Presentation on theme: "Strategies used to identify genes causing? (associated with) asthma or allergy I.Pin, V. Siroux, INSERM U 823. Grenoble, France E. Bouzigon INSERM U 794."— Presentation transcript:
Strategies used to identify genes causing? (associated with) asthma or allergy I.Pin, V. Siroux, INSERM U 823. Grenoble, France E. Bouzigon INSERM U 794. Paris, France
Discover new genes and pathways Genotype/phenotype analysis Refining phenotypes can help in gene identification PHENOTYPESGENES PHENOTYPES GENES Identification of genes may help in isolating phenotypic entities Pharmacogenetics to improve the adaptation of the treatment to the individualized patient Predictive medicine? Objectives of genetic analysis
Asthma: a complex phenotype Clinical/Physiological phenotypes Phenotypes related to triggers Phenotypes related to inflammation Severity-defined Exacerbation-prone Chronic airflow limitation Treatment resistant Age at onset Aspirin Environmental. Allergens Occupational Allergens Menses Exercise Eosinophilic Neutrophilic Pauci-granulocytic Wenzel, Lancet, 2006 « 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)
ASTHMA G0G0 G1G1 G2G2 G3G3 G4G4 IgE Atopy EOS BHR FEV 1 (SPT/ sIgE) E1E1 E0E0 E2E2 G5G5 E3E3 Biological & physiological « intermediate » phenotypes involved in the pathological process
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
Strategies used to identify genes involved in asthma-related phenotypes Genome-wide screen approach Linkage studies ~ 400 genetic markers (microsatellites) Genome-wide association studies ~ genetic markers (SNP) Candidate gene approach Fine mapping Associations Gene discovery Biological studies Hypothesis-driven No Hypothesis X Y
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
Regions most often replicated across populations RegionAsthmaAtopyIgEEOS BHRFEV 1 1p q p q q q Phenotype linked to several regions: polygenic? One region linked to several phenotypes: one pleiotropy gene or several genes in the same region? > 20 genome screens conducted to date Populations: Europeans +++, Australians, North-Americans, Chinese, Japanese
EGEA STUDY EGEA STUDY Multi-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
GENOME SCAN OF 295 EGEA FAMILIES for 8 asthma-related phenotypes Bouzigon et al, Hum Mol Genet 2004 EOS IgE MultiRAST SPT X Y IgE 12p13 SPT 17q22 FEV 1 SPTQ 21q21 FEV1 6q14 FEV 1 SPTQ Asthma BR
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 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)
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?
Other asthma genes discovered by positional cloning PHF11 (13q14) Nat Genet 2003; 34: associated with FEV 1 and IgE DPP10 (2q14-2q32) Nat Genet 2003; 35: associated with asthma & atopy GPRA (7p) Science 2004; 304: associated with IgE and asthma (replication) HLAG (6p) Am J Hum Genet 2005; 76: 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
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
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) Bouzigon et al, Eur Respir J 2007 Asthma score18p Asthma severity score2p %FEV 1 1p q q Phenotypes Region Position LODp-value Use of asthma score instead of binary phenotype new regions Different genetic components underlie disease spectrum, asthma severity and FEV1.
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= ) No linkage to AST only or AR only (MLS = 0) Asthma + allergic rhinitis: a phenotypic entity determined by gene(s) on 1p31?
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)
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
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
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 SNPs Strong association of several close markers on the 17q21 region Discovery of the association with ORMDL3: encode for transmembrane proteins anchored in the ER. Role?
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 (rs and rs ) in strong LD on chr 15q25 with p value < Adjusted OR for 1 copy of the rare allele was 1.27, for 2 copies 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.
Genome wide association studies GWA for lung cancer Thorgeirsson. (Nature 2008) icelandic smokers Association of the same SNP (rs ) 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.
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
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
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
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