Analyzing networks of phenotypes in complex diseases: methodology and applications in COPD
© Chu et al.; licensee BioMed Central Ltd. 2014
Received: 4 April 2014
Accepted: 19 June 2014
Published: 25 June 2014
The investigation of complex disease heterogeneity has been challenging. Here, we introduce a network-based approach, using partial correlations, that analyzes the relationships among multiple disease-related phenotypes.
We applied this method to two large, well-characterized studies of chronic obstructive pulmonary disease (COPD). We also examined the associations between these COPD phenotypic networks and other factors, including case-control status, disease severity, and genetic variants. Using these phenotypic networks, we have detected novel relationships between phenotypes that would not have been observed using traditional epidemiological approaches.
Phenotypic network analysis of complex diseases could provide novel insights into disease susceptibility, disease severity, and genetic mechanisms.
Complex diseases like diabetes, stroke, many types of cancer, and chronic obstructive pulmonary disease (COPD) are likely heterogeneous syndromes composed of multiple disease subtypes that manifest a similar pathological or physiological outcome. These subtypes may have different genetic determinants. In order to understand this heterogeneity, a variety of clinical, physiological, imaging, pathological, and biochemical disease-related phenotypes have been analyzed . In standard clinical epidemiological approaches, univariate and multivariate regression analyses are performed to determine significant and independent predictors of disease development. However, the available disease-related phenotypes may be crude assessments of disease pathophysiology; any analyses that are performed may be confounded by grouping multiple subtypes together. The challenge we face, in part, is deconvoluting these disease-related phenotypes and defining their relationships to one another and to specific genetic determinants.
Network analysis has the potential to provide a holistic approach to the understanding of disease complexity, rather than focusing on individual components of disease . Network approaches can capture emergent properties that are not apparent when network components are analyzed in a pair-wise manner. However, network medicine approaches to complex diseases have largely focused on relating a disease to the underlying cellular and molecular interaction network . Correlation-based networks have been frequently used to analyze gene expression data [4, 5], but these methods have not been widely applied to the study of disease-related phenotypes. Barabási and colleagues  used diagnostic coding data to assess phenotypic network relationships between different disease categories, but not to analyze multiple quantitative phenotypes within one complex disease. Using COPD as an example, we describe the application of network inference methods to explore the relationships between disease-related phenotypes that have been found to be relevant in determining disease severity and outcome, and, ultimately, to begin to define the complex heterogeneity of the disease.
Network inference and comparison
To infer phenotypic networks, we used the Gaussian graphical model (GGM) introduced by  and . Briefly, the model, which is based on the assumption that the variables have Gaussian distributions, infers the connection between each pair of variables and creates a phenotypic network based on partial correlations.
Here, y j i represents the j th phenotype variable in the i th subject, μ is the mean vector and Σ is the covariance matrix. The covariance matrix Σ Y and the partial correlation matrix (denoted by Ω) for Y are estimated (see ). The partial correlation (PCOR) ω j k measures the correlation between variable j and variable k while controlling for all other variables. Therefore, ω j k represents the conditional dependency between variable j and variable k, with ω j k =0 if the two variables are independent conditional on all other variables and ω j k ≠0 if they are conditionally correlated. For each pair of variables that are conditionally dependent, the presumed causal relationship between the variables is a direct one and independent of all other variables. We assume that these partial correlations represent the hidden connections between phenotypic variables that may help to refine disease subtypes.
where κ is the degrees of freedom (K−P+1). Therefore, we can compute the p-values for the estimated partial correlation coefficients for each pair of phenotypic variables and test for the presence of a significant connection between those variables in the phenotypic network. In addition, we can also test for differences in the network connectivity between two groups of subjects by permutation tests. For example, to test for differential connectivity between COPD cases and controls, we randomly swap the labels of cases and controls and calculate the PCORs in the shuffled groups, repeated 10,000 times, to obtain the distribution of PCORs under the null hypothesis in which the presence or absence of connections is not associated with the case-control status. The empirical p-values are reported. Analogously, we have also tested differential connectivity between different genotypes for two previously identified genome-wide significant SNPs associated with COPD using the same approach.
Opgen-Rhein and Strimmer  have extended the GGM method to infer the directionality of the edges between each pair of variables. They proposed a test of directionality based on the log-ratios of standardized partial variances. This method enables identification of a “partially directed graph” where some of the significant edges identified by GGM methods will have directions, which might imply causality, while other edges remain undirected.
Study populations and phenotypic variable selection
COPD is a disease defined by abnormal physiology, with chronic airflow obstruction as the common, key feature . Chronic airflow obstruction is characterized by reductions in the forced expiratory volume in one second (FEV1) and in the ratio of the FEV1 to the forced vital capacity (FVC), which are assessed by spirometry. Clinical epidemiological studies have identified multiple factors that contribute to COPD, including cigarette smoking (often quantified as pack-years, where an average of one pack of cigarettes smoked per day for one year is one pack-year) and increasing age. In addition, a variety of disease-related phenotypes have been studied related to imaging, exercise capacity, respiratory symptoms, and physiology. Computerized tomography (CT) imaging enables assessment of the severity and distribution of emphysema–the destruction of lung parenchyma–as well as thickening of airways [13–15]. The underlying assumption in our analysis is that these phenotypic variables are not independent, but, rather, interact to define distinct groups of patients (subtypes). By defining these subtypes, we might better be able to classify patients, understand their unique disease characteristics, and ultimately direct them to appropriate therapies.
The COPDGene Study  is a multi-center genetic and epidemiologic investigation to study COPD and other smoking-related lung diseases. In this study, 10,192 smokers (including 6,784 non-Hispanic Whites (NHW) and 3,408 African-Americans (AA)) have completed a detailed protocol, including questionnaires, pre-and post-bronchodilator spirometry, high-resolution CT scanning of the chest, exercise capacity (assessed by six minute walk distance), and blood samples for genotyping. Samples were genotyped using the Illumina OmniExpress platform, which assayed genetic polymorphisms at over 700,000 sites along the genome; the genotype data have gone through standard quality-control procedures for genome-wide association analysis. Briefly, a total of 221 subjects and 83,423 markers were excluded for quality control reasons, including identity-by-descent, gender mismatches, genotype missingness, Hardy-Weinberg disequilibrium in controls, and low minor allele frequency. The details of the quality control procedures are available at http://www.copdgene.org/sites/default/files/GWAS_QC_Methodology_20121115.pdf.
Description of phenotypic variables
FEV1 (% predicted FEV1)
Observed FEV1 (liters)/predicted FEV1 (liters), with predicted valued from Hankinson reference equations
% Emphysema at -950 Hounsfield units(HU)
Emphysema Distribution (EmphDist)
Log ratio of emphysema at -950 HU in the upper 1/3 of lung fields compared to the lower 1/3 of lung fields
Gas Trapping (GasTrap)
Air trapping at -856HU on expiratory chest CT scan
Airway Wall Area (Pi10)
Square root of the wall area of a hypothetical 10 mm internal perimeter airway
Exacerbation frequency (ExacerFreq)
Number of COPD exacerbations during the year before study enrollment
Six minute walk distance (6MWD)
Measure of exercise capacity
Body Mass Index
One pack-year is defined as smoking one pack (20 cigarettes) per day for one year
Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE, ) is a large longitudinal study of COPD patients and controls with comprehensive phenotyping similar to COPDGene. Therefore, we used a subset of 1,705 COPD cases (including 1,667 white subjects) with complete data for the 10 quantitative variables at their baseline study visit to build phenotypic networks. All variables in Table 1 were available in ECLIPSE, except for Emphysema Distribution and Gas Trapping. Therefore, networks with 8 variables were built for both COPDGene and ECLIPSE for comparison.
Whole population phenotypic network in COPDGene
Edges of whole population network with p-values < 0.001
Airway Wall Area
Airway Wall Area
Airway Wall Area
Airway Wall Area
Airway Wall Area
Airway Wall Area
Case-control phenotypic network comparison in COPDGene
Moderate/Severe COPD network comparison in COPDGene
Partial correlation and Pearson correlation coefficients for BMI and CT emphysema
Pearson correlation coefficients
Genetic-based network comparison in COPDGene
We also constructed phenotypic networks for COPDGene subjects defined by their genotypes at two SNPs previously associated with COPD in genome-wide association studies: rs1980057 (HHIP) [20, 21] and rs7671167 (FAM13A) . Separate networks were built for homozygous samples (2 copies of the COPD-risk allele or 2 copies of the non-risk allele) for each of these SNPs. Note that in both loci, the minor allele has been associated with COPD protection. We only built genotype-stratified phenotypic networks for NHW subjects, as this FAM13A SNP did not have a significant association with COPD in the AA population in previous GWAS , and the HHIP SNP was a relatively uncommon variant in AA population (MAF =0.10) with few homozygous minor allele subjects.
ECLIPSE network comparison
Finally, we constructed phenotypic networks from ECLIPSE, another independent COPD population, and compared the results between the ECLIPSE and COPDGene networks. The major difference between these two cohorts is that ECLIPSE contains mostly moderate to severe COPD samples (GOLD 2-4) and mostly Caucasians. Therefore, we performed the comparative studies on two sets of sub-populations: (1) All COPD cases (GOLD 2-4, n =2,894 for COPDGene and n =1,705 for ECLIPSE); (2) NHW COPD cases only (n =2,264 for COPDGene and n =1,667 for ECLIPSE). Only 8 out of 10 variables in Table 1 were available in ECLIPSE, therefore the networks were built with 8 nodes and 28 possible edges. The results show that the networks from two populations were very similar (see Additional file 5: Table S4, Additional file 6: Table S5, and Additional file 7: Figure S2.), with minor differences. In all COPD cases, 15 pairs were significant with p <0.001 in COPDGene, out of which 12 were also significant in ECLIPSE (all of them were in the same direction). In white COPD cases, 16 pairs were significant with p <0.001 in COPDGene, out of which 13 were also significant in ECLIPSE. The most striking difference is that Pi10/BMI had the second highest correlation in ECLIPSE (in both analyses), but it was not significant in COPDGene. Overall, the networks from these two populations are reasonably comparable, and most of the strongest connections from COPDGene can be found in another independent population.
Complex diseases are assessed using an array of disease-related phenotypic variables, which may have subtle, hidden relationships that are not captured by standard epidemiological analyses. Understanding the relationships between these disease-related phenotypes in large, well-characterized study populations may provide insight into disease heterogeneity. Different approaches have been proposed to study the relationship between multiple phenotypes, including structural equation modeling  and mutual information . We have developed an approach for constructing networks of phenotypic variables based on partial correlations between quantitative, disease-related phenotypes; for testing the statistical significance of those partial correlations within one phenotypic network; and for comparing those partial correlations between phenotypic networks constructed using different groups of subjects. The correlation-based networks that we analyzed are highly connected and not scale-free, as opposed to the sparse, scale-free networks that are observed in many biological and physical phenomena . This is not surprising, as we built the networks based on a modest number of pre-selected variables closely related to the complex disease of interest.
The correlation-based networks have enabled us to detect novel relationships between disease-related phenotypes that would not have been observed in a single-variable analysis. Network based approaches are particularly useful in the studies of COPD, which is a complex disease with diverse clinical and molecular phenotypic profiles that might represent different subtypes . In our study, the COPD network in the whole COPDGene study population provided a variety of clinically intuitive observations, such as the central location of gas trapping in the network–which includes both of the major COPD-related causes of airflow obstruction, emphysema and small airway disease. This key role of gas trapping was especially notable in the partially directed network (Figure 2). Comparisons between COPD cases and control subjects showed similar relationships for most variables, but an intriguing switch in the direction of the relationship between body mass index and CT emphysema was observed in controls compared to cases. Cachexia can accompany advanced COPD with severe emphysema, so a negative relationship between BMI and emphysema in COPD cases is clinically reasonable. Since the same radiation dose was used in all COPDGene subjects, the positive relationship between BMI and emphysema in control subjects could relate to the increased radiation noise with higher BMI, which could flatten the density histogram and artifactually increase the estimated degree of emphysema using densitometric thresholds.
Other phenotypic relationships are less intuitive but may point to important biological pathways. Comparison between moderate and severe/very severe COPD subjects showed a variety of interesting correlations between phenotypes. For example, increased emphysema was associated with reduced exercise capacity (6MWD) in severe COPD subjects but not in the moderate COPD group. Exercise capacity, assessed by 6MWD, includes many components but is likely significantly related to inspiratory capacity. In severe disease, inspiratory capacity is limited by baseline hyperinflation, which is observed by emphysema on CT scan. However, in moderate disease, other parameters are major determinants of 6MWD. Inspiratory capacity may be limiting with dynamic hyperinflation in moderate COPD subjects, but inspiratory capacity will likely not be closely associated with emphysema in this subgroup. It is unclear why airway wall area (Pi10) was significantly correlated with body mass index (BMI) in one of our study populations (ECLIPSE) but not the other (COPDGene). One possible explanation is that the CT radiation dose for ECLIPSE was substantially lower than in COPDGene, and this difference in radiation dose could have impacted how BMI influenced airway wall measurements.
Phenotypic networks have previously been studied in the context of multi-dimensional analyses that have included both phenotypic and genetic information [27, 28]. Our method can also be applied in such integrative analyses. In particular, we examined the effects of genetic perturbations on the relationships between the phenotypes. In our COPD example, relatively few phenotypic interactions were different between homozygotes for alternative alleles of COPD GWAS regions near HHIP and FAM13A. Given the modest effects of these variants, and most other complex disease GWAS regions, these results are not surprising. However, the observed differences, such as the FEV1-emphysema relationship in alternate FAM13A genotypes, could provide clues regarding the underlying mechanisms by which these GWAS regions influence disease susceptibility. These results suggest that FAM13A may lead to reduced FEV1 through mechanisms other than increased emphysema, which is a testable hypothesis for future research. Similarly, the weaker relationship of FEV1 and exacerbation frequency in the COPD-associated group could indicate that any relationship of the HHIP locus to exacerbations may not be mediated through reduced FEV1.
Although published studies have described methods for assessing relationships between disease diagnostic categories in a network context [6, 29], we instead focused on multiple disease-related phenotypes within one complex disease. While we believe this represents an important new approach, several limitations of our work need to be acknowledged. It is not clear whether it is preferable to use a weighted network, in which all edges are present but of variable magnitude, or an unweighted network, with an admittedly somewhat arbitrary threshold for placing an edge. Further work will also be required to determine the optimal approach for assessing the impact of genetic factors on phenotypic networks. We have compared alternate homozygous classes, but that approach eliminates the information in the typically larger heterozygous genotype group.
In conclusion, we have presented a framework for analyzing and comparing partial correlations between multiple, quantitative disease-related phenotypes in networks. These phenotypic networks could provide insights into disease susceptibility, disease severity, and genetic mechanisms. Future directions will involve refining the approaches for selecting phenotypes to include in such networks as well as improved approaches for incorporating genetic information. Ultimately, these phenotypic networks may prove useful in developing novel classification systems for complex diseases.
This work was supported by U.S. National Institutes of Health (NIH) grants K99HL114651 (Chu), P01HL105339 (Silverman), R01HL075478 (Silverman), R01HL111759 (Quackenbush/Silverman/Yuan), R01HL089897 (Crapo), R01HL089856 (Silverman), R37HL061795 (Loscalzo), P50HL107192 (Loscalzo), and U01HL108630 (MAPGen Consortium) (Loscalzo) from the National Heart, Lung, and Blood Institute. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Heart, Lung, and Blood Institute or the National Institutes of Health. The COPDGene project is also supported by the COPD Foundation through contributions made to an Industry Advisory Board comprised of AstraZeneca, Boehringer Ingelheim, Novartis, Pfizer, Siemens and Sunovion. The ECLIPSE study (NCT00292552; GSK code SCO104960) was sponsored by GlaxoSmithKline.
COPDGene Investigators – Core Units: Administrative Core: James Crapo, MD (PI), Edwin Silverman, MD, PhD (PI), Barry Make, MD, Elizabeth Regan, MD, PhD, Rochelle Lantz, Lori Stepp, Sandra Melanson; Genetic Analysis Core: Terri Beaty, PhD, Barbara Klanderman, PhD, Nan Laird, PhD, Christoph Lange, PhD, Michael Cho, MD, Stephanie Santorico, PhD, John Hokanson, MPH, PhD, Dawn DeMeo, MD, MPH, Nadia Hansel, MD, MPH, Craig Hersh, MD, MPH, Peter Castaldi, MD, MSc, Merry-Lynn McDonald, PhD, Jin Zhou, PhD, Manuel Mattheissen, MD, PhD, Emily Wan, MD, Megan Hardin, MD, Jacqueline Hetmanski, MS, Margaret Parker, MS, Tanda Murray, MS; Imaging Core: David Lynch, MB, Joyce Schroeder, MD, John Newell, Jr., MD, John Reilly, MD, Harvey Coxson, PhD, Philip Judy, PhD, Eric Hoffman, PhD, George Washko, MD, Raul San Jose Estepar, PhD, James Ross, MSc, Mustafa Al Qaisi, MD, Jordan Zach, Alex Kluiber, Jered Sieren, Tanya Mann, Deanna Richert, Alexander McKenzie, Jaleh Akhavan, Douglas Stinson; PFT QA Core, LDS Hospital, Salt Lake City, UT: Robert Jensen, PhD; Biological Repository, Johns Hopkins University, Baltimore, MD: Homayoon Farzadegan, PhD, Stacey Meyerer, Shivam Chandan, Samantha Bragan; Data Coordinating Center and Biostatistics, National Jewish Health, Denver, CO: Douglas Everett, PhD, Andre Williams, PhD, Carla Wilson, MS, Anna Forssen, MS, Amber Powell, Joe Piccoli; Epidemiology Core, University of Colorado School of Public Health, Denver, CO: John Hokanson, MPH, PhD, Marci Sontag, PhD, Jennifer Black-Shinn, MPH, Gregory Kinney, MPH, PhDc, Sharon Lutz, MPH, PhD.
COPDGene Investigators – Clinical Centers: Ann Arbor VA: Jeffrey Curtis, MD, Ella Kazerooni, MD; Baylor College of Medicine, Houston, TX: Nicola Hanania, MD, MS, Philip Alapat, MD, Venkata Bandi, MD, Kalpalatha Guntupalli, MD, Elizabeth Guy, MD, Antara Mallampalli, MD, Charles Trinh, MD, Mustafa Atik, MD, Hasan Al-Azzawi, MD, Marc Willis, DO, Susan Pinero, MD, Linda Fahr, MD, Arun Nachiappan, MD, Collin Bray, MD, L. Alexander Frigini, MD, Carlos Farinas, MD, David Katz, MD, Jose Freytes, MD, Anne Marie Marciel, MD; Brigham and Women’s Hospital, Boston, MA: Dawn DeMeo, MD, MPH, Craig Hersh, MD, MPH, George Washko, MD, Francine Jacobson, MD, MPH, Hiroto Hatabu, MD, PhD, Peter Clarke, MD, Ritu Gill, MD, Andetta Hunsaker, MD, Beatrice Trotman-Dickenson, MBBS, Rachna Madan, MD; Columbia University, New York, NY: R. Graham Barr, MD, DrPH, Byron Thomashow, MD, John Austin, MD, Belinda D’Souza, MD; Duke University Medical Center, Durham, NC: Neil MacIntyre, Jr., MD, Lacey Washington, MD, H Page McAdams, MD; Fallon Clinic, Worcester, MA: Richard Rosiello, MD, Timothy Bresnahan, MD, Joseph Bradley, MD, Sharon Kuong, MD, Steven Meller, MD, Suzanne Roland, MD; Health Partners Research Foundation, Minneapolis, MN: Charlene McEvoy, MD, MPH, Joseph Tashjian, MD; Johns Hopkins University, Baltimore, MD: Robert Wise, MD, Nadia Hansel, MD, MPH, Robert Brown, MD, Gregory Diette, MD, Karen Horton, MD; Los Angeles Biomedical Research Institute at Harbor UCLA Medical Center, Los Angeles, CA: Richard Casaburi, MD, Janos Porszasz, MD, PhD, Hans Fischer, MD, PhD, Matt Budoff, MD, Mehdi Rambod, MD; Michael E. DeBakey VAMC, Houston, TX: Amir Sharafkhaneh, MD, Charles Trinh, MD, Hirani Kamal, MD, Roham Darvishi, MD, Marc Willis, DO, Susan Pinero, MD, Linda Fahr, MD, Arun Nachiappan, MD, Collin Bray, MD, L. Alexander Frigini, MD, Carlos Farinas, MD, David Katz, MD, Jose Freytes, MD, Anne Marie Marciel, MD; Minneapolis VA: Dennis Niewoehner, MD, Quentin Anderson, MD, Kathryn Rice, MD, Audrey Caine, MD; Morehouse School of Medicine, Atlanta, GA: Marilyn Foreman, MD, MS, Gloria Westney, MD, MS, Eugene Berkowitz, MD, PhD; National Jewish Health, Denver, CO: Russell Bowler, MD, PhD, David Lynch, MB, Joyce Schroeder, MD, Valerie Hale, MD, John Armstrong, II, MD, Debra Dyer, MD, Jonathan Chung, MD, Christian Cox, MD; Temple University, Philadelphia, PA: Gerard Criner, MD, Victor Kim, MD, Nathaniel Marchetti, DO, Aditi Satti, MD, A. James Mamary, MD, Robert Steiner, MD, Chandra Dass, MD, Libby Cone, MD; University of Alabama, Birmingham, AL: William Bailey, MD, Mark Dransfield, MD, Michael Wells, MD, Surya Bhatt, MD, Hrudaya Nath, MD, Satinder Singh, MD; University of California, San Diego, CA: Joe Ramsdell, MD, Paul Friedman, MD; University of Iowa, Iowa City, IA: Alejandro Cornellas, MD, John Newell, Jr., MD, Edwin JR van Beek, MD, PhD; University of Michigan, Ann Arbor, MI: Fernando Martinez, MD, MeiLan Han, MD, Ella Kazerooni, MD; University of Minnesota, Minneapolis, MN: Christine Wendt, MD, Tadashi Allen, MD; University of Pittsburgh, Pittsburgh, PA: Frank Sciurba, MD, Joel Weissfeld, MD, MPH, Carl Fuhrman, MD, Jessica Bon, MD, Danielle Hooper, MD; University of Texas Health Science Center at San Antonio, San Antonio, TX: Antonio Anzueto, MD, Sandra Adams, MD, Carlos Orozco, MD, Mario Ruiz, MD, Amy Mumbower, MD, Ariel Kruger, MD, Carlos Restrepo, MD, Michael Lane, MD.
Principal investigators and centers participating in ECLIPSE (NCT00292552, SC0104960): Bulgaria: Y. Ivanov, Pleven; K. Kostov, Sofia. Canada: J. Bourbeau, Montreal; M. Fitzgerald, Vancouver, BC; P. Hernández, Halifax, NS; K. Killian, Hamilton, ON; R. Levy, Vancouver, BC; F. Maltais, Montreal; D. O’Donnell, Kingston, ON. Czech Republic: J. Krepelka, Prague. Denmark: J. Vestbo, Hvidovre. The Netherlands: E. Wouters, Horn-Maastricht. New Zealand: D. Quinn, Wellington. Norway: P. Bakke, Bergen. Slovenia: M. Kosnik, Golnik. Spain: A. Agusti, J. Sauleda, P. de Mallorca. Ukraine: Y. Feschenko, V. Gavrisyuk, L. Yashina, Kiev; N. Monogarova, Donetsk. United Kingdom: P. Calverley, Liverpool; D. Lomas, Cambridge; W. MacNee, Edinburgh; D. Singh, Manchester; J. Wedzicha, London. United States: A. Anzueto, San Antonio, TX; S. Braman, Providence, RI; R. Casaburi, Torrance CA; B. Celli, Boston; G. Giessel, Richmond, VA; M. Gotfried, Phoenix, AZ; G. Greenwald, Rancho Mirage, CA; N. Hanania, Houston; D. Mahler, Lebanon, NH; B. Make, Denver; S. Rennard, Omaha, NE; C. Rochester, New Haven, CT; P. Scanlon, Rochester, MN; D. Schuller, Omaha, NE; F. Sciurba, Pittsburgh; A. Sharafkhaneh, Houston; T. Siler, St. Charles, MO; E. Silverman, Boston; A. Wanner, Miami; R. Wise, Baltimore; R. ZuWallack, Hartford, CT.
Steering Committee: H. Coxson (Canada), C. Crim (GlaxoSmithKline, USA), L. Edwards (GlaxoSmithKline, USA), D. Lomas (UK), W. MacNee (UK), E. Silverman (USA), R. Tal Singer (Co-chair, GlaxoSmithKline, USA), J. Vestbo (Co-chair, Denmark), J. Yates (GlaxoSmithKline, USA).
Scientific Committee: A. Agusti (Spain), P. Calverley (UK), B. Celli (USA), C. Crim (GlaxoSmithKline, USA), B. Miller (GlaxoSmithKline, USA), W. MacNee (Chair, UK), S. Rennard (USA), R. Tal-Singer (GlaxoSmithKline, USA), E. Wouters (The Netherlands), J. Yates (GlaxoSmithKline, USA).
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