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

I. Pin, V. Siroux, INSERM U 823. Grenoble, France

E. BouzigonINSERM U 794. Paris, France

Discover new genes and pathwaysDiscover new genes and pathways

Genotype/phenotype analysis Refining phenotypes can help in gene identificationRefining phenotypes can help in gene identification

PHENOTYPESPHENOTYPES GENESGENES

Identification of genes may help in isolating phenotypic entitiesIdentification of genes may help in isolating phenotypic entities

Pharmacogenetics Pharmacogenetics to improve the adaptation of the treatment to the individualized to improve the adaptation of the treatment to the individualized patientpatient

Predictive medicine?Predictive medicine?

Objectives of genetic analysis

Asthma: a complex phenotype

Clinical/Physiological phenotypes

Phenotypes related to triggers

Phenotypes related to inflammation

Severity-definedExacerbation-proneChronic airflow limitationTreatment resistantAge at onset

AspirinEnvironmental. AllergensOccupational AllergensMensesExercise

EosinophilicNeutrophilicPauci-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

G0 G1 G2 G3 G4

IgE Atopy EOS BHR FEV1

(SPT/ sIgE)

E1

E0

E2

G5

E3

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 ~ 300 000 genetic markers (SNP)

Candidate gene approach

Fine mapping Associations

Gene discovery

Biological studiesHypothesis-driven

No Hypothesis

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

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 populationsRegion Asthma Atopy IgE EOS BHR FEV1

1p31-36 +++++ ++ +++ +

5q31 ++++ ++ ++ +

6p21 ++++ ++ ++++ +++

11q13 + ++ +++ + +

12q21 +++++ + ++ ++

13q12 ++ ++ + + +

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 STUDYEGEA 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

GENOME SCAN OF 295 EGEA FAMILIES for 8 asthma-related phenotypes

Bouzigon et al, Hum Mol Genet 2004

EOSIgE MultiRAST

SPT

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X Y

IgE 12p13

SPT 17q22

FEV1

SPTQ21q21

FEV16q14

FEV1

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

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: 181-6 associated with FEV1 and IgE

DPP10 (2q14-2q32) Nat Genet 2003; 35:258-63 associated with asthma & atopy GPRA (7p) Science 2004; 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 2007associated 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 score 18p11 41.2 2.40 0.0004Asthma severity score 2p23 47.4 1.80 0.002

 %FEV1 1p36 4.2 1.52 0.004 2q36 221.1 1.59 0.003 6q14 89.8 1.64 0.003

Phenotypes Region Position LOD p-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= 0.0008)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 studiesFirst 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 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 studiesGWA 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.

Genome wide association studiesGWA 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.

Genome wide association studiesGWA 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

ConclusionsAchievements 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

ConclusionsFuture 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