Transcript Genetics of Asthma

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
Objectives of genetic analysis
Discover new genes and pathways
Genotype/phenotype analysis
Refining phenotypes can help in gene identification
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?
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)
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
Biological & physiological « intermediate »
phenotypes involved in the pathological process
G0
E0
G1
G2
IgE
G3
Atopy
G4
EOS BHR
(SPT/ sIgE)
E2
E1
E3
ASTHMA
G5
FEV1
• 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
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2
3
4
5
6
7
8
9
10
11 12 13 14 15 16 17
18 19 20
21 22
X
Y
No Hypothesis
Genome-wide screen approach
Linkage studies
~ 400 genetic markers (microsatellites)
1
2
3
4
5
6
7
8
9
10
11 12
13
14 15
16
17
18 19
Fine mapping
Hypothesis-driven
Candidate gene approach
20
21 22
X
Genome-wide association studies
~ 300 000 genetic markers (SNP)
Y
Associations
Gene discovery
Biological studies
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
> 20 genome screens conducted to date
Populations:
Europeans +++, Australians, North-Americans, Chinese, Japanese
Regions most often replicated across populations
Region
Asthma
Atopy
IgE
EOS
1p31-36
+++++
++
+++
+
5q31
++++
6p21
++++
++
++++
+++
11q13
+
++
+++
+
12q21
+++++
+
++
++
13q12
++
++
+
+
++
BHR
FEV1
++
+
+
+
Phenotype linked to several regions: polygenic?
One region linked to several phenotypes: one
pleiotropy gene or several genes in the same region?
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
1
GENOME SCAN OF 295 EGEA FAMILIES
for 8 asthma-related phenotypes
2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
FEV1
6q14
SPT
17q22
IgE
12p13
X
Y
FEV1
SPTQ
21q21
IgE
Asthma
MultiRAST
FEV1
SPT
BR
EOS
SPTQ
Bouzigon et al, Hum Mol Genet 2004
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
•
•
•
•
Limited number of genes tested
Needs high density of markers
Population stratification in case control design:
Needs replication studies in other populations
 Limitations
 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: 181-6
DPP10
(2q14-2q32) Nat Genet 2003; 35:258-63
associated with FEV1 and IgE
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 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)
Phenotypes
Region
Position
LOD
p-value
Asthma score
Asthma severity score
18p11
2p23
41.2
47.4
2.40
1.80
0.0004
0.002
%FEV1
1p36
2q36
6q14
4.2
221.1
89.8
1.52
1.59
1.64
0.004
0.003
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
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 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 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 (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 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.
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