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Candidate gene association studies of chronic obstructive pulmonary disease and asthma He, Jianqing 2010

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CANDIDATE GENE ASSOCIATION STUDIES OF CHRONIC OBSTRUCTIVE PULMONARY DISEASE AND ASTHMA by JIANQING HE M.Sc., West China University of Medical Sciences, 1990 B.M., West China University of Medical Sciences, 1987  THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE STUDIES (Experimental Medicine)  THE UNIVERSITY OF BRITISH COLUMBIA (VANCOUVER)  April 2010 © Jianqing He, 2010  ABSTRACT COPD and asthma are complex diseases characterized by pulmonary inflammation. The roles of single nucleotide polymorphisms (SNPs) in the inflammatory cytokine IL10, IL10RA, CSF2, CSF3 and IL6 in COPD and SNPs in a Th2-polarizing cytokine thymic stromal lymphopoietin (TSLP) in asthma are not clear. Association studies were performed to explore the genetic contribution of these cytokines to COPD or asthma. Housekeeping genes on airway epithelium were also optimized. In the study of COPD in the Lung Health Study (LHS), no association was found for IL10, IL10RA and CSF2; an increase in the number of CSF3 -1719T alleles was associated with protection against low forced expiratory volume in one second (FEV1), odds ratio (OR) = .73, 95% confidence interval (CI) = .56 - .95, P = .018; three IL6 SNPs were associated with the rate of decline of FEV1 (.023 ≤ P ≤ .041 in additive models). In the National Emphysema Treatment Trial - Normative Aging Study, IL6_-174G/C and four other highly linked SNPs were associated with COPD (.01 ≤ P ≤ .04 in additive genetic models). Association between IL6 and rate of FEV1 decline was the most statistically significant observations in all of our studies in LHS, which has examined more than 40 candidate genes. In the study of TSLP association with asthma, the A allele of rs1837253 was associated with protection from asthma, atopic asthma, and airway hyperresponsiveness (AHR), with ORs (95% CI) and corrected P values for each being .79 (.69 - .90) and .0058; .75 (.63 - .88) and .0074; and .76 (.64 – .89) and .0094, respectively. These associations were the most statistically significant observations in our study, which has examined 98 candidate genes. Cyclophilin A was identified as the most suitable normalizer in gene expression studies involving human airway epithelium.  ii  In summary, we demonstrate for the first time that IL6 SNPs are associated with rapid decline of FEV1 and COPD; TSLP SNPs are associated with asthma and AHR. These results will help our understanding of COPD and asthma and lead to more effective and individualized strategies for the management of COPD and asthma.  iii  TABLE OF CONTENTS  Abstract ………………………………………………………………………………....  ii  Table of Contents………………………………………………………………………..  iv  List of Tables…………………………………………………………………………....  xi  List of Figures…………………………………………………………………………...  xiv  Abbreviations…………………………………………………………………………....  xvi  Acknowledgements ……………………………………….............................................. xxiii Dedication ………………………………………………………………………………  xxvi  Co-Authorship Statement …..…………………………………………………………... xxvii Chapter 1: Introduction…………………………………………………………….........  1  1.1 Definitions of COPD and asthma ……………………………………...............  1  1.2 Complex nature of COPD and asthma ……………………………….………..  2  1.2.1 COPD and asthma as complex diseases………………………………...  2  1.2.2 The pathogenesis of COPD and asthma ………………………………..  2  1.2.3 Phenotypes of COPD and asthma ……………………………………...  4  1.3 Genetics of COPD and asthma ……………………………………….….........  5  1.3.1 Evidence of the genetic background to COPD and asthma…….……....  5  1.3.2 Overview of genetic studies of COPD and asthma …………….……...  6  1.3.2.1 Genome linkage scan following by positional cloning .……….  6  1.3.2.1.1 Genome linkage scans of COPD……….…………....  7  1.3.2.1.2 Genome linkage scans of asthma…….……………...  8  1.3.2.2 Candidate gene association studies……………….…………....  9  1.3.2.3 Genome-wide association study (GWAS)……….…………….  11  iv  1.4 Cellular and molecular regulation………………………………….………….  12  1.5 Clinical relevance…………………………...………………………..………..  15  1.6 Hypothesis specific aims and study subjects ...…………………..……………  16  1.6.1 Hypotheses……………………………………………….………..........  16  1.6.2 Specific aims….……………………….………………….…………….  16  1.6.3 Study subjects……………………………………………….……….....  16  1.6.3.1 Subjects for association study of COPD related phenotypes ….  17  1.6.3.2 Subjects for association study of asthma and related phenotypes………………………………………………….....  17  1.6.3.3 Subjects for house-keeping gene optimization study ……..…...  18  1.7 References………………………………………………………………….…..  29  Chapter 2: Association of polymorphisms of interleukin-10 and its receptor with lung 40 function in COPD……. …………………………………………..………… 2.1 Introduction ………………………………………………………….………...  40  2.2 Methods……………… .….………………………………………..………......  41  2.2.1 Study subjects………...………...……………………….……………...  41  2.2.2 TagSNP selection………………………………….. …….…………….  42  2.2.3 Genotyping …………………..………………………………..………..  42  2.2.4 Statistical analysis……………..…………………………….……….....  43  2.3 Results.……….………...……………………………………………………....  44  2.3.1 Characteristics of the study groups …………………………….………  44  2.3.2 Single SNP analysis………………………………...……………….….  44  2.3.3 Haplotype analysis ……………….……...………………….……….....  45  v  2.3.4 Gene gene interaction ………………….……………………..………...  45  2.4 Discussion ………………………...…………………………….……………..  46  2.5 Acknowledgements..……………………………….……...…….…………......  49  2.6 References…...………...………….....................................................................  57  Chapter 3: Association of genetic variations in the CSF2 and CSF3 genes with lung 60 function in smoking-induced COPD …………………………..…………… 3.1 Introduction ……………………………………………….…………………...  60  3.2 Methods………… .….………………………………………………..……......  62  3.2.1 Study subjects…………………...……………………………………...  62  3.2.2 TagSNP selection………………………………….. ……………..……  63  3.2.3 Genotyping …………………..…………………………………..……..  63  3.2.4 Statistical analysis………..………………………………….……….....  64  3.3.5 Power analysis…………………………………………………..………  66  3.3 Results.………………………………………………………………………....  67  3.3.1 Characteristics of the study groups ……………………………….……  67  3.3.2 Haplotypes resolved with the genotyped tagSNPs ………………….…  67  3.3.3 Single SNP association analysis…………….……...………….…….....  67  3.3.4 Haplotype association analysis………………………………………....  69  3.3.5 Gene-gene interaction……………………………………………...…...  70  3.3.6 Power of the study…………………………………………………...….  70  3.4 Discussion ……………………………………………………………………..  70  3.5 References………....……………………………….……...……………….......  85  Chapter 4: Associations of IL6 polymorphisms with lung function decline and COPD  89  vi  4.1 Introduction …………………………………………………………………....  89  4.2 Methods………… .….……………………………………………………........  90  4.2.1 Study participants…………………………….........................................  90  4.2.2 TagSNP selection and genotyping methods ...…….. ……………..……  91  4.2.3 Measurements of serum IL6 concentration in the LHS participants …...  92  4.2.4 Statistical analysis……………...…………………………………….....  92  4.3 Results.………...……………………………………………………….……....  93  4.3.1 Characteristics of the study participants.………………………….……  93  4.3.2 The linkage disequilibrium pattern, Hardy-Weinberg disequilibrium 93 and performance of tagSNPs…...………...…………………………………. 4.3.3 Associations of SNPs and haplotypes in the IL6 gene with rate of 94 decline and baseline of FEV1…………………………….…………………. 4.3.4 Associations of IL6 SNPs and haplotypes with serum IL6 concentrations……………………………..………………………………...  95  4.3.5 Associations of serum IL6 concentrations with rate of decline and 95 baseline FEV1……………………………………………………………….. 4.3.6 Replication of novel IL6 associations in the NETT-NAS participants…  95  4.4 Discussion ……………….………...…………………………………………..  96  4.5 Acknowledgements..……………………………….……...……………….......  100  4.6 References…………...……………....................................................................  108  Chapter 5: Selection of housekeeping genes for real-time PCR in atopic human 111 bronchial epithelial cells……….…………………………………………… 5.1 Introduction …………………  111  vii  5.2 Methods……………… .….………………………………………..……….....  112  5.2.1 Sample collection..…...………...…………………………………..…...  112  5.2.2 RNA extraction and cDNA synthesis ….………….. …………….……  113  5.2.3 Reference gene selection and real time QPCR………………….……...  114  5.2.4 Statistical analysis…………………………………………………........  115  5.3 Results.………………………………………………………………………....  116  5.3.1 Quality control and amplification efficiency .……………………….…  116  5.3.2 Expression levels of candidate housekeeping genes ….…………………  117  5.3.3 Determination of housekeeping gene expression stability ...……..….....  117  5.3.4 Expression levels of candidate HKGs in three groups of subjects ..…...  118  5.3.5 Co-regulation of studied HKGs ..…………………………….………...  120  5.4 Discussion ………………………...……………………………………….…..  120  5.5 References……….....……………………………….……...………………......  134  Chapter 6: A thymic stromal lymphopoietin gene variant is associated with asthma and 136 airway hyperresponsiveness…………………………………………..…….. 6.1 Introduction ……………………………………………………………….…...  136  6.2 Methods……………… .….…………………………………………………....  137  6.2.1 Populations for genetic association studies ……..………………..….....  137  6.2.1.1 The Canadian Asthma Primary Prevention Study (CAPPS)..  137  6.2.1.2 The Study of Asthma Genes and the Environment (SAGE)..  138  6.2.1.3 The Saguenay-Lac-Saint-Jean and Québec City Familial 138 Asthma Collection (SLSJ)…………………….……………. 6.2.1.4 The Busselton Health Study Population…………….………  138  viii  6.2.2 Phenotypes……………………......………………………….…………  139  6.2.2.1 Asthma………………………………………….…….....…..  139  6.2.2.2 Atopy………………………………………………..……….  139  6.2.2.3 Airway hyperresponsiveness (AHR)...………………….…..  139  6.2.2.4 Atopic asthma……………………………………….………  140  6.2.3 TagSNP selection, genotyping, and data cleaning ………………...…...  140  6.2.4 Statistical analysis and correction for multiple testing ……….…...…...  141  6.3 Results.………..…………………………………………………………..…....  144  6.3.1 LD study……………………………….……………………………..…  144  6.3.2 Association study……………………………...….…………………….  144  6.3.3 Associated SNP and proxies in the International HapMap Database…..  145  6.4 Discussion ………………………...…………………………………….……..  145  6.5 Acknowledgements.……………………………….……...……………..…......  149  6.6 References…………...……………....................................................................  155  Chapter 7: Summary…………………………………………………………….………  160  7.1 Overview ………………………………………………………………….…...  160  7.2 Significant findings of the project……………....….…………………………..  161  7.2.1 IL6 as a COPD gene ……..……………………………...………….......  161  7.2.2 TSLP as an asthma gene …………………......…………………..……..  161  7.2.3 PIAA as an optimal house-keeping gene for epithelial cell gene 162 expression studies……………………………………………………..... 7.3 Clinical implication of the project …………………...………………………..  162  7.4 Future directions………………...………………....….………………………  163  ix  7.4.1 Molecular biology and cellular biology studies of IL6 and TSLP ….….  163  7.4.1.1 IL6 and TSLP gene resequencing …………………….…….  163  7.4.1.2 IL6 and TSLP gene expression studies.…………….……….  164  7.4.1.3 IL6 and TSLP reporter gene studies...………………….……  164  7.4.2 Genetic epidemiology studies of IL6 and TSLP …………………...…..  164  7.4.2.1 Replication of the associations with large sample studies.….  164  7.4.2.2 Copy number variations and epigenetic studies….………….  165  7.5 References…………...……………....................................................................  169  APPENDICES……………………………………………………….………………….  173  Appendix A: Supplement to “Chapter 4: Associations of IL6 polymorphisms with 173 lung function decline and COPD”.………….……………………………. A1.1 Methods………………………………………………………………..……..  173  A1.1.1 Study participants……………………………......................................  173  A1.1.1.1 LHS participants……………………......……………..……  173  A1.1.1.2 NETT-NAS Study participants…………………...…....…..  174  A1.1.2 TagSNP selection …...………………………......................................  175  A1.1.3 Genotyping……...…...………………………......................................  176  A1.1.4 Statistical analysis …...……………………….....................................  177  A1.2 Results.………...………………………………………………….…….……  179  A1.2.1 Performance of tagSNPs………….…………………………………..  179  A1.3 References………………...……………………………………..……...  185  Appendix B: List of publications, abstracts, and presentations…………......................  187  Appendix C: Research Ethics Board Certificates of Approval……………….………...  193  x  LIST OF TABLES Table 1-1. COPD phenotypes ……………………………………………......................  22  Table 1-2. Asthma phenotypes…………………………………………………..……...  23  Table 1-3. Asthma genes identified by genome linkage scan following by positional cloning……………………………………..………………………………...  24  Table 1-4. Candidate genes associations with COPD and related phenotypes that have been replicated……………………………………………………..………..  25  Table 1-5. Candidate genes associations with asthma that have been replicated for the same SNP and at least one study with sample size more than 300 ……..….  26  Table 1-6. COPD and Asthma genes identified by genome-wide association studies ....  28  Table 2-1. Nomenclature of the polymorphisms utilized in the study……………..…...  50  Table 2-2. The distribution of demographic characteristics for the fast/non-decline and high/low lung function groups…………………………………………..…..  51  Table 2-3. Minor allele frequencies of IL10 and IL10RA SNPs in the study groups…....  52  Table 2-4. Haplotype frequencies inferred using the EM algorithm in non/fast decline and high/low baseline lung function participants…………………...………  53  Table 2-5. Association studies of IL10 SNPs with rate of decline or level of lung function……………………………………………………………………...  54  Table 3-1. TagSNP selection using the LDSelect program and nomenclature of the SNPs…………………………………………………………………………  77  Table 3-2. TaqMan Primer and Probe Sequences……………………………………….  78  Table 3-3. The distribution of demographic characteristics for the longitudinal FEV1 change study……………………………………………...………………….  79  xi  Table 3-4. The distribution of demographic characteristics for the cross-sectional FEV1 study………………………………………………………….…….....  80  Table 3-5. Single SNP association of CSF2 and CSF3 genes in FEV1 longitudinal decline and cross-sectional level of FEV1 studies……………………..……  81  Table 3-6. Single SNP association of CSF2 and CSF3 with FVC % predicted in the longitudinal decline in FEV1 study group (overall comparison of differences of FVC % among three genotype groups)………………………  83  Table 3-7. Haplotype association of CSF2 and CSF3 in the cross-sectional level of FEV1 study…………………………………………………………….…….  84  Table 4-1. The distribution of demographic characteristics for all subjects and those in the two nested case control study groups in the LHS……………………….  103  Table 4-2. Table 4-2: The distribution of demographic characteristics for NETT COPD cases and NAS controls……………………………………………...  105  Table 4-3. Associations of SNPs in IL6 with rate of decline of FEV1 in the LHS and association with COPD in the NETT NAS…….............................................  106  Table 4-4. Association of serum concentrations of IL6 and IL6 genotypes (linear regression)…………………………………………………………...………  107  Table 5-1. The demographic characteristics of the three groups of children …..........….  125  Table 5-2. Housekeeping genes evaluated in this study ……………………………..…  126  Table 5-3. HKG expression stability results as determined by three different VBA applets…………………………..…………...................................................  127  Table 6-1. Clinical characteristics of subjects from the four study samples……….…...  150  Table 6-2. Associations of TSLP SNPs with asthma, atopy, atopic asthma, and AHR in  151  xii  the separate and combined analyses………………………………………… Table 6-3. Effect modification by gender for rs2289276 T allele with asthma, atopy, atopic asthma, and AHR in the separate and combined analyses……..…….  152  Table 7-1. Candidate genes studied for lung function decline in the Lung Health Study subjects…………………………………………………………………...….  166  Table A1-1. TagSNP selection using the LDSelect program and the nomenclature for the SNPs………………………………………………………………….….  182  Table A1-2. Associations of IL6 SNPs with circulating IL6 concentrations……………  183  xiii  LIST OF FIGURES Figure 1-1. Summary of some candidate genes involved in the pathogenesis of COPD..  20  Figure 1-2. Results of 19 whole genome linkage scans for asthma and its associated phenotypes ……...…………………….........................................................  21  Figure 2-1. TagSNP selection for the IL10 SNPs using the LDSelect program …….....  55  Figure 2-2. Position of SNPs within the IL10 gene……………………………..……...  29  Figure 2-3. Position of SNPs within the IL10RA gene………………………..………..  43  Figure 3-1. The relationship between two LD-selected tagSNPs and haplotypes resolved by those tagSNPs ……………………………………….…………  74  Figure 3-2. Power curves for dominant models (P2 is the proportion in the risk genotype group) for the baseline FEV1 study…………………...…………..  75  Figure 3-3. Power curves for recessive models (P2 is the proportion in the risk genotype group) for the baseline FEV1 study…………………..…………..  76  Figure 4-1. The IL6 gene structure and position of single nucleotide polymorphisms genotyped in the Lung Health Study subjects………..……………………..  101  Figure 4-2. Linkage Disequilibrium of Single Nucleotide Polymorphisms (SNPs) of IL6 in the Lung Health Study subjects using HAPLOVIEW …...………….  102  Figure 5-1. The results of RNA analysis by Agilent bioanalyzer ………………...…….  128  Figure 5-2. Raw Q-RT-PCR cycle threshold values for 12 candidate housekeeping genes among 30 child airway epithelial samples …...……..………………..  129  Figure 5-3. Expression stability of housekeeping genes using the GeNorm expression analysis software…………………….…….………...………………………  130  Figure 5-4. Determination of optimal number of reference genes for normalization.…..  131  xiv  Figure 5-5A. The comparisons of expression levels of 6 HKGs between three groups ..  132  Figure 5-5B. The comparisons of expression levels of 3 HKGs between three groups...  133  Figure 6-1. Pair wise linkage disequilibria of TSLP SNPs evaluated by the r2 value..….  153  Figure 6-2. The combined results of the associations of SNPs in the TSLP gene with four asthma-related phenotypes ………………………..…………………..  154  Figure A1-1. Performance of five tagSNPs in the IL6 gene ……….…………….……..  181  xv  LIST OF ABBREVIATIONS Abbreviations  Full Name  AA  atopic asthma  ABI  Applied Biosystems  ACS  acute coronary syndrome  ACTB  beta-actin  α1AT, SERPINA1  α1-antitrypsin, Serpin peptidase inhibitor, clade A, member 1  α1-ACT, SERPINA3  α1-antichymotrypsin  α2-MG  α2-macroglobulin  ADAM33  a disintegrin and metalloproteinase domain 33  ADRB2  beta-2-adrenergic receptor  AECs  airway epithelial cells  AHR  airway hyperresponsiveness  BAL  bronchoalveolar lavage  BMI  body mass index  B2M  β2-Microglobulin  CAMP  Childhood Asthma Management Program  CAPPS  Canadian Asthma Primary Prevention Study  CAT  catalase  CatG  cathepsin G  CCL11  chemokine, cc motif, ligand 11  CCL24  chemokine, cc motif, ligand 24  CCL5  chemokine, cc motif, ligand 5  CD14  monocyte differentiation antigen cd14  CD/CV  the Common Disease/Common Variants hypothesis  xvi  CEPH  the Centre d'Etude Polymorphisme Humain family panel  CFTR  cystic fibrosis transmembrane regulator  CHIT3L1  chitinase 3-like-1  CHIA  chitinase, acidic  CHITI  chitinase 1 (chitotriosidase)  CHRNA5  cholinergic receptor, nicotinic, alpha 5  CI  confidence interval  CO  carbon monoxide  COPD  Chronic Obstructive Pulmonary Disease  CRHR1  corticotropin-releasing hormone receptor 1  CRP  C-reactive protein  CSF  colony stimulating factor  Ct  threshold cycle  CTLA4  T-lymphocyte-associated protein 4  Cu/Zn-SOD  copper-zinc superoxide dismutase  CX3CR1  chemokine (C-X3-C motif) receptor 1  CYFIP2  cytoplasmic FMRP-interacting protein 2  CYSLTR2  cysteinyl leukotriene receptor 2  DEFA1A3  defensin, alpha 1 and alpha 3, variable copy number locus  DEFB1  defensin, beta 1  DLco  carbon monoxide (CO) diffusing capacity  DPP10  dipeptidyl peptidase X  EC-SOD  extracellular SOD  EDN1  cndothelin 1  FCER1A  Fc IgE receptor, alpha chain  xvii  FCER1B  the Fc fragment of IgE, high affinity I, beta subunit  FEF  forced mid expiratory flow  FEV1  forced expiratory volume in one second  FITF  focused interaction testing framework  FLG  filaggrin  FVC  forced expiratory vital capacity  GAPDH  glyceraldehyde-3-phosphate dehydrogenase  GC  glucocorticoid receptor  GC, VDBP  group-specific component, Vitamin D Binding Protein  GM-CSF  granulocyte-macrophage colony-stimulating factor  GNB2L1  guanine nucleotide-binding protein, β-peptide 2-like 1  GOLD  Global Initiative on Obstructive Lung Disease  GPRA  G protein-coupled receptor for asthma susceptibility  GSTP  glutathione S-transferase  GUSB  β-Glucuronidase  GWAS  genome wide association study  HA  healthy atopic  HAVCR1  Hepatitis a virus cellular receptor 1  HCK  hemopoietic cell kinase  HHIP  hedgehog interacting protein  HKGs  house-keeping genes  HLA-G  histocompatibility antigen, class I  HMOX1  heme oxygenase-1  HNA  nonatopic nonasthmatic  HPRT1  hypoxanthine ribosyl transferase  xviii  HRCT  high resolution computed tomography  ICS  inhaled corticosteroids  IFNG  interferon, gamma  IL  interleukin  IL1RN  interleukin 1 receptor antagonist  IL4RA  interleukin-4 receptor alpha  INPP4A  inositol polyphosphate-4-phosphatase, type I  IRAK-3  interleukin-1 receptor-associated kinase 3  ITGB3  ITGB3 and Name: integrin, beta 3 (platelet glycoprotein IIIa, antigen CD61)  LD  linkage disequilibrium  LHS  Lung Health Study  LTA  lymphotoxin-alpha  M  a measure of gene expression stability  MAF  minor allele frequency  matSpD  matrix spectral decomposition  MDR  multifactor dimensionality reduction  MEH, EPHX1  microsomal epoxide hydrolase  MHC  major histocompatibility complex  MMP  matrix metalloproteinases  MYB  V-myb avian myeloblastosis viral oncogene homolog  MYC  myelocytomatosis viral oncogene homolog  MYLK  myosin light chain kinase  NAEPP  National Asthma Education Program Panel  NAS  Normative Aging Study  NAT2  N-acetyltransferase 2  xix  ne  effective number  NE  neutrophil elastase  NETT  National Emphysema Treatment Trial  NFE2L2  nuclear factor (erythroid-derived 2)-like 2  NOD1  nucleotide-binding oligomerization domain containing 1  NOS3  nitric oxide synthase 3  NPPA  natriuretic peptide precursor A  OR  odds ratio  ORMDL3  orm1-like protein 3  PDE4D  phospholipase A2, group VII (platelet-activating factor acetylhydrolase, plasma) provocative concentration of methacholine chloride inducing a 20% fall in forced expiratory volume Phosphodiesterase 4D, cAMP-specific  PGA  Program for Genomic Applications  PGK1  Phosphoglycerokinase  PHF11  PHD finger protein 11  PLAT  plasminogen activator, tissue  PLAU  plasminogen activator, urokinase  PPIA  peptidyl-prolyl isomerase A  Pr3  proteinase 3  PTGDR  prostaglandin D2 receptor  P-450  cytochrome P-450  qPCR  quantitative polymerase chain reaction  RAD50  rad50, S. cerevisiae, homolog of  RAST  radioallergosorbent test  RFLP-PCR  restriction fragment length polymorphism-PCR  PAFAH PC20  xx  RIN  RNA integrity number  RNR1  18S rRNA  ROS  reactive oxygen species  RPLP0  acidic ribosomal protein  SAGE  Study of Asthma Genes and the Environment  SAM  sentrix array matrix  SD  standard deviation  SERPINE1  serpin peptidase inhibitor, clade E (nexin, plasminogen activator inhibitor type 1), member 1  SFTPB  surfactant proteins B  SLSJ  Saguenay-Lac-Saint-Jean and Québec City Familial Asthma Collection  SNPs  single nucleotide polymorphisms  SOD3  superoxide dismutase 3  SpD  spectral decomposition  SPs  surfactant proteins  STAT6  signal transducer and activator of transcription 6  TBP  TATA-binding protein  TBX21  T-box 21  TBXA2R  thromboxane A2 receptor  TDT  transmission disequilibrium test  TFRC  transferrin receptor  TGFB1  transforming growth factor, beta-1  TGFBR3  transforming growth factor B receptor3  TIMP  tissue inhibitors of metalloproteinases  TLR  Toll-like receptor  xxi  TNF-α  tumor necrosis factor-α  TSLP  thymic stromal lymphopoietin  TSLPR  TSLP receptor  UGB (CC10)  secretoglobin, family 1A, member 1 (uteroglobin)  VDR  vitamin D (1,25- dihydroxyvitamin D3) receptor  WDR36  WD repeat-containing protein 36  3’UTR  3' untranslated region  xxii  ACKNOWLEDGEMENTS This dissertation is the product of more than four years of work whereby I have been supported by many people. This work would not have been possible without their great help. First, I would like to express my deepest gratitude to my supervisor Dr. Peter Paré for his understanding, mentorship, knowledge, dedication, and guidance. As a medical student in the 80s and a clinician in the 90s, since the first PCR report in 1985, especially after its universal utilization and the 1993 Nobel Prize in Chemistry awarded to Dr. Kary B. Mullis, I have been fascinated with performing transitional molecular medical research to promote health care. Dr. Paré gave me a chance to work in his group for several years. When I expressed my desire to gain systematic training in molecular genetics, Dr. Paré understood and supported me to fulfill the dream that I have had since I became a clinician. Dr. Paré’s broad knowledge extending from multiple basic disciplines to clinical medicine has significantly inspired me. In my study, Dr. Paré’s guidance extends far beyond this dissertation. Peter, thanks for your tremendous help on all aspects of my career. It was a great pleasure for me to work under your supervision. As well, I would like to give my most sincere thanks to my supervisor Dr. Andrew Sandford. Thanks Andy for accepting me as a graduate student in the group. Andy is a deep thinker with broad knowledge. He has been very patient, as well as a quick problem solver with a clear mind. Andy was always available to answer my questions and respond very promptly. Also thanks Andy for your hands on guidance from basic concepts to bioinformatics tools and data analysis, etc. I would also like to thank the members of my supervisory committee. Thanks to Drs. Robert Schellenberg, Darryl Knight and Keith Walley. You were all there whenever I needed research advice and practical suggestions throughout the work.  xxiii  I would like to thank the Lung Health Study (LHS) investigators: Dr. John Connett at the University of Minnesota, Dr. Nicholas Anthonisen at the University of Manitoba, Dr. Robert Wise at Johns Hopkins University. Through them I was able to access the LHS materials. I would like to extend my thanks to Dr. Don Sin and Dr. SF Paul Man at the iCAPTURE Center for providing serum IL6 and CRP levels in the LHS participants. I would like to extend my thanks to a team at the Channing Laboratory, Harvard Medical School, including Dr. Edwin Silverman, Dr. G. Foreman, Dr. Dawn DeMeo and Dr. Augusto Litonjua for their replication of the IL6 association with COPD. I also want to extend my thanks to Dr. Moira Chan-Yeung at the University of Hong Kong and Dr. Allan Becker at the University of Manitoba for establishing the two asthma cohorts; thanks to Dr. Tom Hudson at the Ontario Institute for Cancer Research and Dr. Catherine Laprise at the Université du Québec à Chicoutimi for establishing the SLSJ cohort; thanks to Dr. Alan James, through him I was able to access the Busselton Health study; thanks to Drs. Anthony Kicic and Stephen Stick at University of Western Australia for providing airway epithelial cells for the house-keeping gene studies. Special thanks to my colleagues for their constant assistance in my research, particularly, Dr. Denise Daley, Ms. Karey Shumansky, Mr. Ben Tripp and Mr. David Zamar for data analysis; Dr. Ji Yuan and Dr. Decheng Yang for technical consultation; Ms. Dorota Stefanowicz, Ms. Loubna Akhabir and Ms. Treena McDonald for technical assistance and project management. I gratefully acknowledge the financial support by the Michael Smith Foundation for Health Research, the Izaak Walton Killam Memorial Scholarship and the Department of Experimental Medicine at the University of British Columbia.  xxiv  Last but not least, I would like to thank my wife and my son whose love, understanding and support enabled me to complete my work.  xxv  DEDICATION I would like dedicate this work to my mentors Drs. Peter D. Paré and Andrew J. Sandford.  xxvi  CO-AUTHORSHIP STATEMENT Chapter 1: I drafted the manuscript, my supervisor Dr. Sandford provided critical comments and helped revise the manuscript. Chapter 2 – 4: My supervisors Drs. Paré and Sandford identified and designed the research program, I designed the specific research project including selection of candidate genes and SNPs, performed the majority of genotyping assays and part of statistical analysis and drafted the manuscripts. Chapter 5: I identified and designed the research project, performed real time PCR assays, did all statistical analysis and drafted the manuscript. Chapter 6: My supervisors Drs. Paré and Sandford identified and designed the research program, I helped select candidate genes and performed SNP selection. I drafted the manuscript.  xxvii  Chapter 1: INTRODUCTION 1.1 DEFINITIONS OF COPD AND ASTHMA Chronic Obstructive Pulmonary Disease (COPD) is characterized by decreased maximal expiratory flow and the Global Initiative on Obstructive Lung Disease (GOLD) has defined COPD as “a preventable and treatable disease with some significant extra-pulmonary effects that may contribute to the severity in individual patients. Its pulmonary component is characterized by airflow limitation that is not fully reversible. The airflow limitation is usually progressive and associated with an abnormal inflammatory response of the lungs to noxious particles or gases” (1, 2). COPD encompasses chronic obstructive bronchitis, with obstruction of small airways, and emphysema, with enlargement of air spaces due to destruction of lung parenchyma, and loss of lung elasticity. The airflow obstruction may be accompanied by airway hyperreactivity which is partially reversible (3). Emphysema is defined as a progressive destruction of the alveolar wall leading to enlargement of airspaces without obvious fibrosis and with loss of normal architecture. Chronic bronchitis is defined as the presence of a productive cough which can not be explained by other causes for at least three months in each year over two consecutive years. Asthma is a chronic inflammatory pulmonary disorder characterized by reversibility of airflow obstruction caused by a multiplicity of stimuli. The National Asthma Education Program Panel (NAEPP) Guidelines on Diagnosis and Management of Asthma published a A version of this chapter has been published. He JQ, Sandford AJ. COPD and asthma, genetics. In: Detlev Ganten and Klaus Ruckpaul., editors. Encyclopedic Reference of Genomics and Proteomics in Molecular Medicine. pp. 333-338. Spring Verlag Heidelberg, 2006  1  definition of asthma recently , it states “asthma is a common chronic disorder of the airways that is complex and characterized by variable and recurring symptoms, airflow obstruction, bronchial hyperresponsiveness, and an underlying inflammation”. Asthma is manifested physiologically by a widespread narrowing of the air passages, which may be relieved spontaneously or as a result of therapy, and clinically by paroxysms of shortness of breath, cough, and wheezing. 1.2 COMPLEX NATURE OF COPD AND ASTHMA 1.2.1 COPD and asthma as complex diseases Both COPD and asthma are complex diseases, because they result from the interactions of multiple genetic and environmental factors. Cigarette smoking is the most important environmental risk factor for COPD; other important factors that contribute to COPD include exposure to indoor and outdoor air pollution, occupational dust, vapors, fumes, and infections (5). Aging, gender and socioeconomic status also play certain roles (5). Asthma risk factors include maternal and environmental tobacco smoke, allergy, animal and occupational exposure, stress, use of antibiotics and infection, lung function impairment, socio-economic status and family size, sex and gender, breastfeeding, low birth weight or overweight and diet (4, 6, 7). The genetics of COPD and asthma involve the study of the function, variations and expression of genes involved in phenotypes associated with these diseases. I will review the genetics of COPD and asthma in later sections of this chapter. 1.2.2 The pathogenesis of COPD and asthma: The protease-antiprotease imbalance and oxidant-antioxidant imbalance are the two major hypothesizes in the pathogenesis of smoking-related COPD (8, 9). Both hypotheses are  2  closely related to modulation of the consequences of the inflammatory process including autoimmunity (10). Some genes involved in inflammation, the protease-antiprotease balance and the oxidant-antioxidant balance, which may play a role in the pathogenesis of COPD are summarized in Figure 1-1. Inflammation in COPD is characterized by airway infiltration with neutrophils, macrophages and lymphocytes (11-13). Neutrophils and macrophages play an important role in the pathogenesis of smoking induced COPD because they release a number of mediators including proteinases (such as elastase, metalloproteinases) and oxygen radicals which promote tissue inflammation and damage (14). It has been suggested that the accumulation of these cells in the airways of COPD patients is driven by increased release of cytokines which exert a chemotactic effect on them (13). CD8+ T cells have been shown to contribute to the pathogenesis of tissue remodeling in COPD: CD8+ cells were increased in the lungs, peripheral airways and peripheral blood of COPD patients (15-17) and CD8+ cell numbers and the CD4+/CD8+ ratio were associated with forced expiratory volume in one second (FEV1) in COPD patients (15, 18). A recent study further emphasized the importance of CD8+ T cells in COPD by showing that the absolute number of CD8+ cells in the peripheral airways increased as COPD progressed (19). In the past decade, COPD has been recognized as a systemic disease characterized by low-grade, chronic systemic inflammation (20, 21). Potential underlying mechanisms include the spread of local inflammation in the lungs into the systemic circulation or the systemic translocation of cigarette smoke constituents into the systemic circulation. The concept that COPD is a systemic disorder has important therapeutic implications in the management of COPD (20, 22, 23).  3  A popular explanation for the development of allergic diseases is the “hygiene hypothesis”, which states that early childhood exposure to infection or endotoxins alters the TH1/TH2 balance and prevents the TH2 immune response and so inhibits the development of asthma and other allergic diseases (24, 25). Inflammation in asthma is characterized by an allergic type response dominated by TH2-type T helper lymphocytes, mast cells, eosinophils, macrophages, IgE and cytokines (26). With the discovery of several new asthma and allergy genes such as dipeptidyl peptidase X (DPP10), G protein-coupled receptor for asthma susceptibility (GPRA), histocompatibility antigen, class I (HLA-G), filaggrin (FLG), chitinase 3-like-1 (CHIT3L1), the importance of the epithelium and the epidermis has been realized recently (27, 28). The airway epithelium in particular has become an important tissue to identify novel mechanisms of asthma pathogenesis. The epithelium is important because it serves as a barrier and also as a source of secreted biomolecules. In addition, defects in the repair functions of the epithelial-mesenchymal trophic unit could also contribute to the susceptibility to asthma (29). 1.2.3 Phenotypes of COPD and asthma For research purposes, clinicians, epidemiologists and pathologists have created terminology based on a variety of criteria for COPD and asthma. In Table 1 I briefly summarize COPD phenotypes. Among them, the most common phenotypes in COPD genetic studies are the presence and degree of airflow obstruction and its rate of change over time. It has long been recognized that airflow obstruction can occur on the basis of either of two very different pathophysiologic processes in the lung: inflammation and fibrotic narrowing of the small airways (obstructive bronchiolitis) and emphysematous destruction of the alveolar surface resulting in loss of lung recoil (emphysema). In any individual patient one of these  4  processes may predominate although both usually exist simultaneously. In recent years, high resolution computed tomography (HRCT) has been used to classify COPD and these subphenotypes have been used in COPD genetic studies (30-32). The phenotypic heterogeneity of asthma is even more marked than that of COPD. Common asthma subphenotypes are summarized in Table 1-2. The most common subphenotypes in asthma genetic studies are atopic asthma, non-atopic asthma, airway hyperresponsiveness, and serum total IgE. However, based on different classification criteria, asthma can be divided into many additional subgroups, and these classification systems have important implications for clinical management. Recently, Pillai et al. identified five major quantitative asthma phenotypes from analysis in the Genetics of Asthma International Network family study (33). These five factors are the minimum number of sets of features required to subgroup asthmatics. They are baseline lung function measurement of FEV1 and forced expiratory vital capacity (FVC), allergen skin tests, self-reported allergies, rhinitis symptoms and respiratory symptoms. More recently, Prescott et al. divided asthma into Th2high and Th2-low sub-phenotypes (32). These subgroups may be useful in future asthma genetics studies. 1.3 GENETICS OF COPD AND ASTHMA 1.3.1 Evidence of the genetic background to COPD and asthma: Severe α1-antitrypsin (α1AT) deficiency, which follows a simple Mendelian inheritance pattern, has been known to be a genetic risk factor for COPD for four decades (34). However, convincing evidence of other genetic factors for COPD has been provided by epidemiological studies (35). There is an increased risk of COPD within the families of COPD probands but without clear Mendelian inheritance (36). Lower forced expiratory  5  volume in one second (FEV1), chronic bronchitis and COPD are more prevalent among the first-degree relatives of cases after correction for other risk factors such as smoking habits and α1AT deficiency (37). Twin studies have found estimates of heritability for FEV1 that range from 52–76% (38). The prevalence of COPD and similarity in lung function decrease with increased genetic distance (39). The evidence of a genetic risk for asthma comes from similar epidemiological studies. First, asthma prevalence is especially high in individuals with a positive family history of asthma (40, 41). Second, twin studies show that the concordance rates for asthma are higher in identical twins than in non-identical twins, irrespective of environmental risk factors (42). However, the fact that concordance in identical twins is not complete and the significant increase in incidence of asthma during the past two decades, indicates that other factors than genetics play a major role in asthma pathogenesis. Currently, it is believed that both genetic and environmental factors contribute to asthma with a heritability of 36-68% (43, 44). 1.3.2 Overview of genetic studies of COPD and asthma: Three major approaches have been used to identify susceptibility genes: positional cloning following genome linkage scan, candidate gene association study and genome wide association study (GWAS). 1.3.2.1 Genome linkage scan following by positional cloning: A genome linkage scan involves searching the entire genome for regions that harbor disease-susceptibility genes by linkage analysis using affected families. Two advantages of genome screens are that novel genes can be identified in the pathogenesis of a disease and the approach is not confounded by population stratification. However, one disadvantage is the  6  requirement for family data. This requirement makes genetic study of COPD difficult because of the late age of onset of the disease. 1.3.2.1.1 Genome linkage scans of COPD Only a few groups have reported full genome linkage scans in COPD and related phenotypes. Silverman et al. enrolled 72 individuals who had severe, early-onset COPD and 585 of their relatives. Five linkage studies have been published from this population (45-49). Using qualitative phenotypes of airway obstruction and chronic bronchitis (46), suggestive evidence for linkage (LOD score > 1.21) on chromosomes 8, 12p, 19q, and 22 was found. The highest two point LOD score (3.14) was seen when the analysis was restricted to smokers and when mild obstruction was the phenotype. For the quantitative phenotypes, FEV1, FVC and FEV1/FVC, the highest LOD score was 4.12 for FEV1/FVC on chromosome 2q (47). The highest LOD score for FEV1 was 2.43 on chromosome 12p in the same region that was linked in the qualitative study. Using post-bronchodilator quantitative phenotypes increased strength of the linkage (45), the LOD score was 4.42 for FEV1/FVC on chromosome 2q and 3.3 for FEV1 on chromosome 8p. Using forced mid expiratory flow (FEF)25-75% (48), the linkages were strong for chromosomes 2q and 12p. Using emphysema as a phenotype, the linkages were strong for 1p in families of emphysema-predominant probands in the stratified analysis (49). Joost et al (50) used markers spaced at ~10cM in a genome-wide scan of 1,578 members of 330 families participating in the Framingham Study to test for linkage of genetic markers to level of lung function during middle age (48-55 years). Lung function at this age reflects lung growth, the maximal lung function achieved and the rate of lung function decline. After correction for age and smoking status they found evidence of linkage for FEV1 and FVC on chromosomes 4, 6q and 21p (maximum LOD  7  score for FEV1 was 2.4 on chromosome 6 and for FVC was 2.6 on chromosome 21). Wilk et al. performed a whole genome linkage scan in 391 pedigrees with 2178 subjects of the Family Heart Study, 4p was found linked to FEV1/FVC and chromosome 18 for FEV1 and FVC (51). In 264 members of 26 Utah Genetic Reference pedigrees, suggestive evidence of linkage for the ratio FEV1/FVC was found on chromosome 2 (heterogeneity LOD = 2.36, dominant model) and chromosome 5 (heterogeneity LOD = 2.23, recessive model) (52). There have been more than 19 full genome linkage scans for asthma and its related phenotypes (53-61) . There is considerable consensus concerning regions of genetic linkage that are relevant to asthma. The linkages that have been replicated include at least 15 regions. The results of the 19 genomic screens are shown in Figure 1-2 with the regions that have and have not been replicated highlighted. Studies have shown that evidence of linkage to asthma could depend on exposure to an environmental factor (62-64), such as environmental tobacco smoke. To identify susceptibility genes, positional cloning has to be performed to follow up results from genome linkage scans. This is another drawback of genome linkage scans since the linked regions may encompass hundreds of genes. Due to the limited number of COPD genome linkage studies, there are very few genes identified by this approach. SERPINE2 on chromosome 2q is the first COPD gene identified by this method. Recently, transforming growth factor B receptor3 (TGFBR3) has been identified associated with emphysema by genome-wide linkage analysis (49). 1.3.2.1.2 Genome linkage scans of asthma Van Eerdewegh and colleagues (65) performed a genetic linkage scan of 460 pairs of siblings from affected asthma families and identified a locus on chromosome 20q, which was  8  not linked to asthma in previous whole genome screens, but was linked to asthma and bronchial hyperresponsiveness in this population. They assessed 135 single nucleotide polymorphisms (SNPs) in 23 genes in this region and identified that a disintegrin and metalloproteinase domain 33 (ADAM33) was associated with asthma. Since the identification of ADAM33 as an asthma gene by a linkage scan following by positional cloning (65), five other novel asthma genes have been identified by this approach (Table 1-3) (65-70). Hersh et al. summarized these studies and attempted to replicate the association for five of these six genes (excluding Cytoplasmic FMRP-interacting protein 2 (CYFIP2) with two populations, They found replication for GPR154 and PHF11 with asthma susceptibility (71). Replication for ADAM33 has been reported by many others but not in the study of Hersh et al. (71). More recently, Blakey et al. attempted replication for the same 5 genes with asthma and related phenotypes in the British 1958 Birth Cohort; replication for two genes, ADAM33 and DPP10, was obtained (72). 1.3.2.2 Candidate gene association studies Genetic association study is to investigate the relationship between genetic markers and phenotypes in a case control or prospective cohort design. The main problem with the association study approach has been lack of replication. When comparing association studies, three possibilities need to be borne in mind: a false negative study due to lack of power, a false positive original report, and true differences between study populations. Three common reasons of inconsistency of association studies cannot be overemphasized. Firstly, there is a risk of false positive and false negative results due to population stratification. Several approaches such as “genomic controls” have been advocated to attempt to correct for population stratification (73). Secondly, multiple comparisons inevitably result in false  9  positive associations; this is particularly true for asthma association studies, since numerous asthma-related phenotypes have been reported. Thirdly, different phenotypes of COPD and asthma in different studies also contribute to the difficulties of comparisons among association studies. The majority of our knowledge of COPD genetics is derived from candidate gene association studies. Candidate genes for COPD include genes involved in the proteaseantiprotease and oxidant-antioxidant balances, inflammation and airway defense. To date, a total of 26 candidate genes have been identified by association studies and have been replicated with COPD and related phenotypes (see Table 1-4) (74, 75). For asthma, Weiss et al. recently summarized all association studies in the English language publications available on PubMed and identified 43 genes that were associated with asthma, that had been replicated for the same SNP in at least one study with a sample size more than 300 (76, 77). Table 1-5 lists 37 of these 43 genes since I have excluded the four positionally cloned genes which have been reviewed in the previous section and two genes from genome wide association study which will be summarized in the next section. Among them, TNFα has the largest number of replication studies (n=17). Genes in the interleukin-4 (IL4)/IL13 pathway are frequently reported as being associated with asthma and these genes include IL4, IL13, and interleukin-4 receptor alpha (IL4RA). In addition, another four genes with ≥ 5 replications are the beta-2-adrenergic receptor (ADRB2), the receptor for the Fc fragment of IgE, high affinity I, beta subunit (FCER1B), Glutathione S-transferase P1 (GSTP1) and prostaglandin D2 receptor (PTGDR). Therefore, not many genes have been replicated in multiple studies.  10  1.3.2.3 Genome-wide association study (GWAS) The completion of human genome and HapMap projects, along with advanced new technology, has enabled researchers to rapidly genotype 300,000 to 1,000,000 selected markers across the genome in a large number of subjects. Using this method to identify the association of genetic variation with a particular phenotype is called genome-wide association study (GWAS). Compared with candidate gene association studies, the advantage of GWAS is that novel genes and pathways can be discovered. Compared with genome-wide linkage studies, the advantages of GWAS are a) it is easier to identify disease-causing alleles since the associated regions are much smaller than those identified by genome-wide linkage studies and b) it is possible to identify slightly increased risk such as odd ratios of 1.1-1.4, which is the usual range for most complex genetic diseases. Since Orm1-like protein 3 (ORMDL3), the first GWAS identified asthma gene, was reported in 2007, a total of 7 genes have been discovered by GWAS for COPD, asthma and their related phenotypes (Table 1-6) (78-84). GWAS can be used to discover novel disease-causing genes in an unbiased way but further functional studies are usually required to uncover the pathophysiological mechanisms underlying the associations. Wilk et al. recently discovered a chromosome 4 SNP (rs13147758) associated with FEV1/FVC (78), and this SNP is located in an intergenic region that is at least 100 kb away from known genes both upstream and downstream. It remains unknown if this associated SNP is in high linkage disequilibrium (LD) with a SNP in a known gene several hundred kilobase pairs away or within an expressed sequence tag (78). Although ORMDL3 was identified as an asthma gene by GWAS two years ago all the subsequent studies have been limited to replication of the association or exploration of the  11  association in subpopulations of patients (77, 85-90). More functional information regarding ORMDL3 and the effect of the associated SNPs will help us to better understand the pathogenesis of asthma. With the successful identification of COPD and asthma genes by GWAS, should candidate gene association studies be replaced by GWAS? The answer is that it is unlikely, at least at current time, for the following reasons. First, even the most updated Illumina HumanHap650Y chip only captures 66–89% of all HapMap Phase II variation with r2 ≥ 0.8 in different populations (91). Second, although the HapMap Phase II 3.1 million SNPs has good coverage for common variations (minor allele frequency MAF ≥ 0.05), the mean maximum r2 of any SNP to a typed one is 0.90 to 0.96 in different populations, whereas the rarer SNPs with MAF < 0.05 are less well covered (mean maximum r2 of less than 0.8) (91). Third, as the GWAS approach is not hypothesis-testing, the results have to reach a whole genome statistical significance level, which does not consider biologic plausibility. This can lead to false negative results. Last, as half million to one million SNPs are genotyped in GWAS, to achieve reasonable power the sample size must be large and usually needs collaborations between multiple centers and even cross-continental collaborations, which unavoidably creates challenges to homogenize phenotypes and environmental factors, thus leading to reduced power to detect disease association. 1.4 CELLULAR AND MOLECULAR REGULATION DNA sequence variations in the human genome are numerous and there are different ways to categorize them. Variations can be divided to two categories according to frequency: mutations and polymorphisms (92, 93). Mutations are variations with a MAF less than 1%; while polymorphisms are those with MAF equal or more than 1% (92, 93). When classified  12  according to mechanisms, the most common variations are single nucleotide polymorphisms (SNPs). On average, SNPs occur about every 500-1,000 base pairs. Other types of variations such as differences in copy number, insertions, deletions, and duplications also occur. Copy number variation is a rapidly expanding field in genetic studies, and it has been reported in asthma genetic study recently (94). DNA sequence variations can cause disease by various mechanisms. A common mechanism is loss of function of the protein. A classical example is severe α1AT deficiency and emphysema (95). α1-AT is the most abundant circulating proteinase inhibitor. The α1AT gene is a highly polymorphic gene with over 100 variations. Most of these are the result of SNPs that lead to single or sometimes double amino acid changes. Based on their migratory distance on isoelectric focusing analysis, they are categorized as: normal α1AT which migrates in the middle of the gel (M allele), and S and Z variations that migrate more quickly owing to changes in their overall charge. S and Z alleles are caused by amino acid substitutions from Glu to Val at amino acid position 264 and from Glu to Lys at amino acid position 342, respectively. In the Caucasian population, the frequencies of M, S and Z alleles are >95%, 2-3% and 1%, respectively. A small percentage of people inherit a null allele, which leads to complete absence of α1AT production. Individuals with MS and MZ genotypes have ~80% and 60% of normal α1AT levels, respectively. Heterozygous PI SZ is rare and individuals with this genotype have α1AT levels ~40% of normal. Individuals with two Z alleles or one Z and one null allele are referred to as PI Z. PI Z individuals have approximately 15% of normal plasma α1AT levels as 85% of the protein is retained within the rough endoplasmic reticulum of the hepatocyte. The reduced plasma levels are inadequate to protect the lung from the attack of proteinases. Thus the PI Z individuals are  13  susceptible to early onset emphysema. This observation led to the proteinase-antiproteinase hypothesis of lung injury in emphysema which is still a dominant view of COPD pathogenesis. Some sequence variations cause disease through a gain of function, where the protein takes on some new, deleterious or enhanced function. Examples include the coding region SNPs of the IL4 receptor alpha (IL4RA) subunit and asthma. The combination of the V75 allele with R576 resulted in expression of an IL4RA with enhanced sensitivity to IL4. There was also a significant association of V75/R576 with atopic asthma (96). Many functional alterations are located in the coding sequence of genes, which influence the expression of a phenotype as discussed above. However, DNA sequence variations in regulatory region of genes may also change function. For example, a (GT)n dinucleotide repeat in the heme oxygenase-1 (HMOX1) gene shows length polymorphism and could regulate the level of gene transcription. A large size (GT)n repeat may reduce HMOX1 inducibility by reactive oxygen species in cigarette smoke, resulting in the development of emphysema (97). Another example is the T allele of the C-589T polymorphism in the IL4 promoter that was associated with increased luciferase reporter gene activity (98). The T allele was also associated with increased total IgE, asthma and asthma severity (99, 100). DNA sequence variations in other regions of genes also could change function. For example, SNPs within introns and the 3' untranslated region (3’UTR) may affect alternative splicing, splicing efficiency or messenger RNA turnover. For example, several of the SNPs in the ADAM33 gene that were associated with asthma were located in non-coding regions. However, there are no functional data available as to whether the identified sequence variations result in functional changes (65). In addition, it is common that several functional  14  sequence variations in one gene or different genes may contribute independently to a phenotype, such as three SNPs in low LD in ORMDL3 that contribute to childhood asthma independently (80). 1.5 CLINICAL RELEVANCE Studies of genetic factors that could potentially play a role in determining treatment response led to the emergence of pharmacogenetics, and later to pharmacogenomics, which uses genetic approaches to elucidate the inherited basis of differences among persons in response to drugs.  The best examples of asthma pharmacogenomics are the β2  adrenoreceptor (ADRB2), ALOX5 and genes related to corticosteroid response such as corticotropin-releasing hormone receptor 1 (CRHR1), glucocorticoid receptor (GC), IL4, Tbox 21 (TBX21, also known as T-bet),  and several other genes (101, 102). The  pharmacogenomics of COPD and asthma is not within the scope of this chapter, however, several recent reviews have addressed this issue comprehensively (74, 103-105). It is anticipated that the study of the genetics of COPD and asthma will lead to novel or better understanding of the mechanisms of these diseases, thereby, leading to the identification of different types of COPD and asthma. Classification of COPD and asthma according to their underlying pathogenesis may lead to new and better treatments and may help to tailor the most appropriate individual treatments in the future. In this thesis I have taken advantage of phenotypic data and DNA collected on several relatively large studies, the Lung Health Study of COPD and three Canadian asthma cohorts and one Australia case control study for asthma. I have employed the candidate gene association study approach to identify if genetic variations of several cytokine genes are associated with COPD and asthma related phenotypes.  15  1.6 HYPOTHESIS, SPECIFIC AIMS AND STUDY SUBJECTS 1.6.1 Hypotheses •  SNPs in the inflammatory cytokine genes IL10, IL10RA, CSF2, CSF3 and IL6 are associated with COPD related phenotypes in smoking-induced COPD patients  •  SNPs in a Th2-polarizing cytokine TSLP are associated with asthma and related phenotypes.  1.6.2 Specific aims •  To perform genetic association studies between SNPs and haplotypes in IL10, IL10RA, CSF2 and CSF3 with the rate of decline and/or the level of lung function and also investigate gene-gene interactions of IL10 with IL10RA, and CSF2 with CSF3 on these phenotypes.  •  To perform genetic association studies between SNPs and haplotypes in IL6 with the rate of decline and/or the level of lung function and also with the intermediate phenotype of IL6 serum concentrations.  •  To perform genetic association studies between SNPs in epithelium derived cytokine TSLP with asthma and related phenotypes.  •  To optimize house-keeping genes for epithelial gene expression studies for asthma and related phenotypes.  1.6.3 Study subjects More information on the participants is included in each individual chapter; all blood and airway epithelial cells were collected with informed consent obtained in compliance with the Research Ethics Board of each recruiting center. The following is a brief description of the study subjects.  16  1.6.3.1 Subjects for association study of COPD related phenotypes The Lung Health Study (LHS) participants: Two nested case-control studies were constructed from the ~ 4,800 LHS subjects based on the extremes of rate of decline in lung function and baseline lung function (106). In the decline of lung function study, we selected the 287 and 308 non-Hispanic white continuing smokers with the fastest and slowest rate of decline of lung function, respectively during the 5 year follow up (arbitrary cut-off points of ≥ 3.0% predicted decrease /year and ≥ 0.4% predicted increase /year in FEV1 were used for rapid decliners and non-decliners, respectively). The baseline lung function study consisted of the 542 and 554 participants who had the highest and lowest baseline FEV1% predicted, respectively (arbitrary cut-off points of FEV1 % predicted ≥ 88.9% and ≤ 67.0% were used for the high and low baseline groups, respectively) at the beginning of the LHS. There were 144 participants that overlapped between the two sets of nested cases and controls due to the fact that subjects in the rate of decline study group had baseline lung function within one of the categories for baseline lung function. The National Emphysema Treatment Trial (NETT) and the Normative Aging Study (NAS) participants (NETT-NAS participants): We selected 389 non-Hispanic white subjects who were enrolled in the NETT Genetics Ancillary Study (107, 108). The control group was composed of 420 participants with normal spirometry from the NAS, a longitudinal study over the past four decades of healthy adult males that was initiated by the Boston Veterans Administration (109, 110). 1.6.3.2 Subjects for association study of asthma and related phenotypes The Canadian Asthma Primary Prevention Study (CAPPS): A total of 549 children at high risk for developing asthma and their parents who during the second and third trimester  17  of pregnancy, were enrolled in an asthma-prevention study and were recruited from two Canadian cities, Vancouver and Winnipeg (111). The Study of Asthma Genes and the Environment (SAGE): A total of 723 children and their parents were recruited from a population-based sample of 16,320 children, born in the Province of Manitoba, Canada in 1995 (112-114). Children were assessed for asthma and other allergic phenotypes by a pediatric allergist. The Saguenay-Lac-Saint-Jean and Québec City Familial Asthma Collection (SLSJ): This collection is comprised of 306 families from the Saguenay-Lac-Saint-Jean (n = 227) and Québec City (n = 79) regions of Québec, Canada (115-120). The Busselton Health Study Population: Residents of the town of Busselton in the southwest of Western Australia have been involved in a series of health surveys since 1966. From this population a nested case-control study was designed consisting of individuals who participated in one or more surveys and had a methacholine challenge. Cases and controls were designated upon the presence (679 cases) or absence of asthma (870 controls) (121, 122). 1.6.3.3 Subjects for house-keeping gene optimization study Uncultured human airway epithelial cells (AECs) are obtained from 10 healthy nonatopic nonasthmatic (HNA), 10 healthy atopic (HA) and 10 atopic asthmatic (AA) children.  18  Figure legend Figure 1-1: Summary of some candidate genes involved in the pathogenesis of COPD. (ROS = reactive oxygen species; TNF-α = tumor necrosis factor-α; VDBP = vitamin Dbinding protein; IL1b/IL1RN = interleukin-1β/interleukin-1 receptor-antagonist; IL6 = interleukin-6, CRP = C-reactive protein; CSF = colony stimulating factor; α1-AT = α1antitrypsin; TIMP = tissue inhibitors of metalloproteinases; α1-ACT = α1-antichymotrypsin; α2-MG = α2-macroglobulin; MMPs = matrix metalloproteinases; NE = neutrophil elastase; CatG = cathepsin G; Pr3 = proteinase 3; mEH = microsomal epoxide hydrolase; P450 = cytochrome P-450; GST = glutathione S-transferase; HO-1 = heme oxygenase-1; Cu/ZnSOD = copper-zinc superoxide dismutase; EC-SOD = extracellular SOD; CFTR = cystic fibrosis transmembrane regulator; SPs = surfactant proteins).  19  Cigarette Smoke Inflammatory mediators TNFα VDBP, IL13 IL8, CXCR2 CRP, IL6 CSF2, CSF3  Anti-inflammatory  Inflammation and repair  Increased oxidants  Increased proteases  α-1AT TIMPs α-1ACT α-2MG  IL1β/IL1RN IL10  MMP 1,9,12 NE Cat G Cathepsins K,L,S Pr 3 Pr C  ROS  Emphysema Mucus hypersecretion  Tissue injury  mEH P-450 GSTs HO-1 Catalase Cu/Zn-SOD EC-SOD  Mucocilliary clearance CFTR Defensins SPs mucin  COPD  20  Figure 1-2: Results of 19 whole genome linkage scans for asthma and its associated phenotypes. Regions that have been replicated are showed by a red circle, and regions that have not been replicated are showed by a blue circle.  21  Table 1-1: COPD phenotypes Traditional COPD subphenotypes: •  Chronic bronchitis  •  Emphysema  •  Cross-sectional lung function and rate of change of lung function  COPD subphenotypes based on different classification systems: •  Based on pathogenesis -  Proteinase and antiproteinase imbalance: such as alpha1-antitrypsin deficiency  •  -  Oxidant and antioxidant imbalance  -  Cell type: macrophages, neutrophils, CD8+ T lymphocytes  Based on symptoms: -  •  •  Stable COPD, COPD exacerbations  Based on lung function: -  Airway responsiveness to different stimuli such as β2-agonists, etc  -  Carbon monoxide (CO) diffusing capacity (DLco)  Based on radiological study including high Resolution Computed Tomography (HRCT):  •  -  Parenchymal disease (emphysema)  -  Airway disease (airway thickening and narrowing)  Based on therapeutic responsiveness: -  Corticosteroid responsive  22  Table 1-2: Asthma phenotypes  Traditional asthma subphenotypes: •  Atopy (defined by positive skin tests to common allergens)  •  Airway hyperresponsiveness  •  Serum total IgE  •  Eosinophilia  •  Lung function test  Asthma subphenotypes based on different classification systems: •  •  •  •  Based on pathogenesis -  Cell type: Eosinophilic, neutrophilic, mixed cell  -  Atopy: Atopic and non-atopic  -  Th2 response: Th2 high and Th2 low  Based on symptoms -  Age at onset  -  Severity  -  Natural history  Based on lung function -  Baseline lung function and its longitudinal change  -  Airway responsiveness to different stimuli such as medications, cold air, etc.  Based on therapeutic responsiveness -  Corticosteroid responsive  23  Table 1-3: Asthma genes identified by genome linkage scan following by positional cloning Chromosomal Gene identified  Sample used for Phenotype  location  Ref. linkage study  A disintegrin and metalloproteinase domain 33  20p13  (ADAM33) PHD finger protein 11 (PHF11)  Asthma  460 Caucasian families  (65)  BHR 13q14.1  IgE  80 Caucasian families  (66)  2q12.3-14.2  Asthma  244 Caucasian families  (67)  7p15-p14  Asthma  86 Finnish families  (68)  6p21.3  Asthma  36 Caucasian families  (69)  5q33.3  Asthma  155 Japanese families  (70)  Dipeptidyl peptidase X (DPP10) G protein-coupled receptor for asthma susceptibility (GPRA, GPR154), also called Neuropeptide S receptor 1 (NPSR1) Histocompatibility antigen, class I (HLA-G) Cytoplasmic FMRP-interacting protein 2 (CYFIP2)  24  Table 1-4: Candidate genes associations with COPD and related phenotypes that have been replicated Category Antiprotease  Candidate gene (symbol) α1-antitypsin (SERPINA1) Tissue inhibitors of metalloproteinases2 (TIMP2) α1-Antichymotrypsin (SERPINA3) Protease Matrix metalloproteinase-1 (MMP1) Matrix metalloproteinase-9 (MMP9) A disintegrin and metalloproteinase domain 33 (ADAM33) Heme oxygenase1(HMOX1) Antioxidant Superoxide dismutase 3 (SOD3) Nitric oxide synthase 3 (NOS3) Xenobiotic Microsomal epoxide hydrolase metabolizing (EPHX1) enzyme Glutathione S-transferase M1 (GSTM1) Glutathione S-transferase T1 (GSTT1) Glutathione S-transferase P1 (GSTP1) Cytochrome P4501A1 (CYP1A1) Cytochrome P4502E1 (CYP2E1) Inflammatory Tumor necrosis factor, alpha (TNF) mediator Lymphotoxin-alpha (LTA) Vitamin D Binding Protein (GC) Interleukin-1B (IL-1β) Interleukin-6 (IL-6) Interleukin-10 (IL-10) Interleukin-13 (IL13) Transforming growth factor, beta-1 (TGFB1) Airway Cystic fibrosis transmembrane defense conductance regulator (CFTR) Surfactant proteins B (SFTPB) Others ABO blood group (ABO) Major histocompatibility complex (HLA) Beta-2-adrenergic receptor (ADRB2)  Chromosomal location 14q32.1 17q25 14q32.1 11q22-q23 20q11.2-q13.1 20p13 22q12 4p15.3-p15.1 7q36 1q42.1 1p13.3 22q11.2 11q13 15q22-q24 10q24.3-qter 6p21.3 6p21.3 4p12 2q14 7p21 1q31-32 5q31 19q13.1 7q31.2 2p12-p11.2 9q34 6p21.3 5q32-q34  25  Table 1-5: Candidate genes associations with asthma that have been replicated for the same SNP and at least one study with sample size more than 300  Gene symbol  Gene name  Chromosomal location  Number of replications  ADRB2  Beta-2-adrenergic receptor  5q32-34  5  CCL11  Chemokine, cc motif, ligand 11  17q21.1-22.2  3  CCL24  Chemokine, cc motif, ligand 24  7q11.23  2  CCL5  Chemokine, cc motif, ligand 5  17q11.2-12  3  CD14  Monocyte differentiation antigen cd14  5q31.1  4  CTLA4  T-lymphocyte-associated protein 4  2q33  2  CX3CR1  Chemokine (C-X3-C motif) receptor 1  3p21.3  2  CYSLTR2  Cysteinyl leukotriene receptor 2  13q14.12-q21.1  3  EDN1  Endothelin 1  6p24.1  3  FCER1B  Fc fragment of IgE, high affinity I, receptor 11q13 for, beta subunit  9  GSTP1  Glutathione S-transferase, P1  11q13  8  HAVCR1  Hepatitis a virus cellular receptor 1  5q33.2  1  IFNG  Interferon, gamma  12q14  2  IL10  Interleukin 10  1q31-q32  4  IL12b  Interleukin 12B  5q31.1-33.1  2  IL13  Interleukin 13  5q31  8  IL4  Interleukin 4  5q31.1  11  IL4RA  Interleukin 4 receptor α  16p12.1-11.2  7  INPP4A  Inositol polyphosphate-4-phosphatase, type 2q11.2 I  2  26  Gene symbol  Gene name  Chromosomal location  Number of replications  IRAK-3  Interleukin-1 receptor-associated kinase 3  12q14.3  2  ITGB3  ITGB3 and Name: integrin, beta 3 (platelet glycoprotein IIIa, antigen CD61)  17q21.32  3  LTA  Lymphotoxin alpha (TNF superfamily, member 1)  6p21.3  3  MYLK  Myosin light chain kinase  3q21  2  NAT2  N-acetyltransferase 2  8p22  3  NOD1  Nucleotide-binding oligomerization domain 7p15-p14 containing 1  4  NOS3  Nitric oxide synthase 3  7q36  1  NPPA  Natriuretic peptide precursor A  1p36.21  2  PAFAH  Phospholipase A2, group VII (plateletactivating factor acetylhydrolase, plasma)  6p21.2-12  3  PTGDR  Prostaglandin D2 receptor  14q22.1  5  TBXA2R  Thromboxane A2 receptor  19p13.3  2  TGFB1  Transforming growth factor, beta 1  19q13.1  2  TLR4  Toll-like receptor 4  9q32-q33  2  TLR9  Toll-like receptor 9  3p21.3  3  TLR10  Toll-like receptor 10  4p14  2  TNF  Tumor necrosis factor (TNF superfamily, member 2)  6p21.3  17  UGB (CC10)  Secretoglobin, family 1A, member 1 (uteroglobin)  11q12.3-13.1  4  VDR  Vitamin D (1,25- dihydroxyvitamin D3) receptor  12q13.11  3  27  Table 1-6: COPD and Asthma genes identified by genome-wide association studies  Gene identified hedgehog interacting protein  Chromosom phenotype al location  4q31  FEV1/FVC  (HHIP) alpha-nicotinic acetylcholine receptor  15q25  (CHRNA3/5) HHIP  4q31  Orm1-like protein 3 (ORMDL3)  17q21  Chitinase 3–like 1 (CHI3L1)  1q32.1  Fc IgE receptor, alpha chain (FCER1A),  1q23  Rad50, s. cerevisiae, homolog of (RAD50), Signal transducer and activator of transcription 6 (STAT6) Interleukin-1 receptor like 1 (IL1RL1), IL33,  Phosphodiesterase 4D, cAMP-specific (PDE4D)  Reference  7,691 subjects from Framingham Heart Study  (78)  Norway: case: 823 control: 810  (79)  Asthma  Asthma: 994  BHR  Control: 1243  Serum YKL-40 levels  Hutterites: 753  (81)  European origin: 1530  (82)  Icelanders: 9392  (83)  (80)  IgE, 55q31 12q13  Asthma, Atopic dermatitis  2q12.2 9q24.1  WD repeat-containing protein 36 (WDR36), V-myb avian myeloblastosis viral oncogene homolog (MYB)  COPD  Primary sample used for GWAS study  Eosinophil, 5q21.3-q22.1  Asthma  6q22 CAMP study: Case: 359 5q12  Asthma  Control: 846  (84)  28  1.7 REFERENCES 1.  2.  3.  4.  5. 6. 7. 8. 9. 10. 11. 12. 13. 14.  15.  16.  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Arch Pediatr Adolesc Med 2000;154:657-663. Kozyrskyj AL, Mai XM, McGrath P, Hayglass KT, Becker AB, Macneil B. Continued exposure to maternal distress in early life is associated with an increased risk of childhood asthma. Am J Respir Crit Care Med 2008;177:142-147. Mai XM, Becker AB, Sellers EA, Liem JJ, Kozyrskyj AL. The relationship of breast-feeding, overweight, and asthma in preadolescents. J Allergy Clin Immunol 2007;120:551-556. Kozyrskyj AL, Hayglass KT, Sandford AJ, Pare PD, Chan-Yeung M, Becker AB. A novel study design to investigate the early-life origins of asthma in children (sage study). Allergy 2009. Begin P, Tremblay K, Daley D, Lemire M, Claveau S, Salesse C, Kacel S, Montpetit A, Becker A, Chan-Yeung M, Kozyrskyj AL, Hudson TJ, Laprise C. Association of urokinase-type plasminogen activator with asthma and atopy. Am J Respir Crit Care Med 2007;175:1109-1116. Laprise C, Sladek R, Ponton A, Bernier MC, Hudson TJ, Laviolette M. Functional classes of bronchial mucosa genes that are differentially expressed in asthma. BMC Genomics 2004;5:21. Poon AH, Laprise C, Lemire M, Hudson TJ, Schurr E. Nramp1 is not associated with asthma, atopy, and serum immunoglobulin e levels in the french canadian population. Genes Immun 2005;6:519-527. Poon AH, Laprise C, Lemire M, Montpetit A, Sinnett D, Schurr E, Hudson TJ. Association of vitamin d receptor genetic variants with susceptibility to asthma and atopy. Am J Respir Crit Care Med 2004;170:967-973. Raby BA, Lazarus R, Silverman EK, Lake S, Lange C, Wjst M, Weiss ST. Association of vitamin d receptor gene polymorphisms with childhood and adult asthma. Am J Respir Crit Care Med 2004;170:1057-1065. Tremblay K, Lemire M, Provost V, Pastinen T, Renaud Y, Sandford AJ, Laviolette M, Hudson TJ, Laprise C. Association study between the cx3cr1 gene and asthma. Genes Immun 2006;7:632-639. James AL, Knuiman MW, Bartholomew HC, Musk AB. What can busselton population health surveys tell us about asthma in older people? Med J Aust 2005;183:S17-19.  38  122.  James AL, Palmer LJ, Kicic E, Maxwell PS, Lagan SE, Ryan GF, Musk AW. Decline in lung function in the busselton health study: The effects of asthma and cigarette smoking. Am J Respir Crit Care Med 2005;171:109-114.  39  Chapter 2: ASSOCIATION OF POLYMORPHISMS OF INTERLEUKIN-10 AND ITS RECEPTOR WITH LUNG FUNCTION IN COPD 2.1 INTRODUCTION Chronic Obstructive Pulmonary Disease (COPD) is characterized by airway obstruction that is not fully reversible. Airway obstruction is defined as a ratio of forced expiratory volume in one second (FEV1) to forced vital capacity (FVC) of less than 0.70. The value of FEV1, represented as a percentage of predicted for age, height, weight and race defines disease severity. An accelerated rate of decline in FEV1 is the hallmark of COPD. Accumulating evidence suggests that genetic factors account for 30-50% of the variability in cross-sectional FEV1 (1, 2), and account for 18% of the variability of longitudinal change in FEV1 (3). However, the genetic determinants of cross-sectional and longitudinal lung function are not fully understood in smoking-induced COPD. The interleukin-10 (IL10) gene is located at 1q31-32, a region not linked to lung function in COPD linkage studies (4-6). IL10 was selected as a candidate gene based on its known function and previous association studies. IL10 is a Th2 cytokine that has a wide spectrum of anti-inflammatory actions. IL10 levels are reduced in induced sputum from patients with COPD, indicating that this might be a mechanism for increasing lung inflammation (7). Several single nucleotide polymorphisms (SNPs) and haplotypes in IL10 have been associated with the level of expression of IL10 (8, 9). The SNPs of IL10 were associated with FEV1 in asthmatic subjects (10). A study showed that one IL10 SNP was associated with rate of decline of lung function in firefighters (11). A version of this chapter has been published. He JQ, Shumansky K, Zhang X, Connett JE, Anthonisen NR and Sandford A. Polymorphisms of interleukin-10 and its receptor and lung function in COPD. Eur Respir J. 2007 Jun;29(6):1120-26.  40  The IL10 receptor (IL10R) is a heterotetramer composed of two of each of the receptor chains IL10RA and IL10RB. The IL10RA chain plays a dominant role in mediating high affinity ligand binding and signal transduction, whereas the IL10RB subunit is thought to be required for signaling only. It was reported that two SNPs in IL10RA (located at 11q23.3), SER138GLY and GLY330ARG, have functional implications (12). There is an interaction between IL10 and IL10R complex: IL10 stabilizes dimerization of both IL10R subunits, and exerts a variety of immunoregulatory activities (13). We hypothesized that IL10 and IL10RA SNPs and their interaction will influence the decline and/or the cross-sectional level of FEV1 in smoking-induced COPD. The LHS, sponsored by the National Heart, Lung and Blood Institute, was a clinical trial of smoking intervention and bronchodilator treatment on the progression of COPD (14). This dataset provides an excellent opportunity to explore (15-18) genetic determinants of decline and/or the crosssectional level of lung function in smoking induced COPD patients. 2.2 METHODS 2.2.1 Study subjects: The LHS recruited a total of 5887 smokers aged 35-60 with spirometric evidence of mild-moderate lung function impairment from 10 North American medical centers (14). From the LHS cohort, two nested case control studies were designed to study genetic determinants of rate of decline of FEV1 and cross-sectional FEV1. Based on the change of FEV1 during 5 years follow up, we selected the 287 and the 308 non-Hispanic whites with the highest rate of decline (fast decline group) and the slowest rate of decline of FEV1 (non decline group) among 3,216 continuous smokers. From all remaining LHS participants, we selected non-Hispanic whites with the highest (high function group, n=484) and the lowest  41  post bronchodilator FEV1% predicted (low function group, n=468) at the start of the LHS. Since 144 individuals from the rate of decline study groups had baseline FEV1 within one of these categories (58 individuals are in the high function group and 86 individuals are in the low lung function group), they were also analyzed in the baseline function study. Thus, there were 542 and 554 participants in the high and low lung function groups, respectively. Informed consent was obtained from all participants and this investigation received the approval of our internal Institutional Review Board. 2.2.2 TagSNP selection: The IL10 and IL10RA SNP discovery data were downloaded from the SeattleSNPs website (http://pga.gs.washington.edu) (accessed October 2003). The IL10 discovery data from the InnateImmunity website (http://innateimmunity.net/) [accessed October 2003] was incorporated into data from SeattleSNPs. From all SNPs identified in the 23 unrelated European-American samples from the Centre d'Etude Polymorphisme Humain family panel (CEPH), a set of tagSNPs was chosen for each gene using the LDSelect program developed by Carlson et al (19). A relatively stringent LD threshold of r2 > 0.8 and minor allele frequency of 10% was used. Four SNPs in IL10 were selected by the LDSelect program (Figure 2-1) and seven SNPs in IL10RA were selected. The positions of selected tagSNPs in the genes are shown in Figures 2-2 and Figure 2-3. Their corresponding rs numbers and positions are shown in Table 2-1. 2.2.3 Genotyping: The genotyping was performed in 384 well plates by the TaqMan 5’ exonuclease assay using primers and probes supplied by ABI. Twelve DNA samples with known genotypes from the CEPH panel were included as positive controls and 8 no template wells  42  as negative controls in each plate. No errors were detected in the 10% of the randomly selected samples that were genotyped in duplicate. 2.2.4 Statistical Analysis: Hardy-Weinberg equilibrium tests and linkage disequilibrium estimation were done using the genetics package for R (20) (www.r-project.org). All single-locus association tests were performed in R. The codominant and additive models were tested first and if there was a significant association the dominant and recessive models were tested to see if those models fit better. If the cell counts were low, significance was assessed by permutation tests. The Armitage trend test (21) was used to test an additive effect of the allele. In the rate of decline study, multivariate logistic regression was used to adjust for confounding factors such as age, sex, pack-years of smoking, and research centre. In the baseline study, multivariate logistic regression was used to adjust for abovementioned confounding factors and an additional rate of decline of lung function covariate. Haplotype association was tested using the hapassoc package for R (22). An Expectation Maximization algorithm from the haplo.stats package for R was used to calculate haplotype frequencies. Gene-gene interactions: The Multifactor Dimensionality Reduction (MDR) method and the Focused Interaction Testing Framework (FITF) method were used to explore genegene interactions. The MDR method is a nonparametric and genetic model-free alternative to logistic regression for detecting and characterizing nonlinear interactions among factors associated with disease risk (23). The more recent FITF approach tests the association of marginal effects and interactions using a logistic regression framework in a series of stages, the number of interacting loci considered increases with each subsequent stage (24).  43  2.3 RESULTS 2.3.1 Characteristics of the study groups: The characteristics of study participants are shown in Table 2-2. Because there was no DNA available for 9 subjects in the decline study and no DNA available for 24 subjects in the baseline study, the numbers of participants in the two studies were 586 and 1072, respectively. There were significant differences in several potential confounding factors such as age, sex and cigarette smoking history (pack-years) between fast and non decline of FEV1 groups and also in high and low baseline FEV1 groups. Therefore, the associations of alleles, genotypes and haplotypes with lung function were analyzed by logistic regression to adjust for these factors. The allele frequencies of all 11 SNPs did not significantly deviate from HardyWeinberg equilibrium in the non lung function decline and high baseline groups. 2.3.2 Single SNP analysis: The minor allele frequencies of the IL10 and IL10RA SNPs in different study groups are shown in Table 2-3. Multivariate logistic regression was used to adjust for confounding factors such as age, sex, pack-years of smoking, and research centre in the rate of decline study, and abovementioned confounding factors and an additional rate of decline of lung function covariate in the baseline study. There was no single SNP association of the 11 SNPs with rate of decline or level of FEV1 in additive and co-dominant genetic models before and after adjusted for confounding factors (all p values > 0.05). In a subgroup analysis stratified by gender, a significant association of the IL10_3368 SNP with rate of decline of FEV1 in female participants was found: the frequencies of  44  IL10_3368A allele were 14.9% and 22.6% in non and fast FEV1 decliners, respectively (p = 0.02 for the additive model), odds ratio (OR) = 1.92, 95% CI = (1.06 - 3.48). However, this association could not survive from correction for multiple comparisons. 2.3.3 Haplotype analysis: Logistic regression was used to estimate the haplotype disease association using the Hapassoc package for R software. The haplotype frequencies inferred by the Expectation Maximization algorithm from haplo.stats are presented in Table 2-4. In the rate of decline study, the haplotypes of the 4 IL10 SNPs and haplotypes of the 7 IL10RA SNPs were not associated with lung function decline (all P values > 0.05). In the baseline lung function study,  the  frequency  of  the  IL10RA  haplotype  5850A/6865A/6881G/9826A/1371G/12488A/14904G (AAGAGAG) was significantly lower in the high versus the low baseline group (14.1% versus 15.0%) when compared with the GGAGGGA haplotype as a reference (adjusted p = 0.02). The frequency of the IL10RA haplotype GGAGGGG was marginally lower in the high versus the low baseline group (7.2% versus 8.9%) when compared with the GGAGGGA haplotype as a reference (adjusted p = 0.05). However, there were no significant differences on global haplotype tests of association. 2.3.4 Gene gene interaction: We explored IL10 and IL10RA interaction of all possible two-to four-locus models using the MDR method. There was no evidence of epistasis (gene-gene interaction) (detailed results not shown). The result of no epistasis was further confirmed by the FITF strategy, which was suggested to be more powerful than MDR (detailed results not shown) (24).  45  2.4 DISCUSSION Association studies of the IL10 and IL10RA SNPs with rate of decline and baseline of FEV1 were performed by using two nested case-control studies in the LHS. No association was found with either the rate of decline or the level of lung function in smoking induced COPD subjects. Several lines of evidence drove us to analyze our data stratified by sex. First, it was reported that women might be more susceptible to the development of severe COPD (25). Second, there was a lower Th1:Th2 cytokine production ratio by stimulated T cells in women compared with men, which was due to women having lower levels of Th1 cytokines (IL2 and interferon-γ) and higher levels of Th2 cytokines (IL10 and IL4) after stimulation (26). Third, several publications demonstrated that associations of IL10 SNPs with IL10 expression levels and diseases were gender specific (27-29). Although in subgroup analysis stratified by gender showed that the IL10_3368A allele was associated with fast decline of FEV1 (p = 0.02 for the additive model), the association disappeared after adjusted for multiple comparisons. There were three previous studies of IL10 SNP and lung function or COPD (Table 25) (10, 11, 30). In order to compare our results with previous studies, the LD information on all studied SNPs must be established. We put the information from the SeattleSNPs and InnateImmunity SNP discovery data in European Americans (Figure 2-1) into Table 2-5. IL10_734G/T was in perfect LD with IL10_-1117A/G (IL10_-1082 in reference (10)) and IL10_3916T/C (IL10_4299 in reference (10)). Similarly, IL10_-627C/A was in almost perfect LD with IL10_-854C/T (R2 = 0.94) (10): IL10_3368G/A was in perfect LD with IL10_1812G/T (Figure 2-1).  46  A comparison of our data and previously reported results is presented in Table 2-5. Although all three previous studies concluded that IL10 SNPs were associated with FEV1 or FEV1 change either the study subjects were normal individuals or had diseases other than COPD, the associations in two of those studies were moderate and the associations would disappear if adjusted for multiple comparisons. In the report of Burgess et al, the rate of FEV1 decline were studied in 379 firefighters with 5 IL10 SNPs, only a single SNP of IL10_1668AA was found associated with FEV1 decline with a p value of 0.023 before adjusting for multiple comparisons. In the paper of Seifart et al (30), five SNPs were studied in 3 groups (COPD, population control and matched control), only mild to moderate association was found for IL10_-1082A/G with COPD when compared with population control before adjusting for multiple comparisons (p = 0.0247 for genotype distribution and 0.047 for allele frequency). In addition, the associations of these two studies were not found for the same SNP or SNPs in a same bin. Therefore, the positive associations in those two studies were probably by chance. In the Lyon et al. paper (10), which studied 518 Caucasian asthmatic children from the Childhood Asthma Management Program (CAMP) and their parents, the IL10_4299T/C was significantly associated with FEV1 percent predicted (P = 0.0002) in both the family and population analyses, which were still significant even after adjusting for multiple comparisons. There were several explanations for the inconsistent results of ours with that of Lyon et al. First and the most important concern is the study of power for a negative study. The IL10_4299T/C was in perfect LD with IL10_734G/T as above-mentioned, the IL10_4299 T allele frequency was 48.5% in CAMP study, which is similar with the IL10_734T allele  47  frequency in our study (48.7% – 52.9% in 4 different phenotypic groups). The results of previous power analysis in our study samples showed that for the allele frequency of 48.7% and genetic models we studied we had > 90% power to detect an allele/genotype relative risk of ~ 1.5 or greater in baseline of lung function groups (31). Therefore, it is unlikely that our negative association was due to lack of power. Second, the subjects of CAMP study were asthmatic children, while our study subjects were smoking induced COPD in adults. The factors that determine baseline of lung function maybe different in these two disease entities and/or these two different age groups. We explored the interaction effects of IL10 and IL10RA SNPs on rate of decline and baseline of FEV1 using both MDR and FITF methods, but there was no evidence of genegene interaction. Since relatively stringent criteria were used to select tag-SNPs and powerful methodologies were used to perform analysis, the observation is likely true. In summary, we reported there was no association of IL10 and IL10RA SNPs with rate of decline or level of FEV1 in mild to moderate smoking-induced COPD patients. We suggested replicating CAMP study’s association of IL10 SNPs with lung function by studying asthmatic children.  48  2.5 ACKNOWLEDGEMENTS This study was supported by grants from the British Columbia Lung Association, the Canadian Institutes of Health Research and National Institutes of Heath Grant 5R01HL064068-04. The Lung Health Study was supported by contract N01-HR-46002 from the Division of Lung Diseases of the National Heart, Lung, and Blood Institute. The authors gratefully acknowledge the NHLBI for the recruitment and characterization of this study. AJS is the recipient of a Canada Research Chair in genetics.  49  Table 2-1 Nomenclature of the polymorphisms utilized in the study  Gene  SNP ID  Position in  Position in gene**  RefSequence* IL10  IL10RA  Position  Names in  in protein  literature  rs1800872  C/A  472  -627 (promoter)  -  -592  rs3024491  G/T  1833  734 (first intron)  -  -  rs3024492  A/T  2767  1668 (third intron)  -  -  rs3024495  G/A  4467  3368 (fourth intron)  -  rs10750121  G/A  8506  5850 (third intron)  -  -  rs2256111  A/G  9521  6865 (fourth exon)  153A/A  -  rs4252272  A/G  9537  6881 (fourth exon)  159S/G  138S/G  rs2512146  A/G  12482  9826 (sixth intron)  -  -  rs4252286  A/G  14028  11372 (sixth intron)  -  -  rs2229113  G/A  15144  12488 (seventh exon)  351G/R  330G/R  rs9610  A/G  17560  14904 (3’ UTR)  -  -  * RefSequence of IL10 is AF418271, and RefSequence of IL10RA is AY195619. **Numbered by denoting the first nucleotide of the initiator methionine codon as +1 (position 1099 in sequence AF418271 of IL10, position 2657 in sequence AY195619 of IL10RA). The presence of an insertion / deletion polymorphism in the IL10 gene leads to a discrepancy between numbering derived from sequence AF418271 and that commonly used in the literature. In order to make the numbering comparable with the published SNP positions, the numbers of three of the IL10 SNPs were adjusted. Thus, rs3024491, rs3024492 and rs3024495 are at positions 735, 1669 and 3369 in sequence AF418271, respectively. The numbers used in Table 2-1 and the rest of the manuscript are those found in the previous literature.  50  Table 2-2 The distribution of demographic characteristics for the fast/non-decline and high/low lung function groups Low F EV1 Fast Decliners Non Decliners High FEV1 p value p value (n = 280) (n = 306) (n = 534) (n = 538) Male/Female  163/117  204/102  0.0346  354/180  333/205  0.1336  Age (years)  49.5 ± 0.38  47.6 ± 0.39  0.0005  46.2 ± 0.3  50.7 ± 0.3  <0.0001  Smoking history (pack-yrs)a  42.9 ± 1.15  38.3 ± 1.04  0.0029  35.3 ± 0.8  45.2 ± 0.8  <0.0001  ∆FEV1/yr (% predicted pre)b  -4.14 ± 0.06  1.08 ± 0.04  <0.0001  -0.55 ± 0.07  -1.27 ± 0.08  <0.0001  ∆FEV1/yr (% predicted post)c  -3.43 ± 0.08  0.67 ± 0.05  <0.0001  -0.75 ± 0.06  -0.79 ± 0.08  0.7227  Baseline FEV1 (% predicted pre)d  72.6 ± 0.53  75.6 ± 0.46  <0.0001  86.5 ± 0.1  61.1 ± 0.2  <0.0001  91.8 ± 0.1  62.6 ± 0.1  <0.0001  91.8 ± 0.1  62.6 ± 0.1  <0.0001  Baseline FEV1 (% predicted post)e  Values are means ± SE for continuous data a  Number of packs of cigarettes smoked per day × number of years smoking.  b  Change in lung function over a 5 year period per year as % predicted FEV1 pre bronchodilator  c  Change in lung function over a 5 year period per year as % predicted FEV1 post bronchodilator  d  Lung function at the start of the Lung Health Study as measured FEV1(%) predicted pre bronchodilator  e  Lung function at the start of the Lung Health Study as measured FEV1(%) predicted post bronchodilator  51  Table 2-3 Minor allele frequencies of IL10 and IL10RA SNPs in the study groups Rate of decline of lung function  Baseline of lung function  Gene  SNP  Non decline  Fast decline  High baseline  Low baseline  IL10  -627C/A  23.1  20.9  22.1  20.6  734G/T  48.7  52.9  49.8  51.7  1668A/T  28.3  27.0  28.1  26.0  3368G/A  14.9  19.1  14.6  15.8  5850G/A  30.8  30.9  31.8  30.5  6865G/A  45.8  45.4  47.1  46.6  6881A/G  16.1  15.9  15.8  16.1  9826G/A  30.6  30.3  31.7  30.1  11372G/A  3.8  2.9  2.6  2.8  12488G/A  30.1  29.6  30.1  29.3  14904G/A  46.7  47.3  46.7  45.5  IL10RA  Note: P > 0.05 for all SNPs in rate of decline of lung function study and baseline lung function study in additive and codominant models.  52  Table 2-4 Haplotype frequencies inferred using the EM algorithm in non/fast decline and high/low baseline lung function participants. Rate of decline of lung function Gene  IL10  Baseline of lung function  Haplotype  AGAG CGAG CTTG CTAG CTAA others  IL10RA GGAGGGA GGAGGGG GGAGAGA GAAGGGG AAAAGAG AAGAGAG others  Non decline  Fast decline  High function  Low function  23.0  20.9  22.1  20.6  28.1  26.3  27.7  31.0  28.0  26.9  28.2  26.0  5.8  7.0  7.4  6.7  14.9  19.1  14.5  15.7  0.2  0.0  0.1  0.0  42.5 7.8 3.8 14.8 14.5 15.3 1.3  43.3 8.1 2.9 14.4 14.5 14.5 2.3  43.1 7.2 2.4 15.2 15.7 14.1 2.3  41.6 8.9 2.8 15.8 13.9 15.0 2.0  The SNPs comprising IL10 haplotypes are: -627C/A, 734G/T, 1668A/T and 3368G/A. The SNPs comprising IL10RA haplotypes are: 5850G/A, 6865G/A, 6881A/G, 9826G/A, 11371A/G, 12488G/A and 14904G/A.  53  Table 2-5 Association studies of IL10 SNPs with rate of decline or level of lung function Study Current study Caucasian (USA and Canada)  Burgess et al (2004) (11)  Phenotype and sample size Smoking induced COPD patients: 280 fast decliner of FEV1 305 non-decliner of FEV1 531 high baseline FEV1 530 low baseline FEV1 Rate of FEV1 decline 379 firefighters  IL10 SNP or haplotype -627  Bin SNP belongs* 2  Association No association was found  734  1  No association was found  1668  4  No association was found  3368  3  No association was found  -1117  1  No association was found  -854 919 1668  2 2 4  1812  3  No association was found No association was found 1668AA associated with increased rate of FEV1 decline, p = 0.023 No association was found  -1117  1  No association for family based association study; p = 0.01 for population based association study  -854 -627  2 2  1668  4  No association was found P = 0.038 for population based association study; no association for family based association study p = 0.01 for population based association study; no association for family based association study  USA  Lyon et al (2004)(10)  Baseline FEV1 518 asthmatic children and their parents  Caucasian (USA)  Seifart et al (2005)(30)  113 COPD 113 matched control 243 health control  4299 (3916 1 in current study)  P = 0.0002 for both population based and family based association study  -1117  1  -854  2  COPD versus matched control: no association COPD versus health control: p = 0.0247 No association  Germany  Note: The bins are derived from LDselector. SNPs in the same bin have high LD and therefore provide similar genetic information.  54  Bin 1  2  3  4  Figure 2-1 TagSNP selection for the IL10 SNPs using the LDSelect program. The IL10 genotype data came from two sources: SeattleSNPs PGA and Innateimmunity PGA. Each row corresponds to an individual DNA sample in the SeattleSNPs and Innateimmunity polymorphism discovery panel; E001–E023 represent European Americans. Columns correspond to polymorphic sites. Sites are ordered by LD, with sites showing similar patterns of genotype put in the same bin. Selected tagSNPs are indicated with stars and genotyped SNPs are indicated with arrows.  55  Interleukin-10  - 627C/A  734G/T  Exon 1  5’  Exon 2  3368G/A  1668A/T  Exon 4  Exon 3  Exon 5  3’ UTR  Figure 2-2 Position of SNPs within the IL10 gene Interleukin-10RA  6848A/G (132ALA/ALA)  5849G/A  6880A/G (159SER/GLY)  5’  Exon 1  Exon 2 Exon 3  Exon 4  Exon 5 Exon 6  9528A/G  12487G/A (351GLY/ARG)  11371A/G  14903A/G  Exon 7  3’ UTR  Figure 2-3 Position of SNPs within the IL10RA gene  56  2.6 REFERENCES 1.  2.  3.  4.  5.  6.  7.  8.  9.  10.  11.  12.  Lewitter, F. I., I. B. Tager, M. McGue, P. V. Tishler, and F. E. Speizer. 1984. Genetic and environmental determinants of level of pulmonary function. Am J Epidemiol 120(4):518-30. Wilk, J. B., L. Djousse, D. K. Arnett, S. S. Rich, M. A. Province, S. C. Hunt, R. O. Crapo, M. Higgins, and R. H. Myers. 2000. Evidence for major genes influencing pulmonary function in the NHLBI family heart study. Genet Epidemiol 19(1):81-94. Gottlieb, D. 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Eur Respir J 29(1):34-41.  59  Chapter 3: ASSOCIATION OF GENETIC VARIATIONS IN THE CSF2 AND CSF3 GENES WITH LUNG FUNCTION IN SMOKING-INDUCED COPD  3.1 INTRODUCTION Chronic obstructive pulmonary disease (COPD) is a complex genetic/environmental disorder that is characterized by airflow obstruction which is not fully reversible and a chronic persistent inflammatory process. The degree of airflow obstruction defines disease severity, which is quantified by post bronchodilator forced expiratory volume in one second (FEV1) calculated as a percentage of a predicted value. Genetic factors contribute to both the level and decline of lung function. There is evidence to suggest that genetic factors account for 28.0 - 51.5% of the variability in cross-sectional FEV1 (1-3), and account for 18% of the variability of longitudinal change in lung function in smokers (4). The inflammatory process is a complex interaction between many inflammatory cells. Among these cells, neutrophils and macrophages play important roles by releasing proteinases that break down connective tissue in the lung parenchyma, resulting in emphysema. Granulocyte-macrophage colony-stimulating factor (GM-CSF), also known as colony-stimulating factor 2 (CSF2), is an important survival, proliferation and differentiation factor of the progenitor cells for neutrophils and macrophages. Granulocyte colonystimulating factor (G-CSF), also known as CSF3, is specific for granulocytes. The CSF2 gene (located at 5q31.1) and CSF3 gene (located at 17q11.2-q12) were selected as candidates for decline and cross-sectional level of lung function in COPD patients based on following A version of this chapter has been published. He JQ, Shumansky K, Connett JE, Anthonisen NR, Peter D Paré and Sandford A. Association of genetic variations in the CSF2 and CSF3 genes with lung function in smokinginduced COPD. Eur Respir J. 2008 Jul;32(1):25-34.  60  reasons. Firstly, CSF2 and CSF3 can induce the expression of pro-inflammatory cytokines and thereby enhance the inflammatory response. It was shown that CSF2 serum and bronchoalveolar lavage (BAL) levels, along with numbers of total cells and polymorphonuclear cells in the BAL were increased in bronchitic patients during exacerbations (5). It was also reported that CSF3 expression in the lung correlated with severity of pulmonary neutrophilia in acute respiratory distress syndrome (6). Secondly, it has been shown that polymorphisms and haplotypes of the CSF2 gene are associated with the prevalence of asthma and other atopic diseases (7-9). COPD and asthma share a common diathesis according to the "Dutch hypothesis" (10, 11); and atopy is a risk factor for COPD (12). The association of a SNP in CSF3 with a significant increase in granulocytes among workers exposed to benzene was also reported (13). Thirdly, a recent study directly linked the CSF2/CSF3 ratio with lung function in cystic fibrosis patients, which suggested that the interaction between CSF2 and CSF3 contributes to lung function in these patients (14). We hypothesized that CSF2 and CSF3 polymorphisms and their interactions will influence the decline of FEV1 and/or the cross-sectional level of FEV1 in smokers with mild to moderate air flow obstruction from the Lung Health Study (LHS) cohort. The LHS, sponsored by the US National Heart, Lung and Blood Institute, was a clinical trial of smoking intervention and bronchodilator treatment on the progression of COPD (15). This dataset provides an excellent opportunity to explore the impact of genetic polymorphisms and their interaction on longitudinal decline and/or the cross-sectional level of FEV1% predicted as has been used previously (16-22).  61  3.2 METHODS 3.2.1 Study subjects: The LHS recruited a total of 5887 smokers aged 35-60 with spirometric evidence of mild-moderate lung function impairment from 10 North American medical centers. Two nested case control studies were designed from the LHS cohort to study genetic determinants of rate of FEV1 decline and cross-sectional level of FEV1. Based on rate of decline of FEV1 during 5 years of follow up, using arbitrary cut-off points of FEV1 % predicted decrease ≥ 3.0%/year and increase ≥ 0.4%/year for rapid decliners and non decliners, respectively, we selected the 287 and the 308 non-Hispanic whites with the highest rate of decline of lung function (fast decline group) and the slowest rate of decline of lung function (non decline group) among 3,216 continuous smokers during the first 5 years of follow up. The rationale to select approximately the 300 highest and 300 lowest phenotypic subjects was that 1) this approach has the advantage of reducing cost while keeping satisfactory statistical efficiency when compared with the full cohort approach (23, 24); 2) the Common Disease/Common Variants hypothesis was suggested one decade ago which states that disease susceptibility alleles of common diseases will be present at high frequencies (25-27), and 3) this sample size has adequate power to detect common genetic risk variants from our previous power analysis (28). From all remaining LHS subjects, we selected non-Hispanic whites with the highest post bronchodilator FEV1% predicted (high function group, n = 484) and the lowest post bronchodilator FEV1% predicted (low function group, n = 468) at the beginning of the LHS. Arbitrary cut-off points of FEV1 % predicted ≥ 88.9% and ≤ 67.0% were used for the high and low lung function groups, respectively. Since 144 subjects from the rate of decline study groups had baseline lung function within one of limits which defined the cross-  62  sectional groups (58 individuals were in the high function group and 86 individuals were in the low lung function group), they were also analyzed in the study of cross sectional FEV1. Thus, there were 542 and 554 subjects in the high and low lung function groups, respectively. Informed consent was obtained from all participants and this investigation received the approval of the Providence Health Care Research Ethics Board. 3.2.2 TagSNP selection: The CSF2 and CSF3 SNP discovery data were downloaded from the SeattleSNPs NHLBI Program for Genomic Applications (PGA), UW-FHCRC, Seattle, WA (URL: http://pga.gs.washington.edu) [accessed October 2003]. From all SNPs identified in the 23 unrelated European-American samples from the Centre d'Etude Polymorphisme Humain family panel (CEPH), a set of tagSNPs was chosen for each gene using the LDSelect program developed by Carlson et al (29). A LD threshold of r2 > 0.64 and minor allele frequency of 5% were used. Two SNPs located at -1440A/G and 1944T/C (I117T) in the CSF2 gene were selected initially, however the assay for the 1944T/C SNP could not be established by the TaqMan assay (ABI) and a PCR-RLFP assay for the same SNP showed that PCR amplification failed for some samples. Therefore, we replaced 1944T/C with an alternative SNP, 1622C/T. Three SNPs located at -1719C/T, -882G/A and 2176T/C in the CSF3 gene were selected and genotyped. TagSNP selection and the nomenclature of the SNPs are presented in Table 3-1. 3.2.3 Genotyping: All SNPs except CSF2_1622C/T were genotyped in 384 well plates with a total volume of 5 µl by the TaqMan 5’ exonuclease assay using primers and probes supplied by ABI (Applied Biosystems) on an ABI Prism 7900HT Sequence Detection System (Applied 63  Biosystems). Probe and primer sequences for each assay are listed in Table 3-2. Major and minor probes were labeled with 5’ FAM or 5’ VIC fluorophores as reporters (Applied Biosystems). Up to 47 DNA samples of the CEPH panel with sequencing information available from the SeattleSNPs PGA were included as quality controls for each SNP genotyping. All genotype results from TaqMan assay were consistent with sequencing results for all CEPH DNA samples which have sequencing information available in the SeattleSNPs database. No discrepancies were detected in the 10% of the randomly selected samples that were genotyped in duplicate. The CSF2_1622C/T polymorphism was detected by a Restriction Fragment Length Polymorphism-PCR (RFLP-PCR) method using the following primers flanking the polymorphic region: 5'-AAG GAA GGG AGG CTA CTT GG-3' (sense) and 5' GTT CCC CAA GGA GTG CAT AG-3' (antisense). Amplification products were digested by the BlpI restriction enzyme. BlpI produced 116-bp and 133-bp fragments when 1622T was present, but did not digest the 249bp PCR product when CSF2_1622C was present. The genotyping method was confirmed by sequencing 10 samples with 3 different genotypes. Sequencing was performed on an ABI 3100 16-capillary automated genetic analyzer (Applied Biosystems) using the same primers as in the PCR reaction to obtain the PCR product to be sequenced. 3.2.4 Statistical Analysis: Hardy-Weinberg equilibrium tests and linkage disequilibrium estimation were done using the genetics package for R (www.r-project.org). All single-locus association tests were performed in R. The codominant and additive models were tested first, and if there was a significant association the dominant and recessive models were further tested to see if those  64  models fit better. If the cell counts were low, significance was assessed by permutation tests. In a codominant model a heterozygote shows the phenotypic effects of both alleles fully and equally. The three genotypic categories of a SNP in the case and control groups constitute a 2x3 contingency table and the analysis does not provide any sense of ordering across the three genotypes. This type of analysis is also called a general genetic model (30). In a dominant model one copy of the minor allele increases disease risk. The homozygotes and heterozygotes for the minor allele are compared as a group with homozygotes for the major allele (30). In a recessive model two copies of minor allele are required to increase disease risk. The homozygotes for the minor allele are compared with heterozygotes and homozygotes for the major allele as a group. In additive model, there is r fold increased disease risk for heterozygotes compared with the homozygotes for the major allele, there is 2r fold increased disease risk for the homozygotes for the minor allele compared with the homozygotes for the major allele (30). The Armitage trend test (31) was used to test an additive effect of the allele. In both the FEV1 decline study and the cross-sectional FEV1 study, in addition to crude analysis by chi-square tests using 2x3 contingency tables, multivariate logistic regression analyses were also used to control for potential confounders that might influence the rate of decline of lung function or the cross-sectional FEV1 level. In the FEV1 decline study, multivariate logistic regression was used to adjust for confounding factors such as age, sex, pack-years of smoking, and research centre. In the cross-sectional FEV1 study, multivariate logistic regression was used to adjust for the abovementioned confounding factors and rate of decline of FEV1. Although other phenotypes such as FVC % predicted and FEV1/FVC ratio were not our primary phenotypes due to our study design, we also analyzed associations of those phenotypes with single SNPs by using one-way ANOVA  65  if the data were normally distributed or a Spearman’s rank test if the data were not normally distributed in the study groups. The effective number (ne) of haplotypes from SNPs with minor allele frequency ≥ 5% (ne was calculated by the equation, ne =  1 , where pi is the frequency of the ith haplotype ∑ pi2 i  (29). The effective number of haplotypes weights the number of haplotypes by frequency, with common haplotypes more heavily weighted. Correction for multiple tests of SNPs in LD in each gene was done on the basis of the spectral decomposition (SpD) of matrices of pairwise LD between SNPs by using SNPSpD (http://genepi.qimr.edu.au/general/daleN/SNPSpD/) (32). This method provides a useful alternative to the very conservative Bonferroni correction. Haplotype association was tested using the hapassoc package for R. This software performs likelihood inference of trait associations with haplotypes and other covariates for generalized linear models, including logistic regression and does not assume haplotype phase is known (33). An additive effect of haplotype on the log-odds of disease was assumed. To calculate haplotype frequencies, an Expectation Maximization algorithm from the haplo.stats package for R was used. Gene-gene interactions: A new approach “focused interaction testing framework” (FITF) was used to identify gene-gene interactions (34).  3.2.5 Power analysis: The power of the two studies was estimated using the two independent proportions and many proportions functions in PASS 2005 (Hintze J, 2004. NCSS and PASS. Number  66  Cruncher Statistical Systems. Kaysville, Utah. www.ncss.com). Plots were created in R (www.r-project.org) using the output from PASS.  3.3 RESULTS 3.3.1 Characteristics of the study groups: The characteristics of study participants are shown in Tables 3-3 and 3-4. Because there was no DNA available for 8 subjects in the rate of decline of FEV1 study and no DNA available for 22 subjects in the cross-sectional level of FEV1 study, the numbers of participants in the two studies were 587 and 1074, respectively. Among non decliners of the rate of decline of FEV1 study and among the high lung function group of the cross-sectional FEV1 study, the allele frequencies of all 5 SNPs did not significantly deviate from Hardy-Weinberg equilibrium (results not shown).  3.3.2 Haplotypes resolved with the genotyped tagSNPs: Haplotypes from SNPs with minor allele frequency ≥ 5% in 23 CEPH samples were inferred by use of PHASE 2.0 (35, 36). The LD-selected CSF2 tagSNPs can resolve 60% (3 out of 5) of the actual number of haplotypes (Figure 3-1) and resolve 87.1% (2.7 out of 3.1) of the effective number (ne) of haplotypes from SNPs with minor allele frequency ≥ 5%. For CSF3, 35.7% of actual haplotypes and 48.1% of effective haplotypes from SNPs with a minor allele frequency ≥ 5% were resolved by the 3 selected tagSNPs.  3.3.3 Single SNP association analysis: In the FEV1 decline study, none of the 5 SNPs were associated with decline of FEV1 in co-dominant and additive models both before and after adjustment for confounding factors (Table 3-5).  67  In the cross-sectional level of FEV1 study, there was a borderline association of CSF3_-1719T with high FEV1 levels in an additive model (p = 0.054) before adjustment for confounding factors; after adjustment for confounding factors, the association was more significant with p = 0.018 (Table 3-5). The Odds Ratio (OR) of having one -1719T allele compared with no -1719T allele and the OR of having two -1719T alleles compared with one -1719T allele was the same and was 0.73, 95% confidence interval (CI) 0.56 to 0.95. The association of CSF3_-1719 with FEV1 level was adjusted for multiple testing on the basis of the SNP spectral decomposition approach (32). The significance threshold required to keep Type I error rate at 5% for CSF3 in our study is 0.019 based on the LD of the 3 SNPs we studied. Therefore, the association of CSF3_-1719 with FEV1 level remained significant after correction for multiple comparisons. In addition, two SNPs showed borderline associations with FEV1 levels before adjustment for confounding factors: CSF2_1622 in a co-dominant model (comparison of the distribution of the three genotypic groups CC, CT and TT in the case and control groups, p = 0.092) and CSF3_-882 in an additive model (the OR of one A allele compared with no A allele was equal to the OR of two A alleles compared with one A allele, p = 0.059). However, after adjustment for confounding factors the p values were > 0.1 for both SNPs (see Table 3-5). Although FVC % predicted and FEV1/FVC ratio at the beginning of the LHS were not our primary phenotypes as a result of our case-control study design, we performed exploratory analyses of single SNP associations with those phenotypes. In the rate of decline group, the FVC% predicted phenotype was normally distributed and therefore a one-way ANOVA was used to compare if FVC % predicted and FEV1/FVC ratio were the same  68  among the three genotypic groups. A significant association of FVC % predicted with CSF3_2176 was found with P = 0.033 (Table 3-6), those individuals with the 2176TT genotype had a lower FVC % predicted. No other significant associations were found (data not shown).  3.3.4 Haplotype association analysis: Haplotypes from CSF2 or CSF3 were not associated with decline of FEV1 in the analysis both without and with adjustment for confounding factors (data not shown). The results of haplotype association in the cross-sectional level of FEV1 study are shown in Table 3-7. The haplotypes from CSF2 were not associated with decline of FEV1 in the analysis both without and with adjustment for confounding factors. The three locus CSF3 haplotypes were associated with levels of FEV1 in a Wald global test (an overall test of haplotype distribution between cases and controls, p = 0.004) before adjustment for confounding factors, although after adjustment for confounding factors, the association became less significant (p = 0.027). The frequency of the haplotype -1719T/-882G/2176C was marginally higher in the high versus low FEV1 group (16.9% versus 14.0%) when compared with the haplotype -1719C/-882G/2176T as a reference (adjusted p = 0.047). Analysis of two locus haplotypes (see Table 3-7) demonstrated that this marginal association was likely driven by both the -1719T allele and 2176C allele. The frequency of the haplotype -1719C/-882A/2176C was lower in the high versus low FEV1 group (34.2% versus 38.7%) when compared with the haplotype -1719C/-882G/2176T as a reference, but the significance became borderline when adjusting for confounding factors (unadjusted P = 0.007, adjusted p = 0.089).  69  3.3.5 Gene-gene interactions: We explored CSF2 and CSF3 interaction of all possible two- to four-locus models using the “focused interaction testing framework” (FITF) method. There was no evidence of epistasis (gene-gene interaction) (detailed results not shown).  3.3.6 Power of the study: First, we calculated the power of our study for a codominant mode of inheritance. A chi-square test with 2 degrees of freedom was used to calculate the associated power. Effect size (a measure of the magnitude of the Chi-Square that is to be detected), a parameter needed for the power calculations, was calculated using the PASS program for each SNP and was used in the calculations. It was found that there was > 80% power to detect an OR of 1.75 for both FEV1 decline and cross-sectional FEV1 level studies.  The power of the  dominant and recessive models was tested with a 2x2 table; the proportions in the control group were set to be close to those observed with the 5 SNPs in the “low” outcome groups (i.e. non decline of FEV1 group and high lung function group). Figure 3-2 and Figure 3-3 give the curves of power versus OR value for the 5 studied SNPs for the baseline FEV1 study for dominant and recessive models, respectively. For the FEV1 decline study, the power was slightly less than that of the baseline FEV1 study due to smaller sample size (Figures not shown).  3.4 DISCUSSION CSF3 is a logical candidate gene for these studies due to its biologic function. In a rat model, neutrophil stimulation by CSF3 aggravates ventilator-induced lung injury manifested by increased lung neutrophils and IL-6 expression, increased alveolar edema on histology, and reduced lung compliance (37). In patients with acute respiratory distress syndrome,  70  CSF3 expression level in the lung correlated with severity of pulmonary neutrophilia (6). Recently, it was shown that the CSF3_2176 SNP (named as exon 4-165C>T in the original paper (13)) was associated with peripheral blood granulocyte count among workers exposed to benzene. Subjects with homozygous TT genotypes had significantly increased blood granulocytes compared with homozygous CC subjects (p = 0.00002) (13). The functional significance of the CSF3 SNPs is unknown. Although we did not find an association of CSF3_2176 with the primary phenotypes of baseline and decline of FEV1, we found that a different SNP (CSF3_1719) was associated with baseline level of FEV1. Interestingly, in an exploratory analysis of single SNPs with other phenotypes such as FVC % predicted and FEV1/FVC ratio, a significant association of CSF3_2176 with FVC % predicted was found (without correction for multiple comparisons). The association of the CSF3_2176TT genotype with lower FVC % predicted is consistent with the previous report that the TT genotype was associated with higher blood granulocytes (13), since neutrophils in the lung and in the blood are important effector cells in COPD (38). There are several explanations for the above observations including genetic heterogeneity between different populations, different phenotypes studied, and choice of tag SNPs. It was reported that tagSNPs selected using the criteria of R2 of 0.64 and minor allele frequency of 5% could resolve 76% of actual and 85% of effective haplotypes in an analysis of 100 genes (29). However, using the same criteria, the CSF3 tagSNPs only resolved 35.7% of the actual haplotypes and 48.1% of the effective haplotypes. If CSF3_2176 is not the causal SNP and there are different LD patterns in our population compared with that of the workers exposed to benzene (13), we might have missed the functional SNP in our study. The fact that our results showed that CSF3_1719 was associated with baseline level of FEV1  71  and CSF3_2176 was associated with FVC % predicted suggests that neither SNP is causal but may be in linkage disequilibrium with a causal SNP which is yet to be identified. There are several explanations for the observation that SNPs from CSF3 but not CSF2 were associated with lung function. Firstly, animal studies have documented that CSF3 plays a more important role than CSF2 in regulation of neutrophil homeostasis. Dogs depleted of CSF3 by a neutralizing antibody developed profound and selective neutropenia (39) but mice depleted of CSF2 did not show impairment of hematopoiesis (40). In addition, CSF3 but not CSF2 knock-out mice display chronic neutropenia (41, 42). Secondly, in patients with acute respiratory distress syndrome, CSF3 but not CSF2 expression in the lung correlated with severity of pulmonary neutrophilia (6), which demonstrated that CSF3 also plays a more important role than CSF2 in regulation of neutrophils in human subjects. Thirdly, it was reported that dexamethasone inhibits human airway smooth muscle cell release of CSF2 but not CSF3 (43), suggesting that CSF3 and CSF2 are released through different mechanisms and thus may play different roles in the development of COPD. It has been suggested that apart from mobilizing granulocytes from the bone marrow, CSF2 and CSF3 are decisive in influencing the subsequent Th1 or Th2 dominance of the immune response by selecting subsets of dendritic cells (14). A recent study demonstrated that a high CSF2/CSF3 ratio was correlated with good lung function in cystic fibrosis patients with chronic Pseudomonas aeruginosa lung infection (14), which prompted us to analyze gene-gene interaction. However, no significant CSF2 and CSF3 interaction was found in our study. There are several explanations for this: first, we might not have had enough power to detect gene-gene interaction with our sample size and minor allele frequencies. Second, cystic fibrosis with chronic P. aeruginosa lung infection is a Th2  72  dominated response (44) while COPD is a Th1 dominated response (45). Therefore, the determinants of lung function in cystic fibrosis patients with chronic P. aeruginosa lung infection and in smoking induced COPD patients are likely different. There are several concerns with this study. First, population stratification could have led to false-positive results. However, it has been reported that in the non-Hispanic white population, significant false-positive associations are unlikely to arise from population stratification, especially in well-designed, moderately-sized, case-control studies such as ours (46, 47).  Second, false positive results might have arisen from multiple comparisons.  Although the results of association of CSF3_-1719 with lung function were corrected for multiple comparisons, we only took into account multiple SNPs in a single gene. No correction for multiple genes and phenotypes was performed. Thirdly, we did not analyze a second cohort to replicate our results. Fourthly, no available function data support our associations. Finally, the nested case-control study (i.e. using individuals from each extreme of the distribution of the phenotype of interest) has the advantages of cost reduction combined with satisfactory statistical efficiency when compared with the full cohort approach (23, 24) . However, this study design prevented analysis of baseline and decline in FEV1 as continuous variables. Therefore, the results from this study should be regarded as hypothesis generating only and it will be necessary to replicate them in different studies, especially in those with a cohort design. In summary, we reported an association of the CSF3_-1719C/T with baseline level of FEV1. However, this association needs to be replicated in different studies. Moreover, further functional study of this SNP or SNPs in LD with it is warranted.  73  Figure 3-1: The relationship between two LD-selected tagSNPs and haplotypes resolved by those tagSNPs  Haplotype  Frequency (%)  1  41.30  2  34.78  3  17.39  4  4.35  5  2.17  SNP position in the CSF2 gene (Genotyped tagSNPs are denoted in bold and underlined) -1916 -1440  -675  1363  1622  1944  2416  2722  3739  Note: The black squares represent major alleles; the white squares represent minor alleles.  74  Power vs O.R. by P2 with A=0.05 N1=538 N2=536 2-Sided Zp Test 1.00  0.3090 CSF3_-1719  P2  Power  0.95  0.90  0.3560 CSF2_1662 0.5680 CSF3_-882 0.6460 CSF3_2176  0.85  0.80 1.0  0.6690 CSF2_-1440  1.5  2.0  2.5  O.R.  Figure 3-2: Power curves for dominant models (P2 is the proportion in the risk genotype group) for the baseline FEV1 study  75  Power vs O.R. by P2 with A=0.05 N1=538 N2=536 2-Sided Zp Test 1.0  0.0290 CSF3_-1719  P2  Power  0.8  0.6  0.0320 CSF2_1662 0.1180 CSF3_-882 0.1530 CSF3_2176  0.4  0.2 1.0  0.1770 CSF2_-1440  1.5  2.0  2.5  O.R.  Figure 3-3: Power curves for recessive models (P2 is the proportion in the risk genotype group) for the baseline FEV1 study  76  Table 3-1: TagSNP selection using the LDSelect program and nomenclature of the SNPs Gene  Bin  SNP ID  SNP  T/C  Position in Ref Sequence 69  CSF2  1  rs2069614  1  rs2069616  A/G  1  rs1469149  1 2  Position in gene  Notes  -1916 (promoter)  Position in protein -  545  -1440 (promoter)  -  genotyped  A/C  1310  -675 (promoter)  -  -  rs743564  T/C  3347  1363 (third intron)  -  -  rs25881  C/T  3606  1622 (third intron)  -  genotyped as a replacement for 1944  CSF3  2  rs25882  T/C  3928  1944 (fourth exon)  I117T  genotype failed  2  rs25883  G/A  4400  2416 (3’ flanking region)  -  -  2  rs25884  A/G  4706  2722 (3’ flanking region)  -  -  2  rs27438  G/A  5723  3739 (3’ flanking region)  -  -  1  rs2227315  A/G  28  -2012 (promoter)  -  -  1  rs2227322  G/C  1980  -60 (promoter)  -  -  1  rs1042658  T/C  4215  2176 (3’ UTR)  -  genotyped  1  rs2512146  G/T  5168  3129 (3’ flanking region)  -  -  2  rs2227319  G/A  1158  -882 (promoter)  -  genotyped  2  rs2227321  G/C  1607  -433 (promoter)  -  -  2  rs25645  G/A  3456  1417 (fifth exon)  L185L  -  2  rs2227333  C/G  4480  2441 (3’ flanking region)  -  -  3  rs2227316  C/T  321  -1719 (promoter)  -  genotyped  3  rs2827  C/T  4050  2011 (3’ UTR)  -  -  Note: Sites are ordered by linkage disequilibrium, with sites showing similar patterns of genotype put into the same bin. The position in the gene is numbered by denoting the first nucleotide of the initiator methionine codon as +1 (position 1985 in sequence AF373868 of CSF2 and position 2040 in sequence AF388025 of CSF3). All SNPs with minor allele frequency > 5% are tagSNPs in this example. One SNP was genotyped in each bin and genotyped SNPs are indicated.  77  Table 3-2. TaqMan Primer and Probe Sequences. SNP  Primer  Allelic Probea  Forward:  G: 6FAM ACTCAGGCCACAGTG  AACTCCCACAGTACAGGGAAACTG  MGBNFQ  Reverse:  A: VIC CTCAGACCACAGTGC  CAGAGAGCAGGTGGAGTTCATG  MGBNFQ  Forward:  Tb: 6FAM CAGCTGAGCTGAGG  GGGAAGGGAGCAAAGTTTGTG  MGBNFQ  Reverse:  Cb: VIC AGCTGGGCTGAGGT  AAACGCCTGCCTTTTTGGT  MGBNFQ  Forward:  C: VIC CCCACCCTCTACTC  GCAATGAGCGAAACTCCATCTC  MGBNFQ  Reverse:  T: 6FAM CCCACTCTCTACTCC  TGATGTGGCCCAGCTCTGTAC  MGBNFQ  Forward:  Gb:6FAM ACGTGACTTCCCTGGT  CAGCCCGTGTCCACTTCAA  MGBNFQ  Reverse:  Ab: VIC ACACGTGATTTCC  TTGGAACTGCGGGATTGG  MGBNFQ  Forward:  Cb: 6FAM CAGTCCCCGTCCAGC  CAGGTGCCTGGACATTTGC  MGBNFQ  Reverse:  Tb: VIC CAGTCCCCATCCAGC  GTCTGCTCCCTCCCACATC  MGBNFQ  CSF2 -1440  CSF2 1622  CSF3 -1719  CSF3 -882  CSF3 2176  a  SNP sequences in the probes are highlighted in bold. b Probes are designed to the reverse strand.  78  Table 3-3. The distribution of demographic characteristics for the longitudinal FEV1 change study Fast Decliners  Non Decliners  (n = 281)  (n = 306)  Male/Female  164/117  204/102  0.038  Age (years)  49.51 ± 0.38  47.61 ± 0.39  0.0006  Smoking history (pack-yrs)a  42.86 ± 1.14  38.38 ± 1.04  0.004  ∆FEV1/yr (% predicted pre)b  -4.14 ± 0.06  1.08 ± 0.04  <0.0001  Baseline FEV1 (% predicted pre)c  72.6 ± 0.53  75.7 ± 0.46  <0.0001  p value  Values are mean ± SE for continuous data. a Number of packs of cigarettes smoked per day × number of years smoking. b Change in FEV1 over a 5 year period per year as % predicted FEV1 pre bronchodilator. c FEV1 at the start of the LHS as measured FEV1(%) predicted pre bronchodilator  79  Table 3-4. The distribution of demographic characteristics for the cross-sectional FEV1 study High FEV1  Low F EV1 p value  (n = 536)  (n = 538)  Male/Female  354/182  332/206  Age (years)  46.24 ± 0.30  50.69 ± 0.26  <0.0001  Smoking history (pack-yrs)  35.32 ± 0.77  45.16 ± 0.81  <0.0001  ∆FEV1/yr (% predicted pre)a  -0.55 ± 0.07  -1.27 ± 0.08  <0.0001  ∆FEV1/yr (% predicted post)b  -0.75 ± 0.06  -0.79 ± 0.08  <0.722  Baseline FEV1 (% predicted pre)a  86.48 ± 0.13  61.08 ± 0.18  <0.0001  Baseline FEV1 (% predicted post)b  91.80 ± 0.10  62.61 ± 0.14  <0.0001  Values are mean ± SE for continuous data.  a  0.139  prebronchodilator. b postbronchodilator.  80  Table 3-5. Single SNP association of CSF2 and CSF3 genes in FEV1 longitudinal decline and cross-sectional level of FEV1 studies  SNP  CSF2 -1440  CSF2 1622  CSF3 -1719  CSF3 -882  CSF3 2176  Genotypea  Rapid Decliners  Nondecliners  n (%)  n (%)  AA  100 (35.7)  101 (33.0)  AG  124 (44.3)  152 (49.7)  GG  56 (20.0)  CC  Co-dominantb P  d  166 (32.2)  180 (34.2)  252 (48.9)  259 (49.2)  53 (17.3)  97 (18.8)  88 (17.7)  194 (69.3)  210 (68.6)  353 (68.2)  340 (64.4)  CT  74 (26.4)  83 (27.1)  140 (27.0)  171 (32.4)  TT  12 (4.3)  13 (4.2)  25 (4.8)  17 (3.2)  CC  199 (71.1)  214 (70.2)  388 (74.3)  364 (69.2)  CT  71 (25.4)  78 (25.6)  124 (23.8)  147 (28.0)  TT  10 (3.6)  13 (4.3)  10 (1.9)  15 (2.9)  GG  117 (41.8)  112 (36.7)  202 (39.1)  224 (43.2)  GA  124 (44.3)  155 (50.8)  234 (45.3)  233 (45.0)  AA  39 (13.9)  38 (12.5)  81 (15.7)  61 (11.8)  CC  97 (35.4)  105 (34.8)  192 (36.8)  185 (35.3)  CT  130 (47.4)  160 (53.0)  247 (47.3)  259 (49.3)  TT  47 (17.2)  37 (12.3)  83 (15.9)  80 (15.3)  n.s.  n.s.  n.s.  n.s.  n.s.  n.s.  n.s.  n.s.  n.s.  n.s.  n.s.  n.s.  n.s.  P  e  High function n (%)  n.s.  P  d  Low function n (%)  n.s.  P  e  Additivec  n.s.  n.s.  n.s.  n.s.  n.s.  Co-dominant P  d  P  f  Additive Pd  Pf  n.s.  n.s.  n.s.  n.s.  0.092  n.s.  n.s.  n.s.  n.s.  0.059  0.054  0.018  n.s.  n.s.  0.059  0.092  n.s.  n.s.  n.s.  n.s.  81  a  Homozyotes for the major allele are listed first.  b  Codominant model: the three genotypes of a SNP in the cases and controls constitute a 2x3  contingency table, the direct calculation of 2x3 tables provides unadjusted p values, while multiple regression models were used to adjust for confounding factors. c  additive model: there is r fold increased disease risk for heterozygotes compared with the  homozygotes for the major allele, there is 2r fold increased disease risk for the homozygotes for the minor allele compared with the homozygotes for the major allele. The Armitage trend test was used to test an additive effect of the allele. d  Unadjusted P value  e  Adjusted for age, sex, center, smoking history (pack years)  f  Adjusted for age, sex, center, smoking history (pack years) and rate of decline of lung function  (% predicted postbronchodilator)  82  Table 3-6. Single SNP association of CSF2 and CSF3 with FVC % predicted in the longitudinal decline in FEV1 study group (overall comparison of differences of FVC % among three genotype groups) FVC % predicted SNP  N  Genotype  (mean ± SE)  CSF2 -1440  413  CC  97.92 ± 0.53  149  CT  97.49 ± 0.89  23  TT  100.65 ± 2.57  229  GG  97.39 ± 0.72  279  GA  97.99 ± 0.65  77  AA  99.27 ± 1.20  201  AA  98.51 ± 0.75  276  AG  97.48 ± 0.67  109  GG  98.03 ± 1.00  404  CC  97.44 ± 0.52  157  CT  99.03 ± 0.93  25  TT  98.99 ± 2.42  202  CC  97.32 ± 0.79  290  CT  98.94 ± 0.60  84  TT  95.65 ± 1.21  CSF2 1622  CSF3 -1719  CSF3 -882  CSF3 2176  F value  P value  0.675  0.513  0.906  0.406  0.528  0.590  1.220  0.302  3.466  0.033  83  Table 3-7. Haplotype association of CSF2 and CSF3 in the cross-sectional level of FEV1 study.  Gene  CSF2  CSF3  Low function  High function  %  %  -1440A/1622C  33.0  36.9  -1440G/1622C  49.7  44.3  -1440G/1622T  17.3  20.0  -1719C/-882G/2176T  39.9  39.9  -1719C/-882A/2176C  38.7  34.2  -1719T/-882G/2176C  14.0  -1719C/-882G/2176C  Haplotypea  Global test P valueb  P valuec  Pd  Pe  Pd  Pe  n.s.  n.s.  n.s.  n.s.  n.s.  n.s.  0.007  0.089  16.9  n.s.  0.047  7.5  8.9  n.s.  n.s.  -1719C/-882G  48.4  48.8  -1719C/-882A  38.7  34.2  n.s.  n.s.  -1719T/-882G  14.0  16.9  n.s.  0.058  -882G/2176T  39.9  39.9  -882A/2176C  38.7  34.2  n.s.  n.s.  -882G/2176C  21.5  25.8  n.s.  0.081  0.004  0.069  0.027  0.0427  CSF3  a  0.051  0.053  Haplotype frequencies were calculated using an Expectation Maximization algorithm from the  haplo.stats package for R b  comparing overall haplotype distribution between cases and controls.  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J Natl Cancer Inst 92(14):1151-8.  88  Chapter 4: ASSOCIATIONS OF IL6 POLYMORPHISMS WITH LUNG FUNCTION DECLINE AND COPD 4.1 INTRODUCTION IL6 is a pleiotropic pro-inflammatory and immunomodulatory cytokine secreted by airway epithelial cells, alveolar macrophages, adipocytes and myocytes as well as other tissues and cells (1, 2). The potential importance of IL6 in the pathogenesis of COPD is suggested by studies showing that high levels of serum or sputum IL6 are associated with impaired lung function or a faster decline in lung function (1, 2). IL6 has been related to skeletal muscle weakness in COPD (3) as well as to exacerbations (4) and pulmonary infections (5) in COPD patients. In addition, IL6 overexpression in the murine lung resulted in airway inflammation and emphysema-like airspace enlargement (6). Furthermore, IL6 is an important mediator of the acute phase response and can upregulate C-reactive protein (CRP) at the transcriptional level (7). CRP has been associated with lung function levels in healthy individuals and/or lung function decline in smoking-induced COPD (8, 9). Taken together, these data support IL6 as an appealing candidate gene for smoking-induced lung function impairment and COPD. The IL6 gene is located on chromosome 7p21. Previous studies have identified a functional SNP, -174G/C, in the promoter region of IL6 (10). Before initiation of the current study, a small study reported there was no association of an IL6 SNP with COPD (11). Recently, another group showed that the IL6_-572C allele was associated with COPD (12).  A version of this chapter has been published. He JQ, Foreman MG, Shumansky K, Zhang X, Akhabir L, Sin DD, Man SF, DeMeo DL, Litonjua AA, Silverman EK,  Connett JE, Anthonisen NR, Wise RA, Paré PD,  Sandford AJ. Novel associations of IL6 genetic variants with lung function decline and COPD. Thorax, , Apr 8, 2009 [Epub ahead of print].  89  Large well-designed studies with carefully-defined COPD phenotypes are required to unravel the exact role of IL6 genetic variants in the pathogenesis of COPD. We investigated smokers with mild to moderate airflow obstruction who were participants in the Lung Health Study (LHS) cohort and hypothesized that there would be significant associations between SNPs and haplotypes in IL6 with the rate of decline and/or the level of lung function and these association would be mediated through influencing IL6 serum concentrations. The LHS cohort provides an excellent opportunity to explore associations between gene polymorphisms and haplotypes with FEV1% predicted (13, 14) as well as the rate of decline in FEV1 (15, 16). To validate novel associations between IL6 SNPs with lung function phenotypes, replication of results was sought in COPD cases from the National Emphysema Treatment Trial (NETT) with participants from the Normative Aging Study (NAS) serving as controls (17, 18). 4.2 METHODS 4.2.1 Study participants: LHS participants: A total of 1488 subjects were selected from the ~ 4,800 LHS subjects for whom DNA and serum were available. The selection generated two nested casecontrol studies based on the extremes of rate of decline in lung function and baseline lung function. In the decline of lung function study, we selected the 266 and 293 non-Hispanic whites with the fastest and slowest rate of decline of lung function, respectively during the 5 year follow up (arbitrary cut-off points of ≥ 3.0% predicted decrease /year and ≥ 0.4% predicted increase /year in FEV1 were used for rapid decliners and non-decliners, respectively). The rationale to select nested case-control studies with the indicated sample sizes is that 1) this approach has the advantage of reducing cost while keeping satisfactory statistical efficiency when compared with the full cohort approach (19, 20); 2) the Common  90  Disease/Common Variants hypothesis (CD/CV) was suggested one decade ago which states that disease susceptibility alleles of common diseases will be present at high frequencies (21), and 3) this sample size has relatively adequate power to detect common genetic risk variants as shown in our previous power analyses (22). The baseline lung function study consisted of the 532 and 527 participants who had the highest and lowest baseline FEV1% predicted, respectively (arbitrary cut-off points of FEV1 % predicted ≥ 88.9% and ≤ 67.0% were used for the high and low baseline groups, respectively). There were 130 participants that overlapped between the two sets of nested cases and controls due to the fact that subjects in the rate of decline study group had baseline lung function within one of the categories for baseline lung function. NETT-NAS participants: We selected 389 non-Hispanic white subjects who were enrolled in the NETT Genetics Ancillary Study. The control group was composed of 420 participants with normal spirometry from the NAS, a longitudinal study over the past four decades of healthy adult males that was initiated by the Boston Veterans Administration. More information on the participants is included in the supplement of the appendix A. 4.2.2 TagSNP selection and genotyping methods: In the LHS, 5 tagSNPs were chosen from the SeattleSNPs database using the LDSelect program based on a relatively stringent LD threshold of r2 ≥ 0.8 and minor allele frequency cutoff of 10%. An additional 2 SNPs selected for the NETT-NAS study were subsequently chosen for genotyping in the decline of lung function study, in order to make the two studies more comparable. The nomenclature for the polymorphisms utilized in the study is summarized in Table S4-1 in the supplement of the appendix A. SNP genotyping was performed using the TaqMan method (Applied Biosystems, Foster City, CA) for 5  91  tagSNPs and the Illumina Bead Array System for additional 2 SNPs. The positions of the selected and successfully genotyped 5 tagSNPs are shown in Figure 4-1. In the NETT-NAS, the same criteria were used to select six LD-tagging IL6 SNPs and three additional IL6 SNPs were also selected for genotyping. The SNPs were genotyped on an Illumina BeadStation 500G System utilizing the GoldenGate assay technology (Illumina Golden Gate Assay, San Diego, CA). SNP selection criteria are shown in more detail in the supplement of the appendix A. 4.2.3 Measurements of serum IL6 concentration in the LHS participants: After collection, the blood samples were separated into their various components and shipped to the LHS data co-coordinating center on dry ice and were kept in -70°C freezers until use. The serum samples were thawed once for IL6 measurements. The concentrations of IL6 were measured using a highly sensitive chemiluminescent multiplexed sandwich immunoassay (SearchLight Proteome Array System®, Rockford, IL). 4.2.4 Statistical Analysis: In the LHS, Hardy-Weinberg equilibrium tests and linkage disequilibrium estimation were calculated using the genetics package for R (www.r-project.org). Multiple logistic regressions for rate of decline and baseline lung function were performed to test for the association with IL6 SNPs and with IL6 serum levels. Confounding factors included body mass index (BMI), age, gender, pack years of smoking, and smoking status. Multiple linear regression was performed for the complete data set to test for association of IL6 SNPs with log IL6 serum levels. Haplotype analysis was done using the R hapassoc package. In the NETT-NAS, similar analyses were performed with SAS Genetics (Cary, NC). Statistical Analysis is described in more detail in the supplement of the appendix A.  92  4.3 RESULTS 4.3.1 Characteristics of the study participants: In the total of 1488 participants from the LHS, genotyping success rates were 96.4% to 98.6% for the 5 studied IL6 tagSNPs in all subjects and 97.9% for additional 2 SNPs in the rate of decline study. The demographic characteristics are shown in Table 4-1. There were significant differences in several potential confounding factors, such as age, gender, pack years of smoking, and smoking status between study groups. Therefore, multiple regressions were performed to adjust for relevant confounding factors. In the total of 809 participants in the NETT-NAS, the genotype call rate for IL6_615A/G (rs2069832) was 85%; for all other SNPs the call rates were > 97%. The demographic characteristics for the study groups are shown in Table 4-2. 4.3.2 The linkage disequilibrium pattern, Hardy-Weinberg disequilibrium and performance of tagSNPs: The LD pattern of the five IL6 tagSNPs in the full set of 1488 LHS study participants is shown in Figure 4-2A. The r2 values were from 0.04 to 0.89. It is worth noting that the r2 values between IL6_-1479 (rs2069825) and IL6_-174 (rs1800795) as well as IL6_3331 (rs2069845) and IL6_-174 were greater than 0.86, which indicates that it is necessary to genotype only one of these 3 SNPs. The LD patterns of the low and high lung function subgroups were similar to that of all subjects (data not shown), as were those of the fast and non-decline subgroups, the LD pattern of all 7 SNPs genotyped in fast and non-decline subgroups are shown in Figure 4-2B. All the studied SNPs were in Hardy-Weinberg equilibrium.  93  More information on performance of tagSNPs is included in the supplement of the appendix A. 4.3.3 Associations of SNPs and haplotypes in the IL6 gene with rate of decline and baseline of FEV1: Three of 7 IL6 SNPs were associated with FEV1 decline (0.023 ≤ P ≤ 0.041 in additive genetic models) (Table 4-3). The well known functional SNP IL6_-174G/C (rs1800795) was among them. The frequency of the IL6_-174C allele was significantly higher in the group with rapid decline of FEV1 than that in the non-decline group (45.2% versus 39.6%, OR 1.30, 95% CI = 1.01 – 1.66, P = 0.041). The association was more significant in the genotype-based analysis (P = 0.006) with 6 out of 7 SNPs reaching a significance level of P < 0.05 (Table 4-3). It is worthwhile to note that the most significant association was found for the IL6_1479CT in/del (rs2069825): the IL6_-1479CT deletion was associated with rapid decline of FEV1 (p < 0.001) (Table 4-3). Three other IL6 SNPs, were not significantly associated in the additive model, but were significant in the genotype-based analysis (Table 4-3). The IL6_1479 CT in/del and another 2 associated IL6 SNPs were in high LD with IL6_-174G/C. Interestingly, the IL6_5909G/A and IL6_1754C/G, which were not in high LD with IL6_174G/C and not in high LD each other (r2 = 0.52), were also significantly associated with decline of lung function (Table 4-3).  No association was found for IL6 haplotypes with rate of decline of FEV1; IL6 SNPs and haplotypes were not associated with the baseline level of FEV1 (data not shown).  94  4.3.4 Associations of IL6 SNPs and haplotypes with serum IL6 concentrations: The associations of IL6 SNPs with serum IL6 concentrations were analyzed in all LHS subjects for 5 tagSNPs and in rate of decline study subjects for 2 additional SNPs by linear regressions adjusted for BMI, age, gender, pack years of smoking, and smoking status (Table 4-4). No significant association was found for IL6 SNPs with IL6 concentrations. IL6 haplotypes were also not associated with IL6 concentrations (data not shown). 4.3.5 Associations of serum IL6 concentrations with rate of decline and baseline FEV1: As shown in Table 4-1, there were no significant differences in IL6 concentrations between the rapid decline and non-decline groups or between high and low FEV1 groups. 4.3.6 Replication of novel IL6 associations in the NETT-NAS participants: In the LHS, IL6 SNPs were significantly associated with rate of decline of FEV1 in mild COPD patients. Since rapid decline of lung function in smokers is the likely method of development of COPD we reasoned that the same SNPs would be associated with advanced COPD. To test this we used a case-control sample that has been very useful in revealing genes associated with COPD (17, 18). In the NETT-NAS study cases had advanced COPD requiring lung volume reduction surgery and controls were derived from a population of smokers who have not developed COPD. The IL6_-174G/C and another four IL6 SNPs, which had high linkage disequilibrium with IL6_-174G/C, were associated with susceptibility to COPD (0.01 ≤ P ≤ 0.04 in additive genetic models). The IL6_-174C allele was associated with susceptibility to COPD (OR 1.3, 95% CI = 1.1 – 1.7, P = 0.01 in an additive genetic model). The frequency of the IL6_-174C allele was significantly higher in the NETT group than that in the NAS group (42% versus 36%). The association was also significant in genotype-based analysis (P = 0.03) (Table 4-3).  95  4.4 DISCUSSION There are only three studies published on associations of IL6 SNPs with COPD. Seifart et al. reported that there was no association of IL6_-174 with COPD.(11) Broekhuizen et al. did not find an association between IL6_−174 and a cachexia phenotype in COPD subjects (23). Recently, Córdoba-Lanús et al. reported that IL6_−572 but not IL6_-174 was associated with COPD (12). All three studies have relatively small sample sizes. The associations of IL6 SNPs with FEV1 decline in the current study are novel and are the most significant findings among all the studies we have published utilizing the LHS cohort (13-16, 24-27). To strengthen our initial finding in the LHS, we incorporated an association study of IL6 SNPs with COPD in the NETT-NAS. All SNPs that were genotyped and in high LD with the IL6_-174G/C showed significant or borderline association with rapid decline of lung function in the LHS and with COPD in the NETT-NAS. We believe that the strength of the associations, the concordant results with several SNPs in high LD with the IL6_-174G/C SNP, the available previous functional data on IL6_-174G/C, the replication in a second population and the biologic plausibility for association provide strong evidence that this is a true association. We examined the association of IL6 SNPs with IL6 serum levels as well as relationships between IL6 serum levels and lung function decline. We did not find any associations. We also found that adjusting the associations between IL6 SNPs and lung function for serum CRP levels in the LHS had no effect on the strength of the associations (data not shown). Therefore, we did not find evidence that the associations we report were mediated through an influence on production of IL6 or CRP. Studies that have examined the effects of IL6 SNPs on IL6 mRNA and protein expression have led to conflicting results. The first reporter gene study demonstrated that a  96  construct containing the -174G allele had higher reporter gene expression in HeLa cells, both under basal conditions and after LPS or IL1 stimulation (10). However, a second reporter gene study showed that a construct containing -174C had higher IL1-induced expression in HeLa cells than that of the -174G construct, although the difference did not reach statistical significance (28). By comparison of the two different cell types, the authors concluded that there is a cell type-specific regulation of IL6 expression (28). Nine of the most recently published studies of IL6 SNPs with circulating IL6 concentrations are summarized in Table S4-2. A recent meta-analysis of 5659 subjects from seventeen studies concluded that the -174 IL6 SNP was not associated with circulating IL6 levels (29). There are several explanations for the lack of consistent associations. First, the IL6_−174G/C polymorphism might not be a strong determinant of serum IL6 levels. Second, the serum half-life of IL6 is short. Serum IL6 levels show marked diurnal variability (30). The blood samples for IL6 measurement in most studies, including our own, were not taken at a specific time of the day. A third explanation is that the SNPs studied may not be the actual functional SNPs. Recently, Samuel and colleagues have identified a novel IL6 transcriptional regulatory region (-5307 to -5202) much farther from IL6_-174 (31). This report coupled with more recent identification of a novel functional SNP, IL6_-6331T/C (rs10499563), with the T allele preferentially binding to Oct-1 transcription factor and producing higher reporter gene expression, provides evidence that additional functional SNPs do exist in IL6 (32). However, since IL6_-6331T/C is in low LD with IL6_-174, our finding is not likely to be explained by these new functional data. If IL6 SNPs are not related to IL6 levels then what is the basis for their association with FEV1 decline and COPD? One possible explanation is that the association is truly driven via local pulmonary IL6 expression or that it is driven by serum IL6 levels but that the  97  variability and lability of serum IL6 levels obscures this relationship; FEV1 may reflect the average IL6 levels and thus the degree of lung inflammation over the years of the study. In addition, the SNPs could influence IL6 levels and thus lung inflammation during exacerbations but not the constitutive levels during stable periods. IL6 is a pleiotropic cytokine which also modulates expression of many other genes (33). It may be that it is the effect of the IL6 variants on these genes that is the underlying mechanism for the associations we observed. How can we explain the observation that IL6 SNPs were not associated with baseline FEV1 in the LHS but were associated with the presence of COPD in the NETT-NAS study? The mean age of the LHS participants was 48 years as opposed to a mean age of 68 years for the NETT-NAS participants. Baseline FEV1 at age 48 is influenced both by maximal attained FEV1 at ~ 25 years of age and by the rate of decline of lung function after age 25 (34). However, the relative contribution of rate of decline in lung function will be much greater by age 68 than at age 48. Thus, FEV1 at age 68 in the NETT-NAS participants is likely to largely reflect the rate of decline of lung function during their long smoking history whereas there is likely a weaker relationship of FEV1 decline and baseline lung function at age 48. Compared with previous studies, strengths of this study include larger sample size and good power. This sample size has adequate power to detect common genetic risk variants as shown in our previous power analyses, for example, it has 80% power to detect a relative risk of 2.0 when the frequency of the risk factor is 10% or above (22). There are several potential limitations of this study. Firstly, population stratification could have led to false-positive results. However, it has been reported that significant falsepositive associations are unlikely to arise from population stratification in the non-Hispanic white population, especially in well-designed, moderately-sized, case-control studies such as  98  ours (35). In addition, there was no significant evidence of population stratification in the NETT-NAS cases and controls (17). Second, false positive results might have arisen from multiple comparisons. However the consistent results in the NETT-NAS replication study make false positive results unlikely. Third, we have not identified the causal SNP for the associations. The identification of a novel functional SNP IL6_-6331T/C (rs10499563), which has low LD with IL6_-174G/C (rs1800795) with r2 of 0.169 in the CEU HapMap database, indicates that the control of IL6 transcription is likely to be complex (32). We cannot exclude the possibility that SNPs other than the IL6_-174G/C are also causal SNPs. Finally, serum IL6 levels were measured at year 5 of the LHS, therefore it may not be appropriate to link IL6 levels at year 5 with the baseline FEV1 as well as the rate of decline of FEV1 during 5 year follow-up. In summary, we report associations of IL6 variants with rate of decline of lung function and with smoking-induced COPD.  99  4.5 ACKNOWLEDGEMENTS: This work was supported by grants from the Canadian Institutes of Health Research and National Institutes of Heath Grant 5R01HL064068-04. The Lung Health Study was supported by contract N01-HR-46002 from the Division of Lung Diseases of the National Heart, Lung, and Blood Institute.  The NETT Genetics Ancillary Study was supported by  National Institutes of Health grants HL075478 and HL71393.  The Normative Aging Study  is supported by the Cooperative Studies Program/ERIC of the U.S. Department of Veterans Affairs, and is a component of the Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC). Dr. He is the recipient of Michael Smith Foundation for Health Research Fellowship and Izaak Walton Killam Memorial Scholarship Award. Dr. Foreman was supported by HL007427. Dr. DeMeo was supported by HL072918.  100  5909G/A  3331G/A  -174G/C  -1363G/T  -1479CT/--  1  2  3  4  3’ UTR  Figure 4-1: The IL6 gene structure and position of single nucleotide polymorphisms genotyped in the Lung Health Study subjects. Numbered regions represent exons. A: adenine; C: cytokine; G: guanine; T: thymidine; UTR: untranslated region.  101  Figure 4-2: Linkage Disequilibrium of Single Nucleotide Polymorphisms (SNPs) of IL6 in the Lung Health Study subjects using HAPLOVIEW. The linkage disequilibrium (r2) between any two SNPs is listed in the cross cell. The darker the color indicates the higher the linkage disequilibrium between any two SNPs. Figure 4-2A: all subjects; Figure 4-2B: top: fast decline group; bottom: slow decline group.  102  Table 4-1: The distribution of demographic characteristics for all subjects and those in the two nested case control study groups in the LHS. Rate of decline study All participants  Fast Decliners  Non Decliners  (N = 1488)  (n = 266)  (n = 293)  Baseline lung p value  High Function  Low Function  (n = 532)  (n = 527)  p value  Men/Women  948/540  158/108  197/96  N/A  352/180  325/202  N/A  Age (years)  48.41 (0.177)  49.47 (0.397)  47.48 (0.399)  <0.001  46.21 (0.772)  50.76 (0.262)  <0.001  Smoking history (pack-yrs)*  40.41 (0.483)  43.23 (1.178)  38.35 (1.064)  0.002  35.33 (0.125)  45.24 (0.809)  <0.001  Continuing smokers  979  266  293  N/A  264  286  N/A  Intermittent quitters  315  N/A  N/A  N/A  157  158  N/A  Sustained quitters  194  N/A  N/A  N/A  111  83  N/A  Body Mass Index (kg/m2)  25.49 (0.099)  25.22 (0.248)  25.77 (0.210)  0.091  25.39 (0.150)  25.57 (0.183)  0.455  ∆FEV1/yr (% predicted pre)‡  -0.98 (0.053)  -4.13 (0.066)  1.087 (0.042)  <0.001  -0.55 (0.067)  -1.22 (0.078)  <0.001  ∆FEV1/yr (% predicted post)§  -0.85 (0.04)  -3.44 (0.078)  0.695 (0.054)  <0.001  -0.75 (0.064)  -0.74 (0.078)  0.949  Baseline FEV1 (% predicted pre)ll  74.15 (0.302)  72.68 (0.542)  75.51 (0.472)  <0.001  86.49 (0.125)  61.07 (0.180)  <0.001  Baseline FEV1 (% predicted post)**  77.57 (0.336)  75.00 (0.561)  79.80 (0.467)  <0.001  91.81 (0.099)  62.57 (0.144)  <0.001  IL6 concentration (pg/ml)  121 (99, 148)  125 (104, 152)  121 (97, 148)  0.1840  119 (97, 140)  121 (101, 152)  0.12  Smoking status during 5 years followup†  103  Values are means (SEM) for continuous data. * Number of packs of cigarettes smoked per day × number of years smoking. † Continuing smokers: participants who reported smoking at each annual visit. Sustained quitters: participants who were validated by salivary cotinine or exhaled CO as abstinent at every annual visit. Intermittent quitters: participants who were not sustained quitters or continuing smokers ‡ Change in lung function over a 5-year period per year as % predicted FEV1 pre bronchodilator § Change in lung function over a 5-year period per year as % predicted FEV1 post bronchodilator ll Lung function at the start of the Lung Health Study as measured by FEV1(%) predicted pre bronchodilator ** Lung function at the start of the Lung Health Study as measured by FEV1(%) predicted post bronchodilator.  104  Table 4-2: The distribution of demographic characteristics for NETT COPD cases and NAS controls.  NETT  NAS  p-value  (n = 389)  (n = 420)  Age (years)  67 + 6  68 + 9  0.9  Pack-years  66 + 30  39 + 27  <0.001  FEV1 (% predicted, post BD) †  28 + 7  92 + 11  Modified BODE score (median + IQR) ‡  5+3  NA  Sex (% male)  64%  100%  Demographic Characteristics*  *Values are + standard deviation unless otherwise listed †FEV1 % predicted values for the NETT and NAS are based on the prediction equations of Crapo and Morris (47); however the NAS-1988 standards were used in the selection of the control group. ‡  Modified BODE score incorporates the University of San Diego Shortness of Breath Questionnaire,  IQR = interquartile range  105  Table 4-3: Associations of SNPs in IL6 with rate of decline of FEV1 in the LHS and association with COPD in the NETT-NAS LD (r2 with  Rate of FEV1 decline study in the LHS  COPD case control study in the  rs1800795) Bin  SNP ID  SNP in  LHS  gene  NETT-NAS  NETT  Fast  Non-  p-value‡  p-  NETT  NAS  p-value§  p-value§  -NAS  decline  decline  genotype  value‡  MAF %  MAF  genotype  additive  MAF %  MAF %  based  additive  %  based  0.41  0.35  0.06  0.02  0.42  0.36  0.03  0.01  1  rs1800797†  -598G/A  NA  0.93  1  rs1800795*†  -174G/C  -  -  1  rs2069832†  615A/G  NA  0.98  0.42  0.37  0.1  0.09  1  rs1474348†  1090G/C  NA  0.97  0.41  0.36  0.1  0.04  1  rs1474347†  1306G/T  NA  0.98  0.42  0.36  0.06  0.03  2  rs1554606*†  1889G/T  0.84  0.87  0.47  0.43  0.011  0.103  0.44  0.38  0.02  0.01  2  rs2069845*  3331G/A  0.89  NA  0.47  0.42  0.007  0.078  3  rs2069825*  -1479CT/-  0.90  NA  0.44  0.37  < 0.001  0.023  4  rs2069840*†  1754C/G  0.36  0.33  0.32  0.37  0.005  0.064  0.35  0.36  0.3  0.7  5  rs1818879*  5909G/A  0.32  NA  0.29  0.34  0.012  0.035  6  rs2069827*†  -1363G/T  0.12  0.14  0.08  0.08  0.786ll  0.669  0.09  0.09  0.9  0.4  other  rs2069849†  4338C/T  NA  0.02  0.03  0.02  0.2  0.2  0.45  0.40  0.006  0.041  * Genotyped in the LHS, † Genotyped in the NETT-NAS  ‡ Adjustment for confounding factors such as age, gender, pack-years of smoking, and research center. § As the NAS controls were uniformly male smokers with normal lung function, the models were adjusted for age and pack-years. ll The p values were from a dominant genetic model because the minor allele frequency of this SNP was very low.  106  Table 4-4: Association of serum concentrations of IL6 and IL6 genotypes (linear regression*) SNP  Genotype  IL6 level [ln IL6 (pg/ml)] N  Coefficient (SE)  p value  II  514  IL6_-1479  ID  681  -0.011 (0.046)  (rs2069825)  DD  229  -0.080 (0.063)  0.424†  IL6_-1363  GG  1185  (rs2069827)  GT+TT  234  0.000 (0.057)  0.997†  GG  483  IL6_-174  GC  691  0.013 (0.047)  (rs1800795)  CC  255  -0.056 (0.061)  CC  234  IL6_1754  CG  234  0.000 (0.048)  (rs2069840)  GG  72  -0.030 (0.064)  GG  169  IL6_1889  GT  261  0.013 (0.044)  (rs1554606)  TT  110  -0.057 (0.054)  AA  431  IL6_3331  AG  689  0.015 (0.049)  (rs2069845)  GG  293  -0.020 (0.060)  AA  685  IL6_5909  AG  593  0.012 (0.044)  (rs1818879)  GG  137  -0.025 (0.074)  0.486†  0.820‡  0.542‡  0.819†  0.877†  * P values were from genotype-based analysis (co-dominant genetic models) except for IL6_-1363 where p values were from a dominant genetic model because the minor allele frequency for this SNP was very low. † Adjusted for BMI, age, gender, pack years of smoking, and smoking status; ‡ Adjusted for BMI, age, gender, pack years of smoking.  107  4.6 REFERENCES 1. 2.  3.  4.  5. 6.  7. 8. 9. 10.  11.  12.  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Am J Hum Genet 71(2):304-11.  110  Chapter 5: SELECTION OF HOUSEKEEPING GENES FOR REAL-TIME PCR IN ATOPIC HUMAN BRONCHIAL EPITHELIAL CELLS  5.1 INTRODUCTION The airway epithelium is emerging as an especially attractive tissue in which to identify novel mechanisms of asthma pathogenesis and new therapeutic strategies (1). The epithelium is important as a barrier and/or a source of secreted biomolecules. Accumulating evidence suggests that intrinsic differences exist between asthmatic and non-asthmatic epithelial cells both biochemically and functionally (2). These intrinsic changes could influence susceptibility to asthma. Careful comparison of gene expression profiles of normal and asthmatic epithelium could further elucidate the molecular basis of the development of asthma. Real-time quantitative polymerase chain reaction (qPCR) is a very powerful technique to compare gene expression profiles under different biological conditions. When comparing gene expression profiles in normal and disease cells and tissues, it is critical to consider factors confounding expression levels such as different amounts and quality of starting material, RNA extraction and reverse transcription efficiencies. Among several normalization approaches proposed (3), house-keeping genes (HKGs) are accepted and frequently used to normalize qPCR and thus reduce possible errors generated in the quantification of gene expression. An ideal HKG should be stably expressed in the cells and tissues of interest without alteration by the experimental conditions or by disease status. A version of this chapter has been published. He JQ, SandfordAJ, Wang I, Stepaniants S, Knight DA, Kicic A, Stick SM, Paré PD. Selection of housekeeping genes for real-time PCR in atopic human bronchial epithelial cells. Eur Respir J. 2008 Sep;32(3):755-62.  111  However, such an ideal HKG does not presently exist (4). Commonly used HKGs have shown variability in expression levels in different tissues and/or disease states (5-8), and this emphasizes the importance of evaluation studies to identify the most stable HKGs in different tissues and/or disease states before commencement of any qPCR study. A previous study reported a reduction in mRNA expression of two commonly used HKGs, namely beta-actin (ACTB) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH), both in bronchoalveolar lavage fluid cells and biopsy tissues from asthmatics not using inhaled corticosteroids (ICS) compared with that from both normal controls and from asthmatic subjects using ICS (7). This provides direct evidence that HKGs need careful validation before their use in quantitative mRNA assays in asthmatics. However, there have been no studies which have systematically compared the stability of common HKGs in normal and atopic human airway epithelial cells (AECs). In the current study, we measured the expression levels of twelve HKGs and analyzed the stability of their expression in uncultured human AECs derived from healthy nonatopic nonasthmatic (HNA), healthy atopic (HA) and atopic asthmatic (AA) children. After analysis and comparison using three different statistical methods, we recommend that Cyclophilin A (also designated peptidyl-prolyl isomerase A or PPIA) should be used as a HKG for gene expression studies in AECs of asthmatics. 5.2 METHODS 5.2.1 Sample collection: The present study received the approval of the Providence Health Care Research Ethics Board and the Princess Margaret Hospital for Children's Human Ethics Committee. Subject selection (Table 5-1) and AEC collection have been described in detail previously (2,  112  9). Asthma was defined as physician-diagnosed asthma plus wheeze documented by a physician in the 12 months prior to sampling. Atopic status was determined by a positive radioallergosorbent test (RAST) to a panel of common allergens including; grass pollens, milk, mould, peanut, egg white and animal hair. All atopic asthmatic children had mild disease, such that none were treated with inhaled or oral glucocorticosteroids. For this study, AECs were obtained from 30 children undergoing elective surgery for nonrespiratory conditions representing three groups of patients. Briefly, immediately prior to surgery, a cytology brush (BC 25105, Olympus, Australia) was inserted directly through the endotracheal tube, and rubbed against the epithelial surface to sample cells. The brush was then withdrawn and the tip cut off into 5 ml of culture media (RPMI 1640 containing 20% (v/v) heat inactivated fetal calf serum). The cell suspension was immediately put on ice and the process repeated at least once more. The collection media was then pooled, centrifuged, resuspended in 5 ml of collection media and macrophages removed by positive selection. Cell number was determined using a Neubauer haemocytometer within 15 minutes of collection. Each sample of epithelial cells obtained by non-bronchoscopic brushing contained on average 3.41 ± 1.40 million cells, and this did not differ significantly between phenotype groups (data not shown). Of the 5 ml cell suspension, 2 ml (~1.4 million cells) was used for RNA extraction. 5.2.2 RNA extraction and cDNA synthesis: Total RNA was extracted from cells using the RNeasy mini extraction kit (QIAGEN, Hilden, Germany) as described by the manufacturer. Genomic DNA was eliminated by RNase-free DNase I digestion (Qiagen, Hilden, Germany) during the isolation procedure. The quality and quantity of randomly selected RNA samples extracted were assayed using  113  the Agilent Bioanalyzer system (Victoria, Australia). All samples were quantified by spectrometer. cDNA was synthesized using hexanucleotide primers and Multiscribe™ Reverse Transcriptase (Applied Biosystems, CA, USA) in a final reaction volume of 20 µL containing 1 X RT buffer, 5.5 mM MgCl2, 0.5 mM of each of the dNTPs, 2.5 µM random hexamers, 0.4 U RNase inhibitor, 1.25 U Multiscribe reverse transcriptase and 200 ng RNA. All reactions were performed under the following conditions: initial primer incubation step at 25 °C for 10 minutes followed by RT incubation at 48 °C for 1 hour and ended by reverse transcriptase inactivation at 95 °C for 5 minutes, and then cooled to 4 °C before storage at – 80 °C. 5.2.3 Reference gene selection and real time QPCR: Twelve HKGs were selected from commonly used reference genes: their full names, abbreviations and functions are listed in Table 5-2. The first eleven genes were the endogenous control panel genes recommended by Applied Biosystems. GNB2L1 was also included as our group has shown it to be stably expressed in isolated human neutrophils (10).  We also identified this as a reliable HKG for macrophages in chronic obstructive  pulmonary disease patients irrespective of disease severity (11). The expression study of all 12 genes was performed in 384 well plates on an ABI Prism 7900HT Sequence Detection System (Applied Biosystems) using pre-made primers and probes (Applied Biosystems). The assay IDs are listed in Table 5-2. The reactions were performed according to the manufacturer's instructions with minor modifications. Briefly, 2 µl working solution of cDNA (20 fold dilution) was used in a final PCR reaction volume of 10 µL containing 1 X TaqMan PCR master mix (Applied Biosystems, CA, USA), 0.9 µM each of forward and reverse primers and 0.25 µM probe. The conditions for the PCR include  114  AmpliTaq Gold activation at 95 °C for 10 minutes followed by 40 cycles of 15 seconds at 95 °C and 1 minute at 60 °C. All reactions were run in triplicate with non-template controls for each gene. We used the E-Method (12) in order to adjust for differences in amplification efficiency of the different genes. Here, standard curves were generated from serial dilutions of stock cDNAs at following folds: 1.33, 4, 12, 36, 108 and 324 for each studied gene. The threshold cycle (Ct) was manually determined at a fixed value of 0.2 with the use of SDS 2.1 software (Applied Biosystems). The Ct values were subsequently used to calculate a linear regression line by plotting the logarithm of template amount against the corresponding threshold cycle. Genomic DNA contamination was checked using a pair of primers that amplify an intronic region of a gene. 5.2.4 Statistical analysis: To determine the stability of the selected reference genes, three different specific VBA applets (application programs) (geNorm, BestKeeper and NormFinder) were used. The geNorm applet provides a measure of gene expression stability (M), which is the mean pairwise variation between an individual gene and all other tested control genes (4). The BestKeeper applet is to determine the "optimal" HKGs by using a pair-wise correlation analysis of all pairs of candidate genes, and calculating the geometric mean of the "best" suited ones (13). The NormaFinder applet focuses on finding the top gene with the least intra- and inter-group expression variation (14). The comparisons of gene expression levels among three groups of subjects were performed by one-way ANOVA (two tailed) when expression levels were normally distributed and by a nonparametric test (Wilcoxon test) when expression levels were not normally distributed. Bonferroni correction was used to adjust for multiple comparisons. All  115  calculations were carried out with the JMP® 5.1 (SAS Institute Inc., Cary, NC, USA) software package. 5.3 RESULTS 5.3.1 Quality control and amplification efficiency: The quality and quantity of RNA extracted from the epithelial cells was assayed in 8 samples randomly selected from asthmatic and healthy children using the Agilent Bioanalyzer system Samples from atopics were not included in this analysis. Results from the Bioanalyzer were generated in the form of an electropherogram. The mean quantity of RNA extracted was 2.7 ± 0.67 µg. The quality of the total RNA, as determined by the RNA integrity number (RIN), was 7.93 ± 0.60. The ratio of 28S to 18S RNA was 1.85. The high RIN and intact rRNA subunits of 28S and 18S observed on the electrophotograms, indicated that degradation of the RNA was at an acceptable level (Figure 5-1). We randomly amplified 10 cDNAs using intron specific primers; all 10 cDNA samples were negative, while genomic DNA control was positive. Standard curves were generated by using folds of dilution of stock cDNA vs. the Ct values for each gene. The linear correlation coefficient (R2) of all the 12 genes ranged from 0.994 to 0.999. Based on the slopes of the standard curves, the amplification efficiencies of the standards were from 89%~109%, which were derived from the formula E = 10(-1/slope) -1. The Ct values of all the 12 genes in all the samples were covered by the range of the standard curves. The E-Method was then used to adjust for amplification efficiency differences of different genes and adjusted expression values were calculated from Ct values of samples from standard curves for each studied gene (12).  116  5.3.2 Expression levels of candidate housekeeping genes: The 12 HKGs demonstrated a wide range of expression level, from the lowest median Ct value of 15.8 for RNR1 (18S RNA), to the highest median Ct value of 34.4 for TBP (52). In triplicate assays performed for each of the 30 cohort subjects, the standard deviation (SD) of Ct value was > 0.3 for 11 samples for HPRT1 and for 16 samples for TBP. Therefore, the accuracy of the data was questionable for these two genes. This may be explained by the fact that the median Ct values were 33.2 and 34.4 for HPRT1 and TBP, respectively. These high Ct values indicate that the target cDNA quantities approached single copy level. Therefore, we excluded the HPRT1 and TBP genes from further analysis. For all remaining 10 HKGs, only one sample from the HA group had a high SD in the triplicate assays for one gene, this sample also had the lowest Ct values for four genes. Another sample from the HNA group had atypically high Ct values which led to outliers in the assays for two of the HKGs. Thus, both of these samples were excluded from further analysis and the final analysis contained 28 samples for 10 HKGs. 5.3.3 Determination of housekeeping gene expression stability: The HKG stability was evaluated by three different VBA applets, geNorm, BestKeeper and NormFinder. The geNorm program calculates the M value of one gene based on the average pairwise variation between all studied genes. All 10 studied genes reached a high expression stability with low M values below the default limit of M = 1.5, with the exception of TFRC which had an M value of 1.7. Figure 5-3 shows plots of the average M values of the candidate HKGs. The curve represents stepwise exclusion of the least stable HKGs. With the initial exclusion of TFRC, both RPLP0 and PPIA were identified as the two most stable genes (Table 5-3). The geNorm program was also used to calculate a  117  normalization factor and to assess the optimal number of reference genes for generating that factor (Figure 5-4). The normalization factor was calculated first from the two most stable two genes, V was used to quantify pairwise variation between the normalisation factors NFn and NFn+1. Add the third gene to the most stable two HKGs, RPLP0 and PPIA, produced a V value of 0.125, which was below the cutoff of 0.15, indicating that it would not be necessary to include additional reference genes for normalization, although including six HKGs had the smallest variation since adding the seventh gene produced the smallest V value of 0.082. The BestKeeper program was used to grade candidate HKG stability. This approach allows a comparative analysis across HKGs. All 10 candidate HKGs were highly correlated and were combined into an index. Subsequently, the correlation between each HKG and the index were calculated. The best correlation between the HKGs and the BestKeeper index was obtained for PPIA (r = 0.988) (Table 5-3). Finally, the NormFinder program was used to rate candidate HKG stability. This approach combined the intragroup and intergroup expression variation into a stability value that enabled the ranking of genes by expression stability. The most and the least stable genes identified by this program were PPIA and TFRC respectively, which identical to that generated from the geNorm and BestKeeper analyses, although the order of stability of the other genes was slightly different as judged by the three different programs (Table 5-3). 5.3.4 Expression levels of candidate HKGs in three groups of subjects: PPIA was the only HKG among the top two most stable HKGs found by the three different analytic methods. Using this gene as the reference, the expression levels of other candidate HKGs were compared in three phenotypically distinct groups of children: healthy  118  nonatopic nonathmatics, healthy atopics and atopic asthmatics (Figure 5-5). The results showed that before correcting for multiple comparisons, the expression levels were not significantly different between the three groups of patients for 3 genes: RPLP0, GNB2L1 and B2M. There were borderline significant differences for one gene PGK1 (p = 0.07), moderately significant differences for three genes GUSB (p = 0.026), TFRC (p = 0.021) and RNR1 (p = 0.037) and highly significant differences for two genes ACTB (p = 0.0047) and GAPDH (p = 0.0008). After Bonferroni correction (nine comparisons) the expression levels of ACTB and GAPDH were still significantly different; atopic asthmatics had the highest expression levels for both genes, while healthy nonatopic nonasthmatics had the lowest expression for both genes (adjusted p values of 0.042 and 0.007, respectively). Although the orders of HKG expression stability were slightly different from the three different VBA applets, the most stable seven HKGs were the same for all three programs. In addition, the geNorm program identified that the six most stable HKGs produced the smallest variation. A normalization factor was calculated from the expression levels of these six genes, the expression levels of all 10 studied HKGs in the three groups of patients were compared, and results obtained were similar with those using a single HKG, namely PPIA. After Bonferroni correction (ten comparisons), the expression levels of ACTB and GAPDH were still significantly different between the groups (corrected p values of 0.011 and 0.006, respectively). Even with the most conservative Bonferroni correction, i.e., correction for 19 comparisons, the results were still significant (adjusted p values of 0.021 and 0.011). However, since the normalization factor calculated from the expression levels of six genes and that from one gene were highly correlated with an r2 of 0.89, the test that used  119  PPIA as a HKG and the test that used 6 genes as KHGs were not independent. Therefore, using the Bonferroni correction was very conservative. 5.3.5 Co-regulation of studied HKGs: When determining HKG stability using a pairwise comparison approach such as geNorm or BestKeeper, co-regulation among the studied genes may influence the results as these algorithms will select any co-regulated genes. To examine potential co-regulation among the studied genes, network analysis was performed using Ingenuity Pathways Analysis (www.ingenuity.com, Redwood City, CA). Based on known relationships from published papers, this web-based application enables the discovery, visualization, and exploration of interaction networks. This analysis showed that except for 2 genes, namely RPLP0 and GNB2L1 there was no correlation between the assessed genes. A more in depth analysis revealed that an indirect link between certain genes did occur in the presence of activation of a tissue by the inflammatory mediator TNF or in the context of cancer, the oncogene MYC (myelocytomatosis viral oncogene homolog). However, the promoter regions of these genes were found not to share any specific binding sites suggesting that other than during TNF or MYC stimulation, the expression of these genes is largely independent. 5.4 DISCUSSION An ideal HKG should maintain a stable RNA transcription level in all subjects regardless of disease status. Hence, the purpose of this study was to evaluate the variation in expression among 12 potential HKGs in AEC from normal, atopic and atopic asthmatic children. A literature search has shown that no single gene has been consistently used as a  120  reference gene in studies of airway epithelium (15-19). Moreover, the HKGs used as normalizers were not systematically evaluated as in the current study. The current study was characterized by: (1) the simultaneous investigation of a panel of 12 common HKGs, (2) TaqMan probe assays were used for gene expression quantification; for all assays except the RNR1 assay the probes spanned across an intron of the associated genes, (3) three different applets were used for data analysis, (4) AECs were the major component (96.6%) of the total cells, as determined by immunostaining with various cellular markers (2, 9). These features are important for data reliability and for meaningful interpretation of the results. All HKGs except GNB2L1 were chosen from the TaqMan Human Endogenous Control panel (Applied Biosystems). The HKGs investigated can be generally classified into eight different groups: glycolysis-related genes: PGK1 and GAPDH; transcription-related genes: TBP; translation-related genes: RNR1; structure/cytoskeleton-related genes: ACTB; genes involved in protein synthesis: RPLP0; lysosomal enzymes: GUSB; nucleotidemetabolism related genes: HPRT1 and finally the genes that do not clearly categorize into one of these groupings including PPIA, B2M, TFRC and GNB2L1 (Table 5-2). Studying HKGs that have a diversity of functions enables successful selection of reference genes for different cells, tissues and disease status. The use of TaqMan assays provides greater specificity, accuracy and reproducibility as a real time detection method than other real time detection methods such as SYBR Green ) (20). In addition, the design of probes spanning across an intron avoids errors from contaminating  genomic  DNA,  an  important  factor  influencing  gene  expression  quantification.  121  The HKGs stability orders determined by the three different VBA applets were not exactly the same, which could be explained by the different principles employed by each of these applets. Both geNorm and BestKeeper use a pairwise comparison approach (4, 13). geNorm chooses the appropriate reference genes based on the geometric mean of expression levels. The principle is that the expression ratio of two ideal HKGs is identical in all samples, regardless of the experimental condition and disease status. Variation of the expression ratios of two tested HKGs may be due to variable expression of one or both of the genes, therefore variation in ratio increased and expression stability decreased (4). The design of the geNorm method was not suitable to identify a single best HKG, but rather combinations of at least two genes that best represented the geometric mean (4). BestKeeper also selects the least variable gene using the geometric mean but uses raw data of Ct value instead of data converted to relative expression level as used in geNorm (13). The BestKeeper also identifies the most stable gene in all HKGs evaluated (13). The pairwise comparison approach has the problem that it tends to select genes with the similar expression profile. The NormFinder uses a model-based approach (14). This approach analyzes subgroups separately, estimates both the intra-and the intergroup expression variation, and calculates a candidate gene “stability value” (14). The best HKG has minimal combined inter-and intragroup expression variation. Compared with pairwise comparison approaches this method has the advantages of considering systematic differences among sample subgroups and is less affected by candidate HKG co-regulation (14). Theoretically this model-based approach is more suitable for our study design. In this study, PPIA was identified as the most stable gene by three programs based on either the pairwise comparison approach or the model-based approach. In addition, RPLP0 and GNB2L1, identified to be correlated by Ingenuity Pathways Analysis, although  122  appear in the top three most stable genes in geNorm analysis; they are still positioned after PPIA. We conclude that even if potential co-regulation exists among some of the HKGs, they were not apparent in our study. A similar result was obtained by Bionaz and Loor (21). Furthermore, several individual studies have used geNorm, BestKeeper and NormFinder programs to identify the most stable HKG and results obtained were variable, being consistent for all three programs in some but not in others (14, 22-24). In those where inconsistent results were identified, the NormFinder program was identified to be superior to that of other programs (14). Hence, we recommend using the NormFinder program to select HKGs in studies with an experimental design similar to ours. PPIA is a member of the immunophilin class of proteins that all possess peptidylprolyl cis/trans isomerase activity and, therefore, are believed to be involved in protein folding and/or intracellular protein transport (25). PPIA is used as a HKG because of its remarkable evolutionary conservation and broad cellular and tissue distribution (26). In a recent report, PPIA was used as a HKG in an expression study of airway epithelial cells because it was not significantly affected by allergen challenge (18). In the current study, PPIA was ranked in the most stable genes by all three methods. In addition, the most stable six genes and the least stable four genes were the same by all three methods, the utilization of PPIA and all six top ranked genes as reference genes produced very similar results. Collectively, our results identify PPIA as the HKG gene of choice for gene expression studies of AECs from asthmatics. It is worthwhile to mention that variations always exist for any HKG and normalization of gene expression with a single HKG can bias the results. Therefore, it has been suggested that multiple HKGs should be used (4). In this particular study our results  123  suggest that it would be more robust to use 3 HKGs (PPIA, PGK1 and RPLP0) to 6 HKGs (PPIA, PGK1, RPLP0 GNB2L1, GUSB and B2M) if experimental resources are available. The current study also demonstrated that there were significant differences in the expression levels of ACTB and GAPDH between normal, atopic and atopic asthmatic children with the highest expression in asthmatics and the lowest expression in normal children. The differences remain significant after correcting for multiple comparisons. This result indicates that both ACTB and GAPDH, the two most commonly used HKGs, cannot be used as HKGs in gene expression study of AECs of asthmatics. Similar results were reported previously by Glare and colleagues (7). However, in the study of Glare et al. asthmatic subjects not using inhaled corticosteroids, had levels of GAPDH and ACTB mRNA that were significantly lower than those from both normal controls and from asthmatic subjects using inhaled corticosteroids. This may be explained by the fact that the cell types in the two studies were different. In the current study, AECs were the dominant cell type (>96.6%), while in the study of Glare et al. epithelial cells were a minority (<1.4%) and macrophage and lymphocytes were the majority (>92.5%). In addition, in the current study the research subjects were children while Glare et al. studied adults. The differences observed between these two studies only further highlights the importance of researchers validating HKGs for each experimental model utilized. In conclusion, PPIA (cyclophilin A) was identified as the most suitable normalizer in gene expression studies involving human AECs derived from normal, atopic and asthmatic children. Moreover, our findings question the suitability of both beta-actin and Glyceraldehyde-3-phosphate dehydrogenase as housekeeping genes for such studies due to their significantly higher expression levels in asthmatic epithelial cells.  124  Table 5-1: The demographic characteristics of the three groups of children Atopic asthmatic (AA)  Healthy atopic (HA)  Healthy nonatopic nonasthmatic (HNA)  Subjects (n)  10  10  10  Age (years)  8.65 ± 2.98  9.27 ± 3.62  8.19 ± 4.65  Male/Female  9/1  6/4  4/6  Mean (min-max) IgE (IU/L)  1022 (129-3625)  218 (128-265)  40 (7-163)  Hay fever or Eczema or both (n)  9  6  1  125  Table 5-2: Housekeeping genes evaluated in this study Gene name  Abbreviation  Gene function  Applied Biosystems assay ID  18S rRNA  RNR1  Ribosomal RNA  Hs99999901_s1  Acidic ribosomal protein  RPLP0  Catalysis of protein synthesis  Hs99999902_m1  β-actin  ACTB  Cytoskeletal structural protein  Hs99999903_m1  Cyclophilin A  PPIA  Serine-threonine phosphatase inhibitor  Hs99999904_m1  Glyceraldehyde-3-phosphate dehydrogenase  GAPDH  Glycolysis enzyme  Hs99999905_m1  Phosphoglycerokinase  PGK1  Glycolysis enzyme  Hs99999906_m1  β2-Microglobulin  B2M  Cytoskeletal protein involved in cell  Hs99999907_m1  locomotion β-Glucuronidase  GUSB  Exoglycosidase in lysosomes  Hypoxanthine ribosyl transferase  HPRT1  Metabolic salvage of purines  TATA-binding protein  TBP  Transcription by RNA polymerases  Transferrin receptor  TFRC  Cellular iron uptake  Guanine nucleotide-binding protein, β-peptide 2-like 1  GNB2L1  Binding and anchorage of protein kinase C  Hs99999908_m1 Hs99999909_m1 Hs99999910_m1 Hs99999911_m1 Hs00272002_m1  126  Table 5-3: HKG expression stability results as determined by three different VBA applets1 GeNorm Gene name  PPIA + RPLP0  1  BestKeeper  NormFinder  Stability value  BestKeeper  coeff. of corr.  Gene name  Stability value  (average M)  index2 vs.  [r]  0.416  PPIA  0.988  PPIA  0.111  PGK1  0.984  PGK1  0.129  GNB2L1  0.449  B2M  0.956  RPLP0  0.169  PGK1  0.488  GUSB  0.953  GNB2L1  0.209  GUSB  0.528  GNB2L1  0.953  GUSB  0.246  B2M  0.564  RPLP0  0.952  B2M  0.258  RNR1  0.615  RNR1  0.925  RNR1  0.323  GAPDH  0.684  ACTB  0.915  ACTB  0.396  ACTB  0.716  GAPDH  0.913  GAPDH  0.405  TFRC  0.903  TFRC  0.847  TFRC  0.594  Stability values are listed from the most stable to the least stable genes for the results from all  three methods. 2  Measures of the correlation coefficients between each control gene and the BestKeeper index.  All HKGs are strictly correlated between each other and are used to calculate the Bestkeeper index.  127  Figure 5-1: The results of RNA analysis by Agilent bioanalyzer. The first peak is a 20 bp molecular marker. The second and the third peaks are 18S and 28S rRNA.  128  Ct Value  RNR1 RPLP0 ACTB PPIA GAPDH PGK1 B2M GUSB HPRT1 TBP TFRC GNB2L1  Figure 5-2: Raw Q-RT-PCR cycle threshold values for 12 candidate housekeeping genes among 30 child airway epithelial samples.  129  Figure 5-3: Expression stability of housekeeping genes using the GeNorm expression analysis software, plotted from least stable to most stable genes (left to right). M is a measure of gene expression stability, which is the mean pair-wise variation between an individual gene and all other tested control genes.  130  Figure 5-4: Determination of optimal number of reference genes for normalization. geNorm calculated normalization factor from leastwise 2 genes at which V defined pairwise variation between 2 sequential normalization factors, for example variation of normalization factor of 3 vs 4 genes (V3/4).  131  Figure 5-5A: The comparisons of expression levels of 6 HKGs between three groups: A) atopic asthmatic, B) healthy atopic and C) healthy nonatopic nonasthmatic. The normalization factors were calculated from the expression level of the gene PPIA. The graph shows average of expression levels ± SE.  132  p = 0.026  p = 0.021  Expression levels  p = 0.129  A  B B2M  C  A  B GUSB  C  A  B  C  TFRC  Figure 5-5B: The comparisons of expression levels of 3 HKGs between three groups: A) atopic asthmatic, B) healthy atopic and C) healthy nonatopic nonathmatic. The normalization factors were calculated from the expression level of the gene PPIA. Boxes indicate the 25th to the 75th percentiles with the median as the lines in the boxes and whiskers indicate ranges.  133  5.5 REFERENCES: 1. 2.  3. 4.  5.  6.  7.  8.  9. 10. 11.  12. 13.  14.  15.  Hackett, T. L., and D. A. Knight. 2007. The role of epithelial injury and repair in the origins of asthma. Curr Opin Allergy Clin Immunol 7(1):63-68. Kicic, A., E. N. Sutanto, P. T. Stevens, D. A. Knight, and S. M. Stick. 2006. Intrinsic biochemical and functional differences in bronchial epithelial cells of children with asthma. Am J Respir Crit Care Med 174(10):1110-8. Huggett, J., K. 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Real-time reverse transcriptasepolymerase chain reaction (RT-PCR) for measurement of cytokine and growth factor mRNA expression with fluorogenic probes or SYBR Green I. Immunol Cell Biol 79(3):213-21. Bionaz, M., and J. J. Loor. 2007. Identification of reference genes for quantitative realtime PCR in the bovine mammary gland during the lactation cycle. Physiol Genomics 29(3):312-9. Robinson, T. L., I. A. Sutherland, and J. Sutherland. 2007. Validation of candidate bovine reference genes for use with real-time PCR. Vet Immunol Immunopathol 115(1-2):160-5. Hibbeler, S., J. P. Scharsack, and S. Becker. 2008. Housekeeping genes for quantitative expression studies in the three-spined stickleback Gasterosteus aculeatus. BMC Mol Biol 9(1):18. Spinsanti, G., C. Panti, E. Lazzeri, L. Marsili, S. Casini, F. Frati, and C. M. Fossi. 2006. Selection of reference genes for quantitative RT-PCR studies in striped dolphin (Stenella coeruleoalba) skin biopsies. BMC Mol Biol 7:32. Luban, J., K. L. Bossolt, E. K. Franke, G. V. Kalpana, and S. P. Goff. 1993. Human immunodeficiency virus type 1 Gag protein binds to cyclophilins A and B. Cell 73(6):1067-78. Koletsky, A. J., M. W. Harding, and R. E. Handschumacher. 1986. Cyclophilin: distribution and variant properties in normal and neoplastic tissues. J Immunol 137(3):1054-9.  135  Chapter 6: A THYMIC STROMAL LYMPHOPOIETIN GENE VARIANT IS ASSOCIATED WITH ASTHMA AND AIRWAY HYPERRESPONSIVENESS 6.1 INTRODUCTION Asthma is a syndrome, characterized by airway hyperresponsiveness (AHR), which results in reversible episodes of airway obstruction associated with inflammation of the bronchial mucosa. An exaggerated production of IgE antibodies in response to common aeroallergens (atopy) is often the basis for the airway inflammation. An imbalance between T helper (Th1 and Th2) immune cell responses resulting in a skewing toward the Th2 phenotype is a major factor contributing to the inflammation of the airways and the development of asthma (1, 2). The thymic stromal lymphopoietin (TSLP) gene codes for an interleukin (IL)-7 like cytokine TSLP that induces myeloid dendritic cells to stimulate the differentiation of naïve CD4+ T cells to Th2 cells. In murine models, TSLP plays a critical role in the initiation and maintenance of allergic airway inflammation (3-5). Accumulating evidence supports TSLP as a candidate gene for asthma and allergic diseases: 1) TSLP is expressed in the airway epithelium, the entry site for allergens; 2) The TSLP receptor is a heterodimer consisting of the IL7 receptor α chain (IL7Rα) and a common γ-like receptor chain called the TSLP receptor (TSLPR) (5-7). Two nonsynonymous SNPs in IL7R have been reported to be associated with inhalational allergy (8); 3) TSLPR knockout mice (TSLPR-/-) have demonstrated resistance to “asthma” (9) and overexpression of TSLP in the lungs and epidermis of transgenic mice induced allergic nflammation (9-10); 4) There is evidence to suggest that overexpression of TSLP may have  A version of this chapter has been published. He JQ, Hallstrand TS, Knight D, Chan-Yeung M, SandfordAJ, Tripp B, Zamar D, Bossé Y, Kozyrskyj AL, James A, Laprise C, Daley D. A Thymic Stromal Lymphopoietin Gene Variant Is Associated with Asthma and Airway Hyperresponsiveness. J Allergy Clin Immunol. Aug;124(2):222-9.  136  sex-specific effects (11, 12) and 5) TSLP overexpression interacts with allergen exposure to produce airway inflammation by stimulating both the innate and adaptive immune responses (13). Additionally, a previous report showed that the number of cells expressing TSLP mRNA is increased in the epithelium of humans who have asthma (14). The expression of TSLP in human airway epithelial cells is induced by rhinovirus (15), an infection that increases the risk of asthma (16). Recently TSLP was identified as a strong candidate in a human linkage analysis. The T allele of the rs2289276 SNP in TSLP was associated with lower levels of cockroach allergen-specific IgE and total IgE in girls and the association with total IgE was replicated in an independent sample (17). However, the findings were limited to females and asthmatic phenotypes were not studied. We hypothesized that there would be associations of TSLP variants with allergy phenotypes, such as asthma and AHR. We included SNPs in and around TSLP for associations with four asthma/allergy phenotypes in a combined total of 5,565 individuals from four asthma studies. After correction for multiple testing, we found evidence for association of a TSLP variant (rs 1837253) with asthma, atopic asthma, and AHR. 6.2 METHODS 6.2.1 Populations for Genetic Association Studies Four well-characterized independent studies of individuals affected with asthma were brought together for this study (Table 6-1). All DNA samples were collected with informed consent obtained in compliance with the Research Ethics Board of each recruiting center. 6.2.1.1 The Canadian Asthma Primary Prevention Study (CAPPS): A total of 549 children at high risk for developing asthma and their parents who during the second and third  137  trimester of pregnancy, were enrolled in an asthma-prevention study and were recruited from two Canadian cities, Vancouver and Winnipeg (18). The children have been followed since birth and have been assessed by a pediatric allergist for the presence of asthma and for allergies all phenotypes used in the analysis were derived from the 7 year follow-up (380 children/families) (19). 6.2.1.2 The Study of Asthma Genes and the Environment (SAGE): A total of 723 children and their parents were recruited from a population-based sample of 16,320 children, born in the Province of Manitoba, Canada in 1995 (20, 21). In 2002, a one-page health survey was mailed to families, children were then stratified according to the self reported presence of asthma (n=392), or allergies (n=192), or neither (n=3002). Children with neither condition were further stratified by rural or urban residence. All children in the asthma and allergy stratum were invited to participate in the nested case-control study, as were a representative sample of controls from both urban and rural environments. Children were assessed for asthma and other allergic phenotypes by a pediatric allergist. 6.2.1.3 The Saguenay-Lac-Saint-Jean and Québec City Familial Asthma Collection (SLSJ): This collection is comprised of 306 families from the Saguenay-Lac-Saint-Jean (n = 227) and the Québec City (n = 79) regions of Quebec, Canada (22-27). 6.2.1.4 The Busselton Health Study Population: Residents of the town of Busselton in the southwest of Western Australia have been involved in a series of health surveys since 1966. Subjects attended one of 6 cross-sectional surveys from 1966 to 1981 and the follow-up survey in 1994. From this population a nested case-control study was designed consisting of individuals who participated in one or more surveys and had a Methacholine challenge.  138  Cases and controls were designated upon the presence (679 cases) or absence of asthma (870 controls) (28, 29). Subjects were considered to have asthma (n=679) if they answered yes to the question “Has your doctor ever told you that you had asthma/bronchial asthma?” at any survey. All other subjects were considered as controls (n=870). After genotyping, sample QC, removal of related individuals, and duplicate samples there were 644 asthma cases and 751 controls available for the analysis. The panel includes a sample of allergic and non-allergic individuals as determined by skin-prick tests to common allergens. Methacholine challenge tests using a modification of the Yan method (30) were performed on all participants of the 1994 follow-up survey. 6.2.2 Phenotypes: 6.2.2.1 Asthma: Defined as doctor-diagnosed asthma at seven years of age in the CAPPS and SAGE cohorts; defined as doctor-diagnosed asthma, present asthma, or past documented clinical history of asthma in the SLSJ sample; defined as self-reported doctor-diagnosed asthma in the Busselton Study. 6.2.2.2 Atopy: Defined as at least one positive response (wheal diameter ≥ 3mm greater than the negative control at 10 min). 6.2.2.3 Airway Hyperresponsiveness (AHR): Defined as PC20 (provocative concentration of methacholine chloride inducing a 20% fall in forced expiratory volume (FEV1)) < 3.2 mg/ml for CAPPS and SAGE subjects, < 8 mg/ml for SLSJ subjects, or PD20 ≤ 3.9 µmol for Busselton subjects. In the Busselton Health Study, controls are subjects who completed a methacholine challenge test, but whose PD20 was >20. Because AHR is more prevalent in  139  children (31, 32), for the childhood samples (CAPPS & SAGE), we used PC20<3.2 mg/ml, because it yields the greatest sum of sensitivity and specificity in children (31). 6.2.2.4 Atopic Asthma: Defined as individuals diagnosed with both asthma and atopy. 6.2.3 TagSNP Selection, Genotyping, and Data Cleaning: Five common SNPs (minor allele frequency, MAF ≥ 0.05) were selected by using pairwise linkage disequilibrium (LD) measures (33) to capture the genetic variation within TSLP and a 10 kb interval upstream from the transcription start site and downstream of the 3’untranslated region. An additional SNP (rs2289276) was subsequently reported in the literature and was added to the study. Simultaneously we genotyped 150 SNPs previously tested for association with at least one asthma-related phenotype, an additional 30 coding non-synonymous SNPs with a MAF ≥0.05, and 761 tagging SNPs that interrogate the bulk of the genetic variation in 98 genes (22, 27). We genotyped samples with the Illumina Bead Array System in accordance with the manufacturer's protocol (34) and according to Lincoln et al (35). For 669 samples in the CAPPS and SAGE cohorts, where there was insufficient genomic DNA to complete the genotyping, we used DNA templates generated using a Whole Genome Amplification method (WGA), using the RepliG Midi kit (Qiagen Cat# 150045). We retained markers for analysis if they had a minimum call rate of 90%, a maximum of four Mendelian errors, and a maximum of one reproducibility error, and showed consistency with Hardy-Weinberg equilibrium at the level P>0.001. An additional SNP (rs2289276) was genotyped by TaqMan, using the manufacturer’s protocol with the following specifications: 5 ng of DNA per reaction, with a total reaction volume of 5ul and drying of DNA template overnight at room temperature.  140  In the family-based samples, relationships between samples were confirmed by comparing pairwise average number of alleles identical by state to what is expected. A total of 24 families (13 CAPPS, 8 SAGE, and 3 SLSJ) were excluded due to either non-paternity or unresolved DNA switches. At most one Mendelian inconsistency was observed for 99.1% of the SNPs in the remaining families. Four sets of twins were identified in the CAPPS cohort (2 monozygotic and 2 dizygotic). The dizygotic twins were retained. A single sib was chosen from the monozygotic pairs for inclusion in the analysis. In the case-control sample (The Busselton Health Study), we identified 73 parent-offspring relationships and 52 sib pairs. To address these relationships we eliminated 125 samples. Moreover we identified two samples that were likely to be of Asian descent, inclusion of which (in a case-control design) could contribute to spurious results because of population differences in allele frequency. We also identified 2 duplicate samples (identical at all loci) but since the two samples differed in phenotype (one case and one control) both samples were removed. This resulted in 644 asthma cases and 751 controls available for the analysis. 6.2.4 Statistical Analysis and Correction for Multiple Testing Associations between SNPs and the four phenotypes were tested by using a general additive allelic likelihood ratio test χ2 test as implemented in UNPHASED (36, 37), which uses a retrospective conditional likelihood similar to the Transmission Disequilibrium Test (TDT) (38) for the family-based studies and a standard retrospective case-control likelihood for case-control designs. In the family studies, the likelihood is the probability of observing the joint genotype distribution (mother, father, and child), conditional on the affectation status of the child. The likelihoods from family and case-control designs can then be combined in a joint analysis of the data. The allelic model is a standard model for TDT  141  designs, where the transmission, and non-transmission, of parental alleles are compared to the expected distribution under the assumption of Mendelian segregation and random mating. Both alleles are expected to be equally transmitted from parent to offspring. Deviation from this distribution is known as transmission distortion, the most likely explanation of which is disease association. For each SNP the common allele is the referent, and an OR is provided for the minor allele. The test statistic is distributed as a χ2 with 1 degrees freedom. We choose this approach because it easily allows for the joint analysis of trio and case-control samples, while maintaining protection against population stratification in the family samples. We used only complete affected trios for analyses in the family-based studies in order to protect against potential bias (39) and population stratification. For the analysis of the CAPPS and SAGE panels, which are independent trios, we used the no-linkage option; as the SLSJ panel has multiple affected sib-pairs, we used the default, which assumes linkage, for the analysis of the SLSJ and combined panels. We included a factor covariate to account for the confounding effects of different population allele frequencies and between sample heterogeneity by using the Busselton panel as the referent group, due to it being a population based study design. Estimates of OR used the common allele as the referent. Stratified analyses were conducted to evaluate the sensitivity of the OR estimates to the inclusion of non-Caucasian samples in the analyses. Our strategy for correction for multiple testing was influenced by the study design. It is recognized that a Bonferroni correction does not take into account the correlation between tagSNPs, and would result in a significant overcorrection and subsequent loss of power (40). Applying a global multiple correction factor to each SNP in this study would have an undesirable effect: densely typed genes would tend to show greater trends of association  142  merely because they use a greater proportion of the total SNP resources. We employed a gene-based approach by applying a correction only with respect to the number of SNPs in that gene and its neighborhood and the number of phenotypes tested (40). P values were adjusted for both the number of independent SNPs (N=4.83) and phenotypes (N=3) tested (40), thus the unit that is tested is the gene (41, 42). We choose this approach as it has been suggested that inconsistencies arising from population differences can be resolved by use of a gene-based approach rather than either a SNP-based or a haplotype-based approach (41, 42). For each gene investigated, an effective number of independent SNPs was calculated by using the definition of Li and Ji (43), as implemented in SNPSpD (40). We only included the five SNPs originally selected for study in this calculation as the additional SNP was genotyped at the request of the reviewers. By using a similar procedure, the Matrix Spectral Decomposition (matSpD) approach, we estimated the number of independent phenotypes to be three in each sample and in the combined analysis. To determine if the genetic effect of TSLP differs between males and females (i.e. effect modification), sex was included in the model; the statistical significance of the effect modification was evaluated by using the test modifier parameter as implemented in UNPHASED. Under the null hypothesis of no gender effects the OR will be equal for males and females. If the OR for females differs significantly from males with the same allele this would provide statistical evidence for effect modification.  143  6.3 RESULTS 6.3.1 LD Study: The LD patterns (Figure 6-1) of the six TSLP SNPs for each population sample were similar; the pairwise r2 values ranged from 0.00 to 0.54, indicating minimal LD among the six genotyped SNPs. 6.3.2 Association Study: In a combined analysis of participants in all four population samples, after correcting for multiple comparisons, the A allele of rs1837253, which is 5.717 kb upstream of the transcription start site of TSLP, was significantly associated with protection from asthma (OR = 0.79, 95% Confidence Interval (CI) = 0.69 – 0.90, adjusted P= 0.0058), atopic asthma (OR = 0.75, 95% CI = 0.63 – 0.88, adjusted P = 0.0074), and AHR (OR = 0.76, 95% CI = 0.64 – 0.89, adjusted P = 0.0094) (Table 6-2). We found the associations of TSLP rs1837253 with asthma, atopic asthma, and AHR to be the most statistically significant observations in our studies to date (44) (Figure 6-2). Analyses of individual samples are consistent with the results from the combined analysis, the A allele of rs1837253, showed an association of borderline significance (adjusted P-values) with protection from asthma in the SLSJ sample (P=0.0053, adjusted P=0.0758) and the Busselton Health Study (P=0.0056, adjusted P=0.0815) (see Table 6-2). There were several associations of borderline statistical significance in both the combined and individual analyses prior to adjustment for multiple comparisons (detail in Table 6-2). To better define the ORs, we stratified by ethnicity. When we then examined the evidence for association in Caucasians, the most prevalent ethnic group in our studies, we obtained similar  144  results  (data  not  shown).  All  association  results  are  available  online  at  http://genapha.icapture.ubc.ca/. Based on the reported gender-specific association of the TSLP (T allele of rs2289276) with allergen-specific IgE (17) and gender-specific hepatic dysfunction caused by overexpression of TSLP in transgenic mice (11, 12, 17), we hypothesized that there may be gender-specific allelic effects. However, we found no significant gender effects in the combined analysis or individual populations for any phenotype, except an increased OR (8.16, 95% CI 1.46 to 45.84) in females vs. a male baseline OR (0.98) for AHR (rs764916, C allele, P=0.0170) in the CAPPS sample. For rs2289276, we found no evidence for association with asthma or a related phenotype. We tested for gender modification and found no evidence for effect modification in the combined analysis, however, we did find evidence to suggest that there may be gender effects in the childhood cohorts (CAPPS and SAGE) for the asthma, atopic asthma, and atopy phenotypes. However, the direction of the effects was not consistent between the samples (see Table 6-3). 6.3.3 Associated SNP and Proxies in the International HapMap Database The rs1837253 polymorphism showed the most significant association in our study. A search of the HapMap database (accessed Dec 15, 2007) failed to identify any perfect proxies (r2 = 1) among 3,045 SNPs in a 2 Mb genomic segment encompassing the TSLP locus. There were no correlated SNPs, even when the moderate criterion of r2 > 0.4 was used. 6.4 DISCUSSION In this large international study of four populations, the A allele of rs1837253 demonstrates protective effects for atopic asthma (OR = 0.75, adjusted P = 0.0074) and for AHR (OR = 0.76, adjusted P = 0.0094) in the SLSJ and Busselton samples that capture adult  145  persistent asthma. Protective effects are also seen in the CAPPS and SAGE cohorts, which are enriched for transient wheezing, but they did not reach statistical significance. Our comprehensive study which to date has examined 98 candidate genes for asthma and related phenotypes (44), provides further evidence to support the hypothesis that common genetic variants within TSLP are associated with the phenotypes of asthma, atopic asthma, and AHR. In fact, the associations with TSLP are the most significant results in all 98 genes studied (44) (see Figure 6-2). Given the heterogeneity in populations, study designs, and age at onset (childhood and adult) present in the four studies, the robustness and consistency of the TSLP associations is especially notable. Further, studying childhood asthma and adult asthma together has the benefit of identifying genetic susceptibility alleles common to both disease subtypes and may lead to the early identification of children whose symptoms are likely to persist into adulthood. Although significantly associated with asthma, atopic asthma and AHR, rs1837253 showed no association with atopy, defined as positive skin-prick tests. However, it should be noted that we did find evidence for association (unadjusted P=0.0182) with atopy and rs3806932 in the combined analysis.  The fact that experimental asthma and allergic  dermatitis can be produced in TSLP transgenic animals lacking T cells and IgE (9, 10, 45), coupled with the observation that TSLP derived from epithelial cells can directly activate mast cells in humans (46), supports the concept that TSLP can initiate local, organ-specific, allergic inflammation in the absence of systemic markers of atopy. Induction of TSLP in the epithelium can also occur via non-allergic stimuli including rhinovirus and dsRNA (15). However, the report of an association of TSLP variants with levels of allergen-specific IgE and total IgE also supports its potential role in classical IgE-mediated mechanisms (17).  146  Associations of rs1837253 with asthmatic phenotypes indicate that it is either a causal SNP or in tight LD with a causal SNP.  The prediction that SNP rs1837253 will disrupt  transcription factor binding sites (47), indicates that the SNP may be functional. Transcription factors capable of binding the TSLP promoter in the epithelium include NF-κB and STAT6 that have been implicated in asthma pathogenesis (15). We speculate that transcription factor regulation by variants in and around TSLP (48), alters TLSP expression, activating TSLP-driven airway inflammation and remodeling (13). Furthermore, there were no SNPs correlated with rs1837253, even when the moderate criterion of r2 > 0.4 was used. A second TSLP SNP (rs2289276) has been reported to be associated with IgE in a genderspecific manner (17). We found evidence to suggest that there may be gender-specific effects, however, these effects are not consistent across samples (see Table 6-3). The two SNPs rs2289276 and rs1837253 show very weak levels of LD (r2 of 0.003) in the HapMap CEU population. These results suggest that multiple SNPs in TSLP may influence asthmatic phenotypes independently, as has been shown for several SNPs in ORMDL3 which contribute to the risk of childhood asthma (49). Because neither SNP may be functional, but rather in LD with a causal SNP or SNPs that are yet to be identified, deep resequencing and functional assays are needed. The increasing incidence of asthma during the past two decades has resulted in a substantial global burden of morbidity, especially in developed countries (50). To help alleviate this worldwide problem, new methods to predict and help prevent asthma must be found. It is hoped that genetic studies will aid in the early identification of persistent asthmatic children, perhaps even prior to airway remodeling (51). Our results have potential clinical significance in that they suggest novel targets for modulating allergic inflammation  147  in the airways. Because TSLP is highly expressed in the airway epithelium, TSLP is accessible to small molecules or antibodies that inhibit its action. Recent studies have also shown that TSLP can induce the expression of OX40 ligand on the surface of dendritic cells (52). Blockade of OX40 ligand inhibits TSLP-driven atopic inflammation (53), thus providing an additional target for local control of TSLP-driven allergic diseases (54, 55). Ultimately, identification of individuals whose disease is related to genetic dysregulation of TSLP pathways may lead to more effective and individualized strategies for the prevention and management of asthma.  148  6.5 ACKNOWLEDGMENTS We thank all subjects who donated time and samples for this project, and numerous health care workers who helped with recruitment and phenotyping. The authors thank Drs. Thomas Hudson and Peter D. Paré who initiated the genetic studies of these cohorts; Dr. Allan Becker for his establishment of the CAPPS and SAGE cohorts; Drs. Lyle Palmer, Bill Musk, John Beilby, and Nicole Warrington for the recruitment, sample handling, and data management of the Busselton cohort; Ms. Treena McDonald for project management; Dr. Vincent Ferretti, Dr. Alexandre Montpetit and Ms Marie-Catherine Tessier for their work in genotyping the cohorts; Dr. Mathieu Lemire for data management of the SLSJ cohort; Dr Paul Bégin, Muriel Grenon, and Charles Morin for recruitment of the Saguenay-Lac-SaintJean panel participants; Dr Louis-Philippe Boulet and Michel Laviolette for ascertainment,of the Québec City family trios; For the Canadian Asthma Primary Prevention Study cohort we acknowledge Drs Alexander Ferguson and Wade Watson who phenotyped participants, Ms Roxanne Rousseau and Ms Marilyn Lilley who ascertained subjects and Ms Anne DyBuncio for data management; Ms. Dorota Stefanowicz and Ms. Loubna Akhabir for sample handling of the CAPPS and SAGE cohorts; Dr. Anthony Kicic for helpful comments and Ms. Veronica Yakoleff for editing of the manuscript.  149  Table 6-1: Clinical characteristics of subjects from the four study samples  Study Design Sample Families Individuals Genotyped (affected and unaffected)  Family Based Trios CAPPS SAGE 545 723 1316 1466  SLSJ 306 1234  Total 1574 4016  Case-Control Busselton  Total  57  139  379  575  1549 individuals Number of Cases 644  Atopy AHR1,2 Atopic Asthma1,2  135 170 43  190 120 92  362 278 305  687 568 440  620 213 382  575 814 400  Characteristics Sex, male: female3 Ethnicity, n (%) Caucasian  1.05:1 301 (79%)  1.26:1 525 (73%)  1:1.32 100%  1:1.46 100%  1:1.26 100%  Complete Trios Asthma1,2 1,2  1549 Number of Controls 751  1395 1195 1027 782  1  Indicates the number of complete trios (genotypes on both parents and affected child) with this phenotype classification who are eligible for the analysis (i.e. passed all QC controls) in each study. 2 In the Busselton Study this indicates the number of cases with the phenotype and the number of controls for that phenotype. 3 In the Busselton study the sex ratio is for asthma cases and controls.  150  Table 6-2. Associations of TSLP snps with asthma, atopy, atopic asthma, and AHR in the separate and combined analyses  rs1837253A Asthma Atopy Atopic A AHR rs2289278G Asthma Atopy Atopic A AHR rs2416258G Asthma Atopy Atopic A AHR rs3806932C Asthma Atopy Atopic A AHR rs764916C Asthma Atopy Atopic A AHR rs2289276T Asthma Atopy Atopic A AHR  27 71 21 89 11 30 10 38 23 65 20 68 42 93 31 115 8 28 8 37 32 68 23 104  1.0000 79 1.09 0.6861 1.0000 157 0.69 0.0053 0.0758 642/750 0.79 0.0056 0.0815 0.79 1.0000 113 1.10 0.5554 1.0000 168 0.71 0.0212 0.3045 618/574 0.92 0.3576 1.0000 0.89 1.0000 53 0.94 0.8026 1.0000 142 0.66 0.0056 0.0801 381/400 0.77 0.0240 0.3478 0.75 1.0000 71 0.78 0.2689 1.0000 129 0.73 0.0586 0.8423 212/813 0.68 0.0025 0.0363 0.76 1.0000 32 0.89 0.7388 1.0000 90 0.88 0.4721 1.0000 644/750 0.91 0.5298 1.0000 0.92 1.0000 52 1.65 0.0536 0.7693 105 0.88 0.4982 1.0000 619/575 0.82 0.2224 1.0000 1.02 1.0000 20 1.09 0.8348 1.0000 84 0.85 0.4231 1.0000 382/800 0.75 0.1876 1.0000 0.86 1.0000 28 0.83 0.6013 1.0000 76 1.15 0.5220 1.0000 213/814 0.91 0.6907 1.0000 1.01 1.0000 67 0.85 0.4911 1.0000 100 1.03 0.8628 1.0000 643/750 0.95 0.6457 1.0000 0.96 1.0000 96 0.87 0.4495 1.0000 121 0.92 0.6493 1.0000 620/574 1.29 0.0299 0.4327 1.06 1.0000 45 0.86 0.5789 1.0000 103 0.95 0.7758 1.0000 382/399 1.18 0.2523 1.0000 1.04 1.0000 55 0.81 0.3848 1.0000 100 0.84 0.3484 1.0000 213/813 0.82 0.2076 1.0000 0.87 1.0000 92 0.98 0.9270 1.0000 230 0.99 0.9183 1.0000 638/747 0.88 0.0879 1.0000 0.91 1.0000 134 0.92 0.5595 1.0000 257 1.04 0.7381 1.0000 617/570 0.81 0.0112 0.1621 0.87 0.7360 61 1.02 0.9136 1.0000 212 1.01 0.9534 1.0000 380/397 0.77 0.0126 0.1823 0.86 1.0000 82 0.91 0.6255 1.0000 199 1.08 0.5412 1.0000 211/810 0.94 0.6027 1.0000 0.99 1.0000 16 0.89 0.8083 1.0000 63 1.09 0.7179 1.0000 644/750 1.09 0.6546 1.0000 1.11 1.0000 30 1.06 0.8618 1.0000 68 1.20 0.4592 1.0000 620/574 1.36 0.1406 1.0000 1.23 1.0000 13 1.00 1.0000 1.0000 57 1.04 0.8946 1.0000 382/399 1.45 0.1453 1.0000 1.26 0.4442 17 0.80 0.6370 1.0000 48 1.43 0.2361 1.0000 213/813 0.78 0.3901 1.0000 1.18 1.0000 46 1.16 0.5860 1.0000 198 1.02 0.8510 1.0000 644/751 1.13 0.6063 1.0000 0.97 1.0000 91 0.91 0.6350 1.0000 197 1.12 0.3488 1.0000 620/575 0.86 0.1138 1.0000 0.94 1.0000 29 1.33 0.3972 1.0000 153 1.02 0.8892 1.0000 382/400 0.88 0.2669 1.0000 0.94 1.0000 40 0.81 0.4654 1.0000 138 1.11 0.4985 1.0000 213/814 0.92 0.4980 1.0000 0.99  Corrected P value  P value  OR*  Corrected P value  Combined P value  OR*  Case/ Controls  Corrected P value  Busselton P value  OR*  Trios  Corrected P value  SLSJ P value  0.5045 0.4629 0.3520 0.8488 0.2437 0.0840 0.3627 0.7576 1.0000 0.4247 0.8348 0.5687 0.1138 0.0783 0.0510 1.0000 0.1480 0.7149 0.1480 0.0308 0.1638 0.5474 0.2213 0.7962  OR*  0.80 0.86 0.71 0.96 2.00 1.83 1.75 1.10 1.00 0.83 0.92 1.14 0.66 0.72 0.55 1.00 3.00 1.14 3.00 2.08 0.65 0.86 0.65 1.04  Corrected P value Trios  P value  Pheno -types  SAGE  OR*  SNP and minor allele  Trios  CAPPS  0.0004 0.0058 0.0869 1.0000 0.0005 0.0074 0.0007 0.0094 0.4430 1.0000 0.8667 1.0000 0.2777 1.0000 0.9280 1.0000 0.6025 1.0000 0.4787 1.0000 0.6837 1.0000 0.1700 1.0000 0.0914 1.0000 0.0182 0.2635 0.0353 0.5104 0.8808 1.0000 0.4538 1.0000 0.1190 1.0000 0.1659 1.0000 0.2969 1.0000 0.6401 1.0000 0.3151 1.0000 0.4194 1.0000 0.8663 1.0000  Definition of abbreviations: AHR: airway hyperresponsiveness; Atopic A: Atopic Asthma: CAPPS: the Canadian Asthma Primary Prevention Study; SAGE: Study of Asthma Genes and the Environment; SLSJ: The Saguenay-Lac-Saint-Jean and Québec City Familial Asthma Collection; Busselton: Busselton Health Study Population; OR: odds ratio  151  TABLE 6-3. Effect Modification by Gender for rs2289276 T allele with asthma, atopy, atopic asthma, and AHR in the seperate and combined analyses  CAPPS  SAGE  SLSJ  Busselton  Combined  Ast  0.47  2.13  Effect Modification P-value 0.0080  1.62  0.41  Effect Modification P-value 0.0024  0.89  1.11  Effect Modification P-value 0.7146  0.85  1.22  atopy  0.77  1.29  0.2372  1.16  0.53  0.0020  0.98  1.02  0.9312  0.79  1.18  0.3631  0.88  1.05  0.7421  AHR  1.03  1.04  0.8075  0.84  0.85  0.6788  1.07  0.95  0.8898  0.81  1.23  0.4192  0.92  1.09  0.6120  AA  0.46  2.17  0.0158  1.88  0.38  0.0165  0.91  1.06  0.8631  0.68  1.57  0.0513  0.79  1.28  0.1532  Phen  Male OR  Female OR  Male OR  Female OR  Male OR  Female OR  Male OR 0.80  Effect Female Modification OR P-value 1.35 0.0850  Male OR  Female OR  Effect Modification P-value 0.1495  Note: P-values in this table have not been corrected for multiple testing.  152  Figure 6-1: Pair wise linkage disequilibria of TSLP SNPs evaluated by the r2 value. The r2 values are calculated pair-wise and the r2 value appears in the corresponding box. Upper left: CAPPS: Canadian Asthma Primary Prevention Study; Lower left: SAGE: Study of Asthma Genes and the Environment; Upper right: SLSJ: Saguenay-Lac-Saint-Jean and Québec City Familial Asthma Collection: Lower Right: Busselton Study Population. Only one common haplotype was identified in each population and a triangle identifies the block boundary.  153  Figure 6-2: The combined results of the associations of SNPs in the TSLP gene with four asthma-related phenotypes. Genes are ordered on the x-axis by chromosome and position. The legend for the phenotypes is in the top left corner. Corrected p values are on the y-axis. SNPs with corrected P values< 0.05 are labeled with gene name.  154  REFERENCES 1. 2. 3. 4.  5. 6.  7.  8.  9.  10.  11.  12.  13.  Strachan, D. P. 1989. Hay fever, hygiene, and household size. Bmj 299(6710):125960. Wills-Karp, M., J. Santeliz, and C. L. Karp. 2001. 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J Clin Invest 117(12):3655-7.  159  Chapter 7: SUMMARY 7.1 OVERVIEW Over recent years, the study of the genetics of complex diseases such as COPD and asthma has rapidly progressed due to the completion of several large-scale genetic projects such as the Human Genome Project (completed in 2003) and the international HapMap project (completed phase I in 2005, phase II in 2007 and phase III in 2008) (1, 2) and also due to the development of advanced genotyping technologies. Novel genetic discoveries have been accelerated especially since the Genome-Wide Association Study (GWAS) approach has been implemented for the study of asthma and COPD over the last two years. Although seven GWASs have been published for COPD, asthma and their related phenotypes, none of the genes which I studied in the LHS has reached a genome-wide significance level in these GWASs. There are several explanations for this observation. First, the coverage of GWAS is variable among different generations of the chips. For example, there are 1, 3, 6 and 16 IL6 SNPs on the 300k, 550k, 650k and 1M Illumina Human chips. There are three IL6 SNPs on the 550k chips which were used for the COPD GWAS of Pillai et al. (3). In Caucasian subjects, one SNP does not exist and one has a MAF < 5%, therefore, only one SNP was left to cover common genetic variants of IL6, which is not enough since we calculated that at least 4 SNPs are needed to cover common IL6 SNPs in Caucasian populations (see chapter 4 and its supplement: based on our result, we combined bins 1-3 as one bin.). Unfortunately, even the 1M chip, although it contains 16 IL6 SNPs, still will not be enough to cover common IL6 SNPs (missing information for bins 4 and 5 in chapter 4) since most IL6 SNPs on the chip are monomorphic or rare in Caucasians. Second, as we mentioned earlier, GWASs have higher false negative rates due to lack of hypotheses and positive  160  results need to reach genome-wide significance levels. Third, but not least, other factors such as the environment, ethnic/racial background of the research subjects, etc, may also contribute to this lack of replication. 7.2 SIGNIFICANT FINDINGS OF THE PROJECT 7.2.1 IL6 as a COPD gene In the 41 COPD candidate genes we have investigated so far for the rate of decline of lung function in the nested case-control sample from the Lung Health Study, the IL6 SNPs have the strongest SNP associations at a single gene level in terms of p values (Table 71).We listed an estimate of the attributable risk for each SNP and haplotypes which was significantly associated with rate of decline in Table 7-1. This value should not, however be interpreted as population attributable risk since the LHS was restricted to smokers who had some evidence of lung function impairment at study initiation. Recently, Yanbaeva et al. studied 355 COPD patients and 195 controls and they reported that a different IL6 SNP rs1818879 (in bin 5 of our study: see Chapter 4) was a significant risk factor for COPD (p = 0.01) and an IL6 haplotype was associated with COPD ( p = 0.003) (4). These authors also found IL6 variants were associated with IL6 and CRP levels (4). These results further support our conclusion that IL6 is a COPD gene. However, the facts that different SNPs were associated with COPD and that different SNPs in IL6 change its gene function (5, 6) suggest the possibility that we have not yet identified the causal SNP or that multiple SNPs in IL6 contribute to COPD susceptibility. 7.2.2 TSLP as an asthma gene Among all 113 genes we have studied for the 5565 subjects from the four study samples (7, 8), including 93 genes that have previously been associated with asthma and  161  related phenotypes (7), TSLP had the most significant associations with asthma, atopic asthma and atopy. A recent study showed that a SNP in the TSLP gene changed the Th2 response to virus infection (9), providing evidence that TSLP genetic variants influence its function, and may therefore play an important role in the development of atopic diseases. It is well established that TSLP initiates and enhances the Th2 response by acting on multiple cells such as dendritic cells, T cells, and mast cells, etc (10, 11). Modification of cellular responses of those cells to TSLP might change the chain of Th2 responses and therefore prevent or reduce allergic reactions. It is likely that novel preventive and therapeutic approaches to asthma and other allergic diseases will be developed eventually based on TSLP research discoveries. 7.2.3 PIAA as an optimal house-keeping gene for epithelial cell gene expression studies With recent studies of the airway epithelium in the context of the Th2 response, innate and adaptive immunity, the importance of epithelial cells in the pathogenesis of asthma and related diseases cannot be overemphasized. The study of gene expression profiles in different scenarios will provide more information about the airway epithelium. It is crucial to optimize the house-keeping genes for expression studies. Our study highlighted the importance of selecting the correct housekeeping gene by showing differential expression of the two most commonly used house-keeping genes GAPDH and β-actin in atopic and normal subjects. The results of this study should prompt researchers to perform the necessary optimization of house-keeping genes before performing any gene expression studies. 7.3 CLINICAL IMPLICATION OF THE PROJECT In general, the aims of genetic studies are to understand how genetic variation relates to human health and disease, to provide novel knowledge about the pathogenesis of disease  162  and therefore to develop new preventive and therapeutic strategies. In our study, we did not establish significant genetic associations of IL10, IL10 receptor and CSF2 polymorphisms with lung function and its change. In addition, the association of CSF3 with lung function needs further validation. However, we have provided very strong genetic evidence that IL6 is a COPD gene and TSLP is an asthma gene. IL6 has been suggested as a therapeutic target for COPD (12), and our study result has the potential clinical application to identify patients who may response to anti-IL6 therapy. Similarly, as mentioned earlier, TSLP has also been suggested as a target for therapy of allergic diseases (11, 13, 14), and based on our study result we can identify those patients who don’t carry the protective allele and thus might respond to anti-TSLP therapy. 7.4 FUTURE DIRECTIONS To gain a better understanding of how IL6 variants influence COPD and how TSLP variants influence asthma, I propose the following experimental and genetic epidemiology studies: 7.4.1 Molecular and cellular biology studies of IL6 and TSLP 7.4.1.1 IL6 and TSLP gene resequencing For both associations of IL6 variations with COPD and TSLP variations with asthma and related phenotypes, the associated SNPs from this project and from other recent publications are neither the same nor in high LD (4, 15-17), which could indicate that the associated SNPs are only markers for unidentified causal variations. Deeper resequencing of subjects with risk alleles and comparison with normal controls in a significant number of subjects may uncover the causal alleles.  163  7.4.1.2 IL6 and TSLP gene expression studies Both IL6 and TSLP are expressed in many cell types as discussed in previous chapters. It would be ideal to investigate if the associated SNPs are associated with variations in mRNA and protein expression in different cell types. Future studies could include performing IL6 mRNA and protein expression studies on neutrophils and macrophages from bronchoalveolar lavage fluid of COPD subjects with known genotype and performing TSLP mRNA and protein expression studies on bronchial epithelial cells and skin cells from subjects with known genotype. This may provide direct links between genetic variants and relevant phenotypes. 7.4.1.3 IL6 and TSLP reporter gene studies The gene expression studies suggested in the previous section are association studies. From such association studies we cannot answer the question of whether the associated SNPs are causal variants. To identify the functional variants, I suggest performing reporter gene and gel shift studies. These studies may identify the relevant transcription factors involved and explore regulatory mechanisms of the studied genes. Due to the complex nature of gene regulation of both genes, the study of different cell types and different haplotypes are likely needed. 7.4.2 Genetic epidemiology studies of IL6 and TSLP 7.4.2.1 Replication of the associations with large sample studies Although we have replicated associations in our own studies, replication of these associations in much larger studies will still be very useful to address the following issues. First, large sample sizes with sufficient power are needed to identify rare susceptibility alleles. Second, large sample size studies can be used to address gene-environmental  164  interaction. Third, gene-gene interactions can also be explored by large sample size studies. It is likely that several genes contribute to COPD and asthma risk with each gene contributing a relatively small portion of the risk. 7.4.2.2 Copy number variations and epigenetic studies There have been no systematic analyses of what percentage of the heritability of COPD and asthma can be explained by genetic polymorphisms. The results of a GWAS of height have demonstrated that the top 20 SNPs with the most significant associations can only explain 3.8% of the variation in height (18). It has been suggested that similar observations would be made for other complex traits/diseases such as asthma (19) as well as COPD. The missing heritability may be partially explained by other genetic variants such as copy number variations. Epigenetics may also contribute to the missing heritability via processes such as methylation and histone modifications. Unfortunately, there are only few studies on this aspect for asthma and COPD (20-28). I suggest that copy number variant and epigenetic studies should be done for the IL6 and TSLP genes. The future directions described above are essentially the same for all genetic studies of complex diseases after GWASs. New statistical tools, systems biology and functional genomics should better elucidate the role of specific genetic changes in COPD and asthma.  165  Table 7-1: Candidate genes studied for lung function decline in the Lung Health Study Subjects Candidate Chromosomal Odds Ratio Allele/Genotype Attributable Reference Risk % Gene location P value frequency Adrenergic, beta-2-, 5q31-q32 0.6 (0.4-0.8) Gly16Arg, -30 (29) receptor, surface (ADRB2) P = 0.0007 Glu27Gln 44% vs. 57% Catalase (CAT) 11p13 1.7 (1.1-2.2) 30% vs. 22% 13 (30) P = 0.012 Chitinase 3-like 1 1q32.1 NS (31) (CHI3L1) Chitinase, acidic (CHIA) 1p13.1-p21.3 NS (31) Chitinase 1 1q31-32 (chitotriosidase) (CHIT1) Cholinergic receptor, 15q24 nicotinic, alpha 5 (CHRNA5) 1q21-q23 C-reactive protein, pentraxin-related (CRP) Colony stimulating factor 2 5q31.1 (CSF2) Colony stimulating factor 3 17q11.2-q12 (CSF3) Defensin, alpha 1 and 8p23.1 alpha 3, variable copy number locus (DEFA1A3) Defensin, beta 1 (DEFB1) 8p23.2-p23.1 Epoxide hydrolase 1, microsomal (EPHX1)  1q42.1  Haplotype: 113His/139His Group-specific component 4q12-q13 (vitamin D binding protein) (GC) Glutathione S-transferase 11q13 pi 1 (GSTP1) Glutathione S-transferase 1p13.3 mu 1 (GSTM1) Glutathione S-transferase 22q11.23 theta 1 (GSTT1) Combination of GSTP1 Ile105>Val, GSTT1& GSTM1) Hemopoietic cell kinase  NS 3.0 (1.2-7.6) P = 0.018  41% vs. 36%  42  (32)  NS  unpublished  NS  (33)  NS  (33)  NS  (34)  NS  (34)  NS  (35)  2.4 (1.1-5.4) P = 0.03 NS  6.7% vs. 2.9%  4  (35) (35)  NS  (36)  NS  (36)  NS  (36)  2.8 (1.1-7.2) P = 0.03 20q11-q12  (31)  NS  5.4% vs. 2.3%  4  (36)  (37)  166  (HCK) Major histocompatibility 6p21.3 complex, class I, B (HLAB7) Heme oxygenase-1 22q13.1 (HMOX1) Interferon, gamma (IFNG) 12q14  1.6 (1.1-2.3) P = 0.028 NS  Interleukin 1, alpha (IL1A) 2q14  NS  (41, 42)  Interleukin 1, beta (IL-1β) 2q14  NS  (41, 42)  (38)  25.5% vs. 17.9% 10  (39) (40)  1.6 (1.1–2.3) P = 0.02  IL1RN_19327A 72% vs. 62%  27  (41)  Haplotype IL1RN A1/IL1B –511T  1.4 (1 - 2.1)  14% vs. 10%  4  (42)  Haplotype IL1RN A2/IL1B –511C  1.5 (1.1 - 2.1) 20% vs. 14%  7  (42)  5  (43)  Interleukin 1 receptor antagonist (IL1RN)  2q14.2  NS  Interleukin-4 receptor alpha (IL4RAQ551R) Interleukin 6 (IL6)  16p12.1-p11.2 2.2  7.1% vs. 3.6%  7p21  Interleukin 8 (IL8)  4q13-q21  1.9 (1.1-3.2) P = 0.006 1.32  -174CC vs. other 11 24% vs. 14% 58% vs. 51% 14  Interleukin 10 (IL10)  1q31-q32  1.9 (1.1-3.5) P = 0.02  Female 12 participants: 3368A vs. 3368G 22.6% vs. 14.9%  Interleukin 10 receptor, alpha (IL10RA) Interleukin-13 (IL13)  11q23  NS  (45)  5q31  NS  (43)  Interleukin 13 receptor, Xq24 alpha 1 (IL13RA1) Lymphotoxin-alpha (LTA) 6p21.3  NS  (43)  NS  (35)  Matrix metallopeptidase 1 11q22.3 (MMP-1) -1607+G Matrix metallopeptidase 9 20q11.2-q13.1 (MMP9) Matrix metallopeptidase 12 11q22.3 (MMP12)  1.61 (1.1-2.4) 28% vs. 19% p = 0.02 NS  10  NS  15  Haplotype of MMP1 G– 1607GG and MMP12 Asn357Ser  1.42 (1.1–1.8) Haplotype P = 0.0007 MMP1– 1607GG/MMP12 Asn357: 49.8% vs. 41.2% NS  Nuclear factor (erythroid- 2q31 derived 2)-like 2 (NFE2L2)  (16) (44) (45)  (46) (46) (46)  unpublished  167  Plasminogen activator, 8p12 tissue (PLAT) Plasminogen activator, 10q24 urokinase (PLAU) Serpin peptidase inhibitor, 14q32.1 clade A (alpha-1 antiproteinase, antitrypsin), member 1 (SERPINA1) Serpin peptidase inhibitor, 7q21.3-q22 clade E (nexin, plasminogen activator inhibitor type 1), member 1 (SERPINE 1) Transforming growth 19q13.1 factor, beta-1 (TGFB1) Tumor necrosis factor, 6p21.3 alpha (TNF)  NS?  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Human plasminogen activator inhibitor 1 polymorphism and rate of decline of lung function. Am J Respir Crit Care Med 165:A433. Ogawa, E., J. Ruan, J. E. Connett, N. R. Anthonisen, P. D. Pare, and A. J. Sandford. 2007. Transforming growth factor-beta1 polymorphisms, airway responsiveness and lung function decline in smokers. Respir Med 101(5):938-43.  172  APPENDICES  APPENDIX A: Supplement to “Chapter 4: Associations of IL6 polymorphisms with lung function decline and COPD” A1.1 METHODS A1.1.1 Study participants: A1.1.1.1 LHS participants: A total of 1488 subjects were selected from the ~ 4,800 LHS subjects for whom DNA and serum are available. The LHS, sponsored by the National Heart, Lung and Blood Institute was a clinical trial to test the effect of an anti-smoking intervention and bronchodilator treatment on the progression of COPD (1). The LHS recruited a total of 5887 smokers aged 35-60 who had spirometric evidence of mild-moderate lung function impairment from 10 North American medical centers (1). Subjects were followed up yearly during the first five-year period (1) and a venipuncture was performed at the fifth annual visit. Non-Hispanic whites account for the majority of participants and therefore participants from other ethnic groups were excluded in this study. In the manuscript the text refers only to the non-Hispanic whites. These subjects were selected to form nested case control studies of two extreme phenotypes. Based on the rate of decline of lung function during the initial 5 year follow up in the 3,216 continuous smokers, we selected the 266 and 293 participants who had the fastest (rapid decline group) and slowest rate of decline of lung function (non-decline group), respectively. Arbitrary cut-off points of ≥ 3.0% predicted decrease /year in FEV1 and ≥ 0.4% predicted increase /year were used for rapid decliners and non-decliners, respectively. Based  This is online supplement of a published manuscript, a version of Chapter 4.  173  on the post bronchodilator FEV1% predicted at the start of the study, we selected 478 and 451 participants with the highest post bronchodilator FEV1% predicted (high baseline group) and the lowest post bronchodilator FEV1% predicted (low baseline group). Arbitrary cut-off points of FEV1 % predicted ≥ 88.9% and ≤ 67.0% were used for the high and low baseline groups, respectively. Since 130 participants from the rate of decline study group had baseline lung function within one of the categories for baseline lung function (54 individuals were in the high baseline range and 76 individuals were in the low baseline range), they were also analyzed in the baseline lung function study. Thus, there were 532 and 527 participants in the high and low baseline groups, respectively. A1.1.1.2 NETT-NAS Study participants: A subset of participants in the National Emphysema Treatment Trial (NETT) (2, 3) was analyzed in a nested case control study of IL6 polymorphisms and susceptibility to COPD. Briefly, the NETT was a randomized, multi-center, clinical trial comparing lung volume reduction surgery to conventional medical therapy in a cohort of individuals with severe COPD. We analyzed 389 non-Hispanic white subjects who were enrolled in the NETT Genetics Ancillary Study, a sub-study of the NETT. The control group was composed of 420 participants from the Normative Aging Study (NAS) (4, 5), a longitudinal study over the past four decades of healthy adult males that was initiated by the Boston Veterans Administration. The healthy smokers in the NAS cohort did not exhibit airflow obstruction and had > 10-pack-year history of smoking at their last study visit.  174  A1.1.2 TagSNP selection: To select SNPs for genotyping in the LHS, the IL6 SNP discovery data were downloaded from the SeattleSNPs NHLBI Program for Genomic Applications, UW-FHCRC, Seattle, WA (URL: http://pga.gs.washington.edu) [accessed May 2003]. From all SNPs identified in the 23 unrelated European-American samples of the Centre d'Etude Polymorphisme Humain (CEPH) family panel, a set of tagSNPs was chosen for each gene using the LDSelect program developed by Carlson et al (6). This program selects a set of maximally informative tagSNPs based on their having linkage disequilibrium (LD) above a threshold with the non-selected SNPs. A relatively stringent LD threshold of r2>0.8 and minor allele frequency cutoff of 10% was used. Six SNPs in the IL6 gene (see Table A1-1) were selected. An assay for one SNP (rs2069840) in IL6 could not be established by the TaqMan method (Applied Biosystems, Foster City, CA). Therefore, five SNPs were genotyped in the LHS participants. An additional 2 SNPs were genotyped in the decline of lung function study by the Illumina GoldenGate method (Illumina Inc., San Diego, CA) i.e. rs2069840 that could not be genotyped by TaqMan, and rs1554606, selected for the NETTNAS study. In the NETT-NAS, six tag SNPs were selected with the ldSNP Tool, a linkage disequilibrium tagging algorithm, from the Innate Immunity in Heart, Lung and Blood Diseases PGA website (http://innateimmunity.net/). SNP selection was limited to polymorphisms with a minor allele frequency of > 0.1. SNPs not included in the panel were captured by an included SNP with an r2 > 0.8. Three additional IL6 SNPs were also genotyped in the panel.  175  A1.1.3 Genotyping: In the LHS, genotyping of the five selected tagSNPs was performed in 384 well plates in a total volume of 5 µl by the TaqMan 5’ exonuclease assay using primers and probes supplied by Applied Biosystems. Major and minor allele probes were labeled with 5’ FAM or 5’ VIC fluorophore as reporters (Applied Biosystems). The detection of the probe fluorescence signal was performed by an ABI PRISM 7900 Sequence Detection System (Applied Biosystems). Twelve DNA samples with known genotypes from the CEPH panel were included as positive controls and 8 no template wells were included as negative controls in each plate. No discrepancies were detected in the randomly selected 10% of the samples that were genotyped in duplicate. The additional 2 SNPs were genotyped using the Illumina Bead Array System in Génome Québec Innovation Centre. In the NETT-NAS, the SNPs were genotyped on an Illumina BeadStation 500G System utilizing the GoldenGate assay technology (Illumina Golden Gate Assay, San Diego, CA). Genomic DNA was hybridized with a pool of assay oligonucleotides. Each assay consists of two allele-specific oligonucleotides and one locus-specific oligonucleotide. Allele-specific extension and ligation was performed, followed by PCR of the extended and ligated templates utilizing three universal products. The fluorescently labeled strands were hybridized to a Sentrix Array Matrix (SAM). The SAM is a fiberoptic assembly of arrays, each holding 1536 different oligonucleotide probe sequences, attached to uniquely detectable 3-micron beads that are assembled into microwells at the ends of optical fibers. The SAM was imaged with a BeadArray Reader, a two-channel, 0.8-micron resolution confocal laser scanner. Cy3 and Cy5 intensity values were collected for each sample array and normalized.  176  The normalized intensities were converted to genotypes by GenCall software using a clustering algorithm to define and call genotypes. A1.1.3 Statistical Analysis: Multiple logistic regressions for the two dichotomous outcomes, rate of decline and baseline lung function, were performed to test for the association with IL6 SNPs. These models were tested separately using the data sets corresponding to the outcome variables: rate of decline in lung function and baseline lung function. Confounding factors were evaluated and all models in which SNPs and IL 6 expression levels were compared with rate of decline were adjusted for age, gender, pack-years of smoking and research centre. All models in which SNPs and expression levels were compared with baseline lung function were adjusted for age, gender, pack-years of smoking, research centre, and ∆FEV1/yr (% predicted post). Haplotype association was tested using Hapassoc, a contributed R package available at www.r-project.org. This software performs likelihood inference of trait associations with haplotypes and other covariates for generalized linear models, including linear and logistic regression. Haplotype phase was not inferred and was handled with an expectationmaximization implementation of linear or logistic regression. An additive effect of haplotype on disease was assumed. Haplotypes of five loci in IL6 were considered for potential haplotype associations. All covariates previously considered in the single SNP association linear regression models were included in the linear regression models testing for association of haplotypes and expression levels. Similarly, covariates considered in the single SNP association logistic regression models were included in the logistic regression models testing for haplotype association with the lung function phenotypes of interest.  177  In the LHS, multiple linear regression was also used to test association of IL6 SNPs with IL6 expression level. Since IL6 expression levels were right skewed and residuals resulting from the multiple linear regressions were also right skewed, a log transformation was applied before using it as an outcome, making outcome variables and residuals relatively normal. The log-transformed IL6 expression variable had a tail at the right of the distribution, so four extreme outliers were trimmed from the upper end of the distribution making the distribution more closely fit a normal distribution. Analyses with and without the 4 outliers were conducted and the results were unchanged by the exclusion. Confounding factors used in previous analyses and previously published articles were evaluated as potential confounding factors in the present analysis. These factors included body mass index (BMI), age, gender, pack-years of smoking, and smoking status (continuing smokers, intermittent smokers, and sustained quitters). In the NETT-NAS, testing for Hardy-Weinberg equilibrium, determining allele and genotype frequencies, and performing genetic association analysis with Armitage trend tests was accomplished with SAS Genetics (Cary, NC). Odds ratios for susceptibility to COPD were determined from multivariable logistic regression models with additive genetic coding performed with SAS.  178  A1.2 RESULTS: A1.2.1 Performance of tagSNPs: Haplotypes from all SNPs with minor allele frequency ≥ 10% in 23 CEPH samples were estimated with an expectation-maximization algorithm using the R haplo.stats package. The performance of tagSNPs was investigated by comparing the number of actual haplotypes and number of effective haplotypes (ne) resolved with tagSNPs as compared with that inferred using all SNPs with minor allele frequency ≥ 10% in both the IL6 and CRP genes (6). As shown in Figure A1-1, 70.0% of actual haplotypes and 84.0% of effective haplotypes from all 12 SNPs with a minor allele frequency ≥ 10% were resolved by the five selected IL6 tagSNPs. Therefore, the tagSNPs selected in this study performed very well in capturing common haplotypes.  179  FIGURE LEGENDS:  Figure A1-1 Performance of five tagSNPs in the IL6 gene. The tagSNPs selected are denoted in red font and their alleles are shown in each box. The common allele is shown as a blue box and the rare allele as a yellow box. The selected tagSNPs resolve 70% (7 out of 10) of the actual haplotypes (haplotypes resolved are indicated by red shading) and 84.0% of the effective number (ne) of haplotypes derived from SNPs with minor allele frequency ≥ 10%. Note: 1) Haplotypes were arranged by a phylogenetic tree inferred from MEGA 3.1 software. The bar of 0.05 represents phylogenetic distance, which is a measure of evolutionary divergence between two homologous sequences 2) ne was calculated as ne =  1 where pi is the frequency of the ith haplotype. ∑ pi2 i  3) At position -1479, “I” or “D” denotes CT insertion or deletion. 4) Haplotype frequency was calculated using the expectation-maximization algorithm in Haplo.stats for R.  180  Figure A1-1  0.05 5  -1479  -1462  D  G  D  -598  -174  615  846  1090  1306  1754  1889  3331  5909  Haplotype  Frequency  C  G  G  1  0.291  T  C  G  G  2  0.114  I  G  C  G  G  3  0.050  D  G  C  G  G  4  0.028  I  G  C  G  G  5  0.012  I  G  G  G  G  6  0.065  I  G  G  A  G  7  0.065  I  G  G  A  G  8  0.075  D  G  G  A  A  9  0.023  I  G  G  A  A  10  0.272  181  Table A1-1. TagSNP selection using the LDSelect program and the nomenclature for the SNPs. Bin  SNP ID  SNP  Minor allele  Position in  frequency  Ref Sequence  Position in gene  Position in protein  1  rs1800797  G/A  A: 0.47  1086  -598 (promoter)  -  1  rs1800795*  G/C  C: 0.50  1510  -174 (promoter)  -  1  rs2069832  A/G  G: 0.50  2298  615 (second intron)  -  1  rs2069833  C/T  T: 0.50  2529  846 (second intron)  -  1  rs1474348  C/G  G: 0.50  2773  1090 (second intron)  -  1  rs1474347  G/T  T: 0.50  2989  1306 (second intron)  2  rs1554606  T/G  G: 0.43  3572  1889 (third intron)  -  2  rs2069845*  G/A  A: 0.45  5014  3331 (fourth intron)  -  3  rs2069825*  CT/-  del: 0.46  205  -1479 (promoter)  -  4  rs2069840  C/G  G: 0.37  3437  1754 (third intron)  -  5  rs1818879*  G/A  A: 0.29  7592  5909 (3’ flanking region)  -  6  rs2069827*  G/T  T: 0.11  321  -1363 (promoter)  -  Note: Sites are ordered by linkage disequilibrium, with sites showing similar patterns of genotype put into the same bin. The position in the IL6 gene is numbered by denoting the first nucleotide of the transcript as +1 (position 1684 in the sequence AF372214). All SNPs with minor allele frequency >10% are tagSNPs. One SNP was genotyped in each bin and genotyped SNPs in two nested LHS studies are indicated by *.  182  Table A1-2: Associations of IL6 SNPs with circulating IL6 concentrations Variation studied  Multiple tagSNPs  8 SNPs including -572G/C and -598C/T (in LD with 174G/C, individuals with -598T always have 174C)  13 SNPs including -174G/C  -174G/C  -174G/C  Association  Population  Elderly (Participants of Cardiovascular Health Study)  Yes, -174G/C, P = 0.04 1889G/T, P = 0.03  European American  4714  No  862  Patients with ACS*  Borderline association for -572G/C, P = 0.07 (the CG genotype was associated with higher IL6 levels compared with the GG genotype); No association for -598C/T, P = 0.95  African American Swedish  ACS patients with subsequent cardiovascular events  Yes, -572G/C: P = 0.01 (the CG genotype had higher IL6 levels compared with the GG genotype) No association for other SNPs including -598C/T. No No  Swedish  369  Swedish Caucasian  447 363  African American  100  Chianti population (Caucasian) Italian  266  388  Italian  436  Yes, P < 0.0001, the GG genotype associated with higher plasma IL6 levels compared with -174 C allele carriers Yes, P < 0.0001, the GG genotype associated with higher plasma IL6 levels compared to -174 C allele carriers Yes, P = 0.016, -174C allele carriers have higher IL6 levels compared with the GG genotype  Italian  146  Italian  144  Italian  200  No  Dutch  641  No  US whites (96%)  737  (14)  No  Swedish  208  (15)  Healthy controls Elderly women (>70 years old)  Elderly with preexisting major disease** Elderly without pre-existing major disease Diabetics with peripheral artery disease Diabetics without peripheral artery disease  -174G/C  -174G/C 7 SNPs including -174G/C -174G/C and -572G/C  Sample size  Subjects studied  Myocardial infarction > 55 years old population Healthy women  Myocardial infarction  Yes, heterozygotes associated with high IL6 levels compared with 174GG homozygotes Yes, homozygous -174GG with low IL6 P < 0.05 Yes, P = 0.027 (frequency of -174C allele carriers increased from the second IL6 quartile upward with respect to the bottom quartile) No  2704  Ref.  (7)  (8)  (9)  (10)  (11)  (12)  (13)  183  Note: *ACS = acute coronary syndrome **Pre-existing major diseases were any one of: myocardial infarction, congestive heart failure, peripheral vascular disease, cerebrovascular disease, dementia, chronic pulmonary disease, connective tissue disease, peptic ulcer disease, liver and kidney disease, diabetes, cancer.  184  A1.3 REFERENCES: 1.  2.  3.  4.  5.  6.  7.  8.  9.  10.  11.  12.  Anthonisen, N. R., J. E. Connett, J. P. Kiley, M. D. Altose, W. C. Bailey, A. S. Buist, W. A. Conway, P. L. Enright, R. E. Kanner, P. O' Hara, G. R. Owens, P. D. Scanlon, D. P. Tashkin, and R. A. Wise. 1994. Effects of smoking intervention and the use of an inhaled anticholinergic bronchodilator on the rate of decline of FEV1. The Lung Health Study. JAMA 272(19):1497-505. National Emphysema Treatment Trial Research Group. 1999. Rationale and design of the National Emphysema Treatment Trial (NETT): A prospective randomized trial of lung volume reduction surgery. J Thorac Cardiovasc Surg 118(3):518-28. Fishman, A., F. Martinez, K. Naunheim, S. Piantadosi, R. Wise, A. Ries, G. Weinmann, and D. E. Wood. 2003. A randomized trial comparing lung-volumereduction surgery with medical therapy for severe emphysema. N Engl J Med 348(21):2059-73. Alexeeff, S. E., A. A. Litonjua, D. Sparrow, P. S. Vokonas, and J. Schwartz. 2007. Statin use reduces decline in lung function: VA Normative Aging Study. Am J Respir Crit Care Med 176(8):742-7. Bosse, R., D. Sparrow, A. J. Garvey, P. T. Costa, Jr., S. T. Weiss, and J. W. Rowe. 1980. Cigarette smoking, aging, and decline in pulmonary function: A longitudinal study. Arch Environ Health 35(4):247-52. Carlson, C. S., M. A. Eberle, M. J. Rieder, Q. Yi, L. Kruglyak, and D. A. Nickerson. 2004. Selecting a maximally informative set of single-nucleotide polymorphisms for association analyses using linkage disequilibrium. Am J Hum Genet 74(1):106-20. Walston, J. D., M. D. Fallin, M. Cushman, L. Lange, B. Psaty, N. Jenny, W. Browner, R. Tracy, P. Durda, and A. Reiner. 2007. IL-6 gene variation is associated with IL-6 and C-reactive protein levels but not cardiovascular outcomes in the Cardiovascular Health Study. Hum Genet 122(5):485-94. Malarstig, A., L. Wallentin, and A. Siegbahn. 2007. Genetic variation in the interleukin-6 gene in relation to risk and outcomes in acute coronary syndrome. Thromb Res 119(4):467-73. Walston, J., D. E. Arking, D. Fallin, T. Li, B. Beamer, Q. Xue, L. Ferrucci, L. P. Fried, and A. Chakravarti. 2005. IL-6 gene variation is not associated with increased serum levels of IL-6, muscle, weakness, or frailty in older women. Exp Gerontol 40(4):344-52. Ravaglia, G., P. Forti, F. Maioli, M. Chiappelli, P. Dolzani, M. Martelli, M. Bianchin, E. Mariani, L. Bolondi, and F. Licastro. 2005. Associations of the -174 G/C interleukin-6 gene promoter polymorphism with serum interleukin 6 and mortality in the elderly. Biogerontology 6(6):415-23. Libra, M., S. S. Signorelli, Y. Bevelacqua, P. M. Navolanic, V. Bevelacqua, J. Polesel, R. Talamini, F. Stivala, M. C. Mazzarino, and G. Malaponte. 2006. Analysis of G(-174)C IL-6 polymorphism and plasma concentrations of inflammatory markers in patients with type 2 diabetes and peripheral arterial disease. J Clin Pathol 59(2):211-5. Chiappelli, M., C. Tampieri, E. Tumini, E. Porcellini, C. M. Caldarera, S. Nanni, A. Branzi, D. Lio, M. Caruso, E. Hoffmann, C. Caruso, and F. Licastro. 2005.  185  13.  14.  15.  Interleukin-6 gene polymorphism is an age-dependent risk factor for myocardial infarction in men. Int J Immunogenet 32(6):349-53. Sie, M. P., F. A. Sayed-Tabatabaei, H. H. Oei, A. G. Uitterlinden, H. A. Pols, A. Hofman, C. M. van Duijn, and J. C. Witteman. 2006. Interleukin 6 -174 g/c promoter polymorphism and risk of coronary heart disease: results from the rotterdam study and a meta-analysis. Arterioscler Thromb Vasc Biol 26(1):212-7. Epub 2005 Nov 3. Qi, L., R. M. van Dam, J. B. Meigs, J. E. Manson, D. Hunter, and F. B. Hu. 2006. Genetic variation in IL6 gene and type 2 diabetes: tagging-SNP haplotype analysis in large-scale case-control study and meta-analysis. Hum Mol Genet 15(11):1914-20. Bennermo, M., C. Held, F. Green, L. E. Strandberg, C. G. Ericsson, L. O. Hansson, H. Watkins, A. Hamsten, and P. Tornvall. 2004. Prognostic value of plasma interleukin-6 concentrations and the -174 G > C and -572 G > C promoter polymorphisms of the interleukin-6 gene in patients with acute myocardial infarction treated with thrombolysis. Atherosclerosis 174(1):157-63.  186  APPENDIX B: List of publications, abstracts, and presentations A. Articles submitted or published in refereed journals 1. He JQ, Bossé Y, Laprise C, Paré P, Daley D, Becker A, Chan-Yeung M, Tripp B, Zamar D, James A, Palmer L, Musk B, Hudson T, Lemire M, Sandford A. Novel associations of genetic polymorphisms in the interleukin-1 receptor/Toll-like receptor signaling pathways with atopy and atopic asthma (in preparation). 2. He JQ, Shumansky K, Connett JE, Anthonisen NR, Paré PD, Sandford AJ. Catalase gene polymorphisms and lung function in smoking-induced COPD (in preparation). 3. He JQ, Shumansky K, Sin DD, Man SF, Connett JE, Anthonisen NR, Paré PD, Sandford A. Associations of interleukin-1 cluster gene polymorphisms with plasma CRP concentrations and lung function decline in smoking-induced COPD (submitted). 4. Kasuga I, Hogg JC, Paré PD, Hayashi S, Sedgwick EG, Ruan J, Wallace AM, He JQ, Zhang X, Sandford AJ. Role of genetic susceptibility to latent adenoviral infection and decreased lung function. Respir Med 2009 Nov;103(11):1672-80. 5. Bossé Y, Lemire M, Poon AH, Daley D, He JQ, Sandford A, White JH, James AL, Musk AW, Palmer LJ, Raby BA, Weiss ST, Kozyrskyj AL, Becker A, Hudson TJ, and Laprise C. Asthma and genes encoding components of the vitamin D pathway. Respiratory Research 2009 Oct 24;10(1):98. 6. He JQ, Hallstrand TS, Knight D, Chan-Yeung M, SandfordAJ, Tripp B, Zamar D, Bossé Y, Kozyrskyj AL, James A, Laprise C, Daley D. A Thymic Stromal Lymphopoietin Gene Variant Is Associated with Asthma and Airway Hyperresponsiveness. J Allergy Clin Immunol 2009 Aug;124(2):222-9. 7. Daley D, Lemire M, Akhabir L, Chan-Yeung M, He JQ, McDonald T, Sanford S, Stefanowicz D, Tripp B, Zamar D, Bosse Y, Ferretti V, Montpetit, Tessier MC, Becker A, Kozyrksyj AL, Beilby J, McCaskie PA, Musk B, Warrington N, James A, Laprise C, Palmer L, Pare PD, Hudson TJ. Systemic analyses of genotype-phenotype associations in asthma and atopy in four population samples from Canada and Australia. Hum Genet 2009 May;125(4):445-59. 8. He JQ, Foreman MG, Shumansky K, Zhang X, Akhabir L, Sin DD, Man SF, DeMeo DL, Litonjua AA, Silverman EK, Connett JE, Anthonisen NR, Wise RA, Paré PD, Sandford AJ. Novel associations of IL6 genetic variants with lung function decline and COPD. Thorax 2009 Aug; 64(8):698-704.. 9. Yuan J, Liu Z, Lim T, Zhang H, He J, Walker E, Shier C, Wang Y, Su Y, Sall A, McManus B, Yang D. CXCL10 inhibits virus replication through recruitment of natural killer cells in coxsackievirus B3-induced myocarditis. Circ Res 2009 Mar 13;104(5):628-38. 10. He JQ, Sandford AJ, Wang I, Stepaniants S, Knight DA, Kicic A, Stick SM, Paré PD. Selection of housekeeping genes for real-time PCR in atopic human bronchial epithelial cells. Eur Respir J 2008 Sep;32(3):755-62.  187  11. He JQ, Shumansky K, Connett JE, Anthonisen NR, Peter D Paré and Sandford A. Association of genetic variations in the CSF2 and CSF3 genes with lung function in smoking-induced COPD. Eur Respir J 2008 Jul;32(1):25-34. 12. He JQ, Shumansky K, Zhang X, Connett JE, Anthonisen NR and Sandford A. Polymorphisms of interleukin-10 and its receptor and lung function in COPD. Eur Respir J 2007 Jun;29(6):1120-26. 13. Zhang X, He JQ, Ding L, Paré PD, Sandford AJ. Promoter polymorphism and expression of beta-arrestin 2 in neutrophils. Clin Chim Acta. 2007; 385(1-2):79-80. 14. Zhang X, Azer S, Connett JE, Anthonisen NR, He JQ, Peter D Paré and Sandford AJ. Association of Hck genetic with gene expression and COPD. Hum Genet 2007 Jan;120(5):681-690. 15. Tanaka G, Sandford AJ, Burkett K, Connett JE, Anthonisen NR, Pare PD, He JQ. Tumour necrosis factor and lymphotoxin A polymorphisms and lung function in smokers. Eur Respir J 2007 Jan;29(1):34-41. 16. He JQ, Burkett K, Connett JE, Anthonisen NR, Paré PD and Sandford A. IFN gamma and its interaction with smoking are associated with lung function in smokers. Hum Genet 2006; May;119(4):365-75. 17. Wallace AM, He JQ, Burkett KM, Ruan J, Connett JE, Anthonisen NR, Pare PD, et al. Contribution of alpha- and beta-defensins to lung function decline and infection in smokers: an association study. Respir Res 2006; 7: 76-84. 18. Tebbutt SJ, He JQ, Burkett KM, Ruan J, Opushnyev IV, Tripp BW, et al. Microarray genotyping resource to determine population stratification in genetic association studies of complex disease. Biotechniques 2004;37:977-985. 19. He JQ, Connett JE, Anthonisen NR, Paré PD, Sandford AJ. Glutathione S-transferase variants and their interaction with smoking on lung function. Am J Respir Crit Care Med 2004;170(4):388-94. 20. He JQ and Sandford AJ. Pharmacogenomics of COPD. Current Pharmacogenomics 2003; 1:229-43. 21. He JQ, Chan-Yeung M, Becker AB, Dimich-Ward H, Ferguson AC, Manfreda J, Wasson WT, et al. Genetic variants of the IL13 and IL4 genes and atopic diseases in at-risk children. Gene and Immunity 2003; 4(5):385-9. 22. He JQ, Connett JE, Anthonisen NR, Sandford AJ. Polymorphisms in the IL13, IL13RA1, and IL4RA genes and rate of decline in lung function in smokers. Am J Respir Cell Mol Biol 2003 Mar;28(3):379-85. 23. He JQ, Ruan J, Connett JE, Anthonisen NR, Pare PD, Sandford AJ. Antioxidant gene polymorphisms and susceptibility to a rapid decline in lung function in smokers. Am J Respir Crit Care Med 2002 Aug 1;166(3):323-8. 24. He JQ, Gaur LK, Nelson K, Stempien-Otero A, Levy WC, O’Brien KD and Reiner AP. Genetic variants of the hemostatic system and development of transplant coronary artery disease. J Heart Lung Transplant 2002;21(6):629-36.  188  25. Joos L, He JQ, Shepherdson MB, Connett JE, Anthonisen NR, Paré PD and Sandford AJ. The role of metalloproteinase polymorphisms in the development of chronic obstructive lung disease. Hum Mol Genet 2002, 11(5):569-76. 26. He JQ, Joos L, Sandford, AJ. Recent developments in the genetics of asthma. Pharmacogenomics 2001 Nov;2 (4):329-39.  B. Abstracts/Presentations 1. He JQ, Hallstrand TS, Knight D, Chan-Yeung M, Sandford AJ, Tripp B, Zamar D, Bossé Y, Kozyrskyj AL, James A, Laprise C, Daley D. A Thymic Stromal Lymphopoietin Gene Variant Is Associated with Asthma and Airway Hyperresponsiveness. 2009 Annual Meeting of American Society of Thorax, San Diego, USA. 2. He JQ, Bossé Y, Laprise C, Paré P, Sandford A, Becker A, Chan-Yeung M, Tripp B, Zamar D, James A, Palmer L, Musk B, Hudson T, Lemire M, Daley D. Novel associations of genetic polymorphisms in the interleukin-1 receptor/Toll-like receptor signaling pathways with atopy and atopic asthma. 2009 Annual Meeting of American Academy of Asthma, Allergy and Immunology, Washington, DC, USA. 3. He JQ, Shumansky K, Connett JE, Anthonisen NR, Paré PD, Sandford AJ, Wise RA. Catalase gene polymorphisms and lung function in smoking-induced COPD. Proc Am Thorac Soc 2008;2:A337. 4. He JQ, Yuan J, Paré D, Sandford A Promoter polymorphisms of the CSF2 gene are associated with its transcription. Presented at the annual meeting of The American Society of Human Genetics, October 24, 2008, Philadelphia, PA. Available from http://www.ashg.org/genetics/ashg08s/index.shtml. 5. He JQ, SandfordAJ, Wang I, Stepaniants S, Knight DA, et al. Selection of housekeeping genes for real-time PCR in atopic human bronchial epithelial cells. Chest 2007:132 (4): 601a. 6. Zhang J, He J, Sandford A, Paré P. Identification of functional SNPs in TIM3 promoter region. Presented at the annual meeting of The American Society of Human Genetics, October 24, 2007, San Diego, California. Available from http://www.ashg.org/genetics/ashg07s/index.shtml. 7. Daley D, Lemire M, Paré PD, Sanford AJ, Kozyrskyj AL, Laprise C, Bosse Y, Montpetit A, Becker A, Zamar D, Tripp B, He J, Tremblay K, James A, Musk AW, Palmer LJ, Hudson, TJ. Candidate Genes for Asthma and Atopy. Presented at the annual meeting of The American Society of Human Genetics, October 24, 2007, San Diego, California. Available from http://www.ashg.org/genetics/ashg07s/index.shtml. 8. He JQ, Shumansky K, Sin DD, Man SF, Connett JE, Anthonisen NR, Paré PD, Sandford A. Associations of interleukin-1 cluster gene polymorphisms with plasma CRP concentrations and lung function decline in smoking-induced COPD. Proc Am Thorac Soc 2007;3:A654. 9. He JQ, Shumansky K, Connett JE, Anthonisen NR, Peter D Paré and Sandford A. Association of genetic variations in the G-CSF gene with lung function decline and cross-sectional levels in smoking induced COPD. Presented at the annual meeting of The American Society of Human Genetics, October 12, 2006, New Orleans. Available from http://www.ashg.org/genetics/ashg06s/index.shtml. 189  10. He JQ, K Shumansky, Connett JE, Anthonisen NR, Paré PD and Sandford A. Association of Genetic Variants in the IL6 and CRP Genes and Baseline of Lung Function in Smokers. Proc Am Thorac Soc 2006;3:A503. 11. Tanaka G, Sandford A, Burkett K, Connett JE, Anthonisen NR, Paré PD and He JQ. Heme Oxygenase-1 Polymorphisms and Lung Function in Smokers. Proc Am Thorac Soc 2006;3:A128. 12. He JQ, Chan-Yeung M, Becker A, Dimich-Ward H, Ferguson AC, Manfreda J, Watson WTA, Sandford A. The Gln551Arg polymorphism of the IL4RA gene is associated with atopic dermatitis in a prospective study. Presented at 2005 annual meeting of American Society of Human Genetics, Salt Lake city, USA. 13. He JQ, Chan-Yeung M, Becker A, Dimich-Ward H, Ferguson AC, Manfreda J, Watson WTA, Sandford A. The Ile117Thr polymorphism of the GM-CSF gene is associated with atopic dermatitis in a prospective study Accepted for presentation at 2005 annual meeting of Canadian Society of Allergy and Clinical Immunology, Winnipeg, Canada. 14. He JQ, Burkett K, Connett JE, Anthonisen NR, Pare PD, Sandford AJ. Association of Tumor Necrosis Factor Gene with Lung Function and Its Decline in Smokers. American Thoracic Society Meeting. Proc Am Thorac Soc 2005;2:A398. 15. He JQ, Pare PD, Man SFP, Sin DD, Connett JE, Anthonisen NR, Sandford AJ. Genetic Variants in Interleukin 6 (IL6) are Associated with Lung Function Decline but Not with the C-Reactive Protein Level in Smokers. American Thoracic Society Meeting. Proc Am Thorac Soc 2005;2:A398 16. He JQ, Chan-Yeung M, Becker A, Dimich-Ward H, Ferguson AC, Manfreda J, Watson WTA, Sandford A. GSTT1 and GSTM1 Polymorphisms and the Risk of Developing Asthma and Other Atopic Diseases in Childhood. American Thoracic Society Meeting. Proc Am Thorac Soc 2005;2:A30 17. He JQ, Paré PD, Connett JE, Anthonisen NR, and Sandford AJ. Heme oxygenase-1 (HO-1) gene is associated with the rate of decline of lung function in smokers. American Society of Human Genetics. Toronto, Canada; 2004:406:2232 18. Burkett KM, He J-Q, Connett JE, Anthonisen NR, Paré PD, Sandford AJ. Interferon gamma gene and its interaction with smoking are associated with lung function in smokers. American Society of Human Genetics. Toronto, Canada; 2004:417:2303 19. He JQ, Connett JE, Anthonisen NR, Paré PD, and Sandford A. No Association between IL10RA SNPs and the Rate of Decline of Lung Function in Smokers. Am J Respir Crit Care Med 2004:169(7):A527. 20. He JQ, Connett JE, Anthonisen NR, Paré PD, and Sandford A. Genetic Variations in the Interferon Gamma (IFNG) Gene Are Not Associated with the Rate of Decline of Lung Function in Smokers. Am J Respir Crit Care Med 2004:169(7):A506. 21. He JQ, Pare PD, Connett JE, Anthonisen NR, Paré PD, and Sandford A. Lack Association of Genetic Variations in the GM-CSF Gene with Lung Function. Am J Respir Crit Care Med 2004:169(7):A664. 22. Clute IV, SP, Zhang Y, He J, Ferrell RE, Maher BS, Sandford A, Paré P, and Choim, AM. Haplotype Analyses of Racial Differences in Polymorphisms of the HemeOxygenase 1 Gene Enhancer Regions, Promoter Sequence, and Microsatellite Marker. Am J Respir Crit Care Med 2004:169(7):A58  190  23. He JQ, Connett JE, Anthonisen NR, Paré PD and Sandford A. Gender-specific association between IL10 polymorphisms and the rate of decline of lung function in smokers. Am J Hum Genet 2003:73(5 Suppl): 395. 24. He JQ, Nelson K, Gaur LK. Cytokine gene polymorphisms are associated with hepatitis C virus induced end-stage liver disease. Hepatology 38(4 Suppl. 1): 536A, 2003. 25. He JQ, Nelson K, Gaur LK. Donor-recipient sharing of HLA class II alleles predicts earlier recurrence of hepatitis C following liver transplantation. Human Immunology 2003:64(10 Suppl 1): S69. 26. He JQ, Nelson K, Gaur LK. Association of HLA-DRB1 and DQB1 polymorphisms with severe hepatitis C virus infection. Hepatology 38(4 Suppl. 1): 528A-529A, 2003. 27. He JQ, Nelson K, Gaur LK. Association of HLA class I alleles with recurrence of hepatitis C virus infection after liver transplantation. Human Immunology 2003:64(10 Suppl 1): S69. 28. He JQ, Nelson K, Gaur LK. Association of TNFα and IL10 promoter polymorphisms with rejection after liver transplantation in Caucasian HCV patients. Human Immunology 2003:64(10 Suppl 1): S52. 29. Tebbutt SJ, He JQ, Sandford AJ, Russell JA, Paré P and Walley KR. Microarraybased genotyping to determine population stratification in gene association studies of complex disease. “From Genome to Disease, a symposium of high throughput biology” July 2003, Maryland. 30. He JQ, Connett JE, Anthonisen NR Kelly Burkett, Peter D Paré and Sandford A. The effects of polymorphisms of the GSTM1, T1 and P1 genes and their interaction with cigarette exposure on lung function. Am J Respir Crit Care Med 2003; 167(7): A744 31. He JQ, Peter D Paré , Connett JE, Anthonisen NR and Sandford A. Genetic variants of the GM-CSF gene are not associated with the rate of decline of lung function in smokers. Am J Respir Crit Care Med 2003; 167(7): A579 32. He JQ, Ruan J, Becker A, Chan-Yeung Y, Paré P, Sandford A. Polymorphisms of the colony-stimulating factor 2 (CSF2) gene and the development of atopy and atopic diseases in at-risk children. Chest 2003 Mar;123(3 Suppl):438S 33. He JQ, Ruan J, Connett JE, Anthonisen NR, Paré P, Sandford A. Antioxidant gene polymorphism and susceptibility to chronic obstructive pulmonary in smokers. Am J Respir Crit Care Med 2002; 163(5): A591 34. He JQ, Paré P, Connett JE, Anthonisen NR, Sandford A. Polymorphisms of the interleukin-8 and CXCR receptor 1 and 2 genes and rate of decline of lung function in smokers. Am J Respir Crit Care Med 2002; 163(5): A442 35. He JQ, Paré P, Becker A, Chan-Yeung Y, Sandford A. The Arg130Gln polymorphism in the IL-13 gene and the risk of allergic disorders in at-risk infants. Am J Respir Crit Care Med 2002; 163(5): A809 36. Joos L, He JQ, Paré P, Connett JE, Anthonisen NR, Sandford A. Association of MMP-1 and MMP-12 haplotypes with rate of decline of lung function in smokers. Am J Respir Crit Care Med 2002; 163(5): A462 37. Wallace A, He JQ, Paré P, Connett JE, Anthonisen NR, Sandford A. Human neutrophil defensins, HNP-1 and HNP-3 and rate of decline of lung function. Am J Respir Crit Care Med 2002; 163(5): A443  191  38. He JQ, Connett JE, Anthonisen NR, Paré P, Sandford A. The G2044A polymorphism in the IL-13 gene is not associated with susceptibility to COPD. Am J Respir Crit Care Med 2001; 163(5): A907  C. Book Chapters  1. He JQ, Sandford AJ. COPD and asthma, genetics. In: Detlev Ganten and Klaus Ruckpaul., editors. Encyclopedic Reference of Genomics and Proteomics in Molecular Medicine. pp. 333-338. Spring Verlag Heidelberg, 2006 2. He JQ, Tebbutt SJ and Paré PD. Genetics of COPD. In: Barnes PJ and Hansel TT., editors. Recent Advances in Pathophysiology of COPD. pp.1-20. Birkhäuser Publishing, Basel , Switzerland, 2004 3. He JQ, Kasuga I and Paré PD. Genetics of COPD. In Bartolome Celli. Pharmacotherapy of COPD. pp. 119-144. Marcel Dekker, Inc., New York, 2004.  D. Awards 1. Izaak Walton Killam Memorial Predoctoral Scholarship Award, 2007-2009 2. Senior Graduate Studentship Award, Michael Smith Foundation for Health Research, 2007-2010 3. The Cornelis van Breemen Outstanding Young Investigator Award, the James Hogg iCAPTURE Centre, St. Paul’s Hospital, UBC, 01/2007-12/2007 4. University Graduate Fellowship, , University of British Columbia, 2006-2007 5. Graduate Student Entrance Award, University of British Columbia, Vancouver, 2005  192  APPENDIX C: Research Ethics Board Certificates of Approval  193  194  195  

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