Tim Aitman

1st Imperial BHF Symposium, June 5th 2009

PROFITING FROM GENOMICS

Physiological Genomics and MedicineMRC Clinical Sciences CentreHammersmith HospitalImperial CollegeLondon

Mendelian traits Complex Traits

1980 1985 1990 1995 2000

Identification of Genes underlying Mendelian and Complex Traits

1980-2002

Glazier, Nadeau, Aitman, Science, 2002

Mendelian traits

All complex traits

Human complex traits

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

• 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?

CONCLUSION

• Statins

• Thiazolidinediones

• Angiotensin receptor blockers in Marfan

Syndrome

Three drug discovery stories

• 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

• 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

CONCLUSION

Could genomics have helped

discover the target of the

TZD’s?

• TZD’s were developed through the classical

drug discovery pipeline

• The target of the TZD’s (Ppar) is a genetic

risk factor for type 2 diabetes

CONCLUSION

Michael Phelps

Marfan Syndrome

Arachnodactyly

Lens dislocation

Dissection of aorta

Marfan – clinical features

Nature 1991

Overactive TGF- in Marfan mice

Anti TGF- neutralising antibodies reduce lung lesions

• 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

CONCLUSION

Genomic approaches to identification

of new genes underlying complex

cardiovascular traits

QTL Plots of Chromosome 4 for Defects in Insulin Action and Fatty

Acid Metabolism

0

2

4

6

8Lod

0

1

2

3

4

Wox21Ae2

Arb13Il6

Wox7Mgh4

Mgh17 Mgh8

10 cM

Ae2Arb13Il6

Wox7Wox21Mgh4

Mgh17 Mgh8

10 cM

F2 cross Backcross

Aitman et al, Nature Genet 1997

Identification of Cd36 as SHR Insulin Resistance Gene

+

Microarray to Detect Differential Gene Expression between Tissues from

Affected and Control Animals

Aitman et al, Nature Genet 1999

Integrated DNA microarray and linkage analysis in the spontaneously hypertensive rat

Can integrated genomic approaches give insights into gene function at the

level of the genome?

eQTL datasets generated in the BXH/HXB RI strains

eQTL mapping(~1,000 microsatellites and ~2,000 SNPs)

FatAorta Skeletal muscleLiverLeft ventricle

Nu

mb

er o

f eQ

TL

s

Tissue

Genome-wide significance

fat LVadrenal aortakidney liver SKM0

1000

2000

3000

4000

5000

6000

0.050.010.0010.00010.000010.000001

Peak LOD 4.0

Previous linkage analysis showed chromosome 17

QTL regulating left ventricular mass in SHR

A cluster of cis-eQTL genes on chromosome 17 shows striking correlation with Left Ventricular Mass

Petretto, Cook

Peak LOD 4.0

Hbld2 Ogn

Two cis-eQTL genes reside within 1-Lod support

interval for the chromosome 17 LV mass QTL

Ogn regulates heart mass in the mouse

LV

M (

%)

Baseline Hypertrophic stimulation

Ogn-/-

Ogn+/-

Ogn+/+

Ogn-/-

Ogn+/-

Ogn+/+

0.0

0.1

0.2

0.3

0.4

0.5

** *

ns ns

Probeset ID Gene title Gene name Fold

change1 FDR (%)2

Correlation with LVMI3

P-value of correlation

218730_s_at Osteoglycin OGN 1.8 2.8 0.62 8E-04

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

205478_at Protein phosphatase 1subunit 1A PPP1R1A -1.6 1.4 -0.59 2E-03

210096_at Cytochrome P450, family 4 CYP4B1 -1.5 2.8 -0.60 1E-03

213524_s_at G0/G1switch 2 G0S2 -2.1 1.4 -0.60 1E-03

TGFbeta / fibroblast

Ogn is most strongly correlated with LVM in humans out of ~22,000 possible transcripts

Cook, Petretto, Pinto

0 2 4 6 8 10 12 140

25

50

75

100

Su

rviv

al (

%)

Days post-MI

WT (n=9)

Ogn -/- (n=17)

Ogn deletion predisposes to cardiac rupture post-MI

Stuart Cook

Nature Genetics – Rat Focus IssueMay 2008

TranscriptionFactor activity

eQTL

GO:0002376 7.5 x 10-12 immune system

GO:0006955 2.1 x 10-11 immune response

Enriched in inflammatory response genes

Posterior probability for non-zero edge = 0.95

Identification of inflammatory network in rat heart

Corresponding network now replicated in human monocytes

Inflammatory Network Rat heart

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, PravenecBirney, Flicek, Hubner, Cuppen, Kurtz, Jones

EURATRANS – building a multimodality phenotypic model

• 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

CONCLUSION

ACKNOWLEDGEMENTS

IC/Clinical Sciences CentreEnrico PetrettoSantosh AtanurLaurence GameStuart Cook Terry CookJames Scott

FundingBHFMRCWellcomeEU FP6Leducq Foundation

Prague/San FranciscoMichal Pravenec

Vladimir Kren

Ted Kurtz

Berlin/UtrechtNorbert Hübner/Edwin Cuppen

OxfordJonathan Flint

VancouverSteve Jones

EBIEwan Birney, Xose Fernandez

Paul Flicek