Chronic obstructive pulmonary disease (COPD) was the third leading cause of global mortality in 2011 but receives limited attention and research funding. This Review describes the current knowledge on COPD risk factors, including genetic and epigenetic determinants and their interactions with the microbiome and environmental exposures. Preclinical models are being refined and single-cell transcriptomic, metabolomic, and proteomic technologies are being implemented to investigate the molecular mechanisms of disease progression. Patient cohorts to define biomarkers of early disease and the latest approaches to diagnose pre-COPD are essential to accelerate the development of novel and effective therapeutic interventions and translate new findings into clinical trials. This Review is a summary of topics covered by a symposium organized by the COPD-iNET consortium, an international network of researchers who have established a platform that facilitates collaboration of this multidisciplinary group of preclinical, translational, and clinical researchers.

Background:The majority of COPD patients are characterized by non-type 2 inflammation, yet there are no available non-type 2 biomarkers, as opposed to blood eosinophil count for type 2 inflammation. We aimed to test readily obtainable immune cell ratios as biomarkers for clinical phenotypes in COPD and to determine pathways represented by these ratios using multi-omics data. Methods:Using complete blood counts with differential collected at the Phase 2 (5-year) visit in the COPDGene Study, we calculated three immune cell ratios previously described in COPD and other diseases: the neutrophil-lymphocyte ratio (NLR), the platelet-lymphocyte ratio (PLR), and the Systemic Immune-Inflammation Index (SII = NLR*platelets). We tested for associations with COPD outcomes, including lung function, chest CT scan phenotypes, and exacerbations. Blood RNA-sequencing and proteomics data were used to identify genes, proteins and pathways associated with the ratios. Results:In univariate analyses, the three biomarkers were associated with COPD severity measures. In zero inflated Poisson regression models, all three were associated with increased odds of having an exacerbation but were not associated with exacerbation counts. Conversely, the three biomarkers were generally associated with prospective exacerbation counts, but not the zero-inflation term. In logistic regression models, the three biomarkers were significantly associated with having two or more exacerbations in the prior year; however, receiver operating characteristic analyses did not lead to clear cutoff values. Complement and PI3K signaling pathways were enriched across more than one ratio in both the RNA-sequencing and proteomics results. Other inflammatory pathways relevant in COPD appeared in different enrichment sets in either omics data type. Conclusions:Higher levels of three easily obtained blood cell ratios were associated with COPD severity and exacerbations outcomes; however, there are not clear thresholds which would be required for clinical application. Blood RNA-sequencing and proteomics identified inflammatory pathways associated with the three biomarkers, including targets for COPD therapies currently in human trials.

Chronic Obstructive Pulmonary Disease (COPD) is a complex, heterogeneous disease. Traditional subtyping methods generally focus on either the clinical manifestations or the molecular endotypes of the disease, leading to classifications that only partially reflect disease heterogeneity. Here, we introduce a variational autoencoder-based subtyping pipeline that jointly embeds clinical and gene expression data into a single subject-level representation. We evaluate the framework in the COPDGene study, a large study of current and former smoking individuals with and without COPD. Prediction experiments show that the embeddings have predictive accuracy comparable to or better than other unsupervised embedding approaches. Using trajectory learning approaches, we identify five well-separated subtypes with distinct clinical phenotypes, expression signatures, and longitudinal outcomes. Finally, we show that our findings generalize to an external validation cohort. Overall, our approach enables a transition from isolated phenotypic or molecular subtyping toward an integrated and clinically meaningful understanding of COPD heterogeneity.

Background:Alternative splicing, the mechanism by which intronic sequences are excised from pre-mRNAs to produce mature mRNA, affects >95% of human protein-coding genes and is a major driver of human disease states. The spliceosome, a protein-RNA complex responsible for splicing pre-mRNA, identifies candidate splice sites partly through the recognition of characteristic sequence motifs at exon-intron junctions. Deep learning models that predict the presence of splice sites from pre-mRNA sequence have achieved breakthrough performance relative to previous machine-learning techniques, and these models have improved our ability to identify pathogenic genetic variants that alter splicing. Results:We show that, while overall performance measures from these models suggest near-perfect performance, substantial gaps in prediction remain, including the identification of splice sites with low usage rates and tissue-specific splice sites. We leverage one of the largest paired RNA and genotyping datasets used to date to train a novel splicing model optimized for a specific cell type, human airway epithelial cells. We trained a dilated convolutional neural network on data from cultured airway epithelial cells from 100 donors, and showed that this model outperforms current state-of-the-art models on splice site identification and splice site usage quantification, including on multiple tissues not included in the model training data. Conclusions:We present the most comprehensive evaluation of state-of-the-art splicing models published to date, revealing reasonable performance across models for genetic variant effect prediction along with important performance gaps and insights into directions for future model development.

Chronic obstructive pulmonary disease (COPD) exhibits marked heterogeneity in lung function decline, mortality, exacerbations, and other disease-related outcomes. Omic risk scores (ORS) estimate the cumulative contribution of omics, such as the transcriptome, proteome, and metabolome, to a particular trait. This study evaluated associations between blood-based ORS and COPD-related traits in both smoking-enriched and general population cohorts. ORS were developed and tested in 3,339 participants of Genetic Epidemiology of COPD (COPDGene) with blood RNA-sequencing, proteomic, and metabolomic data. Single- and multi-omic risk scores were trained on 24 cross-sectional and five longitudinal traits using 80

RATIONALE: Chronic Obstructive Pulmonary Disease (COPD) exacerbations, manifesting as episodes of respiratory symptoms worsening, are a major cause of COPD morbidity and mortality. While severe COPD patients are prone to exacerbations, there is marked heterogeneity in exacerbation frequency, duration, severity, and response to treatment. Understanding the molecular mechanisms behind this heterogeneity could inform the development of targeted therapies. OBJECTIVES: To identify COPD exacerbation endotypes – i.e. individuals with shared biology – within individuals with severe COPD (GOLD spirometry grade 3-4) using individual-level networks. METHODS: We use a gene regulatory network (GRN) modeling approach to dissect heterogeneity among participants in the COPDGene study with severe COPD (GOLD 3-4). Generally, GRNs model molecular interactions regulating gene expression as networks of genes and transcription factors (TF). We reconstruct individual GRNs for each study subject by applying the PANDA and LIONESS algorithms to predicted TF-binding and whole blood RNA-Seq data from the COPDGene 5-year follow-up visit. These GRNs are composed of weighted edges representing subject-specific TF-gene interactions. For each TF-gene pair, we perform linear regression of subjects’ edge weights against annual exacerbation rates, adjusting for confounders, and select TF-gene edges with significant associations (FDR<0.1). We cluster COPD subjects by their weights in this significant edge subset and compare spirometry, imaging, and molecular traits across clusters. Marker regulatory edges—edges with weight distributions differing significantly in a given cluster compared to others—are identified for each group. Finally, we perform Gene Set Enrichment Analysis. RESULTS: We include 418 GOLD 3-4 COPD participants with individual-level regulatory networks. Clustering analysis identifies two endotypes with similar exacerbation rates and quantitative emphysema but different clinical and molecular characteristics. One endotype (low airway group) exhibits significantly lower (p-val<0.1) CT-assessed wall area percentage and Pi10, lower FEV1/FVC ratio, and higher peripheral blood lymphocyte percentage. Marker nodes of this cluster are associated (FDR<0.1) with the Cell Adhesion Molecules, Antigen Processing and Presentation, and Asthma KEGG pathways. Conversely, another cluster (high airway group) displays significantly higher (p-val<0.1) airway wall area percentage, Pi10, and neutrophil percentage, and lower lymphocyte percentage. Enriched pathways (FDR<0.1) in its marker nodes include the Cytokine-cytokine Receptor Interaction, WNT signaling, and Purine Metabolism KEGG pathways. CONCLUSION: By reconstructing the individual GRNs of severe COPD subjects, our study identifies two endotypes with distinct physiological, clinical, and molecular patterns. These results shed light on the molecular processes underlying COPD exacerbation risk, generating testable predictions that could improve the drug-target selection process.

Background Chronic Obstructive Pulmonary Disease (COPD) has a broad spectrum of clinical characteristics. The aetiology of these differences is not well understood. The objective of this study is to assess whether respiratory genetic variants cluster by phenotype and associate with COPD heterogeneity. Methods We clustered genome-wide association studies of COPD, lung function, and asthma and phenotypes from the UK Biobank using non-negative matrix factorization. We constructed cluster-specific genetic risk scores and tested these scores for association with phenotypes in non-Hispanic white subjects in the COPDGene study. Findings We identified three clusters from 482 variants and 44 traits from genetic associations in 379,337 UK Biobank participants. Variants from asthma, COPD, and lung function were found in all three clusters. Clusters displayed varying effects on white blood cell counts, height, and body mass index (BMI)-related phenotypes in the UK Biobank. In the COPDGene cohort, cluster-specific genetic risk scores were associated with differences in steroid use, BMI, lymphocyte counts, and chronic bronchitis, as well as variations in gene and protein expression. Interpretation Our results suggest that multi-phenotype analysis of obstructive lung disease-related risk variants may identify genetically driven phenotypic patterns in COPD.

Rationale:Chronic obstructive pulmonary disease (COPD) is characterized by decline in lung function, assessed by forced expiratory volume in one second (FEV1). Understanding the genetic basis of FEV1 decline is crucial for unraveling the pathophysiology of COPD and developing effective therapies. We hypothesized that gene expression patterns in inflammatory pathways are associated with FEV1 decline. We investigated this hypothesis using whole blood RNA-sequencing data from the COPDGene Study. Methods:We employed linear regression models on data from 435 participants with available gene expression and FEV1 data to assess the association between gene expression and FEV1, adjusted for age, sex, race, smoking history, and white blood cell count and differential. Additional adjustments were made for height in cross-sectional analysis and baseline FEV1 in FEV1 change analysis, using three approaches: 1. Cross-sectional: We assessed associations between gene expression and FEV1 at the 5-year and 10-year follow-up visits independently. 2. FEV1 change: We assessed the association between gene expression at the 5-year follow up visit and changes in FEV1 observed between 5-year and 10-year follow up visits. 3. Longitudinal: We assessed the association between changes in gene expression and changes in FEV1 within individuals from the 5-year to 10-year follow up visits. We also performed pathway enrichment analysis using genes with P<0.05 generated from the longitudinal analysis to identify relevant biological pathways. Furthermore, we generated a gene signature from the 5-year follow up visit associated with change in FEV1 in three-time intervals: baseline to 5 years (N=3850), 5 years to 10 years (N=2043), and baseline to 10 years (N=2035) selecting participants based on the availability of gene expression data at the 5-year follow-up and FEV1 measurements across these intervals. Results:Distinct gene expression results were found for each approach (Cross-sectional: 2055; FEV1 Change: 101; Longitudinal: 322). Genes previously implicated in lung function, such as MMP9, IL1RL1, ALOX5AP, and OPTN, were identified in the longitudinal study. Pathway enrichment analysis revealed significant enrichment of oxidative stress and MAPK/ERK signaling pathways. The FEV1 change gene signature was associated with the 5-year visit outcomes of COPD progression, exacerbations, and chest CT scan measures of airway wall thickness and emphysema. We also validated the majority of the gene signature and phenotype associations in an independent sample of 673 subjects from the ECLIPSE study. Conclusions:These findings highlight key genes associated with FEV1 decline, offering new insights into the genetic underpinnings of COPD and potential therapeutic targets.

Rationale: Emphysema, a hallmark of COPD, exhibits substantial heterogeneity in severity and anatomical distribution. The relationship between specific emphysema patterns and clinical outcomes remains incompletely characterized. Objectives: To determine if distinct emphysema patterns, identified through CT-based local histogram analysis and clustering techniques, are associated with specific COPD-related outcomes. Methods: We performed local histogram analysis of lung density from Visit 1 chest CT scans in the COPDGene cohort, quantifying low attenuation areas as paraseptal, centrilobular, or panlobular emphysema. K-medoids clustering was applied to identify distinct emphysema pattern groups. Cross-sectional and longitudinal associations with COPD-related outcomes were assessed using univariable and multivariable analyses. Clinical and imaging differences between MM and MZ smokers were also analysed. Results: In 9,167 non-Hispanic White and African American smokers, four distinct clusters emerged, characterized by varying distributions of paraseptal, panlobular, and centrilobular emphysema (P-values<0.001). These clusters demonstrated significant associations with smoking status, dyspnea scores, frequency of respiratory exacerbations, 5-year lung function decline and emphysema progression, and self-reported cardiometabolic comorbidities. MZ smokers exhibited greater emphysema per pack-year of smoking compared to MM smokers and were predominantly represented in the severe emphysema cluster. All associations remained significant after adjustment for potential confounders. Conclusion: CT-based emphysema patterns identified through local histogram analysis and clustering are associated with distinct clinical outcomes. The findings also underscore the heightened susceptibility of MZ smokers to severe emphysema patterns. Ongoing multi-omics and validation studies may reveal the molecular mechanisms of emphysema and foster personalized treatment approaches.

Background: Although cigarette smoke induces lung inflammation in all smokers, only a subset develops COPD. Inflammation often persists even after smoking cessation, suggesting a self-sustaining pathogenic process similar to autoimmune responses. Increased B cell activity and autoantibodies have been associated with emphysema severity, yet their specific role in COPD progression remains underexplored in a well-defined COPD cohort. Methods: We assessed the association of 502 autoantibodies (IgG, IgA, IgM, and IgE) targeting 100 common lung antigens in plasma from 100 COPDGene participants, with emphasis on their relationship to emphysema. Emphysema was measured using volume noise-bias-adjusted lung density. Statistical models adjusted for age, sex, smoking history (pack-years), current smoking status, and lung function parameters (FEV1% predicted and FEV1/FVC ratio). To control for multiple testing, we used immunoglobulin correlation-based adjustments as described by Galwey et al. Additionally, we analyzed associations without lung function adjustments and explored other emphysema measures. Results: The study cohort had an average age of 66, was approximately 50% female, and included both non-Hispanic White and African American participants, with 65% of individuals classified as GOLD 2 or higher for COPD severity. Findings revealed that most elevated autoantibodies in COPD were IgM, notably Myeloperoxidase-IgM (MPO-IgM), an anti-neutrophil cytoplasmic antibody (ANCA). Lung density showed a significant association with MPO-IgM (B = 0.6, P adjusted = 5.6x10⁻³), which was even stronger in analyses without lung function adjustments (P = 1.7x10⁻⁸). This MPO-IgM association suggests a link between autoimmunity and emphysema. Conclusions: Our study identifies autoreactive IgM levels in COPD patients, with MPO-IgM showing a significant inverse association with emphysema. IgM antibodies are part of the innate immune response, potentially clearing dead cells and cellular debris and thus reducing inflammation. However, excessive or dysregulated IgM may shift from beneficial clearance functions to targeting healthy tissues. The specific role of MPO-IgM in milder emphysema forms presents an intriguing finding, as ANCAs are generally associated with systemic autoimmune diseases. Further research is warranted to clarify whether MPO-IgM contributes directly to emphysema progression or represents an immune response byproduct, potentially offering new insights into the autoimmune aspects of COPD pathogenesis.

Micro-ribonucleic acids (miRNAs) are key post-transcriptional regulators of the immune system and may play a role in Chronic Obstructive Pulmonary Disease (COPD). In this paper, we constructed subject-specific miRNA-mRNA regulatory networks using bulk and deconvoluted whole blood RNA-sequencing, whole blood miRNA-sequencing, and B-cell receptor-sequencing data from up to 570 miRNAs, 11,859 mRNAs, and 3,190 participants in the COPDGene study. Analysis of whole blood networks revealed two subnetworks of miRNA-mRNA interactions significantly (FDR<0.05) associated with changes in FEV 1 / FVC . We found that miRNAs (and mRNAs) in the network-identified groups had distinct expression patterns, with miRNAs (and mRNAs) in one group having overall higher expression in COPD (decreasing FEV 1 / FVC ) and miRNAs (and mRNAs) in the other group having overall higher expression in controls (increasing FEV 1 / FVC ). In addition, miRNAs (and mRNAs) within the same group were positively correlated, while those in different groups were negatively correlated, indicating distinct functional roles for these miRNAs (and mRNAs) as a function of increased COPD severity. Network analysis also identified PAX5, a transcription factor master regulator of B-cell development, as the main mRNA network hub. Using ChIP-seq data in lymphoblastoid cells, we identified a PAX5 binding site overlapping with a COPD genome-wide association signal in the promoter region of ADAM19. We also found a loss of co-expression between PAX5 and ADAM19 in COPD subjects. Furthermore, in B-cell deconvoluted data, PAX5 was differentially co-expressed with genes associated with B-cell activation and differentiation, revealing a possible mechanism for the regulation of the immune response in COPD. Finally, in B-cell receptor sequencing data, PAX5 and the identified mRNA subnetworks were negatively associated (FDR<0.05) with immunoglobulin class switching, and positively associated with IgM and IgD counts. In conclusion, PAX5 is a known regulator of B-cell identity. B cells are recognized as key players in chronic inflammation and immune dysregulation in COPD. Our work suggests that PAX5 plays a mediating role both in ADAM19 regulation and in miRNA regulation of early B cells in COPD.

Rationale: Acute exacerbations of COPD (AE-COPD) are associated with a significant disease burden. Furthermore, evidence supports the existence of an exacerbation-susceptible subtype which may represent an important target group for treatment. Here, we identify unique gene expression profiles associated with change in exacerbation frequency over a 5-year period specific to exacerbation susceptible and non-susceptible individuals. Methods: Blood RNA sequencing data (n=5,118) from the COPDGene (Genetic Epidemiology of COPD) Study was analyzed, and complete quality control passing data from 2,714 subjects was available for both phases 2 and 3 (5 and 10-year visits, respectively) and was included in longitudinal analysis. We tested for association between blood gene expression and change in AE-COPD frequency across phase 2 and phase 3 using Limma-Voom, and pathway analysis was performed using Sigora. Results: We found a significant decline in exacerbation frequency from phase 2 to phase 3 (frequency = 0.253 vs 0.202, p=0.002), but not proportion of severe exacerbations (number = 198 vs 205, p=0.48). Initial exacerbation status was an important predictor of five-year exacerbation trajectory. Most subjects (85.0%) in the cohort were nonexacerbators at Phase 2, and the majority of those subjects (93.0%) remained nonexacerbators at Phase 3. Subjects with this “stable nonexacerbator” trajectory had significantly (p<0.01 each) higher FEV1 and FEV1/FVC ratio, and decreased incidence of severe exacerbations when compared to all other exacerbator groups. We subdivided the subjects into five groups based on change in exacerbation status between phases: significant improvement (exacP3-exacP2 <= -2), mild improvement (exacP3-exacP2 = -1), no change (exacP3-exacP2 = 0), mild decline (exacP3-exacP2 = 1), significant decline (exacP3-exacP2 >= 2). We found that differential expression of 3989 genes, corresponding to 87 pathways was associated with improvement in exacerbation frequency, while 181 genes corresponding to 11 pathways were associated with mild decline. We next focused on genes specific to “improvers” by removing genes that are differentially expressed in subjects with frequent exacerbations compared to non-exacerbators in phase 2. We found 1167 differentially expressed genes and 26 pathways that were unique to these “improver” subjects, and 2822 genes and 61 pathways that were shared between exacerbation-susceptible and improver subjects. Conclusions: We identified a gene expression profile enriched for pathways involved in metabolism, which is unique to subjects whose exacerbation frequency improves between Phase 2 to 3, and a separate profile specific to exacerbation-susceptible subjects that improve over phases enriched for viral response pathways.

BACKGROUND:Chronic Obstructive Pulmonary Disease (COPD) is a complex and heterogeneous disease. Emphysema-predominant and non-emphysema predominant COPD are two major disease subtypes capturing important aspects of COPD heterogeneity. Molecular differences between these COPD subtypes are unknown. METHODS:We assessed plasma proteomic associations (using SomaScan) with emphysema-predominant vs. non-emphysema predominant COPD subtypes in COPDGene; replication of significant associations was performed in SPIROMICS. We performed pathway analyses on COPD subtype plasma proteomic associations and used weighted gene correlation network analysis to find COPD subtype-associated protein correlation networks. We tested previously reported COPD genetic variants for association with COPD subtypes and COPD subtype-associated proteomic biomarkers. FINDINGS:One hundred and twenty-four proteins were significantly associated with COPD subtypes in COPDGene, with 64 proteins (65 SOMAmers) validated in SPIROMICS. Higher correlations were observed between proteomic biomarkers with greater expression levels in non-emphysema predominant participants with COPD. Cell adhesion, collagen-containing extracellular matrix, and epithelial mesenchymal transition were biological pathways enriched for COPD subtype proteomic associations. One COPD subtype-associated correlation network module was identified, including highly connected proteomic biomarkers like PXDN and EFNA2. We observed significant genetic effects on COPD subtypes for rs2579762 in LRMDA and on COPD subtype-associated proteomic biomarkers including sRAGE and Ganglioside GM2 Activator. INTERPRETATION:We identified and replicated multiple plasma proteomic biomarkers associated with emphysema-predominant vs. non-emphysema predominant COPD. Pathway analyses, correlation-based network analyses, and genetic association analyses of these proteins may provide insight into the molecular heterogeneity of COPD. FUNDING:National Heart, Lung, and Blood Institute (NIH).

Background: Eosinophilic chronic obstructive pulmonary disease (EoCOPD) is defined by blood eosinophil count, which serves as a biomarker to guide therapy. However, only a small subset of COPD patients fall into the EoCOPD endotype, and clinical trials targeting eosinophils have produced mixed results. Recently, distinct eosinophil subsets with different molecular and functional characteristics have been identified. We hypothesized that eosinophils in COPD may consist of heterogeneous subsets which can be identified by single-cell RNA sequencing (scRNA-seq) of peripheral blood eosinophils from EoCOPD patients. Method: We isolated eosinophils from whole blood using magnetic depletion of other cell types, followed by scRNA-seq (10x Chromium and BDRhapsody). Gene set variation analysis (GSVA) was used to calculate eosinophil subset scores from blood bulk RNA-seq from COPDGene and lung tissue bulk RNA-seq from Lung Tissue Research Consortium (LTRC) data. We investigated expression of eosinophil subset signatures in lung tissue with Visium Spatial Transcriptomics in two COPD subjects and one control subect. Results:We profiled 1,039 eosinophils from two EoCOPD subjects and identified three subsets, designated as type 1, 2 and 3 eosinophils. Type 1 eosinophils (10%) expressed SELL and CD275 and had increased expression of IFNg pathway genes. Type 2 eosinophils (89%) expressed CD101 and type 2 immune response genes. Type 3 eosinophils expressing EGR1, CD151 and CD69 only comprised of 0.3% of total population. Type 1 and type 3 eosinophil subset signatures were increased in COPD compared to controls, with no difference in type 2 eosinophil subset signature in blood or lung (Table 1). We found an inverse correlation between type 1 eosinophil score and lung function (FEV1% predicted), driven by non-EoCOPD: blood: Kendall's tau rank correlation coefficient -0.03 p= 0.5 for EoCOPD and -0.01, p <0.001 for non-EoCOPD; lung: Kendall's tau -0.07 p = 0.4 for EoCOPD and -0.07, p= 0.04 for non-EoCOPD. Spatial transcriptomic analysis showed that type 2 eosinophil signature clustered around airways, type 1 eosinophil signature clustered in the lung parenchyma, and type 3 eosinophil signature was present in both airways and parenchyma. Conclusions:Our investigation of eosinophil heterogeneity from peripheral blood from EoCOPD identified a type 1 (IFNg) inflammation related eosinophil subset signature that is increased in blood and lung tissue gene expression in COPD. The study is limited by a small number of scRNA-seq samples and spatial data, which we plan to expand in future research with additional scRNA-seq and histologic validation.

Genes associated with the same disease frequently engage in mutual biological interactions, e.g., perturbation within a specific neighborhood in the molecular interactome, often referred to as the disease module. This has propelled the advancement of network-based approaches toward elucidating the molecular bases of human diseases. Although many computational methods have been developed to integrate the molecular interactome and omics profiles to extract such context-dependent disease modules, approaches that leverage multi-omics for disease-module detection are still lacking. Here, we developed a statistical physics approach based on the random-field O(n) model (RFOnM) to fill this gap. We applied the RFOnM approach to integrate gene-expression data and genome-wide association studies or mRNA data and DNA methylation for several complex diseases with the human interactome. We found that the RFOnM approach outperforms existing single omics methods in most of the complex diseases considered in this study.

ObjectiveResearchers strive to develop more precise prediction models to understand smoking behaviors, facilitate tailored tobacco treatment and improve early detection of lung cancer, including the use of polygenic risk scores (PRS). This study aimed to better understand participants' knowledge, interest and recommendations for receipt of PRS information. Its specific aims were to (1) describe participants' knowledge and interest in obtaining PRS in the context of smoking behaviors, tobacco treatment and/or early detection of lung cancer and (2) identify patient-reported recommendations for incorporating genetic risk information into clinical care.MethodsA descriptive qualitative approach was used to gather data. A one-time semi-structured interview was conducted at the conclusion of a lung health intervention among individuals who smoked long-term and were eligible for lung cancer screening. Sociodemographic, tobacco, alcohol, and comorbidity data were gathered through an electronic survey. Interviews were audio-recorded, transcribed and analyzed using Braun and Clarke's methods for thematic analysis.ResultsForty-six participants were interviewed. The themes for aim 1 included: (1) knowing about PRS and (2) wanting PRS to prevent and treat tobacco addiction. The themes for aim 2 included: (1) receiving information from health professionals and (2) considering the risks of learning PRS.ConclusionsResults indicate high interest in PRS in clinical settings to help people who smoke to understand their health habits and change behaviors. The need for appropriate framing of risk messages and shared decision making emerged in the interviews.Trial RegistrationNCT0469129T

Cannabis is commonly used as a self-prescribed treatment for anxiety and depression, but few studies have evaluated these associations using both validated mental health scales and biological cannabinoid markers. This study aimed to test associations between cannabis use frequency and symptoms of anxiety and depression, and to examine whether frequent cannabis users with high symptom scores were less likely to use FDA-approved medications. This is a secondary analysis of a cross-sectional study on sleep and cannabis use, including 195 participants who completed the Hospital Anxiety and Depression Scale (HADS), Beck Anxiety Inventory (BAI), Beck Depression Inventory-II (BDI-II), and self-reported cannabis use. Urinary tetrahydrocannabinol (THC) metabolites validated recent cannabis exposure. Regression models adjusted for demographic and clinical variables. Frequent cannabis use (≥ 15 uses in the past 30 days) vs. infrequent use (14 or fewer uses in the past 30 days) was associated with higher likelihood of anxiety, AOR = 1.06 (95

Most genetic variants associated with complex traits and diseases occur in non-coding genomic regions and are hypothesized to regulate gene expression. To understand the genetics underlying gene expression variability, we characterize 14,324 ancestrally diverse RNA-sequencing samples from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program and integrate whole genome sequencing data to perform cis and trans expression and splicing quantitative trait locus (cis-/trans-e/sQTL) analyses in six tissues and cell types, most notably whole blood (N=6,454) and lung (N=1,291). We show this dataset enables greater detection of secondary cis-e/sQTL signals than was achieved in previous studies, and that secondary cis-eQTL and primary trans-eQTL signal discovery is not saturated even though eGene discovery is. Most TOPMed trans-eQTL signals colocalize with cis-e/sQTL signals, suggesting many trans signals are mediated by cis signals. We fine-map European UK BioBank GWAS signals from 164 traits and colocalize the resulting 34,107 fine-mapped GWAS signals with TOPMed e/sQTL signals, finding that of 10,611 GWAS signals with a colocalization, 7,096 GWAS signals colocalize with at least one secondary e/sQTL signal. These results demonstrate that larger e/sQTL analyses will continue to uncover secondary e/sQTL signals, and that these new signals will benefit GWAS interpretation.

RATIONALE: Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of mortality worldwide. We previously published the polygenic transcriptome risk score (PTRS) which uses the cumulative effect of predicted gene expression to construct genetic predictors for complex diseases, and we demonstrated the value of PTRS for COPD built on Genotype-Tissue Expression (GTEx) Lung tissue with significantly improved cross-ancestry portability. However, the postmortem collection and lack of lung disease status on GTEx samples likely affect results. We hypothesized that the performance of PTRS will be improved by using disease-relevant expression quantitative trait loci (eQTLs) to construct the score. Here, we aimed to improve performance of PTRS by leveraging disease-relevant eQTLs. METHODS: We constructed two transcriptome prediction models, Elastic Net (EN) and Prediction Using Models Informed by Chromatin conformation and Epigenomics (PUMICE), using TOPMed lung RNA-seq data from the Lung Tissue Research Consortium (LTRC), which included COPD patients undergoing lung surgery. We compared their performance with two published models built on RNA-seq from GTEx Lung tissue, GTEx-EN and GTEx-PUMICE. We first integrated each of four transcriptome models with multi-ancestry GWAS of FEV1/FVC ratio (Shrine et al. 2023) to generate transcriptome-wide association study (TWAS) results, which produced trait-associated genes. Pathway analysis was then conducted on significant TWAS genes. Finally, the PTRS was computed on TWAS genes that were included in the top 20 significantly enriched pathways. We tested performance of PTRS in race-stratified analysis for moderate-to-severe and severe COPD in COPDGene. RESULTS: Our models produced more significant TWAS genes (FDR<0.05) and had more overlaps with genes identified for FEV1/FVC ratio from Shrine et al. 2023 compared to two GTEx models (Fig. A). Additionally, our models had significantly higher AUC for predicting both moderate-to-severe and severe COPD in race-stratified analysis. The mean AUC from LTRC-EN for predicting severe COPD was 0.57 and 0.55 respectively for EUR and AFA, which was significantly higher than mean AUC of 0.54 and 0.51 from GTEx-EN (Delong p-value=8.15x10-10 for EUR and 1.42x10-3 for AFA) (Fig. B). Similarly, the PTRS derived from our models produced higher Odds Ratios (OR) per standard deviation. The mean OR from LTRC-PUMICE for moderate-to-severe COPD was 1.25 and 1.18 respectively for EUR and AFA, while it was 1.17 and 1.12 from GTEx-PUMICE (Fig. C). CONCLUSIONS: Our study demonstrates the value of leveraging disease-relevant RNA-seq to construct transcriptome prediction models and their improvement on identifying lung function genes and predicting COPD across ancestries.