Research articles

By Dr. Yohan Bosse , Dr. Don Sin , Dr. Michel Laviolette , Dr. Andrew Sandford , Dr. James Hogg , Dr. Denise Daley , Dr. Lude Franke , Dr. David Nickle , Dr. Ke Hao , Dr. Wim Timens , Prof. Dirkje Postma , Dr. Peter D Pare
Corresponding Author Dr. Peter D Pare
University of British Columbia, St Paul's Hospital, iCAPTURE Centre, - Canada V6Z 1Y6
Submitting Author Dr. Peter D Pare
Other Authors Dr. Yohan Bosse
Institut universitaire de cardiologie et de pneumologie de Quebec and ) Department of Molecular Medi, - Canada

Dr. Don Sin
University of British Columbia James Hogg Research Centre, Division of Respiratory Medicine, Departm, - Canada

Dr. Michel Laviolette
Institut universitaire de cardiologie et de pneumologie de Quebec, - Canada

Dr. Andrew Sandford
University of British Columbia James Hogg Research Centre, Division of Respiratory Medicine, Departm, - Canada

Dr. James Hogg
University of British Columbia James Hogg Research Centre, Department of Pathology and Laboratory Me, - Canada

Dr. Denise Daley
University of British Columbia James Hogg Research Centre, Division of Respiratory Medicine, Departm, - Canada

Dr. Lude Franke
Departments of Pathology and Pulmonology, University Medical Center Groningen, - Netherlands

Dr. David Nickle
Molecular Profiling and Research Informatics, Merck Research Labs, Merck , - United States of America

Dr. Ke Hao
Molecular Profiling and Research Informatics, Merck Research Labs, Merck , - United States of America

Dr. Wim Timens
Departments of Pathology and Pulmonology, University Medical Center Groningen, - Netherlands

Prof. Dirkje Postma
Departments of Pathology and Pulmonology, University Medical Center Groningen, - Netherlands


Genome Wide Association Study, Chronic Obstructive Lung Disease, Gene Expression, Expression Quantitative Trait loci, Emphysema

Bosse Y, Sin D, Laviolette M, Sandford A, Hogg J, Daley D, et al. Hypothesis-driven Research on Genomic Data Derived from a Large Scale Lung EQTL Mapping Study. WebmedCentral LUNG 2010;1(9):WMC00724
doi: 10.9754/journal.wmc.2010.00724
Submitted on: 24 Sep 2010 02:38:23 AM GMT
Published on: 24 Sep 2010 07:28:52 AM GMT


The introduction of Genome Wide Association Studies (GWAS) has allowed the identification of reproducible disease-associated loci for more than 100 complex disorders and biological traits. Although these discoveries show the power of a hypothesis-free, unbiased approach to gene association studies, the downstream effects of most of these genetic variants remain to be clarified. The simultaneous genome-wide assay of gene expression in a specific tissue from many individuals can be coupled with the interrogation of the same individuals’ genetic variation. This allows the discovery of gene variants that contribute to inter-individual differences in gene expression (tissue-specific expression of quantitative trait loci (expression QTLs or eQTLs)). Discovery of eQTLs gives rise to a new level of data for the dissection of complex traits. The drawback of coupling GWAS data to genome-wide expression data is that it increases the number of possible comparisons by several orders of magnitude and requires large sample sizes. One approach to decrease multiple comparisons is to limit the analysis to relatively few candidate genes based on biologically plausible hypotheses. To increase creditability and to avoid data dredging, the hypothesis to be tested should to be stated and registered in the public domain before the data are examined. The purpose of this report is to register a priori hypotheses to be tested on a unique data set that will soon be available. The study is based on genome wide-genotypes and genome-wide lung tissue-specific gene expression for ~1200 well phenotyped individuals.  The aim of this study is to discover lung specific eQTLs and their relationship to COPD, asthma and lung cancer phenotypes.
The following is a list of hypotheses that will be tested once the data are available.
1.Confirm candidate genes previously associated with rate of decline in lung function.
2.Replicate the top previously reported and most likely susceptibility genes for COPD.
3.Replicate genome-wide association loci for COPD and related-phenotypes.
4.Replicate susceptibility loci for lung cancer.
5.Test for overlapping susceptibility loci of COPD and lung cancer.

Hypothesis and methods

The following is a list of hypotheses that we will test. They are grouped in two main categories: 1) Studies of genetic variants without reference to gene expression, and 2) Studies of gene expression without reference to genotype. However, it should be noted that some hypotheses take advantage of both genotyping and expression data.
Studies of genetic variants without reference to gene expression
1) Confirm candidate genes previously associated with rate of decline in lung function. The NHLBI funded Lung Health Study represents one of the largest longitudinal COPD cohorts worldwide (N=5,887 smokers). The UBC group has previously reported multiple candidate gene association studies using a nested case-control design in which the allele frequencies in candidate genes in the ~300 individuals who had the fastest rate of decline in lung function over the first 5 years of the study were compared with those of the ~300 who had the slowest decline over this time course [10-24]. The odds ratios associated with the alleles and the attributable risk for being in the fast decline group are shown in Table 2. Here we propose to test these genes for association with lung function related phenotypes in the present data set. These genes were chosen based on the strength of the associations and the prevalence of the polymorphisms. SNPs present on the Illumina Human1M BeadChip covering ten kilobases up- and downstream of these 17 genes will be tested in the present study.
2) Confirm additional candidate genes previously associated with level of lung function and rate of decline in the Vlagtwedde-Vlaardingen cohort study [25-29]. This cohort contains 1,390 subjects who had 8,159 measurements of forced expiratory volume in one second (FEV1) over eight surveys. This cohort was prospectively followed for 25 years with FEV1 measurements performed every three years. The genes which were significantly associated with lung function level or decline are presented with their respective chromosomal region, rs number and in vivo function in Table 3. SNPs present on the Illumina Human1M BeadChip covering ten kilobases up- and downstream of these genes will be tested in the present study.
3) Replicate the top previously reported and most likely susceptibility genes for COPD. In addition to the above mentioned genes and SNPs associated with rate of decline of lung function, a large number of genes have been associated with COPD and related phenotypes [30] and/or have been reported to associated with COPD in a literature based meta-analysis [31]. Varying levels of evidence support the contribution of these genes to the development of COPD. By reviewing the literature, we have identified the top genes that are most likely to be true susceptibility genes for COPD (see Table 4). SNPs present on the Illumina Human1M BeadChip covering ten kilobases up- and downstream of these ten genes will be tested in the present study.
4) Replicate genome-wide association loci for COPD and related-phenotypes. The results of GWAS for COPD and lung function were recently published [32-36]. Table 5 shows the susceptibility loci that were identified. We plan to test a set of uncorrelated SNPs within these loci for COPD and lung function measures using the samples genotyped in the present study.
5) Replicate susceptibility loci for lung cancer. Recent GWAS and follow-up investigations unequivocally identified three genetic loci associated with lung cancer, namely 15q25, 5p15, and 6p21. Based on previous publications, we have selected uncorrelated SNPs strongly associated with lung cancer in these regions (Table 6). We will attempt to replicate these findings by combining the present data set and a shared control group (Illumina’s iControlDB).
6) Test for overlapping susceptibility loci of COPD and lung cancer. Since COPD is an established risk factor for the development of lung cancer, we will assess which genes impart overlapping susceptibility for COPD and lung cancer.
Studies of gene expression without reference to genotype
In addition to testing for genetic associations, we have also formulated several hypotheses regarding the relationships of gene expression to lung function and disease (sub)phenotypes. We will employ both an unbiased approach, as with steroid treatment, and a hypothesis driven approach, as with heterogeneity of COPD.
1) Effects of steroids and combined steroids with long-acting β2 agonists (LABA). Lapperre and co-workers [37] recently described the effects of a treatment intervention on the long-term course of inflammation and airway function in patients with moderate to severe COPD. The COPD cohort was followed for over 2.5 years with clinical data, airway wall biopsies and sputum. Airway wall biopsies were taken at baseline, 6 months and 30 months following treatment with fluticasone (either 6 months followed by 24 months of placebo, or 30-month treatment), fluticasone/salmeterol combination for 30 months, or placebo for 30 months. Inhaled corticosteroid therapy decreased inflammation and attenuated the decline in lung function in previously steroid-naive COPD patients with moderate to severe airflow limitation. Adding LABAs did not modify these effects [37]. We are currently performing arrays on these airway wall biopsies in order to associate these with effects of treatment. It will be of great interest to compare the outcome of this study with a large group of lung tissues in individuals either treated or not treated with the same drug regimens. Furthermore, we will assess whether specific genes that are differentially expressed are also associated with SNPs in these respective genes, as derived from GWAS findings.
2) Heterogeneity of COPD: smoking, severity and gender. COPD is a heterogeneous disease and it has been shown that gender is one factor that may drive its heterogeneity, i.e. gender differences exist in susceptibility as well as in the type and progression of disease [38-42]. We will assess gene expression in the following pathways of interest with respect to their association with COPD, in conjunction with COPD severity, current smoking, treatments and gender effects. This will be followed by association with SNPs in genes in these pathways of interest. The pathways have been chosen based on previous findings in relation to COPD pathogenesis.
a. B-cell related pathways. Hogg et al. [43] were the first to show that infiltration of the airway wall by innate and adaptive inflammatory immune cells that form lymphoid follicles are coupled to a repair and remodeling process that thickens the walls of the small airways in COPD. Additionally we found that the large airways of patients with COPD contain higher numbers of B-cells compared with healthy controls, and that the number of B cells increases with the severity of COPD [44]. When studying the peripheral blood of patients who have COPD and controls, current smokers had higher percentages of (class-switched) memory B cells than ex-smokers and never smokers, irrespective of COPD [45]. This increase in (class-switched) memory B cells in current smokers is intriguing and suggests that smoke-induced neo-antigens may be constantly induced in the lung. The negative correlation between B cells and Tregs in blood is in line with previously published observations that Tregs can suppress B cells. Future studies focusing on the presence of these (class switched) memory B cells in the lung, their antigen specificity and their interaction with Tregs are necessary to further elucidate the specific B-cell response in COPD. Furthermore, B-cell follicles with an oligoclonal, antigen-specific reaction are present in both men and mice with emphysema. We hypothesize that these B cells contribute to the inflammatory process and/or the development and perpetuation of emphysema by producing antibodies against either tobacco smoke residues or ECM components [46]. Given these findings we will investigate whether differential B- and T-reg signatures exist in lung tissue.
b. Multidrug resistance (MDR) pathway, in particular MRP1 (ABCC1). Multidrug resistance-associated protein 1 (MRP1) is a member of the ATP-binding cassette (ABC) superfamily of transporters, which transport physiologic and toxic substrates across cell membranes. MRP1 is highly expressed in lung epithelium. Since it protects against toxic compounds and oxidative stress it might play a role in protection against smoke-induced disease progression [47]. We have found reduced expression of MRP1 in lung tissue of COPD patients [48] and polymorphisms in the MRP1 (ABCC1) gene are associated with a risk for the development of COPD in two independent populations [25]. Reduced inflammatory response to cigarette smoke is observed in MRP1 deficient mice [49]. Furthermore, SNPs in MRP1 are associated with airway wall inflammation in COPD patients as well [50]. Interestingly, cigarette smoke as well as drugs used in the management of COPD symptoms affect MRP function in an epithelial cell line [51, 52]. Thus, MRP1 as well as its downstream pathway is an interesting gene to study both for expression and genetics in the context of COPD severity, treatments, smoking and gender.
3) Lung injury and repair pathways.
a. Damage and repair. Gosselink et al. [53] used real time PCR to interrogate 54 genes which are involved in lung injury and repair in the lungs of patients with varying degrees of airflow obstruction. They hypothesized that an imbalance of expression of these genes in the small airway and lung parenchyma can lead to small airway remodelling and emphysema, respectively. The expression of the 54 genes was measured in 126 paired samples of small bronchiolar tissue and surrounding lung parenchymal tissue separated by laser capture microdissection in the lungs of 63 patients who required surgery for either lung cancer or lung transplantation for very severe COPD. Gene expression was measured by quantitative polymerase chain reaction and compared to the FEV1 by linear regression analysis. Following corrections for false discovery rates expression of only 2 out of 10 genes (SERPINE2 and MMP10) increased while 8 (MMP2, ITGA1, VEGF, ADAM33, SF/HGF, TIMP2, FN1 and COL3A1) decreased in small airways in association with FEV1. In contrast, 8/12 genes (EGR1, MMP1, MMP9, MMP10, PLAU, PLAUR, TNF and IL13) increased and 4/12 (MMP2, TIMP1, COL1A1 and TGFB3) declined in the surrounding lung tissue in association with progression of COPD. Table 7 shows the genes, the p and R2 values and the direction of the relationships.
We will test for relationships between the level of expression of these genes and the FEV1% predicted of the smokers in the eQTL data set at the time of their surgery. We do not have separate measures of emphysema and airway disease in these subjects but we do have a measure of lung diffusing capacity (DLCO) from 206 current-smokers and since diffusing capacity is a measure of emphysema we will determine if the level of expression of any of the genes which were correlated with lung parenchymal destruction in the study of Gosselink et al. [53] are related to the DLCO.
b. The TGFβ-SMAD pathways. Transforming growth factor (TGF)β1, is one of the important cytokines that induces fibroblast proliferation and stimulates production of extracellular matrix (ECM) constituents like decorin, biglycan, versican and collagens. Additionally, TGFβ1 is also a chemotactic factor for many inflammatory cells involved in COPD pathogenesis [54, 55]. We have demonstrated previously that small airways of patients with severe COPD contain less decorin, an important proteoglycan of the ECM [56, 57]. In addition, our studies have shown that pulmonary fibroblasts of COPD patients with GOLD state IV (very severe COPD) produce less decorin after TGFβ stimulation than those of controls [58].
The major intracellular signalling effector of TGFß1 is the Smad protein family. We previously showed that epithelial expression of the inhibitory Smad 7 gene is significantly lower in patients with GOLD stages II and IV than in controls. The expression of TGFß1 and TGFß receptor type I was significantly lower in stage II patients, whereas decorin staining of the adventitia and alveolar walls was significantly lower in COPD stage IV. Therefore we put forward that the TGFß1 - Smad pathway is aberrantly expressed in COPD, leading to abnormal tissue repair that ultimately results in reduced decorin production. We subsequently showed that the fibroblasts of COPD patients and controls differ in their regulation of the Smad pathway, the contrast being most pronounced during exposure to cigarette smoke. Smad3, 4 and 7 have to be considered as important factors in the defective repair process of COPD fibroblasts, since smoke exposure affects expression of these genes in COPD but not in control fibroblasts [59, 60]. Our preliminary data (unpublished) have shown that these effects may be changed by steroid treatment, whilst Chen et al. [61] have shown that VEGF production is affected as well, possibly via the SMAD pathway.
Similar as the pathways above the TGFβ and SMAD-pathway will be studied in conjunction with COPD severity, current smoking, treatments and gender effects.
4) Replicate previous molecular signatures of COPD and emphysema. A number of gene expression profiling experiments in human lung tissues have been conducted to identify molecular signatures for COPD and emphysema and/or for level of lung function. We will attempt to validate these molecular signatures in our data set using the Gene Set Enrichment Analysis (GSEA) program [62]. Briefly, genes tested in our microarray experiment will be ordered according to their differential expression between disease classes (cases and controls). Molecular signatures (or gene sets) derived from previous publications will be tested against our ranked list using the GSEA program. This analysis will determine whether the members of the gene sets are randomly distributed throughout our ranked list or primarily found at the top or bottom. Table 8 shows the gene sets that will be tested.
5) Genes differentially expressed in asthma. Laprise et al. [63] have conducted a microarray experiment comparing gene expression levels in bronchial biopsies of nonallergic healthy controls and patients with allergic asthma. They identified 74 genes that show significant differences in expression in asthmatics compared to controls. More recently, a second microarray experiment identified 40 genes differentially expressed in bronchial biopsies of patient with or without asthma [64]. Despite the use of different subjects, microarrays, and data analyses, 17 genes overlapped between the first and the second microarray experiments (Table 9). We hypothesize that these 17 genes will also be differentially expressed in the same direction in tumor-free lung tissues of asthmatics compared to non-asthmatics.
6) Replicate previous molecular signatures of lung cancer. Gene expression profiling with microarrays has the ability to clarify disease subtypes and could aid in prognosis and choice of therapies. By analyzing mRNA expression levels of human lung tumors, Bhattacharjee et al. [65] demonstrated that major lung cancer classes (small-cell lung carcinomas, adenocarcinomas, squamous cell carcinomas, and large-cell carcinomas) can be defined by a unique set of marker genes. They were also able to generate a subclassification of lung adenocarcinomas and detect metastases of extra-pulmonary origin from the microarray gene expression data. It has also been demonstrated that cancer-specific gene expression signatures can be detected in histologically normal large-airway epithelial cells from patients with lung cancer [66]. Accordingly, we propose that the molecular signatures defining lung cancer subtypes found in tumors will also be present in histologically normal lung tissues of patients with carcinomas.
7) Molecular signature of smoking. Molecular signatures of smoking have been identified in human airway epithelial cells. Spira et al. [67] obtained bronchial airway epithelial cells from brushings and identified 97 genes differentially expressed between current and never smokers. They also demonstrated that the vast majority of these genes return to normal levels in former smokers after 2 years of smoking cessation. Interestingly, a small list of smoking-induced genes (n = 13) were irreversibly altered by cigarette smoke. Boelens et al. [68] have also identified a molecular signature of smoking consisting of 246 genes differentially expressed in laser-microdissected bronchial epithelium between current and ex-smokers. Whether these reversible and irreversible molecular signatures of smoking are observed in the whole lung remained to be tested. The large number of former smokers in the lung eQTL data set, as well as the wide distribution in the number of years of smoking cessation among them, will provide a unique opportunity to differentiate genes that respond favourably from those that are resistant to a smoking cessation regimen.
8) Replication of >200 trans-eQTLs that have been detected in peripheral blood. Recently we have completed a meta-analysis on 1,469 unrelated individuals for whom we have genome-wide genotype and peripheral blood gene expression data. By applying a novel statistical method [69] that enables the removal of expression differences due to physiological effects we have been able to identify over 200 human trans-eQTLs. Some of these trans-eQTLs are clearly blood specific (as supported by very recent GWAS studies that assessed haematological traits), while others seem not to be tissue specific. We will compare lung eQTLs with these data to assess what the proportion of peripheral blood trans-eQTLs is present and to assess which additional trans-eQTLs can be identified when doing a combined meta-analysis (which will increase statistical power considerably).We expect that through meta-analysis we should be able to identify many genetic variants that affect gene-expression levels in trans, which is likely to result in the identification of previously unknown pathways.


This manuscript proposes a series of replication and pathways analyses which will be conducted on a large and unique data set. The intent of this manuscript is to state, up front, the hypotheses that will be tested a priori. We will also analyze the data set in an unbiased manner to discover new associations and develop new hypotheses. However it seems parsimonious to utilize the data using a different statistical approach, to validate previously reported associations and correlations of genetic variation and gene expression with lung phenotypes.
The strategy will enable us to 1) replicate genetic associations with disease, 2) replicate reported gene expression profiles with disease, 3) dissect gene expression profiles for disease heterogeneity and treatments, and 4) to detect whether the gene expression- disease association is linked by underlying genetic mechanisms. The analyses will help to provide insight into the biological pathways affected by these diseases and unravel the regulatory networks underlying disease development and disease heterogeneity. It will help to identify new targets for disease detection and prevention. In this collaborative research effort, we will use a combination of hypothesis driven and unbiased approaches to investigate the rich tissue samples we have combined from three academic centers. This strategy will increase the power to ultimately find new targets for disease intervention.


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