering binding of Forkhead domain transcription factors, including FOXP2. Strikingly, language-associated HAQER variants show higher prevalence in Neanderthals than modern humans, have been stable throughout recent human history, and show evidence of convergent evolution across other mammalian vocal learners. An unexpected pattern of balancing selection acting on these apparently beneficial alleles is explained by their pleiotropic effects on prenatal brain development contributing to birth complications, reflecting an evolutionary trade-off between language capability and reproductive fitness. By developing the Evolution Stratified-Polygenic Score analysis, we show that language capabilities likely emerged before the human-Neanderthal split - far earlier than previously thought[3, 6, 7]. Our findings establish the first direct link between ancient genomic divergence and present-day variation in language abilities, while revealing how evolutionary constraints continue to shape human cognitive development.

SingleBrain: A Meta-Analysis of Single-Nucleus eQTLs Linking Genetic Risk to Brain Disorders

Most genetic risk variants for neurological diseases are located in non-coding regulatory regions, where they may often act as expression quantitative trait loci (eQTLs), modulating gene expression and influencing disease susceptibility. However, eQTL studies in bulk brain tissue or specific cell types lack the resolution to capture the brain's cellular diversity. Single-nucleus RNA sequencing (snRNA-seq) offers high-resolution mapping of eQTLs across diverse brain cell types. Here, we performed a meta-analysis, "SingleBrain," integrating publicly available snRNA-seq and genotype data from four cohorts, totaling 5.8 million nuclei from 983 individuals. We mapped cis-eQTLs across major brain cell types and subtypes and employed statistical colocalization and Mendelian randomization to identify genes mediating neurological disease risk. We observed up to a 10-fold increase in cis-eQTLs compared to previous studies and uncovered novel cell type-specific genes linked to Alzheimer's disease, Parkinson's disease, and schizophrenia that were previously undetectable in bulk tissue analyses. Additionally, we prioritized putative causal variants for each locus through fine-mapping and integration with cell type-specific enhancer and promoter regulatory elements. SingleBrain represents a comprehensive single-cell eQTL resource, advancing insights into the genetic regulation of brain disorders.

Single-cell multiomics of neuronal activation reveals context-dependent genetic control of brain disorders

Despite hundreds of genetic risk loci identified for neuropsychiatric disorders (NPD), most causal variants/genes remain unknown. A major hurdle is that disease risk variants may act in specific biological contexts, e.g., during neuronal activation, which is difficult to study in vivo at the population level. Here, we conducted a single-cell multiomics study of neuronal activation (stimulation) in human iPSC-induced excitatory and inhibitory neurons from 100 donors, and uncovered abundant neuronal stimulation-specific causal variants/genes for NPD. We surveyed NPD-relevant transcriptomic and epigenomic landscape of neuronal activation and identified thousands of genetic variants associated with activity-dependent gene expression (i.e., eQTL) and chromatin accessibility (i.e., caQTL). These caQTL explained considerably larger proportions of NPD heritability than the eQTL. Integrating the multiomic data with GWAS further revealed NPD risk variants/genes whose effects were only detected upon stimulation. Interestingly, multiple lines of evidence support a role of activity-dependent cholesterol metabolism in NPD. Our work highlights the power of cell stimulation to reveal context-dependent "hidden" genetic effects.

The impact of ambient contamination on demultiplexing methods for single-nucleus multiome experiments

Sample multiplexing has become an increasingly common design choice in droplet-based single-nucleus multi-omic sequencing experiments to reduce costs and remove technical variation. Genotype-based demultiplexing is one popular class of methods that was originally developed for single-cell RNA-seq, but has not been rigorously benchmarked in other assays, such as snATAC-seq and joint snRNA/snATAC assays, especially in the context of variable ambient RNA/DNA contamination. To address this, we develop ambisim, a genotype-aware read-level simulator that can flexibly control ambient molecule proportions and generate realistic joint snRNA/snATAC data. We use ambisim to evaluate demultiplexing methods across several important parameters: doublet rate, number of multiplexed donors, and coverage levels. Our simulations reveal that methods are variably impacted by ambient contamination in both modalities. We then applied the demultiplexing methods to two joint snRNA/snATAC datasets and found highly variable concordance between methods in both modalities. Finally, we develop a new metric, variant consistency, which we show is correlated with cell-level ambient molecule fractions in singlets. Applying our metric to two multiplexed joint snRNA/snATAC datasets reveals variable ambient contamination across experiments and modalities. We conclude that improved modelling of ambient material in demultiplexing algorithms will increase both sensitivity and specificity.

Early cell cycle genes in cortical organoid progenitors predict interindividual variability in infant brain growth trajectories

Human induced pluripotent stem cell (iPSC) derived cortical organoids (hCOs) model neurogenesis on an individual's genetic background. The degree to which hCO phenotypes recapitulate the brain growth of the participants from which they were derived is not well established. We generated up to 3 iPSC clones from each of 18 participants in the Infant Brain Imaging Study, who have undergone longitudinal brain imaging during infancy. We identified consistent hCO morphology and cortical cell types across clones from the same participant. hCO cross-sectional area and production of cortical hem cells were associated with in vivo cortical growth rates. Cell cycle associated genes expression in early progenitors at the crux of fate decision trajectories were correlated with cortical growth rate from 6-12 months of age, and were enriched in microcephaly and neurodevelopmental disorder genes. Our data suggest the hCOs capture inter-individual variation in cortical cell types influencing infant cortical surface area expansion.

The impact of ambient contamination on demultiplexing methods for single-nucleus multiome experiments

Sample multiplexing has become an increasingly common design choice in droplet-based single-nucleus multi-omic sequencing experiments to reduce costs and remove technical variation. Genotype-based demultiplexing is one popular class of methods that was originally developed for single-cell RNA-seq, but has not been rigorously benchmarked in other assays, such as snATAC-seq and joint snRNA/snATAC assays, especially in the context of variable ambient RNA/DNA contamination. To address this, we develop ambisim, a genotype-aware read-level simulator that can flexibly control ambient molecule proportions and generate realistic joint snRNA/snATAC data. We use ambisim to evaluate demultiplexing methods across several important parameters: doublet rate, number of multiplexed donors, and coverage levels. Our simulations reveal that methods are variably impacted by ambient contamination in both modalities. We then applied the demultiplexing methods to two joint snRNA/snATAC datasets and found highly variable concordance between methods in both modalities. Finally, we develop a new metric, variant consistency, which we show is correlated with cell-level ambient molecule fractions in singlets. Applying our metric to two multiplexed joint snRNA/snATAC datasets reveals variable ambient contamination across experiments and modalities. We conclude that improved modelling of ambient material in demultiplexing algorithms will increase both sensitivity and specificity.

Multiplexing cortical brain organoids for the longitudinal dissection of developmental traits at single-cell resolution

Dissecting human neurobiology at high resolution and with mechanistic precision requires a major leap in scalability, given the need for experimental designs that include multiple individuals and, prospectively, population cohorts. To lay the foundation for this, we have developed and benchmarked complementary strategies to multiplex brain organoids by pooling cells from different pluripotent stem cell (PSC) lines either during organoid generation (mosaic models) or before single-cell RNA sequencing (scRNA-seq) library preparation (downstream multiplexing). We have also developed a new computational method, SCanSNP, and a consensus call to deconvolve cell identities, overcoming current criticalities in doublets and low-quality cell identification. We validated both multiplexing methods for charting neurodevelopmental trajectories at high resolution, thus linking specific individuals' trajectories to genetic variation. Finally, we modeled their scalability across different multiplexing combinations and showed that mosaic organoids represent an enabling method for high-throughput settings. Together, this multiplexing suite of experimental and computational methods provides a highly scalable resource for brain disease and neurodiversity modeling.

Cross cell-type systems genetics reveals the influence of eQTL at multiple points in the developmental trajectory of mouse neural progenitor cells

Genetic variation leads to phenotypic variability in pluripotent stem cells that presents challenges for regenerative medicine. Although recent studies have investigated the impact of genetic variation on pluripotency maintenance and differentiation capacity, less is known about how genetic variants affecting the pluripotent state influence gene regulation in later stages of development. Here, we characterized expression of more than 12,000 genes in 127 donor-matched Diversity Outbred (DO) mouse embryonic stem cell (mESC) and neural progenitor cell (mNPC) lines. Quantitative trait locus (QTL) mapping identified 2,947 expression QTL (eQTL) unique to DO mNPCs and 1,113 eQTL observed in both mNPCs and mESCs with highly concordant allele effects. We mapped three eQTL hotspots on Chromosomes (Chrs) 1, 10, and 11 that were unique to mNPCs. Target genes of the Chr 1 hotspot were overrepresented for those involved in mRNA processing, DNA repair, chromatin organization, protein degradation, and cell cycle. Mediation analysis of the Chr 1 hotspot identified Rnf152 as the best candidate mediator expressed in mNPCs, while cross-cell type mediation using mESC gene expression along with partial correlation analysis strongly implicated genetic variant(s) affecting Pign expression in the mESC state as regulating the mNPC Chr 1 eQTL hotspot. Together these findings highlight that many local eQTL confer similar effects on gene expression in multiple cell states; distant eQTL in DO mNPCs are numerous and largely unique to that cell state, with many co-localizing to mNPC-specific hotspots; and mediation analysis across cell types suggests that expression of Pign early in development (mESCs) shapes the transcriptome of the more specialized mNPC state.

Shared genetic architecture and bidirectional clinical risks within the psycho-metabolic nexus

BACKGROUND

Increasing evidence suggests a complex interplay between psychiatric disorders and metabolic dysregulations. However, most research has been limited to specific disorder pairs, leaving a significant gap in our understanding of the broader psycho-metabolic nexus.

METHODS

This study leveraged large-scale cohort data and genome-wide association study (GWAS) summary statistics, covering 8 common psychiatric disorders and 43 metabolic traits. We introduced a comprehensive analytical strategy to identify shared genetic bases sequentially, from key genetic correlation regions to local pleiotropy and pleiotropic genes. Finally, we developed polygenic risk score (PRS) models to translate these findings into clinical applications.

FINDINGS

We identified significant bidirectional clinical risks between psychiatric disorders and metabolic dysregulations among 310,848 participants from the UK Biobank. Genetic correlation analysis confirmed 104 robust trait pairs, revealing 1088 key genomic regions, including critical hotspots such as chr3: 47588462-50387742. Cross-trait meta-analysis uncovered 388 pleiotropic single nucleotide variants (SNVs) and 126 shared causal variants. Among variants, 45 novel SNVs were associated with psychiatric disorders and 75 novel SNVs were associated with metabolic traits, shedding light on new targets to unravel the mechanism of comorbidity. Notably, RBM6, a gene involved in alternative splicing and cellular stress response regulation, emerged as a key pleiotropic gene. When psychiatric and metabolic genetic information were integrated, PRS models demonstrated enhanced predictive power.

INTERPRETATION

The study highlights the intertwined genetic and clinical relationships between psychiatric disorders and metabolic dysregulations, emphasising the need for integrated approaches in diagnosis and treatment.

FUNDING

The National Key Research and Development Program of China (2023YFC2506200, SHH). The National Natural Science Foundation of China (82273741, SY).

Endophenotype 2.0: updated definitions and criteria for endophenotypes of psychiatric disorders, incorporating new technologies and findings

Recent genetic studies have linked numerous loci to psychiatric disorders. However, the biological pathways that connect these genetic associations to psychiatric disorders' specific pathophysiological processes are largely unclear. Endophenotypes, first defined over five decades ago, are heritable traits, independent of disease state that are associated with a disease, encompassing a broad range of neurophysiological, biochemical, endocrinological, neuroanatomical, cognitive, and neuropsychological characteristics. Considering the advancements in genetics and genomics over recent decades, we propose a revised definition of endophenotypes as 'genetically influenced phenotypes linked to disease or treatment characteristics and their related events.' We also updated endophenotype criteria to include (1) reliable measurement, (2) association with the disease or its related events, and (3) genetic mediation. 'Genetic mediation' is introduced to differentiate between causality and pleiotropic effects and allows non-linear relationships. Furthermore, this updated Endophenotype 2.0 framework expands to encompass genetically regulated responses to disease-related factors, including environmental risks, illness progression, treatment responses, and resilience phenotypes, which may be state-dependent. This broadened definition paves the way for developing new endophenotypes crucial for genetic analyses in psychiatric disorders. Integrating genetics, genomics, and diverse endophenotypes into multi-dimensional mechanistic models is vital for advancing our understanding of psychiatric disorders. Crucially, elucidating the biological underpinnings of endophenotypes will enhance our grasp of psychiatric genetics, thereby improving disease risk prediction and treatment approaches.

Cell type and dynamic state govern genetic regulation of gene expression in heterogeneous differentiating cultures

Identifying the molecular effects of human genetic variation across cellular contexts is crucial for understanding the mechanisms underlying disease-associated loci, yet many cell types and developmental stages remain underexplored. Here, we harnessed the potential of heterogeneous differentiating cultures (HDCs), an in vitro system in which pluripotent cells asynchronously differentiate into a broad spectrum of cell types. We generated HDCs for 53 human donors and collected single-cell RNA sequencing data from over 900,000 cells. We identified expression quantitative trait loci in 29 cell types and characterized regulatory dynamics across diverse differentiation trajectories. This revealed novel regulatory variants for genes involved in key developmental and disease-related processes while replicating known effects from primary tissues and dynamic regulatory effects associated with a range of complex traits.

Comprehensive Gene Expression Analysis Using Human Induced Pluripotent Stem Cells Derived from Patients with Sleep Bruxism: A Preliminary In Vitro Study

Sleep bruxism (SB) involves involuntary jaw movements during sleep and is potentially caused by motor neuronal hyperexcitability and GABAergic system dysfunction. However, the molecular basis remains unclear. In this study, we aimed to investigate changes in the expression of several genes associated with the pathophysiology of SB. Bulk RNA sequencing (bulk RNA-seq) and single-nucleus RNA sequencing (snRNA-seq) of neurons derived from patient and control human induced pluripotent stem cells (hiPSCs) were performed to comprehensively assess gene expression and cell type-specific alterations, respectively. Bulk RNA-seq revealed significant upregulation of calcium signaling-related genes in SB neurons, including those encoding G protein-coupled receptors and receptor-operated calcium channels. snRNA-seq confirmed the increased expression of GRIN2B (an N-methyl-D-aspartate receptor subunit) and CHRM3 (an M3 muscarinic acetylcholine receptor), particularly in glutamatergic and GABAergic neurons. These alterations were linked to hyperexcitability, with GRIN2B contributing to glutamatergic signaling and CHRM3 contributing to cholinergic signaling. These findings suggest that disrupted calcium signaling and overexpression of GRIN2B and CHRM3 drive neuronal hyperexcitability, providing insight into the pathophysiology of SB. Targeting these pathways may inform therapeutic strategies for SB treatment.

Stimulating Wnt signaling reveals context-dependent genetic effects on gene regulation in primary human neural progenitors

Gene regulatory effects have been difficult to detect at many non-coding loci associated with brain-related traits, likely because some genetic variants have distinct functions in specific contexts. To explore context-dependent gene regulation, we measured chromatin accessibility and gene expression after activation of the canonical Wnt pathway in primary human neural progenitors (n = 82 donors). We found that TCF/LEF motifs and brain-structure-associated and neuropsychiatric-disorder-associated variants were enriched within Wnt-responsive regulatory elements. Genetically influenced regulatory elements were enriched in genomic regions under positive selection along the human lineage. Wnt pathway stimulation increased detection of genetically influenced regulatory elements/genes by 66%/53% and enabled identification of 397 regulatory elements primed to regulate gene expression. Stimulation also increased identification of shared genetic effects on molecular and complex brain traits by up to 70%, suggesting that genetic variant function during neurodevelopmental patterning can lead to differences in adult brain and behavioral traits.

Unraveling the mechanisms underlying diabetic cataracts: insights from Mendelian randomization analysis

BACKGROUND

Diabetic cataract (DC) is a major cause of blindness, with its pathogenesis involving oxidative stress and ferroptosis, according to recent studies.

METHODS

We performed a Mendelian Randomization (MR) study using GWAS data to select SNPs and assess the causal link between diabetes and cataracts. DC datasets were analyzed for differential gene expression, WGCNA, and protein-protein interactions to identify key oxidative stress and ferroptosis genes. An SVM-RFE algorithm developed a diagnostic model, and ImmuCellAI analyzed immune infiltration patterns.

RESULTS

MR analysis confirmed diabetes as a cataract risk factor and identified core genes related to oxidative stress and ferroptosis in DC. Four key genes (Hspa5/Nfe2l2/Atf3/Stat3) linked to both processes were discovered. Immune infiltration analysis revealed an imbalance associated with these genes.

CONCLUSIONS

A functional interaction between oxidative stress and ferroptosis genes in DC is suggested, with a 4-gene model, indicating their potential as a 'bridge' in DC pathogenesis.

Efficient differential expression analysis of large-scale single cell transcriptomics data using dreamlet

Advances in single-cell and -nucleus transcriptomics have enabled generation of increasingly large-scale datasets from hundreds of subjects and millions of cells. These studies promise to give unprecedented insight into the cell type specific biology of human disease. Yet performing differential expression analyses across subjects remains difficult due to challenges in statistical modeling of these complex studies and scaling analyses to large datasets. Our open-source R package dreamlet (DiseaseNeurogenomics.github.io/dreamlet) uses a pseudobulk approach based on precision-weighted linear mixed models to identify genes differentially expressed with traits across subjects for each cell cluster. Designed for data from large cohorts, dreamlet is substantially faster and uses less memory than existing workflows, while supporting complex statistical models and controlling the false positive rate. We demonstrate computational and statistical performance on published datasets, and a novel dataset of 1.4M single nuclei from postmortem brains of 150 Alzheimer's disease cases and 149 controls.

Genome-wide meta-analysis identifies 22 loci for normal tension glaucoma with significant overlap with high tension glaucoma

Primary open-angle glaucoma typically presents as two subtypes. This study aimed to elucidate the shared and distinct genetic architectures of normal-tension (NTG) and high-tension glaucoma (HTG), motivated by the need to develop intraocular pressure (IOP)-independent drug targets for the disease. We conducted a comprehensive multi-ethnic meta-analysis, prioritized variants based on functional annotation, and explored drug-gene interactions. We further assessed the genetic overlap between NTG and HTG using pairwise GWAS analysis. We identified 22 risk loci associated with NTG, 17 of which have not previously been reported for NTG. Two loci, BMP4 and TBKBP1, have not previously been associated with glaucoma at the genome-wide significance level. Our results indicate that while there is a significant overlap in risk loci between tension subtypes, the magnitude of the effect tends to be lower in NTG compared to HTG, particularly for IOP-related loci. Additionally, we identified a potential role for biologic immunomodulatory treatments as neuroprotective agents.

INTERSPECIES ORGANOIDS REVEAL HUMAN-SPECIFIC MOLECULAR FEATURES OF DOPAMINERGIC NEURON DEVELOPMENT AND VULNERABILITY

The disproportionate expansion of telencephalic structures during human evolution involved tradeoffs that imposed greater connectivity and metabolic demands on midbrain dopaminergic neurons. Despite the central role of dopaminergic neurons in human-enriched disorders, molecular specializations associated with human-specific features and vulnerabilities of the dopaminergic system remain unexplored. Here, we establish a phylogeny-in-a-dish approach to examine gene regulatory evolution by differentiating pools of human, chimpanzee, orangutan, and macaque pluripotent stem cells into ventral midbrain organoids capable of forming long-range projections, spontaneous activity, and dopamine release. We identify human-specific gene expression changes related to axonal transport of mitochondria and reactive oxygen species buffering and candidate cis- and trans-regulatory mechanisms underlying gene expression divergence. Our findings are consistent with a model of evolved neuroprotection in response to tradeoffs related to brain expansion and could contribute to the discovery of therapeutic targets and strategies for treating disorders involving the dopaminergic system.

Single-cell eQTL mapping in yeast reveals a tradeoff between growth and reproduction

Expression quantitative trait loci (eQTLs) provide a key bridge between noncoding DNA sequence variants and organismal traits. The effects of eQTLs can differ among tissues, cell types, and cellular states, but these differences are obscured by gene expression measurements in bulk populations. We developed a one-pot approach to map eQTLs in Saccharomyces cerevisiae by single-cell RNA sequencing (scRNA-seq) and applied it to over 100,000 single cells from three crosses. We used scRNA-seq data to genotype each cell, measure gene expression, and classify the cells by cell-cycle stage. We mapped thousands of local and distant eQTLs and identified interactions between eQTL effects and cell-cycle stages. We took advantage of single-cell expression information to identify hundreds of genes with allele-specific effects on expression noise. We used cell-cycle stage classification to map 20 loci that influence cell-cycle progression. One of these loci influenced the expression of genes involved in the mating response. We showed that the effects of this locus arise from a common variant (W82R) in the gene GPA1, which encodes a signaling protein that negatively regulates the mating pathway. The 82R allele increases mating efficiency at the cost of slower cell-cycle progression and is associated with a higher rate of outcrossing in nature. Our results provide a more granular picture of the effects of genetic variants on gene expression and downstream traits.

Genomic analysis of intracranial and subcortical brain volumes yields polygenic scores accounting for variation across ancestries

Subcortical brain structures are involved in developmental, psychiatric and neurological disorders. Here we performed genome-wide association studies meta-analyses of intracranial and nine subcortical brain volumes (brainstem, caudate nucleus, putamen, hippocampus, globus pallidus, thalamus, nucleus accumbens, amygdala and the ventral diencephalon) in 74,898 participants of European ancestry. We identified 254 independent loci associated with these brain volumes, explaining up to 35% of phenotypic variance. We observed gene expression in specific neural cell types across differentiation time points, including genes involved in intracellular signaling and brain aging-related processes. Polygenic scores for brain volumes showed predictive ability when applied to individuals of diverse ancestries. We observed causal genetic effects of brain volumes with Parkinson's disease and attention-deficit/hyperactivity disorder. Findings implicate specific gene expression patterns in brain development and genetic variants in comorbid neuropsychiatric disorders, which could point to a brain substrate and region of action for risk genes implicated in brain diseases.

Modeling common and rare genetic risk factors of neuropsychiatric disorders in human induced pluripotent stem cells

Recent genome-wide association studies (GWAS) and whole-exome sequencing of neuropsychiatric disorders, especially schizophrenia, have identified a plethora of common and rare disease risk variants/genes. Translating the mounting human genetic discoveries into novel disease biology and more tailored clinical treatments is tied to our ability to causally connect genetic risk variants to molecular and cellular phenotypes. When combined with the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRISPR-associated (Cas) nuclease-mediated genome editing system, human induced pluripotent stem cell (hiPSC)-derived neural cultures (both 2D and 3D organoids) provide a promising tractable cellular model for bridging the gap between genetic findings and disease biology. In this review, we first conceptualize the advances in understanding the disease polygenicity and convergence from the past decade of iPSC modeling of different types of genetic risk factors of neuropsychiatric disorders. We then discuss the major cell types and cellular phenotypes that are most relevant to neuropsychiatric disorders in iPSC modeling. Finally, we critically review the limitations of iPSC modeling of neuropsychiatric disorders and outline the need for implementing and developing novel methods to scale up the number of iPSC lines and disease risk variants in a systematic manner. Sufficiently scaled-up iPSC modeling and a better functional interpretation of genetic risk variants, in combination with cutting-edge CRISPR/Cas9 gene editing and single-cell multi-omics methods, will enable the field to identify the specific and convergent molecular and cellular phenotypes in precision for neuropsychiatric disorders.

Computational methods for allele-specific expression in single cells

Allele-specific expression (ASE) is a powerful signal that can be used to investigate multiple molecular mechanisms, such as cis-regulatory effects and imprinting. Single-cell RNA-sequencing (scRNA-seq) enables ASE characterization at the resolution of individual cells. In this review, we highlight the computational methods for processing and analyzing single-cell ASE data. We first describe a bioinformatics pipeline to obtain ASE counts from raw reads synthesized from previous literature. We then discuss statistical methods for detecting allelic imbalance and its variability across conditions using scRNA-seq data. In addition, we describe other methods that use single-cell ASE to address specific biological questions. Finally, we discuss future directions and emphasize the need for an integrated, optimized bioinformatics pipeline, and further development of statistical methods for different technologies.

Development and validation of a machine learning-based diagnostic model for Parkinson's disease in community-dwelling populations: Evidence from the China health and retirement longitudinal study (CHARLS)

BACKGROUND

Parkinson's disease (PD) is a major neurodegenerative disorder in Middle-aged and elderly people.There is a pressing need for effective predictive models, particularly in chinese population.

OBJECTIVE

This study aims to develop and validate a machine learning-based diagnostic model to identify individuals with PD in community-dwelling populations using data from the China Health and Retirement Longitudinal Study (CHARLS).

METHODS

We utilized data from 19,134 individuals aged 45 and above from the CHARLS dataset, with 265 adults reported to have PD. The external validation cohort included 1500 individuals, with 21 (1.4 %) having PD.The random forest (RF) algorithm was used to develop an interpretable PD prediction model, which was internally validated using 10-fold cross-validation and externally validated with a dataset from Northern Jiangsu People's Hospital. SHapley Additive exPlanation (SHAP) values were employed to elucidate the model's predictions.

RESULTS

The RF model demonstrated robust performance with an Area Under the Curve (AUC) of 0.884 and high sensitivity, specificity, and F1 scores. The model's performance in external validation cohort, highlighting an AUC of 0.82 and an accuracy of 0.99. The model's performance remained consistent across internal and external validation cohorts. SHAP analysis provided insights into the importance and interaction of various predictors, enhancing model interpretability.

CONCLUSION

The study presents a highly accurate and interpretable machine learning-based diagnostic model to identify individuals with PD in middle-aged and older Chinese adults. By combined with predictive risk factors and chronic disease information, the model offers valuable insights for early identification and intervention, potentially mitigating PD progression.

Clinical trials in-a-dish for cardiovascular medicine

Cardiovascular diseases persist as a global health challenge that requires methodological innovation for effective drug development. Conventional pipelines relying on animal models suffer from high failure rates due to significant interspecies variation between humans and animal models. In response, the recently enacted Food and Drug Administration Modernization Act 2.0 encourages alternative approaches including induced pluripotent stem cells (iPSCs). Human iPSCs provide a patient-specific, precise, and screenable platform for drug testing, paving the way for cardiovascular precision medicine. This review discusses milestones in iPSC differentiation and their applications from disease modelling to drug discovery in cardiovascular medicine. It then explores challenges and emerging opportunities for the implementation of 'clinical trials in-a-dish'. Concluding, this review proposes a framework for future clinical trial design with strategic incorporations of iPSC technology, microphysiological systems, clinical pan-omics, and artificial intelligence to improve success rates and advance cardiovascular healthcare.

scCTS: identifying the cell type-specific marker genes from population-level single-cell RNA-seq

Single-cell RNA-sequencing (scRNA-seq) provides gene expression profiles of individual cells from complex samples, facilitating the detection of cell type-specific marker genes. In scRNA-seq experiments with multiple donors, the population level variation brings an extra layer of complexity in cell type-specific gene detection, for example, they may not appear in all donors. Motivated by this observation, we develop a statistical model named scCTS to identify cell type-specific genes from population-level scRNA-seq data. Extensive data analyses demonstrate that the proposed method identifies more biologically meaningful cell type-specific genes compared to traditional methods.

Identifying genetic variants that influence the abundance of cell states in single-cell data

Disease risk alleles influence the composition of cells present in the body, but modeling genetic effects on the cell states revealed by single-cell profiling is difficult because variant-associated states may reflect diverse combinations of the profiled cell features that are challenging to predefine. We introduce Genotype-Neighborhood Associations (GeNA), a statistical tool to identify cell-state abundance quantitative trait loci (csaQTLs) in high-dimensional single-cell datasets. Instead of testing associations to predefined cell states, GeNA flexibly identifies the cell states whose abundance is most associated with genetic variants. In a genome-wide survey of single-cell RNA sequencing peripheral blood profiling from 969 individuals, GeNA identifies five independent loci associated with shifts in the relative abundance of immune cell states. For example, rs3003-T (P = 1.96 × 10-11) associates with increased abundance of natural killer cells expressing tumor necrosis factor response programs. This csaQTL colocalizes with increased risk for psoriasis, an autoimmune disease that responds to anti-tumor necrosis factor treatments. Flexibly characterizing csaQTLs for granular cell states may help illuminate how genetic background alters cellular composition to confer disease risk.

Individual variation in the emergence of anterior-to-posterior neural fates from human pluripotent stem cells

Variability between human pluripotent stem cell (hPSC) lines remains a challenge and opportunity in biomedicine. In this study, hPSC lines from multiple donors were differentiated toward neuroectoderm and mesendoderm lineages. We revealed dynamic transcriptomic patterns that delineate the emergence of these lineages, which were conserved across lines, along with individual line-specific transcriptional signatures that were invariant throughout differentiation. These transcriptomic signatures predicted an antagonism between SOX21-driven forebrain fates and retinoic acid-induced hindbrain fates. Replicate lines and paired adult tissue demonstrated the stability of these line-specific transcriptomic traits. We show that this transcriptomic variation in lineage bias had both genetic and epigenetic origins, aligned with the anterior-to-posterior structure of early mammalian development, and was present across a large collection of hPSC lines. These findings contribute to developing systematic analyses of PSCs to define the origin and consequences of variation in the early events orchestrating individual human development.

Oxygen-induced stress reveals context-specific gene regulatory effects in human brain organoids

The interaction between genetic variants and environmental stressors is key to understanding the mechanisms underlying neurological diseases. In this study, we used human brain organoids to explore how varying oxygen levels expose context-dependent gene regulatory effects. By subjecting a genetically diverse panel of 21 brain organoids to hypoxic and hyperoxic conditions, we identified thousands of gene regulatory changes that are undetectable under baseline conditions, with 1,745 trait-associated genes showing regulatory effects only in response to oxygen stress. To capture more nuanced transcriptional patterns, we employed topic modeling, which revealed context-specific gene regulation linked to dynamic cellular processes and environmental responses, offering a deeper understanding of how gene regulation is modulated in the brain. These findings underscore the importance of genotype-environment interactions in genetic studies of neurological disorders and provide new insights into the hidden regulatory mechanisms influenced by environmental factors in the brain.

Effects of pesticide exposure on the expression of selected genes in normal and cancer samples: Identification of predictive biomarkers for risk assessment

Pesticides pivotal in controlling pests, can represent a threat for human health. Regulatory agencies constantly monitor their harmful effects, regulating their use. Several studies support a positive association between long-term exposure to pesticides and chronic pathologies, such as cancer. Geno-toxicological biomonitoring has proven to be valuable to assess genetic risks associated with exposure to pesticides, representing a promising tool to improve preventive measures and identify workers at higher risk. In this study, a differential gene expression analysis of 70 candidate genes deregulated upon pesticide exposure, was performed in 10 GEO human gene expression DataSets. It was found that six genes (PMAIP1, GCLM, CD36, SQSTM1, ABCC3, NR4A2) had significant AUC predictive values. Also, CD36 was upregulated in non-transformed cell samples and healthy workers, but downregulated in cancer cells. Further validation in larger groups of workers will corroborate the importance of the identified candidates as biomarkers of exposure/effect.

Emerging Functional Connections Between Metabolism and Epigenetic Remodeling in Neural Differentiation

Stem cells possess extraordinary capacities for self-renewal and differentiation, making them highly valuable in regenerative medicine. Among these, neural stem cells (NSCs) play a fundamental role in neural development and repair processes. NSC characteristics and fate are intricately regulated by the microenvironment and intracellular signaling. Interestingly, metabolism plays a pivotal role in orchestrating the epigenome dynamics during neural differentiation, facilitating the transition from undifferentiated NSC to specialized neuronal and glial cell types. This intricate interplay between metabolism and the epigenome is essential for precisely regulating gene expression patterns and ensuring proper neural development. This review highlights the mechanisms behind metabolic regulation of NSC fate and their connections with epigenetic regulation to shape transcriptional programs of stemness and neural differentiation. A comprehensive understanding of these molecular gears appears fundamental for translational applications in regenerative medicine and personalized therapies for neurological conditions.

Genomic analysis of intracranial and subcortical brain volumes yields polygenic scores accounting for variation across ancestries

Subcortical brain structures are involved in developmental, psychiatric and neurological disorders. We performed GWAS meta-analyses of intracranial and nine subcortical brain volumes (brainstem, caudate nucleus, putamen, hippocampus, globus pallidus, thalamus, nucleus accumbens, amygdala and, for the first time, the ventral diencephalon) in 74,898 participants of European ancestry. We identified 254 independent loci associated with these brain volumes, explaining up to 35% of phenotypic variance. We observed gene expression in specific neural cell types across differentiation time points, including genes involved in intracellular signalling and brain ageing-related processes. Polygenic scores for brain volumes showed predictive ability when applied to individuals of diverse ancestries. We observed causal genetic effects of brain volumes with Parkinson's disease and ADHD. Findings implicate specific gene expression patterns in brain development and genetic variants in comorbid neuropsychiatric disorders, which could point to a brain substrate and region of action for risk genes implicated in brain diseases.

Macular structural integrity estimates are associated with Parkinson's disease genetic risk

BACKGROUND

Optical coherence tomography (OCT) is a non-invasive technique to measure retinal layer thickness, providing insights into retinal ganglion cell integrity. Studies have shown reduced retinal nerve fibre layer (RNFL) and ganglion cell inner plexiform layer (GCIPL) thickness in Parkinson's disease (PD) patients. However, it is unclear if there is a common genetic overlap between the macula and peripapillary estimates with PD and if the genetic risk of PD is associated with changes in ganglion cell integrity estimates in young adults.

METHOD

Western Australian young adults underwent OCT imaging. Their pRNFL, GCIPL, and overall retinal thicknesses were recorded, as well as their longitudinal changes between ages 20 and 28. Polygenic risk scores (PRS) were estimated for each participant based on genome-wide summary data from the largest PD genome-wide association study conducted to date. We further evaluated whether PD PRS was associated with changes in thickness at a younger age. To evaluate the overlap between retinal integrity estimates and PD, we annotated and prioritised genes using mBAT-combo and performed colocalisation through the GWAS pairwise method and HyPrColoc. We used a multi-omic approach and single-cell expression data of the retina and brain through a Mendelian randomisation framework to evaluate the most likely causal genes. Genes prioritised were analysed for missense variants that could have a pathogenic effect using AlphaMissense.

RESULTS

We found a significant association between the Parkinson's disease polygenic risk score (PD PRS) and changes in retinal thickness in the macula of young adults assessed at 20 and 28 years of age. Gene-based analysis identified 27 genes common to PD and retinal integrity, with a notable region on chromosome 17. Expression analyses highlighted NSF, CRHR1, and KANSL1 as potential causal genes shared between PD and ganglion cell integrity measures. CRHR1 showed consistent results across multiple omics levels.

INTERPRETATION

Our findings suggest that retinal measurements, particularly in young adults, could be a potential marker for PD risk, indicating a genetic overlap between retinal structural integrity and PD. The study highlights specific genes and loci, mainly on chromosome 17, as potential shared etiological factors for PD and retinal changes. Our results highlight the importance of further longitudinal studies to validate retinal structural metrics as early indicators of PD predisposition.

Human Genetics and Genomics for Drug Target Identification and Prioritization: Open Targets' Perspective

Open Targets, a consortium among academic and industry partners, focuses on using human genetics and genomics to provide insights to key questions that build therapeutic hypotheses. Large-scale experiments generate foundational data, and open-source informatic platforms systematically integrate evidence for target-disease relationships and provide dynamic tooling for target prioritization. A locus-to-gene machine learning model uses evidence from genome-wide association studies (GWAS Catalog, UK BioBank, and FinnGen), functional genomic studies, epigenetic studies, and variant effect prediction to predict potential drug targets for complex diseases. These predictions are combined with genetic evidence from gene burden analyses, rare disease genetics, somatic mutations, perturbation assays, pathway analyses, scientific literature, differential expression, and mouse models to systematically build target-disease associations (https://platform.opentargets.org). Scored target attributes such as clinical precedence, tractability, and safety guide target prioritization. Here we provide our perspective on the value and impact of human genetics and genomics for generating therapeutic hypotheses.

Population Diversity at the Single-Cell Level

Population-scale single-cell genomics is a transformative approach for unraveling the intricate links between genetic and cellular variation. This approach is facilitated by cutting-edge experimental methodologies, including the development of high-throughput single-cell multiomics and advances in multiplexed environmental and genetic perturbations. Examining the effects of natural or synthetic genetic variants across cellular contexts provides insights into the mutual influence of genetics and the environment in shaping cellular heterogeneity. The development of computational methodologies further enables detailed quantitative analysis of molecular variation, offering an opportunity to examine the respective roles of stochastic, intercellular, and interindividual variation. Future opportunities lie in leveraging long-read sequencing, refining disease-relevant cellular models, and embracing predictive and generative machine learning models. These advancements hold the potential for a deeper understanding of the genetic architecture of human molecular traits, which in turn has important implications for understanding the genetic causes of human disease.

From genetic associations to genes: methods, applications, and challenges

Genome-wide association studies (GWASs) have identified numerous genetic loci associated with human traits and diseases. However, pinpointing the causal genes remains a challenge, which impedes the translation of GWAS findings into biological insights and medical applications. In this review, we provide an in-depth overview of the methods and technologies used for prioritizing genes from GWAS loci, including gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, linking GWAS variants to target genes through enhancer-gene connection maps, and network-based prioritization. We also outline strategies for generating context-dependent xQTL data and their applications in gene prioritization. We further highlight the potential of gene prioritization in drug repurposing. Lastly, we discuss future challenges and opportunities in this field.

scBoolSeq: Linking scRNA-seq statistics and Boolean dynamics

Boolean networks are largely employed to model the qualitative dynamics of cell fate processes by describing the change of binary activation states of genes and transcription factors with time. Being able to bridge such qualitative states with quantitative measurements of gene expression in cells, as scRNA-seq, is a cornerstone for data-driven model construction and validation. On one hand, scRNA-seq binarisation is a key step for inferring and validating Boolean models. On the other hand, the generation of synthetic scRNA-seq data from baseline Boolean models provides an important asset to benchmark inference methods. However, linking characteristics of scRNA-seq datasets, including dropout events, with Boolean states is a challenging task. We present scBoolSeq, a method for the bidirectional linking of scRNA-seq data and Boolean activation state of genes. Given a reference scRNA-seq dataset, scBoolSeq computes statistical criteria to classify the empirical gene pseudocount distributions as either unimodal, bimodal, or zero-inflated, and fit a probabilistic model of dropouts, with gene-dependent parameters. From these learnt distributions, scBoolSeq can perform both binarisation of scRNA-seq datasets, and generate synthetic scRNA-seq datasets from Boolean traces, as issued from Boolean networks, using biased sampling and dropout simulation. We present a case study demonstrating the application of scBoolSeq's binarisation scheme in data-driven model inference. Furthermore, we compare synthetic scRNA-seq data generated by scBoolSeq with BoolODE's, data for the same Boolean Network model. The comparison shows that our method better reproduces the statistics of real scRNA-seq datasets, such as the mean-variance and mean-dropout relationships while exhibiting clearly defined trajectories in two-dimensional projections of the data.

Brain Chimeroids reveal individual susceptibility to neurotoxic triggers

Interindividual genetic variation affects the susceptibility to and progression of many diseases1,2. However, efforts to study how individual human brains differ in normal development and disease phenotypes are limited by the paucity of faithful cellular human models, and the difficulty of scaling current systems to represent multiple people. Here we present human brain Chimeroids, a highly reproducible, multidonor human brain cortical organoid model generated by the co-development of cells from a panel of individual donors in a single organoid. By reaggregating cells from multiple single-donor organoids at the neural stem cell or neural progenitor cell stage, we generate Chimeroids in which each donor produces all cell lineages of the cerebral cortex, even when using pluripotent stem cell lines with notable growth biases. We used Chimeroids to investigate interindividual variation in the susceptibility to neurotoxic triggers that exhibit high clinical phenotypic variability: ethanol and the antiepileptic drug valproic acid. Individual donors varied in both the penetrance of the effect on target cell types, and the molecular phenotype within each affected cell type. Our results suggest that human genetic background may be an important mediator of neurotoxin susceptibility and introduce Chimeroids as a scalable system for high-throughput investigation of interindividual variation in processes of brain development and disease.

A robust model for cell type-specific interindividual variation in single-cell RNA sequencing data

Single-cell RNA sequencing (scRNA-seq) has been widely used to characterize cell types based on their average gene expression profiles. However, most studies do not consider cell type-specific variation across donors. Modelling this cell type-specific inter-individual variation could help elucidate cell type-specific biology and inform genes and cell types underlying complex traits. We therefore develop a new model to detect and quantify cell type-specific variation across individuals called CTMM (Cell Type-specific linear Mixed Model). We use extensive simulations to show that CTMM is powerful and unbiased in realistic settings. We also derive calibrated tests for cell type-specific interindividual variation, which is challenging given the modest sample sizes in scRNA-seq. We apply CTMM to scRNA-seq data from human induced pluripotent stem cells to characterize the transcriptomic variation across donors as cells differentiate into endoderm. We find that almost 100% of transcriptome-wide variability between donors is differentiation stage-specific. CTMM also identifies individual genes with statistically significant stage-specific variability across samples, including 85 genes that do not have significant stage-specific mean expression. Finally, we extend CTMM to partition interindividual covariance between stages, which recapitulates the overall differentiation trajectory. Overall, CTMM is a powerful tool to illuminate cell type-specific biology in scRNA-seq.