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Imaging genetics: Adventures in the dopaminergic system Christian Büchel. HBM Barcelona 2010. buechel@uke.uni-hamburg.de NeuroImage Nord Hamburg University Medical School Eppendorf. Outline. Introductory remarks Hypothesis driven association studies Reward processing Predictions? - PowerPoint PPT Presentation
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Imaging genetics: Adventures in the dopaminergic system Christian Büchel
buechel@uke.uni-hamburg.de
NeuroImage NordHamburg University Medical School Eppendorf
HBM Barcelona2010
Introductory remarks Hypothesis driven association studies
Reward processing– Predictions?
– Genetic influence on predictions
Novelty and memory– The role of DRD4
General remarks
Outline
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?)
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?
Activ
atio
n in
PFC
Innate values – sucrose vs quinine
Adapted from K. Berridge
Conditioned reward
3 7
choice anticipation outcome
0time (s)
2 x 2 x 2 factorial design: PROBABILITY (12.5 [26%] – 50% [66%]) MAGNITUDE (1 – 5€) OUTCOME (win – lose)
20€ 15€
20€ 21€
Anticipation phase: Expected reward magnitude & probability
probability high > low
y = 15 mm R
y=3mmmagnitude 5€ > 1€
Rz = 0 mmy = 3 mm R
Yacubian et al., J Neuroscience 2006
Which one would you chose ?
10€ / 70% or 100€ / 50% EV 7 EV 50
Val/Val
Met/Met
Val/Met
Schott et al., 2006 Bertolino et al., 2006
DAT- COMT interactions
DAT reuptake of dopamine Variable number of tandem
repeats (VNTR) polymorphism (40bp) mainly 9R and 10R
10R Probably higher activity
DAT - COMT interactions
COMT degrades dopamine SN polymorphism
(val158met) met158
Low enzyme activity
Ventral striatumBilder et al., 2004
from PFC
Effect of COMT and DAT on predictions
Genetic influence on expected value coding during anticipation
1€/p-lo 1€/p-hi 5€/p-lo
BO
LD s
igna
l (a.
u.)
5€/p-hi
COMTMet/Met
Val/Met
Val/Val
DAT 9R 10R
Yacubian et al., PNAS 2007
Inverted u-shape response
“Phasic DA“
COM
T M
et/M
etDA
T 10
RCO
MT
Met
/Met
DAT
9R
COM
T Va
l/Met
DAT
10R
COM
T Va
l/Met
DAT
9RCO
MT
Val/V
alDA
T 10
RCO
MT
Val/V
alDA
T 9R
Slop
e of
fMR
I res
pons
eSe
nsat
ion
seek
ing
≈
r=-0.77, p<0.05
Reuter et al., Nature Neuroscience 2005
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)
Some thoughts on …
Split half testing
Yacubian et al., PNAS 2007
Odd samples
Even samples
Whole group
“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
Opinions – Sample size
Dopamine D4 receptor polymorphisms and novelty
Novelty and Dopamine Dopamine activity signals unexpected, salient, motivationally-
relevant 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
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
Some thoughts on …
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
gain-related effects: conjunction Met/Met & Val/Valp<0.001, FWE corrected
Prelim. neuroimaging results: MID task
Sample Val158met (rs4680) Focus on homozygotes
(Met/Met, Val/Val) n=110 (Met/Met) vs. n=115
(Val/Val)
y=10Ventral striatum (peak t=4.86)
Outcome–related activation
Val/Val > Met/Metp<0.001, uncorrected
Peters et al., in preparation
Ventral striatumBilder et al., 2004
from PFC
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
Some thoughts on …
Combining Imaging and Genetics A very promising approach ( endophenoytpe) As usual there are many pitfalls Field is in a stage of maturation
Interpretability Control for substructure
Candidate vs whole genome approach 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
Summary
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