Using twin designs in social and political
psychology
Gary Lewis
[email protected]
A valuable resource…
Core questions…
• 1. Why isn't everyone the same?
• 2. Why are children like their parents?
• 3. Why aren't children from the same parents all alike?
Variation is (seemingly)
ubiquitous…
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Height
Psychiatric conditions
Personality
Intelligence
Social attitudes
Ad infinitum?
Sources of variation
P = phenotypic variation
Sources of variation
P = phenotypic variation
P = A + D/I + C + E + G*E + GEr
Sources of variation
P = phenotypic variation
A = additive genetic effects (e.g. allele ‘i’ + allele ‘j’…+ allele ‘z’)
Sources of variation
P = phenotypic variation
D/I = dominance/epistatic genetic effects
Sources of variation
P = phenotypic variation
C = shared-environment effects (e.g. family environment)
Sources of variation
P = phenotypic variation
E = unique-environment effects (e.g. random events, measurement
error)
Sources of variation
P = phenotypic variation
G*E = gene-environment interaction
Sources of variation
P = phenotypic variation
GEr = gene-environment correlation
Sources of variation
P = phenotypic variation
P = A + D/I + C + E + G*E + GEr
Decomposing variation
• Q: How do we parse these sources of variation
into their constituent components?
• A: Twin and family studies
Monozygotic/identical twins
Monozygotic (MZ) twins share 100% of their
segregating genes
Dizygotic/fraternal twins
Dizygotic (DZ) twins share 50% of their (variable)
segregating genes
Controlling for environment
• Environment is (more or less) identical within
twin pairs.
• i.e. same womb, same parents, same home, etc.
Causes of similarity among
(reared-together) twins
• Identical/Monozygotic (MZ) twins:
• A + D/I + C
• Fraternal/Dizygotic (DZ) twins:
• ½*A + ¼*D + C
• Note: E, by definition, is an influences that serves to makes
individuals within a twin pair more discordant for a given trait
Genetic architectures and
implications
• Imagine the following trait:
• Additive genetic effects entirely explain variation in Trait “A”:
What would the pattern of MZ and DZ correlations look like?
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Answers:
1. MZr = .50 & DZr = .25?
2. MZr = 1.0 & DZr = .50?
3. MZr = .50 & DZr = .50?
Genetic architectures and
implications
• Imagine the following trait:
• Additive genetic effects entirely explain variation in Trait “A”:
What would the pattern of MZ and DZ correlations look like?
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•
•
•
Answers:
1. MZr = .50 & DZr = .25?
2. MZr = 1.0 & DZr = .50?
3. MZr = .50 & DZr = .50?
Genetic architectures and
implications
• Imagine the following trait:
• Shared environment effects entirely explain variation in Trait “A”:
What would the pattern of MZ and DZ correlations look like?
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•
•
•
Answers:
1. MZr = .50 & DZr = .25?
2. MZr = 1.0 & DZr = .50?
3. MZr = 1.0 & DZr = 1.0?
Genetic architectures and
implications
• Imagine the following trait:
• Shared environment effects entirely explain variation in Trait “A”:
What would the pattern of MZ and DZ correlations look like?
•
•
•
•
Answers:
1. MZr = .50 & DZr = .25?
2. MZr = 1.0 & DZr = .50?
3. MZr = 1.0 & DZr = 1.0?
Genetic architectures and
implications
• Imagine the following trait:
• Dominance genetic effects entirely explain variation in Trait “A”:
What would the pattern of MZ and DZ correlations look like?
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Answers:
1. MZr = 1.0 & DZr = .25?
2. MZr = 1.0 & DZr = .50?
3. MZr = 1.0 & DZr = 1.0?
Genetic architectures and
implications
• Imagine the following trait:
• Dominance genetic effects entirely explain variation in Trait “A”:
What would the pattern of MZ and DZ correlations look like?
•
•
•
•
Answers:
1. MZr = 1.0 & DZr = .25?
2. MZr = 1.0 & DZr = .50?
3. MZr = 1.0 & DZr = 1.0?
Parameter estimation
• With only information from MZs and DZs, we can only
estimate 3 parameters:
• Usually…
• ACE, or
• ADE
• This decision is usually driven by eyeballing MZ vs. DZ
correlations:
• If MZs are more than twice as similar as DZs, this is evidence for
possible dominance effects
• In such a case, an ADE model may be fitted
Parameter estimation
• Falconer’s method:
• rmz = A + C
• rdz = ½A + C
Parameter estimation
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• Therefore…
• A = 2 (rmz – rdz) (i.e. MZs differ from DZs only because of twice the
genetic similarity)
• C = 2* rdz – rmz
• E = 1 – rmz
Parameter estimation
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• Limitations with this approach:
• Doesn’t extend to the multivariate case
• Inefficient with missing data
• Doesn’t allow formal significance testing (e.g. likelihood ratio
test)
Parameter estimation
• Covariance matrix – model fitting approach
• Hypothetical trait: Big 5 “Extraversion”
MZ var/covar
1.3
0.8
DZ var/covar
1.2
0.4
1.1
A+C+E
A+C
A+C+E
1.5
A+C+E
1/2A+C
A+C+E
Parameter estimation
• What if: A = .4; C = .3; and E = .6 ?
Observed
Expected
MZ var/covar
1.3
0.8
DZ var/covar
1.2
0.4
MZ var/covar
1.3
0.7
DZ var/covar
1.3
0.5
1.1
A+C+E
A+C
A+C+E
1.5
A+C+E
1/2A+C
A+C+E
1.3
.4 + .3 + .6
.4 + .3
.4 + .3 + .6
1.3
.4 + .3 + .6
1/2 *.4 + .3 .4 + .3 + .6
Maximum-likelihood
estimation
• These parameters are then fitted using a likelihood function
• The wonders of modern computing * Ronald Fisher! 
• The fit-function is a measure of how closely the expected
variances-covariance matrices (i.e. A, C, and E as specified in
the previous slide) match the observed ones
Example ACE figure
C
A
.5
.2
E
.3
Extraversion
Multivariate extension
• Just a quick slide to show the MV extension
Observed
Expected
MZ var/covar
1.3
0.8
0.7
DZ var/covar
1.2
0.4
0.2
1.3
0.7
0.6
1.3
0.5
0.3
1.1
0.6
A+C+E
A+C
1.3 A+C
A+C+E
A+C
A+C+E
1.5
0.3
A+C+E
1/2A+C
0.13 1/2A+C
A+C+E
1/2A+C
A+C+E
1.3
0.8
1.3
0.3
.4 + .3 + .6
.4 + .3
1.5 .4 + .3
.4 + .3 + .6
1/2 *.4 + .3
1.4 1/2 *.4 + .3
.4 + .3 + .6
.4 + .3
.4 + .3 + .6
.4 + .3 + .6
1/2 *.4 + .3 .4 + .3 + .6
Core assumptions
• Equal environments between twin pairs
• E.g. MZs not treated more similarly simply because they are MZs
• No GEr
• (i) passive (e.g. books), ii) active (e.g. “niche picking”), iii)
evocative (e.g. aggression begets punishment)
• No G*E
• genes controlling sensitivity to the environment
• environment controlling gene expression
• No assortative mating
• No gene-gene interaction
Violations of assumptions!
• GEr = inflation of A
• Because genes lead to environment construction, which in turn
influences the phenotype: MZs have more genes in common, so they will
thus appear more similar
• NB: GEr requires large A and E (i.e. possible GEr doesn’t falsify the
existence of A)
Violations of assumptions!
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• G*E = inflation of E
• Differential environment (that interacts with genotype) will decrease
twin concordances
Violations of assumptions!
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• Assortative mating = inflation of C; underestimates A
• Because DZ’s will become more concordant due to greater genetic
similarity
Violations of assumptions!
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• Equal environments = (possible) inflation of A
• Because MZs are more similar than DZs for more than just genetic
reasons
• Only critical if the trait in question is related to the EEA violation
Violations of assumptions!
• Gene-gene interaction = Inflation of A
• MZ twins share all possible higher-order gene-gene interactions
• DZ twins share 1/4th dominance effects; 1/8th 3-way interactions; 1/16th 4-way
interactions…
• As such, if epistasis is present MZs may be much more than twice as similar as DZs
Assumption-free methods
• Possible to use molecular genetic data to estimate heritability
without the limiting assumptions of the classical twin design
Visscher et al. (2006) PLOS Genetics
But are MZs really identical?
• MZ twins emerge from a splitting of the zygote = equal DNA?
• Not necessarily…
• De novo mutations exist (i.e. acquired during the lifespan)
Mutation types
• Some examples:
• Point mutations
Mutation types
• Some examples:
• Insertions
Mutation types
• What do de novo mutations mean for the twin method?
• Always easiest to think of the problem in terms of how mutations
will effect MZ and DZ covariances…
Mutations: effects on twin
model parameterization
• If mutations are random…
• Outcome: MZs and DZs both become less concordant
proportionally
• E is increased
A quick note on determinism…
• Large heritable component ≠ immutable effects
• Estimates of genetic influence are specific to the population
under study
• Environments can (sometimes radically) alter heritability
estimates
• Q. Would teaching quality effect the heritability of reading ability? If
so, in what direction?
Teacher ability and genetics of
reading
Taylor et al. (2010) Teacher quality moderates the genetic effects on early reading, Science.
Discussion time…
• Qs:
• 1. What are the core arguments against the existence of heritable
effects on social and political attitudes?
• 2. How well do these arguments hold up to scrutiny?
• 3. What alternative etiologies of political attitudes explain the
data?
• 4. How might these heritability estimates look in Syria, Libya, or
China?