Download Case Study: Genetic Control of Reward System

Imaging genetics: Adventures in the
dopaminergic system
Christian Büchel
HBM Barcelona
2010
[email protected]
NeuroImage Nord
Hamburg University Medical School Eppendorf
Outline

Introductory remarks

Hypothesis driven association studies

Reward processing
– Predictions?
– Genetic influence on predictions

Novelty and memory
– The role of DRD4

General remarks
Imaging genetics - Imaging neuroscience
meets genetics

Commonalities
 Are interested in interindividual differences
 Battle the multiple comparisons problem in statistical analysis
of their data

What a Geneticist might think about
Neuroscientists…

They have no clue about methodology in genetics (eg
never heard of Plink)

They don’t care about the heritability of their traits

They use ridiciously small sample sizes

They stick to boring candidate gene approaches and
will never find out anything exciting

What a Neuroscientist might think about
Geneticists …

They have no clue about methodology in neuroimaging

They don’t know anything about the brain (i.e. my
ground breaking hypotheses)

They advocate whole genome approaches that nobody
is able to interpret

They have no clue about the costs of an MR scan

Their gold standard is an uncorrected p-value of ~10^-?
and think that solves the multiple comparisons problem
(havn’t they used FDR before we did?)
Activation in PFC
Explaining interindividual variance
Volunteer

Simple model : 1-sample t-test
 Significant deactivation for the whole group in PFC
 A lot of unexplained interindividual variance
 Age effects? Gender effects? Personality effects? Genetics
effect?
Innate values – sucrose vs quinine
Adapted from K. Berridge
Conditioned reward
20€
21€
20€
15€
2 x 2 x 2 factorial design:
PROBABILITY (12.5 [26%] – 50% [66%])
MAGNITUDE (1 – 5€)
OUTCOME (win – lose)
anticipation outcome
choice
0
7
3
time (s)
Anticipation phase: Expected reward magnitude & probability
magnitude 5€ > 1€
y=3mm
= 3 mm
z = 0y mm
R
probability high > low
y = 15 mm
Which one would you
chose ?
R
10€ / 70% or 100€ / 50%
EV 7
EV 50
Yacubian et al., J Neuroscience 2006
Val/
Met
Val/
Val
Met/
Met
DAT- COMT interactions
Schott et al., 2006
Bertolino et al., 2006
DAT - COMT interactions

DAT




from PFC
reuptake of dopamine
Variable number of tandem
repeats (VNTR)
polymorphism (40bp)
mainly 9R and 10R
10R
 Probably higher activity
COMT



degrades dopamine
SN polymorphism
(val158met)
met158
 Low enzyme activity
Ventral striatum
Bilder et al., 2004
Effect of COMT and DAT on predictions
Genetic
influence on
expected value
coding during
anticipation
DAT
9R
10R
COMT
Met/Met
BOLD signal (a.u.)

Val/Met
1€/p-lo 1€/p-hi 5€/p-lo 5€/p-hi
Val/Val
Yacubian et al., PNAS 2007
Slope of fMRI response
Inverted u-shape response
Sensation seeking
“Phasic DA“
r=-0.77, p<0.05
Reuter et al., Nature Neuroscience 2005
≈
Some thoughts on …

robustness

Encourage publication of null results of imaging
genetics data (given adequate methodology
e.g. sample size etc.)

As usual, large n is helpful

Consider split half testing (e.g. odd-even
samples)
Split half testing
Odd samples
Whole group
Even samples
Yacubian et al., PNAS 2007
Opinions – Sample size

“While the sample size in this study was fairly substantial
for an imaging study, it is rather small for a genetics
study. The reviewer appreciates the logistical problems
and cost of a very large scale imaging x genetics study,
and their sample size certainly falls within the scope of
others of this type. However, the authors should at least
acknowledge the possibility that such studies fall into the
complex trait category (looking for an effect of allelic
variants in the brain induced by a behavioral paradigm is,
by definition, complex) and are therefore subject to the
type I error problem that has plagued behavioral genetics
research.” (the unknown reviewer)

N = 105

Consider stratified sample
Dopamine D4 receptor polymorphisms and
novelty



Novelty and Dopamine
 Dopamine activity signals unexpected, salient, motivationallyrelevant information
 mediated via reciprocal dopaminergic projections between
hippocampus, ventral striatum and dopaminergic midbrain
The role of the Dopamine D4 receptor
 D4 receptor is preferentially expressed in limbic regions, cortex,
basal ganglia and midbrain (SN/VTA)
 association between novelty seeking and a C to T polymorphism in
the DRD4 promoter region (-521C>T; rs1800955) in LD with the
exon III VNTR
 T allele associated with reduced transcription levels of 40%
Study:
 N=46, stratified for rs1800955 (DRD4 -521C>T)
Strange et al., in preparation
Experimental paradigm and behavioural data

Behavioural effects
 Effect only for perceptually salient stimulus (-521C>T)
Strange et al., in preparation
Neuroimaging results
Strange et al., in preparation
Some thoughts on …

Candidate gene vs. whole genome approach

Interpretability of the results (cf. neuroimaging
as a mapping technique vs neuroimaging as a
neurophysiology tool)

Very strong hypotheses: You can only find what
you already know

In between approaches (i.e. reducing genetic
dimensionality to signal cascades that might be
involved in the process (cf. small volume
correction in neuroimaging)

Both can be interesting
Integrated Project FP 6:
Reinforcement-related
behaviour in normal brain
function and
psychopathology

Study design




Investigate 2000+ 14 years old adolecents across Europe since Dec 2007
Predictive Markers for drug abuse
 Neuropsychology, Behavioural testing, personality assessment, environment
assessment
 Brain function (Reward: MID, Impulsivity: SSRT), Brain structure: T1, DTI
 Whole genome approach
Berlin, Dresden, Dublin, Hamburg, Mannheim, Nottingham, London, Paris
Current status: ~1200 volunteers included
Prelim. neuroimaging results: MID task

Sample
 Val158met (rs4680)
 Focus on homozygotes
(Met/Met, Val/Val)
 n=110 (Met/Met) vs. n=115
(Val/Val)
gain-related effects: conjunction Met/Met & Val/Val
p<0.001, FWE corrected
Outcome–related activation
Ventral striatum (peak t=4.86)
from PFC
y=10
Val/Val > Met/Met
p<0.001, uncorrected
Ventral striatum
Bilder et al., 2004
Peters et al., in preparation
Some thoughts on …

substructures

Imaging genetics: explaining interindividual
variance in activation patterns of a certain
brain region by a certain marker / genotype

Make sure that the marker of interest is
uncorrelated to
– Other markers (e.g. check indicator SNPs on other
chromosomes)
“Only five genes were analyzed. In order to identify substructures in a study
population to rule out type I error from stratification, a more intensive
genomic control analysis is necessary (approximately 50-100 genes)”
(from the
unknown reviewer)
– But also to other variables (e.g. age, personality)

Again, large n is helpful
Summary
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Combining Imaging and Genetics

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
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Interpretability


Control for substructure
Candidate vs whole genome approach





A very promising approach ( endophenoytpe)
As usual there are many pitfalls
Field is in a stage of maturation
Both have their merits (data vs hypothesis driven)
Ideally have a large sample to do both
Entertain immediate approaches: e.g. signalling cascades
GWAS: Cooperation with an advanced functional genetics unit is helpful
Sample size


Candidate genes: Stratification from a large pool of genotyped volunteers
Multi-site data acquisition: Feasible for fMRI and sMRI