Biology subjects

Ha, J.

Publications and source records attributed to Ha, J..

At least 19 recordsLinked to original sources

bioRxiv2026

Network Localization of Anxiety as an Early Manifestation of Alzheimer's Disease-related Neurodegeneration

Objective Anxiety is a prominent neuropsychiatric symptom in preclinical Alzheimer's disease (AD), but the neural circuitry linking anxiety and AD remains uncharacterized. We assessed whether anxiety in preclinical AD is independently associated with AD-related neurodegeneration and localizes to a common anxiety network. Methods In cognitively unimpaired (CU) older adults with 3T MRI and amyloid-beta PET (N=1,775), we examined whether an established AD-related atrophy network is associated with anxiety (STAI-6). Then, using Atrophy Network Mapping (ANM), which maps individual cortical atrophy patterns onto a normative functional connectome (N=1000), we derived an anxiety network. We tested its 1) spatial convergence with the AD network and with a causal anxiety network derived from lesion and neuromodulation data, 2) specificity relative to depression, 3) generalizability in samples from independent cohorts with AD and Parkinson's disease (total n=878), and 4) association with longitudinal outcomes. Results In CU older adults, greater AD-network atrophy was associated with higher anxiety, independent of depression, cognition, cortical atrophy, and amyloid burden. Anxiety localized to a network closely resembling the AD network (r=0.74, p=0.04) and the causal anxiety network (r=0.72, p=0.02). This convergence extended across the AD continuum and to the Parkinson's disease cohort and was specific to anxiety relative to depression. Greater anxiety-network atrophy was associated with cognitive decline over 4.6 years. Conclusion Anxiety localizes to a brain circuit resembling the AD-related atrophy network, is consistent across the AD continuum and may extend beyond AD. These findings support anxiety as an independent marker of early AD-related neurodegeneration.

neuroscience↗

bioRxiv2026

Endocrine-Adapted Pituitary Macrophages Regulate Gonadotropin Secretion through CXCL5-CXCR2-MAPK Signaling

Chronic inflammation disrupts hormonal balance in the Hypothalamic-Pituitary-Gonadal (HPG) axis, contributing to reproductive disorders. While immune cells in the hypothalamus and ovaries have been extensively studied, their impact on the pituitary remains largely unexplored. Our research identifies pituitary macrophages (PitMacs) as the dominant pituitary immune cell population with a role in regulating reproductive gonadotropin secretion both in vitro and in vivo. Using a targeted AAV-based depletion strategy, we demonstrate that a reduction of PitMacs decreases serum gonadotropins, luteinizing hormone (LH) and follicle-stimulating hormone (FSH), in female mice. PitMacs are transcriptomically distinct from other tissue-resident macrophages and harbor a unique translational program that reflects the pituitarys endocrine identity, including active translation of growth hormone (Gh) and prolactin (Prl). Cytokine profiling identified CXCL5 and IFN-{gamma} as key PitMac-derived mediators of gonadotropin regulation. Mechanistically, CXCL5 signals through CXCR2 to activate the MAPK pathway, converging with Gonadotropin-Releasing Hormone (GnRH) signaling in a time-dependent manner to regulate LH secretion and GnRH receptor surface expression. These findings establish PitMacs as essential endocrine-immune integrators, opening new avenues for understanding inflammation-driven reproductive disorders. One Sentence SummaryPituitary macrophages are unique hormone-producing immune cells that regulate hormone secretion via cytokine signaling.

immunology↗

bioRxiv2026

Differential Regulation of Branched-Chain Amino Acids During Early Germination of Mungbean (Vigna radiata L.)

Branched-chain amino acids (BCAAs) are essential amino acids involved in protein synthesis and energy metabolism. Because animals cannot synthesize BCAA de novo, plant-derived BCAAs are important to human nutrition. Although mungbean sprouts are widely consumed as functional plant-based food materials, changes in individual BCAA accumulation and their transcriptional regulation during mungbean germination remain poorly understood. In this study, amino acid contents and transcriptomic profiles were analyzed at three germination stages, 8H, 24H, and 72H. Total BCAA content increased during germination, whereas individual BCAAs exhibited distinct temporal accumulation patterns. Isoleucine and valine increased until 72H, while leucine increased during early germination and decreased after 24H. Transcriptome analysis revealed time-dependent expression changes in BCAA biosynthesis and degradation genes associated with the leucine decrease after 24H. These findings suggest that 24H represents an important transition point for BCAA accumulation and compositional change during mungbean germination. This study provides molecular evidence for the regulation of BCAA metabolism during mungbean germination and supports the potential use of germinated mungbean as a plant-based amino acid resource.

plant biology↗

bioRxiv2026

Species and Accession Diversity of Secondary Metabolites and Antioxidant Activity in Legume Sprouts

Legume sprouts contain diverse bioactive phytochemicals, yet species- and accession-level comparisons of antioxidant properties and secondary metabolites remain limited. Antioxidant capacity, total phenolic content (TPC), total flavonoid content (TFC), and 19 secondary metabolites were evaluated in sprouts of soybean (Glycine max (L.) Merr.), mungbean (Vigna radiata (L.) R. Wilczek), cowpea (Vigna unguiculata (L.) Walp.), and peanut (Arachis hypogaea L.), using ten accessions per species under standardized conditions. Mungbean and cowpea sprouts showed significantly higher antioxidant activity, TPC, and TFC than soybean and peanut; across accessions, ABTS and DPPH activities ranged from 13.67 to 49.33% and 7.91 to 55.16%, and TPC and TFC from 3.91 to 13.81 mg GAE/g and 0.05 to 1.04 mg QE/g, respectively. Metabolite profiling revealed species-specific patterns, including isoflavones in soybean, rutin in mungbean, coumestrol in cowpea and resveratrol in peanut. Both antioxidant and phytochemical profiles varied with species and accession, so both can be selected to obtain sprouts with targeted properties.

plant biology↗

bioRxiv2026

Genotype-Dependent Variation in Vitamins B1, B2, B3, B6, B9, and C in Mungbean Sprouts

Water-soluble vitamins are essential micronutrients that are not stored extensively in the human body and must therefore be replenished through dietary intake. Mungbean sprouts are widely consumed in some populations; however, their contributions as dietary sources of water-soluble vitamins and the extent of genotype-dependent variations remain insufficiently characterized. In this study, six water-soluble vitamins (B1, B2, B3, B6, B9, and C) were quantitatively and qualitatively analyzed using ultra performance liquid chromatography in sprouts derived from 34 mungbean genotypes. With the exception of pyridoxine (vitamin B6), all analyzed vitamins were detected across all genotypes, and their contents showed significant genotype-dependent variation. To determine whether differences in vitamin composition are reflected in functional responses, antioxidant capacity and enzyme-based bioactivity were evaluated. Antioxidant activity varied significantly among genotypes and showed a positive correlation with nicotinamide (B3) content, while tyrosinase inhibition was positively correlated with thiamine (B1). Evaluation of vitamin contents on a fresh-weight basis demonstrated that a single 104 g serving of mungbean sprouts can provide 46%- 206% of the recommended daily allowance of thiamine depending on genotype, while supplying all analyzed water-soluble vitamins, including B1, B2, B3, B6, B9, and C. This studys findings support the potential of mungbean sprouts to serve as plant-based dietary sources of water-soluble vitamins and offer a basis for selecting genetic resources to improve the vitamin-related nutritional quality of mungbean sprouts.

plant biology↗

bioRxiv2025

Image-Based Profiling of Induced Trophoblast Stem Cells Identifies Signatures Associated with Sex, Schizophrenia Genomic Risk and Placental Stress

Schizophrenia (SCZ) is a neurodevelopmental disorder where both genetic and environmental risks converge during pregnancy. Recent studies have highlighted the importance of placental biology in influencing risk of developing SCZ. However, the pathways by which genetic risk factors for SCZ interact with environmental influences to alter placental development are poorly understood. Through image-based profiling using Cell Painting, we leveraged trophoblast cultures derived from human induced pluripotent stem cells (hiPSCs) from male and female SCZ and neurotypical donors with varying placental genomic risk scores (PlacGRS) to explore the developmental dynamics of placental cells under normal growth and hypoxic stress. We employed both classical (e.g., CellProfiler) and deep learning feature extraction combined with downstream supervised machine learning to analyze high-dimensional data obtained from this hiPSC-derived model system, highlighting that these approaches overcome the inherent line-to-line variability in phenotypic analysis. Our findings reveal a salient nucleus-localized SCZ risk signature across cell lines, along with clear sexual dimorphism. This research underscores the capability of hiPSC-derived placenta models to elucidate complex interactions between genetic risk and environmental factors implicated in the neurodevelopment of SCZ, paving the way for future studies aimed at developing targeted therapeutic and prevention strategies.

neuroscience↗

bioRxiv2025

SpaGene: A Deep Adversarial Framework for Spatial Gene Imputation

Integrating transcriptome-wide single-cell gene expression data with spatial context significantly enhances our understanding of tissue biology, cellular interactions, and disease progression. Although single-cell RNA sequencing (scRNA-seq) provides high-resolution gene expression data, it lacks crucial spatial context, whereas spatial transcriptomics techniques offer spatial resolution but are limited in the transcriptomic coverage. To address these limitations, integrating scRNA-seq and spatial transcriptomics data is essential. We introduce SpaGene, a novel deep learning framework designed to integrate scRNA-seq data and spatial transcriptomics data. SpaGene consists of two encoder-decoder pairs combined with two translators and two discriminators to effectively impute missing gene expressions within spatial transcriptomics datasets. We benchmarked SpaGene against existing state-of-the-art methods across diverse datasets. Across the datasets, SpaGene achieved an average 33% higher Pearson correlation coefficient (PCC), 21% higher Structural similarity index (SSIM), and 6.6% lower Root mean squared error (RMSE) compared to the existing approaches, highlighting its capability to reliably impute missing genes and provide comprehensive transcriptomics profiles. Application of our model to lung tumor tissue revealed immune cell enrichment at tumor boundaries, restricted myeloid cell trafficking in adjacent normal regions, and microenvironmental-driven pathways linked to immune neighborhoods. These results provide novel insight into immune exclusion and tumor-immune interactions that drive tumor progression, highlighting potential avenues for therapeutic development. Thus, SpaGene extends the power of spatial transcriptomics by delivering spatially resolved, enhanced transcriptome data that enable deeper biological understanding.

bioinformatics↗

bioRxiv2025

Hypoxia inducible factors regulate Pneumovirus replication by enhancing innate immune sensing

The immune mechanisms responsible for protection and pathogenesis in pneumoviral infection are not well defined. We demonstrated that pharmacological activation of the hypoxic inducible factor (HIF) signalling axis using Daprodustat limited viral replication through enhanced immune signalling. Transcriptomic analysis revealed HIF augmented activation of innate immune response genes, including interferon-stimulated gene 15 (Isg15), in the lung and spleen of mice infected with pneumonia virus of mice (PVM). In human respiratory syncytial virus (hRSV) infected airway epithelial cells, Daprodustat inhibited viral replication and enhanced ISG15 expression in a HIF-dependent manner. Importantly, inhibition of type I interferon signalling or the RIG-I sensing pathway abrogated the antiviral activity of HIF. Moreover, Daprodustat increased interferon signalling in response to viral RNA, suggesting that HIF inhibits pneumovirus replication through enhancing viral RNA sensing. Mechanistically, Daprodustat reduced N6-methyladenosine modification of viral RNA through upregulation of RNA demethylases, promoting detection by innate immune sensors. This study highlights the intricate interplay between hypoxia and antiviral immunity and offers valuable insights into pneumovirus-host interactions and potential therapeutic interventions. SIGNIFICANCE STATEMENTThis study explores the role of the hypoxic inducible factor (HIF) signalling pathway in limiting pneumoviral infections, such as respiratory syncytial virus (hRSV) and pneumonia virus of mice (PVM). Using Daprodustat, a clinically approved activator of HIF signalling we demonstrated that stimulation of this pathway enhanced innate immune signalling and limited viral replication. Transcriptomic analysis showed that HIF promoted innate immune response genes in mice infected with PVM and human airway epithelial cells infected with hRSV. Importantly, inhibition of type I interferon signalling or the RIG-I sensing pathway abrogated the antiviral activity of HIF. We uncovered a role for HIF to regulate the levels of N6-methyladenosine modification of viral RNA transcripts, resulting in the increased activation of nucleic acid sensing.

microbiology↗

bioRxiv2025

Spatial Frequency Maps in Human Visual Cortex: A Replication and Extension

In a step toward developing a model of human primary visual cortex, a recent study introduced a model of spatial frequency tuning in V1 (Broderick et al., 2022). The model is compact, using just 9 parameters to predict BOLD response amplitude for locations across all of V1 as a function of stimulus orientation and spatial frequency. Here we replicated this analysis in a new dataset, the nsdsynthetic supplement to the Natural Scenes Dataset (Allen et al., 2022), to assess generalization of model parameters. Furthermore, we extended the analyses to extrastriate maps V2 and V3. For each retinotopic map in the 8 NSD subjects, we fit the 9-parameter model. Despite many experimental differences between NSD and the original study, including stimulus size, experimental design, and MR field strength, there was good agreement in most model parameters. The dependence of preferred spatial frequency on eccentricity in V1 was similar between NSD and Broderick et al. Moreover, the effect of absolute stimulus orientation on spatial frequency maps was similar: higher preferred spatial frequency for horizontal and cardinal orientations compared to vertical and oblique orientations in both studies. The extension to extrastriate maps revealed that the biggest change in tuning between maps was in bandwidth: the bandwidth in spatial frequency tuning increased by 70% from V1 to V2 and 100% from V1 to V3. This implies that higher visual areas are sensitive to a greater range of stimulus spatial frequencies, paralleling the fact that higher visual areas are sensitive to a greater range in location (i.e., larger receptive fields). Together, the results show robust reproducibility and bring us closer to a systematic characterization of spatial encoding in the human visual system.

neuroscience↗

bioRxiv2024

Vasculature segmentation in 3D hierarchical phase-contrast tomography images of human kidneys

Efficient algorithms are needed to segment vasculature in new three-dimensional (3D) medical imaging datasets at scale for a wide range of research and clinical applications. Manual segmentation of vessels in images is time-consuming and expensive. Computational approaches are more scalable but have limitations in accuracy. We organized a global machine learning competition, engaging 1,401 participants, to help develop new deep learning methods for 3D blood vessel segmentation. This paper presents a detailed analysis of the top-performing solutions using manually curated 3D Hierarchical Phase-Contrast Tomography datasets of the human kidney, focusing on the segmentation accuracy and morphological analysis, thereby establishing a benchmark for future studies in blood vessel segmentation within phase-contrast tomography imaging.

bioinformatics↗

bioRxiv2024

Topical ABT-1 263 treatment reduces aged skin senescence and improves subsequent wound healing

Senescent cells (SnC) accumulate in aging tissues, impairing their ability to undergo repair and regeneration following injury. Previous research has demonstrated that targeting tissue senescence with senolytics can enhance tissue regeneration and repair by selectively eliminating SnCs in specific aged tissues. In this study, we focused on eliminating SnC skin cells in aged mice to assess the effects on subsequent wound healing. We applied ABT-263 directly to the skin of 24-month-old mice over a 5-day period. Following topical ABT-263, aged skin demonstrated decreased gene expression of senescent markers p16 and p21, accompanied by reductions in SA-{beta}-gal and p21-positive cells compared to DMSO controls. However, ABT-263 also triggered a temporary inflammatory response and macrophage infiltration in the skin. Bulk RNA sequencing of ABT-263-treated skin revealed prompt upregulation of genes associated with wound healing pathways, including hemostasis, inflammation, cell proliferation, angiogenesis, collagen synthesis, and extracellular matrix organization. Aged mice skin pre-treated with topical ABT-263 exhibited accelerated wound closure. In conclusion, topical ABT-263 effectively reduced several senescence markers in aged skin, thereby priming the skin for improved subsequent wound healing. This enhancement may be attributed to ABT-263-induced senolysis which in turn stimulates the expression of genes involved in extracellular matrix remodeling and wound repair pathways.

cell biology↗

bioRxiv2024

Applying multi-state modeling using AlphaFold2 for kinases and its application for ensemble screening

Structure-based virtual screening (SBVS) is a pivotal computational approach in drug discovery, enabling the identification of potential drug candidates within vast chemical libraries by predicting their interactions with target proteins. The SBVS relies on the receptor protein structures, making it sensitive to structural variations. Kinase, one of the major drug targets, is known as one of the typical examples of an active site conformation change caused by the type of binding inhibitors. Examination of human kinase structures shows that the majority of conformations have the DFGin state. Thus, SBVS using the structures might cause a favor of type of ligand type I inhibitors, bind to the DFGin state, rather than finding the diverse scaffolds. Recent advances in protein structure prediction, such as AlphaFold2 (AF2), offer promising solutions but may still be possibly influenced by the structural bias in existing templates. To address these challenges, we introduce a multi-state modeling (MSM) protocol for kinase structures. We apply MSM to AF2 by providing state-specific templates, allowing us to overcome structural biases and thus apply them to kinase SBVS. We benchmarked our MSM models in three categories: quality of predicted models, reproducibility of ligand binding poses, and identification of hit compounds by ensemble SBVS. The results demonstrate that MSM-generated models exhibit comparable or improved structural accuracy compared to standard AF2 models. We also show that MSM models enhance the accuracy of cognate docking, effectively capturing the interactions between kinases and their ligands. In virtual screening experiments using DUD-E compound libraries, our MSM approach consistently outperforms standard AF2 modeling. Notably, MSM-based ensemble screening excels in identifying diverse hit compounds for kinases with structurally diverse active sites, surpassing standard AF2 models. We highlight the potential of MSM in broadening the scope of kinase inhibitor discovery by facilitating the identification of chemically diverse inhibitors. Author SummaryOne of the main problems with structure-based virtual screening is structural flexibility. Ensemble screening is one of the conventional approaches to solving the issue. Gathering experimental structures or molecular simulations could be used to compile the receptor structures. Recent developments in algorithms for predicting protein structures, like AlphaFold2, suggest that different receptor conformations could be produced. However, the prediction approaches produce biased structures because of the bias in the structure database. In order to solve the problem, we developed a protocol called multi-state modeling for kinases. Rather than supplying multiple sequence alignments as an input, we gave the AlphaFold2 a specific template structure and the sequence alignment between the template and query. Our findings imply that our technique can yield a particular structural state of interest with an enhanced or comparable structural quality to AlphaFold2 and predict highly accurate protein-ligand complex structures. Lastly, compared to the typical AlphaFold2 models, ensemble screening using the multi-state modeling approach improves the structure-based virtual screening performance, particularly for diverse active molecular scaffolds.

bioinformatics↗

bioRxiv2023

Hypoxia inducible factors inhibit respiratory syncytial virus infection by modulation of nucleolin expression

Respiratory syncytial virus (RSV) is a global healthcare problem, causing respiratory illness in young children and elderly individuals. However, our knowledge of the host pathways that define susceptibility to RSV infection and disease severity is limited. Hypoxia inducible factors (HIFs) define cellular metabolic processes in response to low oxygen and are recognised to play a key role in regulating inflammatory responses in the lower respiratory tract. We demonstrate a role for hypoxia inducible factors (HIFs) to suppress RSV entry and RNA replication. We show that hypoxia and HIF prolyl-hydroxylase inhibitors significantly reduce expression of the RSV entry receptor nucleolin and inhibit viral driven cell-cell fusion. We identify a HIF regulated microRNA, miR-494, that regulates nucleolin expression. In RSV-infected mice, treatment with the clinically approved HIF prolyl-hydroxylase inhibitor, Daprodustat, reduced the level of infectious virus and infiltrating monocytes and neutrophils in the lung. This study highlights a role for HIF-signalling to limit multiple aspects of RSV infection and associated inflammation and informs future therapeutic approaches for treating this respiratory pathogen.

molecular biology↗

bioRxiv2023

Long-term three-dimensional high-resolution imaging of live unlabeled small intestinal organoids using low-coherence holotomography

The prevailing challenges in live unlabeled high-resolution imaging of native organoids stem from technical issues like complex sample handling and optical scattering in three-dimensional architectures. In this study, we introduce low-coherence holotomography as an advanced, label-free, quantitative imaging modality, designed to overcome related technical obstacles for long-term live imaging of 3D organoids. We successfully captured high-resolution morphological intricacies and dynamic events within mouse small intestinal organoids at a subcellular resolution. Furthermore, this method provides a unique advantage in differentiating between viable and non-viable organoids, thereby expanding its potential applications in organoid-based research.

developmental biology↗

bioRxiv2023

Estimating Insulin Sensitivity and Beta-Cell Function from the Oral Glucose Tolerance Test: Validation of a new Insulin Sensitivity and Secretion (ISS) Model

Efficient and accurate methods to estimate insulin sensitivity (SI) and beta-cell function (BCF) are of great importance for studying the pathogenesis and treatment effectiveness of type 2 diabetes. Many methods exist, ranging in input data and technical requirements. Oral glucose tolerance tests (OGTTs) are preferred because they are simpler and more physiological. However, current analytical methods for OGTT-derived SI and BCF also range in complexity; the oral minimal models require mathematical expertise for deconvolution and fitting differential equations, and simple algebraic models (e.g., Matsuda index, insulinogenic index) may produce unphysiological values. We developed a new ISS (Insulin Secretion and Sensitivity) model for clinical research that provides precise and accurate estimates of SI and BCF from a standard OGTT, focusing on effectiveness, ease of implementation, and pragmatism. The model was developed by fitting a pair of differential equations to glucose and insulin without need of deconvolution or C-peptide data. The model is derived from a published model for longitudinal simulation of T2D progression that represents glucose-insulin homeostasis, including post-challenge suppression of hepatic glucose production and first- and second-phase insulin secretion. The ISS model was evaluated in three diverse cohorts including individuals at high risk of prediabetes (adult women with a wide range of BMI and adolescents with obesity). The new model had strong correlation with gold-standard estimates from intravenous glucose tolerance tests and hyperinsulinemic-euglycemic clamp. The ISS model has broad clinical applicability among diverse populations because it balances performance, fidelity, and complexity to provide a reliable phenotype of T2D risk.

physiology↗

bioRxiv2022

Physics of sliding on water predicts morphological and behavioral allometry across a wide range of body sizes in water striders (Gerridae)

Laws of physics shape morphological and behavioral adaptations to locomotion at different body sizes. Water striders serve as a model taxon to study how simple physical constraints of water-surface habitats affect their behavior and morphology, and hydrodynamics of rowing by midlegs on the surface is well understood. However, the physics of the subsequent passive sliding has been less explored. We created a model of sliding on the water surface to simulate the effect of body mass, striding type, and wetted leg lengths on an insects ability to float on the surface and on the sliding resistance. The model predicts that to support their weight on the surface during sliding, the heavy species should either develop long forelegs that support the frontal part of its body during symmetrical striding (when two midlegs thrust) or use asymmetrical striding (when one forward-extended midleg supports the body while the other midleg and contra-lateral hindleg thrust). These predictions are confirmed by the behavior and morphology of various Gerridae species. Hence, the results illustrate how simple physical processes specific to a certain habitat type have far-reaching consequences for the evolution of morphological and behavioral diversification associated with body size among biological organisms in these habitats.

biophysics↗

bioRxiv2022

Engineering a Single Extracellular Vesicle Protein and RNA Assay (siEVPRA) via In Situ Fluorescence Microscopy in a UV Micropatterned Array

The physical and molecular heterogeneity of extracellular vesicles (EVs) confounds bulk biomarker characterization, thus encouraging the development of novel assays capable of profiling EVs at a single-vesicle resolution. Here, we present a single EV (siEV) protein and RNA assay (siEVPRA) to simultaneously detect proteins, messenger RNAs (mRNAs), and microRNAs (miRNAs) in siEVs. The siEVPRA consists of an array of microdomains embedded on a polyethylene glycol (PEG)-coated glass surface produced via UV photopatterning, functionalized with antibodies to target siEV subpopulations. Fluorescently labeled antibodies and RNA-targeting molecular beacons (MBs) were used to generate signals for proteins, mRNAs, and miRNAs on siEVs detected by total internal reflection fluorescence microscopy (TIRFM), outperforming the sensitivities of ELISA and PCR by three orders of magnitude. Using the siEVPRA, we analyzed EVs harvested from glioblastoma (GBM) cell lines and demonstrated vesicular heterogeneity in protein, mRNA, and miRNA expression through colocalization analyses, and validated the results by bulk RNA sequencing. We further demonstrated the clinical utility of the siEVPRA by detecting different mRNAs and miRNAs associated with GBM in patient samples. Together, these results indicate that the siEVPRA provides an effective platform to investigate the heterogeneity of proteins and RNAs in subpopulations of EVs.

bioengineering↗

bioRxiv2022

iPheGWAS : an intelligent computational framework to integrate and visualise genome-phenome wide association studies

Estimating the genetic correlations by LDSC is computationally demanding and visualising multiple GWAS results along with their genetic relationships is restricted. This study developed iPheGWAS, a novel approach which applied hierarchical clustering to GWAS summary statistics to (i) calculate their genetic relatedness, and (ii) enable three-dimensional visualisation of multiple ordered GWAS plots. Simulation and real-world data analysis demonstrated that when investigating genetic relationships among multiple phenotypes, iPheGWAS can deliver comparable results with LDSC but with 8 times faster computational speed. It can also provide novel findings in studying genetically-correlated comorbidities, such as mental illness and rheumatoid arthritis.

bioinformatics↗