Supplementary materials
Epistatic interactions of AKT1 on human hippocampal biology and
pharmacogenetic implications - Supplement
Hao Yang Tan, Anthony G Chen, Qiang Chen, Lauren B Browne, Beth Verchinski,
Bhaskar Kolachana, Fengyu Zhang, Jose Apud, Joseph H Callicott, Venkata S Mattay,
Daniel R Weinberger
Clinical Brain Disorders Branch, Genes Cognition and Psychosis Program, Division of
Intramural Research Programs, National Institute of Mental Health, Bethesda, Maryland
20892, USA.
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Supplementary materials
Supplementary Methods:
Subjects:
Research subjects were ascertained as part of the Clinical Brain Disorders Branch
Sibling Study
1
. Subjects were all of European ancestry to minimize genetic
heterogeneity and stratification artifacts. All subjects were from 18 to 55 years of age,
were above 70 in IQ, and gave written informed consent. Exclusion criteria were
significant medical problems, history of loss of consciousness for greater than 5 minutes,
alcohol or drug abuse/ dependence within the last 12 months, and electroconvulsive
therapy within the last 6 months. All subjects were interviewed by a psychiatrist using the
Structured Interview for DSM-IV. For probands, data from psychiatric records were also
evaluated during diagnostic ascertainment.
DNA collection and genotyping:
DNA was extracted from whole blood using standard procedures. All genotypes
were determined using the 5’ exonuclease Taqman assay; SNP probe and primer sets
were acquired as “Assays on Demand” from Applied Biosystems, CA. We genotyped the
AKT1 rs1130233 2, COMT Val158Met 1 and BDNF Val66Met 3 for the primary analyses
reported here. Genotype accuracy was assessed by regenotyping within a subsample, and
reproducibility was routinely greater than 99%. Genotyping completion rate was >95%.
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Cognitive testing:
The neurocognitive battery used, including the Wechsler Adult Intelligtence
Scale-Revised (WAIS-R) was previously described 4. The current IQ estimate was
obtained from a 4-subtest version of the WAIS-R, comprising the Arithmetic, Digit
Symbol Substitution, Picture Completion, and Similarities subtests
4, 5
. The estimate of
premorbid IQ was based on the Reading subtest of the Wide Range Achievement Test
(WRAT-R). The Reading subtest is thought to be a valid reflection of preserved abilities
acquired before the onset of disease 4, 6.
Functional imaging of memory function:
To examine memory-dependent hippocampal function, BOLD fMRI data was
acquired from a group of 96 healthy individuals as they performed a memory encoding
and retrieval task that robustly engaged hippocampal function 7. For each of the encoding
and retrieval sessions, 8 blocks of scenes selected from the International Affective Picture
System were presented with 18s blocks of aversive or neutral scenes alternating with
blocks with a fixation-cross. The retrieval session followed encoding after a 2min delay.
In each block, 6 scenes were presented every 3 seconds. During the encoding blocks,
subjects were to respond with a right or left button-press depending on whether the scene
represented an “indoor” or “outdoor” scene. During retrieval, subjects were presented a
set of scenes where half were seen previously and half were new. Subjects were to
respond with a right button press for scenes seen before during the encoding session (i.e
“old), or with a left button press for the new scenes.
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Whole brain BOLD fMRI data were collected on a 3-T scanner (General Electric
Systems, Milwaukee, WI) with a GE-EPI pulse sequence acquisition of 24 contiguous
slices (echo time=30 msec, repetition time=2 seconds, flip angle=90, field of
view=24cm, matrix=64x64, voxel dimensions= 3.75x3.75x6mm). All fMRI data were
pre-processed and spatially normalized to a common stereotaxic space (Montreal
Neurologic Institute template) with SPM2 software (Wellcome Department of Cognitive
Neurology, London http://www.fil.ion.ucl.ac.uk/spm), and individually examined for
motion artifacts.
Single-subject contrast images were then entered into a second level of analysis
with subject as a random factor. Given our hypotheses about the effect of AKT1 on
potential hippocampal neuroplasticity effects during memory encoding, we primarily
examined gene effects at encoding > baseline within the bilateral hippocampal and
parahippocampal regions-of-interest (ROI). The ROI was defined from the Wake Forest
University Pickatlas (http://www.fmri.wfubmc.edu/download.htm) hippocampus and
parahippocampus regions with a dilation factor of 3 voxels8. The AKT1 SNP rs1130233
A-carrier versus G-homozygote effect was subsequently interrogated within this smaller
search volume and statistically corrected for false-discovery rate9 at p<0.05. Epistatic
interaction between AKT1 and BDNF Val66Met effects, and 3-way AKT1, BDNF and
COMT Val158Met effects were then extracted from significant peaks with this
orthogonal AKT1 main effect, and examined using fully parameterized ANOVAs at
p<0.05 in these higher-level interaction analyses. Finally, we performed permutation tests
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to further determine the conservative rate of false positivity from which the reported set
of results could have arisen anywhere within the bilateral hippocampal and
parahippocampal regions-of-interest. This was to ensure that the strategy to examine the
AKT1 main effect and hypothesis driven interaction effects was statistically conservative
within the multi-voxel ROI search space. Permutation tests were conducted over 10,000
random label changes of subject identity within this sample, keeping constant the
genotype labels to maintain the same genotype frequencies, thus identifying the rate at
which a set of similar false positive main, 2-way and 3-way interaction effects at these
statistical thresholds occurred.
Structural imaging:
To examine hippocampal brain structure, MRI images from 171 healthy subjects,
distinct from those in the functional imaging study, were acquired in a 1.5T GE scanner
using a T1-weighted spoiled grass sequence (repetition time 24s, echo-time 5s, field-ofview 24cm, matrix 256x256 voxels, flip-angle 45o) with 124 saggital slices
(0.94x0.94x1.5mm resolution). These images were processed and analyzed using
optimized VBM with customized templates as previously described
10
. Resulting gray-
matter images were smoothed with a 12-mm Gaussian kernel before statistical analysis
using a generalized linear model. The effects of AKT1 SNP rs1130233 genotypes G/G
versus A-carriers were examined using an analysis of covariance model adjusted for the
orthogonalized first and second polynomials of age, gender, WAIS-IQ and total gray
matter volume, as previously described
10
. We reported peaks at p<0.001 uncorrected in
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the bilateral hippocampal-parahipoocampal regions of interest defined using the Wake
Forest University Pickatlas (http://www.fmri.wfubmc.edu/download.htm) with a dilation
factor of 3 voxels8. Epistatic interactions with BDNF Val66Met and COMT Val158Met
were extracted and examined as in the fMRI data from orthogonally-defined peaks with
the main AKT1 effect. Similarly, we performed permutation tests to further ensure that
the strategy and initial uncorrected statistical thresholds used in examining the AKT1
main effect, hypothesis driven interaction effects, and the set of findings thus obtained
were statistically conservative within the multi-voxel ROI search space. We performed
permutation tests over 10,000 random label changes of subject identity, keeping constant
genotype frequencies within this dataset to estimate the conservative rate of false
positivity by which the same set of results could be obtained with the same or better
statistical thresholds anywhere within the bilateral hippocampal and parahippocampal
regions of interest.
Pharmacogenetic effects on brain structure:
In further examining the pharmacogenetic effects on brain structure, we examined
a subset of 138 schizophrenia patients with available structural MRI data. The structural
MRI data were acquired, processed and analyzed similarly as described earlier. The
hypothesized pharmacogenetic interaction effects of mood stabilizer treatment and AKT1
SNP rs1130233 genotypes G/G versus A-carriers on prefrontal and hippocampal gray
matter volume were examined using an analysis of covariance model adjusted for the
orthogonalized first and second polynomials of age, gender, cognitive change, and total
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gray matter volume. In the first instance, we reported interaction effects in the bilateral
prefrontal, hippocampal and parahippocampal regions defined with WFU Pickatlas
(http://www.fmri.wfubmc.edu/download.htm) surviving nominal p<0.001 uncorrected.
We further determined the conservative rate of false positivity by which the same set of
pharmacogenetic cognitive and structural imaging findings could be obtained at the same
or better statistical thresholds over 10,000 random permutations of subject labels.
Supplementary Results:
AKT1 epistatic interactions and risk for schizophrenia:
We examined if the AKT1, BDNF and COMT genetic variations interacted to
contribute to risk for schizophrenia. We first examined the NIMH/CBDB case-control
sample (n=282 patients, 329 controls) in which we previously reported marginal
association with this AKT1 variant2.
While there were no marginal single gene
associations for COMT or BDNF, the AKT1, COMT and BDNF variants showed a 3way interaction (p<0.041) in a full logistic regression model controlled for gender. The 3way interaction was driven by a divergence of risk for schizophrenia in the context of the
AKT1-A minor allele (Supplementary Figure S1). Individuals at the highest risk were
those who were also COMT-Val homozygotes and BDNF-Met carriers; individuals who
instead carried the COMT-Met allele had the lowest risk (odds ratio between these two
groups was 2.61, 95% CI 0.97-7.00, p=0.056). Accordingly, within the group of AKT1A-carriers, there was a COMT-by-BDNF interaction (p=0.039, Supplementary Figure S1),
but not in AKT1-G homozygotes.
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We examined an independent case-control sample (936 patients, 1,190 controls)
from the Genetic Association Information Network (GAIN) cohort (public release
http://dbgap.ncbi.nlm.nih.gov/). Note that the AKT1 SNP genotyped in the GAIN sample
was rs2494731 and not rs1130233 as in our clinical samples, although these SNPs were
in the same Hapmap-derived haplotype block (r2>0.8). There was the same 3-way AKT1,
BDNF and COMT interaction on risk for schizophrenia (one-tailed p=0.036) and no
significant individual gene main effects in a logistic regression model controlled for
gender. A trend divergence of relative risk for schizophrenia in the context of the AKT1A carriers was similarly observed: individuals carrying all 3 minor alleles for AKT1,
COMT and BDNF had the highest risk for schizophrenia, although it was nonsignificantly increased relative to those with the COMT-Met allele (odds ratio across
these two groups was 1.42, 95% CI 0.89-2.27, p=0.14). Correspondingly, in AKT1-A
carriers but not in G-homozygotes, there was a COMT-by-BDNF interaction (p=0.045;
Supplementary Figure S2). Permutation analysis revealed that the likelihood that these
same three loci would show the identical allelic direction of interactions in these two
independent datasets is p<7x10-6.
Pharmacogenetic effects of BDNF on cognition and brain structure in schizophrenia:
As a supplementary test of the relative specificity of the pharmacogenetic
interaction involving the AKT1 variant and AKT1 activating drugs, we explored the
effect of the functional BDNF Val/Met variant on illness associated cognitive change.
We found that there was a marginal BDNF effect on cognitive change, where patients
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with the BDNF Met-allele had relatively larger cognitive decline (n=182, F(1,174)=4.58,
p=0.034, Supplementary Figure S3). However, there was no pharmacogenetic interaction
with use of mood stabilizers and this BDNF variant on cognition.
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Supplementary Table S1: Demographic characteristics of distinct groups of subjects
fMRI study on
memory encoding
(n=96 healthy
controls)
Structural MRI study
(n=171 healthy
controls)
Pharmacogenetic
study (n=186
schizophrenia
patients)
Age, yrs
Gender, % male
Education, yrs
WAIS IQ
AKT1 G-allele frequency
AKT1 A-allele frequency
BDNF Val-allele frequency
BDNF Met-allele frequency
COMT Val-allele frequency
COMT Met-allele frequency
Mean
31.14
40.6
16.60
108.6
0.73
0.27
0.80
0.20
0.52
0.48
SD
9.68
2.16
9.1
-
Mean
34.04
45.0
17.08
108.4
0.78
0.22
0.81
0.19
0.52
0.48
SD
9.87
2.90
8.76
-
Mean
36.6
77.7
13.85
93.02
0.76
0.24
0.81
0.19
-
SD
9.37
2.25
11.9
-
Age of illness onset, yrs
PANSS Positive score
PANSS Negative score
PANSS General score
Antipsychotic dose, CPZ-eq
Premorbid IQ (WRAT)
-
-
-
-
21.9
12.4
19.9
24.6
551
102.7
5.44
5.2
9.0
8.0
444
11.8
CPZ-eq: Chlorpromazine-equivalent dose; WRAT: Wide Range Achievement Test - Reading
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Supplementary Table S2: Behavioral performance during memory encoding and retrieval in fMRI according to genotype (n=96
healthy controls).
Encoding
Accuracy (AKT GG/ A)
Reaction time, sec (AKT GG/ A)
Accuracy (BDNF VV/M)
Reaction time, sec (BDNF VV/M)
Accuracy (COMT VV/M)
Reaction time, sec (COMT VV/M)
Mean
0.880/ 0.893
1.510/ 1.465
0.885/ 0.884
1.495/ 1.477
0.880/ 0.888
1.472/ 1.488
Retrieval
SD
0.081/ 0.078
0.184/ 0.167
0.077/ 0.086
0.176/ 0.176
0.080/ 0.079
0.165/ 0.183
Mean
0.880/ 0.902
1.432/ 1.400
0.893/ 0.881
1.403/ 1.440
0.894/ 0.890
1.418/ 1.404
SD
0.152/ 0.100
0.150/ 0.161
0.142/ 0.105
0.142/ 0.177
0.085/ 0.145
0.144/ 0.153
AKT GG: AKT1 rs1130233 G-homozygotes; A: AKT1 rs1130233 A-carriers; BDNF VV: Val-homozygotes, M: Met-carriers; COMT VV: COMT-Val
homozygotes, M: COMT-Met-carriers.
Behavioral performance differences across genotype groups were all p>0.22.
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CBDB
AKT rs1130233 GG
4.3
COMT Val/Val
COMT Met-carrier
3.8
Odds ratio
3.3
2.8
2.3
1.8
1.3
0.8
BDNF 1Val/Val BDNF2Val/Val BDNF3 Metcarrier
BDNF4Metcarrier
AKT rs1132033 A-carrier
4.3
COMT Val/Val
COMT Met-carrier
Odds ratio
3.8
3.3
2.8
2.3
1.8
1.3
0.8
1
2
3
BDNF
Val/Val BDNF
Val/Val BDNF
Metcarrier
4
BDNF
Metcarrier
Supplementary Figure S1: AKT1 epistasis and schizophrenia in the CBDB/NIMH sample.
Functional AKT1, COMT and BDNF variants showed a 3-way interaction (n=282
patients, 329 controls; p<0.041) in a full logistic regression model controlled for gender.
Odds ratios were computed relative to individuals who were AKT-A, BDNF-Met and
COMT-Met allele-carriers. Schizophrenia risk diverged in the context the AKT1-A minor
allele. In AKT1-A-carriers, there was a COMT-by-BDNF interaction (p<0.039) but not in
AKT1-G homozygotes. Individuals at the highest risk were those who were also COMTVal homozygotes and BDNF-Met carriers (n=14 patients 10 controls), relative to
individuals who instead carried the COMT-Met allele (n=27 patients 49 controls) and had
the lowest risk (odds ratio difference between these two groups was 2.61, 95% CI 0.977.00, p=0.056). Error bars represent 95% confidence interval. These results were
replicated in an independent sample (n=936 patients, 1,190 controls; Figure S2 below).
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GAIN
AKT rs2494731 GG
2
COMT Val/Val
COMT Met-carrier
Odds ratio
1.8
1.6
1.4
1.2
1
0.8
BDNF 1Val/Val BDNF2Val/Val BDNF3 Metcarrier
BDNF4Metcarrier
AKT rs2494731 C-carrier
2
Odds ratio
1.8
COMT Val/Val
COMT Met-carrier
1.6
1.4
1.2
1
0.8
1 Val/Val BDNF
2 Val/Val BDNF
3 MetBDNF
carrier
4 MetBDNF
carrier
Supplementary Figure S2: AKT1 epistasis and schizophrenia in the Genetic Association
Information Network (GAIN) cohort (public release http://dbgap.ncbi.nlm.nih.gov/). The
AKT1, COMT and BDNF variants showed a 3-way interaction (n=936 patients 1,190
controls; one-tailed p=0.036) in a full logistic regression model controlled for gender.
Odds ratios were computed relative to individuals who were AKT-C, BDNF-Met and
COMT-Met allele-carriers. Schizophrenia risk diverged in the context the AKT1-C minor
allele. In the context of AKT1-C-carriers, there was a COMT-by-BDNF interaction
(p<0.045) but not in AKT1-G homozygotes. Individuals at the highest risk were those
who were also COMT-Val homozygotes and BDNF-Met carriers. Error bars represent
95% confidence interval.
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