Biology subjects

Wang, G.

Publications and source records attributed to Wang, G..

At least 91 records · Page 5Linked to original sources

bioRxiv2025

Spatial and Single-Cell Transcriptomics Decipher the Crosstalk Environment of DEFB1+ Cancer Cells and IFI30+ Macrophages in Intrahepatic Cholangiocarcinoma

Intrahepatic cholangiocarcinoma (ICC) is a rare but highly aggressive primary liver malignancy distinguished by a profoundly heterogeneous tumor microenvironment, which underlies its limited response to targeted and immune-based therapies. Deciphering the cellular composition and intercellular signaling within this complex ecosystem is critical to understanding ICC progression and to identifying actionable therapeutic targets. Here, we integrated single-cell RNA sequencing and spatial transcriptomics to construct a comprehensive cellular and transcriptional atlas of human ICC. Our analyses revealed a continuous trajectory of T-cell state transition from activation to exhaustion, with CD8+ proliferating T cells exhibiting two distinct exhaustion programs, namely terminal and progenitor-like exhaustion. Notably, DEFB1+ cholangiocytes and IFI30+ macrophages displayed a strong positive correlation across independent ICC cohorts and were found in close spatial proximity within the tumor microenvironment. Mechanistically, their interaction appears to be mediated by the TGFB1 - TGFBR1 signaling pathway, contributing to tumor progression and immunotherapy resistance. Together, these findings delineate the cellular architecture and spatial organization of the ICC microenvironment, uncovering a key cholangiocyte-macrophage axis that shapes immune dysfunction and offers a potential therapeutic entry point to enhance immunotherapy efficacy in ICC.

bioinformatics↗

bioRxiv2025

Metabolomic analysis of Chinese chestnut (Castanea mollissima Bl.) leaves in response to Oligonychus ununguis feeding stress

Piercing-sucking insects such as the chestnut red mite (CRM; Oligonychus ununguis) damage leaves by extracting mesophyll cell nutrients, thereby reducing photosynthesis and nut yield in Chinese chestnut (Castanea mollissima Blume). However, the metabolic basis of the chestnut response to mite stress remains elusive. To address this, leaf metabolomics was conducted on four varieties with different resistance levels. Resistance grades were determined by the leaf chlorosis index, and three comparison groups were established. Secondary metabolome profiling revealed that resistant varieties showed greater metabolic changes under infestation than susceptible ones. In total, 713 total metabolites and 275 metabolites with significantly differential accumulation among germplasms were identified across the comparisons. KEGG enrichment highlighted 246 metabolites and 75 differential metabolites involved in flavonoid, flavonol, and phenylpropanoid biosynthesis. Notably, 27 phenylpropanoids and flavonoids accumulated significantly in resistant varieties but were largely absent or unchanged in susceptible ones. These findings suggest that specialized phenylpropanoids and flavonoids, along with their associated pathways, play key roles in CRM resistance, providing a foundation for breeding mite-resistant chestnut varieties.

plant biology↗

bioRxiv2025

Multi-Dimensional Spatiotemporal Attention Neural Network for Next Generation Sequencing Basecalling

Next-generation sequencing (NGS) remains the most used sequencing technique in the field of genomics. Traditional basecall methods face significant challenges in decoding high density sequencing data due to inherent noise in biochemical reactions and limitations of instruments. Here, we present a multi-dimensional deep learning neural network based on spatiotemporal attention mechanism named AICall. The network skips computationally heavy but less effective steps of peak finding and brightness extraction/correction, and directly basecalls from the time sequence of multi-dimensional image stacks obtained in real time. By introducing attention mechanism, it effectively extracts spatial and time-related key information including spatial crosstalk, spectral crosstalk, phasing, base-quenching, and intensity decay, and significantly improves basecall accuracy. We demonstrate that AICall achieves an average error rate less than 0.01% and provides more reliable sequencing results for downstream analysis.

bioinformatics↗

bioRxiv2025

HLA-DQ2/8 genotype imprints strain-level gut microbiome and constrains pantothenate availability in healthy adults

HLA-DQ2/8 haplotypes are established genetic risk factors for autoimmune diseases and are known to influence gut microbiota assembly in early life. However, their impact on the adult microbiome and functional consequences for host physiology remain unclear. Here, we performed a genotype-stratified multi-omics analysis of 60 healthy adults, including 28 HLA-DQ2/8 carriers and 32 non-carriers. We found that host HLA-DQ2/8 genotype was significantly associated with gut microbiome composition, with an effect size exceeding that of sex and BMI. HLA-DQ2/8 carriers exhibited higher gut microbial alpha-diversity, lower virulence factor abundance, and a distinct species profile enriched in butyrate-producing taxa. We identified pervasive intra-species phylogenetic and functional divergence linked to the DQ2/8 genotype. This diversification reflects predicted HLA-restricted microbial peptide-binding specificities, suggesting a possible role for antigen presentation-mediated immune selection, a mechanism further supported by AlphaFold3 structural modeling. We found an enrichment of microbial pathways for pantothenate and coenzyme A (CoA) biosynthesis in carriers, primarily driven by functionally divergent Blautia obeum strains. This functional shift paralleled lower levels of serum pantothenate and HDL-cholesterol in the host. Our findings suggest a potential genotype-microbiome-host axis where antigen presentation-mediated immune selection may modulate microbial adaptation, with possible implications for the host availability of essential cofactor precursors and lipid metabolism.

microbiology↗

bioRxiv2025

CTDP: Identifying cell types associated with disease phenotypes using scRNA-seq data

Single-cell RNA sequencing enables transcriptome-wide analysis at single-cell resolution, offering unprecedented insights into cellular heterogeneity across biological conditions. However, accurately comparing transcriptomic distributions of cells from distinct biological states, such as healthy versus diseased individuals, remains challenging. To address this, we developed CTDP, a robust and interpretable computational framework that identifies disease phenotype-associated cell types of interest by integrating Lasso-regularized logistic regression with permutation testing. Through comprehensive evaluations on both simulated and real-world datasets, including melanoma immunotherapy, COVID-19 severity, and liver cirrhosis, CTDP consistently outperformed existing methods such as DA-seq, scDist, and PENCIL in both accuracy and robustness. In melanoma, CTDP uncovered immune-responsive clusters and revealed transcriptional regulators like PTPRC, CREM, and JUNB linked to immunotherapy efficacy. In COVID-19, it identified critical severity-associated cell types, such as B cells, NK cells, epithelial cells, and macrophages, which contribute to dysregulated immune responses and inflammation in severe cases. These results highlight CTDPs power in uncovering disease-relevant cell populations and its potential to advance precision medicine through single-cell analysis.

bioinformatics↗

bioRxiv2025

CoxMDS: Multiple Data Splitting for High-dimensional Mediation Analysis with Survival Outcomes in Epigenome-wide Studies

Causal mediation analysis investigates whether the effect of an exposure on an outcome operates through intermediate variables known as mediators. Although progress has been made in high-dimensional mediation analysis, current methods do not reliably control the false discovery rate (FDR) in finite samples, especially when mediators are moderately to highly correlated or follow non-Gaussian distributions. These challenges frequently arise in DNA methylation studies. We introduce CoxMDS, a multiple data splitting method that uses Cox proportional hazards models to identify putative causal mediators for survival outcomes. CoxMDS ensures finite-sample FDR control even in the presence of correlated or non-Gaussian mediators. Through simulations, CoxMDS is shown to maintain FDR control and achieve higher statistical power compared with existing approaches. In applications to DNA methylation data with survival outcomes, CoxMDS identified eight CpG sites in The Cancer Genome Atlas (TCGA) that are consistent with the hypothesis that DNA methylation may mediate the effect of smoking on lung cancer survival, and two CpG sites in the Alzheimers Disease Neuroimaging Initiative (ADNI) that are consistent with the hypothesis that DNA methylation may mediate the effect of smoking on time to Alzheimers disease conversion.

bioinformatics↗

bioRxiv2025

HMGB1 assists in overcoming cisplatin resistance in chemoresistant human ovarian cancer cells

Cisplatin is one of the most effective chemotherapeutic agents used in the treatment of ovarian cancer. However, the frequent development of cisplatin resistance remains a significant limitation, leading to therapeutic failure and poor patient outcomes. Cisplatin cytotoxicity is attributed to the generation of toxic DNA lesions, which can be recognized and processed by a variety of proteins, including the high mobility group box 1 (HMGB1) protein. HMGB1 is a multifunctional protein, which is involved in chromatin remodeling and multiple DNA damage repair pathways. In this study, we investigated the role of HMGB1 in modulating cisplatin sensitivity in human ovarian cancer cells. Using cisplatin-sensitive and cisplatin-resistant human ovarian cancer cell lines, we employed siRNA-mediated HMGB1 knockdown to assess its impact on the cellular responses to cisplatin treatment. In clonogenic survival assays, HMGB1 depletion resulted in a significant reduction in colony formation in cisplatin-resistant cells upon cisplatin exposure, compared with non-targeting siRNA treated cells. Additionally, HMGB1 inhibition significantly enhanced cisplatin-induced apoptosis in the cisplatin-resistant cells. Mechanistically, HMGB1-depleted cells exhibited altered DNA damage responses via modulation of ATM/CHK2 and ATR/CHK1 activity following cisplatin treatment. Notably, DNA immunoblot and modified alkaline comet assay results demonstrated that HMGB1 depletion stimulated cisplatin-DNA adduct formation and impaired the removal of cisplatin-DNA adducts, particularly in the cisplatin-resistant cells. Collectively, these findings uncover novel functions of HMGB1 in mediating cisplatin sensitivity, emphasizing its potential as a therapeutic target to overcome cisplatin resistance in ovarian cancer.

cancer biology↗

bioRxiv2025

Mapping disease critical spatially variable gene programs by integrating spatial transcriptomics with human genetics

Spatial gene expression patterns underlie tissue organization, development, and disease, yet current methods for detecting spatially variable genes (SVGs) lack the flexibility to capture multi-scale structure, ensure robustness across platforms, and integrate with genetic data to assess disease relevance. We present Spacelink, a unified framework that models spatial variability of a gene at both whole-tissue and cell-type resolution using an adaptive mixture of data-driven spatial kernels and summarizes it using an Effective Spatial Variability (ESV) metric. Spacelink achieved up to 3.2x higher detection power over eight existing global SVG and cell-type SVG methods while showing consistently superior FDR control across 34 different simulation settings and also showed superior cross-platform concordance in matched tissue Visium and CosMx datasets. Applied to 3 healthy CosMx human tissues (brain cortex, lymph node, liver), Spacelink revealed that SVGs are highly informative for 113 complex traits and diseases (average GWAS sample size = 340,406). Spacelink showed up to 2.2x higher disease informativeness over competing methods in tissue-relevant complex diseases and traits, conditional on putative non-spatial expression-level confounders. Applied to a mouse organogenesis Stereo-seq atlas (8 developmental stages), Spacelink identified 145 genes with stage-associated ESV within brain independent of mean expression, that are enriched in pathways like Wnt signaling and Rap1 signaling characterizing early and late development, respectively. Integration with in vivo Perturb-seq targeting 35 de novo ASD risk genes revealed that perturbations in excitatory neurons and astrocytes preferentially altered spatially structured downstream gene programs (1.7-2.2x higher average ESV across stages than other cell types), many of which were enriched for polygenic autism GWAS loci. In neurodegeneration, analysis of 32 Visium dorsolateral prefrontal cortex samples spanning Alzheimers disease (AD) pathology stages identified 334 genes with decreasing ESV along amyloid burden (enriched for glycolysis) and 216 genes with decreasing ESV along tau tangle accumulation (enriched for apoptotic pathways). Several AD risk genes (PKM, CLU, GPI) showed conserved reductions in spatial variability with AD pathology in both human and 5xFAD mouse, with PKM linking to a colocalized splicing QTL and amyloid burden QTL variant. These results highlight the utility of Spacelink in decoding spatially variable gene programs that connect tissue architecture to disease genetics.

genomics↗

bioRxiv2025

A Respiratory Syncytial Virus trailer sequence modulates viral replication and copy-back defective viral genome generation and propagation kinetics

Copy-back defective viral genomes (cbDVGs) are key inducers of antiviral responses during negative-sense RNA virus infection. Once considered byproducts of in vitro viral replication, cbDVGs have since been detected in clinical specimens and implicated in affecting infection outcomes. The molecular mechanism of cbDVG generation remains unclear, thereby hindering our ability to manipulate cbDVG production during infection for therapeutic gain. Previous work showed that respiratory syncytial virus (RSV) cbDVG re-initiation sites cluster in trailer-end hotspots R1, R2, and R3, and that a poly-U mutation in R1 selectively reduced cbDVG formation at the mutated region. Here, we reported that a 10U mutation in R2 drastically reduced cbDVGs in this region in both minigenome and recombinant virus systems. Furthermore, during high-MOI passaging of the R2-10U virus, we observed delayed detection of cbDVGs with re-initiation sites in R1-R3 (trailer cbDVGs) compared to WT, while no differences in virus titers were observed. Interestingly, we observed the rapid emergence and accumulation of a viral variant bearing a 2-ribonucleotide deletion (R2-8U) within the R2-10U mutation sequence as early as P0. Compared to R2-10U, the R2-8U virus was stable, displayed faster generation and accumulation of trailer cbDVGs, restored cbDVGs with R2 re-initiation sites, and exhibited enhanced genomic replication. Overall, our data identify a sequence in the RSV trailer whose mutation critically modulates both viral replication and the generation/propagation of trailer cbDVGs. Our data also suggest that cbDVG generation, particularly near the trailer, may be an evolutionary tradeoff for more rapid virus genomic replication.

microbiology↗

bioRxiv2025

Bodily Self in Macaque Monkeys and Human Infants

Whether monkeys possess the concept of bodily self remains controversial. We found that macaque monkeys trained for mirror self-recognition (MSR) could spontaneously recognize themselves when viewing video images of their back from a third-person perspective (3PP), but this generalization required a few minutes of "exploration". In contrast, through visual-tactile synchronization training similar to that used in human out-of-body studies, naive monkeys could recognize themselves from 3PP back images and demonstrated MSR within seconds. Interestingly, in human infants, we found that 3PP back self-recognition developed spontaneously several months after MSR. These results demonstrate the existence of hierarchical bodily self in macaques and similar stepwise development of bodily self in human infants, offering insights into the emergence and manifestation of bodily self in natural and artificial intelligence.

neuroscience↗

bioRxiv2025

How do visual and conceptual factors predict the composition of typical scene drawings?

Imagine you are asked to draw a typical bedroom, what would you put on paper? Your choice of objects is likely to depend on visual occurrence statistics (i.e., the objects present in previously encountered bedrooms) and semantic relations between objects and scenes (i.e., the semantic relationship between the bedroom and its constituent objects). To investigate how these two factors contribute to the composition of typical scene drawings, we analyzed 1,192 drawings of six indoor scene categories, obtained from 303 participants. For each object featured in the drawings, we estimated its visual occurrence frequency from the ADE20K dataset of annotated scene images, and its semantic relatedness to the scene concept from a word2vec language processing model. Across all scenes of a given category, generalized linear models revealed that visual and conceptual factors both predicted the likelihood of an object featuring in the scene drawings, with a combined model outperforming both single-factor models. We further computed the visual and semantic specificity of objects for a given scene, that is, how diagnostic an object is for the scene. Object specificity offered only weak predictive power when predicting the selection of objects, yet even infrequently drawn objects remained diagnostic of their scenes. Taken together, we show that visual and conceptual factors jointly shape the composition of typical scene drawings. By releasing a large dataset of typical scene drawings alongside this work, we further provide a starting point for future studies exploring other critical properties of human drawings.

neuroscience↗

bioRxiv2025

Cryo-EM Reveals Regulatory Mechanisms Governing Substrate Selection and Activation of Human LONP1

The human AAA+ protease LONP1 plays a central role in maintaining mitochondrial proteostasis. LONP1 processes a vast array of substrates, ranging from damaged or unfolded proteins to specific subunits stably integrated into respiratory complexes. Previous cryo-EM studies of LONP1 uncovered two distinct conformational states corresponding to inactive or active forms of the enzyme. While these states have shed light on the intricacies of LONP1 substrate translocation and proteolytic processing, little is known about the decision-making involved in LONP1 substrate engagement and subsequent initiation of its unfoldase activity. Here, we use cryo-EM to determine a novel ADP-bound, C3-symmetric intermediate state of LONP1 (LONP1C3) with putative substrate "fold-sensing" capabilities. Our biochemical and structural data indicate that LONP1C3 is an on-pathway intermediate and that is stabilized by interaction with folded substrates. Moreover, we identify additional symmetric and asymmetric conformational states, including a two-fold symmetric split-hexamer conformation, that we associate with the transition from LONP1C3 to LONP1ENZ. We propose that the C3-state regulates substrate selection and enables LONP1 to efficiently surveil the matrix proteome to ensure selective removal of damaged and dysfunctional proteins as well as privileged LONP1 substrates. These findings collectively provide further mechanistic insights into LONP1 substrate recruitment and engagement and inform on its diverse roles in maintaining homeostasis within the mitochondria.

molecular biology↗

bioRxiv2025

GSDME unlocks astrocyte-driven neurotoxicity in Alzheimer's Disease

Astrocytic calcium dysregulation and reactivity precede A{beta} deposition in amyloid-{beta} deposition in Alzheimers disease (AD) but the neurotoxic mechanisms remain unclear. We show that GSDME acts as a switch, linking MAM-mediated calcium release to astrocyte-driven neurotoxicity. Specifically, A{beta}-activated microglial signals activate astrocytic GSDME, releasing its N-terminal fragment, which targets MAMs and triggers ER calcium efflux. This induces biphasic CaMKII phosphorylation, initially boosting NRF2 defenses, then activating NF-{kappa}B-driven inflammation, shifting astrocytes from protective to toxic states. GSDME activation also drives astrocyte-derived exosomes (ADEs) to carry neurotoxic tau, proinflammatory miRNAs, and toxic lipids, propagating toxicity. GSDME deletion in AD mice reduces A{beta} burden, restores NF-{kappa}B/NRF2 balance, reprograms astrocytes and ADEs to protective states, and rescues cognition. Multi-omics profiling of serum ADEs from AD patients reveals a disease-specific signature with central neurotoxicity and peripheral immune regulation. These findings position GSDME as a promising dual diagnostic and therapeutic target for early AD invention.

neuroscience↗

bioRxiv2025

A Spatially Resolved Atlas of Alternative Polyadenylation Across 18 Human Tissues and 76 Disease States

Alternative polyadenylation (APA) is a key regulator of gene expression and cellular dynamics, yet systematic investigations of spatially resolved APA across diverse human tissues remain limited. Here, we developed SpatialAPA (https://github.com/Omicslab-Zhang/spatialAPA), a framework that benchmarks multiple APA identification methods and integrates spatial APA data with gene expression and cellular dynamics at spatial resolution. Applying SpatialAPA to 363 spatial transcriptomic data from 56 projects across 18 human tissues and 76 diseases, we constructed a spatially resolved APA atlas comprising 346,932 APA events across 52,175 genes. This atlas reveals organ-specific APA patterns and provides new insights into how APA regulates tissue homeostasis and disease progression beyond transcriptional control. To ensure cross-sample comparability, we applied batch correction, while spatial cell deconvolution uncovered cell-type-specific dynamics and interactions. In triple-negative breast cancer, integrated spatial and single-cell analyses identified TSPAN8-positive epithelial subpopulations whose distinct APA regulation and transcriptional programs drive differentiation and malignant progression. To facilitate community access, we developed an online platform (http://www.biomedical-web.com/spatialAPAdb/home) for exploring APA, gene expression, and cellular dynamics in health and disease. Together, this study establishes the first comprehensive spatial APA atlas, providing a valuable resource and analytical framework for investigating molecular mechanisms and therapeutic targets.

bioinformatics↗

bioRxiv2025

Structural and functional analysis of VYD222: a broadly neutralizing antibody against SARS-CoV-2 variants

Extensive mutations in SARS-CoV-2 spike protein have rendered most therapeutic monoclonal antibodies (mAbs) ineffective. However, here we describe VYD222 (pemivibart), a human mAb re-engineered from ADG20 (adintrevimab), which maintains potency despite substantial virus evolution. VYD222 received FDA Emergency Use Authorization for pre-exposure prophylaxis of COVID-19 in certain immunocompromised adults and adolescents. Here we show potent neutralization of this antibody against a broad range of emerging variants, including Omicron KP.3 and KP.3.1.1. X-ray crystal structures of VYD222 complexed with the receptor-binding domains of prototype SARS-CoV-2 and Omicron BA.5 demonstrate the binding epitope spans from the receptor binding site to the conserved CR3022 site. Notably, many of the matured residues between ADG20 and VYD222 occur outside the paratopic region. Deep mutational scanning indicates that SARS-CoV-2s ability to escape VYD222 is constrained by structural compatibility and the need to maintain receptor binding. These findings provide crucial insights into the escape-resistant neutralization of VYD222 against a broad panel of clinically relevant SARS-CoV-2 variants and offer valuable guidance for risk assessment of emergent variants.

biophysics↗

bioRxiv2025

EnzymeTuning: a Deep-learning-based Toolbox for Optimizing Enzyme-constrained Metabolic Modeling with Enhanced Proteome Abundance Prediction

AbstractThe accuracy of enzyme kinetic parameters, particularly the turnover number (kcat), is critical for the predictive power of enzyme-constrained genome-scale metabolic models (ecGEMs). However, current kinetic datasets remain sparse and often fail to capture in vivo enzyme behavior, compromising model predictive capacity. To address these challenges, we developed EnzymeTuning----a generative adversarial network (GAN)-based framework for the global kcat optimization. This approach significantly enhances both the accuracy and proteome-level coverage of ecGEM predictions. Moreover, by incorporating literature-derived protein degradation constants (kdeg), we inferred the protein synthesis rate and systematically evaluated their impact on model performance. The framework was validated across distinct yeast species, including Saccharomyces cerevisiae, Kluyveromyces lactis, Kluyveromyces marxianus, and Yarrowia lipolytica, demonstrating its generalizability. Further, we found that EnzymeTuning facilitates the identification of context-specific enzyme usage patterns and adaptive catalytic resource allocation under varying carbon-to-nitrogen (C/N) ratios, showcasing the substantial potential of our toolbox for integrative omics analysis. Overall, EnzymeTuning provides a robust and scalable solution for refining kinetic parameters in ecGEMs, thereby promoting the wide applications of these computational models in systems and synthetic biological studies.

systems biology↗

bioRxiv2025

An activated wheat CCG10-NLR immune receptor forms an octameric resistosome

Nucleotide-binding and leucine-rich repeat (LRR) receptors (NLRs) are widespread intracellular immune sensors across kingdoms. In plants, the G10-type coiled-coil (CCG10)-NLRs form a distinct phylogenetic clade that remains poorly characterized. Here, we identified a gain-of-function (GOF) mutant of Wheat Autoimmunity 3 (WAI3), designated WAI3GOF, which encodes a constitutively activated CCG10-NLR protein due to an amino acid substitution. Cryo-EM structural analysis revealed that activated WAI3 assembles into a distinctive octameric resistosome. Arabidopsis RPS2, another CCG10-NLR, also forms an octamer, indicating a conserved structural property across monocot and dicot plants. The WAI3 resistosome mediates a prolonged and sustained increase in cytosolic calcium influx, facilitated by a unique channel architecture arising from its divergent CC domain configuration. Notably, this domain arrangement may be shared by many plant NLRs that lack the conserved EDVID motif in their CC domains. Our findings uncover a previously uncharacterized resistosome structure and provide insights into plant immune receptor plasticity.

plant biology↗

bioRxiv2025

fSuSiE enables fine-mapping of QTLs from genome-scale molecularprofiles

Molecular quantitative trait locus (QTL) studies seek to identify the causal variants affecting molecular traits like DNA methylation and histone modifications. However, existing fine-mapping tools are not well suited to high-dimensional molecular traits, and so analyses of these traits typically proceed by considering each variant and each molecular measurement independently, ignoring the LD among variants and the spatial correlation in effects between nearby sites. This severely limits accuracy in identifying causal variants and quantifying their molecular trait effects. Here, we introduce fSuSiE ("functional Sum of Single Effects"), a fine-mapping method that addresses these challenges by explicitly modeling the spatial structure of genetic effects on molecular traits. fSuSiE integrates wavelet-based functional regression with the computationally efficient "Sum of Single Effects" framework to simultaneously finemap causal variants and identify the molecular traits they affect. In simulations, fSuSiE identified causal variants and affected CpGs more accurately than methods that ignore spatial structure. In applications to DNA methylation and histone acetylation (H3K9ac) data from the ROSMAP study of the dorsolateral prefrontal cortex, fSuSiE achieved dramatically higher resolution than existing methods (e.g., identifying 6,355 single-variant methylation credible sets compared to only 328 from an existing approach). Applied to Alzheimers disease (AD) risk loci, fSuSiE identified potential causal variants colocalizing with AD GWAS signals for established genes, including CASS4 and CR1/CR2, suggesting specific potential regulatory mechanisms underlying these AD risk loci.

genetics↗