Transcript Interpretation of Clinical and Genetic Association Studies

Genetics of Osteoporosis
Dr. Tuan V. Nguyen
Associate Professor, Senior Fellow
Bone and Mineral Research Program
Garvan Institute of Medical Research
Sydney, Australia
Overview






Osteoporosis – definition and consequences
Risk factors of fracture
Genetics of bone mineral density
Gene hunting
Candidate genes
Future ?
Increase in life expectancy
75
80
70
55
Years
60
50
43
40
30
33
22
20
10
0
Roman
Empire
Middle Age
Mid-19th
century
Early 1900
Now
WHO. Human Population: Fundamentals of Growth World Health, 2000.
The ageing of population
Percent of population aged 65+
25
World
Australia
20
Percent
15
10
5
0
1996
2001
2011
2021
2031
2041
ABS and US Bureau of Census, 1996.
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
Incidence of all-limb fractures
Rate per 100,000 population
500
400
300
200
100
0
0-4
5-14
15-24 25-34 35-44 45-54 55-64 65-74 75-84
Age group
85+
Annual fracture incidence in Australia 1996-2051
250
207.66
200
1000
150.74
150
100
93.75
104.42
115.27
125.86
50
0
2001
2006
2011
2016
2026
2051
Projected annual number of all-limb fractures in Australia aged
35+ (Sanders et al, MJA 1999)
Hip, vertebrae, and Colles fractures
Fracture
2006
2051
Hip
20,700
60,000
Vertebrae
14,500
31,700
Colles
11,900
23,000
Humerus
7,500
16,300
Pelvis
4,100
9,800
Projected annual number of all-limb fractures in Australia aged
35+(Sanders et al, MJA 1999)
Lifetime risk of some diseases women
Any osteoporotic fracture
1/2
Hip fracture
1/6
Clinical vertebral fracture
1/4
Cancer (any site)*
2/5
Breast cancer*
Lung/bronchus*
1/8
1/16
Coronary heart diseases
1/4
Diabetes Mellitus
0
*, from birth
1/3
10
20
30
40
50
Residual lifetime risk (%)
(from the age of 50)
60
70
Lifetime risk of some diseases - men
1/3
Any osteoporotic fracture
Hip fracture
1/16
Clinical vertebral fracture
1/8
Cancer (any site)*
3/7
Prostate cancer*
Lung/bronchus*
1/8
1/16
Coronary heart diseases
1/3
Diabetes Mellitus
0
*, from birth
1/2
10
20
30
40
Residual lifetime risk (%)
(from the age of 50)
50
60
Survival probability in those
with and without fracture
A
Women
B
1.0
1.0
0.9
0.9
0.8
Non-fracture
0.7
0.6
0.5
0.4
0.3
Any fracture
0.2
0.1
Cummulative survival proportion
Cummulative survival proportion
Men
0.8
0.7
Non-fracture
0.6
0.5
0.4
0.3
0.2
0.1
0.0
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15
Time to follow-up (year)
Any fracture
0.0
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15
Time to follow-up (year)
Nguyen et al, 2005
Risk factors of fracture
A model for predicting fracture
Bone mineral
Density (BMD)
Bone strength
Bone quality
(ultrasound ?)
Fracture
Fall
Trauma / mechanical
Force of impact
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
-8
-6
-4
-2
0
2
Effect on Bone Mass
4
6
8
Risk factors for low BMD
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
Change in BMD with Age
Relationship between Femoral Neck BMD and Age
0.8
0.4
0.6
Femoral neck BMD
1.0
0.8
0.2
0.6
BMD L2-L4
1.2
1.0
1.4
1.2
Relationship between LSBMD and Age
10
20
40
30
Age
50
60
10
20
40
30
Age
50
60
<0
.4
0
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
-
Prevalence
18
T < 2.5
osteoporosis
16
0.8
14
0.7
12
0.6
10
0.5
8
0.4
6
0.3
4
0.2
2
0.1
0
0
Femoral neck BMD
10-year Risk of Fx
Bone mineral density and fracture
0.9
Low BMD and fracture - women
1287women
Osteoporosis
345 (27%)
Fx = 137
(40%)
No Fx =
208 (60%)
42%
Non-osteop.
942 (73%)
Fx = 191
(20%)
No Fx = 751
(80%)
Interaction between BMD and falls
n=56
60
n=11
40
30
n=17
20
n=3
n=3
10
to
3-5
n=4
fa
c
n=0
isk
2
0
fr
n=0
ro
< -2.5
FNBM
D (T-
> -1.0
scor
e)
Nguyen et al, JBMR 2005
m
be
0-1
-2.4 to -1.1
rs
n=7
Nu
cture
Rate of Hip fra
n
o -years)
(per 1000 pers
50
Genetics of Osteoporosis
Heritability of femoral neck
BMD
MZ
r =0.75
1.4
1.3
1.3
1.2
1.2
1.1
1.1
1
Twin 2
Twin 2
1.4
DZ
0.9
r =0.45
1
0.9
0.8
0.8
0.7
0.7
0.6
0.6
0.5
0.5
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
Twin 1
Nguyen et al, Am J Epidemiol 1998
Genetics of fracture risk

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)
Gene search
Genotype
Mathematical function
Phenotype
Polymorphisms
Fracture
Genetic markers
Bone mineral density
SNPs
Quantitative ultrasound
Strategies for gene search

Linkage analysis

Genome-wide screen

Association analysis

“Candidate gene”
Linkage analysis – identical by
descent (ibd)
AB
AC
AB
AC
IBD = 0
AB
CD
AC
AD
IBD = 1
AB
CD
BC
BC
IBD = 2
Linkage analysis: basic model
Squared
difference
in BMD
among
siblings
o
oo
oo
oo
o
o
o
oo
oo
oo
o
o
Regression line
o
oo
oo
oo
o
o
0
1
2
Number of alleles shared IBD
Population-based association analysis
Fracture
AC AB AC BC AA AB BB AA AC
AB
AC BB BC BC CC AB BB CC
BB
No fracture
BC
Family-based association analysis
AB
AA
AB
AB
AC
BC
BC
AA
AB
Genome-wide vs candidate gene approach
Genome-wide screen
Candidate gene analysis
Complex
Simple
No prior knowledge of
mechanism
Prior knowledge of
mechanism
Expensive
Inexpensive
No specific genes
Specific genes
Linkage vs association phenomena
Linkage
Association
Magnitude of “effect”
No
Yes
Transmission
Yes
No/Yes
Study design
complexity
Power
Complex
Simple
Low
High
High
High
False +ve
Some recent “osteoporosis genes”

Vitamin D receptor gene (Morrison et al, Nature
1994)

Collagen I alpha 1 gene – COLIA1 (Grant et al,
Nat Genet, 1996).

LRP5 gene (Am J Hum Genet, 1998)
Candidate genes of osteoporosis
Location
Name
Symbol
1q25
2q13
3q21-24
3q27
4q11-13
4q21
5q31
6q25.1
7p21
7q21.3
7q22
11p15
12q13
17q22
19q13
19q13
Osteocalcin
IL-1 Receptor Antagonist
Calcium Sensing Receptor
2HS Glycoprotein
Vitamin D binding protein
Osteopontin
Osteonectin
Estrogen receptor 
Interleukin-6
Calcitonin receptor
Collagen type I2
Parathyroid hormone
Vitamin D receptor
Collagen Type I1
Transforming growth factor 1
Apolipoprotein E
BGLAP
CASR
CASR
AHSG
DBP/GCv
SPP1
SPOCK
ESR
IL-6
CALCR
COLIA2
PTH
VDR
COLIA1
TGF-1
ApoE
Localization of genes for BMD
VDR, COLIA1 and fracture
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 associations

600 positive associations between common gene
variants and disease reported 1986-2000

166 were studied 3+ times

6 have been consistently replicated
J N Hirschhorn et al. Genetics in Medicine 2002
Evolution of
the strength
of an
association as
more
information is
accumulated
Ioannidis et al,
Nat Genet 2001
Problems of gene search – p-value

“Traditional” model of inference
Hypothesis H
 Collecting data D
 Computing p-value = Pr(D | H)

If p-value < 0.05  reject H
 If p-value > 0.05  accept H

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
Diagnostic analogy
Diagnosis
Genetic research
Has cancer
test +ve
OK
Has cancer
test –ve
! (false -ve)
No cancer
test +ve
! (false +ve)
No cancer
test –ve
OK
Association
Significant
Association
NS
No assoc.
Significant
No assoc.
NS
Power
P-value
What do we want to know?
Clinical
P(+ve | cancer), or
P(cancer | +ve) ?
Research
P(Significant test | Association), or
P(Association | Significant test) ?
Breast cancer screening
Prevalence = 1%; Sensitivity = 90%; Specificity = 91%
Population
Cancer (n=10)
No Cancer (n=990)
+ve
-ve
+ve
-ve
N=9
N=1
N=90
N=900
P(Cancer| +ve result) = 9/(9+90) = 9%
Probability of a true association
Prior prob. association = 0.05; Power = 90%; P-value = 5%
1000 SNPs
True (n=50)
False (n=950)
+ve
-ve
+ve
-ve
N=45
N=5
N=48
N=902
P(True association| +ve result) = 45/(45+48) = 48%
Risk factors for fracture




Blonde hair
Being tall
Wear trouser (women)
High heel (women)

Drinking coffee

Drinking tea

Coca cola

High protein intake
“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
Can genes be used to predict
fracture?
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)

“… 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)
Positive predictive value as a function of
gene frequency and relative risk
What is the probability that I will sustain a fracture if I have “high risk” genotype?
Susceptibility
genotype frequency
Relative
Risk =1.5
Relative
Risk =2.0
Relative
Risk =5.0
Relative
Risk =10
0.1%
15.0
20.0
49.8
99.1
0.5%
15.0
19.9
49.0
95.7
1%
14.9
19.8
48.1
91.7
10%
14.3
18.2
35.7
52.6
20%
13.6
16.7
27.8
35.7
PPV (%) of susceptibility genotype for a disease with lifetime risk of 10%
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
19.8
19.8
1%
39.2
95.2
10%
33.0
55.0
1%
38.7
91.6
10%
34.6
68.0
1%
52.9
87.4
10%
36.0
64.9
Disregard cofactor
1%
5%
10%
How many fractures are due to genes?
Population attributable risk fraction as a function of gene
frequency and relative risk
Susceptibility
genotype frequency
RR=1.5
RR=2.0
RR=5.0
RR=10
0.1%
0.05
0.1
0.4
0.9
0.5%
0.25
0.5
2.0
4.3
1%
0.5
1.0
3.9
8.3
10%
4.8
9.1
28.6
47.4
20%
9.1
16.7
44.4
64.3
Summary

Osteoporosis and fracture: serious public
health problem

Bone mineral density: primary predictor of
fracture risk

BMD is largely regulated by genetic factors
Summary

BMD is largely regulated by genetic factors

Finding genes for fracture: challenge


Genetics, clinical medicine, statistics, bioinformatics
Predictive value of genes in fracture
prediction: consider environmental risk factors