Download Genetic Epidemiological Strategies to the Search for Osteoporosis

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
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•
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Epidemiologic results
Clinical features of osteoporosis
Determinants of fracture risk
Genetics of bone mineral density
The search for osteoporosis genes
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)
Magnitude of the Problem
Prevalence of Osteoporosis
Incidence of all Fractures
%
Per 100,000
80
12000
70
Males
10000
60
Females
8000
50
6000
40
30
4000
20
2000
10
0
0
60-64
65-69
70-74
75-79
80+
60-64
65-69
70-74
75-79
80+
Incidence of Hip Fx Worldwide
Type of Fractures
Types of Fractures
A Model of Fracture
Bone density
Bone
strength
Bone quality
Fracture
Fall
Trauma
Force impact
Risks Factors for Hip Fractures
Females
Unit
Relative Risk
Femoral neck BMD 0.12 g/cm2
2.1 (1.6 - 2.6)
Falls
Each fall
2.4 (1.5 - 3.8)
Postural sway
2000 mm2
1.3 (1.1 - 1.5)
Femoral neck BMD 0.12 g/cm2
2.4 (1.6 - 3.7)
Falls
Each fall
3.9 (1.7 - 9.3)
Age
5 years
1.7 (1.2 - 2.3)
Males
Relationship between Fracture
and Bone Mineral Density
Change in BMC and BMD with Age
5
0
1
4
Hip BMC
4
0
3
0
Hip BMD
1
0
Percentperyear
Percentperyear
2
0
1
0
6
2
0
-1
0
5
1
0
1
5
2
0
-2
4
2
5
8
1
2
B
a
se
lin
ea
g
e
1
6
2
0
2
4
2
8
B
a
se
lin
ea
g
e
5
0
Spine BMC
4
0
Spine BMD
1
4
3
0
1
0
Percentperyear
Percentperyear
2
0
1
0
6
2
0
-1
0
5
1
0
1
5
B
a
se
lin
ea
g
e
2
0
2
5
-2
5
1
0
1
5
B
a
se
lin
ea
g
e
2
0
2
5
Determinants of Peak Bone Mass
Genetic factors
Nutritional factors
Peak Bone Mass
16-25 yr of age
Exercise and
environmental factors
Hormonal factors
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
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
-8
-6
-4
-2
0
2
Percent change in BMD
4
6
8
Genetics of Bone Mineral Density
Clues to Genetics and Environment
Epidemiol characteristics
Geographic variation
Ethnic variation
Temporal variation
Epidemics
Social class variation
Gender variation
Age
Family variables
History of disease
Birth order
Birth interval
Co-habitation
Genetics
+
+
+/+
+/+
+/-
Environment
+
+
+
+
+
+
+
+
+
+
+
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.
Basic Genetic Model
Phenotype (P) = Genetics + Environment
Genetics = Additive (A) + Dominant (D)
Environment = Common (C) + Specific (E)
=> P = A + D + C + E
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
Expected Kinship Coefficients
Expected coefficient for
Relative
Spouse-spouse
Parent-offspring
Full sibs
Half-sibs
Aunt-niece
First cousins
Dizygotic twins
Monozygotic twins
s2(a)
0
1/2
1/2
1/4
1/4
1/8
1/2
1
s2(d)
0
0
1/4
0
0
0
1/4
1
s2(c)
1
1
1
1
1
0
1
1
A Genetic Model for Twins Study
r=1
r = .5 / .25
r = 1 / .5
E1
C1
a
c
D1
d
Twin 1
A1
e
A2
D2
a
C2
d
c
E2
e
Twin 2
A=additive; D=dominant; C=common environment; E=specific environment
Intraclass Correlation: Femoral Neck BMD
MZ
1.4
1.4
rMZ = 0.73
rMZ = 0.47
1.3
1.2
1.2
1.1
1.1
1
Twin 2
Twin 2
1.3
DZ
0.9
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
Genetic Determination of Lean, Fat and Bone Mass
rMZ
rDZ
H2 (%)
Lumar spine BMD
0.74 (0.06)
0.48 (0.10)
77.8
Femoral neck BMD
0.73 (0.06)
0.47 (0.11)
76.4
Total body BMD
0.80 (0.05)
0.48 (0.10)
78.6
Lean mass
0.72 (0.06)
0.32 (0.12)
83.5
Fat mass
0.62 (0.08)
0.30 (0.12)
64.8
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
Multivariate Analysis:
The Cholesky Decomposition Model
G1
G2
G3
G4
Lean
mass
Fat
mass
LS
BMD
FN
BMD
TB
BMD
E3
E4
E5
E1
E2
G5
LS=lumbar spine, FN=femoral neck, TB=total body, BMD = bone mineral density
Genetic and Environmental Correlation between
Lean, Fat and Bone Mass
LM
Lean mass (LM)
FM
LS
0.52
0.39
0.23
0.51
0.41
0.36
0.70
0.57
0.70
Ft mass (FM)
0.16
Lumbar spine BMD (LS)
0.08
0.02
Femoral neck BMD (FN)
0.16
0.05
0.64
Total body BMD (TB)
0.09
0.31
0.75
FN
TB
0.61
0.58
Upper diagonal: genetic correlation; lower diagonal: environmental correlation
LS=lumbar spine, FN=femoral neck, TB=total body, BMD = bone mineral density
Strategies for Finding Genes
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 (?)
Distribution of the number of genes
Polygenes
Number of genes
Oligogenes
Major genes
Effect size
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.
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
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
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.
Two-point linkage analysis: an example
D
142
D
d
134 142
138 /142
??
134 /142
142 /146 142 /154
Non
Rec
134 / 146
Non
142 / 154
Non
146 / 154
134 / 146
Non
134 / 154 134 / 146 134 / 154
Non
Non = non-recombination; Rec = recombination
Rec
Non
No linkage
D
Complete linkage
d
D
d
134
1/4
1/4
134
0
1/2
142
1/4
1/4
142
1/2
0
Incomplete linkage
134
D
d
q/2
(1-q)/2
6
LOD  log 10
142
(1-q)/2
1 θ   θ 

  
 2  2
8
1
 
4
q/2
q: Recombination fraction
2
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
Estimated value of q
0.5
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
If b < 0
=>
=>
s2(g) = 0; q = 0.5, i.e. No linkage
s2(g) > 0; q ne 0.5, i.e. Linkage
Behav Genet 1972; 2:3-19
Identical-by-Descent (IBD)
126 / 130
126 / 134
A
126 / 138
B
134 / 138
130 / 134
C
130 / 138
D
126 / 138
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
Sibpair linkage analysis: an example
25
20
o
o
ooo
oo
o
o
o
oo
oo
oo
o
o
o
oo
oo
oo
o
o
Intrapair difference in BMD (%)
Squared
difference
in BMD
among
siblings
15
10
5
0
0
1
2
Number of alleles shared IBD
0
1
Alleles shared IBD
Nature 1994; 367:284-287
2
Association analysis: an example
1.1
g/cm2
1
0.9
0.8
BB
Bb
bb
VDR genotype
Association between vitamin D receptor gene and bone mineral density
Candidate BMD genes : association analysis
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
a2HS Glycoprotein
Vitamin D binding protein
Osteopontin
Osteonectin
Estrogen receptor a
Interleukin-6
Calcitonin receptor
Collagen type Ia2
Parathyroid hormone
Vitamin D receptor
Collagen Type Ia1
Transforming growth factor b1
Apolipoprotein E
BGLAP
CASR
CASR
AHSG
DBP/GCv
SPP1
SPOCK
ESRa
IL-6
CALCR
COLIA2
PTH
VDR
COLIA1
TGF-b1
ApoE
Localization of BMD genes in humans
Some notable genes
•
•
•
•
•
Vitamin D receptor (VDR)
Collagen I alpha 1 (COLIA1)
Estrogen receptor (ER)
Interleukin-6 (IL6)
Transforming growth factor b (TGFb)
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
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
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.
Perspective
• Can genes be found?
– Definitely.
• The Human Genome Project role?
– Very helpful.
• Influences of biotechnology?
– Great realization.
• Gene therapy?
– Quite possible.
• Lôøi queâ (genes) chaép nhaët doâng daøi
• Mua vui cuõng ñöôïc moät vaøi troáng canh (phuùt
giaây)
• Nguyeãn Du