1. Genetics of Osteoporosis Dr. Tuan V. Nguyen Associate Professor, Senior Fellow Bone and Mineral Research Program Garvan Institute of Medical Research Sydney, Australia

2. Overview Osteoporosis – definition and consequences Osteoporosis – definition and consequences Risk factors of fracture Risk factors of fracture Genetics of bone mineral density Genetics of bone mineral density Gene hunting Gene hunting Candidate genes Candidate genes Future ? Future ?

3. Increase in life expectancy WHO. Human Population: Fundamentals of Growth World Health, 2000.

4. The ageing of population Percent of population aged 65+ ABS and US Bureau of Census, 1996.

5. Osteoporosis – definitions “[…] compromised bone strength predisposing a person to an increased risk of fracture. Bone strength primarily reflects the integration of bone density and bone quality” (NIH Consensus Development Panel on Osteoporosis JAMA 285:785-95; 2001) Osteoporosis  Risk factor Fracture  Outcome

6. Incidence of all-limb fractures

7. Annual fracture incidence in Australia 1996-2051 Projected annual number of all-limb fractures in Australia aged 35+ (Sanders et al, MJA 1999)

8. Hip, vertebrae, and Colles fractures Fracture20062051 Hip20,70060,000 Vertebrae14,50031,700 Colles11,90023,000 Humerus7,50016,300 Pelvis4,1009,800 Projected annual number of all-limb fractures in Australia aged 35+(Sanders et al, MJA 1999)

9. Lifetime risk of some diseases - women Any osteoporotic fracture Hip fracture Clinical vertebral fracture Cancer (any site)* Breast cancer* Lung/bronchus* Coronary heart diseases Diabetes Mellitus *, from birth (from the age of 50)

10. Lifetime risk of some diseases - men Any osteoporotic fracture Hip fracture Clinical vertebral fracture Cancer (any site)* Prostate cancer* Lung/bronchus* Coronary heart diseases Diabetes Mellitus *, from birth (from the age of 50)

11. Survival probability in those with and without fracture Nguyen et al, 2005

12. Risk factors of fracture

13. A model for predicting fracture Bone mineral Density (BMD) Bone quality (ultrasound ?) Fall Force of impact Bone strength Trauma / mechanical Fracture

14. Risk factors for low bone mass 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

15. Risk factors for low BMD Genetics GeneticsRace, Sex, Familial prevalence Hormones HormonesMenopause, Oophorectomy, Body composition Nutrition NutritionLow calcium intake, High caffeine intake, High sodium intake, High animal protein intake Lifestyles LifestylesCigarette use, High alcoholic intake, Low level of physical activity Drug Drug Heparin, Anticonculsants, Immunosuppressants Chemotherapy, Corticosteroids, Thyroid hormone

16. Change in BMD with Age

17. Bone mineral density and fracture T < 2.5 osteoporosis

18. Low BMD and fracture - women 1287women Osteoporosis 345 (27%) Non-osteop. 942 (73%) Fx = 137 (40%) No Fx = 208 (60%) No Fx = 751 (80%) Fx = 191 (20%) 42%

19. Interaction between BMD and falls Nguyen et al, JBMR 2005

20. Genetics of Osteoporosis

21. Heritability of femoral neck BMD MZ DZ Nguyen et al, Am J Epidemiol 1998 r =0.75r =0.45

22. Genetics of fracture risk MZ twins have higher concordance in fracture rate than DZ twins (Kannus, BMJ 1999) MZ twins have higher concordance in fracture rate than DZ twins (Kannus, BMJ 1999) Around 1/3 variance of fracture risk is due to genetic factors (Deng et al, JBMR 2000) Around 1/3 variance of fracture risk is due to genetic factors (Deng et al, JBMR 2000)

23. Gene search GenotypePhenotype Fracture Bone mineral density Quantitative ultrasound Polymorphisms Genetic markers SNPs Mathematical function

24. Strategies for gene search Linkage analysis Linkage analysis Association analysis Association analysis Genome-wide screen Genome-wide screen “Candidate gene” “Candidate gene”

25. Linkage analysis – identical by descent (ibd) ABAC ABABACAC ABCD ACACADAD ABCD BCBCBCBC IBD = 0IBD = 1IBD = 2

26. Linkage analysis: basic model 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 Squared difference in BMD among siblings Number of alleles shared IBD 0 1 2 Regression line

27. Population-based association analysis ABACBCAAABBBAAACABAC Fracture BBBC CCABBBCCBCBBAC No fracture

28. Family-based association analysis ABAA ABAB ABAC BCBC BCAA ABAB

29. Genome-wide vs candidate gene approach Genome-wide screen Candidate gene analysis Complex No prior knowledge of mechanism Expensive No specific genes Simple Prior knowledge of mechanism Inexpensive Specific genes

30. Linkage vs association phenomena LinkageAssociation Magnitude of “effect” NoYes TransmissionYesNo/Yes Study design complexity ComplexSimple PowerLowHigh False +ve HighHigh

31. Some recent “osteoporosis genes” Vitamin D receptor gene (Morrison et al, Nature 1994) Vitamin D receptor gene (Morrison et al, Nature 1994) Collagen I alpha 1 gene – COLIA1 (Grant et al, Nat Genet, 1996). Collagen I alpha 1 gene – COLIA1 (Grant et al, Nat Genet, 1996). LRP5 gene (Am J Hum Genet, 1998) LRP5 gene (Am J Hum Genet, 1998)

32. Candidate genes of osteoporosis

33. Localization of genes for BMD

34. VDR, COLIA1 and fracture Risk GenotypePrevalence (%) Relative Risk 1 Attributable Risk Fraction (%) Taq-1 tt15.42.619.8 Sp-1 ss5.03.812.3 tt AND ss1.03.02.0 tt OR ss19.83.532.1 Nguyen et al, JCEM 2005

35. Poor replication of genetic associations 600 positive associations between common gene variants and disease reported 1986-2000 600 positive associations between common gene variants and disease reported 1986-2000 166 were studied 3+ times 166 were studied 3+ times 6 have been consistently replicated 6 have been consistently replicated J N Hirschhorn et al. Genetics in Medicine 2002

36. Evolution of the strength of an association as more information is accumulated Ioannidis et al, Nat Genet 2001

37. Problems of gene search – p-value “Traditional” model of inference “Traditional” model of inference Hypothesis H Hypothesis H Collecting data D Collecting data D Computing p-value = Pr(D | H) Computing p-value = Pr(D | H) If p-value < 0.05  reject H If p-value < 0.05  reject H If p-value > 0.05  accept H If p-value > 0.05  accept H

38. The logic of P-value If Tuan has hypertension, he is unlikely to have red hair Tuan has red hair Tuan is unlikley to have hypertension If there was truly no association, then the observation is unlikely The observation occurred The no-association hypothesis is unlikely

39. Diagnostic analogy Has cancer test +ve OK Has cancer test –ve ! (false -ve) No cancer test +ve ! (false +ve) No cancer test –ve OK Diagnosis Genetic researchAssociationSignificantPowerAssociationNS No assoc. SignificantP-value NS

40. What do we want to know? Clinical P(+ve | cancer), or P(cancer | +ve) ? Research P(Significant test | Association), or P(Association | Significant test) ?

41. Breast cancer screening Population Cancer (n=10) No Cancer (n=990) +ve N=9 -ve N=1 +ve N=90 -ve N=900 P(Cancer| +ve result) = 9/(9+90) = 9% Prevalence = 1%; Sensitivity = 90%; Specificity = 91%

42. Probability of a true association 1000 SNPs True (n=50) False (n=950) +ve N=45 -ve N=5 +ve N=48 -ve N=902 P(True association| +ve result) = 45/(45+48) = 48% Prior prob. association = 0.05; Power = 90%; P-value = 5%

43. Risk factors for fracture Blonde hair Blonde hair Being tall Being tall Wear trouser (women) Wear trouser (women) High heel (women) High heel (women) Drinking coffee Drinking coffee Drinking tea Drinking tea Coca cola Coca cola High protein intake High protein intake

44. “Half of what doctors know is wrong. Unfortunately we don’t know which half.” Quoted from the Dean of Yale Medical School, in “Medicine and Its Myths”, New York Times Magazine, 16/3/2003

45. Can genes be used to predict fracture?

46. Genetics in medicine: hope “ within the next decade genetic testing will be used widely for predictive testing in healthy people and for diagnosis and management of patients.... The excitement in the field has shifted to the elucidation of the genetic basis of the common diseases.” (J Bell, BMJ 1998) “ within the next decade genetic testing will be used widely for predictive testing in healthy people and for diagnosis and management of patients.... The excitement in the field has shifted to the elucidation of the genetic basis of the common diseases.” (J Bell, BMJ 1998) “… new understanding of genetic contributions to human disease and the development of rational strategies for minimizing or preventing disease phenotypes altogether.” (F. S Collins NEJM 1999) “… new understanding of genetic contributions to human disease and the development of rational strategies for minimizing or preventing disease phenotypes altogether.” (F. S Collins NEJM 1999)

47. Positive predictive value as a function of gene frequency and relative risk Susceptibility genotype frequency Relative Risk =1.5 Relative Risk =2.0 Relative Risk =5.0 Relative Risk =10 0.1%15.020.049.899.1 0.5%15.019.949.095.7 1%14.919.848.191.7 10%14.318.235.752.6 20%13.616.727.835.7 PPV (%) of susceptibility genotype for a disease with lifetime risk of 10% What is the probability that I will sustain a fracture if I have “high risk” genotype?

48. Positive predictive value as a function of gene frequency and relative risk and co-factor Frequency of co-factor Frequency of genotype RR associated with co-factor = 2.0 RR associated with co-factor = 5 Disregard co- factor 19.819.8 1%1%39.295.2 10%33.055.0 5%1%38.791.6 10%34.668.0 10%1%52.987.4 10%36.064.9

49. How many fractures are due to genes? Susceptibility genotype frequency RR=1.5RR=2.0RR=5.0RR=10 0.1%0.050.10.40.9 0.5%0.250.52.04.3 1%0.51.03.98.3 10%4.89.128.647.4 20%9.116.744.464.3 Population attributable risk fraction as a function of gene frequency and relative risk

50. Summary Osteoporosis and fracture: serious public health problem Osteoporosis and fracture: serious public health problem Bone mineral density: primary predictor of fracture risk Bone mineral density: primary predictor of fracture risk BMD is largely regulated by genetic factors BMD is largely regulated by genetic factors

51. Summary Finding genes for fracture: challenge Finding genes for fracture: challenge Genetics, clinical medicine, statistics, bioinformatics Genetics, clinical medicine, statistics, bioinformatics Predictive value of genes in fracture prediction: consider environmental risk factors Predictive value of genes in fracture prediction: consider environmental risk factors