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Record W2151436348 · doi:10.3109/15412555.2011.558864

Opportunities and Challenges in the Genetics of COPD 2010: An International COPD Genetics Conference Report

2011· article· en· W2151436348 on OpenAlexaff
Edwin K. Silverman, Jørgen Vestbo, Àlvar Agustí, Wayne H. Anderson, Per Bakke, Kathleen C. Barnes, R. Graham Barr, Eugene R. Bleecker, H. Marike Boezen, Kristin M. Burkart, Bartolomé R. Celli, Michael H. Cho, William Cookson, Thomas L. Croxton, Denise Daley, Dawn L. DeMeo, Weiniu Gan, Judith García‐Aymerich, Ian P. Hall, Nadia N. Hansel, Craig P. Hersh, Noor Kalsheker, James P. Kiley, Woo Jin Kim, Diether Lambrechts, Sang‐Do Lee, Augusto A. Litonjua, David A. Lomas, Stephanie J. London, Masaharu Nishimura, Børge G. Nordestgaard, Christopher J. O’Donnell, Dirkje S. Postma, Milo A. Puhan, Yohannes Tesfaigzi, Martin D. Tobin, Claus Vogelmeier, Jemma B. Wilk, Emiel F.�M. Wouters, Robert P. Young, Loems Ziegler‐Heitbrock, William MacNee, James D. Crapo

Bibliographic record

VenueCOPD Journal of Chronic Obstructive Pulmonary Disease · 2011
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsUniversity of British Columbia
FundersNational Heart, Lung, and Blood InstituteNational Institute for Health and Care Research
KeywordsCOPDMedicineDiseaseCigarette smokeRisk factorGenetic predispositionPathogenesisCigarette smokingTobacco smokeImmunologyIntensive care medicineInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

Chronic obstructive pulmonary disease (COPD) is defined by the Global Initiative for Chronic Obstructive Lung Disease (GOLD) as a disease state characterized by airflow limitation that is not fully reversible (1). Cigarette smoking is the most important risk factor for the development of COPD. Although the dose-response relationship between cigarette smoking and pulmonary function is well-established, there is considerable variability in the reduction in FEV1 among smokers with similar smoking exposures (2, 3). The low percentage of variance in pulmonary function explained by smoking suggests that there could be genetic differences in susceptibility to the effects of cigarette smoking (4, 5). In addition to genetic factors, other environmental determinants such as indoor biomass smoke exposure can be important risk factors for COPD (6). A small percentage of COPD patients (estimated at 1-2%) inherit severe alpha-1 antitrypsin (AAT) deficiency, which proves that genetic factors can in-fluence COPD susceptibility. The discovery of AAT deficiency was a major factor in the development of the Protease-Antiprotease Hypothesis for COPD, which has been one of the prevailing models of disease pathogenesis for more than 40 years. With the substantial impact of AAT deficiency on our understanding of COPD pathogenesis, it was natural to hope that the identification of other COPD susceptibility genes would lead to similar novel insights into COPD. Until recently, however, progress in the identification of additional genetic risk factors for COPD has been slow. To facilitate the development of such research, a meeting of COPD genetics investigators was held on July 13-14,2010 in Boston. The goals of the meeting were: To review the current state of COPD genetics research; To discuss existing study populations for COPD genetics research throughout the world; To consider opportunities for collaborations between different COPD research groups through an International COPD Genetics Consortium; To recognize challenges in building COPD genetics collaborations and to discuss them openly; and, To develop a framework for future collaborative studies. Current status of COPD genetics research Many candidate gene association studies have been performed over the past 40 years, but the results have been largely inconsistent. These inconsistencies likely relate to a variety of methodological issues, including small sample sizes, variable definitions of case and control groups, failure to adjust for multiple statistical testing, and inadequate adjustments for population stratification and smoking exposure. Most of the studies describing COPD-associated polymorphisms were performed in White populations (7). A meta-analysis of 20 polymorphisms in 12 candidate genes involved in the protease-antiprotease balance and several an-tioxidant pathways showed that, after combining independent studies, many of these candidate genes had no association with COPD (8). Another factor likely impeding the progress of identifying COPD susceptibility genes is the lack of accurate phenotypic characterization of this complex and heterogeneous disease. Airflow limitation determined by spirometry has been the most common approach to classify and monitor the disease. Structural changes of the lung including emphysema and small airway obstruction are the primary processes that affect lung function (9), but they are not easily discernable with the simple spirometric measures commonly used for phe-notyping COPD. Recent advances in characterizing pathologic changes such as emphysema and remodeling of the small and large airways by quantitative analyses of image data from multidetector computed tomography (CT), together with physiological testing, have been helpful to differentiate COPD phenotypes (emphysema-predominant, airway-predominant, or mixed)(10). Study populations that have chest CT data may help to better identify COPD-associated genetic variations (11). Other potentially relevant COPD phenotypes, such as cachexia and low exercise capacity, have not been widely analyzed in COPD genetic studies. Perhaps the greatest problem in the candidate gene era of COPD genetic studies was improper candidate gene selection, which reflects our limited understanding of COPD pathogenesis. However, the application of genome-wide association studies (GWAS), which provide an unbiased and comprehensive search throughout the genome for common susceptibility loci, has changed the landscape of COPD genetics. Based on GWAS, three genetic loci have been unequivocally associated with COPD susceptibility, located on chromosome 4 near the HHIP gene, on chromosome 4 in the FAM13A gene, and on chromosome 15 in a block of genes which contains several components of the nicotinic acetyl-choline receptor as well as the IREB2 gene. In 2009, a series of studies provided convincing support for these three genetic loci in COPD susceptibility. Pillai and colleagues found genome-wide significant associations of the CHRNA3/CHRNA5/IREB2 region to COPD (12). DeMeo and colleagues performed gene expression studies of normal vs. COPD lung tissues followed by genetic association analysis of COPD (13), suggesting that at least one of the key COPD genetic determinants in the chromosome 15 GWAS region was IREB2. In the Framingham Heart Study (14), the HHIP region was associated with FEV1/FVC at genome-wide significance with replication of the effect on FEV1/FVC demonstrated in an independent sample drawn from the Family Heart Study, and this same region nearly reached genome-wide significance with COPD susceptibility in the Pillai paper (12). Recently, two papers published in Nature Genetics from large general population samples have provided strong support for the association of HHIP SNPs with FEV1/FVC (15, 16). One of these articles, from the CHARGE Consortium, also found evidence for association of FEV1/FVC with the FAM13A locus (15), which has been strongly associated with COPD susceptibility (17). Moreover, several case-control studies from other European populations have replicated these findings by confirming significant associations to the chromosome 15q25 locus (CHRNA3/CHRNA5/IREB2) (18, 19), chromosome 4q31 locus (HHIP) (20, 21), and chromosome 4q22 locus (FAM13 A) (22). Thus, the frustration of inconsistent genetic association results in COPD from the beginning of the last decade has been replaced by optimism regarding the likely importance of the IREB2/CHRNA3/CHRNA5, HHIP, and FAM13A loci in COPD susceptibility.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.444
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.132
GPT teacher head0.320
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations50
Published2011
Admission routes1
Has abstractyes

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