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Enregistrement 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 sur 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

Notice bibliographique

RevueCOPD Journal of Chronic Obstructive Pulmonary Disease · 2011
Typearticle
Langueen
DomaineMedicine
ThématiqueChronic Obstructive Pulmonary Disease (COPD) Research
Établissements canadiensUniversity of British Columbia
Organismes subventionnairesNational Heart, Lung, and Blood InstituteNational Institute for Health and Care Research
Mots-clésCOPDMedicineDiseaseCigarette smokeRisk factorGenetic predispositionPathogenesisCigarette smokingTobacco smokeImmunologyIntensive care medicineInternal medicineEnvironmental health

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,444
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,001
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,132
Tête enseignante GPT0,320
Écart entre enseignants0,188 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations50
Publié2011
Routes d'admission1
Résumé présentoui

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