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Most transcriptome-wide association studies (TWASs) so far focus on European ancestry and lack diversity. To overcome this limitation, we aggregated genome-wide association study (GWAS) summary statistics, whole-genome sequences and expression quantitative trait locus (eQTL) data from diverse ancestries. We developed a new approach, TESLA (multi-ancestry integrative study using an optimal linear combination of association statistics), to integrate an eQTL dataset with a multi-ancestry GWAS. By exploiting shared phenotypic effects between ancestries and accommodating potential effect heterogeneities, TESLA improves power over other TWAS methods. When applied to tobacco use phenotypes, TESLA identified 273 new genes, up to 55% more compared with alternative TWAS methods. These hits and subsequent fine mapping using TESLA point to target genes with biological relevance. In silico drug-repurposing analyses highlight several drugs with known efficacy, including dextromethorphan and galantamine, and new drugs such as muscle relaxants that may be repurposed for treating nicotine addiction.

While variation in emphysema between COPD patients is well-recognized, clinically applicable definitions of emphysema-predominant (EPD) and non-emphysema-predominant (NEPD) subtypes have not been established. To study the clinical relevance of EPD and NEPD subtypes, we tested the association of these subtypes to prospective FEV1 decline and mortality in 3,427 subjects with GOLD spirometric grade 2-4 COPD at baseline in the COPDGene Study, an ongoing national multicenter study that started in 2007. NEPD was defined as airflow obstruction with =10% CT emphysema. Mixed effects models for FEV1 demonstrated larger average annual FEV1 loss in EPD versus NEPD subjects (-10.2 ml/yr, p 0.9. NEPD and EPD COPD subtypes capture important aspects of COPD heterogeneity and are associated with different rates of disease progression and mortality.

Structured AbstractINTRODUCTIONDiabetes and dementia are diseases of high healthcare burden worldwide. Individuals with diabetes have 1.4 to 2.2 times higher risk of dementia. Our objective was to evaluate evidence of causality between these two common diseases.METHODSWe conducted a one-sample Mendelian randomization (MR) analysis in the U.S. Department of Veterans Affairs Million Veteran program. The study included 334,672 participants ≥65 years of age with type 2 diabetes and dementia case-control status and genotype data.RESULTSFor each standard deviation increase in genetically-predicted diabetes, we found increased odds of three dementia diagnoses in non-Hispanic White participants (all-cause: OR=1.07[1.05-1.08],P=3.40E-18; vascular: OR=1.11[1.07-1.15],P=3.63E-09, Alzheimer’s: OR=1.06[1.02-1.09],P=6.84E-04) and non-Hispanic Black participants (all-cause: OR=1.06[1.02-1.10],P=3.66E-03, vascular: OR=1.11[1.04-1.19],P=2.20E-03, Alzheimer’s: OR=1.12 [1.02-1.23],P=1.60E-02) but not in Hispanic participants (allP>.05).DISCUSSIONWe found evidence of causality between diabetes and dementia using a one-sample MR study, with access to individual level data, overcoming limitations of prior studies utilizing two-sample MR techniques.

BACKGROUND: Emerging data from longitudinal studies suggest that preserved ratio impaired spirometry (PRISm), defined by proportionate reductions in FEV(1) and FVC, is a heterogeneous population with frequent transitions to other lung function categories relative to individuals with normal and obstructive spirometry. Controversy regarding the clinical significance of these transitions exists (eg, whether transitions merely reflect measurement variability or noise). RESEARCH QUESTION: Are individuals with PRISm enriched for transitions associated with substantial changes in lung function? STUDY DESIGN AND METHODS: Current and former smokers enrolled in the Genetic Epidemiology of COPD (COPDGene) study with spirometry available in phases 1 through 3 (enrollment, 5-year follow-up, and 10-year follow-up) were analyzed. Postbronchodilator lung function categories were as follows: PRISm (FEV(1) < 80% predicted with FEV(1)/FVC ratio ≥ 0.7), Global Initiative for Chronic Obstructive Lung Disease grade 0 (FEV(1) ≥ 80% predicted and FEV(1)/FVC ≥ 0.7), and obstruction (FEV(1)/FVC < 0.7). Significant transition status was affirmative if a subject belonged to two or more spirometric categories and had > 10% change in FEV(1) % predicted and/or FVC % predicted between consecutive visits. Ever-PRISm was present if a subject had PRISm at any visit. Logistic regression examined the association between significant transitions and ever-PRISm status, adjusted for age, sex, race, FEV(1) % predicted, current smoking, pack-years, BMI, and ever-positive bronchodilator response. RESULTS: Among subjects with complete data (N = 1,775) over 10.1 ± 0.4 years of follow-up, the prevalence of PRISm remained consistent (10.4%-11.3%) between phases 1 through 3, but nearly one-half of subjects with PRISm transitioned into or out of PRISm at each visit. Among all subjects, 19.7% had a significant transition; ever-PRISm was a significant predictor of significant transitions (unadjusted OR, 10.3; 95% CI, 7.9-13.5; adjusted OR, 14.9; 95% CI, 10.9-20.7). Results were similar with additional adjustment for radiographic emphysema and gas trapping, when lower limit of normal criteria were used to define lung function categories, and when FEV(1) alone (regardless of change in FVC % predicted) was used to define significant transitions. INTERPRETATION: PRISm is an unstable group, with frequent significant transitions to both obstruction and normal spirometry over time. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov; No.: NCT000608764; URL: www.clinicaltrials.gov

RATIONALE: The course of lung function, respiratory symptoms, and functional status over time in people who smoke cigarettes is still incompletely understood. The COPDGene (Genetic Epidemiology of Chronic Obstructive Pulmonary Disease [COPD]) study provides a unique cohort to examine these trajectories, and now 10-year follow-up data are available. OBJECTIVES: This study aims to provide insight into the progression of spirometric parameters, respiratory symptoms, and functional capacity over 10 years in current and former cigarette smokers. METHODS: We analyzed available longitudinal data for COPDGene participants who did not change smoking status over three visits spanning approximately 10 years of follow-up. Change in postbronchodilator forced expiratory volume in 1 second (FEV(1)), St. George’s Respiratory Questionnaire (SGRQ), and 6-minute walk distance (6MWD) from Phase 1 to Phase 3 were examined using linear mixed models. Terms were included in the models to estimate mean progression separately for current and former cigarette smokers. Models were stratified by baseline Global Initiative for Chronic Obstructive Lung Disease (GOLD) spirometry stages as well as by new 2019 COPDGene classification. RESULTS: The mean age at enrollment of the 9,103 participants in this analysis was 59.8 years (SD = 9.2 yr); 46.4% were women, and 32.6% were African American. In all GOLD COPD groups, including participants with normal spirometry, and all groups categorized by 2019 COPDGene classification, FEV(1) decreased, SGRQ increased (indicating higher symptom burden), and 6MWD decreased over the 10-year follow-up period. Current smokers exhibited a greater mean loss of FEV(1) over the study period than former smokers for all groups except those with preserved ratio impaired spirometry. For both SGRQ and 6MWD, rates of progression tended to be similar for former and current smokers except for 6MWD in the highest severity groups, in which former smokers had greater progression. However, this could be impacted by some current smokers with faster progression who had quit smoking and were dropped from analyses. CONCLUSIONS: Progression in FEV(1), SGRQ, and 6MWD overall appears to be slow, and the change over time in groups traditionally characterized as not having disease closely mirrors that of the groups with COPD at all GOLD stages. Current cigarette smokers had greater loss of FEV(1) than former smokers, whereas SGRQ and 6MWD changes were more similar between current and former cigarette smokers.

Rationale: The ability of peripheral blood biomarkers to assess COPD risk and progression is unknown. Genetics and gene expression may capture important aspects of COPD-related biology that predict...

BACKGROUND: Smokers manifest varied phenotypes of pulmonary impairment. RESEARCH QUESTION: Which pulmonary phenotypes are associated with coronary artery disease (CAD) in smokers? STUDY DESIGN AND METHODS: We analyzed data from the University of Pittsburgh COPD Specialized Center for Clinically Oriented Research (SCCOR) cohort (n = 481) and the Genetic Epidemiology of COPD (COPDGene) cohort (n = 2,580). Participants were current and former smokers with > 10 pack-years of tobacco exposure. Data from the two cohorts were analyzed separately because of methodologic differences. Lung hyperinflation was assessed by plethysmography in the SCCOR cohort and by inspiratory and expiratory CT scan lung volumes in the COPDGene cohort. Subclinical CAD was assessed as the coronary artery calcium score, whereas clinical CAD was defined as a self-reported history of CAD or myocardial infarction (MI). Analyses were performed in all smokers and then repeated in those with airflow obstruction (FEV(1) to FVC ratio, < 0.70). RESULTS: Pulmonary phenotypes, including airflow limitation, emphysema, lung hyperinflation, diffusion capacity, and radiographic measures of airway remodeling, showed weak to moderate correlations (r < 0.7) with each other. In multivariate models adjusted for pulmonary phenotypes and CAD risk factors, lung hyperinflation was the only phenotype associated with calcium score, history of clinical CAD, or history of MI (per 0.2 higher expiratory and inspiratory CT scan lung volume; coronary calcium: OR, 1.2; 95% CI, 1.1-1.5; P = .02; clinical CAD: OR, 1.6; 95% CI, 1.1-2.3; P = .01; and MI in COPDGene: OR, 1.7; 95% CI, 1.0-2.8; P = .05). FEV(1) and emphysema were associated with increased risk of CAD (P < .05) in models adjusted for CAD risk factors; however, these associations were attenuated on adjusting for lung hyperinflation. Results were the same in those with airflow obstruction and were present in both cohorts. INTERPRETATION: Lung hyperinflation is associated strongly with clinical and subclinical CAD in smokers, including those with airflow obstruction. After lung hyperinflation was accounted for, FEV(1) and emphysema no longer were associated with CAD. Subsequent studies should consider measuring lung hyperinflation and examining its mechanistic role in CAD in current and former smokers.

Common genetic variants explain less variation in complex phenotypes than inferred from family-based studies, and there is a debate on the source of this 'missing heritability'. We investigated the contribution of rare genetic variants to tobacco use with whole-genome sequences from up to 26,257 unrelated individuals of European ancestries and 11,743 individuals of African ancestries. Across four smoking traits, single-nucleotide-polymorphism-based heritability ([Formula: see text]) was estimated from 0.13 to 0.28 (s.e., 0.10-0.13) in European ancestries, with 35-74% of it attributable to rare variants with minor allele frequencies between 0.01% and 1%. These heritability estimates are 1.5-4 times higher than past estimates based on common variants alone and accounted for 60% to 100% of our pedigree-based estimates of narrow-sense heritability ([Formula: see text], 0.18-0.34). In the African ancestry samples, [Formula: see text] was estimated from 0.03 to 0.33 (s.e., 0.09-0.14) across the four smoking traits. These results suggest that rare variants are important contributors to the heritability of smoking.

Objective: Diabetes and dementia are diseases of high healthcare burden worldwide, and studies have shown that diabetes is associated with increased relative risk of dementia. We set out to examine whether type 2 diabetes-associated genetic variants were associated with dementia and if they differed by race/ethnicity or clinical dementia diagnosis. Research Design and Methods: We evaluated associations of two type 2 diabetes genetic risk scores (GRS and GRS-NonAPOE: a score without rs429358, a variant associated with Alzheimer’s disease [AD]) with three classifications of clinical dementia diagnoses in the Million Veteran Program (MVP): all-cause-dementia, vascular dementia (VaD), and AD. We conducted our analysis stratified by European (EUR), African (AFR), and Hispanic (HIS) race/ethnicities. Results: In EUR, we found associations of the GRS with all-cause-dementia (OR = 1.06, P = 1.60e-07), and clinically diagnosed VaD (OR = 1.12, P = 5.2e-05) but not clinically diagnosed AD (OR = 1.02, P = .43). The GRS was not associated with any dementia outcome in AFR or HIS. When testing with GRS-NonAPOE, we found that effect size estimates in EUR increased and P values decreased (for all-cause-dementia OR = 1.08, P = 2.6e-12; for VaD OR = 1.14, P = 7.2e-07and for AD OR = 1.06, P = .018). For AFR, the association of GRS-NonAPOE and clinically diagnosed VaD (OR = 1.15, P = .016) was statistically significant. There were no significant findings for HIS. Conclusions: We found evidence suggesting shared genetic pathogenesis of diabetes with all-cause-dementia and clinically diagnosed VaD.

Recently, Mendelian Randomization (MR) has gained in popularity as a concept to assess the causal relationship between phenotypes in genetic association studies. An extension of standard MR methodology, the MR Steiger approach, has recently been developed to infer the causal direction between two phenotypes in prospective studies. Through simulation studies, we examine and quantified the ability of the MR Steiger approach to determine the causal direction between 2 phenotypes (i.e. effect direction). Through simulation studies, our results show that the MR Steiger approach may fail to correctly identify the direction of causality. This is true, especially in the presence of pleiotropy. We also applied the MR Steiger method to the COPDGene study, a case-control study of Chronic Obstructive Pulmonary Disease (COPD) in current and former smokers, to examine the role of smoking on lung function. We have created an R package on Github called reverseDirection which runs simulations for user-specified scenarios in order to examine when the MR Steiger approach can correctly determine the causal direction between two phenotypes in any user specified scenario. In summary, our results emphasize the importance of caution when the MR Steiger approach is used in to infer direction of causality.

ObjectivesMuscle wasting is a recognised extra-pulmonary complication in chronic obstructive pulmonary disease and has been associated with increased risk of death. Acute respiratory exacerbations are associated with reduction of muscle function, but there is a paucity of data on their long-term effect. This study explores the relationship between acute respiratory exacerbations and long-term muscle loss using serial measurements of CT derived pectoralis muscle area (PMA).Design and settingParticipants were included from two prospective, longitudinal, observational, multicentre cohorts of ever-smokers with at least 10 pack-year history.ParticipantsThe primary analysis included 1332 (of 2501) participants from Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE) and 4384 (of 10 198) participants from Genetic Epidemiology of COPD (COPDGene) who had complete data from their baseline and follow-up visits.InterventionsPMA was measured on chest CT scans at two timepoints. Self-reported exacerbation data were collected from participants in both studies through the use of periodic longitudinal surveys.Main outcome measuresAge-related and excess muscle loss over time.ResultsAge, sex, race and body mass index were associated with baseline PMA. Participants experienced age-related decline at the upper end of reported normal ranges. In ECLIPSE, the exacerbation rate over time was associated with an excess muscle area loss of 1.3% (95% CI 0.6 to 1.9, pConclusionsExacerbations are associated with accelerated skeletal muscle loss. Each annual exacerbation was associated with the equivalent of 6 months of age-expected decline in muscle mass. Ameliorating exacerbation-associated muscle loss represents an important therapeutic target.

Introduction: Smoking cessation counseling is a central part of the Medicare guidelines for lung cancer screening. With increasing age, many heavy smokers eventually stop smoking, however, factors influencing the decision to stop smoking are poorly understood. We postulated that declining health or physician-diagnosis of a medical condition may be associated with successful smoking cessation. Methods: A total of 4448 current and former smokers in Phase 2 of the COPD Genetic Epidemiology (COPDGene®) study answered a question about reasons for stopping smoking. Participants were classified as successful quitters (n=3345), and unsuccessful quitters (n=1003). Reasons cited for quitting were grouped as: medical diagnoses, social factors, symptoms. Logistic modeling of factors associated with successful quitting were adjusted for age, gender, race, and education. Results: The most common factors cited for a quit attempt by all respondents were medical diagnoses (48%), followed by social factors (47%), and respiratory symptoms (36%). Successful quitters were more likely to be older, male, and non-Hispanic White. An adjusted model found increased age, White race, education beyond high school, and male sex favored successful quitting while the cited medical diagnoses, social factors, and “other” reasons were associated with unsuccessful quitting. Fagerstrom Nicotine Dependence scores were ³ 5 in 54% of the unsuccessful group compared to 45% for successful quitters(p

Yisha Li,1 Ran Dai,2 Yeongjin Gwon,2 Stephen I Rennard,3 Barry J Make,4 Dinah Foer,5 Matthew J Strand,4 Erin Austin,6 Kendra A Young,1 John E Hokanson,1 Katherine A Pratte,7 Rebecca Conway,1 Gregory L Kinney1 On behalf of COPDGene investigators1Department of Epidemiology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; 2Department of Biostatistics, School of Public Health, University of Nebraska Medical Center, Omaha, NE, USA; 3Division of Pulmonary, Critical Care and Sleep Medicine, University of Nebraska Medical Center, Omaha, NE, USA; 4Department of Medicine, National Jewish Health, Denver, CO, USA; 5Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA; 6Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO, USA; 7Department of Biostatistics, National Jewish Health, Denver, CO, USACorrespondence: Gregory L Kinney, Department of Epidemiology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA, Tel +1 303-724-4437, Email GREG.KINNEY@CUANSCHUTZ.EDU

BackgroundBody composition measures, specifically low weight or reduced muscle mass, are associated with mortality in COPD, but the effect of longitudinal body composition changes is undefined.Research questionIs the longitudinal loss of fat-free mass (FFM) associated with increased mortality, including in those with initially normal or elevated body composition metrics?Study design and methodsParticipants with complete data for at least one visit in the COPDGene study (n= 9,268) and the ECLIPSE study (n= 1,760) were included and monitored for 12 and 8 years, respectively. Pectoralis muscle area (PMA) was derived from thoracic CT scans and used as a proxy for FFM. A longitudinal mixed submodel for PMA and a Cox proportional hazards submodel for survival were fitted on a joint distribution, using a shared random intercept parameter and Markov chain Monte Carlo parameter estimation.ResultsBoth cohorts demonstrated a left-shifted distribution of baseline FFM, not reflected in BMI, and an increase in all-cause mortality risk associated with longitudinal loss of PMA. For each 1-cm2 PMA loss, mortality increased 3.1%(95%CI, 2.4%-3.7%; P< .001) in COPDGene, and 2.4%(95%CI, 0.9%-4.0%; P< .001) in ECLIPSE. Increased mortality risk was independent of enrollment values for BMI and disease severity [BODE (body mass, airflow obstruction, dyspnea, and exercise capacity) index quartiles] and was significant even in participants with initially greater than average PMA.InterpretationLongitudinal loss of PMA is associated with increased all-cause mortality, regardless of BMI or initial muscle mass. Consideration of novel screening tests and further research into mechanisms contributing to muscle decline may improve risk stratification and identify novel therapeutic targets in ever smokers.

Risk of chronic obstructive pulmonary disease (COPD) is determined by both cigarette smoking and genetic susceptibility, but little is known about gene-by-smoking interactions. We performed a genome-wide association analysis of 179,689 controls and 21,077 COPD cases from UK Biobank subjects of European ancestry recruited from 2006 to 2010, considering genetic main effects and gene-by-smoking interaction effects simultaneously (2-degrees-of-freedom (df) test) as well as interaction effects alone (1-df interaction test). We sought to replicate significant results in COPDGene (United States, 2008–2010) and SpiroMeta Consortium (multiple countries, 1947–2015) data. We considered 2 smoking variables: 1) ever/never and 2) current/noncurrent. In the 1-df test, we identified 1 genome-wide significant locus on 15q25.1 (cholinergic receptor nicotinic β4 subunit, or CHRNB4) for ever- and current smoking and identified PI*Z allele (rs28929474) of serpin family A member 1 (SERPINA1) for ever-smoking and 3q26.2 (MDS1 and EVI1 complex locus, or MECOM) for current smoking in an analysis of previously reported COPD loci. In the 2-df test, most of the significant signals were also significant for genetic marginal effects, aside from 16q22.1 (sphingomyelin phosphodiesterase 3, or SMPD3) and 19q13.2 (Egl-9 family hypoxia inducible factor 2, or EGLN2). The significant effects at 15q25.1 and 19q13.2 loci, both previously described in prior genome-wide association studies of COPD or smoking, were replicated in COPDGene and SpiroMeta. We identified interaction effects at previously reported COPD loci; however, we failed to identify novel susceptibility loci.

Purpose Medication patterns include all medications in an individual's clinical profile. We aimed to identify chronic co-morbidity treatment patterns through medication use among COPDGene participants and determine whether these patterns were associated with mortality, acute exacerbations of chronic obstructive pulmonary disease (AECOPD) and quality of life. Materials and methods Participants analyzed here completed Phase 1 (P1) and/or Phase 2 (P2) of COPDGene. Latent class analysis (LCA) was used to identify medication patterns and assign individuals into unobserved LCA classes. Mortality, AECOPD, and the St. George's Respiratory Questionnaire (SGRQ) health status were compared in different LCA classes through survival analysis, logistic regression, and Kruskal-Wallis test, respectively. Results LCA identified 8 medication patterns from 32 classes of chronic comorbid medications. A total of 8110 out of 10,127 participants with complete covariate information were included. Survival analysis adjusted for covariates showed, compared to a low medication use class, mortality was highest in participants with hypertension+diabetes+statin+antiplatelet medication group. Participants in hypertension+SSRI+statin medication group had the highest odds of AECOPD and the highest SGRQ score at both P1 and P2. Conclusion Medication pattern can serve as a good indicator of an individual's comorbidities profile and improves models predicting clinical outcomes.

COPD is a heterogeneous syndrome. Many COPD subtypes have been proposed, but there is not yet consensus on how many COPD subtypes there are and how they should be defined. The COPD Genetic Epidemiology Study (COPDGene), which has generated 10-year longitudinal chest imaging, spirometry, and molecular data, is a rich resource for relating COPD phenotypes to underlying genetic and molecular mechanisms. In this article, we place COPDGene clustering studies in context with other highly cited COPD clustering studies, and summarize the main COPD subtype findings from COPDGene. First, most manifestations of COPD occur along a continuum, which explains why continuous aspects of COPD or disease axes may be more accurate and reproducible than subtypes identified through clustering methods. Second, continuous COPD-related measures can be used to create subgroups through the use of predictive models to define cut-points, and we review COPDGene research on blood eosinophil count thresholds as a specific example. Third, COPD phenotypes identified or prioritized through machine learning methods have led to novel biological discoveries, including novel emphysema genetic risk variants and systemic inflammatory subtypes of COPD. Fourth, trajectory-based COPD subtyping captures differences in the longitudinal evolution of COPD, addressing a major limitation of clustering analyses that are confounded by disease severity. Ongoing longitudinal characterization of subjects in COPDGene will provide useful insights about the relationship between lung imaging parameters, molecular markers, and COPD progression that will enable the identification of subtypes based on underlying disease processes and distinct patterns of disease progression, with the potential to improve the clinical relevance and reproducibility of COPD subtypes.

Background: Genetic factors influence chronic obstructive pulmonary disease (COPD) risk, but the individual variants that have been identified have small effects. We hypothesised that a polygenic risk score using additional variants would predict COPD and associated phenotypes. Methods: We constructed a polygenic risk score using a genome-wide association study of lung function (FEV1 and FEV1/forced vital capacity [FVC]) from the UK Biobank and SpiroMeta. We tested this polygenic risk score in nine cohorts of multiple ethnicities for an association with moderate-to-severe COPD (defined as FEV1/FVC 1

OBJECTIVE Diabetes and dementia are diseases of high health care burden worldwide, and studies have shown that diabetes is associated with an increased relative risk of dementia. We set out to examine whether type 2 diabetes–associated genetic variants were associated with dementia and whether they differed by race/ethnicity or clinical dementia diagnosis. RESEARCH DESIGN AND METHODS We evaluated associations of two type 2 diabetes genetic risk scores (GRS and GRS-nonAPOE: a score without rs429358, a variant associated with Alzheimer disease [AD]) with three classifications of clinical dementia diagnoses in the Million Veteran Program (MVP): all-cause dementia, vascular dementia (VaD), and AD. We conducted our analysis stratified by European (EUR), African (AFR), and Hispanic (HIS) races/ethnicities. RESULTS In EUR, we found associations of the GRS with all-cause dementia (odds ratio [OR] 1.06, P = 1.60e−07) and clinically diagnosed VaD (OR 1.12, P = 5.2e−05) but not with clinically diagnosed AD (OR 1.02, P = 0.43). The GRS was not associated with any dementia outcome in AFR or HIS. When testing with GRS-nonAPOE, we found that effect size estimates in EUR increased and P values decreased for all-cause dementia (OR 1.08, P = 2.6e−12), for VaD (OR 1.14, P = 7.2e−07), and for AD (OR 1.06, P = 0.018). For AFR, the association of GRS-nonAPOE and clinically diagnosed VaD (OR 1.15, P = 0.016) was statistically significant. There were no significant findings for HIS. CONCLUSIONS We found evidence suggesting shared genetic pathogenesis of diabetes with all-cause dementia and clinically diagnosed VaD.

Genome-wide association studies (GWAS) have made impactful discoveries for complex diseases, often by amassing very large sample sizes. Yet, GWAS of many diseases remain underpowered, especially for non-European ancestries. One cost-effective approach to increase sample size is to combine existing cohorts, which may have limited sample size or be case-only, with public controls, but this approach is limited by the need for a large overlap in variants across genotyping arrays and the scarcity of non-European controls. We developed and validated a protocol, Genotyping Array-WGS Merge (GAWMerge), for combining genotypes from arrays and whole-genome sequencing, ensuring complete variant overlap, and allowing for diverse samples like Trans-Omics for Precision Medicine to be used. Our protocol involves phasing, imputation, and filtering. We illustrated its ability to control technology driven artifacts and type-I error, as well as recover known disease-associated signals across technologies, independent datasets, and ancestries in smoking-related cohorts. GAWMerge enables genetic studies to leverage existing cohorts to validly increase sample size and enhance discovery for understudied traits and ancestries.

RATIONALE: Multiple studies have demonstrated an increased risk of chronic obstructive pulmonary disease (COPD) in heterozygous carriers of the AAT (alpha-1 antitrypsin) Z allele. However, it is not known if MZ subjects with COPD are phenotypically different from noncarriers (MM genotype) with COPD. OBJECTIVES: To assess if MZ subjects with COPD have different clinical features compared with MM subjects with COPD. METHODS: Genotypes of SERPINA1 were ascertained by using whole-genome sequencing data in three independent studies. We compared outcomes between MM subjects with COPD and MZ subjects with COPD in each study and combined the results in a meta-analysis. We performed longitudinal and survival analyses to compare outcomes in MM and MZ subjects with COPD over time. MEASUREMENTS AND MAIN RESULTS: We included 290 MZ subjects with COPD and 6,184 MM subjects with COPD across the three studies. MZ subjects had a lower FEV(1)% predicted and greater quantitative emphysema on chest computed tomography scans compared with MM subjects. In a meta-analysis, the FEV(1) was 3.9% lower (95% confidence interval [CI], −6.55% to −1.26%) and emphysema (the percentage of lung attenuation areas

Genome-wide association studies (GWAS) have made impactful discoveries for complex diseases, often by amassing very large sample sizes. Yet, GWAS of many diseases remain underpowered, especially for non-European ancestries. One cost-effective approach to increase sample size is to combine existing case-only cohorts with public controls, but this approach is limited by the need for a large overlap in variants across genotyping arrays and the scarcity of non-European controls. We developed and validated a protocol, Genotyping Array-WGS Merge (GAWMerge), for combining genotypes from arrays and whole genome sequencing, ensuring complete variant overlap, and allowing for diverse samples like Trans-Omics for Precision Medicine to be used. Our protocol involves phasing, imputation, and filtering. We illustrated its ability to control type I error and recover known disease-associated signals across technologies, independent datasets, and ancestries in smoking-related cohorts. GAWMerge enables genetic studies to leverage existing cohorts to validly increase sample size and enhance discovery.

Chronic obstructive pulmonary disease (COPD) is a common and progressive disease that is influenced by both genetic and environmental factors. For many years, knowledge of the genetic basis of COPD was limited to Mendelian syndromes, such as alpha-1 antitrypsin deficiency and cutis laxa, caused by rare genetic variants. Over the past decade, the proliferation of genome-wide association studies, the accessibility of whole-genome sequencing, and the development of novel methods for analyzing genetic variation data have led to a substantial increase in the understanding of genetic variants that play a role in COPD susceptibility and COPD-related phenotypes. COPDGene (Genetic Epidemiology of COPD), a multicenter, longitudinal study of over 10,000 current and former cigarette smokers, has been pivotal to these breakthroughs in understanding the genetic basis of COPD. To date, over 20 genetic loci have been convincingly associated with COPD affection status, with additional loci demonstrating association with COPD-related phenotypes such as emphysema, chronic bronchitis, and hypoxemia. In this review, we discuss the contributions of the COPDGene study to the discovery of these genetic associations as well as the ongoing genetic investigations of COPD subtypes, protein biomarkers, and post–genome-wide association study analysis.

Type 1 diabetes mellitus (T1DM) increases the risk of atherosclerotic cardiovascular disease (CVD) in humans by poorly understood mechanisms. Using mouse models of T1DM-accelerated atherosclerosis, we found that relative insulin deficiency rather than hyperglycemia elevated levels of apolipoprotein C3 (APOC3), an apolipoprotein that prevents clearance of triglyceride-rich lipoproteins (TRLs) and their remnants. We then showed that serum APOC3 levels predict incident CVD events in subjects with T1DM in the Coronary Artery Calcification in Type 1 Diabetes (CACTI) study. To explore underlying mechanisms, we investigated the impact of Apoc3 antisense oligonucleotides (ASOs) on lipoprotein metabolism and atherosclerosis in a mouse model of T1DM. Apoc3 ASO treatment abolished the increased hepatic Apoc3 expression in diabetic mice - resulting in lower levels of TRLs - without improving glycemic control. APOC3 suppression also prevented arterial accumulation of APOC3-containing lipoprotein particles, macrophage foam cell formation, and the accelerated atherosclerosis in diabetic mice. Our observations demonstrate that relative insulin deficiency increases APOC3 and that this results in elevated levels of TRLs and accelerated atherosclerosis in a mouse model of T1DM. Because serum levels of APOC3 predicted incident CVD events in the CACTI study, inhibiting APOC3 might reduce CVD risk in T1DM patients.

Rationale: Cor pulmonale (right ventricular [RV] dilation) and cor pulmonale parvus (RV shrinkage) are both described in chronic obstructive pulmonary disease (COPD). The identification of emphysema as a shared risk factor suggests that additional disease characterization is needed to understand these widely divergent cardiac processes. Objectives: To explore the relationship between computed tomography measures of emphysema and distal pulmonary arterial morphology with RV volume, and their association with exercise capacity and mortality in ever-smokers with COPD enrolled in the COPDGene Study. Methods: Epicardial (myocardium and chamber) RV volume (RV(EV)), distal pulmonary arterial blood vessel volume (arterial BV5: vessels

Genome-wide association studies (GWAS) have identified multiple associations with emphysema apicobasal distribution (EABD), but the biological functions of these variants are unknown. To characterize the functions of EABD-associated variants, we integrated GWAS results with 1) expression quantitative trait loci (eQTL) from the Genotype Tissue Expression (GTEx) project and subjects in the COPDGene (Genetic Epidemiology of COPD) study and 2) cell type epigenomic marks from the Roadmap Epigenomics project. On the basis of these analyses, we selected a variant near ACVR1B (activin A receptor type 1B) for functional validation. SNPs from 168 loci with P values less than 5 × 10(−5) in the largest GWAS meta-analysis of EABD were analyzed. Eighty-four loci overlapped eQTL, with 12 of these loci showing greater than 80% likelihood of harboring a single, shared GWAS and eQTL causal variant. Seventeen cell types were enriched for overlap between EABD loci and Roadmap Epigenomics marks (permutation P

Genetic association studies have increasingly recognized variant effects on multiple phenotypes. Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease with environmental and genetic causes. Multiple genetic variants have been associated with COPD, many of which show significant associations to additional phenotypes. However, it is unknown if these associations represent biological pleiotropy or if they exist through correlation of related phenotypes ("mediated pleiotropy"). Using 6,670 subjects from the COPDGene study, we describe the association of known COPD susceptibility loci with other COPD-related phenotypes and distinguish if these act directly on the phenotypes (i.e., biological pleiotropy) or if the association is due to correlation (i.e., mediated pleiotropy). We identified additional associated phenotypes for 13 of 25 known COPD loci. Tests for pleiotropy between genotype and associated outcomes were significant for all loci. In cases of significant pleiotropy, we performed mediation analysis to test if SNPs had a direct association to phenotype. Most loci showed a mediated effect through the hypothesized causal pathway. However, many loci also had direct associations, suggesting causal explanations (i.e., emphysema leading to reduced lung function) are incomplete. Our results highlight the high degree of pleiotropy in complex disease-associated loci and provide novel insights into the mechanisms underlying COPD.

Reduced lung function predicts mortality and is key to the diagnosis of chronic obstructive pulmonary disease (COPD). In a genome-wide association study in 400,102 individuals of European ancestry, we define 279 lung function signals, 139 of which are new. In combination, these variants strongly predict COPD in independent patient populations. Furthermore, the combined effect of these variants showed generalizability across smokers and never-smokers, and across ancestral groups. We highlight biological pathways, known and potential drug targets for COPD and, in phenome-wide association studies, autoimmune-related and other pleiotropic effects of lung function associated variants. This new genetic evidence has potential to improve future preventive and therapeutic strategies for COPD., Editorial summary: A genome-wide association study in over 400,000 individuals identifies 139 new signals for lung function. These variants can predict chronic obstructive pulmonary disease in independent, trans-ethnic cohorts.

Author(s): Regan, Elizabeth A; Lowe, Katherine E; Make, Barry J; Lynch, David A; Kinney, Gregory L; Budoff, Matthew J; Mao, Song Shou; Dyer, Debra; Curtis, Jeffrey L; Bowler, Russell P; Han, MeiLan K; Beaty, Terri H; Hokanson, John E; Kern, Elizabeth; Humphries, Stephen; Curran-Everett, Douglas; van Beek, Edwin JR; Silverman, Edwin K; Crapo, James D; Finigan, James H; COPDGene® Investigators | Abstract: BackgroundLung cancer screening (LCS) via chest computed tomography (CT) scans can save lives by identifying early-stage tumors. However, most smokers die of comorbid smoking-related diseases. LCS scans contain information about smoking-related conditions that is not currently systematically assessed. Identifying these common comorbid diseases on CT could increase the value of screening with minimal impact on LCS programs. We determined the prevalence of 3 comorbid diseases from LCS eligible scans and quantified related adverse outcomes.MethodsWe studied COPD Genetic Epidemiology study (COPDGene®) participants (n=4078) who met criteria for LCS screening at enrollment (age g 55 years, and l 80 years, g 30 pack years smoking, current smoker or former smoker within 15 years of smoking cessation). CT scans were assessed for coronary artery calcification (CAC), emphysema, and vertebral bone density. We tracked the following clinically significant events: myocardial infarctions (MIs), strokes, pneumonia, respiratory exacerbations, and hip and vertebral fractures.ResultsOverall, 77% of eligible CT scans had one or more of these diagnoses identified. CAC (g 100 mg) was identified in 51% of scans, emphysema in 44%, and osteoporosis in 54%. Adverse events related to the underlying smoking-related diseases were common, with 50% of participants reporting at least one. New diagnoses of cardiovascular disease, emphysema and osteoporosis were made in 25%, 7% and 46%, of participants respectively. New diagnosis of disease was associated with significantly more adverse events than in participants who did not have CT diagnoses for both osteoporosis and cardiovascular risk.ConclusionsExpanded analysis of LCS CT scans identified individuals with evidence of previously undiagnosed cardiovascular disease, emphysema or osteoporosis that corresponded with adverse events. LCS CT scans can potentially facilitate diagnoses of these smoking-related diseases and provide an opportunity for treatment or prevention.

Rationale: Increasing awareness of the prevalence and significance of Preserved Ratio Impaired Spirometry (PRISm), alternatively known as restrictive or Global Initiative for Chronic Obstructive Lu...