Transcript Aitman.BHF Sympos.v6 - Workspace

1st Imperial BHF Symposium, June 5th 2009
PROFITING FROM GENOMICS
Tim Aitman
Physiological Genomics and Medicine
MRC Clinical Sciences Centre
Hammersmith Hospital
Imperial College
London
Identification of Genes underlying
Mendelian and Complex Traits
1980-2002
Mendelian traits
Complex Traits
Mendelian traits
All complex traits
Human complex traits
1980
1985
1990
1995
2000
Glazier, Nadeau, Aitman, Science, 2002
Published Genome-Wide Associations through 3/2009,
398 published GWA at p < 5 x 10-8
NHGRI GWA Catalog
www.genome.gov/GWAStudies
Most GWAS SNPs have very low odds ratios
March, 2009
CONCLUSION
• Genome-wide association studies have
dramatically advanced our understanding
of the molecular genetic basis of common
human diseases, and potentially disease
prediction
• But do genomic approaches have any
relevance to drug discovery pipelines?
Three drug discovery stories
• Statins
• Thiazolidinediones
• Angiotensin receptor blockers in Marfan
Syndrome
• Genomic approaches to understanding
cardiovascular phenotypes
Statins and the cholesterol synthesis pathway
Prior to loss of patent protection (2006),
the statin market was worth over
16 billion dollars
Could genomics have helped
discover the target of the
statins?
Nature Genetics, 2008
Kathiresan et al, Nat Genet, 2008
CONCLUSION
• Development of statins followed the
discovery of the LDL receptor as a cause of
familial hypercholesterolaemia, and HMG CoA
reductase as the rate-limiting enzyme in
cholesterol synthesis
• Thirty years later, GWAS identifies SNPs in
HMG CoA reductase (and other genes) as
(minor) cause of hypercholesterolaemia
Could genomics have helped
discover the target of the
TZD’s?
CONCLUSION
• TZD’s were developed through the classical
drug discovery pipeline
• The target of the TZD’s (Pparg) is a genetic
risk factor for type 2 diabetes
Michael Phelps
Marfan Syndrome
Marfan – clinical features
Arachnodactyly
Lens dislocation
Dissection of aorta
Nature 1991
Overactive TGF-b
in Marfan mice
Anti TGF-b neutralising
antibodies reduce lung lesions
CONCLUSION
• Positional cloning of the Marfan gene, and
study of disease mechanism in a mouse
model led to rational development of a new
treatment for this rare, single gene disorder
Genomic approaches to identification
of new genes underlying complex
cardiovascular traits
Integrated DNA microarray and linkage analysis in the
spontaneously hypertensive rat
QTL Plots of Chromosome 4 for
Defects in Insulin Action and Fatty
Acid Metabolism
Microarray to Detect Differential Gene
Expression between Tissues from
Affected and Control Animals
Lod
8
F2 cross
4
6
3
4
2
Backcross
+
10 cM
2
10 cM
1
0
Il6
Ae2Arb13
Mgh17 Mgh8
Wox7
Wox21 Mgh4
0
Ae2 Il6
Arb13
Mgh17 Mgh8
Wox7
Wox21 Mgh4
Aitman et al, Nature Genet 1997
Aitman et al, Nature Genet 1999
Identification of Cd36 as SHR Insulin Resistance Gene
Can integrated genomic approaches
give insights into gene function at the
level of the genome?
eQTL datasets generated in the BXH/HXB RI strains
Aorta
Left ventricle
Liver
Skeletal muscle
Fat
eQTL mapping
Number of eQTLs
(~1,000 microsatellites and ~2,000 SNPs)
6000
Genome-wide
significance
5000
0.05
0.01
0.001
0.0001
0.00001
0.000001
4000
3000
2000
1000
0 adrenal
fat
kidney
aorta
Tissue
LV
liver
SKM
Previous linkage analysis showed chromosome 17
QTL regulating left ventricular mass in SHR
Peak LOD 4.0
A cluster of cis-eQTL genes on chromosome 17 shows
striking correlation with Left Ventricular Mass
Petretto, Cook
Two cis-eQTL genes reside within 1-Lod support
interval for the chromosome 17 LV mass QTL
Peak LOD 4.0
Hbld2
Ogn
Ogn regulates heart mass in the mouse
0.5
LVM (%)
0.4
Ogn+/+
**
*
ns ns
0.3
0.2
0.1
0.0
Baseline
Hypertrophic
stimulation
Ogn+/Ogn-/-
Ogn is most strongly correlated with LVM in humans
out of ~22,000 possible transcripts
Probeset ID
Gene title
Gene name
Fold
change1
FDR (%)2
Correlation
with LVMI3
P-value of
correlation
OGN
1.8
2.8
0.62
8E-04
218730_s_at
Osteoglycin
208370_s_at
Down syndrome critical region 1
DSCR1
2.0
1.4
0.61
9E-04
207173_x_at
Cadherin 11, type 2,
CDH11
1.8
2.8
0.54
4E-03
204472_at
GTP binding protein
GEM
2.7
1.4
0.53
5E-03
205841_at
Janus kinase 2
JAK2
2.1
2.1
0.53
6E-03
219087_at
Asporin
ASPN
2.6
1.4
0.52
7E-03
213765_at
Microfibrillar associated protein 5
MFAP5
2.2
1.4
0.51
7E-03
203570_at
Lysyl oxidase-like 1
LOXL1
1.7
1.4
0.51
7E-03
209101_at
Connective tissue growth factor
CTGF
3.0
1.4
0.51
8E-03
213764_s_at
Microfibrillar associated protein 5
MFAP5
1.8
1.4
0.51
8E-03
211161_s_at
Collagen, type III, alpha 1
COL3A1
3.2
1.4
0.50
9E-03
PPP1R1A
-1.6
1.4
-0.59
2E-03
205478_at
Protein phosphatase 1subunit 1A
210096_at
Cytochrome P450, family 4
213524_s_at
TGFbeta / fibroblast
G0/G1switch 2
CYP4B1
-1.5
2.8
-0.60
1E-03
G0S2
-2.1
1.4
-0.60
1E-03
Cook, Petretto, Pinto
Ogn deletion predisposes to cardiac rupture post-MI
WT
Survival (%)
100
(n=9)
75
50
25
Ogn -/(n=17)
0
0
2
4
6
8
10
12
14
Days post-MI
Stuart Cook
Nature Genetics – Rat Focus Issue
May 2008
Identification of inflammatory network in rat heart
Posterior probability for
non-zero edge = 0.95
Inflammatory
Network
Rat heart
Enriched in inflammatory response genes
GO:0002376
7.5 x 10-12
immune system
GO:0006955
2.1 x 10-11
immune response
Transcription
Factor activity
eQTL
Corresponding network now
replicated in human monocytes
Generation of SHR Genome
Sequence by short-read sequencing
• Paired-end sequence, Illumina GAII
• Mapped to BN reference sequence
– MAQ 0.6.6
• 78 lanes, 11 x coverage
• SNP calling
–
–
–
–
3 or more reads, MAQ score>30
3.1 Million SNPs
436K short indels (1-5bp)
22K indels (5bp-1Mbp)
Aitman, Cook, Pravenec
Birney, Flicek, Hubner, Cuppen, Kurtz, Jones
EURATRANS – building a
multimodality phenotypic model
CONCLUSION
• High throughput and integrative genomic
techniques are increasing our understanding of
the molecular pathogenesis of common
diseases
• Multiple types of genome-wide data, together
with informatics and modelling stand to identify
new preventive strategies, including new
approaches to screening and new drug targets
ACKNOWLEDGEMENTS
Prague/San Francisco
Michal Pravenec
Vladimir Kren
Ted Kurtz
Berlin/Utrecht
Norbert Hübner/Edwin Cuppen
Oxford
Jonathan Flint
Vancouver
Steve Jones
EBI
Ewan Birney, Xose Fernandez
Paul Flicek
IC/Clinical Sciences
Centre
Enrico Petretto
Santosh Atanur
Laurence Game
Stuart Cook
Terry Cook
James Scott
Funding
BHF
MRC
Wellcome
EU FP6
Leducq Foundation