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Epidemiologic results Clinical features of osteoporosis Determinants of fracture risk

Genetic Epidemiological Strategies to the Search for Osteoporosis Genes Dr. Tuan V. Nguyen Bone and Mineral Research Program Garvan Institute of Medical Research Sydney, Australia. Contents of Presentation. Epidemiologic results Clinical features of osteoporosis Determinants of fracture risk

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Epidemiologic results Clinical features of osteoporosis Determinants of fracture risk

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  1. Genetic Epidemiological Strategiesto the Search for Osteoporosis GenesDr. Tuan V. NguyenBone and Mineral Research ProgramGarvan Institute of Medical ResearchSydney, Australia

  2. Contents of Presentation • Epidemiologic results • Clinical features of osteoporosis • Determinants of fracture risk • Genetics of bone mineral density • The search for osteoporosis genes

  3. Definition • Osteoporosis is a metabolic bone disease, which is characterised by low bone mass, microarchitectural deterioration of bone tissue leading to enhanced bone fragility and a consequent increase in fracture risk • (Consensus Development Conference, 1991)

  4. Magnitude of the Problem

  5. Prevalence of Osteoporosis Incidence of all Fractures % Per 100,000

  6. Incidence of Hip Fx Worldwide

  7. Type of Fractures

  8. Types of Fractures

  9. A Model of Fracture Bone density Bone strength Bone quality Fracture Fall Trauma Force impact

  10. Risks Factors for Hip Fractures

  11. Relationship between Fracture and Bone Mineral Density

  12. Change in BMC and BMD with Age Hip BMC Hip BMD Spine BMC Spine BMD

  13. Determinants of Peak Bone Mass Genetic factors Peak Bone Mass 16-25 yr of age Nutritional factors Hormonal factors Exercise and environmental factors

  14. Risk Factors for Osteoporosis Genetics Race, Sex, Familial prevalence Hormones Menopause, Oophorectomy, Body composition Nutrition Low calcium intake, High caffeine intake, High sodium intake, High animal protein intake Lifestyles Cigarette use, High alcoholic intake, Low level of physical activity Drug Heparin, Anticonculsants, Immunosuppressants Chemotherapy, Corticosteroids, Thyroid hormone

  15. Risk factors for Low Bone Density Smoker Age (per 5 years) Maternal history of fx Steroid use Caffeine intake Activity score Age at menopause Milk intake Ever pregnant Surgical menopause Waist/hip ratio Weight Grip strength Height Thiazide use Oestrogen use

  16. Genetics of Bone Mineral Density

  17. Clues to Genetics and Environment Epidemiol characteristics Genetics Environment Geographic variation + + Ethnic variation + + Temporal variation - + Epidemics +/- + Social class variation - + Gender variation + + Age +/- + Family variables History of disease + + Birth order +/- + Birth interval - + Co-habitation - +

  18. Methods of Investigation • Family studies. Examine phenotypes (diseases) in the relatives of affected subjects (probands). • Twin studies. Examine the intraclass correlation between MZ (who share 100% genotypes) and DZ twins (who share 50% genotypes). • Adoption studies. Seek to distinguish genetic from environmental effects by comparing phenotypes in children more closely resemble their biological than adoptive parents. • Offspring of discordant MZ twins. Control for environmental effect; test for large genetic contribution to etiology.

  19. Basic Genetic Model Phenotype (P) = Genetics + Environment Genetics = Additive (A) + Dominant (D) Environment = Common (C) + Specific (E) => P = A + D + C + E

  20. Statistical Genetic Model Cov(Yi,Yj) = 2Fijs2(a) + Dijs2(d) + gijs2(c) + dijs2(e) Fij : kinship coefficient Dij : Jacquard’s coefficient of identical-by-descent gij : Probability of sharing environmental factors dij : Residual coefficient VP = VA + VD + VC + VE V = variance; P = Phenotype; A, D, C, E = as defined

  21. Expected Kinship Coefficients Expected coefficient for Relative s2(a) s2(d) s2(c) Spouse-spouse 0 0 1 Parent-offspring 1/2 0 1 Full sibs 1/2 1/4 1 Half-sibs 1/4 0 1 Aunt-niece 1/4 0 1 First cousins 1/8 0 0 Dizygotic twins 1/2 1/4 1 Monozygotic twins 1 1 1

  22. A Genetic Model for Twins Study r = 1 r = .5 / .25 r = 1 / .5 E1 C1 D1 A1 A2 D2 C2 E2 a c d e a d c e Twin 1 Twin 2 A=additive; D=dominant; C=common environment; E=specific environment

  23. Intraclass Correlation: Femoral Neck BMD DZ MZ rMZ = 0.73 rMZ = 0.47

  24. Genetic Determination of Lean, Fat and Bone Mass rMZ and rDZ are shown in coefficient of correlation and standard error in brackets; H2, Heritability index: proportion of variance of a traited attributed to genetic factors

  25. Multivariate Analysis: The Cholesky Decomposition Model G1 G2 G3 G4 G5 Fat mass LS BMD FN BMD TB BMD Lean mass E1 E2 E3 E4 E5 LS=lumbar spine, FN=femoral neck, TB=total body, BMD = bone mineral density

  26. Genetic and Environmental Correlation between Lean, Fat and Bone Mass Upper diagonal: genetic correlation; lower diagonal: environmental correlation LS=lumbar spine, FN=femoral neck, TB=total body, BMD = bone mineral density

  27. Strategies for Finding Genes

  28. How many genes ? • Initial estimate: 120,000. • DNA sequence: 60,000 - 70,000. • Estimates from the Human Genome Project: 32,000 - 39,000 (including non-functional genes = inactive genes). • Osteporosis genes = 50 - 70 (?)

  29. Distribution of the number of genes Polygenes Number of genes Oligogenes Major genes Effect size

  30. Finding genes: a challenge One of the most difficult challenges ahead is to find genes involved in diseases that have a complex pattern of inheritance, such as those that contribute to osteoporosis, diabetes, asthma, cancer and mental illness.

  31. Why search for genes? • Scientific value • Study genes’ actions at the molecular level • Therapeutic value • Gene product and development of new drugs; • Gene therapy • Public health value • Identification of “high-risk” individuals • Interaction between genes and environment

  32. Genomewise screening vs Candidate gene • Genome-wide screening approach • No physiological assumption • Systematic screening for chromosomal regions of interest in the entire genome • Candidate gene approach • Proven or hypothetical physiological mechanism • Direct test for individual genes

  33. Linkage vs Association • Linkage • traces cosegregation and recombination phenomena between observed markers and unobserved putative trait. Significance is shown by a LOD (log-odds) score. • Association • compares the frequencies of alleles between unrelated cases (diseased) and controls. • Transmission disequilibrium test (TDT) • examines the transmission of alleles from heterozygous parents to those children exhibiting the phenotype of interest.

  34. Two-point linkage analysis: an example D 142 138 /142 D 142 d 134 ?? 134 /142 146 / 154 142 /146 142 /154 134 / 146 142 / 154 134 / 146 134 / 154 134 / 146 134 / 154 Non Rec Non Non Non Non Rec Non Non = non-recombination; Rec = recombination

  35. No linkage Complete linkage D d D d 1/4 1/4 0 1/2 134 142 134 142 1/4 1/4 1/2 0 Incomplete linkage D d q/2 (1-q)/2 134 142 (1-q)/2 q/2 q: Recombination fraction

  36. Estimation of the recombination fraction q Max LOD score +6 +4 LOD score +2 0 -2 -4 -6 0 0.1 0.2 0.3 0.4 0.5 Estimated value of q

  37. A model for sibpair linkage analysis Xi1 = value of sib 1; Xi2 = value of sib 2 Di = abs(Xi1 - Xi2)2 pi = probability of genes shared identical-by-descent E(Di | pi) = a + b pi If b = 0 => s2(g) = 0; q = 0.5, i.e. No linkage If b < 0 => s2(g) > 0; q ne 0.5, i.e. Linkage Behav Genet 1972; 2:3-19

  38. Identical-by-Descent (IBD) 126 / 130 134 / 138 126 / 134 126 / 138 130 / 134 130 / 138 126 / 138 A B C D E Alleles ibd if they are identical and descended from the same ancestral allele • A and D share no alleles • A, B and E share 1 allele (126) ibd; C vs D; A vs C; B, D and E • B and E share 2 (126 and 138) alleles ibd

  39. Sibpair linkage analysis: an example Squared difference in BMD among siblings o o o o o o o o o o o o o o o o o o o o o o o o o o o 0 1 2 Number of alleles shared IBD Nature 1994; 367:284-287

  40. Association analysis: an example Association between vitamin D receptor gene and bone mineral density

  41. Candidate BMD genes : association analysis

  42. Localization of BMD genes in humans

  43. Some notable genes • Vitamin D receptor (VDR) • Collagen I alpha 1 (COLIA1) • Estrogen receptor (ER) • Interleukin-6 (IL6) • Transforming growth factor b (TGFb)

  44. Problems • None of the candidate genes have clinically meaningful effect on BMD. • Inconsistent (even conflicting) results. • Past studies have suffered serious problems in experimental design and methodology. • Association • Inadequate sample size • Univariate analysis • Sibpair analysis

  45. New paradigms • Sampling design • large multi-generational families • Phenotypes • consideration of multitraits rather than a single trait. • Analysis • Combine linkage and association analyses • Animal model • Mouse genome and transgenic model

  46. Summary • Fracture is an ultimate and clinically relevant outcome of osteoporosis. • BMD is a primary predictor of fracture. • Variation in BMD is largely determined by genetic factors. • The search for specific genes that are linked to BMD has not been successful nor productive.

  47. Perspective • Can genes be found? • Definitely. • The Human Genome Project role? • Very helpful. • Influences of biotechnology? • Great realization. • Gene therapy? • Quite possible.

  48. Lôøi queâ (genes) chaép nhaët doâng daøi • Mua vui cuõng ñöôïc moät vaøi troáng canh (phuùt giaây) • Nguyeãn Du

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