Genetic heterogeneity of asthma phenotypes identified by a clustering approach

Transcription

1 Online Supplementary Material Genetic heterogeneity of asthma phenotypes identified by a clustering approach Valérie Siroux 1,2 *, Juan R González 3,4,5 *, Emmanuelle Bouzigon 6,7, Ivan Curjuric 8,9, Anne Boudier 1,2, Medea Imboden 8,9, Josep Maria Anto 3,5,10,11, Ivo Gut 12,13, Deborah Jarvis 14, Mark Lathrop 7,12, Ernst Reidar Omenaas 15,16, Isabelle Pin 1,2,17, Mathias Wjst 18,19, Florence Demenais 6,7, Nicole Probst-Hensch 8,9, Manolis Kogevinas 3,5,11,20, Francine Kauffmann 21,22 * equal first 1 Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, Inserm, U823, Grenoble, France 2 Univ Joseph Fourier, Grenoble, France 3 Centre for Research in Environmental Epidemiology (CREAL), Barcelona; 4 Department of Mathematics, Autonomous University of Barcelona; 5 CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain 6 Inserm, UMRS-946, F Paris, France 7 Univ Paris Diderot, Sorbonne Paris Cité, Institut Universitaire d Hématologie, F-75007, Paris, France 8 Swiss Tropical and Public Health Institute, Basel, Switzerland 9 University of Basel, Basel, Switzerland 10 Universitat Pompeu Fabra. Departament de Ciències Experimentals i de la Salut, Barcelona; 1

2 11 IMIM (Hospital del Mar Medical Research Institute), Barcelona 12 CEA-Centre National de Genotypage, 2 rue Gaston Cremieux, Evry France 13 Centro Nacional de Analisis Genomico, C/Baldiri Reixac 4, Barcelona, Spain. 14 Respiratory Epidemiology and Public Health, Imperial College, and MRC-HPA Centre for Environment and Health, London, United Kingdom 15 Bergen Respiratory Research Group, Institute of Medicine, University of Bergen, Bergen, Norway 16 Center for Clinical Research, Haukeland University Hospital, Bergen, Norway 17 Department of pediatrics, CHU Grenoble, France 18 Comprehensive Pneumology Center (CPC), Helmholtz Zentrum Muenchen, German Research Center for Environmental Health (GmbH), Neuherberg, Germany 19 Institute of Medical Statistics and Epidemiology, Klinikum rechts der Isar der TU Muenchen, Munchen, Germany 20 National School of Public Health, Athens, Greece 21 Inserm, U1018, CESP Centre for research in Epidemiology and Population Health, Respiratory and environmental epidemiology Team, Villejuif, France 22 Université Paris Sud, UMRS 1018, Villejuif, France 2

3 Methods Cluster analysis Personal characteristics (age and sex), age at asthma onset, respiratory symptoms over the past 12 months (woken up by attack of coughing, asthma symptom score combining 5 symptoms (wheeze and breathless, woken up with a feeling of chest tightness, attack of shortness of breath at rest, attack of shortness of breath after exercise and woken by attack of shortness of breath) as previously proposed [1], chronic cough or phlegm, asthma attacks), asthma exacerbation (either hospitalisation or oral steroids used in the past 12 months), allergic characteristics (rhinitis, eczema, atopy (defined by at least one positive skin prick test to 11 aeroallergens in EGEA, specific IgE to cat, Dermatophagoides pteronyssinus, Cladosporium and timothy grass in ECRHS and specific IgE to cat, Dermatophagoides pteronyssinus, and timothy grass in SAPALDIA), total IgE), lung function (FEV1%predicted) and bronchial hyperresponsiveness to methacholine (PD20 < 1mg) have been considered in the LCA model. Daily use of asthma medication was not included in the present LCA analysis as it was unavailable in SAPALDIA2 and was highly related to several variables already included in the model (presence/absence of an asthma attack in the past 12 months, asthma symptoms, asthma exacerbations). To address to which extent the lack of asthma treatment variable in the classification may have impacted the classification, the 14-variable model was compared to a 15-variable model including a 3-class asthma treatment variable (No treatment, other than inhaled corticosteroids (ICS), daily ICS, as previously used [2]) in ECRHSII and EGEA2; overall 87.0% of the subjects were assigned in the same cluster with the two models. 3

4 Genotypic data The subjects were genotyped in the framework of the European GABRIEL consortium. Genotyping was carried out using the Illumina Human610 quad array at the French national genotyping centre (CNG). Stringent quality control criteria were applied [3]. Family relationships were confirmed or revised based on the results of an identity-by-state (IBS) analysis. An ancestry analysis was carried out and putative non-european samples were eliminated from subsequent analyses. The genetic association analysis was restricted to a reliable collection of SNPs fulfilling the following quality control criteria in each study: (1) genotype missing rate <3 %; (2) minor allele frequency 5% in controls; (3) consistency with Hardy-Weinberg equilibrium by a 1 degree-of-freedom goodness-of-fit test in controls (p>10-4 ). 4

5 Results Asthma phenotypes identified by latent class analysis LCA models with 1 to 6 clusters were conducted and compared using the BIC criteria. The BIC observed for each model are presented in Figure E2. A really small BIC decrease (in absolute numbers) was observed between the 4-cluster model and the 5-cluster model as compared with change in BIC observed with the model with lower number of clusters. In order to choose between the 4-cluster model and the 5-cluster model, both models were developed and compared : 91% of the subjects assigned in the phenotype A in the 4-class model were also assigned in the phenotype A in the 5-class model; these results for phenotypes B, C and D were 93%, 97% and 84%. The 5-class model lead to the identification of a fifth class representing only 5% of the population. This additional phenotype E was mainly composed by older men with airflow limitation. The most parsimonious 4-class model was retained first because the BIC were very similar between the 4-class and the 5-class model, indicating that not much was gained by adding a further class and secondly because the 5-class model leads to the identification of a small class (5% of the population under study) preventing from conducting GWAS on this low prevalent phenotype. 5

6 References 1. Sunyer J, Pekkanen J, Garcia-Esteban R, Svanes C, Kunzli N, Janson C, de MR, Anto JM, Burney P. Asthma score: predictive ability and risk factors. Allergy 2007; 62: Siroux V, Basagana X, Boudier A, Pin I, Garcia-Aymerich J, Vesin A, Slama R, Jarvis D, Anto J, Kauffmann F, Sunyer J. Identifying adult asthma phenotypes using a clustering approach. Eur Respir J 2011; 38: Imboden M, Bouzigon E, Curjuric I, Ramasamy A, Kumar A, Hancock DB, Wilk JB, Vonk JM, Thun GA, Siroux V, Nadif R, Monier F, Gonzalez JR, Wjst M, Heinrich J, Loehr LR, Franceschini N, North KE, Altmuller J, Koppelman GH, Guerra S, Kronenberg F, Lathrop M, Moffatt MF, O'Connor GT, Strachan DP, Postma DS, London SJ, Schindler C, Kogevinas M, Kauffmann F, Jarvis DL, Demenais F, Probst-Hensch NM. Genome-wide association study of lung function decline in adults with and without asthma. J Allergy Clin Immunol 2012; 129:

7 Figure legend Figure E1 : Quantile - Quantile plots for A) phenotype A, B) phenotype B, C) Phenotype C and D) phenotype D. Figure E2 : Bayesian information criterion for LCA models with 1 to 6 clusters Figure E3: Regional association plots for each region identified in the GWAS analyses. Chromosome position (NCBI build 36.3) and recombination rate (hg18 build). The sentinel SNP is represented as a diamond and r2 for SNPs to the sentinel SNP (HapMap CEU phase II). A) ALCAM region with phenotype D, B) CD200 region with phenotype D, C) GRIK2 region with phenotype A, D) Region on chromosome 7 with phenotype A, E) LRRC6 region with phenotype A, F) SBF2 region with phenotype A Figure E4 : Forest plot for the most significant SNPs within each gene identified in the GWAS. A) Phenotype D and rs ; B) Phenotype D and rs ; C) Phenotype D and rs ; D) Phenotype A and rs279931; E) Phenotype A and rs ; F) Phenotype A and rs ; G) Phenotype A and rs

8 Table E1: Description of the population with asthma in EGEA2, ECRHSII and SAPALDIA2 population ECRHS II n=1895 EGEA2 n=641 SAPALDI A2 n=465 P value adjusted on age and sex Age 40, % < Sex, men, % < Age of asthma onset: 4 years, % ]4-16] years, % >16 years, % < Woken by cough last 12m, % Asthma symptom score, last 12 months 0 symptom, % 1 or 2 symptoms, % 3 symptoms, % < Chronic cough or phlegm, % Asthma attack, last 12 months, % < Exacerbation, last 12 months, % < Eczema, % Rhinitis, % < Atopy*, % < Total IgE 100 IU/ml, % < FEV 1 < 80% predicted, % BHR, PD20 1mg, % < *assessed with skin prick tests or specific IgEs 8

9 Table E2: Description of the smoking status and the asthma treatment in the past 3 months for each LCA-derived asthma phenotype Phenotype Phenotype B Phenotype C Phenotype D A % (n) % (n) % (n) % (n) Smoking status* Never smokers Past smokers Current smokers 40.8 (225) 33.9 (187) 25.4 (140) 50.3 (550) 25.3 (276) 24.4 (267) 48.4 (388) 29.4 (236) 22.2 (178) 44.9 (240) 29.3 (157) 25.8 (138) In ECRHSII and EGEA2 Asthma treatment 3 months** No treatment Other than daily ICS 67.4 (257) 22.6 (86) 10.0 (38) 62.5 (524) 25.1 (211) 12.4 (104) 13.6 (80) 60.8 (358) 25.6 (151) 23.1 (84) 43.8 (159) 33.1 (120) Daily ICS *p chi2 = **p chi2 <

10 Table E3 : Fisher p-value for SNPs retained in the GWAS Chr Gene Rs number Position Ref/alt all.* Alt all. freq Fisher p-value (asthma phenotype vs control) Phenotype A Phenotype B Phenotype C Phenotype D 3 ALCAM rs G/A e-05 3 ALCAM* rs A/G e-05 3 ALCAM* rs G/A e-05 3 CD200* rs C/T e-04 6 GRIK2 rs A/G e LOC401410* rs A/G e LOC401410* rs A/G e LOC401410* rs T/C e LOC401410* rs T/C e LRRC6 rs C/T e LRRC6 rs G/A e SBF2 rs C/T e SBF2 rs C/T e *reference versus alternative allele 10

11 Table E4: Linkage disequilibrium assessed with r² between SNPs within the same gene identified in the GWAS rs rs rs rs rs rs rs rs rs rs rs rs Alcam rs Alcam rs Alcam rs LOC rs LOC rs LOC rs LOC rs LRRC6 rs LRRC6 rs SBF2 rs SBF2 rs SBF2 rs

12 Table E5: P-values of association from the GWAS analysis using LCA probabilities (linear model). This corresponds to a sensitivity analysis of p-values obtained in Table 3 from main manuscript. P value Chr Gene Rs number Phenotype Phenotype Phenotype Phenotype A B C C 3 ALCAM rs e-7 3 ALCAM rs e-6 3 ALCAM rs e CD200 rs e-8 6 GRIK2 rs e LOC rs e LOC rs e LOC rs e LOC rs e LRRC6 rs e LRRC6 rs e SBF2 rs e SBF2 rs e

13 Figure E1 : Quantile - Quantile plots for A) phenotype A B) phenotype B C) Phenotype C and D) phenotype D. 13

14 14

15 Figure E2 : Bayesian information criterion for LCA models with 1 to 6 clusters 15

16 Figure E3: Regional association plots for each region identified in the GWAS analyses. Chromosome position (NCBI build 36.3) and recombination rate (hg18 build). The sentinel SNP is represented as a diamond and r2 for SNPs to the sentinel SNP (HapMap CEU phase II). A) ALCAM region with phenotype D, B) CD200 region with phenotype D, C) GRIK2 region with phenotype A, D) Region on chromosome 7 with phenotype A, E) LRRC6 region with phenotype A, F) SBF2 region with phenotype A A) B) 16

17 C) 17

18 D) E) F) 18

19 19

20 Figure E4 : Forest plot for the most significant SNPs within each gene identified in the GWAS. A) Phenotype D and rs B) Phenotype D and rs C) Phenotype D and rs

21 D) Phenotype A and rs E) Phenotype A and rs F) Phenotype A and rs G) Phenotype A and rs