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GENE ASSOCIATION STUDIES OF
SCHIZOPHRENIA & TARDIVE DYSKINESIA
By
Clement Zai
A thesis submitted in conformity with the requirements For the degree of Doctor of Philosophy
Institute of Medical Science University of Toronto
Figure 2. Linkage disequilibrium plot among the five GABRG2 gene polymorphisms used. Shown are values for D’ and 95% Confidence Intervals, with the top right triangle derived from case-control data, and bottom left triangle derived from family data). Polymorphism rs183294 rs209356 rs766349 rs12520992 rs211013
rs183294 0.94 0.89-0.97
0.96 0.81-0.99
0.32 0.14-0.48
0.09 0.00-0.20
rs209356 0.91 0.77-0.97
0.70 0.53-0.82
0.17 0.02-0.38
0.12 0.03-0.21
rs766349 0.74 0.33-0.91
0.20 0.02-0.47
1.00 0.62-1.00
0.94 0.82-0.99
rs12520992 0.49 0.27-0.66
0.35 0.05-0.63
0.61 0.07-0.89
1.00 0.90-1.00
rs211013
0.12 0.01-0.31
0.31 0.15-0.44
0.93 0.69-0.99
1.00 0.79-1.00
Zai et al II. GABRG2 in Schizophrenia
60
Table 5. Results considering suicidal behaviour in SCZ patients and GABRG2 polymorphisms.
Genotypes Suicide attempt(s) SNP Alleles Yes No
p-value Suicide specifier
p-value
1/1 1/2 2/2
13 44 60
31 120 104
0.16 2.1+/-1.7 1.6+/-1.8 1.9+/-1.8
0.07 rs183294
1 2
70 164
182 328
0.12
1/1 1/2 2/2
28 54 32
35 137 81
0.04 2.2+/-1.9 1.6+/-1.8 1.8+/-1.7
0.10 rs209356
1 2
110 118
207 299
0.06
1/1 1/2 2/2
84 28 1
183 67 2
*0.90 1.8+/-1.8 1.7+/-1.8 2.0+/-1.7
0.94 rs766349
1 2
196 30
433 71
0.77
1/1 1/2 2/2
1 26 87
9 51
195
*0.34 1.2+/-1.6 1.7+/-1.9 1.8+/-1.8
#0.67 rs12520992
1 2
28 200
69 441
0.64
1/1 1/2 2/2
33 52 29
77 118 56
0.80 1.7+/-1.8 1.8+/-1.8 1.8+/-1.8
0.82 rs211013
1 2
118 110
272 230
0.54
*p-value calculated from two-tailed Fisher’s Exact Test. #p-value from Kruskal-Wallis Test.
Zai et al III. BDNF & DRD3 in Schizophrenia
61
CHAPTER 3
ASSOCIATION STUDY OF BDNF AND DRD3 GENES IN SCHIZOPHRENIA
Clement C. Zai(1,2), Julien Renou(1), Vincenzo De Luca(1,3), Greg W. H. Wong(1), Bernard Le
Foll(1), James L. Kennedy(1,2,3)
(1) Neurogenetics Section, Centre for Addiction and Mental Health, Toronto, Ontario M5T 1R8
Canada
(2) Institute of Medical Science, University of Toronto, Toronto, Ontario M5S 1A8 Canada
(3) Department of Psychiatry, University of Toronto, Toronto, Ontario M5T 1R8 Canada
Mr. Zai designed the experiment (with guidance from faculty), performed all of the genotyping
for the BDNF and DRD3 gene polymorphisms, corresponded with the clinical collaborators to
refine the details of the phenotype, performed all the statistical analyses, and wrote the
rs2134655, rs2087017, and rs1025398, as well as six BDNF gene polymorphisms, Promoter
C/A, C270T, BDNF_4, BDNF_3, BDNF_2, and Val66Met, were genotyped using TaqMan
allele-specific assays on the ABI Prism® 7500 Sequence Detection System with the Allelic
Discrimination program within the ABI software (Applied Biosystems, Foster City, CA). The
positions of the DRD3 and BDNF polymorphisms are indicated in Figures 3a and 3b (also see
Table 9).
3.3.3 Statistical Analyses
Adherence to Hardy-Weinberg equilibrium was determined using the chi-square test in
Haploview version 3.3 (Barrett et al., 2005). Genetic association with the paired case-control
sample was done using chi-square test both in terms of allele frequencies and genotype
frequencies. Two-tailed Fisher’s Exact Tests were used where expected cell counts were less
than five (URL: http://home.clara.net/sisa/fiveby2.htm). Association within the family sample
was done using the family based association test (FBAT) under the additive model (Hovarth et
al., 2001). The quantitative suicide scores were analysed allele-wise using the student t-test and
genotype-wise using One-way ANOVA (SPSS version 14.0, 2005). Linkage disequilibrium
across DRD3 and BDNF was determined using Haploview (Figures 4a, 4b). Haplotype analyses
were done using COCA-PHASE (UNPHASED) for the case-control sample, FBAT for the
family sample, and QT-PHASE (UNPHASED) for the SCZ cases with scores of suicide
behaviour (Dudbridge, 2003). Haplotypes with frequencies of less than 5% were excluded from
the analyses. Gene-gene interaction analysis was performed using HELIXTREE (GoldenHelix),
Zai et al III. BDNF & DRD3 in Schizophrenia
71
and post-hoc analyses of the continuous variable were carried out using univariate general linear
model in SPSS.
Zai et al III. BDNF & DRD3 in Schizophrenia
72
4.4 RESULTS
4.4.1 Sample Characteristics
Both the paired case-control and family samples did not differ significantly from Hardy-
Weinberg equilibrium (p>0.1) except for rs1025398 for the case-control sample (p=0.02).
4.4.2 DRD3 and BDNF are not associated with SCZ
Upon testing for allele and genotype frequency distribution differences in our matched
SCZ case-control sample, none of the ten DRD3 and six BDNF polymorphisms was associated
with SCZ (Tables 6a, 6b). From FBAT analysis on our sample of small nuclear families, we did
not observe biased transmission of alleles in any of the 16 polymorphisms tested (Table 7).
4.4.3 DRD3 and BDNF are not associated with Suicidal behaviour in SCZ patients
To test for an association of suicidal behaviour in SCZ patients, we compared the allele
and genotype frequency distributions of SCZ patients who had had at least one suicide attempt
with those who had not (Table 8). None of the ten DRD3 and six BDNF polymorphisms was
associated with suicidality in SCZ. In line with the qualitative analyses, comparison of the
quantitative suicide behaviour scale among genotypes did not yield significant results in any of
the DRD3 and BDNF polymorphisms.
4.4.4 BDNF-DRD3 interaction in SCZ and Suicidal behaviour in SCZ patients
Because of the functional relationship between BDNF and DRD3 in vivo, we performed
interaction analysis with polymorphisms between BDNF and DRD3 using HELIXTREE. We
did not find significant association between any BDNF-DRD3 two-marker combinations in SCZ
Zai et al III. BDNF & DRD3 in Schizophrenia
73
diagnosis (Figure 5), but we observed a significant association between BDNF Val66Met and
DRD3 Ser9Gly in suicide specifier (Figure 6; Bonferroni p<0.05). More specifically, SCZ
patients who are heterozygous for both Val66Met and Ser9Gly appeared to be more likely to
have attempted suicide(s) than those with other genotype combinations (28/50 or 56% versus
87/314 or 28%; OR=2.02 CI: 1.20-3.40).
Zai et al III. BDNF & DRD3 in Schizophrenia
74
4.5 DISCUSSION
The current study on a small nuclear family sample and an independent paired case-
control sample reports no significant association of the DRD3 and BDNF genes with SCZ.
These results are different from a recent study of the DRD3 gene (Talkowski et al, 2006b), where
the authors found Ser9Gly to be consistently associated with SCZ. The mixed results could be
due to more stringent criteria for our case-control sample in which each SCZ subject was
matched with a control subject on age, sex, and ethnicity. As for our family sample, we used
FBAT that takes advantage of the availability of family structures other than triads, thus
increasing the sample size and power. It should be noted that even though the majority of our
subjects are Caucasians, ethnic differences in SCZ susceptibility and linkage disequilibrium
block structure could have influenced the results. When only Caucasians were included in the
analyses, rs6762200 became marginally significant (p<0.05). It is also important to note that,
except for the HELIXTREE gene-gene interaction analysis, the p-values reported in the current
study were not corrected for multiple testing. The findings suggest that the selected
polymorphisms may not influence schizophrenia risk or that the current sample size is too small
to have enough power to detect a small effect size.
This is the first reported study of DRD3 and BDNF with suicidal behaviour in SCZ.
Although we did not find a significant association with individual polymorphisms, we found a
significant interaction between the functional polymorphisms Val66Met and Ser9Gly in the
history of suicide attempt(s). One interpretation is that since BDNF has been associated with
depression (Strauss et al, 2004; 2005; Martinowich et al, 2007; Ribeiro et al, 2007), and DRD3
has been associated with impulsivity (Retz et al, 2003), suicide attempts may require the
interaction between the depressive and impulsive trait (Mann, 2003). Therefore, even though
Zai et al III. BDNF & DRD3 in Schizophrenia
75
BDNF and DRD3 did not confer risk of suicide individually, the combination of the two genes
did. Further replication studies and functional analyses are required to better understand this
interaction. The role of other confounding factors in suicidal behaviour, including alcohol use,
should also be considered (Sher, 2006). The present findings do not support a role of DRD3 and
BDNF in SCZ development, but they encourage more studies of BDNF and DRD3 in SCZ-
associated phenotypes including suicidal behaviour.
Zai et al III. BDNF & DRD3 in Schizophrenia
76
Figure 3a. Schematic diagram of the DRD3 gene with its exons and introns. The positions of the 12 polymorphisms used for the present study are indicated within the gene. See Table 9 on page 95 for more details. Figure 3b. Schematic diagram of the BDNF gene with its exons and introns. The positions of the 6 polymorphisms used for the present study are indicated within the gene. See Table 9 on page 95 for more details.
5’ 3’
kb ~13kb ~20kb ~256kb
rs905568
rs2399504
rs7611535
rs6762200
rs1394016
rs6280, MscI, BalI, Ser9Gly
rs167770
rs2134655
rs1025398
1 2 3 4 5 6 7
rs2087017
5’ 3’
P1CA C270T BDNF4 BDNF3 BDNF2 Val66Met
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77
Table 6a. Genetic analysis of DRD3 markers and SCZ using paired case-control samples. Polymorphism Assay
*2-tailed Fisher’s Exact Tests #Sliding-window two-marker haplotype analysis using COCA-PHASE.
Zai et al III. BDNF & DRD3 in Schizophrenia
78
Table 6b. Genetic analysis of BDNF markers and SCZ using paired case-control samples. Polymorphism Assay
Genotypes SCZ(Y/N) Allele SCZ(Y/N) p-value#
Promoter C/A rs28383487 C-281A
1/1 (A/A) 1/2 (A/C) 2/2 (C/C) P
0/1 6/5
221/221 1.00*
Allele 1 (A) Allele2 (C) P
6/7 448/447
0.78
0.23
C270T HinfI
1/1 (A/A) 1/2 (A/G) 2/2 (G/G) P
201/209 24/16
1/1 0.43*
Allele 1 (A) Allele2 (G) P
426/434 26/18
0.22
0.43
BDNF_4 rs7103411
1/1 (A/A) 1/2 (A/G) 2/2 (G/G) P
131/126 79/85 15/14 0.84
Allele 1 (A) Allele 2 (G) P
341/337 109/113
0.76
0.53
BDNF_3 rs2049045
1/1 (C/C) 1/2 (C/G) 2/2 (G/G) P
158/146 59/72
9/8 0.40
Allele 1 (C) Allele2 (G) P
375/364 77/88
0.34
0.57
BDNF_2 rs11030104
1/1 (C/C) 1/2 (C/T) 2/2 (T/T) P
16/15 75/82
135/129 0.79
Allele 1 (C) Allele2 (T) P
107/112 345/340
0.70
Val66Met rs6265
1/1(A/A) 1/2(A/G) 2/2(G/G) P
15/12 68/77
140/134 0.60
Allele 1 (A) Allele 2 (G) P
98/101 348/345
0.81
0.73
*2-tailed Fisher’s Exact Tests #Sliding-window two-marker haplotype analysis using COCA-PHASE.
Zai et al III. BDNF & DRD3 in Schizophrenia
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Table 7. Family-based association test using FBAT for DRD3 and BDNF single-nucleotide polymorphisms and HBAT for two-marker haplotypes. Polymorphism #informative
families S
(allele 1/2) E(S)
(allele 1/2) Var(S) Z (allele 1) P-value P
(Haplotypes)
rs905568 38 46/36 43/39 13.9 0.68 0.50 0.63
rs2399504 34 47/23 51/20 9.7 -1.12 0.26 0.20
rs7611535 41 35/51 29/58 13.2 1.79 0.07
0.14 rs6762200 41 42/46 35/53 12.7 1.90 0.06
0.19 rs1394016 42 45/45 50/40 13.8 -1.30 0.19
0.44 Ser9Gly 39 45/37 48/34 13.5 -0.88 0.38
0.55 rs167770 39 34/46 30/50 12.8 1.07 0.28
0.19 rs2134655 31 17/49 22/44 9.4 -1.72 0.09
0.16 rs2087017 40 41/39 36/44 13.5 1.28 0.20
0.47 rs1025398 35 45/31 44/32 12.7 0.23 0.82
NA Promoter C/A 3 ND ND ND ND ND
0.88 C270T 11 16/8 17/7 3.5 -0.45 0.65
0.91 BDNF_4 31 38/26 39/25 9.7 -0.19 0.85
0.98 BDNF_3 28 41/21 41/21 8.4 -0.03 0.98
0.85 BDNF_2 31 26/36 23/39 9.2 0.85 0.39
Val66Met 28 23/39 23/39 8.7 -0.14 0.89 0.98
ND Not determined.
Zai et al III. BDNF & DRD3 in Schizophrenia
80
rs
9055
68
rs23
9950
4
rs76
1153
5
rs67
6220
0
rs13
9401
6
Ser
9Gly
rs16
7770
rs21
3465
5
rs20
8701
7
rs10
2539
8
rs905568
0 . 1 6 0.01-0.41
0 . 1 0 0.00-0.32
0.007 -0.01-0.16
0 . 0 2 -0.01-0.19
0 . 0 9 0.00-0.26
0 . 2 4 0.06-0.41
0 . 7 0 0.50-0.83
0 . 1 6 0.04-0.28
0 . 2 1 0.05-0.37
rs2399504 0 . 0 6 0.00-0.23
1 . 0 0
0.93-1.00
1 . 0 0
0.90-1.00
0 . 9 6
0.83-1.00
0 . 9 7
0.85-1.00
0 . 9 7
0.87-1.00
0 . 9 1 0.60-0.98
0 . 5 3 0.25-0.73
0.004 -0.01-0.24
rs7611535 0 . 0 3 -0.01-0.17
0 . 9 9
0.95-1.00
0 . 9 8
0.89-1.00 0 . 9 7 0.88-1.00
0 . 7 9 0.66-0.88
0 . 7 6 0.64-0.84
0 . 9 3 0.67-0.98
0 . 1 8 0.02-0.42
0 . 0 2 -0.01-0.20
rs6762200 0 . 1 2 0.01-0.24
0 . 9 4
0.87-0.98
0 . 9 3 0.87-0.97
0 . 9 9
0.93-1.00 0 . 8 7 0.80-0.92
0 . 8 7 0.78-0.93
0 . 9 1 0.73-0.97
0 . 2 4 0.07-0.40
0 . 0 5 -0.01-0.20
rs1394016 0 . 2 0 0.09-0.30
0 . 8 3 0.72-0.90
0 . 8 9 0.82-0.94
0 . 9 3 0.88-0.96
0 . 9 8 0.92-1.00
1 . 0 0 0.94-1.00
0 . 9 6
0.81-0.99
0 . 2 8 0.11-0.43
0 . 0 8 0.00-0.24
Ser9Gly 0 . 2 0 0.08-0.31
0 . 8 3 0.74-0.90
0 . 6 5 0.56-0.72
0 . 8 3 0.78-0.87
0 . 9 8
0.94-1.00
1 . 0 0 0.95-1.00
1 . 0 0 0.86-1.00
0 . 4 1 0.22-0.57
0 . 1 1 0.01-0.26
rs167770 0 . 2 6 0.12-0.38
0 . 8 4 0.76-0.90
0 . 6 7 0.60-0.74
0 . 8 2 0.76-0.87
1 . 0 0
0.97-1.00
1 . 0 0
0.98-1.00
1 . 0 0
0.83-1.00 0 . 3 5 0.14-0.53
0 . 0 1 -0.01-0.17
rs2134655 0 . 7 3 0.60-0.82
0 . 6 9 0.42-0.85
0 . 7 5 0.54-0.87
0 . 8 2 0.69-0.90
0 . 9 4
0.85-0.98
0 . 9 4
0.85-0.98
0 . 9 8
0.87-1.00
0 . 9 6
0.82-1.00
0 . 1 9 0.02-0.46
rs2087017 0 . 3 0 0.21-0.37
0 . 7 2 0.57-0.82
0 . 3 1 0.16-0.45
0 . 3 6 0.25-0.46
0 . 3 2 0.22-0.41
0 . 4 8 0.37-0.57
0 . 4 6 0.33-0.57
0 . 9 4
0.86-0.98
0.003 -0.01-0.02
rs1025398 0 . 0 7 0.00-0.17
0.003 -0.01-0.14
0 . 0 3 -0.01-0.13
0 . 0 8 0.00-0.17
0 . 0 7 0.00-0.17
0 . 0 9 0.01-0.18
0 . 0 7 0.00-0.16
0 . 2 4 0.05-0.41
0 . 0 4 -0.01-0.17
Figure 4a. Linkage disequilibrium plot among the 10 DRD3 gene polymorphisms used in the present study. The numbers represents D’ values and the 95% confidence intervals of D’, while the color darkness within each box corresponds to strength of linkage. Values in the upper right triangle were derived from the family sample, while values in the lower left triangle were derived from the case-control sample. The markers boxed in thick lines have the highest linkage disequilibrium given by D’>0.90 and lower boundary of the 95% confidence intervals of D’>0.70.
Zai et al III. BDNF & DRD3 in Schizophrenia
81
P
rom
oter
C/A
C27
0T
BD
NF
_4
BD
NF
_3
BD
NF
_2
Val
66M
et
Promoter C/A
1 . 0 0 0.05-0.98
1 . 0 0 0.13-0.99
1 . 0 0 0.09-1.00
1 . 0 0 0.13-0.99
1 . 0 0 0.11-0.99
C270T 0 . 4 6 0.04-0.96
1 . 0 0 0.35-1.00
1 . 0 0 0.30-1.00
1 . 0 0 0.36-1.00
1 . 0 0 0.26-1.00
BDNF_4 1 . 0 0 0.22-1.00
1 . 0 0 0.23-0.99
0 . 9 5
0.85-0.99
0 . 9 6
0.91-0.99
0 . 9 6
0.88-0.99
BDNF_3 1 . 0 0 0.13-0.99
1 . 0 0 0.16-0.99
0 . 9 7
0.93-1.00
1 . 0 0
0.93-1.00
1 . 0 0
0.93-1.00
BDNF_2 1 . 0 0 0.21-1.00
1 . 0 0 0.31-1.00
0 . 9 8
0.95-1.00
0 . 9 9
0.95-1.00
1 . 0 0
0.94-1.00
Val66Met 1 . 0 0 0.18-1.00
1 . 0 0 0.28-1.00
0 . 9 8
0.94-1.00
0 . 9 7
0.92-0.99
1 . 0 0
0.98-1.00
Figure 4b. Linkage disequilibrium plot among the six BDNF gene polymorphisms used in the present study. The numbers represents D’ values and the 95% confidence intervals of D’, while the color darkness within each box corresponds to strength of linkage. Values in the upper right triangle were derived from the family sample, while values in the lower left triangle were derived from the case-control sample. The markers boxed in thick lines have the highest linkage disequilibrium given by D’>0.90 and lower boundary of the 95% confidence intervals of D’>0.70.
Zai et al III. BDNF & DRD3 in Schizophrenia
82
Table 8. Results considering suicidal behaviour in SCZ patients with BDNF and DRD3 polymorphisms.
*p-value calculated from two-tailed Fisher’s Exact Test. #p-value from Kruskal-Wallis Test.
Zai et al III. BDNF & DRD3 in Schizophrenia
84
Figure 5. P-values from analyses of two-marker interactions between BDNF and DRD3 polymorphisms in relation to SCZ diagnosis given by HELIXTREE program. Top left triangle indicates significance with the raw p-values, while the bottom right triangle indicates significance with Bonferroni adjusted p-values.
Raw p-values
Bonferroni p-values
rs1025398rs2087017rs2134655rs167770Ser9Gly
rs1394016rs6762200rs7611535rs905568
Val66MetBDNF_2BDNF_3BDNF_4
C270TP1CA
rs1025398
rs2087017
rs2134655
rs167770
Ser9G
ly
rs1394016
rs6762200
rs7611535
rs905568
Val66M
et
BD
NF
_2
BD
NF
_3
BD
NF
_4
C270T
P1C
A
10-8 -
10-7 -
10-6 -
10-5 -
10-4 -
10-3 -
10-2 -
10-1 -
1 -
Clement Zai II. GABRG2 and Schizophrenia
Page 85
Figure 6. P-values from analyses of two-marker interactions between BDNF and DRD3 polymorphisms in association with the history of suicide attempt(s) given by HELIXTREE program. Top left triangle indicates significance with the raw p-values, while the bottom right triangle indicates significance with Bonferroni adjusted p-values. *The interaction between BDNF Val66Met and DRD3 Ser9Gly was significant in history of suicide attempt(s).
Raw p-values
Bonferroni p-values
rs1025398rs2087017rs2134655rs167770*Ser9Gly
rs1394016rs6762200rs7611535rs905568
*Val66MetBDNF_2BDNF_3BDNF_4
C270TP1CA rs1025398
rs2087017
rs2134655
rs167770
*Ser9G
ly
rs1394016
rs6762200
rs7611535
rs905568
*Val66M
et
BD
NF
_2
BD
NF
_3
BD
NF
_4
C270T
P1C
A
10-8 -
10-7 -
10-6 -
10-5 -
10-4 -
10-3 -
10-2 -
10-1 -
1 -
Zai et al IV. BDNF & DRD3 in Tardive Dyskinesia
Page 86
CHAPTER 4
GENETIC STUDY OF BDNF, DRD3, AND THEIR INTERACTION IN TARDIVE
DYSKINESIA
Manuscript to be submitted
Clement C. Zai(1,2), Vincenzo De Luca(1,2), Daniel J. Müller(1,3), Natalie Bulgin (1),
Nicole King (1), Aristotle N. Voineskos(1), Herbert Y. Meltzer(4), Jeffrey A.
Lieberman(5), Steven G. Potkin(6), Gary Remington(1), James L. Kennedy(1,2)
(1) Centre for Addiction and Mental Health, Toronto, Ontario, CANADA
(2) Institute of Medical Science, University of Toronto, Toronto, Ontario, CANADA
(3) Department of Psychiatry, Charité University Medicine Berlin, Campus Charité
Mitte, Berlin, Germany
(4) Psychiatric Hospital at Vanderbilt University, Nashville, Tennessee, USA
(5) New York State Psychiatric Institute, Columbia University Medical Centre, New
York City, New York, USA
(6) Brain Imaging Center, Irvine Hall, University of California at Irvine, California, USA
Mr. Zai designed the experiment (with guidance from faculty), performed all the
genotyping for the BDNF and ZNF80 gene polymorphisms and over 90% of the
genotyping for the DRD3 gene polymorphisms (50% of the Ser9Gly genotypes were
Zai et al IV. BDNF & DRD3 in Tardive Dyskinesia
Page 87
reported previously in Basile et al (1999), and the remaining 50% of the Ser9Gly
genotypes were performed by Ms. Nancy Chung, a former summer student working with
Dr. Vincenzo De Luca). Mr. Zai corresponded with the clinical collaborators to refine
the details of the phenotype, performed all the statistical analyses, and wrote the
such as smoking, alcohol and substance use could increase the risk of TD and contribute
to our findings, though we do not have the information available for our entire sample
(Menza et al, 1991). Further, our sample was drawn from four different clinical sites.
Even though the study Caucasian population was in Hardy-Weinberg equilibrium for all
10 polymorphisms and the sex ratios among the four geographical groups do not differ
significantly (p>0.1), the mean ages differ significantly among them (p<0.001).
Therefore, the possibility of ascertainment bias cannot be ignored. However, when we
analyzed the rs905568 polymorphism in subjects recruited from the US and Canada
separately, the same trend remained for both sub-samples.
The DRD3 gene is unlikely to be the only genetically determined factor for TD, as
other genes have been found associated with TD. Meta-analyses have identified DRD2
and HTR2A to be associated with TD (Zai et al, 2007b; Lerer et al, 2005). Studies in
other genes such as HTR2C, CYP1A2, and manganese superoxide dismutase require
replication studies to confirm their association (Basile et al, 2000; Segman et al, 2000;
Schulze et al, 2001; Hori et al, 2000). As all antipsychotics target more than one
receptor, it is likely that TD is a polygenic condition with each gene contributing a small
proportion of the risk to the disorder. Additional gene-gene interaction studies may help
in identifying and clarifying pathways that contribute to TD. TD risk is also likely to be
influenced by many environmental factors. Acquiring these information will help
immensely in limiting effects of potential confounders in genetic studies of TD. The
Zai et al IV. BDNF & DRD3 in Tardive Dyskinesia
Page 102
present study encourages further studies into the rs905568 and adjacent polymorphisms
surrounding the 5’ region of the DRD3 gene in TD.
Zai et al IV. BDNF & DRD3 in Tardive Dyskinesia
Page 103
Table 9. ABI Assays-on-Demand and Assays-by-Design with information on their corresponding BDNF and DRD3 polymorphisms used in the study. (See Figures 3a, 3b on page 72) Gene Assay-on-
Demand / -by-Desgn
Allele — FAM
Allele — VIC
Polymorphism Name(s)
Location in DRD3 gene
References
DRD3 rs905568 C G CG Promoter Talkowski et al, 2006 DRD3 rs2399504 G A CT Promoter Talkowski et al, 2006 DRD3 rs7611535 A G CT Promoter DRD3 rs6762200 A G CT Promoter DRD3 rs1394016 C T AG Promoter Talkowski et al, 2006 DRD3 rs6280 A(S) G(G) Ser9Gly Exon 2 DRD3 rs167770 C T A11277G Intron 2 DRD3 rs2134655 A G C32638T Intron 5 Talkowski et al, 2006 DRD3 rs2087017 C T A48826G 3’ DRD3 rs1025398 A G CT 3’ Talkowski et al, 2006 BDNF rs6265 A(M) G(V) Val66Met Exon 2 Pezawas et al, 2004 BDNF rs7103411 A G BDNF_4 Intron 1 BDNF rs2049045 C G BDNF_3 Intron 1 BDNF rs11030104 C T BDNF_2 Intron 1 BDNF HinfI G A C270T Intron 1 Tongiorgi et al, 2006 BDNF rs28383487 A C C-281A,
Table 10a. Statistical analyses on demographics (sex, age) as well as total AIMS scores and TD diagnoses with DRD3 genotypes. DRD3 markers N (M/F) Age (years) Total AIMS score TD (Yes/No) rs905568 1/1 (C/C)
1/2 (C/G) 2/2 (G/G) P
62(41/21) 103(64/39) 26(22/4) 0.095
38.08+/-9.03 37.73+/-10.43 37.08+/-10.04 0.910
7.33+/-7.32 6.28+/-8.06 2.64+/-4.55 0.007!
32/30 39/64 5/21 0.015
rs2399504 1/1 (G/G) 1/2 (G/A) 2/2 (A/A) P
122(88/34) 65(38/27) 5(2/3) 0.060*
38.31+/-9.95 36.98+/-10.25 37.80+/-6.65 0.689
6.09+/-8.14 6.22+/-6.40 4.50+/-4.20 0.909
48/74 27/38 1/4 0.769*
rs7611535 1/1 (A/A) 1/2 (A/G) 2/2 (G/G) P
14(5/9) 82(56/26) 96(67/29) 0.045*
35.64+/-9.24 37.82+/-9.89 38.20+/-10.18 0.671
5.10+/-4.75 5.81+/-7.16 6.44+/-8.11 0.800
6/8 31/51 39/57 0.898
rs6762200 1/1 (A/A) 1/2 (A/G) 2/2 (G/G) P
30(16/14) 95(65/30) 65(45/20) 0.259
34.87+/-8.82 38.03+/-9.46 39.05+/-10.59 0.152
6.13+/-6.38 5.18+/-6.87 7.50+/-8.67 0.261!
12/18 33/62 31/34 0.259
rs1394016 1/1 (C/C) 1/2 (C/T) 2/2 (T/T) P
58(40/18) 102(71/31) 33(18/15) 0.256
38.67+/-10.84 37.75+/-9.97 36.67+/-8.13 0.648
7.44+/-8.85 5.07+/-6.44 6.65+/-7.86 0.370!
26/32 39/63 11/22 0.527
Ser9Gly 1/1(A/A) 1/2(A/G) 2/2(G/G) P
71(51/20) 93(62/31) 24(12/12) 0.147
38.46+/-10.19 37.03+/-10.68 37.58+/-7.74 0.671
6.87+/-8.50 5.44+/-6.85 5.22+/-6.99 0.490
32/39 35/58 6/18 0.207
rs167770 1/1 (C/C) 1/2 (C/T) 2/2 (T/T) P
16(5/11) 87(60/27) 89(63/26) 0.007
37.88+/-7.96 37.92+/-10.33 37.78+/-10.01 0.995
7.00+/-8.22 5.77+/-6.34 6.24+/-8.42 0.848
5/11 34/53 37/52 0.733
rs2134655 1/1 (A/A) 1/2 (A/G) 2/2 (G/G) P
9(5/4) 69(48/21) 112(75/37) 0.619*
38.33+/-14.98 38.38+/-9.34 37.41+/-9.86 0.807
6.50+/-5.68 6.19+/-7.51 5.86+/-7.69 0.954
5/4 28/41 41/71 0.489*
rs2087017 1/1 (C/C) 1/2 (C/T) 2/2 (T/T) P
36(26/10) 95(64/31) 60(37/23) 0.551
38.69+/-9.47 36.31+/-9.83 40.15+/-9.81 0.053
6.55+/-8.65 6.00+/-7.64 6.08+/-6.71 0.942
13/23 42/53 21/39 0.460
rs1025398 1/1 (A/A) 1/2 (A/G) 2/2 (G/G) P
67(44/23) 96(67/29) 28(16/12) 0.452
38.85+/-9.23 36.92+/-10.20 39.07+/-10.74 0.381
6.68+/-7.12 6.04+/-7.79 5.09+/-7.82 0.698
30/37 38/58 8/20 0.338
* With at least 1 expected cell count <5; Fisher Exact Test used. ! Variances among comparisons groups differ significantly; Kruskal-Wallis test used. Bolded numbers indicate 0.05<p<0.10; bolded and italicized numbers indicate p<0.05.
Zai et al IV. BDNF & DRD3 in Tardive Dyskinesia
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Table 10b. Statistical analyses on demographics (sex, age) as well as total AIMS scores and TD diagnoses with BDNF and ZNF80 genotypes. Markers N (M/F) Age (years) Total AIMS score TD (Yes/No) Promoter C/A 1/1 (/)
1/2 (/) 2/2 (/) P
0(0/0) 7(6/1) 185(122/63) 0.428*
N/A 37.29+/-6.58 37.87+/-10.08 0.879
N/A 3.50+/-4.73 6.16+/-7.58 0.487
0/0 1/6 75/110 0.248*
C270T 1/1 (/) 1/2 (/) 2/2 (/) P
171(115/56) 18(10/8) 2(2/0) 0.438*
37.68+/-9.86 38.89+/-11.48 45.50+/-3.54 0.494
5.86+/-7.38 8.72+/-8.70 2.00+/-2.83 0.234
65/106 10/8 1/1 0.331*
BDNF_4 1/1 (A/A) 1/2 (A/G) 2/2 (G/G) P
118(83/35) 63(37/26) 9(7/2) 0.237*
38.96+/-10.11 36.17+/-9.65 37.44+/-6.89 0.194
5.47+/-6.93 6.57+/-7.86 13.50+/-12.08 0.035
45/73 26/37 5/4 0.561*
BDNF_3 1/1 (C/C) 1/2 (C/G) 2/2 (G/G) P
133(95/38) 55(30/25) 4(3/1) 0.060*
38.05+/-10.07 37.82+/-9.90 31.50+/-5.51 0.434
5.70+/-7.17 6.76+/-7.98 12.00+/-14.18 0.288
52/81 22/33 2/2 0.954*
BDNF_2 1/1 (C/C) 1/2 (C/T) 2/2 (T/T) P
10(8/2) 67(40/27) 115(80/35) 0.291*
37.40+/-10.63 36.67+/-9.96 38.57+/-9.92 0.459
12.38+/-11.75 6.26+/-7.27 5.49+/-7.11 0.211!
5/5 28/39 43/72 0.633*
Val66Met 1/1 (/) 1/2 (/) 2/2 (/) P
4(4/0) 64(37/27) 125(88/37) 0.085*
36.75+/-7.04 36.86+/-10.08 38.38+/-9.96 0.595
9.67+/-15.89 6.90+/-8.04 5.57+/-7.02 0.651!
1/3 27/37 48/77 0.748*
rs6438191 1/1 (A/A) 1/2 (A/G) 2/2 (G/G) P
90(62/28) 88(54/34) 13(11/2) 0.211*
37.64+/-9.36 37.86+/-10.72 40.08+/-8.86 0.713
6.19+/-7.25 6.41+/-8.01 3.91+/-6.25 0.592
37/53 36/52 3/10 0.443
rs3732781 1/1 (G/G) 1/2 (G/T) 2/2 (T/T) P
10(4/6) 96(65/31) 85(58/27) 0.209*
33.90+/-7.08 38.42+/-9.47 37.93+/-10.53 0.388
5.14+/-8.69 6.41+/-6.82 5.90+/-8.29 0.862
4/6 43/53 29/56 0.332
rs3732782 1/1 (A/A) 1/2 (A/C) 2/2 (C/C) P
14(11/3) 88(55/33) 88(61/27) 0.460*
41.14+/-9.40 37.88+/-10.52 37.34+/-9.49 0.417
5.00+/-7.06 6.12+/-7.91 6.27+/-7.33 0.865
4/10 34/54 37/51 0.617
* With at least 1 expected cell count <5; Fisher Exact Test used. ! Variances among comparisons groups differ significantly; Kruskal-Wallis test used. Bolded numbers indicate 0.05<p<0.10; bolded and italicized numbers indicate p<0.05.
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Table 11. Results from χ2 test of allele frequencies of each of the 19 polymorphisms versus TD diagnoses for our Caucasian and African-American samples.
TD (Yes/No) TD (Yes/No) DRD3 markers Caucasian Black
BDNF markers Caucasian Black
rs905568 Allele 1 (C) Allele2 (G) P
99/120 43/106 0.002
18/23 8/15 0.476
Promoter C/A
Allele 1 () Allele 2 () P
0/6 146/226 0.086*
0/0 22/38 N/A
rs2399504 Allele 1 (G) Allele2 (A) P
117/185 29/45 0.944
18/30 6/6 0.517*
C270T Allele 1 () Allele 2 () P
136/220 10/10 0.292
21/34 1/4 0.643*
rs7611535 Allele 1 (A) Allele2 (G) P
42/67 100/165 0.885
6/8 20/30 0.847
BDNF_4 Allele 1 (A) Allele 2 (G) P
113/177 35/45 0.439
1/1 21/37 0.999*
rs6762200 Allele 1 (A) Allele2 (G) P
57/98 89/130 0.450
19/19 3/15 0.017
BDNF_3 Allele 1 (C) Allele 2 (G) P
120/193 24/37 0.883
1/1 21/37 0.999*
rs1394016 Allele 1 (C) Allele2 (T) P
84/127 58/105 0.404
2/10 24/26 0.048
BDNF_2 Allele 1 (C) Allele 2 (T) P
36/49 110/183 0.423
21/37 1/1 0.999*
Ser9Gly Allele 1 (A) Allele 2 (G) P
98/134 48/92 0.128
4/9 22/25 0.302
Val66Met Allele 1 () Allele 2 () P
28/41 122/187 0.866
18/35 4/3 0.405*
rs167770 Allele 1 (C) Allele2 (T) P
43/75 101/157 0.616
13/24 7/14 0.890
ZNF80 markers
rs2134655 Allele 1 (A) Allele2 (G) P
38/49 110/183 0.303
3/7 23/31 0.510*
rs6437191 Allele 1 (A) Allele 2 (G) P
110/158 42/72 0.443
17/25 5/13 0.350
rs2087017 Allele 1 (C) Allele 2 (T) P
66/97 80/131 0.613
14/18 8/20 0.224
rs3732781 Allele 1 (G) Allele 2 (T) P
51/65 101/165 0.271
2/4 20/34 0.999*
rs1025398 Allele 1 (A) Allele2 (G) P
93/132 51/96 0.199
18/25 8/11 0.986
rs3732782 Allele 1 (A) Allele 2 (C) P
42/74 108/156 0.388
4/13 16/25 0.258
* with at least one expected cell count <5. Fisher Exact Test was used. Bolded numbers indicate 0.05<p<0.10; bolded and italicized numbers indicate p<0.05.
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rs
6438
191
rs37
3178
1
rs37
3278
2
rs90
5568
rs23
9950
4
rs76
1153
5
rs67
6220
0
rs13
9401
6
Ser
9Gly
rs16
7770
rs21
3465
5
rs20
8701
7
rs10
2539
8
rs6438191
1 . 0 0
0.82-1.00
1 . 0 0
0.97-1.00
0 . 9 1 0.82-0.96
0 . 0 4 -0.01-0.25
0 . 0 7 0.00-0.43
0 . 2 6 0.05-0.47
0 . 2 2 0.03-0.44
0 . 2 0 0.02-0.44
0 . 1 5 0.01-0.44
0 . 6 9 0.37-0.86
0 . 2 6 0.09-0.41
0 . 0 2 -0.01-0.19
rs3732781
0 . 9 4
0.72-0.99
0 . 9 3 0.78-0.98
0 . 2 3 0.02-0.69
0 . 3 3 0.05-0.60
0 . 3 7 0.11-0.57
0 . 4 7 0.22-0.64
0 . 3 6 0.08-0.58
0 . 2 7 0.03-0.56
0 . 3 1 0.14-0.45
0 . 0 5 0.00-0.25
0 . 2 6 0.04-0.48
rs3732782
0 . 9 0 0.81-0.95
0 . 0 1 0.00-0.49
0 . 1 2 0.01-0.46
0 . 2 7 0.05-0.48
0 . 2 4 0.04-0.45
0 . 2 3 0.03-0.46
0 . 2 1 0.02-0.48
0 . 6 9 0.36-0.86
0 . 2 6 0.09-0.41
0 . 0 1 -0.01-0.27
rs905568
0 . 3 1 0.04-0.60
0 . 1 9 0.02-0.43
0 . 0 3 -0.01-0.18
0 . 0 5 -0.01-0.20
0 . 0 6 0.00-0.21
0 . 2 3 0.04-0.44
0 . 8 2 0.60-0.92
0 . 1 5 0.03-0.28
0 . 1 5 0.02-0.28
rs2399504
1 . 0 0
0.94-1.00
1 . 0 0
0.91-1.00
0 . 9 4 0.81-0.98
0 . 9 2 0.79-0.97
0 . 9 1 0.80-0.97
0 . 6 7 0.21-0.88
0 . 7 5 0.51-0.88
0 . 3 2 0.06-0.58
rs7611535
1 . 0 0
0.95-1.00 0 . 9 4 0.85-0.98
0 . 7 3 0.61-0.82
0 . 7 6 0.66-0.83
0 . 7 6 0.45-0.90
0 . 1 8 0.02-0.37
0 . 1 2 0.00-0.34
rs6762200
0 . 9 2 0.86-0.96
0 . 8 8 0.81-0.93
0 . 8 7 0.77-0.93
0 . 6 8 0.43-0.82
0 . 3 9 0.23-0.53
0 . 0 5 0.00-0.25
rs1394016
0 . 9 9 0.93-1.00
0 . 9 8 0.91-1.00
1 . 0 0
0.88-1.00
0 . 4 2 0.26-0.55
0 . 0 1 -0.01-0.17
Ser9Gly
1 . 0 0 0.95-1.00
0 . 9 4 0.73-0.99
0 . 6 5 0.50-0.77
0 . 0 0 -0.01-0.16
rs167770
0 . 9 2 0.64-0.98
0 . 5 3 0.34-0.68
0 . 0 6 0.00-0.30
rs2134655
0 . 9 6
0.81-0.99
0 . 4 3 0.13-0.65
rs2087017
0 . 0 4 -0.01-0.19
rs1025398
Figure 7a. Linkage disequilibrium plot among the three ZNF80 and ten DRD3 gene polymorphisms used in the present study. The numbers represents D’ values and the 95% confidence intervals of D’, while the color darkness within each box corresponds to strength of linkage. The blocks (1, 2, and 3) encompass areas with highest linkage disequilibrium given by D’>0.90 and lower boundary of the 95% confidence intervals of D’>0.70.
Zai et al IV. BDNF & DRD3 in Tardive Dyskinesia
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P
rom
oter
C/A
C27
0T
BD
NF
_4
BD
NF
_3
BD
NF
_2
Val
66M
et
Promoter C/A
1 . 0 0 0.05-0.97
0 . 9 6 0.05-0.97
1 . 0 0 0.05-0.97
1 . 0 0 0.05-0.97
1 . 0 0 0.06-0.98
C270T
0 . 4 5 0.04-0.86
0 . 4 8 0.04-0.96
0 . 3 8 0.03-0.85
0 . 8 2 0.06-0.97
BDNF_4
0 . 9 1 0.81-0.97
0 . 9 4
0.87-0.98
0 . 8 9 0.79-0.94
BDNF_3
1 . 0 0
0.94-1.00
0 . 9 4
0.86-0.98
BDNF_2
0 . 9 6
0.89-0.99
Val66Met
Figure 7b. Linkage disequilibrium plot among the six BDNF gene polymorphisms used in the present study. The numbers represents D’ values and the 95% confidence intervals of D’, while the color darkness within each box corresponds to strength of linkage. The blocks (1, 2, and 3) encompass areas with highest linkage disequilibrium given by D’>0.90 and lower boundary of the 95% confidence intervals of D’>0.70.
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Table 12. P-values from analyses of two-marker haplotypes across ZNF80, DRD3, and BDNF in association to TD and AIMS using COCA-PHASE and QT-PHASE respectively.
Figure 8. P-values from analyses of two-marker interactions between BDNF and DRD3 polymorphisms in association to AIMS given by HELIXTREE program. Top left triangle indicates significance with the raw p-values, while the bottom right triangle indicates significance with Bonferroni adjusted p-values
calculated using TD status on the 12 polymorphisms is provided as a reference in designing
future genetic studies (Figure 14).
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5.5 DISCUSSION
Previous studies yielded conflicting results with regard to the DRD2 gene and TD.
Besides the positive findings by Chen et al (1997a), none of the others have found a significant
association using DRD2 polymorphisms. There are several reasons for these mixed results.
First, different polymorphisms were used in many of the studies, and in most cases only a few
polymorphisms were tested without haplotype analyses. Only Kaiser et al (2002) analyzed nine
polymorphisms spanning the entire 65kb long DRD2 gene. Second, populations with different
ethnic backgrounds were used in the studies, making findings difficult to compare due to
potentially undetected stratification effects. Further, in some studies, the sample sizes were
small, limiting the power to detect an association. Finally, many studies did not take into
account statistical advantages of the continuous AIMS scores and only used the dichotomous TD
occurrence for the analyses. The aim of our study has been to investigate twelve DRD2 gene
polymorphisms for genetic association with TD in a relatively large sample involving haplotype
analyses.
Results from the present study are consistent with most previous studies in that the A-
241G, -141C Ins/Del, TaqID, and TaqIA polymorphisms were found to be not significantly
associated with TD. The previous positive finding with TaqIA could be due to higher linkage
disequilibrium in the 3’ portion of DRD2 as shown in our sample and others (Figure 2; Kaiser et
al, 2002; Ritchie and Noble, 2003), and that the causative variant may reside within DRD2.
Indeed, we found a significant association between TD and the C957T polymorphism as well as
its neighboring C939T polymorphism in our Caucasian sample. To our knowledge, the C957T
polymorphism has not been investigated previously for its effect on TD. Although the
polymorphism does not affect the amino acid sequence, the T variant has been associated with
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decreased striatal D2 levels in vivo and decreased DRD2 mRNA stability in vitro (Duan et al,
2003; Hirvonen et al, 2004). Using MFOLD, Duan and coworkers showed that the predicted
mRNA folding structures were different between the two alleles (Duan et al, 2003). They
hypothesized that T957 may decrease DRD2 expression through its effect on D2 mRNA
secondary structure. The change in secondary structure may affect binding of mRNA stabilizing
proteins at the 5’-cap and 3’-poly(A) as well as translation initiation factors, thus decreasing both
translation efficiency and mRNA half-life (Duan et al, 2003; Perkins et al, 2005). An under-
representation of the T allele in patients with TD and a decrease in TD severity in T-allele
carriers suggest that decreased D2 levels may decrease TD susceptibility and severity. Our
present study also reported a positive association with the nearby C939T, a polymorphism not
found to be associated with TD in a previous study on a Japanese sample (Inada et al, 1997). It
is possible, though, that TD susceptibility and risk factors may be different among different
ethnic groups. Ethnic differences were reflected by findings in our African-American sample, in
which a significant association was only detected between C957T alleles and TD.
TD occurrence and severity were found to increase with age in the current sample,
supporting previous studies from our laboratory and others (Basile et al, 1999; Jeste, 2000; van
Os et al, 1997; Kaiser et al, 2002). Age and gender were not likely responsible for the positive
findings in this study, because mean age and gender proportions did not differ significantly
among the genotypes of C939T and C957T.
Since both C939T and C957T are located in exon 7, it is possible that they are linked to a
region that affects splicing around exon 6, resulting in a different ratio of the long (D2L) and
short (D2S) isoforms. The two isoforms have distinct functions in vivo, and only the
postsynaptic D2L isoform appears to be a major target of haloperidol (Centonze et al, 2004;
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Usiello et al, 2000). Thus, splicing changes could have contributed to changes in DRD2
function, expression levels and patterns, affecting antipsychotic response and adverse effects.
Understanding the mechanisms that regulate splicing of the DRD2 gene will help answer the
question of the locations of polymorphisms in the DRD2 gene that affect the splicing efficiency.
From the epigenetics standpoint, the C957T polymorphism may have arisen from deamination of
methylated C957 to T957. Though C957T has been reported to regulate D2 expression through
its effect on mRNA stability, it is possible that C957 could be methylated in a subset of
individuals. Methylated C957 may be functionally different from unmethylated C957 at the
DNA level, thus opening another dimension of regulation of DRD2 expression and function.
This may also be the case for other polymorphisms in DRD2, especially in the promoter region
where methylation has been reported (Popendikyte et al, 1999). Because association studies of
DRD2 have not considered CpG methylation and genetic polymorphisms do not capture the
variability of regional CpG methylation (Flanagan et al, 2006), it may have given rise to the
mixed results in previous findings. Understanding the role of epigenetics in the regulation of
gene expression will help resolve at least some of the variability of results from genetics studies.
The present study encourages further examinations into C957T, C939T, as well as
adjacent polymorphisms and TD, but it has several limitations. First, not all clinical data were
available for our study. These include medication history such as antipsychotic dose and
duration, schizophrenia disease history including age of onset, clinical subtypes,
psychopathology, and co-morbidities. These factors or variables have been previously
associated with TD (reviewed in Müller et al, 2004). Medications taken by patients for other
adverse effects such as parkinsonism could have masked the TD phenotype (Shale and Tanner,
1996; Egan et al, 1997; Glazer, 2000). Moreover, we did not have information on tobacco,
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alcohol, or substance use for our entire sample. Further, our sample was drawn from four
different clinical sites. Even though the study Caucasian population was in Hardy-Weinberg
equilibrium for all 12 polymorphisms and the gender ratios among the four geographical groups
do not differ significantly (p=0.165), the mean ages differ significantly among them (p<0.001).
Therefore, the possibility of ascertainment bias cannot be ignored. About half of the present
sample has been analyzed previously for other genes with TD, with DRD3 findings being
replicated (Basile et al, 1999; Lerer et al, 2002; Bakker et al, 2006). The other half of the sample
has not been published previously for TD. Nonetheless, when the two halves were analyzed
separately, the trend remained for C939T and C957T (data not shown). Also, heterogeneity of
the TD phenotype could have confounding effects; only total AIMS scores, but not the sub-
scores, were available for most of our sample for genetic analyses, preventing further dissection
of the phenotype. Finally, the marginally significant association could be due to the possibility
that the polymorphisms have only a small contributing effect to the risk for TD as expected in
complex phenotypes. The sample size in the current study was not large enough to provide
sufficient power to detect a significant difference in AIMS scores between the genotypes. False-
positive results from multiple testing are possible; indeed, if we corrected for multiple testing
taking linkage disequilibrium into account using the online SNPSpD program, the significance
threshold (α) in order to keep the Type I error rate at 5% would have become 0.005 (Nyholt,
2004). As a result, only our findings from haplotype analyses would have remained statistically
significant. Larger sample sizes are required to detect small effects of genotypes on TD risk and
severity, especially for our African–American sample where we did not detect significant
associations between TD and any DRD2 polymorphisms after correction for multiple testing.
With α set at 0.005, the Caucasian portion of the sample used for the present study has only 18%
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power to detect the differences in AIMS scores observed among the C939T genotypes (Glantz,
1992). To detect such differences, a sample size of at least 120 per genotype group will be
needed for future studies.
The DRD2 gene is unlikely to be the only genetically determined factor for TD, as other
genes have been associated with TD as well. Genetic studies have identified DRD3 to be
reproducibly associated with TD (Steen et al, 1997; Basile et al, 1999; Segman et al, 1999;
Lovlie et al, 2000; Liao et al, 2001; Garcia-Barcelo et al, 2001; Lerer et al, 2002). Studies in
other genes such as HTR2A, HTR2C, CYP1A2, and manganese superoxide dismutase require
further investigation (Hori et al, 2000; Basile et al, 2001; Segman et al, 2000; Segman et al,
2001; Schulze et al, 2001; Tan et al, 2001). As nearly all antipsychotics target more than one
receptor, it is likely that TD is not related to one receptor gene, but rather it is a polygenic
condition with each gene contributing a small proportion of the risk to the disorder. Gene-gene
interaction studies may help in identifying and clarifying pathways that contribute to TD. TD
risk is also likely to be influenced by several environmental factors (Müller et al, 2004), and
acquiring this information will help immensely in increasing the statistical power and limiting
the effects of potential confounders in genetic studies of TD.
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Figure 9. Schematic diagram of the DRD2 gene with its exons and introns. The positions of the 12 polymorphisms used for the present study are indicated within the gene.
5’ 3’
1 2 3 4 5 6 7 8 1 kb ~15kb ~34kb ~5kb
A-241G
-141C Ins/Del
rs4648317
rs1125394
rs1079598
TaqID
C957T
C939T
rs2242591
rs2242593
rs2242592 TaqIA
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Table 13. Statistical analyses on demographics (gender, age) as well as total AIMS scores and TD diagnoses with genotypes of the 12 polymorphisms in DRD2.
TD DRD2 markers N (F/M) Age (years) Total AIMS score +/- SD (N) Yes No
* With at least 1 expected cell count <5; Fisher Exact Test used. ! Variances among comparisons groups differ significantly; Kruskal-Wallis test used. Bolded numbers indicate 0.05<p<0.10; bolded and italicized numbers indicate p<0.05.
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Table 14. Results from Chi-squared test of allele frequencies of each of the 12 DRD2 polymorphisms versus TD diagnoses for both Caucasian and African-American populations.
Caucasian African American TD TD
DRD2 markers
Yes No Yes No
Allele 1 (A) Allele2 (G)
130 12
220 16
15 7
33 5
A-241G
P 0.548 0.102* Allele 1 (Del/C) Allele2 (Ins/CC)
16 122
20 210
9 13
16 22
-141C Ins/Del
P 0.365 0.928 Allele 1 (C) Allele2 (T)
124 16
200 30
19 3
34 4
rs4648317
P 0.648 0.700* Allele 1 (A) Allele2 (G)
122 26
197 35
19 3
37 1
rs1125394
P 0.521 0.135* Allele 1 (C) Allele2 (T)
24 126
36 200
2 20
0 38
rs1079598
P 0.844 0.131* Allele 1 (C) Allele2 (T)
73 67
110 128
13 9
18 20
TaqID
P 0.266 0.381 Allele 1 (C) Allele2 (T)
80 60
165 69
6 16
14 24
C939T
P 0.0085 0.449 Allele 1 (C) Allele 2 (T)
83 59
107 129
20 2
26 12
C957T
P 0.0135 0.0472 Allele 1 (A) Allele2 (G)
110 28
193 39
20 2
34 4
rs2242591
P 0.401 1.000* Allele 1 (C) Allele2 (T)
56 88
67 167
14 8
20 18
rs2242592
P 0.039 0.687 Allele 1 (A) Allele2 (G)
114 28
198 34
21 1
37 1
rs2242593
P 0.201 1.000* Allele 1 (C) Allele2 (T)
31 105
53 189
4 18
8 30
TaqIA
P 0.841 1.000*
* with at least one expected cell count <5. Fisher Exact Test was used. Bolded numbers indicate 0.05<p<0.10; bolded and italicized numbers indicate p<0.05.
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A-2
41G
-141
C I
/D
rs46
4831
7
rs11
2539
4
rs10
7959
8
Taq
ID
C93
9T
C95
7T
rs22
4259
1
rs22
4259
2
rs22
4259
3
Taq
IA
A-241G 1.00 0.07-0.98
0.41 0.04-0.95
0.04 0.00-0.28
0.003 -0.01-0.25
0.04 0.00-0.46
0.31 0.05-0.58
0.59 0.16-0.83
0.03 0.00-0.28
0.40 0.10-0.64
0.04 0.00-0.28
0.14 0.01-0.41
-141C Ins/Del
1.00
0.17-0.99
1.00 0.14-0.99
0.57 0.06-0.88
0.38 0.07-0.65
0.61 0.36-0.78
0.58 0.24-0.79
0.61 0.08-0.89
0.57 0.32-0.75
0.54 0.06-0.88
0.45 0.05-0.80
rs4648317
0.02 -0.01-0.20
0.30 0.03-0.76
0.05 0.00-0.40
0.12 0.00-0.36
0.04 0.00-0.42
0.06 0.01-0.63
0.04 0.00-0.54
0.02 -0.01-0.20
0.07 0.00-0.27
rs1125394 0.98 0.91-1.00
0.61 0.33-0.79
0.74 0.40-0.89
0.95 0.77-0.99
0.96 0.89-0.99
1.00 0.71-1.00
0.96 0.89-0.99
0.91 0.81-0.96
rs1079598
Block 1 0.66 0.38-0.82
0.91 0.61-0.98
1.00 0.84-1.00
0.98 0.91-1.00
1.00 0.73-1.00
0.96 0.89-0.99
0.98 0.89-1.00
TaqID 0.24 0.07-0.40
0.51 0.39-0.61
0.57 0.31-0.74
0.24 0.06-0.41
0.62 0.35-0.79
0.37 0.12-0.57
C939T 0.96 0.88-0.99
0.86 0.58-0.95
0.96 0.91-1.00
0.83 0.52-0.94
0.73 0.43-0.87
C957T
Block 2 0.85 0.64-0.94
1.00 0.94-1.00
1.00 0.85-1.00
0.66 0.43-0.79
rs2242591 1.00 0.77-1.00
1.00 0.95-1.00
0.92 0.82-0.97
rs2242592
1.00 0.73-1.00
0.85 0.56-0.95
rs2242593 0.95 0.86-0.99
TaqIA
Block 3
Figure 10. Linkage disequilibrium plot among the 12 DRD2 gene polymorphisms used in the present study. The numbers represents D’ values and the 95% confidence intervals of D’, while the color darkness within each box corresponds to strength of linkage. The blocks (1, 2, and 3) encompass areas with highest linkage disequilibrium given by D’>0.90 and lower boundary of the 95% confidence intervals of D’>0.70 (Gabriel et al, 2002).
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Table 15. Global p-values from analyses of DRD2 two-marker haplotypes in association to TD and AIMS using COCA-PHASE and QT-PHASE respectively. Haplotypes with frequencies of less than 0.05 were excluded from the analyses.
*Allele and Genotype information were reported for Ser311Cys, but allele names, frequencies, and reference of Grandy et al, 1993 (Ritchie and Noble, 2003) matched TaqIA.
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CHAPTER 7
GENETIC STUDY OF EIGHT AKT1 GENE POLYMORPHISMS AND THEIR
INTERACTION WITH DRD2 GENE POLYMORPHISMS IN TARDIVE
DYSKINESIA
Submitted to Neuropsychopharmacology
Clement C. Zai(1,2), Marco A. Romano-Silva(3), Rudi Hwang(1,2), Gwyneth C.
Zai(1,2), Vincenzo DeLuca(1,2), Daniel Müller(4), Nicole King (1), Aristotle N.
Voineskos(1,2), Herbert Y. Meltzer(5), Jeffrey A. Lieberman(6), Steven G. Potkin(7),
Gary Remington(1), James L. Kennedy(1,2)
(1) Centre for Addiction and Mental Health, Toronto, Ontario, CANADA
(2) Institute of Medical Science, University of Toronto, Toronto, Ontario, CANADA
(3) Laboratorio de Neurociencia, Dept. Saude Mental, Faculdade de Medicina,
Universidade Federal de Minas Gerais, Brazil
(4) Department of Psychiatry, Charité University Medicine Berlin, Campus Charité
Mitte, Berlin, Germany
(5) Psychiatric Hospital at Vanderbilt University, Nashville, Tennessee, USA
(6) New York State Psychiatric Institute, Columbia University Medical Centre, New
York City, New York, USA
(7) Brain Imaging Center, Irvine Hall, University of California at Irvine, California, USA
Mr. Zai designed the experiment (with guidance from faculty and Dr. Marco Romano-
Silva, a collaborator), performed genotyping on the AKT1 gene polymorphisms in
approximately 50% of the tardive dyskinesia sample. Dr. Marco Romano-Silva
genotyped the remaining 50% of the sample and used them to analyze other phenotypes,
not tardive dyskinesia. Mr. Zai corresponded with the clinical collaborators to refine the
details of the phenotype, performed all the statistical analyses, and wrote the manuscript.
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Keywords: Schizophrenia, tardive dyskinesia, gene association, polymorphism, Protein
Kinase B PKB/AKT1, Dopamine Receptor DRD2, Abnormal Involuntary Movement
Scale (AIMS)
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7.1 ABSTRACT
Tardive dyskinesia (TD) represents a potentially irreversible motor side effect
associated with chronic antipsychotic exposure. Dopamine neurotransmission system
changes have been implicated, and a number of studies have focused on the association of
dopamine system gene polymorphisms and TD; for example, we recently found an
association between polymorphisms in the dopamine D2 receptor gene DRD2 and TD.
The small odds ratio, though, suggests additional factors are involved in the
etiopathology of TD. All antipsychotic drugs block the D2 receptors AKT1 acts
downstream of the D2 receptor, and all antipsychotic drugs block the D2 receptor to some
degree. Haloperidol has been shown to alter AKT1 activity. Although AKT1 has been
identified as a candidate gene for schizophrenia, it has not been investigated in TD.
Thus, in the present study, we examined eight polymorphisms spanning the AKT1 gene
and their association with TD in our Caucasian (N=193) and African-American (N=30)
samples. AKT1 polymorphisms and haplotypes were not significantly associated with TD
occurrence or severity as measured by AIMS (Abnormal Involuntary Movement Scale).
However, interaction analysis showed a significant interaction between rs6275 of DRD2
and rs3730358 of AKT1 (p<1X10-5). Taken together, the present study suggests that the
interaction of DRD2 and AKT1 is involved in TD development, though further studies are
required.
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7.2 INTRODUCTION
Tardive dyskinesia (TD) is a motor side effect linked to chronic antipsychotic
treatment that affects between 16 and 43% of SCZ patients treated with conventional or
typical antipsychotics (Tarsy and Baldessarini, 2006). Older age, female gender, and
African American ethnicity have all been suggested to increase the risk and severity of
TD (Kane et al, 1988; Woerner et al, 1998; Smith and Dunn, 1979; van Os et al, 1997;
Jeste et al, 2000). The use of alcohol, tobacco, and recreational drugs can further the risk
of TD (Menza et al, 1991; Bailey et al, 1997; Olivera et al, 1990). As a class the newer,
‘atypical’ antipsychotics have been linked to a diminished risk of motor side effects, but
this advantage has been tempered by substantially higher costs, questionable clinical
superiority, and their own substantial side effects in the form of weight gain and
metabolic disturbances (Lieberman et al, 2005). As a result, there has been a renewed
interest in typical antipsychotic use once again. This, in combination with the fact that
atypicals are not devoid of TD liability, requires that we continue in our pursuit to better
understand TD and those at risk for this potentially irreversible side effect.
Though the etiopathology of TD remains elusive, a number of mechanisms have
been proposed. TD has been postulated to arise from a hypersensitivity of dopamine
receptors secondary to chronic antipsychotic exposure, resulting in excessive function of
dopamine in the central nervous system (CNS). This theory is compatible with the fact
that all marketed antipsychotics block dopamine receptors, albeit to varying degrees
(Tarsy and Baldessarini, 1977; Klawans et al, 1980; Gerlach and Casey, 1988; Abilio et
al, 2003).
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Family studies have suggested a genetic component underlying TD development
(Müller et al, 2001), and several studies have investigated the role of dopaminergic
system genes in TD. The association between DRD3 Ser9Gly and TD (Badri et al, 1996;
Steen et al, 1997) has been confirmed by two meta-analyses (Lerer et al, 2002; Bakker et
al, 2006), while our laboratory recently analyzed twelve DRD2 polymorphisms in TD and
found that haplotypes containing rs6275 and rs6277 were associated with TD (Zai et al,
2007a). A recent meta-analysis conducted by our laboratory (Zai et al, 2007b) also
confirmed the association between DRD2 Taq1A and TD, detected initially by Chen et al
(1997a). Nonetheless, the odds ratios obtained from the meta-analyses ranged from 1.1
to 1.5, supporting the notion that multiple genetic factors influence TD risk. Most TD
genetic studies thus far have analyzed genes that directly affect dopamine metabolism or
dopamine receptor function, but none have investigated the role of gene products that
transduce signals downstream of dopamine receptors.
AKT1, also known as protein kinase B (PKB), is a serine/threonine kinase that is
involved in numerous signaling pathways (Nicholson and Anderson, 2002). It is
important in the regulation of neuronal plasticity (Kim and Chung, 2002; Wang et al,
2003) and synaptic transmission (Beaulieu et al, 2005). Moreover, it has been linked to
both SCZ and dopamine function. Specifically, the AKT1 gene, located at 14q32.32, has
been associated with SCZ in some studies (Emamian et al, 2004; Ikeda et al, 2004;
Schwab et al, 2005; Bajestan et al, 2006), but not others (Ohtsuki et al, 2004; Ide et al,
2006; Liu et al, 2006; Turunen et al, 2007). Beaulieu et al (2004) found that
pharmacological or genetic upregulation of dopamine neurotransmission decreased the
activating phosphorylation of AKT1; the decrease was blocked or attenuated by
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raclopride, a D2/D3 receptor antagonist, and not by SCH23390, a D1 receptor antagonist.
The results were corroborated by the observation that activating phosphorylation of
AKT1 was increased in mice deficient in D2 or D3 (Beaulieu et al, 2007), or by
haloperidol treatment in mice (Emamian et al, 2004). The results suggest that AKT1 acts
downstream of D2 receptors and may be relevant in dopamine related disorders including
schizophrenia and TD.
In the present study, we tested for an association between the AKT1 gene and TD
in our samples of Caucasian and African American schizophrenia patients using eight
polymorphisms spanning the AKT1 gene (Emamian et al, 2004; Schwab et al, 2005;
Table 17, Figure 11). The polymorphisms were selected because they were used
previously in schizophrenia genetic studies and they have high minor allele frequencies.
The linkage disequilibrium chart showing D’ values on the eight polymorphisms is also
provided as a reference in designing future genetic studies (Table 4). In addition, because
AKT1 acts downstream of D2, we also tested for an interaction between DRD2 and AKT1
polymorphisms in TD.
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7.3 PATIENTS AND METHODS
7.3.1 Subjects
The sample for this study is largely the same as the one used in Zai et al (2007a).
Subjects were recruited from four clinical sites in North America: Center for Addiction
and Mental Health in Toronto, Ontario (Remington, N=92); Case Western Reserve
University in Cleveland, Ohio (Meltzer, N=69); Hillside Hospital in Glen Oaks, New
York (Lieberman, N=50); University of California at Irvine, California (Potkin, N=12).
Subjects were selected based on their diagnoses for SCZ or schizoaffective Disorder
according to DSM-III-R or DSM-IV (APA, 2000). All patients had undergone at least
one year of treatment with typical or atypical antipsychotic treatment, and the presence of
TD was evaluated using the Abnormal Involuntary Movement Scale (AIMS) (Guy, 1976;
Schooler and Kane, 1982) or the modified Hillside Simpson Dyskinesia Scale (HSDS) for
the 50 patients from the Hillside Hospital (Basile et al, 1999).
In all, 223 SCZ patients were studied. Of this sample, 193 are Caucasians, with
76 of these subjects positive for the diagnosis of TD. The remaining 30 are African-
Americans, of which 11 are TD-positive. Because of the small sample size, the African-
Americans were used only in the allele frequency association tests.
7.3.2 Gene polymorphism analysis
Genomic DNA was purified from whole blood samples using a non-enzymatic
method previously described (Lahiri and Nurnburger, 1991). The 10µL Polymerase
Chain Reactions were performed on 20ng genomic DNA using ABI TaqMan genotyping
assays under the following conditions: 95oC for 10min, followed by 50 cycles of 92oC for
Clement Zai VII. DRD2 & AKT1 in Tardive Dyskinesia
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15sec and 60oC for 1min. The rs numbers for the markers and their relative positions in
the AKT1 gene are given in Table 17 and Figure 11. Genotypes were determined using
Allelic Discrimination software in the ABI Prism® 7500 Sequence Detection System
(Applied Biosystem, Foster City, CA, USA).
7.3.3 Statistics
Statistical analyses of individual polymorphisms were conducted using the SPSS
program v14.0. The χ2 test was used for fit of genotypes to Hardy-Weinberg equilibrium
and to test for gender differences. The association of genotype frequencies with age and
AIMS was assessed using one-way ANOVA. Where the Levene Test for Homogeneity
of Variance was violated, the Kruskal-Wallis test was performed. The differences in
allele and genotype frequencies between patients with and without TD were analyzed by
the χ2 test. For contingency tables with at least one expected cell count of less than five,
Fisher Exact Tests were performed (http://home.clara.net/sisa/fiveby2.htm). Haplotype
analyses and linkage disequilibrium calculations were conducted using the UNPHASED
v2.402 (Dudbridge et al, 2003) and Haploview v3.2 (Barrett et al, 2005) Programs
respectively. Gene-gene interaction analysis was performed using HELIXTREE
(GoldenHelix), and post-hoc analyses of the continuous variable (AIMS) were carried out
using univariate general linear model in SPSS.
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7.4 RESULTS
7.4.1 Sample Characteristics
The genotype distributions of all the AKT1 gene polymorphisms in the Caucasian
and African American samples did not differ significantly from the Hardy-Weinberg
equilibrium (p>0.10). There was a significant difference in genotype frequencies of
rs2498784 in males versus females (Table 18), while age was not found to be associated
with any of the eight polymorphisms. We assume that the association with sex is
spurious; nonetheless, we included sex as a covariate in the ANCOVA for this marker.
7.4.2 Association analysis of individual polymorphisms with TD occurrence and AIMS
In our Caucasian sample, we did not find a significant association between any of
the eight AKT1 polymorphisms tested and TD, although we found a trend for allele 1 (G)
of rs10149779 to be under-represented in the TD-positive group (p=0.08; Tables 18, 19).
Next, we tested for an association between genotype frequencies and AIMS scores, but
did not find a significant association with any of the eight polymorphisms. Using the
Haploview program, strong evidence for linkage disequilibrium was found between
rs3803304 and rs2494731, prompting us to test for association between TD and two-
marker haplotypes across AKT1 (Figure 12). None of the two-marker haplotypes were
associated with TD or AIMS scores (data not shown). For the African-American sample,
preliminary results indicated a significant association between TD and rs2494738, as well
as a trend for the adjacent rs3730358 (Table 19).
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7.4.3 Significant interaction between DRD2 and AKT1 polymorphisms in TD
Using the Caucasian sample, we analyzed the interaction between the 12 DRD2
polymorphisms reported previously (Zai et al, 2007a) and the eight AKT1 polymorphisms
in this study. Due to the large number of pairwise comparisons, we used the Bonferroni
correction to account for multiple testing. Results are summarized in Figure 13. We
found DRD2 rs6275 to interact with AKT1 rs3730358 (Bonferroni p=0.007), and to
confirm the findings, we conducted a two-way ANOVA under the univariate general
linear model analysis option using SPSS with AIMS as the dependent variable and with
rs3730358 and rs6275 as fixed factors. A graphical representation of the interaction is
given in Figure 14. The interaction between rs3730358 and rs6275 was significant
(p<0.001). Because the assumption of equal variances for ANOVA was violated by the
significant Levene’s Test, we conducted the same analysis using ranked AIMS as
dependent variable in place of AIMS. The results were equally significant (p=0.008).
More detailed analysis showed that the average total AIMS scores of subjects with
genotype 2/2 (T/T) at rs6275 were higher than subjects with genotype 1/2 (C/T), and
those with genotype 1/2 (C/T) were higher than those with genotype 1/1 (C/C), as
reported previously {linear regression: r=0.244, F=9.978, p(1,158)=0.002} (Zai et al,
2007a). While the same association was observed at rs6275 in patients with genotype 1/1
(C/C) at rs3730358 {linear regression: r=0.409, F=21.529, p(1,107)<0.001}, those of
carriers with allele 2 (T) at rs3730358 did not follow the trend at rs6275. Instead, carriers
of allele 2 (T) at rs3730358 had similar total average AIMS scores among the three
rs6275 genotype groups {linear regression: r=0.061, F=0.156, p(1,42)=0.695}. When we
examined rs6275 and rs3730358 in TD, we found a significant under-representation of
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the 1/1 (C/C) genotype at rs6275 in TD-positive patients with 1/1 (C/C) genotype at
rs3730358 (p=0.006), but not in TD-positive patients carrying at least one copy of allele 2
(T) at rs3730358 (p=0.99).
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7.5 DISCUSSION
This is the first reported genetic association study between AKT1 and TD. While
the results are not significant for AKT1 by itself in our Caucasian sample, we found that
DRD2 rs6275 interacts with AKT1 rs3730358 in TD. Specifically, allele 2 (T) at
rs3730358 restricts the genotypic effects of rs6275 on TD occurrence and severity. This
finding is in agreement with the strong evidence that AKT1 is a downstream signal
transducer of the D2 receptor (Beaulieu et al, 2007). While this positive gene-gene
interaction result is intriguing, the methods for gene-gene interaction analyses are not
well established, thus these results should therefore be considered preliminary.
Nevertheless, the genetic interaction reported herein survived Bonferroni correction and
encourages further investigations into the molecular mechanisms underlying this
association.
We recently reported an association of rs6275 and an adjacent functional
polymorphism rs6277 with TD (Zai et al, 2007a). While rs6277 genotypes are associated
with D2 expression (Duan et al, 2003; Hirvonen et al, 2004), they do not appear to
interact strongly with AKT1 polymorphisms to affect AIMS score (Figure 3). rs6275
does not appear to be associated with D2 expression (Duan et al, 2003); nevertheless, it
may influence RNA splicing at exon 6, thus changing the ratio of long and short D2
isoforms. AKT1 haplotypes containing the C allele at rs3730358 have been associated
with altered AKT1 expression (Emamian et al, 2004). Interaction analysis using
haplotypes may provide valuable information about the regions of biological interest
within DRD2 and AKT1, but larger samples will be required for such extensive
examinations.
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The lack of association between AKT1 and TD in our Caucasian sample could be
a true negative result; alternatively, the polymorphisms may only have a small
contributing effect to the risk for TD, and the sample size in the current study provides
insufficient power to detect the small difference in AIMS observed among the genotypes.
Under reasonable assumptions (α=0.05, risk allele frequency=0.3), the current sample
has 82.1% power to detect a risk ratio for TD of as low as 2.0. The association between
rs2494738 and TD in our African-American sample, as well as the rs6275-rs3730358
interaction requires replication in larger samples.
The present study has several limitations. First, not all relevant clinical data were
available for our study. These include medication history (antipsychotics, dose,
duration), schizophrenia disease history (onset, clinical subtypes, severity), and co-
morbidities. Medications taken by patients for other adverse effects (e.g., parkinsonism,
anxiety) could mask TD (Shale and Tanner, 1996; Egan et al, 1997; Glazer, 2000).
Conversely, environmental risk factors such as smoking have been identified as a risk
factor for TD and could contribute to our findings, though we do not have such
information available for our entire sample. Finally, different manifestations (e.g., facial
versus truncal) of the TD phenotype could have different genetic contributions. We
evaluated total AIMS scores because our sample sizes would have been too small to have
enough power to detect an association with specific subtypes of TD. On the other hand,
TD is likely less sensitive to design-related variables than presumably more complex
phenotypes such as antipsychotic response.
Meta-analyses support the association of DRD2 (Zai et al, 2007b), DRD3 (Lerer
et al, 2002; Bakker et al, 2006), HTR2A (Lerer et al, 2005), and CYP2D6 (Patsopoulos et
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al, 2005) with TD. Studies of other genes such as HTR2C, CYP1A2, and manganese
superoxide dismutase are preliminary and require replication to confirm their potential
association with TD (Basile et al, 2000; Segman et al, 2000; Schulze et al, 2001; Hori et
al, 2000). As all antipsychotics target more than one receptor and behavioural
phenotypes are complex, it is likely that TD is a polygenic condition with each gene
contributing a small proportion of the risk. TD risk is also likely to be influenced by
many environmental factors (Menza et al, 1991; Bailey et al, 1997; Olivera et al, 1990).
Acquiring this additional information for future samples will help in limiting effects of
potential confounds in genetic studies of TD and, in doing so, increase the predictive
value of future genetic tests for TD. The present study encourages further investigations
of the interactions among genes along signaling pathways in deciphering the
pathophysiology of TD and other complex genetic diseases.
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Figure 11. Schematic diagram of the AKT1 gene with its exons and introns. The darker color indicates the coding region. The positions of the eight polymorphisms used for the present study are indicated within the gene.
5’ 3’
1 2 3 4 5 6 7 8 9 10 11 12 13 14 1 kb
rs2498784
rs1130214
rs2494746
rs10149779
rs2494738
rs3730358
rs2494731
rs3803304
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Table 17. ABI Assays-on-Demand with information on their corresponding AKT1 polymorphisms used in the present study. VIC and FAM are fluorescent dyes that are conjugated onto probes specific for the corresponding alleles of each polymorphism. Assay-on-Demand
Allele 1 FAM
Allele 2 VIC
Polymorphism Name(s)
Location with respect to AKT1 gene
References
rs2498784 C T SNP1a, G-5983A 5’ Schwab et al, 2005 rs1130214 G T SNP2, C-754A Intron 1 Emamian et al,
2004 rs2494746 G C G1261C Intron 2 rs10149779 C T SNP2a, G7894A Intron 2 Schwab et al, 2005 rs2494738 C T G12294A Intron 2 rs3730358 C T SNP3, G12573A,
G3+18A Intron 3 Emamian et al,
2004 rs3803304 G C G19834C,
G11+69C Intron 11
rs2494731 G C G21300C Intron 12
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Table 18. Statistical analyses on demographics (sex, age) as well as total AIMS scores and TD occurrence with each of the eight AKT1 polymorphisms. DRD2 markers N (M/F) Age (years) Total AIMS score TD (Yes/No) rs2498784 1/1 (C/C)
1/2 (C/T) P
162(116/46) 27(11/16) 3(1/2) 0.002*
38.04+/-10.14 37.63+/-8.84 29.67+/-8.74 0.353
6.28+/-7.90 5.50+/-4.85 0.00+/-0.00 0.177#
63/99 13/14 0/3 0.266*
rs1130214 1/1 (G/G) 1/2 (G/T) 2/2 (T/T) P
111(68/43) 59(44/15) 17(12/5) 0.201
37.81+/-10.13 37.27+/-9.46 38.71+/-10.90 0.863
5.58+/-7.36 7.04+/-7.91 6.38+/-7.58 0.544
38/73 28/31 7/10 0.239
rs2494746 1/1 (G/G) 1/2 (G/C) 2/2 (C/C) P
149(106/43) 31(16/15) 4(2/2) 0.060*
38.05+/-10.08 37.97+/-9.05 27.25+/-8.62 0.101
6.20+/-7.82 4.58+/-4.37 3.67+/-6.35 0.749#
58/91 12/19 1/3 1.000*
rs10149779 1/1 (C/C) 1/2 (C/T) 2/2 (T/T) P
113(70/43) 57(41/16) 16(11/5) 0.417
37.53+/-10.09 38.12+/-9.68 37.75+/-10.98 0.936
5.52+/-7.35 7.79+/-7.98 6.00+/-7.46 0.239
39/74 29/28 7/9 0.116
rs2494738 1/1 (C/C) 1/2 (C/T) 2/2 (T/T) P
172(115/57) 13(8/5) 1(1/0) 0.842*
37.73+/-10.02 39.31+/-9.40 21.00 0.211
6.04+/-7.58 2.91+/-2.66 8.00 0.498#
66/106 4/9 1/0 0.526*
rs3730358 1/1 (C/C) 1/2 (C/T) 2/2 (T/T) P
131(84/47) 55(39/16) 1(0/1) 0.221*
38.31+/-9.91 37.25+/-9.94 25.00 0.345
6.04+/-7.87 6.11+/-6.51 7.00 0.991
51/80 22/33 1/0 0.540*
rs3803304 1/1 (G/G) 1/2 (G/C) 2/2 (C/C) P
10(6/4) 76(51/25) 102(67/35) 0.873*
31.60+/-9.12 38.13+/-9.84 38.02+/-10.06 0.137
3.90+/-3.70 7.10+/-8.00 5.81+/-7.59 0.369
4/6 31/45 40/62 0.969*
rs2494731 1/1 (G/G) 1/2 (G/C) 2/2 (C/C) P
80(53/27) 88(60/28) 22(13/9) 0.722
38.01+/-10.21 38.57+/-9.89 33.95+/-9.12 0.149
6.00+/-7.98 6.41+/-7.56 5.84+/-6.07 0.931
32/48 32/56 12/10 0.298
* With at least 1 expected cell count <5; Fisher Exact Test was used. # Variances among comparisons groups differ significantly; Kruskal-Wallis test was used. Bolded numbers indicate 0.05<p<0.10; bolded and italicized numbers indicate p<0.05.
Clement Zai VII. DRD2 & AKT1 in Tardive Dyskinesia
Page 157
Table 19. Results from χ2 tests of allele frequencies of each of the eight AKT1 polymorphisms versus TD occurrence for both Caucasian and African-American populations.
TD (Yes/No) DRD2 markers Caucasian African American
rs2498784 Allele 1 (C) Allele 2 (T) P
139/212 13/20 0.981
16/31 6/7 0.520*
rs1130214 Allele 1 (G) Allele 2 (T) P
104/177 42/51 0.165
15/22 7/14 0.587
rs2494746 Allele 1 (G) Allele 2 (C) P
128/201 14/25 0.714
12/22 10/16 0.801
rs10149779 Allele 1 (C) Allele 2 (T) P
107/176 43/46 0.080
15/21 7/15 0.453
rs2494738 Allele 1 (C) Allele 2 (T) P
136/221 6/9 0.882
18/34 4/0 0.020*
rs3730358 Allele 1 (C) Allele 2 (T) P
124/193 24/33 0.672
17/24 3/14 0.082
rs3803304 Allele 1 (G) Allele 2 (C) P
39/57 111/169 0.865
4/9 18/29 0.751*
rs2494731 Allele 1 (G) Allele 2 (C) P
96/152 56/76 0.482
11/24 11/14 0.319
* with at least one expected cell count <5. Fisher Exact Test was used. Bolded numbers indicate 0.05<p<0.10; bolded and italicized numbers indicate p<0.05.
Clement Zai VII. DRD2 & AKT1 in Tardive Dyskinesia
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rs
2498
784
rs11
3021
4
rs24
9474
6
rs10
1497
79
rs24
9473
8
rs37
3035
8
rs38
0330
4
rs24
9473
1
rs2498784 1 . 0 0
0.46-1.00 1 . 0 0
0.90-1.00 1 . 0 0
0.43-1.00
0 . 7 0 0.45-0.86
0 . 5 9 0.07-0.89
0 . 0 1 -0.01-0.27
0 . 5 5 0.26-0.75
rs1130214
1 . 0 0
0.50-1.00
0 . 9 9
0.93-1.00 0 . 0 9 0.02-0.73
0 . 5 7 0.41-0.70
0 . 2 8 0.15-0.40
0 . 2 6 0.09-0.40
rs2494746
1 . 0 0
0.47-1.00 0 . 6 6 0.40-0.84
0 . 2 6 0.02-0.75
0 . 0 5 0.01-0.55
0 . 4 7 0.21-0.67
rs10149779
0 . 0 4 0.01-0.72
0 . 6 0 0.45-0.73
0 . 2 8 0.14-0.41
0 . 2 0 0.04-0.36
rs2494738
1 . 0 0
0.12-0.99
1 . 0 0
0.12-0.99
0 . 7 2
0.30-0.90
rs3730358
0 . 7 7 0.61-0.87
0 . 7 3 0.53-0.85
rs3803304
1 . 0 0
0.95-1.00 rs2494731
Block 1
Figure 12. Linkage disequilibrium plot among the eight AKT1 gene polymorphisms used in the present study. The numbers represent D’ values and the 95% confidence intervals of D’ using the Haploview program. The color intensity within each box corresponds to strength of linkage, with the darkest having strong evidence for linkage, intermediate having uninformative results, and white having evidence for recombination. Block 1 encompasses an area with highest linkage disequilibrium given by D’>0.90 and lower boundary of the 95% confidence intervals of D’>0.70.
Clement Zai VII. DRD2 & AKT1 in Tardive Dyskinesia
Page 159
Figure 13. p-values from analyses of two-marker interactions between DRD2 and AKT1 polymorphisms in association to AIMS given by HELIXTREE program. Top left triangle indicates significance with the raw p-values, while the bottom right triangle indicates significance with Bonferroni adjusted p-values. Note that AKT1 rs3730358 interacts with DRD2 rs6275 (Bonferroni p=7x10-3).
Clement Zai VII. DRD2 & AKT1 in Tardive Dyskinesia
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Figure 14. Interaction between DRD2_rs6275 (C939T) and AKT1_rs3730358 in AIMS. We conducted a two-way ANOVA that showed a significant interaction between the two polymorphisms (p=0.008).
Zai et al VIII. Discussion
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CHAPTER 8
GENERAL DISCUSSION
8.1 SUMMARY OF FINDINGS AND IMPLICATIONS
In the first manuscript, we investigated the GABRG2 gene in SCZ. It resides in the SCZ
susceptibility region at 5q31-35 (Sklar et al, 2004; Lewis et al, 2003). It gene product also
interacts physically with the D5 receptor in a mutually inhibitory manner (Liu et al, 2000). We
tested five polymorphisms within GABRG2 for association with SCZ diagnosis and suicidal
behaviour in the sixth manuscript. We found a nominally significantly association with rs183294
in the 5’ region of GABRG2 in our case-control sample, and a trend for the same polymorphism
in our independent family sample. The significance level increased when the case-control and
family samples were combined. The results are not in agreement with previous studies showing
GABRG2 not being associated with SCZ in Caucasian and East Asian samples. They are also not
consistent with the previous positive finding where the 3’ region of GABRG2 was found to be
most significantly associated. The mixed results could be due to a number of factors. Sampling
could have contributed to the positive findings in our sample. Only one other GABRG2 study
utilized both case-control and family samples (Petryshen et al, 2005). Also, although our family
and case-control samples were mostly Caucasians, the African American and East Indian subjects
were also included. These additional ethnic groups have not previously been studied in GABRG2
and SCZ. However, when we analyzed only Caucasian subjects, our findings remained
significant for the case-control sample. Another possible reason for the mixed findings could be
the different sets of polymorphisms used in previous studies. A separate polymorphism in
Zai et al VIII. Discussion
Page 162
linkage disequilibrium with rs183294, and not rs183294 itself, may be conferring SCZ
susceptibility because the results became more significant with haplotype analysis. Yet another
explanation could be that our reported positive results were spurious. Nonetheless, our results
would have survived multiple-testing correction for five markers. Replication in independent
samples with larger sizes is required, especially if the effect conferred by GABRG2 is small.
Functional analysis of polymorphisms in GABRG2 is needed to find functional variants for more
targeted genetic studies. The GABRG2-coded GABAA receptor γ2 subunit interacts directly with
the dopamine D5 receptor in a mutually inhibitory manner (Liu et al, 2000). Exploring the
genetic interaction between GABRG2 and DRD5 polymorphisms may reveal a larger combined
genetic effect, and shed some light on the discrepant findings on GABRG2.
In the second manuscript, we investigated a member of the D2 dopamine receptor family,
D3. Ser9Gly has been investigated in numerous SCZ genetic studies, and the results have been
mixed (Jonsson et al, 2004). A number of meta-analyses have yielded both positive and negative
findings with odds ratios of close to one (Jonsson et al, 2004). We also examined BDNF, which
is required for the expression of D3 DA receptor in the striatum (Guillin et al, 2001). The
previous findings for BDNF in SCZ were mixed, with three meta-analyses failing to find a
significant effect of Val66Met or C270T in SCZ (Naoe et al, 2007; Qian et al, 2007; Xu et al,
2007; Kanazawa et al, 2007; Zintzaras et al, 2007). Most previous studies focussed on Ser9Gly
in DRD3, and Val66Met and C270T in BDNF, and did not explore other regions of the DRD3
and BDNF genes. Therefore, we examined ten polymorphisms across and surrounding DRD3
and six polymorphisms spanning BDNF for association with SCZ. Our analyses of BDNF and
DRD3 did not yield significant findings in either family or case-control samples, suggesting that
neither BDNF nor DRD3 plays a major role in SCZ. Although the results were negative for the
Zai et al VIII. Discussion
Page 163
diagnosis of SCZ, BDNF and DRD3 may affect phenotypes that are related to SCZ. Age of onset
was recently found to be affected by Ser9Gly in our sample in that the high-activity Gly allele is
associated with earlier onset (Renou et al, 2007). We found a significant interaction between
Ser9Gly and Val66met in the analysis of suicide data in our SCZ sample using the HELIXTREE
program. Specifically, schizophrenia patients who are heterozygous at both Ser9Gly and
Val66Met are at higher risk of attempting suicide. One possible explanation for this observation
is heterosis, a term that refers to increased vigour as a result of outbreeding. It was first observed
in maize where the hybrid maize offspring by cross-fertilization of two different inbred maize
parents are 25% taller than the parental maize (Hochholdinger and Hoecker, 2007). On the other
hand, heterozygous mRNA products may in some cases interfere with each other and cause
overall reduction in gene expression (Wang et al, 1995). The mechanism behind this
phenomenon is poorly understood.
Smoking data is currently being analyzed (Le Blanc et al, in preparation). Additional
phenotypes important in SCZ, including alcohol and substance use, should be analyzed in future
studies. While it appears that the interaction between BDNF and DRD3 does not play a
significant role in SCZ development, the observed interaction between Val66Met and Ser9Gly in
In the third manuscript, we investigated the DRD3 gene in TD, a motor side effect of
long-term antipsychotic treatment that occurs in a subset of SCZ patients. The association of
Ser9Gly in TD has been replicated in two meta-analyses (Lerer et al, 2002; Bakker et al, 2006).
However, its effect may be small given the small odds ratio estimated from the meta-analyses.
Thus, we investigated ten polymorphisms spanning the DRD3 gene in search of additional
variants that may contribute to TD development. We did not find Ser9Gly to be significantly
associated with TD in our Caucasian or African-American sample. We found rs905568 in the 5’
Zai et al VIII. Discussion
Page 164
region to be associated with TD. This polymorphism is adjacent to a gene coding for a putative
transcription factor further upstream, ZNF80. We genotyped three non-synonymous
polymorphisms within the ZNF80 gene, and found them not to be significantly associated with
TD. The results suggest that either rs905568 itself or a DNA variant in linkage disequilibrium
with rs905568 influences TD risk. The function of rs905568 has not been investigated. It may
be involved in enhancer elements that regulate the expression of DRD3.
Preliminary consensus sequence analysis showed that the polymorphism alters the
recognition for the transcription factor Pax3, a developmentally regulated transcription factor.
The Pax3 consensus sequence is relatively common within the human genome (random chance of
one Pax3 site for every 20000 bases); therefore, the functional consequence of rs905568 on Pax3
recognition should be viewed with caution. Pax3 is expressed in the neural tube and neural crest
during embryogenesis (Pruitt et al, 2004). Another possible function for rs905568 may be the
use of a rare and yet unidentified alternative promoter for DRD3. For example, the first intron in
DRD2 is approximately 50kb in size, so it is not unreasonable to hypothesize the presence of
additional DRD3 exon(s) in the intergenic region between DRD3 and ZNF80. To date, only 9kb
of genomic sequence upstream of DRD3 gene has been investigated (Anney et al, 2002). More
comprehensive analysis of the 5’ region of DRD3 is required to answer these questions.
We also investigated six polymorphisms spanning the BDNF gene in TD. We did not
find significant association in single-marker or haplotype tests of BDNF, nor did we find
significant gene-gene interactions between BDNF and DRD3 polymorphisms using
HELIXTREE. Thus, the interaction of variations in the genes for BDNF and D3 does not appear
to play a major role in the pathophysiology of TD. Investigation in independent samples is
required before the role of BDNF in TD can be dismissed.
Zai et al VIII. Discussion
Page 165
In the fourth manuscript, we found an association between TD and two adjacent DRD2
gene polymorphisms, C939T and C957T. The association did not appear to be specific for either
polymorphism, because the significance level increased with haplotype analysis. TD could be
associated with a polymorphism in linkage disequilibrium with the two polymorphisms. In fact,
our data showed generally high linkage disequilibrium observed in the 3’ region of DRD2 that
encompasses the TaqIA polymorphism. In the fifth manuscript, TaqIA, a polymorphism 3’ to
DRD2, was associated with TD, while –141C Ins/Del, a polymorphism 5’ of DRD2 was not.
Both TaqIA and C957T have been associated with altered D2 DA receptor expression (Jonsson et
al, 1999b; Noble et al, 1991; Pohjalainen et al, 1998; Ritchie and Noble, 2003; Thompson et al,
1997; Hirvonen et al, 2004; Duan et al, 2003). T957 has been associated with decreased in vivo
D2 DA receptor binding in healthy human subjects, possibly through its effect on D2 mRNA
stability (Duan et al, 2003; Hirvonen et al, 2004). TaqIA is now identified as a non-synonymous
polymorphism (K713E) in ANKK1, a novel Serine/Threonine protein kinase gene adjacent to
DRD2, with its mRNA detected in the placenta and spinal cord (Neville et al, 2004). Functional
studies of ANKK1 are required. Nonetheless, most studies have associated the A1 allele with
decreased D2 levels (Ritchie and Noble, 2003). Both A1 and T957 alleles were associated with
decreased TD risk, suggesting that decreased D2 levels may protect against TD occurrence.
Conversely, increased D2 levels predict the occurrence of TD. This is in agreement with brain
imaging studies showing increased occupancy at the D2 DA receptor is associated with TD
(Kapur et al, 2000).
In the sixth manuscript, we investigated the possible association between TD and eight
AKT1 polymorphisms. We did not find a significant association. Since we investigated AKT1
due to its role in signalling downstream of the D2 DA receptor (Beaulieu et al, 2007), we
conducted a gene-gene interaction analysis using pairs of DRD2 and AKT1 polymorphisms in the
Zai et al VIII. Discussion
Page 166
HELIXTREE program. Preliminary findings show the association of DRD2 C939T became
more significant on the G allele background of AKT1 rs3730358 in determining TD severity.
Haplotypes with the G allele at rs3730358 have been associated with changes in AKT1 levels
(Emamian et al, 2004), suggesting that AKT1 signalling from the D2 DA receptor may be
important for TD development. Another possible explanation for the association and interaction
could be the proximity of the C957T and C939T polymorphisms to the alternatively spliced exon
six in DRD2. C957T and C939T could be in linkage disequilibrium with polymorphisms that
affect the splicing efficiency of exon six. Aberrant splicing has been demonstrated to be the
mechanism behind the role of the microtubule-associated protein tau (MAPT) in frontotemporal
dementia with Parkinsonism linked to chromosome 17 (FTDP-17). Mutations surrounding exon
10 of MAPT leads to decreased splicing efficiency, leading to impaired microtubule assembly and
resultant neurofibrillary tangles and neurodegeneration (Lee VM et al, 2001). The mechanism of
alternative splicing of exon six in DRD2 is unclear, and may be epigenetically regulated (Young
et al, 2005). While the full-length long D2 isoform is predominantly located postsynaptically,
splicing at exon six produces the short D2 DA receptor isoform that is predominantly presynaptic
(Usiello et al, 2000). It is interesting to note that the two D2 DA receptor isoforms may bind or
respond to antipsychotics differently (Xu et al, 2002; Centonze et al, 2004). Examining the
association of C957T and C939T and their haplotypes on the expression and ratio of the D2 DA
receptor isoforms will help clarify the role of D2 DA receptor in TD, as well as other conditions.
Molecular studies are required to explore the role of AKT1 in signalling from the two D2
DA receptor isoforms. The DRD2-AKT1 interaction in TD demonstrates that single genes by
themselves may not affect risk of TD and other complex diseases but may play a role within the
context of particular signalling pathways. AKT1 is not associated with TD by itself possibly
Zai et al VIII. Discussion
Page 167
because it has multiple functions in multiple organ systems. However, its role in dopamine
signalling may play a modifying role in TD susceptibility.
Zai et al VIII. Discussion
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8.2 LIMITATIONS AND CONSIDERATIONS
8.2.1 Sample Characteristics and Power
Our TD results should be regarded with caution because even though the main sample
that was analyzed consisted of European Caucasians only, with age and gender adequately
matched, the medication histories of the subjects were mixed. As discussed in the introduction,
medication type and period could influence TD occurrence and severity. Moreover, smoking is a
risk factor for TD, as is the use of alcohol and illicit drugs. These factors could have confounded
our TD findings in that these factors could have created spurious significant associations or
masked associations that would have otherwise been significant. This point is clearly
demonstrated by the inclusion of multiple factors in determining the risk of TD by Basile et al
(2002). In the review, the authors combined the effects of pharmacodynamic (DRD3_Ser9Gly),
pharmacokinetic (CYP1A2*F), as well as age, ethnicity, and sex, and showed that this group of
variables accounted for over 50% of the risk for TD in their sample (Basile et al, 2002).
The results from the meta-analysis of the DRD2 gene in TD should be viewed as the
current status in the investigation of DRD2 in TD, until further markers or samples are
investigated. Lack of significant heterogeneity in the meta-analysis could be due to the lack of
power with the small number of studies included. Heterogeneity does exist among studies. For
example, the assessment of TD was not the same among the studies. Some included persistent
cases that were examined at least twice, while others included probable cases where patients were
examined for TD only once. The background patient population could be different among studies
in that some included only schizophrenia patients and others included schizophrenia and
schizoaffective disorder patients. Medication histories of the study populations are also different.
Some samples only underwent medication with typical antipsychotics, while others underwent
Zai et al VIII. Discussion
Page 169
both typical and atypical antipsychotic treatment. Additional studies with more homogeneous
samples are required before firm conclusions can be drawn on the association of DRD2 with TD.
The known ethnic heterogeneity within the case-control and family samples could have
diluted any effects of the genetic variants or given rise to spurious positive findings of association
between GABRG2 and SCZ. The significant findings in GABRG2 were maintained even after the
exclusion of ethnicities other than Caucasian in the matched case-control sample. However, the
findings in the family sample became less significant, possibly due to the loss of statistical power
with the removal of over 20% of the sample. As for our entire TD sample, we analyzed the
European Caucasians and African Americans separately because several DRD2 genotypes
deviated from Hardy-Weinberg Equilibrium in the combined sample.
Sample size remains a major limiting factor in genetic studies. Our TD sample, with
approximately 80 SCZ patients with TD and 120 without, has 80% power to detect a genotypic
relative risk for TD of as low as 1.9 if we genotype polymorphisms with minor allele frequencies
of 20% and set the critical p-value α at 0.05. With the marker minor allele frequency of 20% and
α at 0.05, our 229 SCZ case control sample pair has 80% power to detect genotypic relative risk
for SCZ of as low as 1.8. While our TD and SCZ samples are moderate in size, they would be
unlikely to detect the small relative risks of 1.2 to 1.5 that are commonly observed in complex
diseases. In addition, as we are moving toward gene-gene interaction analyses, the samples will
be divided into increasing number of comparison groups, making individual group sizes smaller.
One way to overcome this limitation in addition to larger samples is to replicate the experiments
in independent samples, and to follow up with meta-analyses.
Zai et al VIII. Discussion
Page 170
8.2.2 Multiple Testing
For each single-marker test, we set the critical p-value α at 0.05. In other words, we set
the false-positive (type-I error) rate at 5% for each test. Because we are analyzing multiple
markers in each study, the random chances of yielding a positive result is increased in the overall
study. A common way of controlling for inflated false-positive rate of testing multiple markers is
the Bonferroni correction. Traditionally, Bonferroni correction is used for post-hoc pair-wise
comparisons among independent comparison groups after a global comparison among all
comparison groups yields a significant result. It involves setting the critical p-value α at a level
calculated by dividing 0.05 by the number of pair-wise comparisons. It has been adopted for
many genetic studies. For example, we tested 12 DRD2 markers in TD. The critical p-value
threshold, below which we will consider the results significant, would have been 0.05/12, or
0.0042.
Some argue that Bonferroni correction is over-conservative. In fact, if the 12 DRD2
markers were completely independent, statistically there would have been a 45% chance of
getting a false-positive finding in the study. The critical p-value would have to be set at 0.0043
for each marker test in order to maintain the overall false-positive rate of the study at 0.05.
Another issue with multiple testing corrections is that the markers in the study are often
associated with one another. For example, the polymorphisms in the 3’ region of DRD2 were in
high linkage disequilibrium with one another given by the LD plot. Nyholt et al (2004) offered a
solution by accounting for correlation between markers in the calculation of the critical p-value
adjustment, thus decreasing the effective number of markers used in the study. However, the
correction factor would change every time a different number of polymorphisms are tested
(Perneger, 1998). Some researchers may simply minimize the number of markers tested or
Zai et al VIII. Discussion
Page 171
reported in order to avoid a large correction factor. This situation would prevent them from
exploring many markers required to assess an entire gene of interest in an association study.
Along the same line, how often are researchers willing to adjust their p-values every time they
use the same clinical sample to study additional candidate genes and polymorphisms?
Further, neither the Bonferroni nor Nyholt correction could explain the relationship
among the polymorphisms in that the comparison groups are all derived from the same clinical
data set. The single marker analyses can be considered as the same data set grouped differently
according to genotypes at each polymorphism. A more recent approach to resolve this problem
involves permutation. It is more computationally challenging because it involves iterative
randomization of transmission status for family samples or randomization of affected status for
case-control samples. The number of simulations is specified and the distribution of simulated
data is compared to the observed data to derive the global permutation p-value (UNPHASED
manual). This would control for the multiple testing issue because entire genes or sets of
polymorphisms are considered, not just individual polymorphisms. Unfortunately, most
computers available are not capable of running 10,000 permutations with multiple markers.
Lastly, correction for multiple testing to minimize the chances of false-positive results will
increase the chances of false-negative results, or type-II errors (Perneger, 1998). It is especially
true for complex disorders where multiple factors of small effect sizes contribute to the
conditions. As a result of the above limitations, a consensus on multiple testing correction has
yet to be reached. Perhaps one option is to report the p-values both in their unadjusted raw form
and also their corrected form (by whatever means the authors use), and leave the results up to the
reader to decide whether the reported association is real or not. Replications in independent
samples are usually the gold standard required to strengthen the genetic findings. It is also
Zai et al VIII. Discussion
Page 172
important to report raw data in publications to allow for future systematic reviews and meta-
analyses using pooled samples to detect genetic factors of small effect sizes.
Perhaps the key limitation for genetic association studies is that the results are correlative,
and do not reflect necessarily a cause-and-effect relationship between the polymorphisms within
the genes and TD or SCZ. This is especially true if the study is of retrospective design.
Prospective cohort studies, where the size of each genotype group is matched before the
emergence of the phenotype, be it SCZ or TD, are better at answering causality because it is less
susceptible to sampling bias; however, prospective study design is very costly due to long follow-
up periods and possibly high dropout rates. To establish causality, it would be necessary to
determine the function of each significant polymorphism by cell culture and in-vitro molecular
studies, followed by the use of genetically modified mice to determine the effects of the DNA
variation on phenotypes of interest.
Zai et al VIII. Discussion
Page 173
8.3 FUTURE DIRECTIONS
8.3.1 Gene-gene interactions
The manuscripts in the current thesis demonstrate the utility of using gene-gene
interaction analysis to elucidate the genetic and molecular mechanism underlying complex
disorders such as SCZ and TD. Future studies may include the interaction between other
candidate genes of interest. Other genes in the DRD2-AKT1 signalling pathway could be added
to the analysis to further explore the effects of this pathway on TD development. However, much
larger sample sizes are required because the number of possible genotype combinations will be
large, given that the number of possible genotype combinations is 3n with n being the number of
biallelic markers examined. Another interaction of interest could be that between DRD5 and
GABRG2. Association studies of DRD5 with SCZ have yielded mixed results (Muir et al, 2001;
Hoogendoorn et al, 2005), and GABRG2 has not been associated with SCZ in most studies
(Petryshen et al, 2005). Perhaps the interaction between the two genes, and not the individual
genes, is important for SCZ development. There is evidence for genetic interaction in the case of
BDNF Val66Met and DRD3 Ser9Gly in suicidal behaviour in SCZ patients.
8.3.2 Gene-environment interaction
After decades of intense search for susceptibility loci of SCZ, the underlying genetic
mechanism of SCZ is still far from resolved. SCZ development is also likely influenced by
environmental factors, including marijuana use (Di Forti et al, 2007) and family stress (Phillips et
al, 2007). For the purpose of discussion, we will focus on infectious agents as environmental risk
factors. Several lines of evidence point to a role of prenatal immune challenge in SCZ
development: epidemiological, molecular biological, genetic, and animal models.
Obstetric complications such as premature birth, low birth weight, and hypoxia have been
associated with SCZ (Cannon et al, 2002; Gilmore et al, 2000; Seeman et al, 2005). Winter and
Zai et al VIII. Discussion
Page 174
spring births appear to increase the risk of schizophrenia (Davies et al, 2003). Influenza
epidemics have been associated with subsequent spikes in SCZ incidence (Mednick et al, 1988).
Serological evidence of prenatal exposure to influenza, especially in the first trimester leads to a
seven-fold increase in risk of the offspring developing SCZ (Brown et al, 2004). In these cases,
serum autoantibodies were identified against brain structures and neurotransmitter receptors
(Henneberg et al, 1994; Tanaka et al, 2003). IL-1, IL-2, and IL-4 levels were reported to be
abnormal in CSF of SCZ patients (Licinio et al, 1993; Katila et al, 1994; Mittleman et al, 1997).
A number of association studies and linkage studies have pointed to a role of the HLA region on
chromosome 6p in SCZ (Wright et al, 2001). Specifically, the HLA-DRB1 locus has been
consistently associated with SCZ in East Asian samples (Li et al, 2001; Akaho et al, 2000; Sasaki
et al, 1999; Wright et al, 1996). Studies on the HLA-DQB1 gene have yielded mixed results
(Chowdari et al, 2001; Gibson et al, 1999; Wright et al, 1996; Nimgaonkar et al, 1995a; Campion
et al, 1992). Thus far, over twenty immune system genes have been examined in SCZ. IL10
(Chiavetto et al, 2002; Yu et al, 2004) and TNFA (Meira-Lima et al, 2003; Schwab et al, 2003;
Tan et al, 2003; Boin et al, 2001; Duan et al, 2004; Riedel et al, 2002; Zai et al, 2006) genes were
found to be associated with SCZ, while IL1B was found to be associated in a meta-analysis
(Shirts et al, 2006). Other genes, including IL4 (Schwarz et al, 2006; Jun et al, 2003), IL2RB
(Nimgaonkar et al, 1995b; Tatsumi et al, 1997), PLA2G1B (Strauss et al, 1999; Chowdari et al,
2001), and PNOC (Imai et al, 2001; Blaveri et al, 2001) should not be discounted due to repeated
negative findings or mixed results, as none of these genes was examined thoroughly. The single
positive findings in CCR5 (Rasmussen et al, 2006), and CTLA4 (Jun et al, 2002) genes require
replication. Recently, a genome-wide association study identified IL3RA as a potential candidate
gene for SCZ (Lencz et al, 2007a).
Zai et al VIII. Discussion
Page 175
Administration of human influenza virus (Patterson, 2002; Fatemi et al, 1999; Shi et al,
2003), and viral mimics (Borrell et al, 2002; Shi et al, 2003; Zuckerman et al, 2003; Meyer et al,
2005; Ozawa et al, 2006) in pregnant mice produced offspring that showed acoustic prepulse
inhibition deficit and abnormal social interaction. These SCZ-associated phenotypes could be
reversed by antipsychotics (Patterson, 2002; Fatemi et al, 1999; Shi et al, 2003). In addition to
behavioral changes, decrease in reelin expression and increase in D2 DA receptor levels have
been reported (Fatemi et al, 1999; Beraki et al, 2005; Ozawa et al, 2006) in offspring of immune-
challenged pregnant mice. The observations are in agreement with the reported decrease in reelin
expression in human postmortem SCZ brain samples (Guidotti et al, 2000).
The current body of work is primarily focused on the dopamine hypothesis of SCZ. It has
been suggested the dopamine system and the immune system may interact. All dopamine
receptors have been detected by reverse-transcription PCR in peripheral blood lymphocytes from
healthy individuals (Ostadali et al, 2004). D2 and D3 DA receptors, in particular, have been
implicated in the dopamine induced increased T cell activation (Levite et al, 2001). The immune
system, on the other hand, has been implicated in the regulation of the expression of D2 and D3
DA receptors. Specifically, IL-2, a TH1 cytokine that is found increased in SCZ patients, has
been shown to induce the expression of BDNF and its receptor trkB (Besser and Wank, 1999).
IL-10, a TH2 cytokine that has also been found increased in SCZ, has been linked to the induction
of nerve growth factor (NGF) expression (Brodie C, 1996). NGF increases the expression of D2
DA receptor (Fiorentini et al, 2002), and BDNF increases the expression of D3 DA receptor
(Guillin et al, 2001). Perhaps dysregulation, due to genetic predisposition or environmental
triggers or both, in the interaction between the dopamine and immune systems leads to SCZ.
Future genetic studies should introduce additional research data from medical records on the
history of maternal infections and early life events in SCZ patients, so that the interaction
Zai et al VIII. Discussion
Page 176
between SCZ candidate genes and these environmental variables can be explored. In any case,
having this environmental information will help minimize the heterogeneity within the study
sample.
8.3.3 Whole Genome Association
Recently, there is increasing interest in using genetic markers that are evenly and densely
spaced across the entire genome to identify novel candidate genes. Lencz et al (2007a) published
the initial findings from a genome-wide association study on SCZ and found a novel marker near
CSF2RA and IL3RA to be significantly associated. However, this approach is very costly, so as a
way to minimize expenses of individual genotyping, pooled sample whole genome association
studies have been attempted. Steer et al (2007) were able to replicate association findings for
known candidate genes PTPN22 and MAGI3 in rheumatoid arthritis. A novel candidate gene
diacylglycerol kinase eta (DGKH) was implicated in bipolar disorder from a pooled sample
whole genome association scan (Baum AE et al, 2007). There are likely additional candidate
genes to be discovered for SCZ and TD, and using pooled sample whole genome association may
provide new insights into these debilitating conditions. Also, genome-wide association studies
are revealing deletions and copy-number variations that may be playing a role in SCZ (Lencz et
al, 2007b). Future genome-wide studies could be extended to analyses of global epigenetic and
variable-number repeats profiles.
Zai et al VIII. Discussion
Page 177
9.4 CONCLUDING REMARKS
Through experiments on human genomic DNA, we conclude that:
(1) Sequence variation in the GABAA receptor γ2 subunit GABRG2 gene is associated with risk
of SCZ. Other GABA genes in the vicinity of GABRG2 should also be investigated, as well
as the interaction between the GABRG2 subunit and the D5 DA receptor. Neither BDNF,
DRD3, nor their interaction is a major factor in SCZ, but the interaction between BDNF
Val66Met and DRD3 Ser9Gly is associated with history of suicide attempt. Future studies
should include investigations of subpopulations of SCZ patients that share specific SCZ-
associated phenotypes such as suicidal behaviour, smoking, age of onset, as well as
antipsychotic response and side effects, in order to decrease the heterogeneity of the sample
and increase the power to detect genetic associations. One SCZ-related phenotype that we are
particularly interested in is Tardive Dyskinesia (TD).
(2) Variations in the dopamine receptor DRD2 gene are associated with risk of TD, possibly by
signalling via AKT1. The dopamine receptor DRD3 gene is associated with TD in some
sample populations, but the interaction of BDNF and DRD3 genes does not appear to play a
major role. Our association of the 5’ region of DRD3 with TD encourages more
comprehensive investigations of polymorphisms spanning and surrounding candidate genes
in association studies.
(3) Overall, the body of work presented in this thesis strengthens support for the dopamine
hypothesis, in particular, the role of D2 and D3 dopamine receptors, in TD. Future genetic
studies should involve analyzing more than one gene along pathways in association with TD
(and other complex diseases) rather than testing single genes in isolation. Future studies
should also include deriving genetically modified animals where multiple genes are mutated
Zai et al VIII. Discussion
Page 178
or inactivated to explore the biological effects caused by the genetic variations and their
interactions.
Zai et al IX. References
Page 179
CHAPTER 9
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