Genetics of inhibition and error
processing- implications for ADHD,
schizophrenia and drug abuse
Mark A. Bellgrove, PhD
School of Psychology & Psychiatry
Monash University
mark.bellgrove@monash.edu
Inhibition and Cognitive
Control
What gives rise to individual differences in the
ability to control and inhibit behaviour
and monitor for errors?
What gives rise to individual differences in the
ability to control and inhibit behaviour
and monitor for errors?
GENETICS!
Why study cognitive control and
inhibitory failures?
OCD
Addiction
Inhibition
ADHD
Schizophrenia
Why study cognitive control and
inhibitory failures?
OCD
Addiction
Inhibition
ADHD
Schizophrenia
Biological Bases of ADHD
clues to the biology of cognitive control?
•  Genetics
–  Familiality: ADHD more prevalent among
biological relatives of an affected individual
> 2-8 fold increase in risk in parents and siblings
of children with ADHD (Biederman et al, 2005)
–  Heritability: whether it is defined
categorically or dimensionally, heritability of
ADHD is high (75-90%)
= Strong Genetic Contribution To ADHD
Candidate gene studies of ADHD
A hypothesis-driven approach:
•  Dysfunction to catecholamine (e.g., Dopamine and
Noradrenaline) systems seems likely, since stimulants
act on these systems
•  Candidate gene approach seeks to determine whether
genetic variants, or polymorphisms, are associated with
ADHD at a greater than chance frequency
•  Candidate genes for ADHD include those coding for
receptors, enzymes or transporters, amongst others,
involved in catecholamine function
Candidate Genes for
ADHD
Tyrosine
DOPA
DA and NA
Transporters
Blocked by
Ritalin
Susceptibility Genes for ADHD
DAT1
DRD4
DRD5
SNAP25
DA
NA
DA
Dopamine
Beta
Hydroxylase
COMT
Degrades DA
in PFC
D4 and D5
Receptors
Increasing Strength of Genetic Effects
Endophenotypes for ADHD
Symptom Domains
Hyperactivity/
Impulsivity
Cognitive Systems
Inhibitory Deficits
Neural Systems
Fronto-Striatal
Circuits
Genes
Genes
Measuring Inhibition
Go/No-Go
Stop-Signal
Commission Errors (% correct inhibition)- Inhibition
Omission Errors- Sustained Attention
Reaction Time Variability- Cognitive Control
Stop-Signal Reaction Time (SSRT)- Speed of Inhibition
Behaviour Genetics of Inhibition
•  Twin studies demonstrate high heritability for
measures of response inhibition
Freidman et al, JEP, 2008
Cognitive Neuroanatomy of
Response Inhibition
Transranial Magnetic Stimulation (TMS) of frontal
cortex disrupts inhibition
Chambers et al, 2006, JOCN
Cognitive Neuroanatomy of
Response Inhibition
BG Stop Network
R Frontal Stop Network
1,896 14 Year Old Adolescents
Whelan et al, 2012, Nat Neuro
Neurochemistry of inhibition
•  Mono-amine systems are implicated in inhibitory
control and disorders of inhibitory control
–  Dopamine
>  ↓ SSRT with MPH (Aron et al, 2003) but not others
–  Noradrenaline
>  ↓ Atomoxetine (Chamberlain et al, 2006;2008)
–  Serotonin
>  Tryptophan depletion alters activity in inhibition networks
(Rubia et al, 2005)
>  No effect of Citalopram on SSRT (Chamberlain et al,
2006;2008)
>  Role for serotonin in OCD
Nandam et al, 2011, Biol Psychiatry
Neurochemistry of inhibition
•  24, right-handed, male, Caucasian participants
performed the Stop-task
•  Four-arm, placebo-controlled, randomised,
cross-over design
–  30mg Methylphenidate
–  60mg Atomoxetine
–  30mg Citalopram
–  Placebo
•  Task performed 2 ½ hours post intake
Nandam et al, 2011, Biol Psychiatry
MPH enhances inhibition
450
300
280
400
MRT(ms)
SSRT(ms)
260
240
220
200
350
300
250
180
200
160
MPH
ATM
CIT
[F(3,69)=5.52, p=0.002]
PLAC
MPH
ATM
CIT
PLAC
[F(3,69)=0.14,p=0.935].
MPH<ATM, p<0.05
MPH< CIT, PLAC, p<0.01
Both dopamine and noradrenaline appear important for inhibitory control
Nandam et al, 2011, Biol Psychiatry
Cabergoline enhances inhibition
500
280
450
260
400
240
350
MRT(ms)
SSRT(ms)
300
220
200
180
300
250
200
150
160
100
140
50
0
0
CAB
PLAC
CAB
PLAC
[F(1,20)=5.8, p<0.05]
d’=0.43
D2 receptor modulation of response inhibition
Nandam et al, unpublished
Neurochemistry of Inhibition
NET1, D4, D2,
α-2,
Molecular Targets
Become Candidate
Genes for Genetic
Association
With Inhibition
DAT1, D2
How do we identify genes for inhibition?
•  Variation in many human traits do not appear to
follow simple Mendelian inheritance laws.
•  Rather, complex or quantitative traits are most
likely the result of both multiple genes (exerting
small effects) and environmental factors
•  Examples of quantitative traits are IQ, height, or
your degree of inhibitory control
Traits such inhibitory control vary
normally in the general population. This
variability is driven by differences in many
genes as well as environmental differences
How do we link DNA variation to variation in a
trait, such as inhibition?
AAGCCTA
C- Allele
Individual 1 differs from 2 at a single
Base-pair location
C / T SNP.
Within a Population, you have:
C/C genotypes
C/T genotypes
TT genotypes
T-Allele
Frequency
AAGCTTA
Good
Inhibition
Quantitative Trait
Poor
How do we link DNA variation to variation in a
trait, such as inhibition?
AAGCCTA
C- Allele
Individual 1 differs from 2 at a single
Base-pair location
C / T SNP.
Within a Population, you have:
C/C genotypes
C/T genotypes
TT genotypes
T-Allele
Frequency
AAGCTTA
Non-ADHD
Inhibition
ADHD
Genetic Association Study of Inhibition
•  405 non-clinical adults performed the stopsignal reaction time task and other cognitive
assessments
•  Allelic association (e.g., CC vs. CT vs. TT
genotypes) with inhibition (SSRT) was tested
using multiple regression
•  Full-screen of autosomal catecholamine genes
(DA/NA)
•  141 SNPs; MAF>0.05%, pcritical=5 x 10-4
Cummins et al, 2011, Mol Psych
Genetic Association Study of Inhibition
Cummins et al, 2011, Mol Psych
Additive influence of T allele of rs37020
on SSRT
Cummins et al, 2011, Mol Psych
LD (D ) map of SLC6A3 (DAT1) gene
Strong LD between
rs460000
and rs37020 (14kbp)
No LD with int8 or 3 UTR
VNTRs
Cummins et al, 2011, Mol Psych
Imaging Genetics of Inhibition
Influence of rs37020 genotype
Anterior frontal, superior frontal
Superior medial gyrus
Bilateral Caudate
Inhibition-related activity increased
additively from TT to GT to GG genotype
Cummins et al, 2011, Mol Psych
Imaging Genetics of Inhibition
Association with NET1 N=819
R Frontal Stop Network: T allele of rs36024 SLC6A2 (intron
4)
Whelan et al, 2012, Nat Neuro
Atomoxetine modulates IFG during
response inhibition in humans
Chamberlain et al, 2008, Biol Psych
Single acute 40mg dose of atomoxetine
improved SSRT and modulated activity
in right IFG
Genetics of Error Processing
A
X
Stimulation
Computer
Marker Codes
Filters &
Amplifier
Digitization
Computer
EEG
EEG Recorded from the Pz Electrode Site
B
X
X
O
O
X
X
–
20 µV
+
0
C
1000 2000 3000 4000 5000 6000
Time in milliseconds
EEG Segments
7000 8000 9000
Continuous
EEG
trace at a single
Separate response-locked averages for
Following Marker
Codes
electrode over multiple trials (Luck,
correct and error responses (Amodio et al,
Average of 80 Xs 2006)
2006)
N1
X
Genetics of Error Processing
Chromosome / Gene
ERN
Pe
SNP
MAF(a)
ERN
Pe
4 / DRD5 (5' flanker)
rs10033951
0.37
0.65
0.0008*
5 / DRD1
rs4867798
0.31
0.87
0.041*
rs686
0.40
0.26
0.028*
rs265981
0.40
0.24
0.013*
8 / ADRA1A
rs10503800
0.34
0.25
0.026*
9 / DBH
rs1548364
0.50
0.37
0.033*
11 / ANKK1
rs17115439
0.28
0.0012*
0.36
rs2734849
0.47
0.0085*
0.39
rs6279
0.29
0.043*
0.45
rs12364051
0.38
0.0005#
0.62
rs4245146
0.49
0.72
0.37
rs8118409
0.27
0.39
0.02*
11 / DRD2
20 / ADRA1D
(significance threshold: p < .000647)
1.
2.
D2 Dopamine Receptor gene (DRD2) à ERN amplitude
D5 Dopamine Receptor (DRD5) à Pe amplitude
Genetics of Error Processing
DRD2 (rs12364051) and the ERN
N = 31
N = 43
N = 11
β= -.37
t(81) = -3.65
p = 4.60 x 10-4
rsp2 = .13
DRD5 (rs10033951) and the Pe
β= .39
t(81) = 3.48
N = 27
N = 43
N=7
p = 8.41 x 10-4
rsp2 = .14
Genetics of Error Processing
rs12364051 in DRD2 is not likely to be functional in its own right.
…. Could it be tagging a known functional variant?
•  rs12364051 is located in Intron 1 and
is probably not functional
•  But a DRD2 hit is plausible
(D2-antagonist haloperidol
modulates ERN)
v
•  Our SNP is linked with both Taq1A
and two recently reported SNPs that
Uncorrected Significant Associations with
ERN Amplitude within DRD2 and ANKK1
Chr / Gene
11 / ANKK1
11 / DRD2
SNP
rs17115439
0.0012*
rs2734849
0.0085*
rs6279
0.043*
rs12364051
* p < .05
ERN Amplitude
0.0005**
influence D2-receptor expression
(Zhang et al, 2007)
•  Cluster of nominally significant hits in
this region is encouraging
•  Relevance: First independent
association of DRD2 with ERN?
Genetics of Error Processing
rs10033951 in the 5 flanker region of DRD5 is also not likely to be
functional in its own right.
… but it may also be tagging an existing known variant associated with ADHD…
•  DRD5 in highly conserved region
•  rs10033951 sits between 5 end of
DRD5 and widely reported risk
marker for ADHD.
•  Pe deficits in both child and adult
ADHD
•  Evidence for ADHD-associated
variant also influencing an
ADHD-related trait in healthy
population
MPH modulates error awareness
Inhibition
Error Awareness
Hester et al, J Neuro, 2012
MPH modulates activity in the anterior
cingulate and inferior parietal lobe
Hester et al, J Neuro, 2012
100
Mean % Error Awareness
Mean % Error Awareness
Role for Dopamine D2 receptor in error
awareness?
80
60
40
20
0
CAB
PLAC
Drug Condition
[F(1,24)=4.7, p<0.05]
d’=0.43
100
80
60
40
20
0
0/1 T allele
2 T alleles
DRD2 Genotype (rs6277)
[F(1,20)=6.6, p<0.05]
Summary
•  Measures of cognitive control and inhibition are
heritable traits that inform us about risk for psychiatric
disorder
•  Cognitive-neuroanatomical and pharmacological models
of cognitive control can be used to guide candidate
gene selection
•  DNA variation in the dopamine and noradrenaline
transporter genes(DAT1, NET1) are significant
predictors of individual differences behavioural and
brain measures of inhibition.
•  DNA variation in DRD2 and DRD5 predict
electrophysiological indices of error processing
•  Using behavioural and neural measures of cognitive
control as quantitative traits has helped to identify
genomic regions that may confer susceptibility to range
of psychiatric disorders, inc ADHD, schizophrenia and
drug abuse.
Acknowledgements
Work funded by NHMRC, ARC, & NARSAD
Collaborators:
Rob Hester (Melbourne)
Hugh Garavan (Vermont)
Redmond O’Connell (Dublin)