Long-access heroin self-administration induces region specific reduction of grey matter volume and microglia reactivity in the rat.

In opioid use disorder (OUD) patients, a decrease in brain grey matter volume (GMV) has been reported. It is unclear whether this is the consequence of prolonged exposure to opioids or is a predisposing causal factor in OUD development. To investigate this, we conducted a structural MRI longitudinal study in NIH Heterogeneous Stock rats exposed to heroin self-administration and age-matched naïve controls housed in the same controlled environment. Structural MRI scans were acquired before (MRI1) and after (MRI2) a prolonged period of long access heroin self-administration resulting in escalation of drug intake. Heroin intake resulted in reduced GMV in various cortical and sub-cortical brain regions. In drug-naïve controls no difference was found between MRI1 and MRI2. Notably, the degree of GMV reduction in the medial prefrontal cortex (mPFC) and the insula positively correlated with the amount of heroin consumed and the escalation of heroin use. In a preliminary gene expression analysis, we identified a number of transcripts linked to immune response and neuroinflammation. This prompted us to hypothesize a link between changes in microglia homeostasis and loss of GMV. For this reason, we analyzed the number and morphology of microglial cells in the mPFC and insula. The number of neurons and their morphology was also evaluated. The primary motor cortex, where no GMV change was observed, was used as negative control. We found no differences in the number of neurons and microglia cells following heroin. However, in the same regions where reduced GMV was detected, we observed a shift towards a rounder shape and size reduction in microglia, suggestive of their homeostatic change towards a reactive state. Altogether these findings suggest that escalation of heroin intake correlates with loss of GMV in specific brain regions and that this phenomenon is linked to changes in microglial morphology.

LRT: Integrative analysis of scRNA-seq and scTCR-seq data to investigate clonal differentiation heterogeneity.

Single-cell RNA sequencing (scRNA-seq) data has been widely used for cell trajectory inference, with the assumption that cells with similar expression profiles share the same differentiation state. However, the inferred trajectory may not reveal clonal differentiation heterogeneity among T cell clones. Single-cell T cell receptor sequencing (scTCR-seq) data provides invaluable insights into the clonal relationship among cells, yet it lacks functional characteristics. Therefore, scRNA-seq and scTCR-seq data complement each other in improving trajectory inference, where a reliable computational tool is still missing. We developed LRT, a computational framework for the integrative analysis of scTCR-seq and scRNA-seq data to explore clonal differentiation trajectory heterogeneity. Specifically, LRT uses the transcriptomics information from scRNA-seq data to construct overall cell trajectories and then utilizes both the TCR sequence information and phenotype information to identify clonotype clusters with distinct differentiation biasedness. LRT provides a comprehensive analysis workflow, including preprocessing, cell trajectory inference, clonotype clustering, trajectory biasedness evaluation, and clonotype cluster characterization. We illustrated its utility using scRNA-seq and scTCR-seq data of CD8+ T cells and CD4+ T cells with acute lymphocytic choriomeningitis virus infection. These analyses identified several clonotype clusters with distinct skewed distribution along the differentiation path, which cannot be revealed solely based on scRNA-seq data. Clones from different clonotype clusters exhibited diverse expansion capability, V-J gene usage pattern and CDR3 motifs. The LRT framework was implemented as an R package 'LRT', and it is now publicly accessible at https://github.com/JuanXie19/LRT. In addition, it provides two Shiny apps 'shinyClone' and 'shinyClust' that allow users to interactively explore distributions of clonotypes, conduct repertoire analysis, implement clustering of clonotypes, trajectory biasedness evaluation, and clonotype cluster characterization.

multi-GPA-Tree: Statistical approach for pleiotropy informed and functional annotation tree guided prioritization of GWAS results.

Genome-wide association studies (GWAS) have successfully identified over two hundred thousand genotype-trait associations. Yet some challenges remain. First, complex traits are often associated with many single nucleotide polymorphisms (SNPs), most with small or moderate effect sizes, making them difficult to detect. Second, many complex traits share a common genetic basis due to 'pleiotropy' and and though few methods consider it, leveraging pleiotropy can improve statistical power to detect genotype-trait associations with weaker effect sizes. Third, currently available statistical methods are limited in explaining the functional mechanisms through which genetic variants are associated with specific or multiple traits. We propose multi-GPA-Tree to address these challenges. The multi-GPA-Tree approach can identify risk SNPs associated with single as well as multiple traits while also identifying the combinations of functional annotations that can explain the mechanisms through which risk-associated SNPs are linked with the traits. First, we implemented simulation studies to evaluate the proposed multi-GPA-Tree method and compared its performance with existing statistical approaches. The results indicate that multi-GPA-Tree outperforms existing statistical approaches in detecting risk-associated SNPs for multiple traits. Second, we applied multi-GPA-Tree to a systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA), and to a Crohn's disease (CD) and ulcertive colitis (UC) GWAS, and functional annotation data including GenoSkyline and GenoSkylinePlus. Our results demonstrate that multi-GPA-Tree can be a powerful tool that improves association mapping while facilitating understanding of the underlying genetic architecture of complex traits and potential mechanisms linking risk-associated SNPs with complex traits.

Migrating Type 2 Dendritic Cells Prime Mucosal Th17 Cells Specific to Small Intestinal Commensal Bacteria.

Dendritic cells (DCs) are professional APCs equipped with MHC-restricted Ags, costimulations, and cytokines that effectively prime and differentiate naive T cells into distinct functional subsets. The immune signals that DCs carry reflect the route of Ag uptake and the innate stimuli they received. In the mucosal tissues, owing to the great variety of foreign Ags and inflammatory cues, DCs are predominantly activated and migratory. In the small intestine, CD4 Th17 cells are abundant and have been shown to be regulated by DCs and macrophages. Using a mouse commensal bacteria experimental model, we identified that the early priming step of commensal-driven Th17 cells is controlled by bona fide Zbtb46-expressing DCs. CCR7-dependent migration of type 2 DCs (DC2s) from the small intestine to the mesenteric lymph nodes (MLNs) is essential for the activation of naive CD4 T cells. The migratory DC2 population in the MLNs is almost exclusively Esam+ cells. Single-cell RNA sequencing highlighted the abundance of costimulatory markers (CD40 and OX40) and chemokines (Ccl22 and Cxcl16) on MLN migratory DCs. Further resolution of MLN migratory DC2s revealed that the Th17-polarizing cytokine IL-6 colocalizes with DC2s expressing CD40, Ccl17, and Ccl22. Thus, early Th17 cell differentiation is initiated by a small subset of migratory DC2s in the gut-draining lymph nodes.

Metabolic Regulation of Gene Expression by Histone Lysine ß-Hydroxybutyrylation.

Here we report the identification and verification of a ß-hydroxybutyrate-derived protein modification, lysine ß-hydroxybutyrylation (Kbhb), as a new type of histone mark. Histone Kbhb marks are dramatically induced in response to elevated ß-hydroxybutyrate levels in cultured cells and in livers from mice subjected to prolonged fasting or streptozotocin-induced diabetic ketoacidosis. In total, we identified 44 histone Kbhb sites, a figure comparable to the known number of histone acetylation sites. By ChIP-seq and RNA-seq analysis, we demonstrate that histone Kbhb is a mark enriched in active gene promoters and that the increased H3K9bhb levels that occur during starvation are associated with genes upregulated in starvation-responsive metabolic pathways. Histone ß-hydroxybutyrylation thus represents a new epigenetic regulatory mark that couples metabolism to gene expression, offering a new avenue to study chromatin regulation and diverse functions of ß-hydroxybutyrate in the context of important human pathophysiological states, including diabetes, epilepsy, and neoplasia.

GPA-Tree: statistical approach for functional-annotation-tree-guided prioritization of GWAS results.

MOTIVATION: In spite of great success of genome-wide association studies (GWAS), multiple challenges still remain. First, complex traits are often associated with many single nucleotide polymorphisms (SNPs), each with small or moderate effect sizes. Second, our understanding of the functional mechanisms through which genetic variants are associated with complex traits is still limited. To address these challenges, we propose GPA-Tree and it simultaneously implements association mapping and identifies key combinations of functional annotations related to risk-associated SNPs by combining a decision tree algorithm with a hierarchical modeling framework. RESULTS: First, we implemented simulation studies to evaluate the proposed GPA-Tree method and compared its performance with existing statistical approaches. The results indicate that GPA-Tree outperforms existing statistical approaches in detecting risk-associated SNPs and identifying the true combinations of functional annotations with high accuracy. Second, we applied GPA-Tree to a systemic lupus erythematosus (SLE) GWAS and functional annotation data including GenoSkyline and GenoSkylinePlus. The results from GPA-Tree highlight the dysregulation of blood immune cells, including but not limited to primary B, memory helper T, regulatory T, neutrophils and CD8+ memory T cells in SLE. These results demonstrate that GPA-Tree can be a powerful tool that improves association mapping while facilitating understanding of the underlying genetic architecture of complex traits and potential mechanisms linking risk-associated SNPs with complex traits. AVAILABILITY AND IMPLEMENTATION: The GPATree software is available at https://dongjunchung.github.io/GPATree/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A Bayesian multivariate mixture model for high throughput spatial transcriptomics.

High throughput spatial transcriptomics (HST) is a rapidly emerging class of experimental technologies that allow for profiling gene expression in tissue samples at or near single-cell resolution while retaining the spatial location of each sequencing unit within the tissue sample. Through analyzing HST data, we seek to identify sub-populations of cells within a tissue sample that may inform biological phenomena. Existing computational methods either ignore the spatial heterogeneity in gene expression profiles, fail to account for important statistical features such as skewness, or are heuristic-based network clustering methods that lack the inferential benefits of statistical modeling. To address this gap, we develop SPRUCE: a Bayesian spatial multivariate finite mixture model based on multivariate skew-normal distributions, which is capable of identifying distinct cellular sub-populations in HST data. We further implement a novel combination of Pólya-Gamma data augmentation and spatial random effects to infer spatially correlated mixture component membership probabilities without relying on approximate inference techniques. Via a simulation study, we demonstrate the detrimental inferential effects of ignoring skewness or spatial correlation in HST data. Using publicly available human brain HST data, SPRUCE outperforms existing methods in recovering expertly annotated brain layers. Finally, our application of SPRUCE to human breast cancer HST data indicates that SPRUCE can distinguish distinct cell populations within the tumor microenvironment. An R package spruce for fitting the proposed models is available through The Comprehensive R Archive Network.

A Bayesian framework for pathway-guided identification of cancer subgroups by integrating multiple types of genomic data.

In recent years, comprehensive cancer genomics platforms, such as The Cancer Genome Atlas (TCGA), provide access to an enormous amount of high throughput genomic datasets for each patient, including gene expression, DNA copy number alterations, DNA methylation, and somatic mutation. While the integration of these multi-omics datasets has the potential to provide novel insights that can lead to personalized medicine, most existing approaches only focus on gene-level analysis and lack the ability to facilitate biological findings at the pathway-level. In this article, we propose Bayes-InGRiD (Bayesian Integrative Genomics Robust iDentification of cancer subgroups), a novel pathway-guided Bayesian sparse latent factor model for the simultaneous identification of cancer patient subgroups (clustering) and key molecular features (variable selection) within a unified framework, based on the joint analysis of continuous, binary, and count data. By utilizing pathway (gene set) information, Bayes-InGRiD does not only enhance the accuracy and robustness of cancer patient subgroup and key molecular feature identification, but also promotes biological understanding and interpretation. Finally, to facilitate an efficient posterior sampling, an alternative Gibbs sampler for logistic and negative binomial models is proposed using Pólya-Gamma mixtures of normal to represent latent variables for binary and count data, which yields a conditionally Gaussian representation of the posterior. The R package "INGRID" implementing the proposed approach is currently available in our research group GitHub webpage (https://dongjunchung.github.io/INGRID/).

Long-Term Impact of Daily E-cigarette Exposure on the Lungs of Asthmatic Mice.

INTRODUCTION: Although the greater popularity of electronic cigarettes (EC) among asthmatics is alarming, there is limited knowledge of the long-term consequences of EC exposure in asthmatics. AIMS AND METHODS: Mild asthmatic C57/BL6J adult male and female mice were established by intranasal insufflation with three combined allergens. The asthmatic and age and sex-matched' naïve mice were exposed to air, nicotine-free (propylene glycol [PG]/vegetable glycerin [VG]-only), or PG/VG+Nicotine, 4 hours daily for 3 months. The effects of EC exposure were accessed by measuring cytokines in bronchoalveolar lavage, periodic acid-schiff (PAS) staining, mitochondrial DNA copy numbers (mtCN), and the transcriptome in the lung. Significance was false discovery rate <0.2 for transcriptome and 0.05 for the others. RESULTS: In asthmatic mice, PG/VG+Nicotine increased PAS-positive cells and IL-13 compared to mice exposed to air and PG/VG-only. In naïve mice exposed to PG/VG+Nicotine and PG/VG-only, higher INF-γ was observed compared to mice exposed only to air. PG/VG-only and PG/VG+Nicotine had significantly higher mtCN compared to air exposure in asthmatic mice, while the opposite pattern was observed in non-asthmatic naïve mice. Different gene expression patterns were profoundly found for asthmatic mice exposed to PG/VG+Nicotine compared to PG/VG-only, including genes involved in mitochondrial dysfunction, oxidative phosphorylation, and p21-activated kinase (PAK) signaling. CONCLUSIONS: This study provides experimental evidence of the potential impact of nicotine enhancement on the long-term effects of EC in asthmatics compared to non-asthmatics. IMPLICATIONS: The findings from this study indicate the potential impact of EC in asthmatics by addressing multiple biological markers. The long-term health outcomes of EC in the susceptible group can be instrumental in supporting policymaking and educational campaigns and informing the public, healthcare providers, and EC users about the underlying risks of EC use.

OGT suppresses S6K1-mediated macrophage inflammation and metabolic disturbance.

Enhanced inflammation is believed to contribute to overnutrition-induced metabolic disturbance. Nutrient flux has also been shown to be essential for immune cell activation. Here, we report an unexpected role of nutrient-sensing O-linked ß-N-acetylglucosamine (O-GlcNAc) signaling in suppressing macrophage proinflammatory activation and preventing diet-induced metabolic dysfunction. Overnutrition stimulates an increase in O-GlcNAc signaling in macrophages. O-GlcNAc signaling is down-regulated during macrophage proinflammatory activation. Suppressing O-GlcNAc signaling by O-GlcNAc transferase (OGT) knockout enhances macrophage proinflammatory polarization, promotes adipose tissue inflammation and lipolysis, increases lipid accumulation in peripheral tissues, and exacerbates tissue-specific and whole-body insulin resistance in high-fat-diet-induced obese mice. OGT inhibits macrophage proinflammatory activation by catalyzing ribosomal protein S6 kinase beta-1 (S6K1) O-GlcNAcylation and suppressing S6K1 phosphorylation and mTORC1 signaling. These findings thus identify macrophage O-GlcNAc signaling as a homeostatic mechanism maintaining whole-body metabolism under overnutrition.

Trefoil factor 2 secreted from damaged hepatocytes activates hepatic stellate cells to induce fibrogenesis.

Liver fibrosis is a common characteristic of chronic liver diseases. The activation of hepatic stellate cells (HSCs) plays a key role in fibrogenesis in response to liver injury, yet the mechanism by which damaged hepatocytes modulate the activation of HSCs is poorly understood. Our previous studies have established that liver-specific deletion of O-GlcNAc transferase (OGT)leads to hepatocyte necroptosis and spontaneous fibrosis. Here, we report that OGT-deficient hepatocytes secrete trefoil factor 2 (TFF2) that activates HSCs and contributes to the fibrogenic process. The expression and secretion of TFF2 are induced in OGT-deficient hepatocytes but not in WT hepatocytes. TFF2 activates the platelet-derived growth factor receptor beta signaling pathway that promotes the proliferation and migration of primary HSCs. TFF2 protein expression is elevated in mice with carbon tetrachloride-induced liver injury. These findings identify TFF2 as a novel factor that mediates intercellular signaling between hepatocytes and HSCs and suggest a role of the hepatic OGT-TFF2 axis in the process of fibrogenesis.

Conditional knockout of oestrogen receptor alpha in CD11c+ cells impacts female survival and inflammatory cytokine profile in murine lupus.

Oestrogen and oestrogen receptor alpha (ERα) have been implicated in systemic lupus erythematosus pathogenesis. ERα signalling influences dendritic cell (DC) development and function, as well as inflammation and downstream immune responses. We previously reported that ERα modulates multiple Toll-like receptor-stimulated pathways in both conventional and plasmacytoid DCs in lupus-prone mice. For example, CD11chi MHCII+ cell numbers are reduced in mice with global ERα deficiency or when expressing a short variant of ERα. Herein, RNA-seq analysis of CD11chi cells from bone marrow of NZM2410 mice expressing WT ERα versus ERα short versus ERα null revealed differentially expressed complement genes, interferon-related genes and cytokine signalling (e.g., IL-17 and Th17 pathways). To better understand the role of ERα in CD11c+ cells, lupus prone NZM2410 mice with selective deletion of the Esr1 gene in CD11c+ cells were generated. Phenotype and survival of these mice were similar with the exception of Cre positive (CrePos) female mice. CrePos females, but not males, all died unexpectedly prior to 35 weeks. DC subsets were not significantly different between groups. Since ERα is necessary for robust development of DCs, this result suggests that DC fate was determined prior to CD11c expression and subsequent ERα deletion (i.e., proximally in DC ontogeny). Overall, findings point to a clear functional role for ERα in regulating cytokine signalling and inflammation, suggesting that further study into ERα-mediated regulatory mechanisms in DCs and other immune cell types is warranted.

IRIS-FGM: an integrative single-cell RNA-Seq interpretation system for functional gene module analysis.

SUMMARY: Single-cell RNA-Seq (scRNA-Seq) data is useful in discovering cell heterogeneity and signature genes in specific cell populations in cancer and other complex diseases. Specifically, the investigation of condition-specific functional gene modules (FGM) can help to understand interactive gene networks and complex biological processes in different cell clusters. QUBIC2 is recognized as one of the most efficient and effective biclustering tools for condition-specific FGM identification from scRNA-Seq data. However, its limited availability to a C implementation restricted its application to only a few downstream analysis functionalities. We developed an R package named IRIS-FGM (Integrative scRNA-Seq Interpretation System for Functional Gene Module analysis) to support the investigation of FGMs and cell clustering using scRNA-Seq data. Empowered by QUBIC2, IRIS-FGM can effectively identify condition-specific FGMs, predict cell types/clusters, uncover differentially expressed genes and perform pathway enrichment analysis. It is noteworthy that IRIS-FGM can also take Seurat objects as input, facilitating easy integration with the existing analysis pipeline. AVAILABILITY AND IMPLEMENTATION: IRIS-FGM is implemented in the R environment (as of version 3.6) with the source code freely available at https://github.com/BMEngineeR/IRISFGM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Envelope-based partial partial least squares with application to cytokine-based biomarker analysis for COVID-19.

Partial least squares (PLS) regression is a popular alternative to ordinary least squares regression because of its superior prediction performance demonstrated in many cases. In various contemporary applications, the predictors include both continuous and categorical variables. A common practice in PLS regression is to treat the categorical variable as continuous. However, studies find that this practice may lead to biased estimates and invalid inferences (Schuberth et al., 2018). Based on a connection between the envelope model and PLS, we develop an envelope-based partial PLS estimator that considers the PLS regression on the conditional distributions of the response(s) and continuous predictors on the categorical predictors. Root-n consistency and asymptotic normality are established for this estimator. Numerical study shows that this approach can achieve more efficiency gains in estimation and produce better predictions. The method is applied for the identification of cytokine-based biomarkers for COVID-19 patients, which reveals the association between the cytokine-based biomarkers and patients' clinical information including disease status at admission and demographical characteristics. The efficient estimation leads to a clear scientific interpretation of the results.

Association of Referral Source and Substance Use with Hepatitis C Virus Outcomes at a Southern Academic Medical Center.

OBJECTIVES: Therapeutic advances make the cure of chronic hepatitis C virus (HCV) infection achievable for individuals aware of their diagnosis who can access care. Identifying barriers to accessing care is critical to achieve population-level HCV elimination and improve the cascade of care from diagnosis to cure. METHODS: To identify barriers to HCV care, we performed a retrospective observational analysis of outcomes for patients with chronic HCV referred to an infectious diseases clinic at an academic medical center in Charleston, South Carolina between January 1, 2015 and January 1, 2020. We categorized outcomes in the cascade of care between "never presenting for evaluation" and "completed treatment with documented cure." Patient demographic factors, referral source, ZIP code of residence, insurance status, clinical characteristics, antiviral regimen, psychiatric and substance use history, and route of infection were assessed for associations with care outcomes. RESULTS: Of 407 referrals, 32% of patients never presented for an initial evaluation, an outcome that was associated with active substance use, mental health disease, and referral from an emergency department or obstetrics-gynecology provider. Of the patients who presented for an initial evaluation, 78% of patients initiated treatment. Active substance use was the only variable associated with lack of therapy initiation after presenting for an initial evaluation (odds ratio 2.5, 95% confidence interval 1.07-5.84). Once treatment had been initiated, no clinical or demographic variables were associated with odds of achieving documented or presumed HCV cure. CONCLUSIONS: Active substance use, mental health disease, and referral from an emergency department or obstetrics-gynecology provider were associated with a lower odds of presenting for evaluation and initiation of HCV treatment. Innovative models to improve access to care and increase outreach to vulnerable populations will be essential to eliminate HCV.

PALMER: improving pathway annotation based on the biomedical literature mining with a constrained latent block model.

BACKGROUND: In systems biology, it is of great interest to identify previously unreported associations between genes. Recently, biomedical literature has been considered as a valuable resource for this purpose. While classical clustering algorithms have popularly been used to investigate associations among genes, they are not tuned for the literature mining data and are also based on strong assumptions, which are often violated in this type of data. For example, these approaches often assume homogeneity and independence among observations. However, these assumptions are often violated due to both redundancies in functional descriptions and biological functions shared among genes. Latent block models can be alternatives in this case but they also often show suboptimal performances, especially when signals are weak. In addition, they do not allow to utilize valuable prior biological knowledge, such as those available in existing databases. RESULTS: In order to address these limitations, here we propose PALMER, a constrained latent block model that allows to identify indirect relationships among genes based on the biomedical literature mining data. By automatically associating relevant Gene Ontology terms, PALMER facilitates biological interpretation of novel findings without laborious downstream analyses. PALMER also allows researchers to utilize prior biological knowledge about known gene-pathway relationships to guide identification of gene-gene associations. We evaluated PALMER with simulation studies and applications to studies of pathway-modulating genes relevant to cancer signaling pathways, while utilizing biological pathway annotations available in the KEGG database as prior knowledge. CONCLUSIONS: We showed that PALMER outperforms traditional latent block models and it provides reliable identification of novel gene-gene associations by utilizing prior biological knowledge, especially when signals are weak in the biomedical literature mining dataset. We believe that PALMER and its relevant user-friendly software will be powerful tools that can be used to improve existing pathway annotations and identify novel pathway-modulating genes.

Defects in the Exocyst-Cilia Machinery Cause Bicuspid Aortic Valve Disease and Aortic Stenosis.

BACKGROUND: Bicuspid aortic valve (BAV) disease is a congenital defect that affects 0.5% to 1.2% of the population and is associated with comorbidities including ascending aortic dilation and calcific aortic valve stenosis. To date, although a few causal genes have been identified, the genetic basis for the vast majority of BAV cases remains unknown, likely pointing to complex genetic heterogeneity underlying this phenotype. Identifying genetic pathways versus individual gene variants may provide an avenue for uncovering additional BAV causes and consequent comorbidities. METHODS: We performed genome-wide association Discovery and Replication Studies using cohorts of 2131 patients with BAV and 2728 control patients, respectively, which identified primary cilia genes as associated with the BAV phenotype. Genome-wide association study hits were prioritized based on P value and validated through in vivo loss of function and rescue experiments, 3-dimensional immunohistochemistry, histology, and morphometric analyses during aortic valve morphogenesis and in aged animals in multiple species. Consequences of these genetic perturbations on cilia-dependent pathways were analyzed by Western and immunohistochemistry analyses, and assessment of aortic valve and cardiac function were determined by echocardiography. RESULTS: Genome-wide association study hits revealed an association between BAV and genetic variation in human primary cilia. The most associated single-nucleotide polymorphisms were identified in or near genes that are important in regulating ciliogenesis through the exocyst, a shuttling complex that chaperones cilia cargo to the membrane. Genetic dismantling of the exocyst resulted in impaired ciliogenesis, disrupted ciliogenic signaling and a spectrum of cardiac defects in zebrafish, and aortic valve defects including BAV, valvular stenosis, and valvular calcification in murine models. CONCLUSIONS: These data support the exocyst as required for normal ciliogenesis during aortic valve morphogenesis and implicate disruption of ciliogenesis and its downstream pathways as contributory to BAV and associated comorbidities in humans.

Sparse Linear Discriminant Analysis using the Prior-Knowledge-Guided Block Covariance Matrix.

There are two key challenges when using a linear discriminant analysis in the high-dimensional setting, including singularity of the covariance matrix and difficulty of interpreting the resulting classifier. Although several methods have been proposed to address these problems, they focused only on identifying a parsimonious set of variables maximizing classification accuracy. However, most methods did not consider dependency between variables and efficacy of selected variables appropriately. To address these limitations, here we propose a new approach that directly estimates the sparse discriminant vector without a need of estimating the whole inverse covariance matrix, by formulating a quadratic optimization problem. Furthermore, this approach also allows to integrate external information to guide the structure of covariance matrix. We evaluated the proposed model with simulation studies. We then applied it to the transcriptomic study that aims to identify genomic markers predictive of the response to cancer immunotherapy, where the covariance matrix was constructed based on the prior knowledge available in the pathway database.

hubViz: A Novel Tool for Hub-centric Visualization.

Visualization algorithms have been widely used for intuitive interrogation of genomic data and popularly used tools include MDS, t-SNE, and UMAP. However, these algorithms are not tuned for the visualization of binary data and none of them consider the hubness of observations for the visualization. In order to address these limitations, here we propose hubViz, a novel tool for hub-centric visualization of binary data. We evaluated the performance of hubViz with its application to the gene expression data measured in multiple brain regions of rats exposed to cocaine, the single-cell RNA-seq data of peripheral blood mononuclear cells treated with interferon beta, and the literature mining data to investigate relationships among diseases. We further evaluated the performance of hubViz using simulation studies. We showed that hubViz provides effective visual inspection by locating the hub in the center and the contrasting elements in the opposite sides around the center. We believe that hubViz and its software can be powerful tools that can improve visualizations of various genomic data. The hubViz is implemented as an R package hubviz, which is publicly available at https://dongjunchung.github.io/hubviz/.

Improving SNP prioritization and pleiotropic architecture estimation by incorporating prior knowledge using graph-GPA.

Summary: Integration of genetic studies for multiple phenotypes is a powerful approach to improving the identification of genetic variants associated with complex traits. Although it has been shown that leveraging shared genetic basis among phenotypes, namely pleiotropy, can increase statistical power to identify risk variants, it remains challenging to effectively integrate genome-wide association study (GWAS) datasets for a large number of phenotypes. We previously developed graph-GPA, a Bayesian hierarchical model that integrates multiple GWAS datasets to boost statistical power for the identification of risk variants and to estimate pleiotropic architecture within a unified framework. Here we propose a novel improvement of graph-GPA which incorporates external knowledge about phenotype-phenotype relationship to guide the estimation of genetic correlation and the association mapping. The application of graph-GPA to GWAS datasets for 12 complex diseases with a prior disease graph obtained from a text mining of biomedical literature illustrates its power to improve the identification of risk genetic variants and to facilitate understanding of genetic relationship among complex diseases. Availability and implementation: graph-GPA is implemented as an R package 'GGPA', which is publicly available at http://dongjunchung.github.io/GGPA/. DDNet, a web interface to query diseases of interest and download a prior disease graph obtained from a text mining of biomedical literature, is publicly available at http://www.chunglab.io/ddnet/. Supplementary information: Supplementary data are available at Bioinformatics online.