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A ndré G Uitterlinden, Fernando Rivadeneira + help from friends Genetic Laboratory

Venice 9 nov 2006: HuGE-Network of Networks. Field Synopsis of Genetics of Osteoporosis. A ndré G Uitterlinden, Fernando Rivadeneira + help from friends Genetic Laboratory Department of Internal Medicine Department of Epidemiology&Biostatistics Department of Clinical Chemistry.

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A ndré G Uitterlinden, Fernando Rivadeneira + help from friends Genetic Laboratory

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  1. Venice 9 nov 2006: HuGE-Network of Networks Field Synopsis of Genetics of Osteoporosis André G Uitterlinden, Fernando Rivadeneira + help from friends Genetic Laboratory Department of Internal Medicine Department of Epidemiology&Biostatistics Department of Clinical Chemistry

  2. “A disease characterised by low bone mass and microarchitectural deterioration of bone tissue, leading to enhanced bone fragility and increased fracture risk” (WHO, 1994) Osteoporosis Individuals with BMD > 2.5 SD below the young normal mean (T - Score) 0 20 40 60 80 What is Osteoporosis? Changes in Bone Mass with age 2 1 0 Normal Standard Deviations -1 Osteopaenia -2 -3 Osteoporosis -4 Age

  3. Osteoporotic Fracture *Average length of bed-stay in hospital ~ 10 days *Annual cost of hip fracture Europe ~ euro 4,000,000,000.= Identify subjects at risk: -Clinical Risk Factors -Bone Densitometry -Biochemical Markers -Genetic Markers? Hip fx Spine fx Wrist fx

  4. Monozygotic Genes Shared Resemblance MZ Twin 1 100% r = 0.7 Twin 2 Dizygotic DZ Twin 1 50% r = 0.3 Twin 2 Quantitating the genetic contribution to complex bone traits using twins Heritability of bone Phenotypes • BMD 50-80% • Turnover 40-70% • Geometry 70-85% • Fracture 25-50%

  5. Genetic contribution to Population Variation of Quantitative Traits/Endo-phenoptyes Common Rare Rare Gene polymorphisms with moderate effects Mutations with severe effects Mutations with severe effects Population Frequency of Trait Value BMD Hypertension Glucose level Height Cholesterol level Etc.

  6. Osteoporosis is a “complex” genetic disease: Fracture Risk Clinical Expression: Risk Factors: Bone Strength Impact Force Fall Risk BMD Quality Geometry DNA polymorphisms Environmental factors: diet, exercise, sun exposure, ...

  7. Environmental influences can differ between populations ! HOLLAND BELGIUM

  8. Dissection of a Complex Disease/Trait : identify “risk” alleles of susceptibility genes Resolution Effectiveness Type of approach “Top-down”/hypothesis free * Whole-Genome-Linkage analysis - Pedigrees - Sib-pairs - Human, mouse * Whole-Genome-Association analysis - 100K – 1000K SNP analysis in cases/controls “Bottom-up”/up-front hypothesis * Association analyses of candidate gene polymorphisms (based on biology) -? 5-20 million bp +/? 5-50 thousand bp +/- 1 bp >>All approaches converge to testing (candidate) gene polymorphisms!!

  9. Molecular Genetic Epidemiology

  10. Monogenic mutation model Genome Wide Linkage Genome Wide Association Candidate gene association humans mice humans mice humans humans *Chromosomal region x x x x *Gene x x x x x LD block x x Haplotype alleles x x variant x x x x Field Synopsis Osteoporosis Genetics * Genetic: Type of evidence Genetic resolution * Functional: mRNA, protein, cells, etc

  11. Osteoporosis Genetics Field Synopsis – Linkage Studies

  12. Osteoporosis linkage analysis “in practice”: Many controversial & ir-reproducible results because of: • Humans: • low power • big chromosomal regions • Phenotype heterogeneity • Mice: • high LD • transferibility to humans (biology, phenotype heterogeneity) • limited variation tested (few inbred strains)

  13. IGFI, IGFBP3 GR, 11B-HSD Cortisol IGF/GH TSHR, DIO1, DIO2, DIO3, MCT8 Thyroid Hormone ERα, ERβ, Aromatase, LH, LHR, GnRH VDR,DBP Vitamin D Estrogen Genetic determinants of osteoporosis ? TGFb/BMP/Wnt-signalling homocysteine Matrix molecules MTHFR, MS, MTRR, CBS, THYMS TGFb,LRP5/6, BMP2, FRZB, SOST Collagen Ia1, osteocalcin, AHSG, LOX

  14. Candidate gene association analysis “in practice”: Many controversial & ir-reproducible results because of: • Small sample size • Ill-defined choice of polymorphisms • Lack of standardized genotyping • Lack of standardized phenotype data • Publication bias • >> How to improve? • Combine study populations (across Europe, globally): meta-analysis • Rationalise choice of polymorphisms: functionality, haplotypes • Standardize genotyping methods: reference DNA plate • Standardize phenotypes across populations: meta-analysis individual level data • Run prospective meta-analyses

  15. Field Synopsis Osteoporosis Genetics • Levels of evidence: • Collaborative prospective meta-analysis of individual level data (GENOMOS) • Collaborative meta-analysis of individual level data of published studies • Meta-analysis of published data • >2 large studies (n > 1000? 5000?) • 1-3 smaller studies • - 1 small study (n<500?) Good Not so good

  16. STRUCTURE OF “GENOMOS” WORKPLAN WP 1 GENOTYPING WP 2 FAMILY DATA WP 3 HAPLOTYPING WP 4 MONOGENIC GENES WP 5 DNA POOLING DATA GENERATION GENOTYPE DATA OF POPULATIONS META-ANALYSES DATA ANALYSIS WP 7: GENE ENVIRONMENT INTERACTIONS: Gene-nutrient interaction WP6: ASSOCIATION WITH FRACTURE, BMD, BONE LOSS WP 8: GENE ENVIRONMENT INTERACTIONS: Response-to-Treatment DELIVERABLES OSTEOPOROSIS SUSCEPTIBILITY- ALLELES DIETARY MODIFICATION TREATMENT MODIFICATION DISSEMINATION, COMMERCIALISATION

  17. “GENOMOS” a large-scale, multi-centre study for prospective meta-analyses of osteoporosis candidate gene variants “Genetic Markers for Osteoporosis” EU FP5 sponsored: 3 mio euro Jan 2003 – Jan 2007 Total number of subjects (early 2006): 26,264 18,405 women 7,859 men 6,498 fractures 2,380 vertebral fx Genes analysed: - ESR1 (JAMA, 2004) - COLIA1 (PLoS Medicine, 2006) - VDR (Ann Int Med,2006) - TGFb (submitted) - LRP5&6 (ongoing) Aberdeen 16 2 14 Aarhus 13 15 3 Cambridge 5 Amsterdam 12 Warsaw 1 Rotterdam* 10 7 Antwerp Graz 11 4 Firenze 9 Barcelona 8 = Participant + Epidemiological Cohort Ioannina 9 = Participant 7 *coordinating centre

  18. GENOMOS RESULTS: nov 2006 GENE SNPs n Sample n BMD FX FN LS Vert Non-Vert ESR1 3 18,917 - - 20-30% 10-20% COLI 1 20,786 0.15 SD 0.15 SD 10% (Sp1) - VDR 5 26,242 - - 10% (Cdx) - TGFb 5 28,924 - - - (?) - LRP5 2 >35,000 LRP6 1 >35,000 Drawbacks: - Candidate gene approach - No htSNPs across gene region - Selected phenotypes EU FP7: - Combine GWA studies - htSNPs - phenotype scrutiny

  19. GWA in The Rotterdam Study (ERGO) -Complete ERGO (NWO Groot grant 6 mio euro; just started): *>10.000 ERGO DNA samples with 550.000 ht SNPs (Illumina 550K) *High power for all QTL and many disease endpoints -Pilot study (almost finished): * 500 ERGO samples x 500.000 random SNPs (Affymetrix 500K) * mostly QTL (quantitative traits: BMD, height, cholesterol, etc.) Collaborations: - Wellcome Trust Case Control Consortium (prof.dr. Lon Cardon c.s.) - Framingham Study (prof.dr. Chris O’Donell c.s.)

  20. Field Synopsis Osteoporosis Genetics *Writing team: Fernando Rivadeneira (Netherlands), Hong Wen Deng (USA), Stuart Ralston (UK), John Ioannidis (Greece), André Uitterlinden (Netherlands) *Review and summarize: -Human monogenic diseases several -Mouse models (KO, transgenic, etc.) several -Human genome wide linkage data hits, but no genes? -Mice genome wide linkage data hits, but no genes? -Human candidate gene association studies few good ones -Human GWA studies soon available

  21. OSTEOPOROSIS/BMD LOCI ALPL MTHFR TNF A Human A C =genome search locus A=Devoto ea,1998 B=Mitchell ea,1998 (ab) C=Niu ea,1999 D=Koller ea, 2000 b,c,d 1 IL-6 a b,c,d DBP IL-1A IL-1B IL-1RN CBFA1 D SPP1 D AR 2 BGP CASR B COLIa2 a,b, c,d Mouse SPOCK AHSG D ESRa =genome search locus a,d A a=Klein ea,1998 b=Shimizu ea,1999 c=Beamer ea,1999 d=Benes ea, 2000 a,d PTH B 3 VDR COL2a1 1aOHase A =bone disorder gene a 1=Abers-Schonberg 2=Hypercalciuria 3=HBM-OP 4=van Buchem / SCL 5=FEO / Paget’s ARO OPG a A IGF-1 B C 4 COLIa1 a =candidate gene TGFb 5 a,b,c RANK a ApoE

  22. Erasmus University Rotterdam THANK YOU ! The GENOMOS consortium (sept 2006) Stuart Ralston Bente Langdahl Maria Luisa Brandi Jonathan Reeve Alisoun Carey Daniel Grinberg/Nogues/ Diez- Perez/Ballcells Wim van Hul John Ioannidis Roman Lorenc/Marcin Kruk Barbara Obermayer-Pietsch Paul Lips Olaf Johnell Claes Olsen Kim Brixen Ulrike Petterson Francois Rousseau (Canada) Doug Kiel (USA) Jose Riancho Steve Cummings (USA) Genetic Laboratory Pascal Arp Arjan Bergink Yue Fang Hanneke Kerkhof Mila Jhamai Joyce van Meurs Fernando Rivadeneira Stephanie Schuit Lisette Stolk Rowena Utberg Nahid Yazdanpanah Internal Medicine Carla Baan Joop Janssen Frank de Jong JanWillem Koper Steven Lamberts Hans van Leeuwen Huibert Pols Erik Sijbrands Axel Themmen Theo Visser Epidemiology&Biostatistics Monique Breteler Cornelia van Duijn Bert Hofman Jacqueline Witteman Clinical Chemistry Robert de Jonge Ron van Schaik Jan Lindemans Clinical Genetitics Ben Oostra

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