Strategies used to identify genes causing? (associated with) asthma or allergy

1. Strategies used to identify genes causing? (associated with) asthma or allergy Pin, V. Siroux, INSERM U 823. Grenoble, France E. Bouzigon INSERM U 794. Paris, France

2. Objectives of genetic analysis • Discover new genes and pathways • Genotype/phenotype analysis Refining phenotypes can help in gene identification PHENOTYPESGENES Identification of genes may help in isolating phenotypic entities • Pharmacogenetics to improve the adaptation of the treatment to the individualized patient • Predictive medicine?

3. Asthma: a complex phenotype «  Not a single disease entity but made up of various overlapping phenotypes … in people with different genetic predisposition & susceptible to different environmental triggers » OR «  A symptom (as fever): the clinical manifestation of several distinct diseases »  (F. Martinez)  Wenzel, Lancet, 2006

4. Biological & physiological « intermediate » phenotypes involved in the pathological process G0 G1 G2 G3 G4 G5 ASTHMA E0 IgE Atopy EOS BHR FEV1 (SPT/ sIgE) E2 E1 E3

5. Polymorphism: genetic variant • Single nucleotide • polymorphism: SNP • Microsatellites • Haplotype: combination of alleles in different loci on the same Xme • HapMap project: • catalogue of the most frequent genetic variations (nature, variants, position, distribution) in several human populations

6. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X Y 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X Y Strategies used to identify genes involved in asthma-related phenotypes No Hypothesis Genome-wide screen approach Genome-wide association studies ~ 300 000 genetic markers (SNP) Linkage studies ~ 400 genetic markers (microsatellites) Fine mapping Associations Gene discovery Hypothesis-driven Biological studies Candidate gene approach

7. Genome linkage screen • To identify genomic regions shared by relatives (sibs) who present phenotypic similarities • Genetic markers (micro satellites ~ 500) disseminated within the whole genome • Possibility of fine localization + positional cloning for precise genes identification • Advantages • Identify new genetic regions • Identify regions with large phenotypic effects • Limitations • Family designs: need to examine and genotype all family members • Screened regions include hundreds of genes • Statistical methods • LOD (logarithm of the odds) score to calculate linkage distance

8. > 20 genome screens conducted to date • Populations: • Europeans+++, Australians, North-Americans, Chinese, Japanese Regions most often replicated across populations Phenotype linked to several regions: polygenic? One region linked to several phenotypes: onepleiotropy gene or several genes in the same region?

9. EGEA STUDYMulti-center french study (5 cities) The EGEA was designed to identify the genetic and environmental factors of asthma, BHR and atopy It includes family data & case-control data. 388 families 416 controls DATA Collected: Questionnaire:information on respiratory and allergic symptoms, family history and exposure to environmental factors Clinical/biological/functional tests:Skin prick tests to 11 allergens (SPT), MultiRAST Phadiatop test, total IgE, eosinophils, spirometry, methacholine bronchial challenge test

10. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X Y FEV1 SPTQ 21q21 SPT 17q22 IgE 12p13 FEV1 6q14 MultiRAST FEV1 IgE Asthma EOS SPTQ SPT BR GENOME SCANOF 295EGEAFAMILIES for 8 asthma-related phenotypes Bouzigon et al, Hum Mol Genet 2004

11. Candidate gene approach • Candidate genes chosen: • Physiopathology, biology of the disease: SNP inside genes or promotor regions, functional or in LD with functional PMP • Within linkage regions • Advantages • May detect genes with smaller effects • Case-control study design easier to conduct and less expensive • Increased power • Biological plausability • Limitations • Limited number of genes tested • Needs high density of markers • Population stratification in case control design: • Needs replication studies in other populations • Statistical methods • case/control analysis. Family-based analysis (TDT: transmission of heterozygote parental alleles to sick children) • Need to take into account multiple testing

12. Candidate gene approach > 500 association studies of asthma phenotypes (Ober & Hoffjan 2006) 118 genes associated to asthma or atopy phenotypes 54 genes found in 2 to 5 independent studies 15 genes found in 6 to 10 independent studies 10 genes found in > 10 independent studies IL4, IL13, CD14, IL4RA, ADRB2, HLA-DRB1, HLA-DQB1, TNF, FCER1B, (ADAM33)

13. Positional cloning: combination of linkage and association studies; example of ADAM33 • ADAM33, (Nature 2002; 418; 426) • 460 families, asthma + BHR, 20p13: D20S482, LOD score 3,93 • 40 genes, 135 SNP on 23 genes • SNPs in ADAM33 (A Disintegrine And Metalloprotease) • Replications: confirmation of • relationship between 2 SNPs and asthma (Meta-analysis, Blakey. Thorax 2005) • relationship between SNPs and accelerated decline in lung function in asthmatics (Jongepier. Clin Exp Allergy 2004)and in the general population (Van Diemen. AJRCCM 2005) • Expression: bronchial muscle, pulmonary fibroblast • Effect on remodeling of the airways?

14. Other asthma genes discovered by positional cloning • PHF11 (13q14) Nat Genet 2003; 34: 181-6 associated with FEV1 and IgE • DPP10 (2q14-2q32) Nat Genet 2003; 35:258-63 associated with asthma & atopy • GPRA (7p) Science2004; 304:300-30 associated with IgE and asthma (replication) • HLAG (6p) Am J Hum Genet 2005; 76:349-357 associated with asthma & BHR • CYFIP2 (5q33) Am J Resp Crit Care Med 2005 associated with atopic asthma • IRAKM (12q13-24) Am J Human Genet 2007 associated with early onset asthma

15. How to progress further to disentangle the complex mechanisms involved ? • Improve phenotype definitions: categorical phenotypes, sub-phenotypes • Take into account modifiers of gene expression • Environment • Gene by gene interaction • Epigenetics • Use new technologies: genome wide association studies in the context of large scale collaborative studies

16. Improving phenotype definition: Categorical phenotype instead of binary phenotype Asthma: difficult to define • Consider the whole spectrum of disease expression from mild to severe + unaffecteds Build asthma severity score & asthma score from clinical items and treatment asthma severity score : 1 to 4 asthma score : 0 to 4 (0 = unaffected) Phenotypes Region Position LOD p-value Asthma score18p11 41.2 2.40 0.0004 Asthma severity score 2p23 47.4 1.80 0.002 %FEV11p36 4.2 1.52 0.004  2q36 221.1 1.59 0.003 6q14 89.8 1.64 0.003 Use of asthma score instead of binary phenotype  new regions Different genetic components underlie disease spectrum, asthma severity and FEV1. Bouzigon et al, Eur Respir J 2007

17. Improving phenotype definition: considering sub-phenotypes • Genome screen (EGEA)(Bouzigon. Hum Mol Genet 2004, Dizier. Gen Immun 2005) • 1p31 linked to asthma (AST) or allergic rhinitis (AR) (p=0.005) • Stronger linkage signal for AST + AR (p=0.0002) • Significant test for heterogeneity between ‘one disease phenotype’ vs ‘2 diseases’ phenotype (Dizier. Hum Hered 2007) • Linkage to AS + AR (MLS = 3.05; p= 0.0008) • No linkage to AST only or AR only (MLS = 0) Asthma + allergic rhinitis: a phenotypic entity determined by gene(s) on 1p31?

18. Gene by environment interactions • CD14 and exposure to LPS • Polymorphism of the CD14 gene promotor :-159 C T • TT:  sCD14 in serum &  IgE(Baldini 1999) • Effet of the genetic variant varies according to the level of exposure • low exposure: TT protects from allergy or asthma • high exposure: TT increases the risk of atopy (Eder JACI 2005) • Glutathione S transferase and exposure to ETS • Deficient variants of the GSTM1 and GSTT1 genes are associated with increased asthma risk and descreased lung function in children exposed to ETS, but not in those not exposed (Kabesch. Thorax 2004)

19. Gene by gene interactions • Sample of 1120 children 9-11 years from the general population • SNPs of genes involved in the IgG-IgE switch: Il4, Il13, Il4-αR, STAT6 • Increased risk of asthma with combination of alleles of 3 SNPs than isolated ones. Kabesch JACI 2006, modified by Vercelli

20. Genome wide association studies • New technologies available: genotyping 300,000 – 500,000 SNPS to conduct GWA • Dense sets of SNPs to survey the most common genetic variants covering the whole genome (available on chips developed with the HapMap project) • Large-scale collaborative studies to get large sample sizes with well characterized phenotypes (eg european consortium GABRIEL project ) • Development of statistical & bioinformatics tools to handle large body of data & address complex genetic mechanisms (multiple genes, multiple phenotypes) • Objectives: discover new genes and pathways • Limitations • Replication • Large scale • Statistical challenge (multiple testing) • Functional variants

21. Genome wide association studies • First GWA study in asthma. (Moffatt. Nature 2007) • 994 asthmatic children and 1234 control children from UK and Germany, replication in an other German population and in the UK 1958 birth cohort • 300 000 SNPs • Strong associationof several close markers on the 17q21 region • Discovery of the association with ORMDL3: encode for transmembrane proteins anchored in the ER. Role?

22. Genome wide association studies • GWA for lung cancer • IARC: (Hung. Nature 2008) • 1989 cases and 2625 controls. Logistic regression adjusted on age, sex and country • 2 SNPs (rs8034191 and rs1051730) in strong LD on chr 15q25 with p value < 10-7. • Adjusted OR for 1 copy of the rare allele was 1.27, for 2 copies 1.80. Further adjustment on duration of smoking did not change the OR • Replication in 5 independent studies: > 2000 cases and > 3000 controls. Similar ORs, same trends for homozygotes. • Prevalence of the rare allele: 34 %. Population attribuable risk: 15 % • No association with head and neck KCs. Association exists even in non smokers. No association with nicotine dependence.

23. Genome wide association studies • GWA for lung cancer • Thorgeirsson. (Nature 2008) • 10 995 icelandic smokers • Association of the same SNP (rs1051730) on chr 15q25 with level of active tobacco smoke and nicotine dependence. • Association with lung Kc (OR: 1.31) and CV diseases (OR: 1.19) • Amos. (Nature genetics 2008) • Cases matched to controls on smoking, age and sex: 1154 cases of lung Kc in ever smokers and 1137 ever smoker controls. Replication in 2 sets of cases and matched controls. • Despite matching, smoking cases had  pack/years than smoking controls • Identification of the same SNPs. Similar OR for hetero and homozygotes. • Adjustment on duration of smoking did not change the OR. No association in never smokers.

24. Genome wide association studies • GWA for lung cancer • Region of 100–kb including CHRNA5/CHRNA3: strong candidate genes, associated with tobacco addiction, but also in nicotine-mediated suppression of apoptosis in lung cancer cells. Nicotine has an impact on promotion of lung Kc • Effect dependant on tobacco smoke or independent? • Discussion: • Large data-sets but inprecise environmental exposures • Vs smaller studies with careful exposure assessments

25. Conclusions • Achievements in asthma genetics appear both impressive and confusing. • Many susceptibility genes are robust candidates, new genes have been discovered leading to new hypothesis (functional role?) • Parallele improvement in molecular biology and statistical methods and tools. • Replication of previous results of linkage and associations has been generally poor. • Asthma is a complex disease, with implication of multiple genes of small effects with modulation of expression (gene and/or environment interactions). Importance of careful definition of phenotypes and environmental exposures • Studies are expensive

26. Conclusions • Future challenges are multiples • Large scale studies with well characterized subjects are required to reach the power necessary to improve the analyses. • Due to strong gene/environment interactions, careful assessments of environmental factors are necessary. • Link all the available data from geneticists, biologists, clinicians, epidemiologists • Necessity of analysis taking into account the whole system biology: genome, but also transcriptome and proteome

27. ACKNOWLEDGMENTS EGEA cooperative group: Coordination: F Kauffmann; F Demenais (genetics); I Pin (clinical aspects). Respiratory epidemiology: Inserm U 823, Grenoble: V Siroux; Inserm U 700, Paris M Korobaeff (Egea1), F Neukirch (Egea1); Inserm U 707, Paris: I Annesi-Maesano; Inserm U 780, Villejuif: F Kauffmann, N Le Moual, R Nadif, MP Oryszczyn. Genetics: Inserm U 393, Paris: J Feingold; Inserm U 535, Villejuif: MH Dizier; Inserm U 794, Evry: E Bouzigon , F Demenais; CNG, Evry: I Gut , M Lathrop. Clinical centers: Grenoble: I Pin, C Pison; Lyon: D Ecochard (Egea1), F Gormand, Y Pacheco; Marseille: D Charpin (Egea1), D Vervloet; Montpellier: J Bousquet; Paris Cochin: A Lockhart (Egea1), R Matran (now in Lille); Paris Necker: E Paty, P Scheinmann; Paris-Trousseau: A Grimfeld, J Just. Data and quality management: Inserm ex-U155 (Egea1): J Hochez; Inserm U 780, Villejuif: N Le Moual, C Ravault; Inserm U 794: N Chateigner; Grenoble: J Ferran