Genetic Epidemiological StrategiesGenetic Epidemiological Strategiesto the Search for Osteoporosis Genesto the Search for Osteoporosis Genes

Dr. Tuan V. NguyenBone and Mineral Research ProgramGarvan Institute of Medical Research

Sydney, Australia

• Epidemiologic results

• Clinical features of osteoporosis

• Determinants of fracture risk

• Genetics of bone mineral density

• The search for osteoporosis genes

Contents of PresentationContents of Presentation

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)

DefinitionDefinition

Magnitude of the ProblemMagnitude of the Problem

0

10

20

30

40

50

60

70

80

60-64 65-69 70-74 75-79 80+

0

2000

4000

6000

8000

10000

12000

60-64 65-69 70-74 75-79 80+

Males

Females

Incidence of all FracturesIncidence of all FracturesPrevalence of OsteoporosisPrevalence of Osteoporosis

% Per 100,000

Incidence of Hip Fx WorldwideIncidence of Hip Fx Worldwide

Type of FracturesType of Fractures

Types of FracturesTypes of Fractures

Fracture

Bone strength

Trauma

Bone density

Bone quality

Fall

Force impact

A Model of FractureA Model of Fracture

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)

Males

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)

Risks Factors for Hip FracturesRisks Factors for Hip Fractures

Relationship between Fracture and Bone Mineral Density

Relationship between Fracture and Bone Mineral Density

Change in BMC and BMD with AgeChange in BMC and BMD with Age

Baseline age

Perc

ent p

er y

ear

-10

0

10

20

30

40

50

5 10 15 20 25

Baseline age

Perc

ent p

er y

ear

-10

0

10

20

30

40

50

5 10 15 20 25

Hip BMC

Spine BMC

Baseline age

Perc

ent p

er y

ear

-2

2

6

10

14

4 8 12 16 20 24 28

Baseline age

Perc

ent p

er y

ear

-2

2

6

10

14

5 10 15 20 25

Hip BMD

Spine BMD

Determinants of Peak Bone MassDeterminants of Peak Bone Mass

Peak Bone Mass

16-25 yr of age

Genetic factors

Exercise and

environmental factors

Hormonal factorsNutritional factors

Risk Factors for OsteoporosisRisk Factors for Osteoporosis

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

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

Percent change in BMD

SmokerAge (per 5 years)Maternal history of fxSteroid use

Caffeine intakeActivity score

Age at menopause

Milk intakeEver pregnant

Surgical menopauseWaist/hip ratio

Weight

Grip strengthHeight

Thiazide use

Oestrogen use

Risk factors for Low Bone DensityRisk factors for Low Bone Density

Genetics of Bone Mineral Density

Clues to Genetics and EnvironmentClues to Genetics and EnvironmentClues to Genetics and EnvironmentClues to Genetics and Environment

Epidemiol characteristics Genetics EnvironmentGeographic variation + +Ethnic variation + +Temporal variation - +Epidemics +/- +Social class variation - +Gender variation + +Age +/- +Family variables

History of disease + +Birth order +/- +Birth interval - +Co-habitation - +

Methods of InvestigationMethods of InvestigationMethods of InvestigationMethods 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 ModelBasic Genetic ModelBasic Genetic ModelBasic Genetic Model

Phenotype (P) = Genetics + Environment

Genetics = Additive (A) + Dominant (D)

Environment = Common (C) + Specific (E)

=> P = A + D + C + E

Statistical Genetic ModelStatistical Genetic ModelStatistical Genetic ModelStatistical Genetic Model

Cov(Yi,Yj) = 2ij2(a) + ij2(d) + ij2(c) + ij2(e)

ij : kinship coefficient

ij : Jacquard’s coefficient of identical-by-descent

ij : Probability of sharing environmental factors

ij : Residual coefficient

VP = VA + VD + VC + VE

V = variance; P = Phenotype; A, D, C, E = as defined

Expected Kinship CoefficientsExpected Kinship CoefficientsExpected Kinship CoefficientsExpected Kinship Coefficients

Expected coefficient forRelative 2(a) 2(d) 2(c)Spouse-spouse 0 0 1Parent-offspring 1/2 0 1Full sibs 1/2 1/4 1Half-sibs 1/4 0 1Aunt-niece 1/4 0 1First cousins 1/8 0 0Dizygotic twins 1/2 1/4 1Monozygotic twins 1 1 1

Twin 1 Twin 2

E1 C1 D1 A1 A2 D2 C2 E2

A Genetic Model for Twins StudyA Genetic Model for Twins StudyA Genetic Model for Twins StudyA Genetic Model for Twins Study

r = 1

r = .5 / .25

r = 1 / .5

a c d e a d c e

A=additive; D=dominant; C=common environment; E=specific environment

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

Intraclass Correlation: Femoral Neck BMDIntraclass Correlation: Femoral Neck BMDIntraclass Correlation: Femoral Neck BMDIntraclass Correlation: Femoral Neck BMD

MZ DZ

rMZ = 0.73 rMZ = 0.47

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

Genetic Determination of Lean, Fat and Bone MassGenetic Determination of Lean, Fat and Bone MassGenetic Determination of Lean, Fat and Bone MassGenetic Determination of Lean, Fat and Bone Mass

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: Multivariate Analysis: The Cholesky Decomposition ModelThe Cholesky Decomposition Model

Multivariate Analysis: Multivariate Analysis: The Cholesky Decomposition ModelThe Cholesky Decomposition Model

Leanmass

Fatmass

LSBMD

FNBMD

TBBMD

E1 E2 E3 E4 E5

G1 G2 G3 G4 G5

LS=lumbar spine, FN=femoral neck, TB=total body, BMD = bone mineral density

LM FM LS FN TB

Lean mass (LM) 0.52 0.39 0.23 0.51

Ft mass (FM) 0.16 0.41 0.36 0.70

Lumbar spine BMD (LS) 0.08 0.02 0.57 0.70

Femoral neck BMD (FN) 0.16 0.05 0.64 0.61

Total body BMD (TB) 0.09 0.31 0.75 0.58

Genetic and Environmental Correlation between Genetic and Environmental Correlation between Lean, Fat and Bone MassLean, Fat and Bone Mass

Genetic and Environmental Correlation between Genetic and Environmental Correlation between Lean, Fat and Bone MassLean, Fat and Bone Mass

Upper diagonal: genetic correlation; lower diagonal: environmental correlationLS=lumbar spine, FN=femoral neck, TB=total body, BMD = bone mineral density

Strategies for Finding GenesStrategies for Finding GenesStrategies for Finding GenesStrategies for Finding Genes

How many genes ?How many genes ?How many 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 (?)

Effect size

Num

ber of genes

Major genes

Polygenes

Oligogenes

Distribution of the number of genesDistribution of the number of genes

Finding genes: a challengeFinding 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?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 Genomewise screening vs Candidate geneCandidate 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 AssociationLinkage 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 exampleTwo-point linkage analysis: an exampleTwo-point linkage analysis: an exampleTwo-point linkage analysis: an example

??138 /142

134 /142 146 / 154

142 /146 142 /154 134 / 146 142 / 154 134 / 146 134 / 154 134 / 146 134 / 154

Non Rec Non Non Non Non Rec Non

D142

D142

d134

Non = non-recombination; Rec = recombination

134

142

D d

1/4 1/4

1/41/4

134

142

D d

0 1/2

01/2

134

142

D d

(1-)/2

/2(1-)/2

No linkage Complete linkage

Incomplete linkage

8

26

10

41

221

log

θθ

LOD

Recombination fraction

LODscore

Estimated value of 0 0.1 0.2 0.3 0.4 0.5

Estimation of the recombination fraction Estimation of the recombination fraction

-6

-4

-2

0

+2

+4

+6Max LOD score

A model for sibpair linkage analysisA model for sibpair linkage analysisA model for sibpair linkage analysisA model for sibpair linkage analysis

Xi1 = value of sib 1; Xi2 = value of sib 2 i = abs(Xi1 - Xi2)2

i = probability of genes shared identical-by-descentE(i | i) = + i

If = 0 => 2(g) = 0; = 0.5, i.e. No linkageIf < 0 => 2(g) > 0; ne 0.5, i.e. Linkage

Behav Genet 1972; 2:3-19

Identical-by-Descent (IBD)Identical-by-Descent (IBD)Identical-by-Descent (IBD)Identical-by-Descent (IBD)

126 / 130 134 / 138

126 / 134 126 / 138 130 / 134 130 / 138 126 / 138 A B C D E

• 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

Alleles ibd if they are identical and descended from the same ancestral allele

oooooooo

o

ooooooooo

ooooooooo

Squareddifference in BMDamong siblings

Number of alleles shared IBD

0 1 2

Sibpair linkage analysis: an exampleSibpair linkage analysis: an exampleSibpair linkage analysis: an exampleSibpair linkage analysis: an example

0

5

10

15

20

25

0 1 2

Alleles shared IBD

Intr

apai

r d

iffe

ren

ce i

n B

MD

(%

)

Nature 1994; 367:284-287

Association analysis: an exampleAssociation analysis: an exampleAssociation analysis: an exampleAssociation analysis: an example

0.8

0.9

1

1.1

BB Bb bb

VDR genotype

g/cm

2

Association between vitamin D receptor gene and bone mineral density

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

Candidate BMD genes : association analysisCandidate BMD genes : association analysisCandidate BMD genes : association analysisCandidate BMD genes : association analysis

Localization of BMD genes in humansLocalization of BMD genes in humansLocalization of BMD genes in humansLocalization of BMD genes in humans

Some notable genesSome notable genesSome notable genesSome notable genes

• Vitamin D receptor (VDR)

• Collagen I alpha 1 (COLIA1)

• Estrogen receptor (ER)

• Interleukin-6 (IL6)

• Transforming growth factor (TGF)

Problems Problems Problems 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 paradigmsNew paradigmsNew paradigmsNew 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

SummarySummarySummarySummary

• 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.

PerspectivePerspective

• 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