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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