Biology subjects

Cai, N.

Publications and source records attributed to Cai, N..

At least 19 recordsLinked to original sources

bioRxiv2026

Joint localization and testing reveals spatial context of differential expression

Multi-subject spatial transcriptomics is increasingly used to identify molecular differences across individuals and conditions while resolving where in tissue those differences occur. Yet subject-level differential expression (DE) analyses commonly require spatial regions to be specified in advance, separating localization from testing. Here we present SpaceDX, a framework that uses gene-specific attention-based spatial weighting to jointly test differential expression and localize the spatial expression patterns contributing to these associations. In simulations, SpaceDX outperforms cluster-based DE methods when differential expression does not align with predefined spatial boundaries. Across two multi-subject mouse brain datasets, SpaceDX detected 45.8% more DE genes than regional DESeq2 pseudobulk in a chronic stress model, while in an Alzheimer's disease model combining SpaceDX with DESeq2 increased discovery by 19.1% relative to DESeq2 alone. Across both datasets, the learned maps provided spatial context for condition-associated expression signals in addition to gene-level significance. In the chronic stress model, for example, oligodendrocyte- and myelin-associated genes showed shared localization to corresponding brain structures. Together, these results show that the spatial context of differential expression can be inferred jointly with gene-level associations, recovering signals obscured by regional aggregation without requiring the relevant tissue context to be defined in advance.

bioinformatics↗

bioRxiv2026

A generalizable codesigned platform for solid-state nanopore sensing beyond the capacitive-noise constraints

Solid-state nanopores offer label-free, real-time single-molecule sensing, yet resolving rapidly translocating biomolecules requires high-bandwidth data acquisition, where increased high-frequency noise fundamentally limits reliable recovery of informative events. Here we establish a hardware-software co-designed nanopore sensing platform that overcomes this bandwidth-noise limitation by integrating scalable low-noise device engineering with deep learning-based signal reconstruction. A wafer-scale dielectric-engineering strategy using low-dielectric SU8 photoresist reduces total device capacitance to the picofarad regime and suppresses high-frequency noise by up to fivefold while maintaining facile, controllable and reproducible fabrication. This extends usable acquisition rate to 40 MHz and enables capture of fast molecular features. Combined with a reconstruction neural network trained on synthetic translocation signals embedded in experimentally measured noise, the platform recovers transient blockade sublevels while preserving temporal fidelity. Using engineered DNA molecules carrying dumbbell-like barcodes, we resolve nanometer-scale structural features on sub-microsecond timescales, and experimentally measure translocation velocity within the sub-5 nm regime. Dual-channel measurement on a single nanopore device further demonstrates transferability of the platform by showing robust cross-channel signal reconstruction across distinct baseline noise levels. This platform provides a general route for reliable recovery of previously inaccessible molecular information from high-bandwidth nanopore measurements.

biophysics↗

bioRxiv2025

Chemerin-CMKLR1 mediated OGD/R induced Mitochondrial Dysfunction, Oxidative Stress, and Autophagy differentially in Microglia and Neurons

IntroductionIschemia-reperfusion (I/R) injury exacerbates tissue damage upon reperfusion after ischemia. The impact of chemerin and its receptor, chemokine-like receptor 1 (CMKLR1) on I/R injury remains poorly understood. We hypothesized that chemerin-CMKLR1 differentially regulates signaling in microglia and neuronal cells oxygen-glucose deprivation/reoxygenation (OGD/R), influencing mitochondrial function, oxidative stress, and autophagy. MethodsUsing BV2 microglia and Neuro-2a (N2a) neuronal cells, we examined OGD/R-induced changes in autophagy associated proteins, chemerin and CMKLR1 expression. We investigated the functional consequences of CMKLR1 overexpression and chemerin treatment on oxidative stress, apoptosis, autophagy, and mitochondrial dynamics in BV2 microglia and N2a neuronal cells. ResultsOGD/R downregulated CMKLR1 while upregulating autophagy in both BV2 microglia and N2a cells; While chemerin expression decreased in BV2 microglia but increased in N2a cells following OGD/R. Treatment with chemerin dose-dependently reduced oxidative stress and apoptosis while enhancing mitochondrial fusion, suppressing fission, and promoting autophagy and mitochondrial function in both cell types under OGD/R. CMKLR1 overexpression exacerbated mitochondrial respiratory dysfunction, mitochondrial fusion, fission, and elevated autophagy (LC3II/LC3I and Pink1 levels), with cell-type-specific differences observed in Parkin and P62 regulation. ConclusionOur study demonstrates cell-type-specific regulation of chemerin-CMKLR1 signaling in I/R injury, and distinct mitophagy activation mechanisms in microglia and neurons. These findings suggest cell-type specific modulation of chemerin-CMKLR1 as a potential therapeutic target in preserving mitochondrial homeostasis, modulating autophagy, mitophagy and reducing oxidative stress, apoptosis in both microglia and neurons for mitigating I/R injury.

neuroscience↗

bioRxiv2025

Phage-Nanoparticle Cocktails as a Novel Antibacterial Approach: Synergistic Effects of Bacteriophages and Green-Synthesized Silver Nanoparticles

Bacteriophages have emerged as promising natural antibacterial agents, offering a targeted approach to combating bacterial infections. While phage-antibiotic cocktails are widely explored to enhance antibacterial efficacy and prevent resistance, research on phage-nanoparticle combinations remains limited. However, antibiotic resistance continues to rise, necessitating alternative strategies. Combining bacteriophages with nanoparticles presents a novel approach that could enhance antibacterial potency while reducing the risk of resistance, yet studies in this area are still scarce. We explore the synergy between green tea extract-capped silver nanoparticles (G-TeaNPs) and bacteriophages in combating pathogenic bacteria (Staphylococcus aureus, Salmonella enterica). G-TeaNPs show no antiphage activity, ensuring compatibility in phage-NP formulations. These combinations significantly reduce bacterial counts in a short time (only 3 hours), e.g., S. aureus survival is around 30% after incubations with just 0.001 mg/mL of G-TeaNPs, with G-TeaNPs and phages alone result in around 80% and 70% survival, respectively. Cytotoxicity tests against eukaryotic 3T3 NIH fibroblast cells confirmed biocompatibility at effective concentrations. Additionally, we examine G-TeaNPs impact on the free-living protist Acanthamoeba castellanii. Both green tea extract and G-TeaNPs can reduce A. castellanii cell counts by 80%, but only at concentrations larger than 10 mg/mL. Microscopy revealed nanoparticle uptake by amoebae, causing intracellular accumulation and vacuolization, while green tea extract induced similar changes without uptake. Our findings highlight G-TeaNPs as safe, effective agents in phage-nanoparticle antibacterial formulations with dual antimicrobial and amoebicidal properties for therapeutic and environmental applications.

microbiology↗

bioRxiv2025

Bayesian Aggregation of Multiple Annotations Enhances Rare Variant Association Testing

Gene-level rare variant association tests (RVATs) are essential for uncovering disease mechanisms and identifying potential drug targets. Advances in sequence-based machine learning have expanded the availability of variant pathogenicity scores, offering new opportunities to improve variant prioritization for RVATs. However, existing methods often rely on rigid models or analyze single annotations in isolation, limiting their ability to leverage these advances. To address this, we introduce BayesRVAT, a Bayesian framework for RVATs that models variant effects using multiple annotations. By specifying priors on variant effects and estimating gene-trait-specific burden scores through variational inference, BayesRVAT flexibly accommodates diverse genetic architectures. In simulations, BayesRVAT improved power while maintaining statistical calibration. When applied to real data from the UK Biobank, BayesRVAT detected 10.2% more associations with blood traits than the next-best method and uncovered novel gene-disease associations in analyses of eight disease traits, including a link between PRPH2 and retinal disease. These results highlight BayesRVATs potential to enhance rare variant discovery and improve gene-trait association studies.

genetics↗

bioRxiv2024

The role of inflammation in a humanized mouse model of transthyretin cardiac amyloidosis

BackgroundSystemic amyloidosis represents a group of protein-misfolding diseases that confer significant morbidity and mortality for millions of patients worldwide. Transthyretin cardiac amyloidosis (ATTR) is a particularly devastating amyloid disease that affects middle-aged and elderly individuals and leads to cardiomyopathy (ATTR-CM), which has a median survival of 2.5 to 3.5 years [1, 2]. ATTR-CM can be hereditary, leading to a more aggressive disease course in younger patients. The most prevalent TTR variant in the United States is V122I, which is found in 3-4 % of African Americans [3]. Despite the significant healthcare burden, ATTR-CM remains underdiagnosed due to a lack of disease awareness and limited diagnostic techniques [4]. Informative in vivo models have proven elusive during the past decade [5]. Moreover, there is no available treatment to reverse cardiac dysfunction due to amyloid fibril deposition [1, 6, 7]. Therefore, a better understanding of the molecular mechanisms of ATTR-CM is imperative to developing novel, effective therapies. Method and ResultsTo explore the pathogenesis of ATTR, we created a murine TTR knockout (TTR-KO) model expressing the human V122I TTR variant. To study the gender differences, both male and female TTR-KO mice were utilized in this study. Significant elevations of human TTR were observed in both male and female ATTR murine plasma post-injection 3 months (human TTR level (ng/ml) Male ATTR: 109.9 {+/-} 5.568; Male control: 28.17 {+/-} 7.010; p=0.0008, N=3 mice/group; Female ATTR: 127.5 {+/-} 32.43; Female control: 20.08 {+/-} 8.351; p=0.0327, N=3 mice/group) with preserved cardiac function (FS% Male ATTR: 26.07 {+/-} 3.667; Male control: 22.69 {+/-} 1.585; p=0.3712, N=6-8 mice/group; Female ATTR: 26.62 {+/-} 1.980; Female control: 31.25 {+/-} 4.482;p=0.3397, N=5-6 mice/group). Notably, the mouse model exhibited cardiac amyloid deposits confirmed by amyloidotic-specific Congo Red staining and Thioflavin T Staining. Transmission electron microscopy revealed both immature and mature amyloid fibrils in the extracellular matrix. RNA-sequencing of the ATTR mouse heart identified distinct transcriptomic patterns and conserved inflammation pathways similar to those seen in a cohort of human ATTR heart samples, including leukocyte transendothelial migration, T-cell receptor signaling, and apoptosis, along with upregulation of inflammatory markers CXCL-1/2/3 and CCL20, were observed in ATTR murine hearts. At the posttranslational level, we confirmed an increased level of CCL5 (MFI ATTR: 801 {+/-} 105; Control: 426{+/-} 64; p=0.0061, N=3 mice/group) in murine plasma post-injection 3 months by a luminance-based immunoassay. The CXCL- and CCL-chemokines family are critical for directing leukocytes to inflammation sites. ConclusionIn this study, we developed a humanized V122I ATTR mouse model with elevated circulating human TTR level and Congophilic amyloid deposits in the murine heart and kidneys. Our transcriptomic study suggested that inflammation may contribute to the ATTR-CM pathogenesis. Further studies are needed to decipher the precise interactions between inflammation and ATTR-CM. Highlights/Whats new/Clinical relevanceO_LIWe developed a humanized mouse model to replicate the multisystem complexity and clinical diversity associated with V122I ATTR-CM. C_LIO_LIOur study unveiled the pathogenic molecular mechanisms of amyloid deposition in ATTR-CM via a novel mouse model. C_LIO_LIWe identified signature inflammatory pathways that uncover potential therapeutic targets for ATTR-CM. C_LIO_LIOur ATTR mouse model allows for preclinical pharmacogenomic assessments of novel therapeutics, which will undoubtedly improve outcomes for ATTR-CM patients. C_LI

pathology↗

bioRxiv2024

Tissue-specific apparent mtDNA heteroplasmy and its relationship with ageing and mtDNA gene expression

Heteroplasmy in the mitochondrial DNA (mtDNA) accumulates with age, but how much it occurs in tissues and how it affects function and physiology is not well-studied. Here we present a comprehensive tissue-specific map of mtDNA heteroplasmy and mtRNA modifications, and their relationships with age and mitochondrial gene expression. We propose a robust variant calling pipeline for mtDNA heteroplasmy and mtRNA modifications using bulk RNAseq data from 49 tissues in GTEx, and a calibrated phenotype association framework for both types of variants. We identify 109 associations between them and donor age, and 784 associations with tissue-specific mtDNA gene expression. Of these, 7 and 18 show cell-type specificity within tissue. In addition, we find 9 instances where these variations mediate donor age effects on mtDNA gene expression. Finally, we confirm previously identified relationships between mt-tRNA modifications and gene expression on their 5, but read- and gene-level investigations reveal previously unmodeled complexities.

genomics↗

bioRxiv2024

Interplay between mitochondrial and nuclear DNA in gene expression regulation

Using data from 48 tissues and 684 European individuals in the GTEx dataset, we investigate the role of mitochondrial DNA (mtDNA) and its encoded genes in gene regulation. We perform a comprehensive multi-tissue eQTL analysis on mtDNA encoded protein-coding genes, identifying 111 mtDNA-cis-eQTLs (FDR<5%) and 260 nucDNA trans-eQTLs (P<5x10-8). We further identify 108 mtDNA associations with 68 nucDNA encoded genes (FDR<5%). Our results are not driven by nuclear mitochondrial sequences (NUMTs) or minority cell types within tissues. Incorporating mtDNA trans-eQTLs in gene expression networks improves prediction of nucDNA genes with mitochondrial function. nucDNA trans-eQTLs for mtDNA genes are enriched in genes involved in mitochondrial pathways and GWAS hits for complex traits and diseases, implicating dual regulation by both genomes in maintaining mitochondrial function and organismal health. Our multi-tissue map of dual-genome gene regulation is an essential step towards understanding mito-nuclear cross-talk in health and disease.

genomics↗

bioRxiv2024

Cryo-EM structural insights into Vγ9Vδ2 TCR activation via multiple butyrophilins

Human V{gamma}9V{delta}2 T cells detect cancerous and pathogen-infected cells through an "inside-out" mechanism wherein intracellular domains of butyrophilin (BTN) proteins recognize phosphoantigens (pAgs) to prime BTN ectodomains to engage with the T cell receptor (TCR) to activate V{gamma}9V{delta}2 T cells. Here, we present cryo-electron microscopy (cryo-EM) structures, including the full-length BTN3A1-BTN3A2-BTN2A1 complex stabilized by the pAg HMBPP and an HMBPP-primed BTN multimer complex engaged with the V{gamma}9V{delta}2 TCR. The HMBPP-primed BTN3A1-BTN3A2-BTN2A1 complex confirms that this pAg bridges the intracellular B30.2 domains of BTN3A1 and BTN2A1, while also revealing an association of the ectodomains of BTN3A2 and BTN2A1. Upon TCR engagement, the TCR undergoes a substantial conformational change and BTN3A2-BTN2A1 ectodomain interaction dissociates, allowing BTN2A1 to bind to the lateral surface of the V{gamma}9 chain, while BTN3A2 binds to the apical surface of the V{delta}2 chain. Beyond demonstrating that BTN3A2 as a bona fide TCR ligand, this dissociation/binding sequence suggests a "pliers-like gripping" mechanism for TCR activation. We also obtained cryo-EM structures of BTN proteins in complex with functional antibodies, further elucidating the overall V{gamma}9V{delta}2 T cell activation process. Thus, our structural insights into how BTN proteins sense antigens and drive TCR activation lay the groundwork for developing targeted {gamma}{delta} T cell immunotherapies.

immunology↗

bioRxiv2024

Immune Cell Influence on Diffuse Large B-Cell Lymphoma: A Mendelian Randomization Analysis

ObjectiveTo elucidate the causal relationship between immune cells and diffuse large B-cell lymphoma (DLBCL), we conducted a Mendelian randomization analysis. MethodsMendelian randomization (MR) leverages genetic variants as instruments to infer causal effects from observational data. Here, we performed a two-sample MR analysis to assess the causal impact of 731 immune cell types on DLBCL. We employed various MR techniques, including the weighted median estimator (WME) and inverse variance weighting (IVW), and conducted sensitivity analyses to ensure result robustness. Additionally, reverse MR analysis was performed to explore the potential causal relationship between DLBCL and immune cells. ResultsWe identified seventeen immune features with causal links to DLBCL, categorized across various cellular groups: four in B cells, two in T cell maturation stages, six in Tregs, four in the TBNK group, and one in dendritic cells (DCs). Sensitivity analyses confirmed the absence of heterogeneity, horizontal pleiotropy, and bias in our findings. Reverse causal analysis revealed a causal association between DLBCL and one of the seventeen immune cell types identified. ConclusionsOur MR analysis of seventeen immune cell types uncovers the complex interactions between the immune system and DLBCL, providing crucial insights into the tumor microenvironment and potential avenues for targeted immunotherapy.

immunology↗

bioRxiv2024

Mendelian randomization for multiple exposures and outcomes with Bayesian Directed Acyclic Graphs exploration and causal effects estimation

Current Mendelian randomization (MR) methods do not reflect complex relationships among multiple exposures and outcomes as is typical for real-life applications. We introduce MrDAG the first MR method to model dependency relations within the exposures, the outcomes, and between them to improve causal effects estimation. MrDAG combines three causal inference strategies in a unified manner. It uses genetic variation as instrumental variables to account for unmeasured confounders. It performs structure learning to detect and orientate the direction of the dependencies within exposures and outcomes. Finally, interventional calculus is employed to derive principled causal effect estimates. MrDAG was motivated to unravel how lifestyle and behavioural exposures impact mental health. It highlights education and smoking as key effective points of intervention given their down-stream effects on mental health. These insights would have been difficult to delineate without modelling the causal paths between multiple exposures and outcomes at once.

systems biology↗

bioRxiv2024

An Open-Source Membrane Stretcher for Simultaneous Mechanical and Structural Characterizations of Soft Materials and Biological Tissues

The ability to simultaneously measure material mechanics and structure is central for understanding their nonlinear relationship that underlies the mechanical properties of materials, such as hysteresis, strain-stiffening and -softening, and plasticity. This experimental capability is also critical in biomechanics and mechanobiology research, as it enables direct characterizations of the intricate interplay between cellular responses and tissue mechanics. Stretching devices developed over the past few decades, however, do not often allow simultaneous measurements of the structural and mechanical responses of the sample. In this work, we introduce an open-source stretching system that can apply uniaxial strain at a submicron resolution, report the tensile force response of the sample, and be mounted on an inverted microscope for real-time imaging. Our system consists of a pair of stepper-based linear motors that stretch the sample symmetrically, a force transducer that records the sample tensile force, and an optically clear sample holder that allows for high-magnification microscopy. Using polymer samples and cellular specimens, we characterized the motion control accuracy, force measurement robustness, and microscopy compatibility of our stretching system. We envision that this uniaxial stretching system will be a valuable tool for characterizing soft and living materials.

bioengineering↗

bioRxiv2024

Molecular and neural mechanisms of behavioural integration in the extended-amygdala

Integration of diverse stimuli is crucial for organisms to adapt and communicate effectively, enabling overall homeostasis and survival. Studies have been performed on identifying specific neuronal encoding of individual behaviours, but how neurons integrate diverse behaviours across contexts remains elusive. Here we use Ca2+ imaging in freely moving mice to identify neural ensembles in the extended amygdala encoding behaviours across six distinct contexts. We found extensive flexibility in these ensemble encodings that may act as reserves for behavioural integration, with those encoding aversive stimuli showing greater specificity. Finally, we identified differential gene expression profiles between ensembles that are enriched in associations with human psychiatric and neurodegenerative disorders. Overall, our results demonstrate the molecular mechanisms behind behavioural integration, and their potential implications in health and disease.

neuroscience↗

bioRxiv2023

Missing view completion for multi-timepoints omics data via representation disentanglement and temporal knowledge transfer

Longitudinal multi-view omics data offer unique insights into the temporal dynamics of individual-level physiology, which provides opportunities to advance personalized healthcare. However, the common occurrence of incomplete views makes extrapolation tasks difficult, and there is a lack of tailored methods for this critical issue. Here, we introduce LEOPARD, an innovative approach specifically designed to complete missing views in multi-timepoint omics data. By disentangling longitudinal omics data into content and temporal representations, LEOPARD transfers the temporal knowledge to the omics-specific content, thereby completing missing views. The effectiveness of LEOPARD is validated on three benchmark datasets constructed with data from the MGH COVID study and the KORA cohort, spanning periods from 3 days to 14 years. Compared to conventional imputation methods, such as missForest, PMM, GLMM, and cGAN, LEOPARD yields the most robust results across the benchmark datasets. LEOPARD-imputed data also achieve the highest agreement with observed data in our analyses for age-associated metabolites detection, estimated glomerular filtration rate-associated proteins identification, and chronic kidney disease prediction. Our work takes the first step toward a generalized treatment of missing views in longitudinal omics data, enabling comprehensive exploration of temporal dynamics and providing valuable insights into personalized healthcare.

bioinformatics↗

bioRxiv2023

PSTPIP2 ameliorates aristolochic acid nephropathy by suppressing interleukin-19-mediated neutrophil extracellular trap formation

Aristolochic acid nephropathy (AAN) is a progressive kidney disease caused by herbal medicines. Previously, we found that proline-serine-threonine phosphatase interacting protein 2 (PSTPIP2) and neutrophil extracellular traps (NETs) play important roles in kidney injury and immune defense, respectively; however, the mechanism of AAN regulation by PSTPIP2 and NETs remains unclear. We found that renal tubular epithelial cell (RTEC) apoptosis, neutrophil infiltration, and inflammatory factor and NET production were increased in a mouse model of AAN, while PSTPIP2 expression was low. Conditional knock-in of PSTPIP2 in mouse kidneys inhibited cell apoptosis, reduced neutrophil infiltration, suppressed the production of inflammatory factors and NETs, and ameliorated renal dysfunction. In contrast, restoring normal PSTPIP2 expression promoted kidney injury. In vivo, the use of Ly6G-neutralizing antibody to remove neutrophils and peptidyl arginine deiminase 4 (PAD4) inhibitors to prevent NET formation reduced apoptosis, thereby alleviating kidney injury. In vitro, damaged RTECs released interleukin-19 (IL-19) via the PSTPIP2/nuclear factor (NF)-{kappa}B pathway and induced NET formation via the IL-20R{beta} receptor. Concurrently, NETs promoted the apoptosis of damaged RTECs. PSTPIP2 affected NET formation by regulating IL-19 expression via inhibition of NF-{kappa}B pathway activation in RTECs, inhibiting their apoptosis and reducing kidney damage.

pharmacology and toxicology↗

bioRxiv2022

Phenotype integration improves power and preserves specificity in biobank-based genetic studies of MDD

Biobanks often contain several phenotypes relevant to a given disorder, and researchers face complex tradeoffs between shallow phenotypes (high sample size, low specificity and sensitivity) and deep phenotypes (low sample size, high specificity and sensitivity). Here, we study an extreme case: Major Depressive Disorder (MDD) in UK Biobank. Previous studies found that shallow and deep MDD phenotypes have qualitatively distinct genetic architectures, but it remains unclear which are optimal for scientific study or clinical prediction. We propose a new framework to get the best of both worlds by integrating together information across hundreds of MDD-relevant phenotypes. First, we use phenotype imputation to increase sample size for the deepest available MDD phenotype, which dramatically improves GWAS power (increases #loci ~10 fold) and PRS accuracy (increases R2 ~2 fold). Further, we show the genetic architecture of the imputed phenotype remains specific to MDD using genetic correlation, PRS prediction in external clinical cohorts, and a novel PRS-based pleiotropy metric. We also develop a complementary approach to improve specificity of GWAS on shallow MDD phenotypes by adjusting for phenome-wide PCs. Finally, we study phenotype integration at the level of GWAS summary statistics, which can increase GWAS and PRS power but introduces non-MDD-specific signals. Our work provides a simple and scalable recipe to improve genetic studies in large biobanks by combining the sample size of shallow phenotypes with the sensitivity and specificity of deep phenotypes.

genetics↗

bioRxiv2022

Deep Learning-based Phenotype Imputation on Population-scale Biobank Data Increases Genetic Discoveries

Biobanks that collect deep phenotypic and genomic data across large numbers of individuals have emerged as a key resource for human genetic research. However, phenotypes acquired as part of Biobanks are often missing across many individuals, limiting the utility of these datasets. The ability to accurately impute or "fill-in" missing phenotypes is critical to harness the power of population-scale Biobank datasets. We propose AutoComplete, a deep learning-based imputation method which can accurately impute missing phenotypes in population-scale Biobank datasets. When applied to collections of phenotypes measured across {approx} 300K individuals from the UK Biobank, AutoComplete improved imputation accuracy over existing 2 methods (average improvement in r2 of 18% for all phenotypes and 42% for binary phenotypes). We explored the utility of phenotype imputation for improving the power of genome-wide association studies (GWAS) by applying our method to a group of five clinically relevant traits with an average missigness rate of 83% (67% to 94%) leading to an an increase in effective sample size of {approx}2-fold on average (0.5 to 3.3-fold across the phenotypes). GWAS on the resulting imputed phenotypes led to an increase in the total number of loci significantly associated to the traits from four to 129. Our results demonstrate the utility of deep-learning based imputation to increase power for genetic discoveries in existing biobank data sets.

bioinformatics↗

bioRxiv2022

Multi-ancestry GWAS of major depression aids locus discovery, fine-mapping, gene prioritisation, and causal inference

Most genome-wide association studies (GWAS) of major depression (MD) have been conducted in samples of European ancestry. Here we report a multi-ancestry GWAS of MD, adding data from 21 studies with 88,316 MD cases and 902,757 controls to previously reported data from individuals of European ancestry. This includes samples of African (36% of effective sample size), East Asian (26%) and South Asian (6%) ancestry and Hispanic/Latinx participants (32%). The multi-ancestry GWAS identified 190 significantly associated loci, 53 of them novel. For previously reported loci from GWAS in European ancestry the power-adjusted transferability ratio was 0.6 in the Hispanic/Latinx group and 0.3 in each of the other groups. Fine-mapping benefited from additional sample diversity: the number of credible sets with [≤]5 variants increased from 3 to 12. A transcriptome-wide association study identified 354 significantly associated genes, 205 of them novel. Mendelian Randomisation showed a bidirectional relationship with BMI exclusively in samples of European ancestry. This first multi-ancestry GWAS of MD demonstrates the importance of large diverse samples for the identification of target genes and putative mechanisms.

genetics↗