Ian Day Edited Presentation


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BEN Event: Personalised Healthcare - 8th October 2009

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Ian Day Edited Presentation

  1. 1. The potential of genetics to enable personalization of healthcare Professor Ian N M Day MRC Centre (CAiTE) and Bristol Genetic Epidemiology Laboratory Department of Social Medicine Oakfield House, Oakfield Grove University of Bristol Bristol Enterprise Network event „The Personalised Healthcare Revolution‟ 8th October 2009 Bristol Zoo
  2. 2. What is in your genome? • 3000 million A,C,G,T bases strung together into 23 long molecules (chromosomes) • The base sequence is your blueprint • There are about 20,000 ‘genes’ • Each gene encodes a protein, e.g.haemoglobin • 99% does NOT encode proteins. ?junk • About 1% of bases are ‘polymorphic’ • You may have about 1m more ‘private’ variations • Mutation of one base may cause a major disease • You are 96% the same as chimp, 99.5% in protein coding
  3. 3. Where is your genome? • There is a copy in every cell of your body • Different genes are active in different cells • However, you only copy half your genome into a sperm or an egg
  4. 4. Does the hypercholesterolaemia run in the family with the LDLR gene?
  5. 5. Melt-MADGE Loading the gel Pouring one gel Pouring 16 gels Melt-MADGE system Melt-MADGE tank Glass-gel-glass sandwich
  6. 6. “ScanLab” (same system to run melts and endoVII assays) >>Goal – studying unknown mutations in populations … studies in big case collections also feasible >>Present capacity – 10 systems – 50million base pairs scanned per week
  7. 7. Found 1 severe mutation in MC4R in 1,100 population sample • Khalid K. Alharbi1, Emmanuel Spanakis1, Karen Tan2, Matt J. Smith1, Mohammed A. Aldahmesh1, Sandra D. O'Dell1, Avan Aihie Sayer3, Debbie A. Lawlor4, Shah Ebrahim5, George Davey Smith4, Stephen O’Rahilly3, Sadaf Farooqi2 , Cyrus Cooper3, David I.W.Phillips3 and Ian N M Day1 Prevalence and functionality of paucimorphic and private MC4R mutations in a large, unselected European British population, scanned by meltMADGE (Human Mutation 2006) (cat.P114) MC4R A87D > (Appetite++) > BMI 31.5kg/m2 Does everybody have “one worst gene”? Can we help them (cf phenylketonuria)?
  8. 8. From RV-CD to CV-CD • Rare variant – common disease • Common variant – common disease
  9. 9. BMI GWAS - SNPs near MC4R
  10. 10. BMI GWAS - SNPs near MC4R
  11. 11. Forest Plot / Meta-Analysis of Cohorts MC4R lead SNP in replication studies Common variants near MC4R are associated with fat mass, weight and risk of obesity. Loos RJ, Lindgren CM, Li S, Wheeler E, Zhao JH, Prokopenko I, Inouye M, Freathy RM, Attwood AP, Beckmann JS, Berndt SI; Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial, Jacobs KB, Chanock SJ, Hayes RB, Bergmann S, Bennett AJ, Bingham SA, Bochud M, Brown M, Cauchi S, Connell JM, Cooper C, Smith GD, Day I, Dina C, De S, Dermitzakis ET, Doney AS, Elliott KS, Elliott P, Evans DM, Sadaf Farooqi I, Froguel P, Ghori J, Groves CJ, Gwilliam R, Hadley D, Hall AS, Hattersley AT, Hebebrand J, Heid IM; KORA, Lamina C, Gieger C, Illig T, Meitinger T, Wichmann HE, Herrera B, Hinney A, Hunt SE, Jarvelin MR, Johnson T, Jolley JD, Karpe F, Keniry A, Khaw KT, Luben RN, Mangino M, Marchini J, McArdle WL, McGinnis R, Meyre D, Munroe PB, Morris AD, Ness AR, Neville MJ, Nica AC, Ong KK, O'Rahilly S, Owen KR, Palmer CN, Papadakis K, Potter S, Pouta A, Qi L; Nurses' Health Study, Randall JC, Rayner NW, Ring SM, Sandhu MS, Scherag A, Sims MA, Song K, Soranzo N, Speliotes EK; Diabetes Genetics Initiative, Syddall HE, Teichmann SA, Timpson NJ, Tobias JH, Uda M; SardiNIA Study, Vogel CI, Wallace C, Waterworth DM, Weedon MN; Wellcome Trust Case Control Consortium, Willer CJ; FUSION, Wraight, Yuan X, Zeggini E, Hirschhorn JN, Strachan DP, Ouwehand WH, Caulfield MJ, Samani NJ, Frayling TM, Vollenweider P, Waeber G, Mooser V, Deloukas P, McCarthy MI, Wareham NJ, Barroso I, Jacobs KB, Chanock SJ, Hayes RB, Lamina C, Gieger C, Illig T, Meitinger T, Wichmann HE, Kraft P, Hankinson SE, Hunter DJ, Hu FB, Lyon HN, Voight BF, Ridderstrale M, Groop L, Scheet P, Sanna S, Abecasis GR, Albai G, Nagaraja R, Schlessinger D, Jackson AU, Tuomilehto J, Collins FS, Boehnke M, Mohlke KL. Nat Genet. 2008 Jun;40(6):768-75. Epub 2008 May 4.
  12. 12. Effect size of SNP locus near MC4R
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  16. 16. Helicopter view of genome+diseases: cGWAS browser
  17. 17. What Sells to the Public? • Judged by Internet traffic • 1. Internet (indexing by web crawlers) • 2. Sex • 3. Genealogy
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  22. 22. What Sells to the Doctors? • Genetic information that will help clinical process • 1. diagnosis • 2. prognosis • 3. monitoring • 4. screening • 5. Non-directive counselling /reproductive choice • 6. !! Not the patient’s right to know
  23. 23. Some real clinical examples (single targetted gene tests) • TPMT / thiopurines azathioprine, 6-mercaptopurine and 6- thioguanine / organ Tx / autoimmune disease/ leukaemias • Her2/NEU / breast cancer drug response • VKORC1/CYP2C9 / warfarin dosing • UGT1A1 /Gilbert’s / irinotecan / colon cancer • DPYD, TS / 5-fluorouracil / gastric cancer • (ABO) – blood group 1900-1925
  24. 24. Who gets the statins? Average prescribing cutpoint Red genotype over-represented and blue genotype under-represented relative to black genotype. If we know the curves, then we could just use the genotype ratios in those getting the statin, to determine the average prescribing cutpoint Genotype ratio treatment index (GRTI) NHS research of service delivery?
  25. 25. What does it all cost (2009)? • 600,000 common polymorphisms (GWAS) • £300 from 23andMe [NB £40 200K panels imminent] • ‘Complete’ genome sequencing (NGS) • £50,000 • 5 year goal of 1,000 dollar genome • The cost of making sense of it all. • The cost of leveraging value (to whoever) of that meaning
  26. 26. Urban(e) Dog Tags? (slide modified) • identity • blood type and history of inoculations • Blood group • Statin myopathy risk • CFTR delta508, HBB E6V • ALDH2 status • BCHE D70G • LCT • Norwalk virus resistance • MC1R, OCA2 • What I would really like for Xmas is my complete genome sequence!