Activated sputum eosinophils associated with exacerbations in children on mepolizumab.

BACKGROUND: MUPPITS-2 was a randomized, placebo-controlled clinical trial that demonstrated mepolizumab (anti-IL-5) reduced exacerbations and blood and airway eosinophils in urban children with severe eosinophilic asthma. Despite this reduction in eosinophilia, exacerbation risk persisted in certain patients treated with mepolizumab. This raises the possibility that subpopulations of airway eosinophils exist that contribute to breakthrough exacerbations. OBJECTIVE: We aimed to determine the effect of mepolizumab on airway eosinophils in childhood asthma. METHODS: Sputum samples were obtained from 53 MUPPITS-2 participants. Airway eosinophils were characterized using mass cytometry and grouped into subpopulations using unsupervised clustering analyses of 38 surface and intracellular markers. Differences in frequency and immunophenotype of sputum eosinophil subpopulations were assessed based on treatment arm and frequency of exacerbations. RESULTS: Median sputum eosinophils were significantly lower among participants treated with mepolizumab compared with placebo (58% lower, 0.35% difference [95% CI 0.01, 0.74], P = .04). Clustering analysis identified 3 subpopulations of sputum eosinophils with varied expression of CD62L. CD62Lint and CD62Lhi eosinophils exhibited significantly elevated activation marker and eosinophil peroxidase expression, respectively. In mepolizumab-treated participants, CD62Lint and CD62Lhi eosinophils were more abundant in participants who experienced exacerbations than in those who did not (100% higher for CD62Lint, 0.04% difference [95% CI 0.0, 0.13], P = .04; 93% higher for CD62Lhi, 0.21% difference [95% CI 0.0, 0.77], P = .04). CONCLUSIONS: Children with eosinophilic asthma treated with mepolizumab had significantly lower sputum eosinophils. However, CD62Lint and CD62Lhi eosinophils were significantly elevated in children on mepolizumab who had exacerbations, suggesting that eosinophil subpopulations exist that contribute to exacerbations despite anti-IL-5 treatment.

Peripheral Blood Single-Cell Sequencing Uncovers Common and Specific Immune Aberrations in Fibrotic Lung Diseases.

Rationale and Objectives: The extent and commonality of peripheral blood immune aberrations in fibrotic interstitial lung diseases are not well characterized. In this study, we aimed to identify common and distinct immune aberrations in patients with idiopathic pulmonary fibrosis (IPF) and fibrotic hypersensitivity pneumonitis (FHP) using cutting-edge single-cell profiling technologies. Methods: Single-cell RNA sequencing was performed on patients and healthy controls' peripheral blood and bronchoalveolar lavage samples using 10X Genomics 5' gene expression and V(D)J profiling. Cell type composition, transcriptional profiles, cellular trajectories and signaling, and T and B cell receptor repertoires were studied. The standard Seurat R pipeline was followed for cell type composition and differential gene expression analyses. Transcription factor activity was imputed using the DoRothEA-VIPER algorithm. Pseudotime analyses were conducted using Monocle3, while RNA velocity analyses were performed with Velocyto, scVelo, and CellRank. Cell-cell connectomics were assessed using the Connectome R package. V(D)J analyses were conducted using CellRanger and Immcantation frameworks. Across all analyses, disease group differences were assessed using the Wilcoxon rank-sum test. Measurements and Main Results: 327,990 cells from 83 samples were profiled. Overall, changes in monocytes were common to IPF and FHP, whereas lymphocytes exhibited disease-specific aberrations. Both diseases displayed enrichment of CCL3 hi /CCL4 hi CD14+ monocytes (p<2.2e-16) and S100A hi CD14+ monocytes (p<2.2e-16) versus controls. Trajectory and RNA velocity analysis suggested that pro-fibrotic macrophages observed in BAL originated from peripheral blood monocytes. Lymphocytes exhibited disease-specific aberrations, with CD8+ GZMK hi T cells and activated B cells primarily enriched in FHP patients. V(D)J analyses revealed unique T and B cell receptor complementarity-determining region 3 (CDR3) amino acid compositions (p<0.05) in FHP and significant IgA enrichment in IPF (p<5.2e-7). Conclusions: We identified common and disease-specific immune mechanisms in IPF and FHP; S100A hi monocytes and SPP1 hi macrophages are common to IPF and FHP, whereas GMZK hi T lymphocytes and T and B cell receptor repertoires were unique in FHP. Our findings open novel strategies for the diagnosis and treatment of IPF and FHP.

A hybrid machine learning and regression method for cell type deconvolution of spatial barcoding-based transcriptomic data.

Spatial barcoding-based transcriptomic (ST) data require cell type deconvolution for cellular-level downstream analysis. Here we present SDePER, a hybrid machine learning and regression method, to deconvolve ST data using reference single-cell RNA sequencing (scRNA-seq) data. SDePER uses a machine learning approach to remove the systematic difference between ST and scRNA-seq data (platform effects) explicitly and efficiently to ensure the linear relationship between ST data and cell type-specific expression profile. It also considers sparsity of cell types per capture spot and across-spots spatial correlation in cell type compositions. Based on the estimated cell type proportions, SDePER imputes cell type compositions and gene expression at unmeasured locations in a tissue map with enhanced resolution. Applications to coarse-grained simulated data and four real datasets showed that SDePER achieved more accurate and robust results than existing methods, suggesting the importance of considering platform effects, sparsity and spatial correlation in cell type deconvolution.

iDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) technology has enabled assessment of transcriptome-wide changes at single-cell resolution. Due to the heterogeneity in environmental exposure and genetic background across subjects, subject effect contributes to the major source of variation in scRNA-seq data with multiple subjects, which severely confounds cell type specific differential expression (DE) analysis. Moreover, dropout events are prevalent in scRNA-seq data, leading to excessive number of zeroes in the data, which further aggravates the challenge in DE analysis. RESULTS: We developed iDESC to detect cell type specific DE genes between two groups of subjects in scRNA-seq data. iDESC uses a zero-inflated negative binomial mixed model to consider both subject effect and dropouts. The prevalence of dropout events (dropout rate) was demonstrated to be dependent on gene expression level, which is modeled by pooling information across genes. Subject effect is modeled as a random effect in the log-mean of the negative binomial component. We evaluated and compared the performance of iDESC with eleven existing DE analysis methods. Using simulated data, we demonstrated that iDESC had well-controlled type I error and higher power compared to the existing methods. Applications of those methods with well-controlled type I error to three real scRNA-seq datasets from the same tissue and disease showed that the results of iDESC achieved the best consistency between datasets and the best disease relevance. CONCLUSIONS: iDESC was able to achieve more accurate and robust DE analysis results by separating subject effect from disease effect with consideration of dropouts to identify DE genes, suggesting the importance of considering subject effect and dropouts in the DE analysis of scRNA-seq data with multiple subjects.

Differences in Mortality Among Patients With Asthma and COPD Hospitalized With COVID-19.

BACKGROUND: It remains unclear whether patients with asthma and/or chronic obstructive pulmonary disease (COPD) are at increased risk for severe coronavirus disease 2019 (COVID-19). OBJECTIVE: Compare in-hospital COVID-19 outcomes among patients with asthma, COPD, and no airway disease. METHODS: A retrospective cohort study was conducted on 8,395 patients admitted with COVID-19 between March 2020 and April 2021. Airway disease diagnoses were defined using International Classification of Diseases, 10th Revision codes. Mortality and sequential organ failure assessment (SOFA) scores were compared among groups. Logistic regression analysis was used to identify and adjust for confounding clinical features associated with mortality. RESULTS: The median SOFA score in patients without airway disease was 0.32 and mortality was 11%. In comparison, asthma patients had lower SOFA scores (median 0.15; P < .01) and decreased mortality, even after adjusting for age, diabetes, and other confounders (odds ratio 0.65; P = .01). Patients with COPD had higher SOFA scores (median 0.86; P < .01) and increased adjusted odds of mortality (odds ratio 1.40; P < .01). Blood eosinophil count of 200 cells/µL or greater, a marker of type 2 inflammation, was associated with lower mortality across all groups. Importantly, patients with asthma showed improved outcomes even after adjusting for eosinophilia, indicating that noneosinophilic asthma was associated with protection as well. CONCLUSIONS: COVID-19 severity was increased in patients with COPD and decreased in those with asthma, eosinophilia, and noneosinophilic asthma, independent of clinical confounders. These findings suggest that COVID-19 severity may be influenced by intrinsic immunological factors in patients with airway diseases, such as type 2 inflammation.

A Multi-omic Analysis of the Human Lung Reveals Distinct Cell Specific Aging and Senescence Molecular Programs.

Age is a major risk factor for lung disease. To understand the mechanisms underlying this association, we characterized the changing cellular, genomic, transcriptional, and epigenetic landscape of lung aging using bulk and single-cell RNAseq (scRNAseq) data. Our analysis revealed age-associated gene networks that reflected hallmarks of aging, including mitochondrial dysfunction, inflammation, and cellular senescence. Cell type deconvolution revealed age-associated changes in the cellular composition of the lung: decreased alveolar epithelial cells and increased fibroblasts and endothelial cells. In the alveolar microenvironment, aging is characterized by decreased AT2B cells and reduced surfactant production, a finding that was validated by scRNAseq and IHC. We showed that a previously reported senescence signature, SenMayo, captures cells expressing canonical senescence markers. SenMayo signature also identified cell-type specific senescence-associated co-expression modules that have distinct molecular functions, including ECM regulation, cell signaling, and damage response pathways. Analysis of somatic mutations showed that burden was highest in lymphocytes and endothelial cells and was associated with high expression of senescence signature. Finally, aging and senescence gene expression modules were associated with differentially methylated regions, with inflammatory markers such as IL1B, IL6R, and TNF being significantly regulated with age. Our findings provide new insights into the mechanisms underlying lung aging and may have implications for the development of interventions to prevent or treat age-related lung diseases.

Construction of prediction model of lymph node metastasis of early cervical cancer based on machine learning algorithm and its application: experience of 204 cases in a single center.

OBJECTIVES: The prediction model of para-aortic lymph node metastasis (LNM) in patients with early cervical cancer was constructed based on the logistic regression (LR) and random forest (RF) algorithms in the machine learning algorithm. The prediction efficiencies of the two models were compared. METHODS: The clinical data of 204 patients with early cervical cancer in the First Affiliated Hospital of Guangxi Medical University were retrospectively collected. The 204 patients were randomly divided into a training set and a verification set according to a ratio of 3:1. The training set was used to build the model. The verification set was used to evaluate model effectiveness. The para-aortic LNM prediction model of early cervical cancer was established by LR and RF. Receiver operating characteristic curve (ROC), sensitivity, and specificity were used to evaluate the prediction performances of the two models. RESULTS: LR analysis showed that tumor diameter > 4 cm, choroidal aneurysm embolism, pelvic lymph node metastasis, and high preoperative squamous cell carcinoma antigen (SCC-Ag) level were risk factors for para-aortic LNM in patients with early cervical cancer (P < 0.05). The area under the ROC curve (AUC) was 0.914. The sensitivity, specificity, and accuracy were 92.6%, 66.7%, 87.0%, respectively. The results of the importance analysis of the characteristic variables of the RF showed that the top 5 variables were preoperative SCC-Ag level, tumor diameter > 4 cm, advanced clinical stage, cancer thrombus, and pelvic lymph node metastasis. The AUC of the RF was 0.883. The sensitivity, specificity, and accuracy were 90.7%, 53.3%, 82.6%, respectively. There was no significant difference in AUC between the LR and RF (P > 0.05). CONCLUSIONS: Both LR and RF models based on machine learning algorithm have great predictive value in predicting early cervical cancer para-aortic lymph node metastasis.

Integrated Analysis of Tracheobronchial Fluid from Before and After Cardiopulmonary Bypass Reveals Activation of the Integrated Stress Response and Altered Pulmonary Microvascular Permeability.

Objective: We aim to comprehensively describe the transcriptional activity and signaling of pulmonary parenchymal and immune cells before and after cardiopulmonary bypass (CPB) by using a multi-omic approach coupled with functional cellular assays. We hypothesize that key signaling pathways from specific cells within the lung alter pulmonary endothelial cell function resulting in worsening or improving disease. Methods: We collected serial tracheobronchial lavage samples from intubated patients less than 2-years-old undergoing surgery with CPB. Samples were immediately processed for single cell RNA sequencing (10x Genomics). Cell clustering, cell-type annotation, and visualization were performed, and differentially expressed genes (DEG) between serial samples were identified. Metabolomic and proteomic analyses were performed on the supernatant using mass spectrometry and a multiplex assay (SomaScan) respectively. Functional assays were done using electric cell-substrate impedance sensing to measure resistance across human pulmonary microvascular endothelial cells (HPMECs). Results: Analysis of eight patients showed a heterogeneous mixture of pulmonary parenchymal and immune cells. Cell clustering demonstrated time-dependent changes in the transcriptomic signature indicating altered cellular phenotypes after CPB. DEG analysis was represented by genes involved in host defense, innate immunity, and the mitochondrial respiratory transport chain. Ingenuity pathway analysis showed upregulation of the integrated stress response across all cell types after CPB. Metabolomic analysis demonstrated upregulation of ascorbate and aldarate metabolism. Unbiased proteomic analysis revealed upregulation of proteins involved in cytokine and chemokine pathways. Post-CPB patient supernatant improved HMPEC barrier function, suggesting a protective cellular response to CPB. Conclusion: Children who undergo CPB for cardiac surgery have distinct cell populations, transcriptional activity, and metabolism that change over time. The response to ischemia-reperfusion injury in the lower airway of children appears to be protective, with the need to identify potential targets through future investigations.

Noninvasive determinants of pulmonary hypertension in interstitial lung disease.

Pulmonary hypertension (PH) in interstitial lung disease (ILD) is associated with increased mortality and impaired exertional capacity. Right heart catheterization is the diagnostic standard for PH but is invasive and not readily available. Noninvasive physiologic evaluation may predict PH in ILD. Forty-four patients with PH and ILD (PH-ILD) were compared with 22 with ILD alone (non-PH ILD). Six-min walk distance (6MWD, 223 ± 131 vs. 331 ± 125 m, p = 0.02) and diffusing capacity for carbon monoxide (DLCO, 33 ± 14% vs. 55 ± 21%, p < 0.001) were lower in patients with PH-ILD. PH-ILD patients exhibited a lower gas-exchange derived pulmonary vascular capacitance (GXCAP, 251 ± 132 vs. 465 ± 282 mL × mmHg, p < 0.0001) and extrapolated maximum oxygen uptake (VO2max) (56 ± 32% vs. 84 ± 37%, p = 0.003). Multivariate analysis was performed to determine predictors of VO2 max. GXCAP was the only variable that predicted extrapolated VO2 max among PH-ILD and non-PH ILD patients. Receiver operating characteristic curve analysis assessed the ability of individual noninvasive variables to distinguish between PH-ILD and non-PH ILD patients. GXCAP (area under the curve [AUC] 0.85 ± 0.04, p < 0.0001) and delta ETCO2 (AUC 0.84 ± 0.04, p < 0.0001) were the strongest predictors of PH-ILD. A CART analysis selected GXCAP, estimated right ventricular systolic pressure (eRVSP) by echocardiogram, and FVC/DLCO ratio as predictive variables for PH-ILD. With this analysis, the AUC improved to 0.94 (sensitivity of 0.86 and sensitivity of 0.93). Patients with a GXCAP ≤ 416 mL × mmHg had an 82% probability of PH-ILD. Patients with GXCAP ≤ 416 mL × mmHg and high FVC/DLCO ratio >1.7 had an 80% probability of PH-ILD. Patients with GXCAP ≤ 416 mL × mmHg and an elevated eRVSP by echocardiogram >43 mmHg had 100% probability of PH-ILD. The incorporation of GXCAP with either eRVSP or FVC/DLCO ratio distinguishes between PH-ILD and non-PH-ILD with high probability and may therefore assist in determining the need to proceed with a diagnostic right heart catheterization and potential initiation of pulmonary arterial hypertension-directed therapy in PH-ILD patients.

A novel Bayesian framework for harmonizing information across tissues and studies to increase cell type deconvolution accuracy.

Computational cell type deconvolution on bulk transcriptomics data can reveal cell type proportion heterogeneity across samples. One critical factor for accurate deconvolution is the reference signature matrix for different cell types. Compared with inferring reference signature matrices from cell lines, rapidly accumulating single-cell RNA-sequencing (scRNA-seq) data provide a richer and less biased resource. However, deriving cell type signature from scRNA-seq data is challenging due to high biological and technical noises. In this article, we introduce a novel Bayesian framework, tranSig, to improve signature matrix inference from scRNA-seq by leveraging shared cell type-specific expression patterns across different tissues and studies. Our simulations show that tranSig is robust to the number of signature genes and tissues specified in the model. Applications of tranSig to bulk RNA sequencing data from peripheral blood, bronchoalveolar lavage and aorta demonstrate its accuracy and power to characterize biological heterogeneity across groups. In summary, tranSig offers an accurate and robust approach to defining gene expression signatures of different cell types, facilitating improved in silico cell type deconvolutions.

Biomarkers and molecular endotypes of sarcoidosis: lessons from omics and non-omics studies.

Sarcoidosis is a chronic granulomatous disorder characterized by unknown etiology, undetermined mechanisms, and non-specific therapies except TNF blockade. To improve our understanding of the pathogenicity and to predict the outcomes of the disease, the identification of new biomarkers and molecular endotypes is sorely needed. In this study, we systematically evaluate the biomarkers identified through Omics and non-Omics approaches in sarcoidosis. Most of the currently documented biomarkers for sarcoidosis are mainly identified through conventional "one-for-all" non-Omics targeted studies. Although the application of machine learning algorithms to identify biomarkers and endotypes from unbiased comprehensive Omics studies is still in its infancy, a series of biomarkers, overwhelmingly for diagnosis to differentiate sarcoidosis from healthy controls have been reported. In view of the fact that current biomarker profiles in sarcoidosis are scarce, fragmented and mostly not validated, there is an urgent need to identify novel sarcoidosis biomarkers and molecular endotypes using more advanced Omics approaches to facilitate disease diagnosis and prognosis, resolve disease heterogeneity, and facilitate personalized medicine.

Unagi: Deep Generative Model for Deciphering Cellular Dynamics and In-Silico Drug Discovery in Complex Diseases.

Human diseases are characterized by intricate cellular dynamics. Single-cell sequencing provides critical insights, yet a persistent gap remains in computational tools for detailed disease progression analysis and targeted in-silico drug interventions. Here, we introduce UNAGI, a deep generative neural network tailored to analyze time-series single-cell transcriptomic data. This tool captures the complex cellular dynamics underlying disease progression, enhancing drug perturbation modeling and discovery. When applied to a dataset from patients with Idiopathic Pulmonary Fibrosis (IPF), UNAGI learns disease-informed cell embeddings that sharpen our understanding of disease progression, leading to the identification of potential therapeutic drug candidates. Validation via proteomics reveals the accuracy of UNAGI's cellular dynamics analyses, and the use of the Fibrotic Cocktail treated human Precision-cut Lung Slices confirms UNAGI's predictions that Nifedipine, an antihypertensive drug, may have antifibrotic effects on human tissues. UNAGI's versatility extends to other diseases, including a COVID dataset, demonstrating adaptability and confirming its broader applicability in decoding complex cellular dynamics beyond IPF, amplifying its utility in the quest for therapeutic solutions across diverse pathological landscapes.

Metagenomic sequencing of the bronchoalveolar lavage extracellular virome and cellular transcriptome of sarcoidosis patients does not detect rubella virus.

BACKGROUND:   Sarcoidosis is a multisystem granulomatous inflammatory disease of unclear etiology that involves the lung, skin and other organs, with an unknown antigenic trigger. Recently, evidence has been found in both immune deficient and immune competent patients for rubella virus in cutaneous granulomas. These granulomatous lesions share overlapping features with cutaneous sarcoidosis, raising the question of rubella virus in sarcoidosis. OBJECTIVE: To investigate the presence of rubella virus in sarcoidosis lung samples. METHODS: We employed metagenomic sequencing to interrogate extracellular virome preparations and cellular transcriptomes from bronchoalveolar lavage (BAL) of 209 sarcoidosis patients for rubella virus sequences. RESULTS: We found no evidence for rubella virus genomes in acellular fluid or rubella virus gene expression in BAL cells of sarcoidosis patients. CONCLUSIONS: These findings argue against rubella virus infection or persistence within the lung at time of sampling as a sarcoidosis trigger.

Emerging insights in sarcoidosis: moving forward through reverse translational research.

Sarcoidosis is a chronic granulomatous disease of unknown etiology that primarily affects the lungs. The development of stage IV or fibrotic lung disease accounts for a significant proportion of the morbidity and mortality attributable to sarcoidosis. Further investigation into the active mechanisms of disease pathogenesis and fibrogenesis might illuminate fundamental mediators of injury and repair while providing new opportunities for clinical intervention. However, progress in sarcoidosis research has been hampered by the heterogeneity of clinical phenotypes and the lack of a consensus modeling system. Recently, reverse translational research, wherein observations made at the patient level catalyze hypothesis-driven research at the laboratory bench, has generated new discoveries regarding the immunopathogenic mechanisms of pulmonary granuloma formation, fibrogenesis, and disease model development. The purpose of this review is to highlight the promise and possibility of these novel investigative efforts.

Single-cell multi-omics reveals dyssynchrony of the innate and adaptive immune system in progressive COVID-19.

Dysregulated immune responses against the SARS-CoV-2 virus are instrumental in severe COVID-19. However, the immune signatures associated with immunopathology are poorly understood. Here we use multi-omics single-cell analysis to probe the dynamic immune responses in hospitalized patients with stable or progressive course of COVID-19, explore V(D)J repertoires, and assess the cellular effects of tocilizumab. Coordinated profiling of gene expression and cell lineage protein markers shows that S100Ahi/HLA-DRlo classical monocytes and activated LAG-3hi T cells are hallmarks of progressive disease and highlights the abnormal MHC-II/LAG-3 interaction on myeloid and T cells, respectively. We also find skewed T cell receptor repertories in expanded effector CD8+ clones, unmutated IGHG+ B cell clones, and mutated B cell clones with stable somatic hypermutation frequency over time. In conclusion, our in-depth immune profiling reveals dyssynchrony of the innate and adaptive immune interaction in progressive COVID-19.

Lung Microenvironments and Disease Progression in Fibrotic Hypersensitivity Pneumonitis.

Rationale: Fibrotic hypersensitivity pneumonitis (fHP) is an interstitial lung disease caused by sensitization to an inhaled allergen. Objectives: To identify the molecular determinants associated with progression of fibrosis. Methods: Nine fHP explant lungs and six unused donor lungs (as controls) were systematically sampled (4 samples/lung). According to microcomputed tomography measures, fHP cores were clustered into mild, moderate, and severe fibrosis groups. Gene expression profiles were assessed using weighted gene co-expression network analysis, xCell, gene ontology, and structure enrichment analysis. Gene expression of the prevailing molecular traits was also compared with idiopathic pulmonary fibrosis (IPF). The explant lung findings were evaluated in separate clinical fHP cohorts using tissue, BAL samples, and computed tomography scans. Measurements and Main Results: We found six molecular traits that associated with differential lung involvement. In fHP, extracellular matrix and antigen presentation/sensitization transcriptomic signatures characterized lung zones with only mild structural and histological changes, whereas signatures involved in honeycombing and B cells dominated the transcriptome in the most severely affected lung zones. With increasing disease severity, endothelial function was progressively lost, and progressive disruption in normal cellular homeostatic processes emerged. All six were also found in IPF, with largely similar associations with disease microenvironments. The molecular traits correlated with in vivo disease behavior in a separate clinical fHP cohort. Conclusions: We identified six molecular traits that characterize the morphological progression of fHP and associate with in vivo clinical behavior. Comparing IPF with fHP, the transcriptome landscape was determined considerably by local disease extent rather than by diagnosis alone.

Openness weighted association studies: leveraging personal genome information to prioritize non-coding variants.

MOTIVATION: Identification and interpretation of non-coding variations that affect disease risk remain a paramount challenge in genome-wide association studies (GWAS) of complex diseases. Experimental efforts have provided comprehensive annotations of functional elements in the human genome. On the other hand, advances in computational biology, especially machine learning approaches, have facilitated accurate predictions of cell-type-specific functional annotations. Integrating functional annotations with GWAS signals has advanced the understanding of disease mechanisms. In previous studies, functional annotations were treated as static of a genomic region, ignoring potential functional differences imposed by different genotypes across individuals. RESULTS: We develop a computational approach, Openness Weighted Association Studies (OWAS), to leverage and aggregate predictions of chromosome accessibility in personal genomes for prioritizing GWAS signals. The approach relies on an analytical expression we derived for identifying disease associated genomic segments whose effects in the etiology of complex diseases are evaluated. In extensive simulations and real data analysis, OWAS identifies genes/segments that explain more heritability than existing methods, and has a better replication rate in independent cohorts than GWAS. Moreover, the identified genes/segments show tissue-specific patterns and are enriched in disease relevant pathways. We use rheumatic arthritis and asthma as examples to demonstrate how OWAS can be exploited to provide novel insights on complex diseases. AVAILABILITY AND IMPLEMENTATION: The R package OWAS that implements our method is available at https://github.com/shuangsong0110/OWAS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.