Genome-wide association study of lung function and clinical implication in heavy smokers

Li, Xingnan; Ortega, Victor E.; Ampleford, Elizabeth J.; Barr, R. Graham; Christenson, Stephanie A.; Cooper, Christopher B.; Couper, David J.; Han, Mei Lan K.; Hansel, Nadia N.; Hoffman, Eric A.; Kanner, Richard E.; Kleerup, Eric C.; Martínez, Fernando J.; Paine, Robert; Woodruff, Prescott G.; Hawkins, Gregory A.; Bleecker, Eugene R.; Meyers, Deborah A.

doi:10.1186/s12881-018-0656-z

Cited by 29 publications

(28 citation statements)

References 24 publications

(40 reference statements)

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“…In a genome-wide association study (GWAS) of 1645 non-Hispanic white participants enrolled in SPIROMICS, a functional rare variant in the SERPINA1 gene (rs28929474: Glu342Lys) was associated with lower FEV 1 / FVC and FEV 1 50 . In a model including age, sex and smoking pack-years, the top 10 single nucleotide polymorphisms associated with FEV 1 (including the aforementioned SERPINA1 variant) explained 8.6% of the variance of FEV 1 /FVC.…”

Section: Chest Imagingmentioning

confidence: 99%

Insights into Chronic Obstructive Pulmonary Disease Epidemiology, Phenotypes and Outcomes from SPIROMICS

Labaki¹,

Martínez²,

Han³

2021

BRN

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SubPopulations and InteRmediate Outcome Measures in COPD Study (SPIROMICS) is a multicentre National Institutes of Health funded cohort study of nearly 3000 never-smokers and ever-smokers (≥ 20 pack-years) with and without chronic obstructive pulmonary disease (COPD). It was designed to help fill gaps in our understanding of the biological complexity and clinical heterogeneity of COPD. The original goals for this study were to identify subgroups of smokers who may benefit from targeted therapies and to discover and validate intermediate biomarker endpoints. Here we review important findings from SPIROMICS over the past five years, including the impact of environmental and occupational exposures on the respiratory health of smokers, the characterisation of symptomatic smokers with preserved pulmonary function and the investigation of several diagnostic and prognostic biomarkers such as airway mucin concentration, functional small airways disease on chest computed tomography and a gene expression signature of interleukin 17A response in airway epithelium.

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Section: Chest Imagingmentioning

confidence: 99%

Insights into Chronic Obstructive Pulmonary Disease Epidemiology, Phenotypes and Outcomes from SPIROMICS

Labaki¹,

Martínez²,

Han³

2021

BRN

Self Cite

View full text Add to dashboard Cite

show abstract

“…3). Interestingly, AGER is one of the most well-known candidate genes located in a significant COPD GWAS region with a non-synonymous SNP (located about 2 Kb from the transcription start site), which has been associated with multiple COPD-related phenotypes and COPD affection status 20,21 .…”

Section: Summarizing the Profiles Of The Copd Modules To Summarize Tmentioning

confidence: 99%

Integrated transcriptomic correlation network analysis identifies COPD molecular determinants

Paci

Fiscon

Conte

et al. 2020

Sci Rep

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Chronic obstructive pulmonary disease (COPD) is a complex and heterogeneous syndrome. Networkbased analysis implemented by SWIM software can be exploited to identify key molecular switches-called "switch genes"-for the disease. Genes contributing to common biological processes or defining given cell types are usually co-regulated and co-expressed, forming expression network modules. Consistently, we found that the COPD correlation network built by SWIM consists of three wellcharacterized modules: one populated by switch genes, all up-regulated in COPD cases and related to the regulation of immune response, inflammatory response, and hypoxia (like TIMP1, HIF1A, SYK, LY96, BLNK and PRDX4); one populated by well-recognized immune signature genes, all up-regulated in COPD cases; one where the GWAS genes AGER and CAVIN1 are the most representative module genes, both down-regulated in COPD cases. Interestingly, 70% of AGER negative interactors are switch genes including PRDX4, whose activation strongly correlates with the activation of known COPD GWAS interactors SERPINE2, CD79A, and POUF2AF1. These results suggest that SWIM analysis can identify key network modules related to complex diseases like COPD. Chronic obstructive pulmonary disease (COPD) is a devastating lung disease characterized by progressive and incompletely reversible airflow obstruction. Like many other common diseases, COPD is a heterogeneous and complex syndrome influenced by both genetic and environmental determinants and is one of the main causes of morbidity and mortality worldwide. Cigarette smoking is a major environmental risk factor for COPD, but the substantial heritability of COPD indicates an important role for genetic determinants as well 1. Although multiple genetic loci for COPD have been identified by genome-wide association studies (GWAS), the key genes in those regions are largely undefined. Various contributors to COPD pathogenesis have been also suggested, including protease-antiprotease imbalance, oxidant-antioxidant imbalance, cellular senescence, autoimmunity, chronic inflammation, deficient lung growth and development, and ineffective lung repair. However, the pathobiological mechanisms for COPD remain incompletely understood 2. COPD susceptibility, like other complex diseases, is rarely caused by a single gene mutation, but is likely influenced by multiple genetic determinants with interconnections between different molecular components. Studying the effects of these interconnections on disease susceptibility could lead to improved understanding of COPD pathogenesis and the identification of new therapeutic targets. Previous efforts to identify the network of interacting genes and proteins in COPD have included protein-protein interaction (PPI) network studies. McDonald and colleagues 3 used dmGWAS software to identify a consensus network module within the PPI network based on COPD GWAS evidence. Sharma and colleagues 4 started with "seed" genes based on well-established COPD GWAS genes or Mendelian syndromes that include COPD ...

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“…It is a multiligand receptor, and besides AGE, interacts with other molecules implicated in homeostasis, development, and inflammation ( Figure 3). Interestingly, AGER is one of the most well-known candidate genes located in a significant COPD GWAS region with a non-synonymous SNP (located about 2 Kb from the transcription start site), which has been associated with multiple COPD-related phenotypes and COPD affection status [Kim 2015, Li 2018. The first gene with the highest module membership in module 2 is CAPN2 that is down-regulated in COPD cases (Fold-change = 0.8 and FDR = 7.3 • 10 −7 ) and encodes the large subunit of the ubiquitous enzyme, calpain 2.…”

Section: Summarizing the Profiles Of The Copd Modulesmentioning

confidence: 99%

Integrated transcriptomic correlation network analysis identifies COPD molecular determinants

Paci

Fiscon

Conte

et al. 2019

Preprint

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Chronic obstructive pulmonary disease (COPD) is a heterogeneous and complex syndrome. Network-based analysis implemented by SWIM software can be exploited to identify key molecular switches -called "switch genes" -for disease. Genes contributing to common biological processes or define given cell types are frequently co-regulated and co-expressed, giving rise to expression network modules. Consistently, we found that the COPD correlation network built by SWIM consists of three well-characterized modules: one populated by switch genes, all up-regulated in COPD cases and related to the regulation of immune response, inflammatory response, and hypoxia (like TIMP1, HIF1A, SYK, LY96, BLNK and PRDX4); one populated by well-recognized immune signature genes, all up-regulated in COPD cases; one where the GWAS genes AGER and CAVIN1 are the most representative module genes, both down-regulated in COPD cases. Interestingly, 70% of AGER negative interactors are switch genes including PRDX4, whose activation strongly correlates with the activation of known COPD GWAS interactors SERPINE2, CD79A, and POUF2AF1. These results suggest that SWIM analysis can identify key network modules related to complex diseases like COPD. growth and development, and ineffective lung repair. However, the pathobiological mechanisms for COPD remain incompletely understood [Silverman 2018].COPD susceptibility, like other complex diseases, is rarely caused by a single gene mutation, but is likely influenced by multiple genetic determinants with interconnections between different molecular components. Studying the effects of these interconnections on disease susceptibility could lead to improved understanding of COPD pathogenesis and the identification of new therapeutic targets. Previous efforts to identify the network of interacting genes and proteins in COPD have included protein-protein interaction (PPI) network studies. McDonald and colleagues [McDonald 2014] used dmGWAS software to identify a consensus network module within the PPI network based on COPD GWAS evidence. Sharma and colleagues [Sharma 2018] started with "seed" genes based on well-established COPD GWAS genes or Mendelian syndromes that include COPD as part of the syndrome constellation with a random walk approach to build a COPD network module of 163 proteins.

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Genome-wide association study of lung function and clinical implication in heavy smokers

Cited by 29 publications

References 24 publications

Insights into Chronic Obstructive Pulmonary Disease Epidemiology, Phenotypes and Outcomes from SPIROMICS

Insights into Chronic Obstructive Pulmonary Disease Epidemiology, Phenotypes and Outcomes from SPIROMICS

Integrated transcriptomic correlation network analysis identifies COPD molecular determinants

Integrated transcriptomic correlation network analysis identifies COPD molecular determinants

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