Genetic landscape of chronic obstructive pulmonary disease identifies heterogeneous cell-type and phenotype associations

Sakornsakolpat, Phuwanat; Prokopenko, Dmitry; Lamontagne, Maxime; Reeve, Nicola F.; Guyatt, Anna L; Jackson, Victoria E.; Shrine, Nick; Qiao, Dandi; Bartz, Traci M.; Kim, Deog Kyeom; Lee, Mi Kyeong; Latourelle, Jeanne C.; Li, Xingnan; Morrow, Jarrett; Obeidat, Ma’en; Wyss, Annah B.; Bakke, Per; Barr, R. Graham; Beaty, Terri H.; Belinsky, Steven A.; Brusselle, Guy; Crapo, James D.; Jong, Kim de; DeMeo, Dawn L.; Fingerlin, Tasha E.; Gharib, Sina A.; Gulsvik, Amund; Hall, Ian P.; Hokanson, John E.; Kim, Woo Jin; Lomas, David A.; London, Stephanie J.; Meyers, Deborah A.; O’Connor, George T.; Rennard, Stephen I.; Schwartz, David A.; Śliwiński, Paweł; Sparrow, David; Strachan, David P.; Tal‐Singer, Ruth; Tesfaigzi, Yohannes; Vestbo, Jørgen; Vonk, Judith M.; Yim, Jae Joon; Zhou, Xiaobo; Bossé, Yohan; Manichaikul, Ani; Lahousse, Lies; Silverman, Edwin K.; Boezen, H. Marike; Wain, Louise V.; Tobin, Martin D.; Hobbs, Brian D.; Cho, Michael H.

doi:10.1038/s41588-018-0342-2

Cited by 267 publications

(265 citation statements)

References 126 publications

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“…15q25 is the locus most strongly associated with lung cancer, 1 but also a leading susceptibility locus for smoking behavior 23 and other traits related to lung disease such as chronic obstructive pulmonary disease (COPD). 24 COPD and lung cancer-associated variants in 15q25 are known expression and methylation QTL (eQTL and meQTL) for multiple genes and tissues. 3,25 It has not been possible so far to definitely identify all of the causal gene(s) at this locus, but most evidence points toward CHRNA5 (cholinergic receptor nicotinic alpha 5 subunit) or IREB2 (iron-responsive element binding protein 2).…”

Section: Discussionmentioning

confidence: 99%

Transcriptome‐wide association study reveals candidate causal genes for lung cancer

Bossé

Xia

et al. 2019

Intl Journal of Cancer

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We have recently completed the largest GWAS on lung cancer including 29,266 cases and 56,450 controls of European descent. The goal of our study has been to integrate the complete GWAS results with a large‐scale expression quantitative trait loci (eQTL) mapping study in human lung tissues (n = 1,038) to identify candidate causal genes for lung cancer. We performed transcriptome‐wide association study (TWAS) for lung cancer overall, by histology (adenocarcinoma, squamous cell carcinoma and small cell lung cancer) and smoking subgroups (never‐ and ever‐smokers). We performed replication analysis using lung data from the Genotype‐Tissue Expression (GTEx) project. DNA damage assays were performed in human lung fibroblasts for selected TWAS genes. As expected, the main TWAS signal for all histological subtypes and ever‐smokers was on chromosome 15q25. The gene most strongly associated with lung cancer at this locus using the TWAS approach was IREB2 (pTWAS = 1.09E−99), where lower predicted expression increased lung cancer risk. A new lung adenocarcinoma susceptibility locus was revealed on 9p13.3 and associated with higher predicted expression of AQP3 (pTWAS = 3.72E−6). Among the 45 previously described lung cancer GWAS loci, we mapped candidate target gene for 17 of them. The association AQP3‐adenocarcinoma on 9p13.3 was replicated using GTEx (pTWAS = 6.55E−5). Consistent with the effect of risk alleles on gene expression levels, IREB2 knockdown and AQP3 overproduction promote endogenous DNA damage. These findings indicate genes whose expression in lung tissue directly influences lung cancer risk.

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Section: Discussionmentioning

confidence: 99%

Transcriptome‐wide association study reveals candidate causal genes for lung cancer

Bossé

Xia

et al. 2019

Intl Journal of Cancer

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“…COPD switch genes are all protein-coding, among which 4 transcription factors -including E2F3, HIF4, TAF10 of module 3 and RUNX1 of module 2 -and five other genes located within previously identified genome-wide significant COPD GWAS loci [Sakornsakolpat 2019] (Figure 5c). Functional annotation analysis of switch genes reveals that they are mainly involved in the regulation of several functionalities related to the immune and inflammatory response, mirroring the enrichment results obtained for module 3 (Supplementary Figure 3).…”

Section: Identification and Characterization Of Switch Genesmentioning

confidence: 97%

“…We found 1358 DEGs (65%) down-regulated in COPD cases and the remaining 739 DEGs (35%) up-regulated ( Figure 1a). Among DEGs, we found 145 genes located within genomic regions (+/-1 Mb from the top SNP) previously identified as containing genome-wide significant associations to COPD [Sakornsakolpat 2019] ( Supplementary Table 1).…”

Section: Copd Correlation Networkmentioning

confidence: 99%

“…COPD genetic risk loci were extracted from [Sakornsakolpat 2019], where the authors performed a genomewide association study (GWAS) for a total of 257,811 individuals (i.e., 35,735 cases and 222,076 controls) from 25 different studies, including studies from International COPD Genetics Consortium [Hobbs 2017] and UK Biobank [Sudlow 2015], that collected genetic and phenotypic data with lung function and cigarette smoking assessment. In particular, the authors of [Sakornsakolpat 2019] tested the association of COPD and 6,224,355 variants, identifying 82 loci associated at genome-wide significance (p-value < 5 • 10 −8 ). Genetic loci were defined in [Sakornsakolpat 2019] by using a 2 Mb window (+/-1 Mb) around a lead variant (top SNP).…”

Section: Copd Gwas Genesmentioning

confidence: 99%

“…In particular, the authors of [Sakornsakolpat 2019] tested the association of COPD and 6,224,355 variants, identifying 82 loci associated at genome-wide significance (p-value < 5 • 10 −8 ). Genetic loci were defined in [Sakornsakolpat 2019] by using a 2 Mb window (+/-1 Mb) around a lead variant (top SNP).…”

Section: Copd Gwas Genesmentioning

confidence: 99%

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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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Accuracy of Airflow Obstruction Thresholds for Predicting COPD-Related Hospitalization and Mortality

Vestbo

Lange

2019

JAMA

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Genetic landscape of chronic obstructive pulmonary disease identifies heterogeneous cell-type and phenotype associations

Cited by 267 publications

References 126 publications

Transcriptome‐wide association study reveals candidate causal genes for lung cancer

Transcriptome‐wide association study reveals candidate causal genes for lung cancer

Integrated transcriptomic correlation network analysis identifies COPD molecular determinants

Accuracy of Airflow Obstruction Thresholds for Predicting COPD-Related Hospitalization and Mortality

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