Introduction to Genetic Epidemiology

Introduction to Genetic EpidemiologyHGEN619, 2006

Hermine H. Maes

Genetic Epidemiology Establishing / Quantifying the role of genes and

environment in variation in disease and complex traits ~ Answering questions about the importance of nature and nurture on individual differences

Finding those genes and environmental factors

Genes & Environment How much of the variation in a trait is accounted

for by genetic factors? Do shared environmental factors contribute

significantly to the trait variation? The first of these questions addresses

heritability, defined as the proportion of the total variance explained by genetic factors

Nature-nurture question Sir Francis Galton: comparing the similarity of identical

and fraternal twins yields information about the relative importance of heredity vs environment on individual differences

Gregor Mendel: classical experiments demonstrated that the inheritance of model traits in carefully bred material agreed with a simple theory of particulate inheritance

Ronald Fisher: first coherent account of how the ‘correlations between relatives’ explained ‘on the supposition of Mendelian inheritance’

People and IdeasGalton (1865-ish)

CorrelationFamily Resemblance

TwinsAncestral Heredity

Mendel (1865)Particulate Inheritance

Genes: single in gamete double in zygote

Segregation ratios

Darwin (1858,1871)Natural SelectionSexual Selection

Evolution

Fisher (1918)Correlation & MendelMaximum Likelihood

ANOVA: partition of variance

Spearman (1904)Common Factor Analysis

Wright (1921)Path Analysis

Thurstone (1930's)Multiple Factor Analysis

Mather (1949) &

Jinks (1971)Biometrical Genetics

Model Fitting (plants) Joreskog (1960)Covariance

Structure AnalysisLISREL

Morton (1974)Path Analysis &

Family Resemblance

Watson &

Crick (1953)

Jinks & Fulker (1970)Model Fitting applied to humans

Martin & Eaves (1977)Genetic Analysis of

Covariance Structure

Elston etc (19..)Segregation

Linkage Rao, Rice, Reich,

Cloninger (1970's)Assortment

Cultural Inheritance

Neale (1990) MxMolecularGenetics

PopulationGenetics

2000

Biometrical Model

aa AA

Aa m

d-d

h

To make the simple two-allele model concrete, let us imagine that we are talking about genesthat influence adult stature. Les us assume that the normal range of height for males is from

4 feet 10 inches to 6 feet 8 inches; that is, about 22 inches. And let us assume that eachsomatic chromosome has one gene of roughly equivalent effect. Then, roughly speaking, we

are thinking in terms of loci for which the homozygotes contribute +- 1/2 inch (from themidpoint), depending on whether they are AA , the increasing homozygote, or aa , the

decreasing homozygote. In reality, although some loci may contribute greater effects thanthis, others will almost certaily contribute less; thus we are talking about the kind of model in

which any particular polygene is having an effect that would be difficult to detect by themethods of classical genetics.

in Biometrical Genetics chapter in Methodology for Genetic Studies of Twins and Families

0

1

2

3

1 Gene 3 Genotypes 3 Phenotypes

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1

2

3

2 Genes 9 Genotypes 5 Phenotypes

01234567


0

5

10

15

20


Polygenic Traits

Stature in adolescent twins

Stature

190.0

185.0

180.0

175.0

170.0

165.0

160.0

155.0

150.0

145.0

Women700

600

500

400

300

200

100

0

Std. Dev = 6.40

Mean = 169.1

N = 1785.00

Physical attributes (height, eye color) Disease susceptibility (asthma, anxiety) Behavior (intelligence, personality) Life outcomes (income, children)

Individual differences

Polygenic Model Polygenic model: variation for a trait caused by a

large number of individual genes, each inherited in a strict conformity to Mendel’s laws

Multifactorial model: many genes and many environmental factors also of small and equal effect

Effects of many small factors combined > normal (Gaussian) distribution of trait values, according to the central limit theorem.

The normal distribution is to be expected whenever variation is produced by the addition of a large number of effects, non-predominant

This holds quite often

Quantitative traits

Central Limit Theorem

Body Mass Index vs “obesity” Blood pressure vs “hypertensive” Bone Mineral Density vs “fracture” Bronchial reactivity vs “asthma” Neuroticism vs “anxious/depressed” Reading ability vs “dyslexic” Aggressive behavior vs “delinquent”

Continuous or Categorical ?

unaffected affected

Disease liability

Single threshold

severe

Disease liability

Multiple thresholds

mildnormal mod

Multifactorial Threshold Model of Disease

Imprecise phenotype Phenocopies / sporadic cases Low penetrance Locus heterogeneity/ polygenic effects

Genetically Complex Diseases

Disease Phenotype

Commonenvironment

Marker Gene1

Individualenvironment

Polygenicbackground

Gene2

Gene3

Linkage

Linkagedisequilibrium

Mode ofinheritance Linkage

Association

Complex Trait Model

Causes of Variation pre-1990

estimation of ‘anonymous’ genetic and environmental components of phenotypic variation

genetic epidemiologic studies post-1990

identification of QTL’s: quantitative trait loci contributing to genetic variation of complex (quantitative) traits

linkage and association studies

Stages of Genetic Mapping Are there genes influencing this trait?

Genetic epidemiological studies Where are those genes?

Linkage analysis What are those genes?

Association analysis

Partitioning Variation phenotypic variance (VP) partitioned in genetic

(VG) and environmental (VE) VP = VG + VE Assumptions: additivity & independence of

genetic and environmental effects heritability (h2): proportion of variance due to

genetic influences (h2 = VG /VP) property of a group (not an individual), thus specific

to a group in place & time

Sources of Variance Genetic factors:

Additive (A)Dominance (D)

Environmental factors:Common / Shared (C)Specific / Unique (E)Measurement Error, confounded with E

Genetic Factors Additive genetic factors (A): sum of all the

effects of individual loci

Non-additive genetic factors: result of interactions between alleles at the same locus (dominance, D) or between alleles on different loci (epistasis)

Environmental Factors Shared [common or between-family]

environmental factors (C): aspects of the environment shared by members of same family or people who live together, and contribute to similarity between relatives

Non-shared [specific, unique or within-family] environmental factors (E): unique to an individual, contribute to variation within family members, but not to their covariation

Estimating Components Estimate phenotypic variance components from

data on covariances of related individuals Different types of relative pairs share different

amounts of phenotypic variance Biometrical genetics theory: specify amounts in

terms of genetic and environmental variances Three major types of study: family, adoption and

twin

Designs to disentangle G+E Resemblance between relatives caused by:

Shared Genes (G = A + D)

Environment Common to family members (C)

Differences between relatives caused by:

Non-shared Genes

Unique environment (E)

Informative Designs Family studies – G + C confounded

MZ twins alone – G + C confounded

MZ twins reared apart – rare, atypical, selective placement ?

Adoption studies – increasingly rare, atypical, selective placement ?

MZ and DZ twins reared together

Extended twin design

Classical Twin Study MZ and DZ twins reared together

MZ twins genetically identicalDZ twins share on average half their genes

Equal Environments AssumptionMZ and DZ twins share relevant

environmental influences to same extent

MZ and DZ twins: determining zygosity using ABI Profiler™ genotyping

(9 STR markers + sex)MZ DZ DZ

Identity at marker loci - except for rare mutation

Zygosity

MZ & DZ Correlations rMZ > rDZ: G (heritability) C: increase rMZ & rDZ Relative magnitude of the MZ and DZ

correlations > contribution of additive genetic (G) and shared environmental (C) factors

1-rMZ: importance of specific environmental (E) factors

Twin Correlations

*

*

MZDZ

.5

1.0

**

MZDZ

.6

.8

* *

MZDZ

.7

.8

*

*

MZDZ

.4

.8A

E

C

Example thus if, VP = VA + VC + VE = 2.0

CovMZ = VA + VC = 1.6CovDZ = 1/2VA + VC = 1.2

then, by algebra, VA = 0.8, VC = 0.8, VE = 0.4

but it isn’t always so simple, consider VP = 1.0, CovMZ = 0.6; CovDZ = 0.65

then VA = -0.1, VC = 0.7, VE = 0.4 nonsensical negative variance component

Observed Statistics Trait variance & MZ and DZ covariance as

unique observed statistics Estimate the contributions of additive genes (A),

shared (C ) and specific (E) environmental factors, according to the genetic model

Useful tool to generate the expectations for the variances and covariances under a model is path analysis

Path Analysis Allows us to diagrammatically represent linear

models for the relationships between variables Easy to derive expectations for the variances

and covariances of variables in terms of the parameters of the proposed linear model

Permits translation into matrix formulation

Phenotype

E C A D

UniqueEnvironment

AdditiveGenetic

SharedEnvironment

DominanceGenetic

e

ac

d

Variance ComponentsP = eE + aA + cC + dD

PT1

ACE

PT2

A C E

1

MZ=1.0 / DZ=0.5

e ac eca

ACE Model Path Diagram for MZ & DZ Twins

Model Fitting Evaluate significance of variance components -

effect size & sample size Evaluate goodness-of-fit of model - closeness of

observed & expected values Compare fit under alternative models Obtain maximum likelihood estimates

Mx Structural equation modeling package Software: www.vcu.edu/mx Manual: Neale et al. 2006 Free

Both continuous and categorical variables Systematic approach to hypothesis testing Tests of significance Can be extended to:

More complex questions Multiple variables Other relatives

Structural equation modeling

Are the same genes acting in males and females? (sex limitation)

Role of age on (a) mean (b) variance (c) variance components

Are G & E equally important in age, country cohorts? (heterogeneity)

Are G & E same in other strata (e.g. married/unmarried)? ( G x E interaction)

SEM: more complex questions I

Do the same genes account for variation in multiple phenotypes? (multivariate analysis)

Do the same genes account for variation in phenotypes measured at different ages? (longitudinal analysis)

Do specific genes account for variation/covariation in phenotypes? (linkage/association)

SEM: more complex questions II

Linkage & Association Analysis


Epidemiological studies Where are those genes?



Sharing between relatives Identifies large regions

Include several candidates

Complex disease Scans on sets of small families popular No strong assumptions about disease alleles Low power Limited resolution

Linkage Analysis

Linkage Scan


Epidemiological studies Where are those genes?



Sharing between unrelated individuals Trait alleles originate in common ancestor High resolution

Recombination since common ancestor Large number of independent tests

Powerful if assumptions are met Same disease haplotype shared by many patients

Sensitive to population structure

Association Analysis

Association Scan

Genome Scan Gene 1 Gene 2 Gene 3 Gene 4

Breast cancer DLC-1 Chr 8q Chr 13q

Lung cancer CD44 Chr 22q

Melanoma B-RAF

Type 2 diabetes PPAR PPP1R3A FOXA2 Chr 1q

HDL-C plasma level CETP LPL

Osteoarthritis AGC1

Schizophrenia DDC

Proof of Concept: Genes/Regions

First (unequivocal) positional cloning of a complex disease QTL !

From QTL to gene: the harvest begins: RKorstanje & B Paigen : Nature Genetics 31, 235 – 236 (2002)

Number of genes identified from QTL by year

Documents

Introduction to Genetic Epidemiology