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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