Genetics of Genetics of Osteoporosis Osteoporosis

Dr. Tuan V. NguyenDr. Tuan V. NguyenAssociate Professor, Senior FellowAssociate Professor, Senior Fellow

Bone and Mineral Research ProgramBone and Mineral Research ProgramGarvan Institute of Medical ResearchGarvan Institute of Medical Research

Sydney, AustraliaSydney, Australia

OverviewOverview

Osteoporosis – definition and Osteoporosis – definition and consequencesconsequences

Risk factors of fractureRisk factors of fracture Genetics of bone mineral densityGenetics of bone mineral density Gene huntingGene hunting Candidate genesCandidate genes Future ?Future ?

Increase in life expectancyIncrease in life expectancy

22

33

43

55

75

0

10

20

30

40

50

60

70

80

RomanEmpire

Middle Age Mid-19thcentury

Early 1900 Now

Yea

rs

WHO. Human Population: Fundamentals of Growth World Health, 2000.

The ageing of populationThe ageing of population

0

5

10

15

20

25

1996 2001 2011 2021 2031 2041

Per

cent

World Australia

Percent of population aged 65+

ABS and US Bureau of Census, 1996.

Osteoporosis – definitionsOsteoporosis – 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

Incidence of all-limb Incidence of all-limb fracturesfractures

0

100

200

300

400

500

0-4 5-14 15-24 25-34 35-44 45-54 55-64 65-74 75-84 85+

Age group

Rate

per

100,0

00 p

op

ula

tio

n

Annual fracture incidence in Annual fracture incidence in Australia Australia 1996-20511996-2051

93.75104.42

115.27125.86

150.74

207.66

0

50

100

150

200

250

2001 2006 2011 2016 2026 2051

1000

Projected annual number of all-limb fractures in Australia aged 35+ (Sanders et al, MJA 1999)

Hip, vertebrae, and Colles Hip, vertebrae, and Colles fracturesfractures

FractureFracture 20062006 20512051

HipHip 20,70020,700 60,00060,000

VertebraeVertebrae 14,50014,500 31,70031,700

CollesColles 11,90011,900 23,00023,000

HumerusHumerus 7,5007,500 16,30016,300

PelvisPelvis 4,1004,100 9,8009,800

Projected annual number of all-limb fractures in Australia aged 35+(Sanders et al, MJA 1999)

Lifetime risk of some Lifetime risk of some diseases - womendiseases - women

Any osteoporotic fracture

Hip fracture

Clinical vertebral fracture

Cancer (any site)*

Breast cancer*

Lung/bronchus*

Coronary heart diseases

Diabetes Mellitus

*, from birth Residual lifetime risk (%)

0 10 20 30 40 50 60 70

1/2

1/6

1/4

2/5

1/8

1/16

1/4

1/3

(from the age of 50)

Lifetime risk of some Lifetime risk of some diseases - mendiseases - 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)

Residual lifetime risk (%)

0 10 20 30 40 50 60

1/3

1/16

1/8

3/7

1/8

1/16

1/3

1/2

Survival probability in thoseSurvival probability in thosewith and without fracturewith and without fracture

Time to follow-up (year)

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Cu

mm

ula

tive

su

rviv

al p

rop

ort

ion

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Non-fracture

Any fracture

B Men

Time to follow-up (year)

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Cu

mm

ula

tive

su

rviv

al p

rop

ort

ion

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Non-fracture

Any fracture

A Women

Nguyen et al, 2005

Risk factors of fractureRisk factors of fracture

A model for predicting A model for predicting fracture fracture

Bone mineral Density (BMD)

Bone quality (ultrasound ?)

Fall

Force of impact

Bone strength

Trauma / mechanical

Fracture

Risk factors for low bone Risk factors for low bone massmass

-8 -6 -4 -2 0 2 4 6 8

Effect on Bone Mass

SmokerAge (per 5 years)Maternal history of fxSteroid use

Caffeine intakeActivity score

Age at menopause

Milk intakeEver pregnant

Surgical menopauseWaist/hip ratio

Weight

Grip strength

HeightThiazide use

Oestrogen use

Risk factors for low BMDRisk factors for low BMD

GeneticsGenetics Race, Sex, Familial prevalence

HormonesHormones Menopause, Oophorectomy, Body composition

NutritionNutrition Low calcium intake, High caffeine intake, High sodium intake, High animal protein intake

LifestylesLifestyles Cigarette use, High alcoholic intake, Low level of physical activity

DrugDrug Heparin, Anticonculsants, Immunosuppressants Chemotherapy, Corticosteroids, Thyroid hormone

Change in BMD with AgeChange in BMD with Age

10 20 30 40 50 60

0.6

0.8

1.0

1.2

1.4

Relationship between LSBMD and Age

Age

BM

D L

2-L

4

10 20 30 40 50 60

0.2

0.4

0.6

0.8

1.0

1.2

Relationship between Femoral Neck BMD and Age

Age

Fe

mo

ral n

eck

BM

D

Bone mineral density Bone mineral density and fractureand fracture

0

2

4

6

8

10

12

14

16

18

<0.40

0.40-

0.45-

0.50-

0.55-

0.60-

0.65-

0.70-

0.75-

0.80-

0.85-

0.90-

0.95-

1.00-

1.05-

1.10-

Femoral neck BMD

Pre

vale

nce

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10-y

ear

Ris

k o

f F

x

T < 2.5 osteopor

osis

Low BMD and fracture - Low BMD and fracture - womenwomen1287women

Osteoporosis 345 (27%)

Non-osteop. 942 (73%)

Fx = 137 (40%)

No Fx = 208 (60%)

No Fx = 751 (80%)

Fx = 191 (20%)

42%

Interaction between BMD Interaction between BMD and fallsand falls

Nguyen et al, JBMR 2005

0

10

20

30

40

50

60R

ate

of

Hip

fra

ctu

re

(per

100

0 p

erso

n-y

ears

)

Num

ber o

f ris

k fa

ctor

s

FNBMD (T-score)

3 - 5

2

0 - 1

> -1.0-2.4 to -1.1

< -2.5

n=56

n=11

n=3

n=17

n=7

n=4

n=0

n=0

n=3

Genetics of OsteoporosisGenetics of Osteoporosis

Heritability of femoral Heritability of femoral neck BMDneck BMD

0.5

0.6

0.7

0.8

0.9

1

1.1

1.2

1.3

1.4

0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4

Twin 1

Tw

in 2

0.5

0.6

0.7

0.8

0.9

1

1.1

1.2

1.3

1.4

0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4

Twin 1

Tw

in 2

MZ DZ

Nguyen et al, Am J Epidemiol 1998

r =0.75 r =0.45

Genetics of fracture riskGenetics of fracture risk

MZ twins have higher concordance in MZ twins have higher concordance in fracture rate than DZ twins (Kannus, fracture rate than DZ twins (Kannus, BMJ 1999)BMJ 1999)

Around 1/3 variance of fracture risk is Around 1/3 variance of fracture risk is due to genetic factors (Deng et al, due to genetic factors (Deng et al, JBMR 2000)JBMR 2000)

Gene searchGene search

GenotypeGenotype PhenotypePhenotype

Fracture

Bone mineral density

Quantitative ultrasound

Polymorphisms

Genetic markers

SNPs

Mathematical function

Strategies for gene Strategies for gene searchsearch

Linkage analysisLinkage analysis

Association Association analysisanalysis

Genome-wide Genome-wide screenscreen

““Candidate gene”Candidate gene”

Linkage analysis – identical Linkage analysis – identical by descent (ibd)by descent (ibd)

AB AC

AB AC

AB CD

AC AD

AB CD

BC BC

IBD = 0 IBD = 1 IBD = 2

Linkage analysis: basic Linkage analysis: basic modelmodel

oooooooo

o

ooooooooo

ooooooooo

Squareddifference in BMDamong siblings

Number of alleles shared IBD

0 1 2

Regression line

Population-based association Population-based association analysisanalysis

AB AC BC AA AB BB AA AC ABAC

Fracture

BB BC BC CC AB BB CC BC BBAC

No fracture

Family-based association Family-based association analysisanalysis

AB AA

AB

AB AC

BC

BC AA

AB

Genome-wide vs candidate gene Genome-wide vs candidate gene approachapproach

Genome-wide screenGenome-wide screen Candidate gene analysisCandidate gene analysis

Complex

No prior knowledge of mechanism

Expensive

No specific genes

Simple

Prior knowledge of mechanism

Inexpensive

Specific genes

Linkage vs association Linkage vs association phenomenaphenomena

LinkageLinkage AssociatiAssociationon

Magnitude of Magnitude of “effect”“effect”

NoNo YesYes

TransmissionTransmission YesYes No/YesNo/Yes

Study design Study design complexitycomplexity

ComplexComplex SimpleSimple

PowerPower LowLow HighHigh

False +veFalse +ve HighHigh HighHigh

Some recent “osteoporosis Some recent “osteoporosis genes”genes”

Vitamin D receptor gene (Morrison Vitamin D receptor gene (Morrison et al, Nature 1994)et al, Nature 1994)

Collagen I alpha 1 gene – COLIA1 Collagen I alpha 1 gene – COLIA1 (Grant et al, Nat Genet, 1996).(Grant et al, Nat Genet, 1996).

LRP5 gene (Am J Hum Genet, 1998)LRP5 gene (Am J Hum Genet, 1998)

Candidate genes of Candidate genes of osteoporosisosteoporosis

Location Name Symbol1q25 Osteocalcin BGLAP2q13 IL-1 Receptor Antagonist CASR3q21-24 Calcium Sensing Receptor CASR 3q27 2HS Glycoprotein AHSG4q11-13 Vitamin D binding protein DBP/GCv4q21 Osteopontin SPP15q31 Osteonectin SPOCK6q25.1 Estrogen receptor ESR

7p21 Interleukin-6 IL-67q21.3 Calcitonin receptor CALCR7q22 Collagen type I2 COLIA211p15 Parathyroid hormone PTH12q13 Vitamin D receptor VDR17q22 Collagen Type I1 COLIA119q13 Transforming growth factor 1 TGF-119q13 Apolipoprotein E ApoE

Localization of genes for Localization of genes for BMDBMD

VDR, COLIA1 and VDR, COLIA1 and fracturefracture

Risk Genotype

Prevalence(%)

Relative Risk1

Attributable Risk Fraction

(%)

Taq-1 tt 15.4 2.6 19.8

Sp-1 ss 5.0 3.8 12.3

tt AND ss 1.0 3.0 2.0

tt OR ss 19.8 3.5 32.1

Nguyen et al, JCEM 2005

Poor replication of genetic Poor replication of genetic associationsassociations

600 positive associations between 600 positive associations between common gene variants and disease common gene variants and disease reported 1986-2000reported 1986-2000 166 were studied 3+ times 166 were studied 3+ times

6 have been consistently replicated6 have been consistently replicated

J N Hirschhorn et al. Genetics in Medicine 2002

Evolution Evolution of the of the strength strength of an of an associatioassociation as more n as more informatioinformation is n is accumulataccumulateded Ioannidis et Ioannidis et al, Nat Genet al, Nat Genet 20012001

Problems of gene search – Problems of gene search – p-valuep-value

““Traditional” model of inferenceTraditional” model of inference Hypothesis HHypothesis H Collecting data DCollecting data D Computing p-value = Pr(D | H)Computing p-value = Pr(D | H)

If p-value < 0.05 If p-value < 0.05 reject H reject H If p-value > 0.05 If p-value > 0.05 accept H accept H

The logic of P-valueThe 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

Diagnostic analogyDiagnostic analogy

Has Has cancercancer

test test +ve+ve

OKOK

Has Has cancercancer

test –test –veve

! (false -! (false -ve)ve)

No No cancercancer

test test +ve+ve

! (false ! (false +ve)+ve)

No No cancercancer

test –test –veve

OKOK

Diagnosis

Genetic researchAssociatiAssociationon

SignificaSignificantnt

PowerPower

AssociatiAssociationon

NSNS

No No assoc.assoc.

SignificaSignificantnt

P-valueP-value

No No assoc.assoc.

NSNS

What do we want to What do we want to know?know?ClinicalClinical

P(+ve | cancer), or P(+ve | cancer), or P(cancer | +ve) ?P(cancer | +ve) ?

ResearchResearchP(Significant test | Association), or P(Significant test | Association), or P(Association | Significant test) ?P(Association | Significant test) ?

Breast cancer screeningBreast 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%

Probability of a true Probability of a true associationassociation

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%

Risk factors for fractureRisk factors for fracture

Blonde hairBlonde hair Being tallBeing tall Wear trouser Wear trouser

(women)(women) High heel High heel

(women)(women)

Drinking coffeeDrinking coffee

Drinking teaDrinking tea

Coca colaCoca cola

High protein High protein intakeintake

““Half of what doctors know is Half of what doctors know is wrong. Unfortunately we don’t wrong. Unfortunately we don’t know which half.”know which half.”

Quoted from the Dean of Yale Quoted from the Dean of Yale Medical School, in “Medicine and Its Medical School, in “Medicine and Its Myths”, Myths”, New York Times MagazineNew York Times Magazine, , 16/3/200316/3/2003

Can genes be used to Can genes be used to predict fracture?predict fracture?

Genetics in medicine: hopeGenetics in medicine: hope ““within the next decade genetic testing will within the next decade genetic testing will

be used widely for predictive testing in be used widely for predictive testing in healthy people and for diagnosis and healthy people and for diagnosis and management of patients. . . . The excitement management of patients. . . . The excitement in the field has shifted to the elucidation of in the field has shifted to the elucidation of the genetic basis of the common diseasesthe genetic basis of the common diseases.” (J .” (J Bell, BMJ 1998)Bell, BMJ 1998)

“… “… new understanding of genetic new understanding of genetic contributions to human disease and the contributions to human disease and the development of rational strategies for development of rational strategies for minimizing or preventing disease phenotypes minimizing or preventing disease phenotypes altogetheraltogether.” (F. S Collins NEJM 1999).” (F. S Collins NEJM 1999)

Positive predictive value as a Positive predictive value as a function of gene frequency and function of gene frequency and

relative riskrelative risk

Susceptibility Susceptibility genotype genotype frequencyfrequency

Relative Relative Risk Risk =1.5=1.5

Relative Relative Risk Risk =2.0=2.0

Relative Relative Risk Risk =5.0=5.0

Relative Relative Risk =10Risk =10

0.1%0.1% 15.015.0 20.020.0 49.849.8 99.199.1

0.5%0.5% 15.015.0 19.919.9 49.049.0 95.795.7

1%1% 14.914.9 19.819.8 48.148.1 91.791.7

10%10% 14.314.3 18.218.2 35.735.7 52.652.6

20%20% 13.613.6 16.716.7 27.827.8 35.735.7

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?

Positive predictive value as a Positive predictive value as a function of gene frequency and function of gene frequency and

relative risk and co-factorrelative risk and co-factor

Frequency Frequency of co-factorof co-factor

Frequency Frequency of of genotypegenotype

RR associated RR associated with co-factor = with co-factor =

2.02.0

RR RR associated associated

with co-factor with co-factor = 5= 5

Disregard Disregard co-factorco-factor

19.819.8 19.819.8

1%1% 1%1% 39.239.2 95.295.2

10%10% 33.033.0 55.055.0

5%5% 1%1% 38.738.7 91.691.6

10%10% 34.634.6 68.068.0

10%10% 1%1% 52.952.9 87.487.4

10%10% 36.036.0 64.964.9

How many fractures are due to genes?

Susceptibility Susceptibility genotype genotype frequencyfrequency

RR=1.5RR=1.5 RR=2.0RR=2.0 RR=5.0RR=5.0 RR=10RR=10

0.1%0.1% 0.050.05 0.10.1 0.40.4 0.90.9

0.5%0.5% 0.250.25 0.50.5 2.02.0 4.34.3

1%1% 0.50.5 1.01.0 3.93.9 8.38.3

10%10% 4.84.8 9.19.1 28.628.6 47.447.4

20%20% 9.19.1 16.716.7 44.444.4 64.364.3

Population attributable risk fraction as a function of gene frequency and relative risk

SummarySummary

Osteoporosis and fractureOsteoporosis and fracture: serious : serious public health problempublic health problem

Bone mineral densityBone mineral density: primary : primary predictor of fracture riskpredictor of fracture risk

BMD is largely regulated by genetic BMD is largely regulated by genetic factorsfactors

SummarySummary

BMD is largely regulated by genetic BMD is largely regulated by genetic factorsfactors

Finding genes for fractureFinding genes for fracture: challenge: challenge Genetics, clinical medicine, statistics, Genetics, clinical medicine, statistics,

bioinformaticsbioinformatics

Predictive value of genes in fracture Predictive value of genes in fracture predictionprediction: consider environmental risk : consider environmental risk factorsfactors