zai clement c 200806 phd thesis
TRANSCRIPT
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
© Copyright by Clement C. Zai (2008)
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Thesis Title: Gene Association Studies of Schizophrenia and Tardive Dyskinesia
Degree: Doctor of Philosophy
Year of Convocation: June, 2008
Name: Clement C. H. Zai
Department: Institute of Medical Science, University of Toronto
ABSTRACT
Schizophrenia (SCZ) is a severe neuropsychiatric disorder with a genetic component.
Most candidate gene association studies have given mixed results. We investigated the GABAA
receptor γ2 subunit gene GABRG2, the dopamine receptor gene DRD3, and the Brain-derived
neurotrophic factor gene BDNF that is required for D3 expression by genotyping polymorphisms
spanning and surrounding these genes for association with SCZ, as well as suicidal behaviour.
We also examined the BDNF, DRD3, as well as the dopamine receptor gene DRD2 and Protein
Kinase B gene AKT1 for association with Tardive Dyskinesia (TD), a potentially irreversible
motor side effect of long-term antipsychotic medication. Our analysis included single-marker
tests, haplotype tests, and gene-gene interactions. We found a haplotype in the 5’ region of
GABRG2 to be associated with SCZ in both families and matched case-control samples. We also
found two synonymous DRD2 polymorphisms, rs6275 (C939T) and rs6277 (C957T), and their
haplotypes, as well as a polymorphism 5’ of DRD3, rs905568, to be associated with TD. Further,
we reviewed two putative functional DRD2 polymorphisms, -141C Ins/Del and TaqIA, in TD
and found TaqIA 3’ of the gene to be associated with TD in a meta-analysis. Lastly, we found a
significant interaction between AKT1 rs3730358 and DRD2 C939T in TD. Though replication
studies are required, these results contribute to the future development of genetic tests to assess
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for the risks of SCZ and TD, leading to better outcome for patients suffering from these
debilitating conditions.
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ACKNOWLEDGEMENTS
I would like to thank my supervisor, Dr. James L. Kennedy, and my co-supervisor, Dr.
Albert H. C. Wong, for their guidance, teaching, and insightful comments on my projects as well
as advice on my future career path. I would like to also thank members of my supervisory
committee, Dr. Paul Fletcher and Dr. John Vincent, and other members of my examination
committee, Dr. Jose Nobrega and Dr. Oksana Suchowerski. I would like to acknowledge the
graduate coordinators and administrative staff at the Institute of Medical Science. Many thanks
to members of the Kennedy lab and Wong lab: Nicole King and Mawahib Semeralul for their
help in study designs; Mary Smirniw, Sharah Mar, and Andrea Smart for their administrative
assistance; past and present research analysts (Joanne Brathwaite, Natalie Bulgin, Sahar
Ehtesham, Olga Likhodi, Laura Miler, Sajid Shaikh, David Sibony, Tricia Sicard, Maria
Tampakeras, Subi Tharmalingam, Joseph Trakalo, Gregory Wong, Pamela Zuker) for their
technical support so that my experiments could run smoothly; my past and present student
colleagues (Dr. Paul Arnold, Poonam Batra, Renan deSouza, Dr. Marc Fadel, Laura Feldcamp,
Nipa Haque, Dr. Daniela Hlousek, Rudi Hwang, Dr. Timothy Klempan, Dr. Wiplove Lamba,
Frankie Lee, Dr. Livia Martucci, Anjali Rastogi, Dr. Anil Srivastava, Dr. John Strauss) for
making my doctoral experience stimulating and fun; post-doctoral fellows and visiting scientists
(Drs. Vincenzo De Luca, Yuko Hirata, Mirko Manchia, Daniel Müller, Xingqun Ni, Claudia
Rothe, Marco Romano-Silva, Arun Tiwari) for their valuable input throughout my research
projects. I would further like to thank the collaborators (Drs. Gary Remington, John Roder,
Tatiana Lipina, Bernard Le Foll, Herbert Meltzer, Jeffrey Lieberman, Steven Potkin) for their
contributions in the studies and manuscripts, and of course, the patients without whom my
doctoral studies would not have been possible.
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I would like to thank my many relatives and family friends who have provided
encouragements throughout the years. Lastly, I would like to thank my parents, brother and
sister, who have always believed in me even when I was in doubt, and have supported me
unconditionally through it all. To them I will dedicate this work.
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CONTENTS
CHP SECTION TITLE PAGE
ABSTRACT …………………………………………………………………….. ii
ACKNOWLEDGEMENTS …………………………………………………….. iv
TABLE OF CONTENTS ……………………………………………………….. vi
List of Abbreviations …………………………………………………………… x
List of Figures …………………………………………………………………... xii
List of Tables …………………………………………………………………… xiv
1 INTRODUCTION
1.1 SCHIZOPHRENIA
1.1.1 Diagnostic Criteria and Epidemiology …………………………………..
1.1.2 Molecular Genetics Studies ……………………………………………...
1.1.3 Candidate Pathways and Genes …………………………………………
1.1.4 Suicidal Behaviour in SCZ ……………………………………………...
1.1.5 Pharmacogenetics ………………………………………………………..
1.2 TARDIVE DYSKINESIA
1.2.1 Diagnostic Criteria and Epidemiology …………………………………..
1.2.2 Candidate Pathways and Genes …………………………………………
1.3 DOPAMINE
1.3.1 Dopamine neurotransmission ……………………………………………
1.3.2 The Dopamine DRD2 Gene ……………………………………………..
1.3.3 The Dopamine DRD3 Gene ……………………………………………..
1.4 GABA
1
1
2
7
20
21
23
26
32
33
37
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1.4.1 GABA neurotransmission ……………………………………………….
1.4.2 The GABRG2 Gene ……………………………………………………...
1.5 METHODOLOGIES – GENETIC ASSOCIATION STUDIES
1.5.1 Case-control association studies ………………………………………...
1.5.2 Transmission-Disequilibrium Tests and Family-Based Association Test
39
39
41
41
1.6 RATIONALE ……………………………………………………………… 43
2 ORIGINAL RESEARCH ARTICLE:
The γ-aminobutryic acid type A receptor γ2 subunit gene is associated with
Schizophrenia and Suicidal Behaviour (manuscript to be submitted)
2.1 Abstract ………………………………………………………………….
2.2 Introduction ……………………………………………………………...
2.3 Patients and Methods ……………………………………………………
2.4 Results …………………………………………………………………...
2.5 Discussion ……………………………………………………………….
45
46
47
51
53
55
3 ORIGINAL RESEARCH ARTICLE:
Association study of BDNF and DRD3 genes in Schizophrenia (manuscript to
be submitted)
3.1 Abstract ………………………………………………………………….
3.2 Introduction ……………………………………………………………...
3.3 Patients and Methods ……………………………………………………
3.4 Results …………………………………………………………………...
3.5 Discussion ……………………………………………………………….
61
62
63
69
72
74
4 ORIGINAL RESEARCH ARTICLE: 86-87
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Genetic study of BDNF, DRD3, and their interaction in Tardive Dyskinesia
(manuscript to be submitted)
4.1 Abstract ………………………………………………………………….
4.2 Introduction ……………………………………………………………...
4.3 Patients and Methods ……………………………………………………
4.4 Results …………………………………………………………………...
4.5 Discussion ……………………………………………………………….
88
89
94
97
100
5 ORIGINAL RESEARCH ARTICLE:
Association study of Tardive Dyskinesia and twelve DRD2 polymorphisms in
Schizophrenia Patients (published in International Journal of
Neuropsychopharmacology)
5.1 Abstract ………………………………………………………………….
5.2 Introduction ……………………………………………………………...
5.3 Patients and Methods ……………………………………………………
5.4 Results …………………………………………………………………...
5.5 Discussion ……………………………………………………………….
111-
112
113
114
118
121
124
6 ORIGINAL RESEARCH PUBLICATION:
Meta-Analysis of Two Dopamine D2 receptor gene Polymorphisms with
Tardive Dyskinesia in Schizophrenia Patients (published in Molecular
Psychiatry)
6.1 Introduction ……………………………………………………………...
6.2 Patients and Methods ……………………………………………………
6.3 Results and Discussion …………………………………………………..
134
135
136
138
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7 ORIGINAL RESEARCH ARTICLE
Genetic study of eight AKT1 gene polymorphisms and their interaction with
DRD2 gene polymorphisms in Tardive Dyskinesia (manuscript to be submitted)
7.1 Abstract ………………………………………………………………….
7.2 Introduction ……………………………………………………………...
7.3 Patients and Methods ……………………………………………………
7.4 Results …………………………………………………………………...
7.5 Discussion ……………………………………………………………….
140-
141
142
143
146
148
151
8 DISCUSSION
8.1 Summary of Findings and Implications …………………………………
8.2 Limitations and Considerations
8.2.1 Sample Characteristics and Power …………………………………….
8.2.2 Multiple Testing ……………………………………………………….
8.3 Future Directions
8.3.1 Gene-gene Interactions ………………………………………………...
8.3.2 Gene-environment Interactions ………………………………………..
8.3.3 Whole Genome Association …………………………………………...
8.4 Concluding Remarks …………………………………………………….
161
168
170
173
173
176
177
9 REFERENCES ………………………………………………………………….. 179
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List of Abbreviations
6-OHDA 6-hydroxydopamine AC5 Adenylate cyclase 5 AIMS Abnormal Involuntary Movement Scale AKT1 Protein kinase B gene ANKK1 ankyrin repeat and kinase domain containing 1 AP Antipsychotic BDNF Brain-derived neurotrophic factor gene cAMP 3’-5’-cyclic adenosine momophosphate CATIE Clinical Antipsychotic Trials of Intervention Effectiveness COMT Catechol-O-methyltransferase gene CSF Cerebrospinal fluid CYP Cytochrome P450 genes DA dopamine DAOA/G72 D-amino acid oxidase activator gene DARPP32 Dopamine and cAMP-regulated phosphoprotein gene (aka PPP1R1B) DISC1 Disrupted in Schizophrenia 1 gene DRD2 Dopamine D2 receptor gene DRD3 Dopamine D3 receptor gene DRD4 Dopamine D4 receptor gene DRD5 Dopamine D5 receptor gene DSM Diagnostic and Statistical Manual of Mental Disorders DTNBP1 Dysbindin (Dystrobrevin-binding protein) gene FBAT Family-based association Test GABA γ-amino-butyric-acid GABRA3 GABAA α3 subunit gene GABRB2 GABAA β2 subunit gene GABRG2 GABAA γ2 subunit gene GSK3β Glycogen synthase kinase 3β GSTM1/P1/T1 Glutathione S-transferase µ/π/θ1 gene GPX1 Glutathione peroxidase 1 gene HPL Haloperidol HTR2A Serotonin 5HT2A receptor gene HTR2C Serotonin 5HT2C receptor gene HTR6 Serotonin 5HT6 receptor gene LI Latent inhibition MAO Monoamine oxidase MAPK mitogen-activated protein kinase (aka ERK) MDR1 P-glycoprotein (Multidrug Resistance), aka ABCB1 NMDA N-methyl-D-aspartate NOS1/3 Nitric oxide synthase 1(neuronal)/3(endothelial) gene NQO1 NAD(P)H:Quinone acceptor oxidoreductase type 1 gene NR2B NMDA receptor 2B subunit gene NRG1 Neuregulin gene OR Odds ratio
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PET Positron emission tomography PKC protein kinase C PPI Prepulse inhibition PRODH Proline dehydrogenase gene RGS4 Regulator of G-protein signalling gene SCZ Schizophrenia SLC6A3 Dopamine transporter DAT1 gene SLC6A4 Serotonin transporter 5HTT gene SNP Single nucleotide polymorphism SOD Superoxide dismutase SPECT Single photon emission computed tomography TD Tardive dyskinesia TDT Transmission disequilibrium Test TPH1/2 Tryptophan hydroxylase 1/2 genes
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List of Figures
1 Candidate genes of Schizophrenia 18-19
2 Linkage disequilibrium plot among the five GABRG2 gene polymorphisms used 59
3a Schematic diagram of the DRD3 gene with its exons and introns 76
3b Schematic diagram of the BDNF gene with its exons and introns 76
4a Linkage disequilibrium plot among the 10 DRD3 gene polymorphisms used 80
4b Linkage disequilibrium plot among the six BDNF gene polymorphisms used 81
5 p-values from analyses of two-marker interactions between BDNF and DRD3
polymorphisms in relation to schizophrenia diagnosis given by HELIXTREE
program
84
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
85
7a Linkage disequilibrium plot among the three ZNF80 and 10 DRD3 gene
polymorphisms used
107
7b Linkage disequilibrium plot among the six BDNF gene polymorphisms used 108
8 p-values from analyses of two-marker interactions between BDNF and DRD3
polymorphisms in association to AIMS given by HELIXTREE Program
110
9 Schematic diagram of the DRD2 gene with its exons and introns 129
10 Linkage disequilibrium plot among the 12 DRD2 gene polymorphisms used 132
11 Schematic diagram of the AKT1 gene with its exons and introns 154
12 Linkage disequilibrium plot for the eight AKT1 gene polymorphisms used 158
13 p-values from analyses of two-marker interactions between DRD2 and AKT1 159
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polymorphisms in association to AIMS given by HELIXTREE Program
14 Interaction between DRD2_rs6275 (C939T) and AKT1_rs3730358 in AIMS 160
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List of Tables
1 Candidate Genes of Schizophrenia 17
2 Candidate Gene Studies of Tardive Dyskinesia 31
3 Genetic analysis of GABRG2 markers and schizophrenia using paired case-
control samples
57
4 Family-based association test using FBAT for GABRG2 polymorphisms and
haplotypes
58
5 Results considering suicidal behaviour in schizophrenia patients and GABRG2
polymorphisms
60
6a Genetic analysis of DRD3 markers and schizophrenia using paired case-control
samples
77
6b Genetic analysis of BDNF markers and schizophrenia using paired case-control
samples
78
7 Family-based association test using FBAT for DRD3 and BDNF single-
nucleotide polymorphisms and HBAT for two-marker haplotypes
79
8 Results considering suicidal behaviour in schizophrenia patients with BDNF and
DRD3 polymorphisms
82-83
9 ABI assays-on-demand and assays-by-design with information on their
corresponding BDNF and DRD3 polymorphisms used
103
10a Statistical analyses on demographics as well as total AIMS scores and TD
diagnoses with DRD3 genotypes
104
10b Statistical analyses on demographics as well as total AIMS scores and TD
diagnoses with BDNF and ZNF80 genotypes
105
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11 Results from χ2 tests of allele frequencies of each of the 19 polymorphisms
versus tardive dyskinesia diagnoses for our Caucasian and African-American
samples
106
12 p-values from analyses of two-marker haplotypes across ZNF80, DRD3, and
BDNF genes in association to tardive dyskinesia and AIMS using COCA-PHASE
and QT-PHASE respectively
109
13 Statistical analysis on demographics as well as total AIMS scores and tardive
dyskinesia diagnoses with genotypes of the 12 polymorphisms in DRD2
130
14 Results from χ2 test of allele frequencies of each of the 12 DRD2 polymorphisms
versus tardive dyskinesia diagnoses for both Caucasian and African-American
populations
131
15 Global p-values from analyses of DRD2 two-marker haplotypes in association to
tardive dyskinesia and AIMS using COCA-PHASE and QT-PHASE respectively
133
16 Summary for meta-analysis of DRD2 Taq1A and –141C Ins/Del polymorphisms 139
17 Assays-on-Demand with information on their corresponding AKT1
polymorphisms used in the present study
155
18 Statistical analyses on demographics as well as total AIMS scores and tardive
dyskinesia occurrence with each of the eight AKT1 polymorphisms
156
19 Results from χ2 tests of allele frequencies of each of the eight AKT1
polymorphisms versus tardive dyskinesia occurrence for both Caucasian and
African-American populations
157
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CHAPTER 1
INTRODUCTION
1.1 SCHIZOPHRENIA
1.1.1 Diagnostic criteria and Epidemiology
Schizophrenia (SCZ) is a severe debilitating neuropsychiatric disorder. It is generally
characterized by positive and negative symptoms. Positive symptoms refer to hallucinations,
delusions and thought disorder. Hallucinations in schizophrenia are typically auditory and are of
voices talking with or about the patient. Delusions are often paranoid and can include false
beliefs of persecution, grandiosity, external control, and special powers. Thought disorder is
usually manifest in disorganized speech that can be disjointed and erratic, and disorganized
behaviours. Negative symptoms include poverty of speech (alogia), greatly diminished
motivation (avolition), and a lack of emotion. The diagnostic criteria require that these signs and
symptoms be present for at least six months, and are especially prominent for a significant
portion of a one-month time period unless successfully treated (APA, 2000). They often cause
significant disturbances at work and/or in interpersonal relationships. The lifetime risk of
schizophrenia is estimated to be nearly 1% for the general population, regardless of ethnicity, and
to be approximately equal between the sexes. However, a meta-analysis of non-overlapping
samples from 38 published studies on gender and SCZ found males to be at 40% increased risk
compared to females (Odds Ratio, OR=1.42; 95% CI: 1.30-1.56) (Aleman et al, 2003). The
reported age of onset varies from study to study, ranging from 25 to 35 years (Hafner and an der
Heiden, 1997). There appears to be an earlier age of onset in males compared to females, with
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our sample reporting a difference of 2.1 years (Renou et al, 2007). Being born or living in cities
appears to increase the risk of SCZ two folds compared to living in rural areas (Pedersen et al,
2001; Marcelis et al, 1998). The authors cautioned that the increased SCZ rate in urban areas
could be partially due to easier access to medical care in urban areas, thus artificially increasing
the observed SCZ rates in these areas.
There was an estimated 234,300 SCZ patients in Canada in 2004, with healthcare costs
estimated at over $2 billion CAD. Combined with lost productivity due to unemployment,
morbidity, and mortality due to SCZ, the total cost estimate of SCZ was $6.85 billion CAD
(Goeree et al, 2005). It is important to note that these costs did not take into account emotional
and monetary costs for relatives of individuals with SCZ, as well as the chronic social effects of
SCZ.
1.1.2 Molecular Genetic Studies
Family, twin, and adoption studies have provided evidence for a genetic component to
SCZ risk. Overall, there is a ten-fold increase in the risk of schizophrenia for a first-degree
relative of a SCZ patient compared to an average global prevalence of 0.7-0.8% (Mowry, 2000;
Saha et al, 2005). The concordance rate (presence of the same trait in both members of a twin
pair), between monozygotic twins (sharing ~100% DNA sequence identity) is 45-75%, and that
between dizygotic twins (sharing ~50% sequence identity) is lower at 4-15% (Kendler, 1983;
McGuffin et al, 1984; Farmer et al, 1987; Onstad et al, 1991; Cannon et al, 1998). While the
monozygotic and dizygotic twin concordance rates are well above the expected global average
incidence rate of 1%, the monozygotic concordance rate of well below 100% suggests that
genetic factors are insufficient in totally explaining the etiology of SCZ (LaBuda et al, 1993).
From these twin studies, the heritability of SCZ could be estimated at ~60-70%.
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Adoption studies have been useful in separating the relative contribution of familial
environment from genetic factors in SCZ risk (Wahlberg et al, 1997). In a study of 361 Finnish
families, adopted children whose biological mothers had schizophrenia have over a four-fold
increase in the rate of SCZ compared to adopted children of non-SCZ biological mothers
(Tienari, 1991). A similar study in Denmark showed a ten-fold increase in SCZ rate (Kety et al,
1994). The results from adoption studies should be reviewed with caution, as the adopted
children were exposed to prenatal and often perinatal environment with their biological mothers.
Kety and coworkers (1994) attempted to resolve this issue by comparing the prevalence rate of
SCZ among paternal half-siblings of SCZ and non-SCZ individuals. They found higher rate of
SCZ among paternal half-siblings of SCZ (13%) compared to those of non-SCZ (2%) (Kety et al,
1994). The lifetime risk of schizophrenia of an individual with both parents suffering from SCZ
is ~46%, a rate that is much lower than if SCZ is a dominant trait (75%), or if SCZ is a recessive
trait (100%) (Tsuang et al, 1982; Faraone et al, 1985; Mortensen et al, 1998). Therefore, it is
likely that SCZ is a complex disorder with multiple genetic and environmental components each
contributing a small risk (Tsuang et al, 2001).
To begin unravelling the genetic contribution of schizophrenia, familial syndromes with
schizophrenia-like phenotypes were investigated. Chromosomal aberrations have been reported
in families with schizophrenia and other psychiatric disorders. Inversion at 4(q13;q25) was
detected in a multigenerational Hong Kong family with multiple SCZ probands (Mensah et al,
2007). An extra copy of a portion of the 5q chromosomal region was reported in an extended
family (Bassett et al, 1988), followed by the report of a SCZ patient with an interstitial deletion at
5q21-23.1 (Bennett et al, 1997). Other chromosomal abnormalities have also been reported,
including single families with a balanced translocation between chromosomal regions 1p22 and
7q22 (t(1;7)(p22;q22)) (Gordon et al, 1994), t(2;18)(p11.2;p11.2) (Maziade et al, 1993),
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t(6;11)(q14.2;q25) (Holland and Gosden, 1990), t(4; 13)(p16.1;q21.31) (Itokawa et al, 2004), an
inversion on chromosome 4 (Palmour et al, 1994), or partial trisomy at 5p (Malaspina et al,
1992). 22q11 deletion occurs in velocardiofacial (DiGeorge) syndrome patients of which 18%
have psychotic symptoms, a rate that is much higher than the overall prevalence of 1%;
conversely, at least 2% of schizophrenia patients were reported to have 22q11 deletions
compared to the 0.025% prevalence rate of 22q11 deletion syndrome in the general population
(Karayiorgou et al, 1995; Murphy, 2002). A balanced (1;11)(q42.1;q14.3) translocation was
found in a large Scottish family with high frequency of psychiatric disorders including
schizophrenia (Millar et al, 2000; Blackwood et al, 2001). In each study, however, the
chromosomal aberration does not completely co-segregate with the SCZ phenotype; thus these
chromosomal abnormalities alone are not sufficient to cause SCZ.
Since the first linkage study done by Sherrington et al (1988), over 35 genome scans have
been conducted to search for linkage between genetic markers throughout the human genome and
the hypothetical SCZ locus. Sherrington and coworkers analyzed seven families with 39 SCZ or
schizophreniform disorder patients and found the long arm of chromosome 5 (5q) to be linked to
SCZ (Sherrington et al, 1988). However, investigations in independent samples soon after did
not yield significant results in the same region (Kennedy et al, 1988; St Clair et al, 1989;
McGuffin et al, 1990; Aschauer et al, 1990). To date, many chromosomal regions have been
linked to SCZ, with repeated positive linkage findings in chromosomal regions 1q21-42
(Brzustowicz et al, 2000; Gurling et al, 2001; Hovatta et al, 1999; Ekelund et al, 2000;
Blackwood et al, 2001), 5q21-q33 (Schwab et al, 1997; Camp et al, 2001; Gurling et al, 2001;
Straub et al, 1997; 2002a; DeLisi et al, 2002; Paunio et al, 2001; Devlin et al, 2002; Sklar et al,
2004), 6p24-p22 (Moises et al, 1995; SCLG, 1996; Straub et al, 2002a; Schwab et al, 1995; 2000;
Fallin et al, 2003), 6q21-q25 (Cao et al, 1997; Kaufmann et al, 1998; Martinez et al, 1999,
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Levinson et al, 2000; Lindholm et al, 2001), 8p22-p21 (SCLG, 1996; Blouin et al, 1998;
Kaufmann et al, 1998; Brzustowicz et al, 1999; Gurling et al, 2001; Garver et al, 2001; Straub et
al, 2002a; Stefansson et al, 2002), each having been cited in at least five studies.
The mixed results could be due to variability in sample characteristics such as ethnicity,
as well as diagnostic procedure and criteria. In addition, the presumed mode of inheritance and
penetrance are important. Evidence of linkage is traditionally provided by the odds of observing
the cosegregation of a genetic marker and SCZ by chance. A genetic marker is considered linked
to SCZ if the observed degree of cosegregation could occur only once for every 1000 or more
cases; that is, if the logarithm of the odds is more than 3. If the mode of inheritance is non-
Mendelian, then linkage findings will be difficult to interpret. The lack of strong replication in
linkage studies could also be the result of small effect size for each susceptibility region that can
only be detected with very large sample sizes. Badner and Gershon (2002) attempted to resolve
the inconsistencies by performing a meta-analysis of 18 genome scans using multiple-scan
probability (MSP) and found three chromosomal regions showing significant linkage to SCZ: 8p,
13q, and 22q. Lewis et al (2003) performed a meta-analysis of 20 previously published genome
scans. The multi-centre study group ranked linkage scores of 30cM bins across the genome for
each study, and computed the average rank for each 30cM bin across all 20 studies. Using a
permutation test, they found significant linkage at 2q, as well as a number of nominally
significant regions including 5q, 3p, 11q, 6p, 1q, 22q, 8p, 20q, 14p, 16q, 18q, 10p, 15q, 6q, and
17q (Lewis et al, 2003). Additional linkage support was more recently provided for 4q33-q35.1
(Vawter et al, 2006), 5q31-q35 (Sklar et al, 2004), 6p22 (Maziade et al, 2005), 6q23 (Lerer et al,
2003), 8p23.3-q12 (Suarez et al, 2006), 13q13 (Maziade et al, 2005), 15q26 (Vazza et al, 2007),
and 18q21 (Maziade et al, 2005).
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Another approach in elucidating the genetic basis of SCZ is to study the candidate genes.
DNA variants, polymorphisms, in HTR2A at 13q14-q21, DRD2 at 11q23, DRD3 at 3q13.3,
COMT at 22q11.21, DTNBP1 at 6p22.3, NRG1 at 8p12, RGS4 at 1q23.3, DISC1 at 1q42.1, NR2B
at 12p12, DAOA/G72 at 13q33.2-q34, BDNF at 11p13, PRODH at 22q11.21, AKT1 at 14q32.32
genes have been examined more than once for possible association in SCZ families and case-
control samples. The roles of some of these candidate pathways and genes in SCZ are discussed
below (Figure 1; Table 1). Mice with mutant or deficient expression of some of these candidate
genes have been generated and tested for SCZ related phenotypes, prepulse inhibition (PPI) and
latent inhibition (LI). PPI of the acoustic startle reflex refers to a paradigm that measures
sensorimotor gating where a weak prepulse stimulus reduces the startle reflex to a startle-eliciting
pulse stimulus that follows shortly after (Hoffman and Searle, 1965). PPI has been demonstrated
in a variety of animal species (van den Buuse et al, 2005). PPI deficit has been consistently
reported in schizophrenia patients (Braff et al, 1992; Ludewig et al, 2003). LI is commonly
considered as a form of salience (or attentional) learning, reflecting the ability to ignore stimuli
that do not previously predict any significant outcomes. Hence, LI deficit may indicate a
vulnerability to distraction by irrelevant stimuli. Baruch et al (1988) initially reported LI
deficiency in schizophrenia patients, followed by Gray et al (1992) (Williams et al., 1998). LI
can be disrupted by amphetamine treatment and rescued by antipsychotics in animals (Solomon
and Staton, 1982; Weiner et al., 1984; Feldon and Weiner, 1992; Moser et al., 2000; Weiner,
2003; Meyer et al., 2004) and human subjects (Gray et al., 1992b; Kumari et al., 1999).
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7
1.1.3 Candidate Pathways and Genes
• The Serotonin HTR2A Gene
The serotonin hypothesis of SCZ arose from the pharmacological evidence that Lysergic
acid diethylamide (LSD), an indoleamine that resembles serotonin, produces
psychotic/hallucinogenic symptoms. The affinity and agonistic effects of hallucinogenic agents
to the 5HT2A receptor was correlated to their hallucinogenic potential (Glennon RA et al, 1984;
Vollenweider et al, 1997). The serotonin 2A receptor (HTR2A) gene is located at 13q14-q21, a
SCZ susceptibility region (Blouin et al, 1998; Brzustowicz et al, 1999; Maziade et al, 2005;
Badner and Gershon, 2002). The exon 1 synonymous T102C polymorphism (Ser34Ser; Warren
et al, 1993) has been examined in SCZ samples. The C allele has been associated with a 20%
decrease in 5HT2A receptor levels in the temporal cortex (Polesskaya and Sokolov, 2002). A
meta-analysis of 31 case-control studies showed a significant association between the C allele
and CC genotype and SCZ in European Caucasians but not in East Asians (Abdolmaleky et al,
2004). The odds ratio for the C allele in European Caucasians was 1.2 (CI: 1.1-1.3), and that for
the CC genotype in European Caucasians was 1.5 (CI: 1.1-2.0). This allele distribution could
account for the decreased messenger RNA (mRNA) expression in post-mortem brain samples of
SCZ patients compared to healthy controls (Polesskaya and Sokolov, 2002). The serotonin
system may influence SCZ development by its effect on glutamatergic neurotransmission
(Aghajanian and Marek, 1997). Studying the behavioural phenotypic effects of HTR2A gene
ablation will uncover its possible role in SCZ.
• The Regulator of G-protein Signalling 4 RGS4 Gene
Regulator of G-protein Signalling 4 (RGS4), on 1q23, was identified as a SCZ candidate
gene in a microarray study where RGS4 levels were found decreased (Mirnics et al, 2001). RGS4
Clement Zai I. Introduction
8
was later found genetically associated with SCZ (Chowdari et al, 2002; Chen et al, 2004; Morris
et al, 2004). However, other studies (Sobell et al, 2005), including recent meta-analyses
(Talkowski et al, 2006a; Guo et al, 2006; Li et al, 2006a) did not support these findings. RGS4 is
expressed throughout much of the central nervous system (Erdely et al, 2004), with a role in the
negative regulation of G-protein signalling from receptors for neurotransmitters including
dopamine (Taymans et al, 2003) and glutamate (De Blasi et al, 2001). Mice with the RGS
domain within the endogenous Rgs4 deleted by Cre-mediated recombination exhibit intact PPI
and similar locomotor activity compared to wildtype mice (Grillet et al, 2005), suggesting that
the role of RGS4 in SCZ may be modest.
• The N-methyl-D-aspartate (NMDA) receptor 2β subunit NR2B Gene
The glutamate hypothesis of SCZ arises from observations that altered glutamate system
components are seen in SCZ and that glutamatergic antagonists such as phencyclidine (PCP) and
ketamine produce psychotic symptoms. Examples of this type of evidence include reduced
glutamate in the CSF of SCZ patients (Kim et al, 1980), and the effectiveness of D-cycloserine,
which regulates glutamate receptor function, in treating negative symptoms of SCZ when
combined with standard antipsychotics (Goff et al, 1995; Evins et al, 2002). Glutamate has also
been shown to modulate dopamine function (Floresco et al, 1998; Floresco et al, 2001). More
specifically, N-methyl-D-aspartate (NMDA) receptor activation in the prefrontal cortex and
striatum enhances presynaptic dopamine release (Matsumoto et al, 2003). NR2B at chromosomal
region 12p12, which codes for the ionotropic NMDA glutamate receptor 2β subunit (GRIN2B),
has been investigated in SCZ. Although post-mortem NR2B mRNA levels were not significantly
different between SCZ and controls, two studies found association of NR2B polymorphisms with
SCZ, and association with the T-200C polymorphism in the 5’ region of the gene was recently
Clement Zai I. Introduction
9
replicated (Martucci et al, 2006), and strengthened in an independent meta-analysis of six studies
(Li et al, 2007a). Testing Nr2b-mutant mice for SCZ-related phenotypes (eg PPI, LI) may
uncover the mechanism underlying this association.
• The D-amino acid oxidase activator G72 (DAOA) Gene
D-amino acid oxidase activator (G72, or DAOA) is located at 13q22-q34, another SCZ
susceptibility region (Itokawa et al, 2004; Maziade et al, 2005). DAOA activates D-amino acid
oxidase (DAAO), which metabolizes D-serine, an agonist of the glutamatergic NMDA receptor.
Its expression is increased in post-mortem prefrontal cortical brain regions of SCZ patients
(Korostishevsky et al, 2004). Chumakov et al (2002) found DAOA to be associated with SCZ in
two Caucasian samples. The association between the G72 and SCZ was replicated in eight
samples out of nine that were reported (Shinkai et al, 2007), and confirmed in a recent meta-
analysis by Detera-Wadleigh and McMahon (2006). Since Daoa is not present in rodents, studies
of its function are carried out through transgenic mice that ectopically express human DAOA.
More work is needed to elucidate the function and functional variants of this gene.
• The Neuregulin 1 NRG1 Gene
Neuregulin 1, coded by NRG1 on 8p22-p11, is another strong candidate of SCZ because
of repeated linkage findings of the chromosomal region being linked to SCZ (SCLG, 1996;
Blouin et al, 1998; Kaufman et al, 1998; Brzustowicz et al, 1999; Gurling et al, 2001; Garver et
al, 2001; Straub et al, 2002a; Stefansson et al, 2002). Neuregulin 1 is a growth and
differentiation factor that binds ErbB receptor tyrosine kinases, and is involved in the formation
and remodelling of the synapse, as well as neurotransmitter function (Falls, 2003). NRG1 has
been shown to upregulate NMDAR 2C subunit expression (Ozaki et al, 1997). It has also been
reported to increase and decrease GABAA receptor subunit expression, depending on cell type
Clement Zai I. Introduction
10
(Rieff et al, 1999; Okada and Corfas, 2004). mRNA for one isoform of NRG1 was increased in
SCZ post-mortem brains (Hashimoto et al, 2004). Stefansson et al (2002) originally reported an
association between NRG1 and SCZ in an Icelandic and Scottish sample. Since then, the genetic
association between NRG1 and SCZ has been replicated in a majority of studies, with a recent
meta-analysis showing significant association of two promoter microsatellites in an East Asian
population, and four adjacent single-nucleotide polymorphisms in a Caucasian sample (Li et al,
2006a), highlighting the different linkage disequilibrium structures between the two ethnic
groups. Phenotypes of mice with only one functional copy of Nrg1 (+/-) resemble those of
ErbB4+/- mice, suggesting that the behavioural effects of NRG1 deficiency are transduced
through ErbB4. Both mouse lines were hyperactive and had PPI deficit (Stefansson et al, 2002;
Hong et al, 2008). Additional behavioural tests indicated Nrg1+/- to have LI deficit (Rimer et al,
2005) and delay in habituation to novel environment (O’Tuathaigh et al, 2006).
• The Dystrobrevin-binding protein, or Dysbindin DTNBP1 Gene
The DTNBP1 gene, which codes for dysbindin, resides on 6p24-p21, a region repeatedly
found to be linked to SCZ from genome scans (Moises et al, 1995; Straub et al, 2002a; Schwab et
al, 1995; Fallin et al, 2003; Lerer et al, 2003; Maziade et al, 2005). It binds β-dystrobrevin, a
member of the dystrophin protein complex located at the synapse. The complex may be involved
in glutamate release (Numakawa et al, 2004), as well as recruiting GABAA receptor to the
postsynaptic density (Knuesel et al, 1999). Dysbindin protein (Talbot et al, 2004) and mRNA
(Weickert et al, 2004) levels were significantly lower in the prefrontal cortex of SCZ patients
compared to matched controls. Straub et al (2002b) found several single-nucleotide
polymorphisms (SNPs) and their haplotypes to be associated with SCZ in an Irish family sample.
A recent meta-analysis of all published genetic association papers on nine single-nucleotide
Clement Zai I. Introduction
11
polymorphisms in DTNBP1 showed modest association between five of these SNPs and SCZ (Li
et al, 2007b). Li et al (2003) reported a non-sense mutation in DTNBP1 in Hermansky-Pudlak
syndrome type 7, an albinism marked by bleeding problems and lung fibrosis. Preliminary
behavioural tests on mice with spontaneous Dtnbp1 mutations revealed intact PPI, but additional
SCZ-related behavioural tests need to be conducted. Other components of the dystrophin protein
complex should be investigated for genetic association with SCZ in the future.
• The Disrupted-in-Schizophrenia 1 DISC1 Gene
The Disrupted-in-Schizophrenia 1 gene (DISC1) was discovered because the gene is
truncated by a balanced t(1;11) translocation in a multigenerational Scottish family with multiple
members affected by SCZ and other psychiatric disorders (Blackwood et al, 2001). An exon-12
frameshift mutation was also discovered in an American family affected by SCZ (Sachs et al,
2005). Additional support for a role of DISC1 in SCZ came from linkage studies that point to
chromosomal region 1q42 to be a SCZ susceptibility region for the general population
(Brzustowicz et al, 2000; Gurling et al, 2001; Hovatta et al, 1999; Ekelund et al, 2000;
Blackwood et al, 2001). The nonsynonymous rs821616 (Ser704Cys) polymorphism was
associated with SCZ in a Caucasian sample (Callicott et al, 2005). The results were partially
replicated by Qu et al (2007), albeit with a different risk allele in an East Asian sample. Other
reports did not find association between DISC1 polymorphisms and SCZ (Devon et al, 2001;
Hennah et al, 2003; Thomson et al, 2005; Zhang et al, 2006). A meta-analysis of rs821616 in six
studies did not find a significant association with SCZ (Rastogi et al, in preparation). DISC1
expression appeared similar between SCZ and healthy controls in the Stanley postmortem brain
sample, but Sawamura et al (2005) found nuclear DISC1 mRNA to be enriched in the SCZ
group. Lipska et al (2006) also reported no significant difference in DISC1 expression between
post-mortem brain samples of SCZ patients and controls. They found, however, that the levels of
Clement Zai I. Introduction
12
DISC1-binding proteins FEZ1, LIS1, and NUDEL were significantly lower in SCZ (Lipska et al,
2006). These protein levels were also associated with the risk allele of rs821597, a
polymorphism close to rs831616 (Lipska et al, 2006). Using numerous yeast-two-hybrid
experiments with DISC1 and strong DISC1-binding proteins, Camargo and coworkers (2007)
constructed a DISC1 interactome consisting of 127 proteins involved in microtubule function,
neurodevelopment, cAMP catabolism, among others, and some interacting proteins overlap with
the interactome of dysbindin (Camargo et al, 2007). Several research groups have generated
different mutant or transgenic mouse lines that express various mutant DISC1 proteins (Clapcote
et al, 2007; Li et al, 2007c; Pletnikov et al, 2008; Hikida et al, 2007); these mice were shown to
have SCZ-related phenotypes, including deficits in PPI and LI.
• The Catechol-O-methyltransferase COMT Gene
Catechol-O-methyltransferase (COMT) is linked to a SCZ susceptibility region at 22q11
(Karajiorgou et al, 1995; Lewis et al, 2003). Deletion of this chromosomal region is associated
with velocardiofacial syndrome, where approximately 25% of its sufferers exhibit psychotic
symptoms (Murphy et al, 1999). COMT metabolizes dopamine to homovanillic acid (HVA) in
brain regions including the prefrontal cortex. Its role in dopamine metabolism suggests it may
play a part in the dopamine hypothesis of SCZ (Egan et al, 2001; Joober et al, 2002; Tunbridge et
al, 2006).
The dopamine hypothesis of SCZ has endured for decades because of two key
observations. All antipsychotics block D2 DA receptors to some extent (Carlsson and Lindqvist,
1963; Creese et al, 1976; Seeman et al, 1976; Kapur and Mamo, 2003). Amphetamine, the
dopamine agonist that inhibits dopamine reuptake, induces psychotic features in non-
schizophrenia individuals. Amphetamine also exacerbates psychotic symptoms in SCZ patients
(Casey et al, 1961; Curran et al, 2004). However, it was not until 1996 that a link between
Clement Zai I. Introduction
13
dopamine and SCZ was provided in SCZ patients (Laruelle et al, 1996). Striatal dopamine
release in response to amphetamine was enhanced in acute SCZ patients. In addition, there was a
positive correlation between the release of dopamine and positive symptom severity as well as
response to dopamine antagonists (Breier et al, 1997; Abi-Dargham et al, 1998; Laruelle et al,
1999).
Experimental models also support the dopamine hypothesis of SCZ. Increased dopamine
sensitivity has been found in rodents that have undergone neonatal hippocampal lesions, chronic
administration of various antipsychotics or psychotogenic agents, genetic deletion of dopamine
system genes such as DRD1, or birth by Caesarean sections (Seeman et al, 2005). These
manipulations all resulted in increased D2 DA receptor levels given by autoradiography and
competition assays (Seeman et al, 2005). Using positron emission topography on SCZ patients,
Meyer-Lindenberg et al (2002) observed blunted task-induced increase in regional cerebral blood
flow in the prefrontal cortex that correlated with increased striatal dopamine uptake, suggesting
that hypofunction in the prefrontal cortex may be related to dopamine hyperactivity in the
subcortical striatal region (Meyer-Lindenberg et al, 2002).
Altered HVA levels have been found in SCZ (Davidson and Davis, 1988; Green et al,
1993a, b). The results are difficult to compare due to possible effects of antipsychotic treatment
(Sedvall and Wode-Helgodt, 1980). HVA levels also correlated with the severity of positive
symptoms (Maas et al, 1997) and treatment response (Pickar et al, 1990). They were inversely
correlated to negative symptoms (Lindstrom, 1985).
Association studies of the functional COMT Val158Met polymorphism and SCZ have
yielded mixed results, with some studies indicating increased risk with the low-activity Met allele
and others indicating increased risk with the high-activity Val allele. Some studies reported no
significant association. A meta-analysis of case-control studies did not find Val158Met to be
Clement Zai I. Introduction
14
associated with SCZ (Munafo et al, 2005). Comt-deficient mice exhibit intact PPI and similar
open-field activity levels to wildtype mice (Gogos et al, 1998). However, a meta-analysis of
family studies found the Val allele to be significantly associated with SCZ (Glatt et al, 2003).
Val158Met has been associated with performance on the Wisconsin Card Sort Test and the N-
back Task, both cognitive examinations being dependent on dorsolateral prefrontal cortical
activity (Weinberger et al, 1986; Berman et al, 1995; Callicott et al, 2000). More specifically, the
high-activity Val allele carriers performed significantly worse than Met allele carriers (Joober et
al, 2002; Egan et al, 2001). SCZ patients scored significantly worse in the Wisconsin Card Sort
Test than healthy controls (Egan et al, 2001). Overall, the association of COMT Val158Met with
SCZ and performance on cognitive tests, together with cognitive deficit reported in SCZ patients,
suggest that COMT is associated with cognitive deficit in SCZ. Further studies into other SCZ-
related phenotypes in rodents and humans are warranted.
• The Proline dehydrogenase PRODH Gene
Similar to COMT, Proline dehydrogenase (PRODH) is also located at 22q11. PRODH
may regulate glutamate release (Renick et al, 1999). PRODH is widely expressed in the brain. It
is localized within the mitochondria where it converts proline to D-1-pyrroline-5-carboxylate,
which is a precursor for glutamate and GABA. Liu et al (2002) detected an initial association
between PRODH and schizophrenia in three independent samples. While later genetic studies
yielded mixed results and the meta-analysis did not find a significant association (Li et al,
2006b), mice expressing a truncated form of PRODH exhibit significant deficit in PPI (Gogos et
al, 1999). Revisiting PRODH with additional polymorphisms is warranted.
• The γ-aminobutyric acid β2 subunit GABRB2 Gene
Clement Zai I. Introduction
15
The GABRB2 gene, which codes for the γ-aminobutyric acid A receptor β2 subunit, is
localized on chromosomal region 5q33.2, a SCZ susceptibility region (Schwab et al, 1997; Camp
et al, 2001; Gurling et al, 2001; Straub et al, 2002a; DeLisi et al, 2002; Paunio et al, 2001; Devlin
et al, 2002; Sklar et al, 2004; Lewis et al, 2003). It has been a target of SCZ genetic studies
because of the GABA hypothesis of SCZ. The GABA hypothesis of SCZ stemmed from
pathological data. GABA neuron density was decreased in SCZ (Reynolds et al, 2001; Cotter et
al, 2002). Picrotoxin, a GABAA receptor antagonist, disrupted PPI in rats (Japha et al, 1999).
GAD67, an enzyme that synthesizes GABA, is decreased (Akbarian et al, 1995), while GAT1, a
GABA transporter, is increased (Sundman-Eriksson et al, 2002) in SCZ patients. An earlier
study reported no significantly different mRNA levels of GABAA receptor subunits between SCZ
patients and controls using in-situ hybridization (Akbarion et al, 1995). Recently, mice with
genetic ablation of Gabra3 were generated. These mice exhibited over 50% reduction in PPI,
suggesting that GABRA3 may be a candidate gene for SCZ and other related disorders (Yee et al,
2005). GABA may also influence the development of SCZ through its interaction with the
dopamine system. Early reports of the possible interaction between the GABA and dopamine
systems came from rodent studies. Pycock and Horton (1979) found that dopamine-induced
hyperactivity in rats was suppressed by GABA uptake inhibition or GABA agonists. Dopamine
axons are colocalized with GABA neurons in the prefrontal cortex (Benes et al, 1993), where
GABA may inhibit the dopamine release (Dewey et al, 1992). Dopamine agonists, on the other
hand, have been shown to inhibit pallidal GABA release (Floran et al, 1997).
Genetically, Lo et al (2004) reported an initial association of six intronic polymorphisms
in GABRB2 with SCZ. The findings were partially replicated in some (Petryshen et al, 2005; Liu
et al, 2005a; Yu et al, 2006; Lo et al, 2007; Zhao et al, 2007), but not other studies (Ambrosio et
Clement Zai I. Introduction
16
al, 2005; Ikeda et al, 2005; Jamra et al, 2007). The most consistent findings appeared to be
centred on two adjacent polymorphisms (rs1816071 and rs1816072) in intron 8 (Liu et al, 2005a;
Yu et al, 2006; Lo et al, 2007). These two polymorphisms were associated with changes in
GABRB2 mRNA levels in post-mortem brain tissues, where overall GABRB2 mRNA levels
were reduced in SCZ patients (Zhao et al, 2006). Animal behavioural studies will help uncover
the involvement of its gene product in SCZ development.
Clement Zai I. Introduction
17
Table 1. Some Schizophrenia candidate genes and their association with schizophrenia according
to chromosomal location, genetic association, biology/animal models, expression alterations, and
meta-analysis results. The number of ”+” indicates the strength of association. “ND” not
determined (modified from Ross et al, 2006).
Gene Locus Linkage Association Biology Expression Meta-analysis
AKT1 14q22-32 + ++ ++ ++ ND COMT 22q11 ++++ ++ +++ + + DAOA(G72) 13q32-34 +++ ++++ +++ ND +++ DISC1 1q42 ++++ ++ ++++ + + DRD2 11q23 ++ +++++ ++++ ++++ +++ DRD3 3q13.3 ++ ++ +++ +++ ++ DTNBP1 6p22 ++++ +++++ ++ ++ +++ GABRB2 5q34 ++++ ++++ ++ ++ + HTR2A 13q14-21 +++ ++++ +++ ++ +++ NR2B 12p12 + ++++ ++ ND +++ NRG1 8p12-21 ++++ +++++ +++ + +++ PRODH 22q11 ++++ ++ ++ +++ + RGS4 22q11 ++++ ++ ++ ++ +
Clement Zai I. Introduction
18
Clement Zai I. Introduction
19
Figure 1. Some SCZ candidate genes and their possible interactions in the central nervous
system. Dysbindin (DTNBP1), part of the dystrophin protein complex, is involved in neuronal
survival via AKT1, as well as glutamate and dopamine release (Numakawa et al, 2004).
Neuregulin (NRG1), via its receptor ErbB4, increases the expression of glutmatergic NMDA and
GABAA receptor subunits (Ozaki et al, 1997; Okada and Corfas, 2004). The dopamine D1
receptor is involved in the expression of Brain-derived Neurotrophic Factor (BDNF), which in
turn through its receptor tyrosine kinase TrkB, is required for the expression of the dopamine D3
receptor (DRD3) (Guillin et al, 2001). AKT1 increases the expression of GABAA receptors and
phosphorylates GABAA β2 subunit (GABRB2) (Wang et al, 2003). Proline dehydrogenase
(PRODH) regulates glutamate release (Renick et al, 1999). Regulator of G-protein Signaling 4
(RGS4) regulates dopamine (Taymans et al, 2003) and glutamate receptor (Aghajanian and
Marek, 1997) signaling. The serotonin 2A receptor (HTR2A) inhibits the release of dopamine
(Fink and Gothert, 2007). G72 (D-amino acid oxidase activator) upregulates glutamatergic
NMDA receptor activity by increasing the synthesis of D-serine (Panatier et al, 2006).
Clement Zai I. Introduction
20
1.1.3 Suicidal Behaviour in Schizophrenia
An estimated 12% of SCZ deaths are due to suicide (Brown, 1997). It is the single largest
cause of excess mortality in SCZ compared to the general population (Sartorius et al, 1986).
Suicide attempts in general tend to occur more often within families (Johnson et al, 1998; Brent
et al, 2002). Twin studies showed greater concordance among monozygotic twins than dizygotic
twins (Roy and Segal, 2001; Statham et al, 1998). A recent review of 32 twin studies estimated
the heritability of suicidal behaviour to be 30-55% (Voracek and Loibl, 2007). The serotonergic
system has been examined for genetic associations with suicidal behaviour, but a meta-analysis
of HTR2A did not reveal a major effect of genetic variation on suicidal behaviour in SCZ patients
(Li et al, 2006c). The Tryptophan hydroxylase genes TPH1 and TPH2 have been associated with
suicide (Paik et al, 2000; Zhang et al, 2007), but the results were not replicated by other
investigators (Viana et al, 2006; De Luca et al, 2005a, 2006a; Mann et al, 2008). Variation in the
serotonin transporter gene (SLC6A4) intron 2 VNTR is associated with suicide (DeLuca et al,
2006b; Correa et al, 2004; Bayle et al, 2003) in a majority of studies (except Chong et al, 2000a),
with the short low-activity allele being associated with history of suicide attempt. More
comprehensive genotyping efforts, together with molecular studies may help clarify the role of
serotonin system genes in suicidal behaviour. Only a few genes in other neurotransmitter
systems have been explored for association with suicidal behaviour. Persson and coworkers
(1997) tested the gene coding tyrosine hydroxylase, the rate-limiting enzyme in the synthesis of
monoamines dopamine and noradrenaline, and did not find a tetranucleotide repeat to be
associated with suicide attempts in Swedish mixed psychiatric patients. The gene coding for the
dopamine-metabolizing COMT has also been investigated in suicidal behaviour, with some
reports finding the low-activity Met allele to be associated (Nolan et al, 2000; Ono et al, 2004),
and others not finding an association (Russ et al, 2000; Liou et al, 2001; De Luca et al, 2005b).
Clement Zai I. Introduction
21
The –141C deletion allele in the promoter region of the DRD2 gene was found to be associated
with suicdality in a sample of alcoholics (Johann et al, 2005). The exon 3 variable number
tandem repeat polymorphism within the DRD4 gene coding for the D4 DA receptor was not
found to be associated with suicidal behaviour (Persson et al, 1999; Zalsman et al, 2004). Baca-
Garcia et al (2004) did not find an association between the GABRA3 gene coding for the GABAA
receptor α3 subunit, while Hong CJ et al (2003) did not find an association between the brain-
derived neurotrophic factor gene BDNF and suicidal behaviour. More detailed examinations of
genes in the dopamine and GABA systems in addition to those in the serotonin system are
required.
1.1.4 Pharmacogenetics of SCZ
There is currently no cure for schizophrenia. It is treated with antipsychotic medications.
Efficacy and side effects remain major concerns. Antipsychotics (APs) used to treat SCZ
symptoms are originally categorized into two groups, typical and atypical APs that differ in their
ability to induce catalepsy in rodents. Some clinicans define typical APs, including
chlorpromazine, haloperidol, and perphenazine, as having more specific dopamine D2 DA
receptor antagonism. They define atypical APs, including olanzapine, risperidone, and
quetiapine, as having a different pharmacological profile from typical APs (Geddes et al, 2000;
Meltzer, 1989). Yet others differentiate between them by defining typical APs as being effective
in treating positive symptoms of SCZ but with a high propensity for developing unfavourable
motor side effects such as tardive dyskinesia (TD), and atypical APs as being effective in treating
both positive and negative symptoms of SCZ but with a higher chance of developing metabolic
adverse effects such as weight gain (Meltzer, 2004; Nasrallah, 2008). Recently, the Clinical
Clement Zai I. Introduction
22
Antipsychotic Trials of Intervention Effectiveness (CATIE) study was carried out to determine
and compare the efficacy and side effects of several atypical (second-generation) antipsychotics
(olanzapine, quetiapine, risperidone, ziprasidone) and the typical (or first-generation)
antipsychotic perphenazine (Lieberman et al, 2005). Results from the 18-month long CATIE
trial found that the rate of discontinuation (mostly due to inefficacy of treatment) was similar
among the APs, with the exception of olanzapine being most efficacious. On the other hand, the
rate of discontinuation due to weight gain is highest in the olanzapine group, while the rate of
discontinuation due to extrapyramidal side effects was highest in the perphenazine group.
Overall, the rate of discontinuation due to any intolerable adverse effects was not significantly
different among all antipsychotic groups (Lieberman et al, 2005). This landmark multi-centre
study encourages the development of predictive tests for clinical response and side effects of
APs. Meta-analyses have indicated the role of HTR2A T102C and H452Y in clozapine response
(Arranz et al, 1998a), and HTR2C in clozapine-induced weight gain (De Luca et al, 2007). The
most studied area in pharmacogenetics of SCZ is Tardive Dyskinesia (TD). Because both typical
and atypical antipsychotics have similar efficacy in relieving schizophrenia symptoms
(Lieberman et al, 2005), predicting which patients are vulnerable to TD is a high priority for
psychiatrists in treatment selection.
Clement Zai I. Introduction
23
1.2 TARDIVE DYSKINESIA (TD)
1.2.1 Diagnostic Criteria and Epidemiology
Tardive Dyskinesia (TD) was first mentioned by Faurbye (1964), with “tardive”
emphasizing delayed onset of the impairment of voluntary movements, with the DSM-IV
diagnosis requiring generally at least three months of antipsychotic exposure. It is a potentially
irreversible movement disorder caused by long-term antipsychotic exposure, characterized by
involuntary athetoid (slow), choreiform (fast), and/or rhythmic (stereotypic) movements affecting
mostly orofacial muscles, with more severe cases involving the trunk and limbs. Obvious
manifestations of TD include tongue protrusions, grimaces, lip smacking, puckering, and pursing.
Prevalence data is difficult to interpret due to heterogeneous study populations and
different TD assessment methodologies. Kane and Smith (1982) reviewed 56 studies from 1959
to 1979 and found TD occurrence to range from 0.5 to 65%. Yassa and Jeste (1992) reported a
prevalence of 24% in 39187 patients pooled from 76 studies. Using the Schooler and Kane
(1982) criteria for TD occurrence, Woerner et al (1991) reported that 23.4% of neuroleptic-
treated mixed population had TD. Muscettola et al (1993) reported a prevalence of 19.1% in
1651 patients with various psychiatric disorders using the Schooler and Kane criteria.
Despite the widely variable results, there appears to be a common trend toward higher
prevalence of TD in elderly subjects. Age has been the most consistently reported risk factor for
TD, beginning with a report by Smith and Baldessarini (1980) of a linear correlation between age
and TD severity and prevalence. Fenton (2000) reviewed 14 studies and found a positive
correlation between TD occurrence and age in schizophrenia patients, from 12% in patients at or
below age 30 years, to 42% in patients at least 60 years of age. Two prospective studies reported
cumulative incidence of TD over 3 to 6 years of antipsychotic exposure (Jeste et al, 1995; Kane
et al, 1995). Kane et al (1995) found the incidence increasing from 5% after 1 year to 26% after
Clement Zai I. Introduction
24
6 years in 850 adults with a mean age of 29 years. Jeste et al (1995), on the other hand, reported
the incidence to be 26% after 1 year and to increase to 60% after 3 years in a cohort of 266
patients with a higher mean age of 65 years.
There have been mixed results in the studies of relationship between sex and TD. Some
studies report higher risk in females (Smith and Dunn, 1979; Musettatola et al, 1993); others
report higher risk in males (van Os et al, 1999) or no difference in risk between the sexes
(Caligiuri et al, 1997). Yassa and Jeste (1992) conducted a review of 75 studies on sex
differences in TD. Using six studies with data grouped according to age, they found no
difference in risk between the two sexes in lower age groups but an increased risk in females over
50 years of age compared to males in the same age group (Yassa and Jeste, 1992). This age-
dependent risk may affect results in other studies that do not control for both sex and age, such as
a meta-analysis reporting a higher TD incidence in female subjects (Smith and Dunn, 1979).
Chong et al (2002) assessed TD in 537 East Asian SCZ inpatients and found the TD rate
to be 29-40%, which is comparable to that in Caucasian SCZ patients. African Americans appear
to be more likely to develop TD after adjusting for dose and duration of antipsychotic treatment
(Morgenstern and Glazer, 1993; Glazer et al, 1993). Swartz et al (1997) reviewed literature on
ethnicity and TD and concluded that a mixture of genetic and environmental factors including
diet, alcohol, tobacco, substance use, as well as types and dosages of medication contribute to TD
development.
Since TD is a drug-induced movement disorder, the dose and choice of medication may
affect risk for developing TD. The dose of antipsychotics affects the risk of TD at the lower dose
range, as the target neurotransmitter receptor may be saturated at the lower doses (Baldessarini,
1988). Conventional neuroleptics like haloperidol and chlorpromazine were shown to be more
likely to induce TD than atypical drugs, however clozapine appears to be unique in having
Clement Zai I. Introduction
25
extremely low risk for TD. Newer antipsychotics like risperidone, olanzapine, aripiprazole, and
quetiapine have been reported to be associated with TD as well (Buzan, 1996; Silberbauer, 1998;
Ananth and Kenan, 1999; Herran and Vazquez-Barquero, 1999; Ghelber and Belmaker, 1999;
Karama and Lal, 2004; Gharabawi et al, 2006; Maytal et al, 2006; Bhanji and Margolese, 2004;
Bressan et al, 2004; Rizos et al, 2007). However, the quality of most studies was hindered by
mixed medical/medication history, short study duration, poly-pharmacy, and changes in
medication over time (Tarsy and Baldessarini, 2006).
Several reviews and meta-analyses have been conducted after the year 2000, with most of
them pointing to similar overall tolerability between conventional and atypical antipsychotics
(Geddes et al, 2000; Wahlbeck et al, 2001). Conventional neuroleptics have less favourable
motor side effect profile than atypical antipsychotics only at high doses (Geddes et al, 2000). At
lower doses or in combination with the prophylactic benztropine, conventional neuroleptics have
similar extrapyramidal side effect profiles to atypical antipsychotics (Geddes et al, 2000;
Rosenheck et al, 2003). Recently, the Clinical Antipsychotic Trials of Intervention Effectiveness
(CATIE) study was carried out to determine and compare the efficacy and side effects of several
atypical antipsychotics (olanzapine, quetiapine, risperidone, ziprasidone) and the typical
antipsychotic perphenazine (Lieberman et al, 2005). Results from the 18-month long CATIE
trial found that while the rate of discontinuation due to extrapyramidal side effects was highest in
the perphenazine group, the rate of discontinuation due to any intolerable adverse effect was not
significantly different between perphenazine and the atypical antipsychotics (Lieberman et al,
2005).
Tobacco, alcohol, and substance use have long been associated with TD. Smokers
appeared to be more likely to develop TD (Yassa et al, 1987; Binder et al, 1987), as are
alcoholics (van Os et al, 1997; Dixon et al, 1992; Olivera et al, 1990). One study attributed the
Clement Zai I. Introduction
26
three-fold increased TD risk in alcohol abusers to neurotoxic effects or intermittent neuroleptic
exposure due to poor compliance (Duke et al, 1994). The motor side effects of TD interfere with
normal voluntary movements, and causes discrimination by others, thus these side effects greatly
reduce treatment compliance and worsen outcome. Predicting which patients will likely develop
TD remains an important issue in treatment prescription.
1.2.2 Candidate Pathways and Genes for TD
Genetics is a prominent factor in determining the risk of TD, as suggested by increased
incidence of TD in families reported by several family studies (Yassa and Ananth, 1981; Youssef
et al, 1989; Müller et al, 2001). Genes that influence the pharmacokinetics, pharmacodynamics,
and oxidative stress associated with antipsychotics (APs) have been considered for TD risk (see
Table 2).
Pharmacokinetic factors influence the level of APs at site of action; that is, their
neurotransmitter targets. They include absorption from site of administration, distribution, and
metabolism. CYP2D6 and CYP1A2 genes have been the primary focus for pharmacokinetics
studies of AP treatment. The results related to TD have been promising. CYP2D6 is a non-
inducible enzyme that metabolizes around 30% of commonly prescribed drugs, including many
typical and atypical antipsychotics (Shimada et al, 1994). It has received much attention in TD
genetics studies because it is highly polymorphic (Patsopoulos et al, 2005). A recent meta-
analysis of CYP2D6 studies observed an odds ratio of 1.64 for poor metabolizing genotypes with
TD (Patsopoulos et al, 2005). Basile and coworkers (2000) found an association between the
CYP1A2 CC734 genotype and the AIMS score (p=0.008), with partial replication by an
independent group (Tiwari et al, 2005a). Other CYP genes also deserve attention in TD genetic
studies. Tiwari and coworkers (2005b) found a trend for association with CYP3A4, which
Clement Zai I. Introduction
27
eliminates approximately half of commonly prescribed APs. CYP17 converts
Dehydroepiandrosterone (DHEA) to pregnenolone, both of which have neuroprotective effects
(Waters et al, 1997; Maurice et al, 2000), and pregnenolone increases dopamine release from rat
nucleus accumbens (Barrot et al, 1999). Segman and coworkers (2002) did not find an
association between CYP17 and TD, unless it was combined with the DRD3 Gly9 allele. de Leon
(2005) found that the low-expression alleles of CYP3A5 were over-represented in TD compared
to non-TD, and that the gene coding for Multidrug resistance (MDR1), a drug transporter across
membranes, was not associated with TD.
With regards to pharmacodynamics, dopamine system genes have been popular
candidates since all APs target the dopamine system to some extent. Although the non-human
primate TD model showed irreversible decreases in dopamine turnover in the caudate and
substantia nigra (Shannak and Hornykiewicz, 1980), independent researchers have not found TD-
associated alterations in D1 or D2 DA receptor levels in the vacuous chewing movement model
(Knable et al, 1994) and in post-mortem human tissues (Crow et al, 1982). More recently, brain-
imaging studies have provided stronger evidence supporting the involvement of D2 occupancy in
antipsychotic-induced motor side effects (Kapur et al, 2000; Nordstrom et al, 1993). From PET
scans of SCZ patients using [11C]-raclopride as the substrate, Kapur et al (2000) demonstrated
that high D2 occupancy (>80%) by haloperidol was associated with extrapyramidal side effects.
More long-term, follow-up studies with additional subjects are required to determine the effect of
dopamine receptor occupancy in TD emergence and severity.
The DRD2 gene has received much attention in light of the observation that D2 DA
receptor is the target of all antipsychotics. Chen and coworkers (1997a) found an initial
association of Taq1A with TD. The association could not be replicated in a number of later
studies (Hori et al, 2001a; Chong et al, 2003a; Segman et al, 2003) until recently, when several
Clement Zai I. Introduction
28
laboratories found haplotypes containing Taq1A to be associated with TD (Srivastava et al, 2006;
Liou et al, 2006a). Other DRD2 polymorphisms have been investigated, but no significant
association has been reported. In a number of studies, the sample sizes might have limited the
statistical power to detect an association between DRD2 and TD (Lattuada et al, 2005; Inada et
al, 1997; Segman et al, 2003), especially if the polymorphisms that were used have low minor
allele frequencies (Ser311Cys, for example), and the quantitative AIMS scores were not used in
the analyses. More thorough examination of DRD2 gene using both qualitative and quantitative
analyses are required.
Badri et al (1996) was the first to describe an association between the D3 receptor gene
(DRD3) Ser9Gly polymorphism and TD (Basile et al., 1999). The findings were corroborated by
Steen et al (1997) and two subsequent meta-analyses (Lerer et al., 2002; Bakker et al., 2006).
Odds ratios for the Gly allele of 1.3 and 1.1 suggest a consistent but minor role in TD. Only
Srivastava and coworkers tested polymorphisms other than Ser9Gly in TD, and they found no
significant association with DRD3 in their North Indian sample (Srivastava et al, 2006).
The exon 3 48 base-pair variable number tandem repeat polymorphism in the D4-coding DRD4
gene was found to be associated with TD in one report (Lattuada et al, 2005), but the finding was
not replicated in two other studies (Srivastava et al, 2006; Segman et al, 2003). Other dopamine
system genes, namely COMT (Srivastava et al, 2006; Lai et al, 2005; Matsumoto et al, 2004a;
Herken et al, 2003), monoamine oxidase MAO (Matsumoto et al, 2004a), and dopamine
transporter SLC6A3 (Srivastava et al, 2006; Segman et al, 2003; Inada et al, 1997) genes, have
not been associated with TD. However, their role in TD cannot be dismissed without thorough
examination with additional polymorphisms spanning each gene.
The serotonergic system is also a target of many APs and serotonin may inhibit dopamine
neurotransmission (Kapur and Remington, 1996). Therefore, genes in this system have also been
Clement Zai I. Introduction
29
studied in TD. An association was initially reported for HTR2A C102 and TD (Segman et al.,
2001; Tan et al., 2001). Although two research groups were unable to replicate the findings
(Basile et al., 2001; Deshpande et al, 2005), a meta-analysis later supported this finding,
especially in older patients (Lerer et al., 2005). The S23 allele in the X-linked 2C receptor gene
(HTR2C) was found to be associated with TD (Segman et al, 2000), but the finding was not
replicated by Deshpande et al (2005). Zhang et al (2002a) found an association with another
polymorphism, G-697C. Other serotonergic system genes, serotonin 6 receptor (HTR6) (Segman
et al, 2003; Ohmori et al, 2002), serotonin transporter (SLC6A4) (Segman et al, 2003; Chong et
al, 2000; Herken et la, 2003), and tryptophan hydroxylase (TPH1) (Segman et al, 2003), were not
found to be associated with TD.
Oxidative stress has also been implicated in the pathophysiology of TD. The brain is
vulnerable to oxidative stress for several reasons. It uses a great deal of energy. It has large
amounts of polyunsaturated fatty acids that are substrates for lipid peroxidation cascades.
Further, certain brain regions, the basal ganglia in particular, contain large amounts of transition
metals, some of which are involved in the formation of hydroxyl radicals via superoxide
dismutase (SOD). The basal ganglia is also rich in dopamine, a neurotransmitter that can auto-
oxidize to produce dopamine quinones free radicals, or be metabolized by monoamine oxidase
(MAO), with hydrogen peroxide as a byproduct. Acute haloperidol also increased murine brain
oxidized-to-reduced glutathione ratio; the increase could be blocked by the inhibition of
monoamine oxidase enzyme by deprenyl (Cohen et al, 1989). Brain superoxide dismutase and
catalase levels were significantly reduced accompanied by increased membrane lipid oxidation
products after long-term haloperidol administration in rats (Pillai et al, 2007). Thus, haloperidol
increases oxidative stress at least partly through increased dopamine metabolism. Sagara (1998)
also found increased levels of oxidized glutathione in neuronal cultures, but found that oxidative
Clement Zai I. Introduction
30
stress originated from the mitochondria and not dopamine metabolism. Vitamin E and
melatonin, both with antioxidant activity, have both been tested in TD. A meta-analysis of the
effect of Vitamin E on TD found 28.3% of Vitamin-E treated patients to have decreased AIMS
scores by at least one third compared to 4.6% of placebo-treated patients (Barak et al, 1998). A
double-blind placebo controlled trial found melatonin to decrease average AIMS scores by 2.45
compared to a reduction of 0.77 in the placebo group (Shamir et al, 2001).
Genetic studies have suggested that the manganese superoxide dismutase MnSOD Ala9
allele may be protective against TD (Hori et al., 2000; Galecki et al, 2006), though Zhang and
coworkers (2002b) could not replicate the finding. The nitric-oxide synthase NOS1 gene was not
associated with TD (Shinkai et al., 2002; Wang et al, 2004). The association found between TD
and the NADPH quinone oxidoreductase gene NQO1 T750 (Pae et al. 2004) was not replicated by
Hori and coworkers (2006), though Liou et al (2005) found a trend for higher average AIMS
scores in T750 carriers than in C750 carriers, which is in agreement with the Pae et al paper. The
NOS3 (Liou et al, 2006b) and glutathione S-transferase GSTM1 (Lattuada et al, 2005) findings
require replication, while the GPX1 (Shinkai et al, 2006), GSTT1 (de Leon et al, 2005), GSTP1
(Shinkai et al, 2005), and APOE (Halford et al, 2006) genes need to be examined more
thoroughly.
Although there have been several positive results, conclusions regarding the effects of
these candidate genes (DRD3, DRD2, HTR2A, CYP2D6, CYP1A2) cannot be drawn yet. It is
necessary for these findings to be replicated. In addition a thorough investigation across these
genes needs to be performed (see Table 2).
Clement Zai I. Introduction
31
Table 2. Tardive Dyskinesia candidate genes and their association with tardive dyskinesia.
Polymorphisms investigated, number of studies with positive (+) and negative (-) findings, and
meta-analysis (meta) results. ? Insufficient information.
Gene Polymorphisms (+) (-) Meta Comments
CYP1A2 rs762551 (C163A), rs2470890 (C1545T), *1F, rs2472304, rs3743484, rs762551 (C734A), rs2069514 (G-2964A)
3 4 ND Boke, 2007; Tiwari, 2007; Schulze, 2001; Chong, 2003b; Matsumoto, 2004b; sig in smokers (Basile, 2000)
CYP2D6 *1-*15, *17-*19, *25, *26, *31, *36, *41, *45, duplication
7 9 (+) Patsopooulos, 2005: LOF risk
CYP3A4 rs2740574 (*1B) 0 1 ND Tiwari, 2005 CYP3A5 *3, *6 1 0 ND De Leon, 2005 CYP17 rs743572 (promoter-T/C) 0 1 ND Segman, 2002 MDR1 rs1045642? (C3435T), G2677AT 0 1 ND De Leon, 2005 DRD1 rs686, rs4532 (DdeI), rs13306309
(T229A), A-48G, rs5330 (S50R), rs5331 (A199S)
0 2 ND Srivastava, 2006
DRD2 rs1800497 (TaqIA), rs1799732 (-141C Ins/Del), rs1799978 (A-241G), rs1079597 (TaqIB), rs1800498 (TaqID), rs6275 (NcoI), rs1801028 (S311C)
4 8 ND Chen, 1997a; Inada, 1997; Hori, 2001; Kaiser, 2002; Chong, 2003a; Lattuada, 2005; de Leon, 2005; Srivastava, 2006; Liou, 2006a
DRD3 rs6280 (Ser9Gly), rs324036, rs1503670, rs905568
8 12 (+) Lerer, 2002; Bakker, 2006: Gly9 risk
DRD4 rs1800955 (C-521T), exon3-VNTR, promoter-120bp-repeat
2 2 ND Segman, 2003; Lattuada, 2005; Srivastava, 2006; Lee, 2007
DAT1 3’-VNTR, rs27072 (G2319A) 0 4 ND Inada, 1997; Segman, 2003; Srivastava, 2006
COMT rs4680 (Val158Met), rs11544669? (L112L), rs3838146? (3’UTR-C Ins/Del)
1 3 ND Lai, 2005; Matsumoto, 2004a; Herken, 2003; Srivastava, 2006
MAOA Promoter-30bp-repeat 0 1 ND Matsumoto, 2004a MAOB rs1799836 (intron13A/G) 0 1 ND Matsumoto, 2004a HTR2A rs6313 (T102C), rs6314 (H452Y), rs6311
(A-1438G) 4 3 (+) Lerer, 2005: C102 risk
HTR2C rs6318 (C23S) 2 1 ND Zhang, 2002a; Segman, 2000 HTR6 rs1805054 (C267T) 0 2 ND Segman, 2003; Ohmori, 2002 5HTT LPR 0 3 ND Segman, 2003; Chong, 2000b; Herken,
2003 TPH1 rs1800532 (A218C) 0 1 ND Segman, 2003 MnSOD rs4880 (A9V, A16V) 1 4 ND Zhang, 2002b; Hori, 2000 NOS1 rs2682826? 0 2 ND Wang, 2004; Shinkai, 2002 NOS3 Intron4-27bp-repeat, rs1799983 (D298E),
rs2070744…? (T-786C) 1 0 ND Liou, 2006b
NQO1 rs1800566 (C750T, P187S, “C609T”) 1 2 ND Pae, 2004a; Liou, 2005; Hori, 2006 GPX1 rs1050450 (P197L, P200L) 0 1 ND Shinkai, 2006 GSTM1 Gene deletion 1 1 ND Pae, 2004b; de Leon, 2005 GSTT1 Gene deletion 0 1 ND De Leon, 2005 GSTP1 rs1695 (I105V) 0 1 ND Shinkai, 2005
Clement Zai I. Introduction
32
1.3 DOPAMINE
1.3.1 Dopamine Signalling
Dopamine is a catecholamine neurotransmitter that is synthesized from the amino acid
tyrosine. Tyrosine hydroxylase (TH) is the rate-limiting enzyme that converts tyrosine into L-
dihydroxyphenylalanine (L-DOPA), which then gets decarboxylated to dopamine. Dopamine can
be converted to norepinephrine (NE) by dopamine β-hydroxylase, or it can be metabolized by
COMT or MAO to homovanillic acid (HVA). It can also be taken up presynaptically by
dopamine transporter (DAT1). The brain is thought to have four dopaminergic projections:
mesolimbic, mesocortical, nigrostriatal, and tuberoinfundibular pathways (Lindvall and
Björklund, 1978). The tuberoinfundibular pathway originates from the arcuate nucleus in the
hypothalamus and terminates in the anterior pituitary where dopamine regulates the secretion of
prolactin (Porter et al, 1990). The mesolimbic, and mesocortical pathways originate from the
ventral tegmental area. The mesolimbic pathway projects to limbic areas including ventral
striatum, amgdala, and hippocampus. The mesocortical pathway projects to cortical areas,
including orbitofrontal, medial prefrontal, cingulated, dorsolateral prefrontal, temporal, and
parietal cortices. The mesolimbic and mesocortical tracts are involved in emotions including
reward, motivation, and attention (Majovski et al, 1981). The nigrostriatal tract projects from the
substantia nigra to the dorsal striatum, where it integrates cognitive signals, coordinates
sensorimotor information, and initiates movement (Majovski et al, 1981).
Five dopamine receptors have been cloned. They can be grouped into two categories with
regards to the type of G-proteins they interact with: D1-like receptors (D1 and D5) that stimulate
adenylate cyclase via the activation of Gαs and Gαolf, and D2-like DA receptors (D2, D3, and D4)
that inhibit adenylate cyclase via the activation of Gαi/o (Neve et al, 2004). The dopamine
Clement Zai I. Introduction
33
receptors differ in anatomical localization in the brain (Meador-Woodruff et al, 1996). D1 is most
widely expressed, with distribution in the cortex and striatum (Missale et al, 1998). The D2 DA
receptor is expressed mainly in the striatum, though weak expression had been detected in the
cortical regions (Missale et al, 1998). D3 is more restricted to the islands of Calleja and ventral
striatum (Suzuki et al, 1998), particularly in the nucleus accumbens. D4 is expressed in the
prefrontal cortex and hippocampus, but not in the striatum (Lahti et al, 1998). D5 is expressed
mainly in the hippocampus and the entorhinal cortex (Missale et al, 1998). Thus, from an
anatomical location perspective, the genes coding for the D1, D2, and D3 dopamine receptors are
most attractive as candidates for TD studies.
1.3.2 The Dopamine D2 Receptor Gene
The DRD2 gene was identified from DNA fragments that are homologous to the β2-
adrenergic receptor and later isolated from rat brain cDNA library (Bunzow et al, 1988). The
human DRD2 gene was mapped to chromosome 11q23. It spans approximately 65kb with eight
exons. Structurally, the D2 DA receptor protein is a seven-transmembrane receptor with a large
third cytoplasmic loop and a short carboxyl terminus, structural features that are characteristic of
Gi/o-coupled receptors. The downstream effectors of D2 DA receptor signalling include adenylate
cyclase inhibition, phosphoinositol hydrolysis and calcium mobilization, and potassium ion
influx. Mice homozygous for Drd2 gene deletion displayed Parkinson’s disease-like phenotypes
(Baik et al, 1995). Type 5 adenylate cyclase (AC5) and DARPP32 (dopamine and cAMP-
regulated phosphoprotein) appear to be critical for D2 DA receptor signalling. Cataleptic doses of
antipsychotics haloperidol and sulpiride did not suppress motor activity in mice lacking Ac5 as
they did in wildtype mice (Lee et al, 2002). Similarly, the D2 DA receptor antagonist raclopride
Clement Zai I. Introduction
34
was cataleptic in wildtype mice, but not in mice lacking Darpp32 (Fienberg et al, 1998).
Although Ac5-deficient and Darpp32-deficient mice did not display obvious phenotypes, their
decreased motor response to antipsychotics and dopamine antagonists resembled that of Drd2-
lacking mice (Kelly et al, 1998). The D2 DA receptor has two known isoforms, with the short
isoform lacking 29 amino acids in its third intracellular loop. The short isoform is predominantly
expressed presynaptically, while the long isoform mediates most of the postsynaptic effects of D2
DA receptor (Usiello et al, 2000).
As mentioned earlier, experiments on rodents have shown increased D2 DA receptor
levels due to various neonatal stressors, including hippocampal lesions, Caesarian sections,
amphetamine, and phencyclidine administrations (Seeman et al, 2005). In human SCZ patients,
Wong et al (1986) found elevated caudate nucleus D2 DA receptor levels regardless of whether
the patients were on antipsychotic medication or not. Farde et al (1990) found no such
difference in drug-naive SCZ patients. The mixed results could be due to the age of the subjects
and the use of different D2 DA receptor ligands. More recent studies using PET and SPECT have
revealed elevated striatal D2 DA receptor density in SCZ patients not undergoing antipsychotic
treatments, with a recent meta-analysis showing an overall 12% increase (Guillin and Laruelle,
2007).
Genetically, DRD2 has been examined relatively extensively in SCZ and other
neuropsychiatric conditions. The Del allele at position –141 was associated with lower promoter
activity in vitro (Arinami et al, 1997). Two subsequent studies reported either an increase
(Jonsson et al, 1999b) or no difference (Ritchie and Noble, 2003) in D2 DA receptor binding of
the Del allele compared to the Ins allele. The mixed findings could be due to different ethnicities
being studied. The TaqIA polymorphism approximately 9.5kb downstream of DRD2 was
originally thought to be in the DRD2 gene; it has now been shown to be in the ANKK1 (ankyrin
Clement Zai I. Introduction
35
repeat and kinase domain containing 1) gene (Neville et al, 2004). However, The TaqIA A1
allele has been associated with reduced D2 DA receptor levels in several studies (Jonsson et al,
1999b; Noble et al, 1991; Pohjalainen et al, 1998; Ritchie and Noble, 2003; Thompson et al,
1997). Laruelle and co-workers (1998) did not find a significant association between TaqIA and
D2 DA receptor expression level, a finding that could be influenced by population stratification as
subjects with four different ethnic backgrounds were included. The C957T polymorphism does
not affect the amino acid sequence of the D2 DA receptor protein (Pro319Pro), but its T allele has
been associated with decreased D2 DA receptor binding in vivo (Hirvonen et al, 2004), possibly
through its effect on mRNA stability (Duan et al, 2003).
TaqIA has been associated with alcoholism in a recent meta-analysis (Munafo et al,
2007), and SCZ (Golimbet et al, 1998; Parsons et al, 2007). Many studies have also focussed on
the S311C, TaqIA, and –141C Ins/Del polymorphisms in SCZ. The non-synonymous S311C
polymorphism was found to be associated with SCZ (Arinami et al, 1994; Shaikh et al, 1994), or
of various subtypes of SCZ (Arinami et al, 1996; Serretti et al, 1998). Although several studies
did not find a significant association (Crawford et al, 1996; Verga et al, 1997; Tanaka et al,
1996a; Hori et al, 2001b), a recent meta-analysis of 27 samples comprising of 3707 SCZ patients
and 5363 controls found the Cys allele to confer a 40% increased risk of SCZ compared to the
Ser allele (Glatt and Jonsson, 2006). The C957T was associated with SCZ in a number of recent
studies, with the C allele to appearing over-represented in the SCZ groups compared to controls
(Lawford et al, 2005; Kukreti et al, 2006; Hanninen et al, 2006; Hoenicka et al, 2006). Positive
findings have also been reported for -141C Ins/Del (Arinami et al, 1997; Breen et al, 1999; Inada
et al, 1999; Jonsson et al, 1999a). The Del allele at position –141 is underrepresented in SCZ
patient group compared to the control group (Arinami et al, 1997). Breen et al (1999) found the
opposite allele (Ins) to be underrepresented in their Caucasian sample. Several other studies,
Clement Zai I. Introduction
36
however, could not replicate the DRD2 –141C Ins/Del findings in SCZ (Stober et al, 1998;
Tallerico et al, 1999; Hori et al, 2001b; Glatt et al, 2004). These results suggest a trend toward the
3’ region of DRD2, encompassing the TaqIA, S311C, and C957T polymorphisms, to be
associated with SCZ. A thorough examination of the 3’ region of DRD2 is required to better
understand this association.
AKT1 was recently shown to be regulated specifically by D2 DA receptor in vivo
(Beaulieu et al, 2007). AKT1, also known as protein kinase B, is an intracellular signalling
Serine/Threonine kinase with multiple substrates. It has been historically linked to growth factor
receptors, and it is involved in cell-cycle progression and survival (reviewed in Nicholson et al,
2002). For example, AKT1 phosphorylates GSK3β, a target of the bipolar disorder medication
lithium, at Ser9 and inactivates it. Its increased expression has been documented in multiple
forms of cancers, but its role in neurotransmitter signalling has only recently been uncovered.
Mice deficient in D2 DA receptor had significantly increased activating phosphorylation of AKT1
at Thr308 compared to their wildtype littermates (Beaulieu et al, 2007). Its interaction with the
D2 DA receptor prompted investigations of the AKT1 gene, localized at 14q22-32, in SCZ.
Emamian et al (2004) found AKT1 levels reduced in SCZ patients compared to controls in
transformed lymphocytes and two independent post-mortem brain collections. They also found
an AKT1 haplotype, which they associated with decreased AKT1 levels, to be preferentially
transmitted to SCZ probands. The antipsychotic haloperidol increased the activating
phosphorylation of AKT1 at Ser473 and Thr308. The association was replicated in a number of
studies (Bajestan et al, 2006; Ikeda et al, 2004), but not others (Ide et al, 2006; Ohtsuki et al,
2006; Turunen et al, 2007).
Clement Zai I. Introduction
37
1.3.3 The Dopamine D3 Receptor Gene
The D3 DA receptor-coding DRD3 gene has seven exons and is mapped to the
chromosomal region 3q13.3. The Ser9Gly polymorphism was studied previously because of an
initial report of its association with TD (Badri et al, 1996), and an association of the glycine
variant with increased D3 DA receptor affinity for dopamine as revealed in cell culture
(Lundstrom and Turpin, 1996). Also, the glycine version showed increased activation of
mitogen-activated protein kinase (MAPK) and inhibition of cAMP synthesis (Jeanneteau et al,
2006). The glycine variant has further been demonstrated in vitro to result in a shift in D3
signaling from inhibition of adenylate cyclase to inhibition of prostaglandin production
(Hellstrand et al, 2004). Numerous association studies on DRD3 in SCZ have also been published
with mixed results (Jonsson et al, 2004). The major limitation of previous DRD3 genetic studies,
especially in TD, has been the use of only one polymorphism, Ser9Gly within the amino-terminal
extracellular loop. Until recently, promoter polymorphisms have been largely ignored.
Brain derived neurotrophic factor (BDNF) was found to be required for the expression of
D3 in the ventral striatum in vivo (Guillin et al, 2001). 6-OHDA lesion of dopamine neurons
projecting to the nucleus accumbens downregulated D3 DA receptor, and not D1 or D2,
expression in that area (Guillin et al, 2001). Bdnf gene deletion mimicked the effect of the lesion
on D3 DA receptor expression. Local BDNF infusion restored D3 DA receptor expression.
BDNF is expressed in the hippocampus and cerebral cortex (Metsis et al, 1993). BDNF proteins
are packaged in neuronal vesicles, and are released upon membrane depolarization of the neuron
(Mowla et al, 1999). They bind receptor tyrosine kinase TrkB (Urfer et al, 1995), which
transduces signals downstream through MAPK (Jovanovic et al, 2000; Suzuki et al, 2004),
altering postsynaptic response to neurotransmitters (Levine et al, 1998). BDNF also has trophic
properties. Dopamine neurons, in particular, require BDNF for establishment in the nigrostriatal
Clement Zai I. Introduction
38
system (Baquet et al, 2005) and stimulation of dopamine release (Altar et al, 1998). Decreased
BDNF expression has been reported in patients with Parkinson’s Disease and Alzheimer’s
Disease (Howells et al, 2000; Holsinger et al, 2000).
The BDNF gene, located on 11p13, contains a 3’ coding exon and a number of
alternatively spliced upstream exons (Liu et al, 2005b). The use of alternative upstream exons
determines the tissue-specific expression of BDNF (Metsis et al, 1993). A number of putative
functional polymorphisms have been reported. Magnetic resonance imaging scans showed that
the Met66 allele carriers had significantly lower average hippocampal volume compared to
Val66/Val66 (Pezawas et al, 2004). The C270T polymorphism was shown to affect BDNF
mRNA stability (Tongiorgi et al, 2006). The promoter C-281A polymorphism was shown to
decrease in vitro DNA-binding activity and basal reporter gene activity in cultured neurons (Jiang
et al, 2005). Meta-analyses supported a role of the Val66Met polymorphism in Eating disorders
and substance use disorders (Gratacos et al, 2007), but not in Parkinson’s Disease (Zintzaras and
Hadjigeorgiou, 2005). Genetic studies of BDNF in depression have yielded mixed results
(Strauss et al, 2004; 2005; Ribeiro et al, 2007). While BDNF Val66Met and C270T
polymorphisms do not appear to confer a strong risk for SCZ (negative meta-analyses: Xu et al,
2007; Zintzaras et al, 2007; Kanazawa et al, 2007; positive meta-analysis: Gratacos et al, 2007),
only the Val66Met polymorphism has been tested in one genetic study of TD (Liou et al, 2004)
where they did not find a significant association.
Clement Zai I. Introduction
39
1.4 GABA
1.4.1 GABA signalling
GABA is the major inhibitory neurotransmitter in the central nervous system. It is
synthesized from glutamate by the enzyme Glutamic Acid Decarboxylase (GAD67). GABA
signals through its GABAA ionotropic and GABAB metabotropic receptors. Inhibiton by GABA
is mediated through its GABAA receptors in postsynaptic neurons, resulting in the opening of Cl-
channels. The influx of the negative Cl- ions shunts excitatory currents, decreasing the
probability of action potential initiation. GABAergic interneurons regulate firing rate from
pyramidal neurons that mediate cortical signals for information processing in learning, memory,
and perception (Mountcastle, 1997; McBain and Fisahn, 2001; Freund, 2003; Buzsáki, 2001;
Möhler, 2006; Rao et al, 2000; Benes and Berretta, 2001; Sawaguchi and Iba, 2001). Long-term
changes in GABAergic signalling have been associated with seizures, sedation, or coma (Benes
and Berretta, 2001; Costa et al, 2001; Hensch and Stryker, 2004; Fagiolini et al, 2004).
1.4.2 The GABAA Receptor γ2 Subunit Gene
The GABRG2 gene, mapped on chromosome 5q31.1-33.1, has nine exons spanning 85kb.
The long isoform has an additional eight amino acids in exon 9 that contains an additional
regulatory phosphorylation site by Protein Kinase C (Krishek et al, 1994), and may mediate the
effects of ethanol (Cheng et al, 1997). In a study with very small sample size, Huntsman and
coworkers (1998) found in 5 SCZ patients reduced levels of the short isoform of GABRG2 that
lacks a PKC regulatory phosphorylation site, in comparison to those in matched healthy controls.
An NciI polymorphism of GABRG2 has been associated with prefrontal activity in a study of
event-related potentials (Winterer et al., 2000). The NciI polymorphism is situated near the
Clement Zai I. Introduction
40
alternatively spliced exon 9 (Cheng et al, 1997). GABRG2 has been associated with febrile
seizures (Wallace et al, 2001), a condition that is linked to a 44% increased risk of SCZ
(Vestergaard et al, 2005).
Clement Zai I. Introduction
41
1.5 METHODOLOGIES – GENETIC ASSOCIATION STUDIES:
1.5.1 Case-control association tests
For single marker allelic association tests with dichotomous variables on matched case-
control samples, such as presence or absence of SCZ, suicide attempts, or TD, contingency tables
for observed allelic frequency distribution are tabulated. From the row and column sums, tables
of expected allelic frequency distributions are derived. Pearson χ2 is then calculated as the sum
of the differences between the observed and expected values in proportion to the expected values.
Genotype association tests are done similarly. In cases where a contingency table has at least one
cell count of less than five, Fisher’s Exact Tests are performed. For analysis of continuous
variables such as AIMS and suicide behaviour, analysis of variance (ANOVA) is performed to
compare these variables among genotypes of each marker. In cases where the ANOVA
assumption of equal variances among comparison groups is violated using the Levene’s Test,
Kruskal-Wallis tests are performed with ranked variables.
1.5.2 Transmission Disequilibrium Tests and Family-based association tests (FBAT)
For single marker allelic association tests with dichotomous variables on small nuclear
family samples, Transmission Disequilibrium Tests (TDTs) can be performed (Spielman et al,
1993) using the Haploview program (Barrett et al, 2005). TDT is a modified χ2 test that
measures the degree of biased transmission of one allele over another from the parents to the
affected offspring. It is calculated as [(transmitted-untransmitted)2/(transmitted+untransmitted)].
Familial data can also be analyzed with the FBAT program (Laird et al, 2000), which takes into
consideration a wider range of family structures in addition to triads (parents and a proband;
including diads, sibling pairs, and more distant relatives as well). FBAT outputs Z-scores, and
effectively increases the sample size and power to detect associations. FBAT can also analyze
Clement Zai I. Introduction
42
continuous variables. Family studies are more robust against population stratification because
family members share a common genetic background (Evangelou et al, 2006). However, larger
samples need to be recruited to derive the same amount of information as case-control studies.
Results from case-control and family samples can be combined in cases where the same
trend for over-representation and over-transmission of an allele is observed respectively. In one
method, the case-control data in the contingency table is first converted to proportions to
calculate the corresponding Z-score. Then the Z-score from the case-control sample (z1) is
combined with that from FBAT (z2) using the formula: [(z1+z2)/√2] (Hedges and Olkin, 1985).
For haplotype analyses with case-control samples, the UNPHASED software is used for
both dichotomous and continuous variables using the COCA-PHASE and QT-PHASE programs
respectively in a sliding-window approach (Dudbridge et al, 2003). To prevent the influence of
spurious fluctuations from rare observations, haplotypes with frequencies of less than 5% are
omitted from analysis of individual haplotypes within a two-marker window as well as global test
of the two-marker window. For family samples, HBAT command from the FBAT software
allows testing haplotype windows across genes with both individual haplotype tests and global
tests for each two-marker window.
Power calculations for case-control samples are performed with Primer software, and
power of the family sample is computed using Genetic Power Calculator (Purcell et al, 2003).
For example, a sample size of 200 cases and 200 controls can detect an odds ratio as low as 1.86
with 80% power.
Clement Zai I. Introduction
43
1.6 RATIONALE
From epidemiological and genetic studies, it is clear that the etiopathophysiology of SCZ
is complex. More specifically, the symptoms of SCZ can vary widely among patients. Findings
from genetic association and linkage studies have been mixed. One possible reason for the mixed
findings could be that many genetic association studies of SCZ and TD only examined a few
polymorphisms within the candidate genes and lacked a comprehensive analysis that include
polymorphisms that may affect the promoter activity or splicing efficiency. Another possible
reason could be that genes do not act in isolation but in pathways. It is possible for variations in
genes within signalling pathways to interact in influencing the risk of SCZ. We examined the
dopamine hypothesis and the GABA hypothesis of SCZ using human genetic association design.
Investigating SCZ patients with shared characteristics may reduce the heterogeneity of the
sample, so we examined two SCZ-associated phenotypes, suicidal behaviour and the
antipsychotic-induced motor side effect TD.
(1) The GABAA γ2 subunit gene GABRG2 has been investigated in SCZ in the past, mainly with
negative results, however most of the previous studies did not use both family and case-
control samples, and none has investigated suicidal behaviour in SCZ patients. We
hypothesized that variations in the GABRG2 gene coding for the GABAA receptor γ2 subunit
are involved in SCZ per se and/or suicidal behaviour in SCZ patients.
(2) The Dopamine D3 receptor gene DRD3 has been investigated in relation to SCZ and TD, both
with mixed results, however most of the previous studies only tested the functional Ser9Gly
polymorphism. We hypothesized that other genetic variations in DRD3 in addition to
Ser9Gly are associated with TD and SCZ. We also hypothesized that variations in the BDNF
Clement Zai I. Introduction
44
gene coding for Brain Derived Neurotrophic Factor are involved in TD and SCZ partly
because BDNF controls the expression of D3 (Guillin et al, 2001).
(3) The Dopamine D2 receptor gene DRD2 has been investigated in relation to TD, with mixed
results. Many of the previous studies investigated only a few polymorphisms in DRD2, and
in small samples. We hypothesized that genetic variations spanning DRD2, other than the
markers previously studied, are associated with TD. We also reviewed previous studies
between DRD2 and TD by performing a meta-analysis of the two most studied and putative
functional DRD2 polymorphisms. We also hypothesized that polymorphisms spanning the
AKT1 gene that codes for Protein Kinase B are associated with TD because AKT1 acts
downstream of D2.
Zai et al II. GABRG2 in Schizophrenia
45
CHAPTER 2
THE GAMMA-AMINOBUTYRIC ACID TYPE A RECEPTOR γγγγ2 SUBUNIT GENE IS
ASSOCIATED WITH SCHIZOPHRENIA AND SUICIDALITY
Clement C. Zai(1,2), Nicole King(1), Vincenzo De Luca(1,3), Greg W. H. Wong(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 GABRG2 gene polymorphisms, corresponded with the clinical collaborators to refine the
details of the phenotype, performed all the statistical analyses, and wrote the manuscript.
Key words: Schizophrenia, GABRG2, genetics, suicidal behaviour, haplotype analysis
Zai et al II. GABRG2 in Schizophrenia
46
2.1 ABSTRACT
Schizophrenia (SCZ) is a severe neuropsychiatric disorder with a strong genetic basis.
Because gamma-aminobutyric acid (GABA) is produced in areas of the brain implicated in SCZ,
and it has been shown to interact with the dopamine system, GABA may be an important factor
in the susceptibility to SCZ development. GABA acts mostly through its ionotropic GABAA
receptor, and several genes coding for GABAA subunits, including GABRG2 encoding the γ2
subunit, are clustered at 5q31-q35, a chromosomal region associated with SCZ in genome scan
studies. We tested five polymorphisms spanning GABRG2 for association with SCZ and also
suicidal behaviour. The sample consisted of 109 small nuclear families and 229 SCZ cases
paired with 229 healthy controls. rs183294 in the 5’ region of GABRG2 was found associated
with SCZ in both samples with the C allele over-represented in SCZ cases and over-transmitted
in SCZ families (combined p=3�10-3). Preliminary data also showed rs209356 to be associated
with suicidal behaviour in SCZ patients (p=0.04). Taken together, the results of the present
study suggest GABRG2 may be involved in SCZ susceptibility, but further studies are required.
Zai et al II. GABRG2 in Schizophrenia
47
2.2 INTRODUCTION
Schizophrenia (SCZ) is a severe debilitating neuropsychiatric disorder characterized by a
variety of symptoms ranging from positive symptoms such as paranoia and auditory
hallucinations to negative symptoms including thought poverty, anhedonia, and social
withdrawal, as well as cognitive impairment. It affects approximately 1% of the general
population. Findings from family, twin, and adoption studies support a genetic basis for this
disorder (reviewed in McGuffin, 2004), but its etiology is still unclear. Hypothesis driven
candidate gene studies have examined on the dopaminergic (COMT, DRD2, DRD3) and the
serotonergic (HTR2A) systems among others. Chromosomal breakpoints or deletions point to
the DISC1 gene and 22q11. Genome scans of SCZ families have found chromosomal regions
that showed evidence of linkage and these regions are being examined for specific genes. These
genes raise possibilities of dysfunction of other pathways, including intracellular mechanisms
(DTNBP1, NRG1), myelination (OLIG2), and the glutamatergic system (GRM3, G72).
However, in addition to multiple positive findings, there were also negative results for each of
these genes, suggesting that the mechanism of SCZ etiology is complex and may have variable
overlap among different populations (reviewed in Harrison and Weinberger, 2005).
A growing body of evidence suggests that alterations in γ-aminobutyric acid (GABA)
neurotransmission may underlie the mechanism of pathophysiology in a subset of SCZ cases
(Benes et al., 1992; Delini-Stula and Berdah-Tordjman, 1996; Huntsman et al., 1998; Dean et al.,
1999; Wassef et al., 1999; Guidotti et al., 2005). GABA is the major inhibitory neurotransmitter
in the brain. It is produced mainly by interneurons in the hippocampus, limbic system, and
cerebral cortex. The locations of its synthesis and receptors, as well as neurobiologic studies,
suggest that it regulates other neurotransmitter pathways including dopamine, serotonin, and
Zai et al II. GABRG2 in Schizophrenia
48
norepinephrine, all of which have been implicated in SCZ (Jones and Hendry, 1986; Fiber and
Etgen, 1997; Carlsson et al, 1999; Matsumoto et al, 2003; Clements and Schreck, 2004). About
25% to 50% of neurons use GABA (Young and Chu, 1990), and GABA exerts its effects partly
via the ionotropic GABAA receptors. The GABAA receptor is a heteropentameric chloride
channel with 20 mammalian subunits identified thus far (Barnard et al., 1998).
Pharmacological studies have shed light on the role of GABA in regulating the
excitability of various neuronal circuits, including those affecting memory, learning, anxiety, and
cognition (Olsen and Avoli, 1997; Fritschy et al., 1999; Pratt, 1992; Sarter et al., 1988; Izquierdo
and Medina, 1991). Benzodiazepines are frequently prescribed in psychiatry, especially in more
severe and chronic patients (Veronese et al, 2007). Its use in SCZ in combination with
neuroleptics appears beneficial in temporarily increasing the responsiveness to therapy,
especially in neuroleptic-resistant patients (Volz et al, 2007). Benzodiazepines work partly by
binding to GABAA receptors allosterically (Smith and Olsen, 1995). In SCZ patients, the density
of these receptors and the benzodiazepine-binding sites on GABAA receptors appear to be
increased (Benes et al., 1996, 1997; Dean et al., 1999). GABAA receptor density was reportedly
increased in the dorsolateral prefrontal cortex of SCZ patients (Benes et al., 1996), while a
reduction or no change in the number of benzodiazepine-binding sites on GABAA receptors has
also been reported (Squires et al., 1993; Pandey et al., 1997). The variable results could be
explained by differences in methodologies and different brain regions examined. GABAA
subunit genes are clustered in the genome, with α1, α6, β2, and γ2 gathered around 5q32-q35
(Johnson et al., 1992; Wilcox et al., 1992; Hicks et al., 1994), a chromosomal region associated
with schizophrenia in a number of genome scan studies (Sklar et al., 2004; Lewis et al., 2003).
The α, β, and γ subunits make up most GABAA receptors (Wafford et al., 1993a, b; Pritchett et
Zai et al II. GABRG2 in Schizophrenia
49
al., 1989; Pritchett and Seeburg, 1990; von Blankenfeld et al., 1990; Angelotti and MacDonald,
1993; Benke et al., 1996). The α subunits determine the selectivity of benzodiazepines, and the
β2 subunit is responsible for high-affinity benzodiazepine binding. The γ2 subunit, in particular,
regulates the binding of benzodiazepines to GABAA receptors (Knoflach et al., 1991; Wafford et
al., 1991). The second intracellular loop of the γ2 subunit has been shown to interact directly
with the carboxyl terminus of the dopamine D5 receptor, thus providing a direct link between the
two neurotransmission systems (Liu et al., 2000).
A major contributor to mortality in SCZ is suicide, which accounts for about 10% of
deaths in these patients (Meltzer, 2002). Cheetham et al. (1988) found an increased number of
benzodiazepine binding sites in the frontal cortex of suicide victims. The results were consistent
with the Pandey et al. (1997) study where an increased number of benzodiazepine binding sites
were found in the prefrontal cortex of suicide victims. However, Pandey et al. (1997) did not
find significantly different benzodiazepine sites between suicide and non-suicide post-mortem
brain tissue in schizophrenia sample. The results with the SCZ patient samples may have been
confounded by neuroleptic treatment because SCZ patients on neuroleptics had lower
benzodiazepine binding than neuroleptic-free SCZ patients (Pandey et al, 1997).
The γ2 subunit is of particular interest in SCZ. Huntsman and coworkers found in 5 SCZ
patients reduced levels of the short isoform of GABRG2 in comparison to those in matched
healthy controls (Huntsman et al., 1998). An NciI polymorphism of GABRG2 has been
associated with prefrontal activity in a study of event-related potentials (Winterer et al., 2000).
GABRG2 has been associated with febrile seizures (Wallace et al, 2001), a condition that is
linked to a 44% increased risk of SCZ (Vestergaard et al, 2005). Several recent studies reported
mixed findings for GABRG2 and SCZ using case-control samples and small nuclear families
Zai et al II. GABRG2 in Schizophrenia
50
(Turunen et al., 2003; Ambrosio et al., 2004; Petryshen et al., 2005; Ikeda et al., 2005;
Nishiyama et al., 2005). Turunen and coworkers (2003) first reported an association of GABRG2
with SCZ. It was followed with a number of negative findings. The small number of
polymorphisms examined (Ambrosio et al, 2005), as well as the use of only family or case-
control samples alone and not a combination of both (Ambrosio et al, 2005; Nishiyama et al,
2005; Ikeda et al, 2005) could have contributed to the negative findings. In addition, none of the
previous studies has studied the role of GABRG2 in suicidal behaviours. In the present study, we
tested for the association of five polymorphisms as well as their haplotypes in GABRG2 with
SCZ using independent paired case-control and small nuclear family samples, including
examination of suicidal behaviour.
Zai et al II. GABRG2 in Schizophrenia
51
2.3 PATIENTS AND METHODS
2.3.1 Subjects
Two samples were analyzed in the present study, 229 SCZ unrelated patients paired with
229 healthy controls matched for sex, ethnicity, and where possible, age, as well as 109 SCZ
probands with available first-degree relatives. The small nuclear families consist of 31 triads, 10
triads plus a sibling where 3 of the siblings are affected, 49 diads, 8 families of a proband with a
single parent and a sibling where all siblings are unaffected, as well as 11 families with an
affected proband plus a sibling where 2 of the siblings are affected. The Structured Clinical
Interview for DSM-IV (SCID-I; First et al., 1997) was used as the primary diagnostic tool. A
clinical summary of detailed information about sequence, context, and severity of symptoms was
prepared for each patient (Maxwell, 1992). A best estimate diagnostic consensus was reached
after the clinical summary and SCID-I response were reviewed and discussed among research
psychiatrists. Patients who satisfied the DSM-IV diagnostic criteria for SCZ or schizoaffective
disorder were included (APA, 1994), while patients with history of major neurological disorders,
major substance abuse, and head injury with significant loss of consciousness were excluded
from the study. Out of SCZ probands, 378 had data on suicide along a scale as follows: 0 being
absent, 1 as having thoughts of one’s own death, 2 as having suicidal ideation, 3 as having
planned suicide(s), 4 as having attempted suicide(s), and 5 as having attempted violent
suicide(s).
2.3.2 DNA isolation and polymorphism genotyping
Blood specimens of probands and their family members were obtained by venipuncture
into two 10mL EDTA tubes. Genomic DNA was purified from blood lymphocytes as described
Zai et al II. GABRG2 in Schizophrenia
52
previously (Lahiri and Nurnberger, 1991). Five polymorphisms along the GABRG2 genes, the
NciI site (rs211013), rs12520992, rs766349, rs209356, and rs183294, were genotyped using
TaqMan allele-specific assays on the ABI Prism® 7000 Sequence Detection System with the
Allelic Discrimination program within the ABI software (Applied Biosystems, Foster City, CA).
2.3.3 Statistical Analyses
Adherence to Hardy-Weinberg equilibrium was determined using the chi-square test in
Haploview version 3.2 (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 v10.0.7, 2000). Linkage disequilibrium across
GABRG2 was determined using Haploview. 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. P-values from
family and case-control samples were combined using the formula [(z1+z2)/√2] (Hedges and
Olkin, 1985).
Zai et al II. GABRG2 in Schizophrenia
53
2.4 RESULTS
2.4.1 Sample Characteristics
Both the paired case-control and family samples did not differ significantly from Hardy-
Weinberg equilibrium (p>0.1).
2.4.2 The 5’ region of GABRG2 may be associated with SCZ
Upon testing for allele and genotype frequency distribution differences in our matched
SCZ case-control sample, allele 2 (C) and genotype 22 (CC) of rs183294 are over-represented in
SCZ patients compared to controls (p=0.01; ORC=1.42 [CI: 1.08-1.86]; p=0.04; ORCC=1.57 [CI:
1.06-2.31]; Table 3). From FBAT analysis on our sample of small nuclear families, we observed
a trend for over-transmission of rs183294 allele 2 (C) (p=0.09) and genotype 22 (CC) (p=0.05)
(Table 4). The significance level increased by combining the family and case-control samples
(z=3.0; p=3�10-3) (Hedges and Olkin, 1985). rs183294 and rs209356 were in strong linkage
disequilibrium (Figure 2); therefore, we conducted association analyses using two-marker
haplotypes across GABRG2. Two-marker haplotypes containing rs183294 and rs209356 were
significantly associated with SCZ in the case-control sample (p=8�10-3). More specifically, the
2-2 (C-A) haplotype was present more often in SCZ patients than expected (p=9�10-3; ORC-
A=1.62 [CI: 1.12-2.34]). The same haplotypes were not significant in the nuclear family sample
(p=0.33). However, combining the family and case-control samples yielded more significant
results (z=9.18; p<1�10-3).
2.4.3 The 5’ region of GABRG2 may be associated with Suicidal behaviour in SCZ patients
To test for an association of suicidal behaviour in SCZ patients, we compared the allele
Zai et al II. GABRG2 in Schizophrenia
54
and genotype frequency distributions of SCZ patients who had had at least one suicide attempt
with those who had not (Table 5). For rs209356, allele G (p=0.05) and genotype G/G were over-
represented in the suicide group with genotype frequency distribution reaching statistical
significance (among the three genotype groups: p=0.04; GG versus A-carriers: p=0.01). In line
with the qualitative analyses, comparison of the quantitative suicide behaviour scale among the
three genotypes for rs209356 yielded a trend in the suicide behaviour score in patients with the
G/G genotype to be higher than patients with other genotypes (ANOVA p=0.095; t-test [GG
versus A-carriers] p=0.06).
Zai et al II. GABRG2 in Schizophrenia
55
2.5 DISCUSSION
The current study reports a possible association between the GABRG2 gene and SCZ.
The results from the case-control sample were corroborated by the independent family sample in
that rs183294 in the 5’ end of GABRG2 is associated with SCZ. The positive results are different
from two recent studies of the GABAA receptor gene cluster at chromosomal region 5q31-q35 on
Japanese and Portuguese samples (Ikeda et al., 2005; Nishiyama et al., 2005; Petryshen et al.,
2005), and the associated 5’ region of GABRG2 is different from the 3’ region found associated
in a Finnish family study sample (Turunen et al., 2003). The mixed results could be due to
different sets of polymorphisms being tested (Ikeda et al., 2005). They could also be due to
different selection and analysis procedures. Our cases were more stringently selected, each
being paired with a control subject matched for age, sex, and ethnicity. As for our independent
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
majority of our subjects are Caucasians, ethnic differences in SCZ susceptibility and linkage
disequilibrium block structure could have influenced the results. If only Caucasians were
included in the analyses, the results remained significant for rs183294 in the SCZ case-control
(p=8x10-3) and suicidal behaviour (p=0.02) data, but those from the family sample became less
significant, possibly due to low power of the reduced sample size. It is especially true if the
increased risk of SCZ conferred by rs183294 is small. It is important to note that the p-values
reported in the current study were not corrected for multiple testing. Nonetheless, the results for
rs183294 would remain significant after Bonferroni correction for the five tested markers.
This is the first report of a possible association between GABRG2 and suicidal behaviour in SCZ.
A quantitative mRNA analysis showed several GABAA subunits to be reduced in suicide post-
Zai et al II. GABRG2 in Schizophrenia
56
mortem brains compared to non-suicide brains (Merali et al., 2004). Although the authors did
not find a significant difference in the γ2 subunit between suicide and non-suicide brains, the γ2
subunit may influence the susceptibility of SCZ and suicidal behaviour through its regulatory
activity on other GABAA subunits. Examining the other GABAA receptor subunit genes at 5q31-
q35 may help to resolve the mixed findings in SCZ. The present findings encourage further
investigation of GABRG2 SCZ susceptibility and suicidal behaviour.
Zai et al II. GABRG2 in Schizophrenia
57
Table 3. Genetic analysis of GABRG2 markers and schizophrenia using paired case-control samples.
Polymorphism Assay
Genotypes SCZ NC p-value* (Genotype)
Allele SCZ NC p-value (Allele)
rs183294 (C3167701) 1=T; 2=C
1/1 (T/T) 1/2 (T/C) 2/2 (C/C)
25 104 93
39 113 70
0.04 1 2
154 290
191 253
0.01
rs209356 (C3167710) 1=G; 2=A
1/1 (G/G) 1/2 (G/A) 2/2 (A/A)
52 109 66
51 105 71
0.88 1 2
213 241
207 247
0.24
rs766349 (C985685) 1=T; 2=C
1/1 (T/T) 1/2 (T/C) 2/2 (C/C)
165 48 5
172 44 2
0.46# 1 2
378 58
388 48
0.30
rs12520992 (C3169571) 1=C; 2=A
1/1 (C/C) 1/2 (C/A) 2/2 (A/A)
5 41
177
2 52
169
0.26# 1 2
51 395
56 390
0.61
NciI, rs211013 (C3169568) 1=A, 2=G
1/1 (A/A) 1/2 (A/G) 2/2 (G/G)
54 105 55
49 104 51
0.83 1 2
213 215
222 206
0.54
Haplotype (10,000 permutations)
SCZ NC Haplotype-specific p
Global p
rs183294-rs209356
C-A Not C-A
84 358
56 386
9�10-3
8�10-3
rs209356-rs766349
0.70
rs766349-rs12520992
0.52
rs12520992-rs211013
0.74
*2-tailed Fisher’s Exact Tests #Haplotype analysis using COCA-PHASE.
Zai et al II. GABRG2 in Schizophrenia
58
Table 4. Family-based association test using FBAT for GABRG2 polymorphisms and haplotypes.
Polymorphism #informative families
S (allele 1/2)
E(S) (allele ½)
Var(S) Z (allele 1) P-value
rs183294 42 35/55 41/49 14.2 -1.71 0.09
rs209356 38 40/40 35/45 13.5 1.44 0.15 rs766349 13 19/7 19/7 3.69 0.13 0.90 rs12520992 17 13/23 15/21 5.6 -0.94 0.35 rs211013 38 45/35 45/35 13.6 -0.09 0.93
Haplotype (<10,000 permutations)
frequency #families Haplotype-specific p
P-value
rs183294-rs209356
C-G T-A C-A
0.39 0.34 0.25
38 40 35
0.11 0.10 0.94
0.33
rs209356-rs766349
0.55
rs766349-rs12520992
0.89
rs12520992-rs211013
0.73
Zai et al II. GABRG2 in Schizophrenia
59
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
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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
manuscript.
Key words: Schizophrenia, DRD3, BNDF, genetics, suicidal behaviour, haplotype analysis
Zai et al III. BDNF & DRD3 in Schizophrenia
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3.1 ABSTRACT
Schizophrenia (SCZ) is a severe neuropsychiatric disorder with prominent genetic
etiologic factors. The dopamine receptor DRD3 gene is a strong candidate in genetic studies of
SCZ because of the dopamine hypothesis of SCZ and the selective expression of D3 in areas of
the limbic system implicated in the disease. We examined ten single-nucleotide polymorphisms
(SNPs) in DRD3 in our sample of 109 small nuclear SCZ families and 229 paired case-controls.
We also examined six BDNF SNPs in our samples because of evidence for BDNF regulation of
DRD3 expression (Guillin et al, 2001). Further, we examined the SNPs of both genes for
association with suicidal behaviors in the SCZ patients. We did not find a significant association
between DRD3 or BDNF polymorphisms with SCZ. However, we found a possible interaction
between BDNF Val66Met and DRD3 Ser9Gly in determining the history of suicide attempt(s).
Specifically, a larger proportion of SCZ patients who were heterozygous for Val66Met and
Ser9Gly have attempted suicides compared to patients with other genotypes (p=0.007). Taken
together, the results from the present study suggest that BDNF and DRD3 may not be involved in
SCZ susceptibility, but further studies are required, especially for the role of BDNF and DRD3 in
suicidal behaviour.
Zai et al III. BDNF & DRD3 in Schizophrenia
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3.2 INTRODUCTION
Schizophrenia (SCZ) is a severe disabling neuropsychiatric disorder characterized by a
collection of symptoms that may include positive symptoms such as paranoia and auditory
hallucinations, negative symptoms including thought poverty, anhedonia, and social withdrawal,
as well as cognitive impairment. It affects approximately 1% of the general population.
Findings from family, twin, and adoption studies support a genetic basis for SCZ (reviewed in
McGuffin, 2004), but its etiology is still unclear. Dopamine hypothesis of SCZ arose from the
observations that dopamine-mimetic agents including amphetamine induces SCZ associated
symptoms, and that all antipsychotic medications have a certain degree of dopamine-blocking
capacity.
D3 is a G-protein coupled receptor (GPCR) that, upon binding of dopamine, transduces
signal via inhibition of cAMP synthesis. It is encoded by the DRD3 gene, which is located on
chromosomal region 3q13.3. It is primarily expressed in the striatum, with the highest
expression in the ventral portion. These brain regions have been implicated in SCZ
pathophysiology. Ser9Gly is the only polymorphism that has been studied for its effects on D3
function. The Gly allele has been associated with increased binding affinity of dopamine
(Lundstrom and Turpin, 1996), as well as increased dopamine-induced stimulation of ERK and
inhibiton of cAMP synthesis (Jeanneteau et al, 2006).
Researchers found decreased DRD3 (Sokoloff et al, 1990) mRNA expression in
postmortem brain samples (Schmauss et al, 1993) and peripheral blood lymphocytes (Vogel et
al, 2004) of SCZ patients compared to those of controls. The decrease could be due to increased
splicing of the 3’ region of the DRD3 pre-mRNA (Schmauss et al, 1996), leading to an increased
ratio of truncated (D3nf) to full-length (D3) mRNA. In contrast, Ilani et al (2001) found decreased
Zai et al III. BDNF & DRD3 in Schizophrenia
64
DRD3 mRNA levels in SCZ patients’ blood lymphocytes compared to controls. The mixed
results could be due to different housekeeping genes used (Ilani et al, 2001; Vogel et al, 2004).
They could also be due to the influence of antipsychotics at time of death. Gurevich et al (1997)
found increased D3 levels in the ventral striatum of antipsychotic-free SCZ patients at time of
death, but similar D3 levels in SCZ patients taking antipsychotics up to time of death. These
results suggest that while D3 is up in SCZ, antipsychotics down-regulate it.
In the original genetic study of DRD3 in SCZ, Crocq et al (1992) found Ser9Gly to be
associated with SCZ using two matched case-control samples from France and the UK. Mant
and coworkers from the same research group updated the positive findings with additional
subjects, with an excess of Ser allele and Ser/Ser genotype in SCZ cases compared to controls
(Mant et al, 1994). Our laboratory replicated the original positive findings using two
independent samples from North America and Italy (Kennedy et al, 1995). The positive findings
were also replicated in other independent samples (Shaikh et al, 1996; Spurlock et al, 1998;
Ishiguro et al, 2000). However, they were challenged by results on many other case-control
samples, where the authors did not find a significant association between Ser9Gly and SCZ
(Yang et al, 1993; Nanko et al, 1993; Saha et al, 1994; Inada et al, 1995; Ohara et al, 1996;
Tanaka et al, 1996b; Chen et al, 1997b; Hawi et al, 1998a; Cordeiro et al, 2001; Nimgaonkar et
al, 1993; Nothen et al, 1993; Di Bella et al, 1994; Laurent et al, 1994; Gaitonde et al, 1996;
Rietschel et al, 1996; Dollfus et al, 1996; Krebs et al, 1998; Joober et al, 2000; Virgos et al,
2001; Lorenzo et al, 2007). Transmission disequilibrium tests (Spielman and Ewens, 1993) were
used in a number of studies with no significant findings (Rothschild et al, 1996; Prasad et al,
1999; Kremer et al, 2000; Ambrosio et al, 2004). To sort through the mixed results, several
reviews and meta-analyses have been conducted. Shaikh et al (1996) found the Ser allele to
Zai et al III. BDNF & DRD3 in Schizophrenia
65
confer increased risk of SCZ in ten previous studies, and Dubertret et al (1998) found the Ser/Ser
genotype to be associated with SCZ in 13 studies on European Caucasians. More recently,
Jonsson et al (2003) conducted a meta-analysis using results from 40 non-overlapping samples
and found homozygosity to marginally increase the risk of SCZ. However, after adding four
additional samples, the results were no longer statistically significant (Jonsson et al, 2004).
Polymorphisms in addition to Ser9Gly have been examined in SCZ genetic studies. For
example, the MspI and PvuII polymorphisms were not linked to SCZ in French families (Sabate
et al, 1994). Ishiguro et al (2000) examined three polymorphisms (G-712C, A-205G, Ala38Thr)
in addition to Ser9Gly in two independent Japanese case-control samples; they found Ser9 and
haplotypes containing G-712C, A-205G, and Ser9Gly to be significantly associated with SCZ.
Staddon and coworkers (2005) analyzed two other polymorphisms (G-205A, G-7685C) in
addition to Ser9Gly, and found –7685C to be over-represented in SCZ in an isolate from
Northern Spain. Baritaki et al (2004) reported marginally significant findings with the A-206/A-
206 genotype in SCZ Greeks.
Despite having accumulated genotype data from over 10,000 SCZ patients across
different studies, with most studies having tested only the Ser9Gly polymorphism within this
40kb gene, the role of DRD3 in SCZ remains elusive. Recently, Talkowski et al (2006)
investigated 11 polymorphisms evenly distributed within and around DRD3 for association with
SCZ in two family samples (US and Indian) and a case-control sample. They found consistent
association between Ser9Gly and SCZ in both family samples, but only against a common
haplotype background (Talkowski et al, 2006b). Dominguez and coworkers (2007) genotyped
17 polymorphisms spanning DRD3 and found haplotypes in the 3’ portion of the gene to be
significantly associated in a Galician isolate population. These latest findings suggest that
Zai et al III. BDNF & DRD3 in Schizophrenia
66
Ser9Gly may be a marker for other polymorphism(s) that confer risk to SCZ and encourage
further more comprehensive examination of DRD3 in SCZ.
The brain-derived neurotrophic factor (BDNF) plays a critical role in dopaminergic
neuronal establishment (Baquet et al, 2005). The number of tyrosine hydroxylase-expressing
dopaminergic neurons was reduced in the midbrain-hindbrain regions where the BDNF gene was
selectively deleted (Baquet et al, 2005). BDNF also specifically regulates the in vivo expression
of DRD3 in the nucleus accumbens both during development and in adulthood (Guillin et al,
2001). Postmortem studies in the brains of SCZ patients showed decreases in BDNF protein
(Iritani et al, 2003; Weickert et al, 2003) and mRNA levels (Weickert et al, 2003), while Buckley
et al (2007) and Palomino et al (2006) recently found decreased plasma BDNF levels in first-
episode patients. However, Takahashi et al (2000) also found increased BDNF protein levels in
the hippocampus and nucleus accumbens in SCZ subjects. SCZ patients who were chronically
on antipsychotic medications showed increased (Gama et al, 2007) or decreased (Grillo et al,
2007; Toyooka et al, 2002) serum BDNF levels compared to healthy controls. Similar BDNF
levels between the chronically treated SCZ and controls were also reported (Shimizu et al, 2003).
The mixed results could be due to a number of factors, including insufficient sample sizes,
different patient populations, different brain regions examined, or different antipsychotics at
different doses. Genetically, our laboratory found modest association between the promoter GT-
dinucleotide repeat polymorphism and SCZ, especially with an excess of maternal transmission
of the 170-bp allele (Muglia et al, 2003). However, other studies in various ethnic groups did not
replicate the positive findings (Hawi et al, 1998; Sasaki et al, 1997; Wassink et al, 1999). A
modest association was found with the T allele of C270T polymorphism in the 5’ noncoding
region (Nanko et al, 2003; Szekeres et al, 2003); the association was not replicated in other
Zai et al III. BDNF & DRD3 in Schizophrenia
67
studies (Galderisi et al, 2005; Szczepankiewicz et al, 2005; Xu et al, 2007). Association studies
have shown Val66 to be overrepresented in SCZ cases (Neves-Pereira et al, 2005) and
overtransmitted to affected members in small nuclear families (Rosa et al, 2006). Other studies
did not show association of Val66Met with SCZ in European Caucasians (Hawi et al, 1998;
Skibinska et al, 2004; Galderisi et al, 2005; de Krom et al, 2005), and East Asians (Chen et al,
2006; Tochigi et al, 2006; Watanabe et al, 2006; Xu et al, 2007; Naoe et al, 2007). Individuals
with at least one Met allele performed more poorly on memory and learning tasks, with abnormal
hippocampal activation (Egan et al, 2003), and decreased volume of the hippocampal formation
(Szeszko et al, 2005). Ho et al (2006) found the Met allele to be associated SCZ-specific
visuospatial impairment and correlated decreases in gray matter volumes in the temporal and
occipital lobes. Five recent meta-analyses on Val66Met did not find a significant association
with SCZ (Naoe et al, 2007; Qian et al, 2007; Xu et al, 2007; Kanazawa et al, 2007; Zintzaras et
al, 2007). The C270T polymorphism has also been analyzed in two meta-analyses, which found
weak (Zintzaras et al, 2007) or no (Xu et al, 2007) association with SCZ. However, the role of
the BDNF gene could not be excluded as Qian et al (2007) found a four-marker haplotype to be
protective against SCZ in their Chinese sample.
A major contributor to mortality in SCZ is suicide, which accounts for approximately
10% of deaths in these patients (Meltzer, 2003). Twin studies support a genetic basis of suicidal
behaviour (Voracek and Loibl, 2007). A meta-analysis of excess mortality rates in SCZ by
Brown (1997) found suicides to be the leading cause of excess deaths in SCZ, accounting for
28% of the excess SCZ deaths. BDNF mRNA (Dwivedi et al, 2003) and protein (Dwivedi et al,
2003; Karege et al, 2005) levels were reduced in the prefrontal cortex and hippocampus of
suicidal victims compared to controls. Plasma BDNF levels were reduced in major depressive
Zai et al III. BDNF & DRD3 in Schizophrenia
68
disorder patients who attempted suicide (Kim et al, 2007). BDNF was not significantly different
between suicidal and non-suicidal SCZ patients in one study (Huang et al, 2006). One genetic
study of Val66Met and suicide in mood disorder patients yielded negative results (Hong et al,
2003). Neither the role of the D3 protein nor the DRD3 gene has been examined in suicidal
behaviour. Although expression studies have pointed to a possible role of BDNF gene in suicidal
behaviours, none has studied the combined role of BDNF and DRD3 genes in suicidal
behaviours in SCZ patients.
In the present study, we tested for the association of ten polymorphisms in DRD3 and six
in BDNF. We analyzed individual polymorphisms and two-marker haplotypes in the sliding
window approach on our paired case-control and small nuclear family SCZ samples. We also
examined single-marker interactions between DRD3 and BDNF in SCZ. We further explored
the effect of DRD3 and BDNF on suicidal behaviour of the SCZ patients.
Zai et al III. BDNF & DRD3 in Schizophrenia
69
3.3 PATIENTS AND METHODS
3.3.1 Subjects
Two samples were analyzed in the present study, 229 SCZ unrelated patients paired with
229 healthy controls matched for sex, ethnicity and where possible, age, as well as 109 SCZ
probands with available first-degree relatives. The small nuclear families consist of 31 triads, 10
triads plus a sibling where 3 of the siblings are affected, 49 diads, 8 families of a proband with a
single parent and a sibling where all siblings are unaffected, as well as 11 families with an
affected proband plus a sibling where 2 of the siblings are affected. The Structured Clinical
Interview for DSM-IV (SCID-I; First et al., 1997) was used as the primary diagnostic tool. A
clinical summary of detailed information about sequence, context, and severity of symptoms was
prepared for each patient (Maxwell, 1992). A best estimate diagnostic consensus was reached
after the clinical summary and SCID-I response were reviewed and discussed among research
psychiatrists. Patients who satisfied the DSM-IV diagnostic criteria for SCZ or schizoaffective
disorder were included (APA, 1994), while patients with history of major neurological disorders,
major substance abuse, and head injury with significant loss of consciousness were excluded
from the study. Out of SCZ probands, 378 had data on suicide along a scale as follows: 0 being
absent, 1 as having thoughts of one’s own death, 2 as having suicidal ideation, 3 as having
planned suicide(s), 4 as having attempted suicide(s), and 5 as having attempted violent
suicide(s).
3.3.2 DNA isolation and polymorphism genotyping
Blood specimens of probands and their family members were obtained by venipuncture
into two 10mL EDTA tubes. Genomic DNA was purified from blood lymphocytes as described
Zai et al III. BDNF & DRD3 in Schizophrenia
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previously (Lahiri and Nurnberger, 1991). 10 polymorphisms along the DRD3 genes, the
rs905568, rs2399504, rs7611535, rs6762200, rs1394016, rs6280 (MscI, Ser9Gly), rs167770,
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
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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
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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
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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
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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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Table 6a. Genetic analysis of DRD3 markers and SCZ using paired case-control samples. Polymorphism Assay
Genotypes SCZ(Y/N) Allele SCZ(Y/N) p-value (Haplotype)
rs905568 1/1 (C/C) 1/2 (C/G) 2/2 (G/G) P
64/71 120/108 39/44 0.52
Allele 1 (C) Allele2 (G) P
248/250 198/196
0.89
0.41
rs2399504 1/1 (G/G) 1/2 (G/A) 2/2 (A/A) P
149/146 71/66 4/12 0.12
Allele 1 (G) Allele2 (A) P
369/358 79/90
0.35
0.68
rs7611535 1/1 (A/A) 1/2 (A/G) 2/2 (G/G) P
11/18 92/90
122/117 0.40
Allele 1 (A) Allele2 (G) P
114/126 336/324
0.37
0.70
rs6762200 1/1 (A/A) 1/2 (A/G) 2/2 (G/G) P
32/33 109/100 78/86 0.67
Allele 1 (A) Allele2 (G) P
173/166 265/272
0.63
0.40
rs1394016 1/1 (C/C) 1/2 (C/T) 2/2 (T/T) P
73/72 104/107 46/44 0.95
Allele 1 (C) Allele2 (T) P
250/251 196/195
0.95
0.80
Ser9Gly rs6280 MscI BanI
1/1(A/A) 1/2(A/G) 2/2(G/G) P
87/94 101/91 35/38 0.63
Allele 1 (A) Allele 2 (G) P
275/279 171/167
0.78
0.25
rs167770 1/1 (C/C) 1/2 (C/T) 2/2 (T/T) P
21/27 97/92
104/103 0.64
Allele 1 (C) Allele2 (T) P
139/146 305/298
0.62
0.49
rs2134655 1/1 (A/A) 1/2 (A/G) 2/2 (G/G) P
13/14 80/88
133/124 0.69
Allele 1 (A) Allele2 (G) P
106/116 346/336
0.44
0.69
rs2087017 1/1 (C/C) 1/2 (C/T) 2/2 (T/T) P
50/43 104/112 69/68 0.66
Allele 1 (C) Allele 2 (T) P
204/198 242/248
0.69
rs1025398 1/1 (A/A) 1/2 (A/G) 2/2 (G/G) P
94/107 91/90 39/27 0.22
Allele 1 (A) Allele2 (G) P
279/304 169/144
0.08
0.31
*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.
Polymorphism Assay
Genotypes SCZ(Y/N) Suicide Specifier Allele SCZ(Y/N)
rs905568 1/1 (C/C) 1/2 (C/G) 2/2 (G/G) P
35/92 64/118 17/48 0.24
1.81+/-1.74 1.81+/-1.87 1.60+/-1.80
0.70
Allele 1 (C) Allele2 (G) P
134/302 98/214
0.84
rs2399504 1/1 (G/G) 1/2 (G/A) 2/2 (A/A) P
77/169 37/77
3/9 0.91*
1.72+/-1.82 1.93+/-1.81 1.75+/-1.77
0.59
Allele 1 (G) Allele2 (A) P
191/415 43/95
0.93
rs7611535 1/1 (A/A) 1/2 (A/G) 2/2 (G/G) P
12/20 45/95
59/141 0.63
2.00+/-1.92 1.94+/-1.81 1.63+/-1.80
0.23
Allele 1 (A) Allele2 (G) P
69/135 163/377
0.34
rs6762200 1/1 (A/A) 1/2 (A/G) 2/2 (G/G) P
21/46 59/117 36/93 0.58
1.82+/-1.83 1.87+/-1.84 1.63+/-1.78
0.53
Allele 1 (A) Allele2 (G) P
101/209 131/303
0.49
rs1394016 1/1 (C/C) 1/2 (C/T) 2/2 (T/T) P
31/79 65/114 21/60 0.16
1.68+/-1.79 2.00+/-1.83 1.51+/-1.79
0.094
Allele 1 (C) Allele2 (T) P
127/272 107/234
0.90
Ser9Gly, rs6280 1/1(A/A) 1/2(A/G) 2/2(G/G) P
37/92 66/117 13/43 0.13
1.69+/-1.77 1.98+/-1.85 1.43+/-1.75
0.10
Allele 1 (A) Allele 2 (G) P
140/301 92/203
0.87
rs167770 1/1 (C/C) 1/2 (C/T) 2/2 (T/T) P
10/33 57/108 49/113
0.34
1.51+/-1.76 1.92+/-1.86 1.73+/-1.78
0.38
Allele 1 (C) Allele2 (T) P
77/174 155/334
0.78
rs2134655 1/1 (A/A) 1/2 (A/G) 2/2 (G/G) P
4/18 44/85
68/155 0.32
1.50+/-1.57 1.96+/-1.82 1.69+/-1.83
0.31
Allele 1 (A) Allele2 (G) P
52/121 180/395
0.76
rs2087017 1/1 (C/C) 1/2 (C/T) 2/2 (T/T) P
21/53 65/123 30/80 0.36
1.64+/-1.80 1.88+/-1.85 1.71+/-1.76
0.54
Allele 1 (C) Allele 2 (T) P
107/229 125/283
0.72
rs1025398 1/1 (A/A) 1/2 (A/G) 2/2 (G/G) P
44/112 58/106 15/36 0.36
1.61+/-1.81 2.01+/-1.85 1.62+/-1.71
0.12
Allele 1 (A) Allele2 (G) P
146/330 88/178
0.50
Promoter C/A rs28383487 C-281A
1/1 (A/A) 1/2 (A/C) 2/2 (C/C) P
0/0 4/5
113/255 0.47*
NA 2.33+/-1.87 1.76+/-1.81
0.35
Allele 1 (A) Allele2 (C) P
4/5 230/515
0.47*
Zai et al III. BDNF & DRD3 in Schizophrenia
83
C270T HinfI
1/1 (A/A) 1/2 (A/G) 2/2 (G/G) P
103/227 13/31
1/1 0.76*
1.79+/-1.80 1.68+/-1.91 2.00+/-2.83
0.92
Allele 1 (A) Allele2 (G) P
219/485 15/33
0.98
BDNF_4 rs7103411
1/1 (A/A) 1/2 (A/G) 2/2 (G/G) P
70/148 42/82 5/18 0.52
1.81+/-1.85 1.85+/-1.81 1.61+/-1.64
0.85
Allele 1 (A) Allele 2 (G) P
182/378 52/118
0.64
BDNF_3 rs2049045
1/1 (C/C) 1/2 (C/G) 2/2 (G/G) P
76/187 36/61
5/9 0.29*
1.70+/-1.81 1.98+/-1.84 2.00+/-1.71
0.38
Allele 1 (C) Allele2 (G) P
188/435 46/79
0.15
BDNF_2 rs11030104
1/1 (C/C) 1/2 (C/T) 2/2 (T/T) P
7/16 41/78
68/159 0.69
1.91+/-1.73 1.88+/-1.83 1.72+/-1.82
0.70
Allele 1 (C) Allele2 (T) P
55/110 177/396
0.55
Val66Met rs6265
1/1(A/A) 1/2(A/G) 2/2(G/G) P
6/15 39/72
71/169 0.56
1.90+/-1.70 1.88+/-1.83 1.72+/-1.81
0.69
Allele 1 (A) Allele 2 (G) P
51/102 181/410
0.52
*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
manuscript.
Keywords: Schizophrenia, tardive dyskinesia, genetics, BDNF, DRD3, Abnormal
Involuntary Movement Scale (AIMS)
Zai et al IV. BDNF & DRD3 in Tardive Dyskinesia
Page 88
4.1 ABSTRACT
Tardive dyskinesia (TD) is a movement adverse effect of long-term antipsychotic
medication. The pathophysiology is unclear, but dopamine neurotransmission system
changes have been suggested to be involved. Thus, a number of studies have focused on
the association of dopamine system gene polymorphisms and TD and most consistent
findings have been focused on the Ser9Gly polymorphism of the DRD3 gene. However,
as there were negative results and only one polymorphism within DRD3 has been tested
thus far, the role of DRD3 in TD is still unclear. Further, Brain derived neurotrophic
factor (BDNF), a neuronal growth and survival factor, regulates DRD3 expression and
may be involved in neuronal degeneration observed in TD. In the present study, we
investigated 10 polymorphisms spanning the DRD3 gene and 6 polymorphisms spanning
the BDNF gene for association with TD in our sample (N=223). The rs905568 was
found to be associated with TD diagnosis (p=0.015) and AIMS scores (p=0.007) in our
European Caucasian sample. Taken together, the present study suggests that DRD3 may
be involved in TD development in Caucasian population, though further studies are
warranted.
Zai et al IV. BDNF & DRD3 in Tardive Dyskinesia
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4.2 INTRODUCTION
Tardive dyskinesia (TD) is a potentially irreversible motor side effect that
develops in about 25% of SCZ patients who are undergoing long-term antipsychotic
treatment (reviewed in Tarsy and Baldessarini, 2006). Patients with this condition
display orofacial movements that are athetoid, choreoform, or rhythmic in nature. In
more severe cases, the movements may involve the trunk and limbs. Due to motor
difficulties, patients with TD often struggle with treatment adherence, discrimination, and
poorer quality of life (Marsalek, 2000; Gerlach, 2002), so predicting which patients are
vulnerable to TD remains a high priority for psychiatrists in treatment selection.
The etiology of TD is complex and remains unclear. Age, gender, and ethnicity
are all suggested risk factors for TD. More specifically, Woerner and coworkers found
patients above the age of 50 were three to five folds more likely to develop TD than
younger patients in other studies (Woerner et al, 1998; Kane et al, 1988; Morgenstern and
Glazer, 1993). A review of 13 studies found females to be almost 70% more likely to
develop TD (Smith and Dunn, 1979), though other studies reported the opposite findings
(Morgenstern et al, 1987; van Os et al, 1997). The difference was attributed to possible
selection bias for more severe cases in the female gender group in earlier studies. Jeste
and coworkers reported the yearly TD incidence of over 45% for African-Americans
compared to 27% in Caucasians (Jeste et al, 2000), but socioeconomic factors could have
contributed to the findings. Smoking, drinking, and using street drugs could further
increase the risk for TD (Menza et al, 1991; Bailey et al, 1997; Olivera et al, 1990). TD
appears to be more common among first-degree relatives, thus providing evidence for a
genetic component of TD (Müller et al, 2001). A number of mechanisms leading to TD
Zai et al IV. BDNF & DRD3 in Tardive Dyskinesia
Page 90
have been hypothesized, with the best known hypothesis postulating TD as being caused
by hypersensitivity of dopamine receptors induced by dopamine receptor blocking
medication including antipsychotics (Tarsy and Baldessarini, 1977; Klawans et al, 1980;
Gerlach and Casey, 1988; Abilio et al, 2003).
The dopamine D3 receptor is a member of the G-protein coupled receptor
superfamily. By coupling to inhibitory G-proteins, it regulates the production of cyclic
AMP in response to dopamine binding. Functionally, D3 is localized to the ventral
striatum and putamen of the basal ganglia, an area of the brain that is involved in
locomotor control (Joyce and Meador-Woodruff, 1997; Suzuki et al, 1998). D3 levels
were increased in response to chronic haloperidol administration in rat brains (Buckland
et al, 1992). The increase was also observed in human postmortem schizophrenia patient
basal ganglia after neuroleptic treatment (D’Souza et al, 1997). R-(+)-7-OH-DPAT, a
D3-selective agonist, inhibited locomotor activity when injected into the nucleus
accumbens of rats (Kling-Petersen et al, 1995), while D3 antagonists increased motor
activity (Kling-Petersen et al, 1995; Fink-Jensen et al, 1998; Gendreau et al, 1997; Van
Hartesveldt, 1997). The findings were corroborated in mice lacking functional D3; they
were hyperactive (Accili et al, 1996).
The D3-coding DRD3 gene has seven exons and is mapped to the chromosomal
region 3q13.3. The Ser9Gly polymorphism in the second exon was studied previously
because of our initial report of its association with TD (Badri et al, 1996), and an
association of the glycine variant with increased D3 affinity for dopamine as revealed in
CHO cells (Lundstrom and Turpin, 1996), accompanied by increased activation of ERK
and inhibition of cAMP synthesis (Jeanneteau et al, 2006). The glycine variant has also
Zai et al IV. BDNF & DRD3 in Tardive Dyskinesia
Page 91
been demonstrated in vitro to result in a shift in D3 signaling from inhibition of adenylate
cyclase to inhibition of prostaglandin production (Hellstrand et al, 2004).
The Ser9Gly polymorphism has been examined for possible association with
schizophrenia in numerous studies, with a recent meta-analysis determining the odds ratio
to be around 1.10 for homozygosity (Jönsson et al, 2003). DRD3 has received much
attention in TD studies in light of several positive association results. Specifically, the
Gly variant was found to be associated with increased risk of TD (Badri et al, 1996; Steen
et al, 1997; Segman et al, 1999; Basile et al, 1999; Lovlie et al, 2000; Liao et al, 2001).
Although these findings could not be replicated in other studies (Inada et al, 1997;
Rietschel et al, 2000; Garcia-Barcelo et al, 2001), the Gly allele (or Gly/Gly genotype)
has been found associated with TD in a combined analysis involving 780 patients, and
two meta-analyses, with the latest reported odds ratio for the glycine allele being 1.17
(Lerer et al., 2002; Bakker et al, 2006). Small sample sizes could have contributed to
some of the negative findings, especially if the glycine allele is exerting a small effect on
TD risk and severity. The mixed results could also be due to different ethnic
backgrounds of the study sample populations. Different variants may contribute to TD in
different ethnic groups. However, a comprehensive analysis of the DRD3 gene in TD is
still lacking.
Brain-derived neurotrophic factor (BDNF) is known to exert wide-ranging effects
on the nervous system, including growth, differentiation, and survival of neurons (Lewin
and Barde, 1996; Altar and DiStefano, 1998), and support the activities of dopaminergic,
glutaminergic, cholinergic, and serotonergic neurons (Altar, 1999). More specifically,
the involvement of BDNF in the dopaminergic system (Hyman et al, 1991; Thoenen,
Zai et al IV. BDNF & DRD3 in Tardive Dyskinesia
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1995) includes its stimulation of forebrain dopamine release (Altar et al, 1998) and
prevention of damage of dopaminergic neurons by neurotoxin 1-methyl-4-phenyl-1,2,3,6-
tetrahydropyridine (MPTP) (Ebadi et al, 1998). BDNF is required for neuronal
establishment in the nigrostriatal dopaminergic system that is implicated in TD (Nishio et
al, 1998; Murer et al, 2001; Baquet et al, 2005). BDNF from dopaminergic neurons was
demonstrated in vivo to be required for D3 receptor expression in the nucleus accumbens
during development and adulthood (Guillin et al, 2001). Several studies found BDNF
mRNA expression to be decreased in the rat hippocampus after haloperidol treatment
while clozapine appeared to increase BDNF (Chlan-Fourney et al, 2002; Bai et al, 2003),
but others did not report the differences between typical and atypical antipsychotics on
BDNF levels (Angelucci et al, 2000; Lipska et al, 2001). Recently, Tan and coworkers
found decreased plasma BDNF levels in schizophrenia patients with TD compared to
those without (Tan et al, 2005). The BDNF gene, located on 11p13, contains a number of
putative functional polymorphisms. Magnetic resonance imaging scans showed that the
Met66 allele carriers had significantly lower average hippocampal volume compared to
Val66/Val66 (Pezawas et al, 2004). The C270T polymorphism was shown to affect
BDNF mRNA stability (Tongiorgi et al, 2006). The promoter C-281A polymorphism
was shown to decrease in vitro DNA-binding activity and basal reporter gene activity in
cultured neurons (Jiang et al, 2005). While BDNF Val66Met and C270T polymorphisms
do not appear to confer a strong risk for schizophrenia (Xu et al, 2007; Zintzaras et al,
2007; Kanazawa et al, 2007), only the Val66Met polymorphism has been tested in one
genetic study of TD (Liou et al, 2004).
Zai et al IV. BDNF & DRD3 in Tardive Dyskinesia
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Because BDNF is required for D3 expression, and neither BDNF nor DRD3 has
been extensively examined in TD, we investigated the possible role of polymorphisms
spanning the DRD3 and BDNF genes as well as their interactions in TD risk and severity.
Zai et al IV. BDNF & DRD3 in Tardive Dyskinesia
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4.3 PATIENTS AND METHODS
4.3.1 Subjects
Subjects were recruited from four clinical sites in North America: Center for
Addiction and Mental Health in Toronto, Ontario (Dr. G Remington, N=92); Case
Western Reserve University in Cleveland, Ohio (Dr. HY Meltzer, N=69); Hillside
Hospital in Glen Oaks, New York (Dr. JA Lieberman, N=50); University of California at
Irvine, California (Dr. SG Potkin, N=12). Subjects were selected based on their
diagnoses for Schizophrenia or Schizoaffective Disorder according to DSM-III-R or IV
(APA, 2000). All patients have undergone at least one year of treatment with typical or
atypical antipsychotics. The presence of TD was assessed using the Abnormal
Involuntary Movement Scale (AIMS) or the modified Hillside Simpson Dyskinesia Scale
(HSDS) for 50 patients recruited from the Hillside Hospital (Guy, 1976; Schooler and
Kane, 1982; Basile et al, 1999). AIMS scores were available for 161 patients.
In all, 223 schizophrenia patients were studied. 193 of them were European
Caucasians, of which 76 were positive for the diagnosis of TD. The remaining 30 were
African-Americans, of which 11 were positive for TD. Because of small sample size, the
African-Americans were only used in the test for allele frequency association with TD.
4.3.2 Gene polymorphism analysis
Genomic DNA was purified from whole blood samples using non-enzymatic
method previously described (Lahiri and Nurnburger, 1991). 10µL Polymerase Chain
Reactions on 20ng genomic DNA were performed using Assays-on-Demand (ABI) with
the following conditions: 95oC 10min, followed by 50 cycles of 92oC 15sec, 60oC 1min.
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Genotyping was done after the subjects have completed the follow-up in which all
laboratory staff was blind to the AIMS scores. The assays with their corresponding
polymorphisms and locations are shown in Table 1 and Figure 1. They are as follows:
for DRD3: rs905568, rs2399504, rs7611535, rs6762200, rs1394016, rs6280 (Ser9Gly),
rs167770, rs2134655, rs2087017, rs1025398; for BDNF: Promoter C/A (C-281A),
C270T (HinfI), rs7103411 (BDNF_4), rs2049045 (BDNF_3), rs11030104 (BDNF_2),
rs6265 (Val66Met); for ZNF80: rs6438191 (R201H), rs3732781 (Y245stop), rs3732782
(D253A). Allelic discrimination was performed using ABI7000. More details about the
polymorphisms used in the present study are presented in Table 9.
4.3.3 Statistics
Statistical analyses were conducted using the SPSS program Student version 14.0,
Haploview version 3.3 (Barrett et al, 2005), and UNPHASED version 2.0 (Dudbridge,
2003). Odds ratio calculations were conducted using Program 2BY2 version 2 written by
Jurg Ott. Genotype frequency distribution was tested for fitness to Hardy-Weinberg
equilibrium using Haploview. The association of genotype frequencies with age and
AIMS was assessed using ANOVA, and where the variances of AIMS scores among
genotypes differed significantly using the Levene’s Test for Homogeneity of Variances,
AIMS was tested with the Kruskal-Wallis test on SPSS. Gender differences in genotype
frequencies were assessed using the χ2 test on SPSS. The differences in allele and
genotype frequencies between patients with and without TD were analyzed by χ2 test on
SPSS. For contingency tables with at least one expected cell count of less than five, two-
tailed Fisher’s Exact Tests were performed (URL: http://home.clara.net/sisa/fiveby2.
Zai et al IV. BDNF & DRD3 in Tardive Dyskinesia
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htm). Haplotype analyses and linkage disequilibrium calculations were conducted using
UNPHASED and Haploview respectively. Gene-gene interaction analysis was conducted
using HELIXTREE program (GoldenHelix).
.
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4.4 RESULTS
4.4.1 Sample Characteristics
The genotype distributions of all 19 polymorphisms in the ZNF80, DRD3, and
BDNF genes in the European Caucasian samples did not differ significantly from Hardy-
Weinberg equilibrium (p>0.10). We did not observe significantly different average age
among the genotypes for any of the 19 polymorphisms. A significant association was
found between genotype frequencies of three DRD3 polymorphisms and sex (rs2399504,
rs7611535, rs167770; p<0.05, Table 2a). Analyzing the presence or absence of TD and
AIMS in males and females separately did not yield significant results.
4.4.2 Association study of DRD3 polymorphisms and haplotypes with TD and AIMS
With our Caucasian sample, we found the rs905568 genotype and allele
frequencies to be significantly associated with TD diagnoses (p=0.015, Table 10a, and
p=0.002, Table 11). Specifically, the G allele is under-represented in the TD-positive
group of patients (ORG = 0.49, CI: 0.32-0.77). Similarly, fewer TD-positive patients
were observed with the GG genotype than expected (ORGG = 0.32, CI: 0.11-0.88). The
other DRD3 polymorphisms analyzed did not show significant allele or genotype
association with TD. We compared the average total AIMS scores among the genotype
groups for each polymorphism. rs905568 showed significantly different AIMS scores
among its genotype groups (p=0.007, Table 10a), with patients having the GG genotype
also having the lowest average total AIMS scores compared to patients with other
genotypes.
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Because the rs905568 polymorphism is located adjacent to the gene coding for a
putative zinc-finger protein (ZNF80), we investigated whether the rs905568 association
with TD may have been due to an association of ZNF80 with TD. We genotyped three
non-synonymous polymorphisms in ZNF80, and found that the ZNF80 polymorphisms
were not associated with TD diagnosis or AIMS scores (Table 10b). Using the
Haploview program, strong evidence for linkage was found between markers (Figure 7a).
As an exploratory test, we analyzed two-marker haplotypes across ZNF80 and DRD3
using the sliding window approach in UNPHASED. Only windows containing rs905568
showed significant association with TD and AIMS (Table 12).
4.4.3 Association study of BDNF polymorphisms and haplotypes with TD and AIMS
For BDNF, the promoter C/A genotype and allele distributions were associated
with TD, but the results did not reach statistical significance (Tables 10b and 11). The
other BDNF polymorphisms did not show significant association with TD diagnosis or
AIMS scores (Table 10b and 11). The two-marker haplotypes of BDNF were not
significantly associated with TD in our European Caucasian sample (Table 12).
4.4.4 Interaction analysis of BDNF and DRD3 polymorphisms in TD
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 TD occurrence or in AIMS (Figure 8).
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For the African-American sample, preliminary results indicated a significant
association between TD and alleles of the rs6762200 polymorphism (p=0.017) and
rs1394016 (p=0.048)(Table 11). Neither Ser9Gly nor rs905568 was associated with TD
in our African American sample.
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4.5 DISCUSSION
The present study reports that the region about 50kb upstream of the DRD3 gene
is associated with TD. The region does not appear to extend into the ZNF80 gene
because the ZNF80 polymorphisms are not associated with TD in our sample. The
present study also reports that BDNF is not associated with TD. The rs905568
association with TD might be a false-positive result from multiple testing; however, this
possibility is unlikely because the rs905568 results remained significant after correction
for testing 10 DRD3 markers. Nevertheless, larger sample sizes are required to detect
small effects of genotypes on TD risk and severity, especially for our African–American
sample, where two other polymorphisms in the 5’ region of DRD3 are associated with
TD. Even though Ser9Gly was not significantly associated with TD in our African
American sample, there is a trend for the Gly allele to be the risk allele for TD, which is
in agreement with previous studies in which African Americans have a higher frequency
of the Gly allele (Basile et al, 1999). Age, sex, and ethnicity are suggested risk factors
for TD (Basile et al, 1999; Jeste, 2000; van Os et al, 1997; Kaiser et al, 2002). Age was
not likely responsible for the positive findings in this study, because mean age did not
differ significantly among the genotypes of any of the polymorphisms in the present
study. Also, rs905568 could be in linkage disequilibrium with a nearby polymorphism
that directly affects TD, possibly through variations in transcription factor binding sites.
The present study has several limitations. Firstly, not all clinical data was
available for our study. These include medication history like antipsychotic dose and
duration, schizophrenia disease history such as clinical subtypes, psychopathology, and
co-morbidities. These factors or variables are also associated with TD according to
Zai et al IV. BDNF & DRD3 in Tardive Dyskinesia
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previous studies (reviewed in Müller et al, 2004). It was noted that medications taken by
patients for other adverse effects could have masked the TD phenotype (Shale and
Tanner, 1996; Egan et al, 1997; Glazer, 2000). Moreover, environmental risk factors
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
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present study encourages further studies into the rs905568 and adjacent polymorphisms
surrounding the 5’ region of the DRD3 gene in TD.
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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,
P1CA Promoter Jiang et al, 2005
ZNF80 rs6438191 A(H) G(R) His201Arg Exon 1 ZNF80 rs3732781 G(Stop) T(Y) Tyr245Stop Exon 1 ZNF80 rs3732782 A(D) C(A) Ala253Asp Exon 1
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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.
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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.
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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.
Zai et al IV. BDNF & DRD3 in Tardive Dyskinesia
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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.
Haplotype across ZNF80 and DRD3
P-value (TD+/-) P-value (AIMS)
rs6438191-rs3731781 0.533 1.000 rs3731781-rs3731782 0.458 0.828 rs3731782-rs905568 0.005 0.016
rs905568-rs2399504 0.052 0.086
rs2399504-rs7611535 0.983 0.652 rs7611535-rs6762200 0.318 0.448 rs6762200-rs1394016 0.300 0.351 rs1394016-rs6280 0.202 0.544 rs6280-rs167770 0.234 0.400 rs167770-rs2134655 0.651 0.947 rs2134655-rs2087017 0.640 1.000 rs2087017-rs1025398 0.217 0.552
Haplotype across BDNF P-value (TD+/-) P-value (AIMS)
P1CA-C270T 0.164 0.410 C270T-BDNF_4 0.456 0.102 BDNF_4-BDNF_3 0.089 1.000 BDNF_3-BDNF_2 0.517 0.157 BDNF_2-Val66Met 0.249 0.134 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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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
Raw p-values
Bonferroni p-values
10-8 -
10-7 -
10-6 -
10-5 -
10-4 -
10-3 -
10-2 -
10-1 -
1 -
rs1025398rs2087017rs2134655rs167770Ser9Gly
rs1394016rs6762200rs7611535rs905568
rs3732782rs3732781rs6438191Val66MetBDNF_2BDNF_3BDNF_4
C270TP1CA
rs1025398 rs2087017 rs2134655 rs167770 S
er9Gly
rs1394016 rs6762200 rs7611535 rs905568 rs3732782 rs3732781 rs6438191 V
al66Met
BD
NF
_2 B
DN
F_3
BD
NF
_4 C
270T
P1C
A
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CHAPTER 5
ASSOCIATION STUDY OF TARDIVE DYSKINESIA AND TWELVE DRD2
POLYMORPHISMS IN SCHIZOPHRENIA PATIENTS
Published in the International Journal of Neuropsychopharmacology as an original research
paper
Clement C. Zai(1,2), Rudi W. Hwang(1,2), Vincenzo De Luca(1,2), Daniel J. Müller(1,3),
Nicole King (1), Gwyneth C. Zai(1,2), Gary Remington(1), Herbert Y. Meltzer(4), Jeffrey A.
Lieberman(5), Steven G. Potkin(6), 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 genotyping on the
DRD2 polymorphisms in approximately 50% of the tardive dyskinesia sample. Rudi Hwang,
Clement Zai V. DRD2 and Tardive Dyskinesia
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another graduate student in the lab, genotyped the remaining 50% of the sample and used them
to analyze another phenotype, not tardive dyskinesia. Mr. Zai corresponded with the clinical
collaborators to refine the details of the phenotype, performed all the statistical analyses, wrote
the manuscript, responded to reviewers’ comments from the journal and edited the text
accordingly.
Keywords: Schizophrenia, tardive dyskinesia, genetics, DRD2, Abnormal Involuntary Movement
Scale (AIMS)
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5.1 ABSTRACT
Tardive dyskinesia (TD) is a side effect of chronic antipsychotic medication.
Abnormalities in dopaminergic activity in the nigro-striatal system have been most often
suggested to be involved because the agents which cause TD share in common potent
antagonism of dopamine D2 receptors (DRD2), that notably is not balanced by effects such as
more potent serotonin 5HT2A antagonism. Thus, a number of studies have focused on the
association of dopamine system gene polymorphisms and TD. The most consistent findings
have been found with the Ser9Gly polymorphism of the DRD3 gene. Although the DRD2 has
long been hypothesized to be the main target for antipsychotics, only a few polymorphisms in
DRD2 have been investigated for their potential involvement in the etiology of TD. In the
present study, we investigated 12 polymorphisms spanning the DRD2 gene and their association
with TD in our European Caucasian (N=202) and African-American (N=30) samples. Genotype
frequencies for a functional polymorphism, C957T (Duan et al, 2003; Hirvonen et al, 2004), and
the adjacent C939T polymorphism were found to be significantly associated with TD (p=0.013
and p=0.022, respectively). DRD2 genotypes were not significantly associated with TD severity
as measured by AIMS (Abnormal Involuntary Movement Scale) with the exception of a trend for
C939T (p=0.071). Both TD and total AIMS scores were found to be significantly associated
with two-marker haplotypes containing C939T and C957T (p=0.021 and p=0.0087,
respectively). Preliminary results indicated that C957T was also associated with TD in our
African-American sample (p=0.047). Taken together, the present study suggests that DRD2 may
be involved in TD in the Caucasian population, though further studies are warranted.
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5.2 INTRODUCTION
Tardive dyskinesia (TD) is a potentially irreversible motor side effect that develops in
SCZ patients treated chronically with typical antipsychotic drugs. It is characterized by
involuntary choreoathetotic movements mostly in the orofacial regions, with more severe cases
involving the trunk as well as the upper and lower limbs. Its reported prevalence varies from
16% to 43%, with an annual incidence rate of around 5% (reviewed in Tarsy and Baldessarini,
2006). Patients with this condition often struggle with the immediate difficulties of motor
function, but also with adhering to treatment, discrimination, and poorer quality of life
(Marsalek, 2000; Gerlach, 2002), so predicting which patients are vulnerable to TD remains a
high priority for psychiatrists in treatment selection.
The etiology of TD is complex and remains unclear. Age, gender, and ethnicity are all
suggested risk factors for TD. More specifically, Woerner and coworkers found patients above
age 50 were three to five times more likely to develop TD than younger patients in other studies
(Woerner et al, 1998; Kane et al, 1988; Morgenstern and Glazer, 1993). A review of 13 studies
found females to be more likely to develop TD with an odds ratio of 1.69 (Smith and Dunn,
1979), though other studies reported the opposite findings (Morgenstern et al, 1987; van Os et al,
1997). The difference was attributed to possible selection bias for more severe cases in the
female group in earlier studies. Jeste and coworkers reported the yearly TD incidence of over
45% for African-Americans compared to 27% in Caucasians (Jeste et al, 2000), but
socioeconomic factors could have contributed to the findings. Environmental risk factors such as
smoking as well as alcohol and recreational drug use could further increase the risk for TD
(Menza et al, 1991; Bailey et al, 1997; Olivera et al, 1990). Concordance for the presence or
absence of TD among first-degree relatives provided evidence for a genetic component of TD
Clement Zai V. DRD2 and Tardive Dyskinesia
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(Müller et al, 2001). A number of mechanisms leading to TD have been hypothesized, including
GABA insufficiency (Casey, 2000), free radical-mediated neuronal injury (Andreasson and
Jorgensen, 2000), and structural abnormalities in brain regions involved in motor function such
as the caudate nucleus (Chakos et al, 1994; Corson et al, 1999). The best known hypothesis
postulates TD as being caused by hypersensitivity of dopamine receptors induced by dopamine
receptor blocking medication including antipsychotics (Tarsy and Baldessarini, 1977; Klawans et
al, 1980; Gerlach and Casey, 1988; Abilio et al, 2003). This hypothesis is based on several
observations. Persistent dyskinesia was induced by neuroleptic treatment in a non-human
primate model of TD with significantly decreased dopamine turnover in the caudate and
substantia nigra (Gunne et al, 1984). The dopamine D2 receptor is most densely expressed in the
basal ganglia, an area of the central nervous system that regulates movements (Hall et al, 1994).
Moreover, clinical studies (Kane et al, 1993; Beasley et al, 1999; Jeste et al, 1999a, b) and rodent
vacuous chewing TD models (Johansson et al, 1986; Gao et al, 1998) have revealed that
compared to atypical antipsychotics, TD is more likely to develop after treatment with typical
antipsychotics that generally have higher affinities for D2 given by in vitro competition assays
(Schwartz et al, 2000). Some antipsychotic drugs that have high affinities for D2 have a lower
risk of causing TD, e.g. olanzapine, risperidone and ziprasidone. This appears to be due to them
also being more potent 5HT2A antagonists (Meltzer et al, 2003). Thus, variation in D2 function
and expression may contribute to the risk of TD development.
The dopamine D2 receptor belongs to the G-protein coupled receptor family. It activates
intracellular signaling by inhibiting the synthesis of cAMP. DRD2, the D2 encoding gene,
consists of eight exons and spans approximately 65kb (Figure 1). Several in vivo and in vitro
studies have focused on the effect that DRD2 polymorphisms have on D2 expression. The –141C
Clement Zai V. DRD2 and Tardive Dyskinesia
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Del allele has been reported to decrease DRD2 promoter activity in vitro (Arinami et al, 1997),
while one in vivo study showed that the Del allele is associated with an increase in striatal D2
binding (Jönsson et al, 1999b); however, another in vivo study reported no significant difference
(Ritchie and Noble, 2003). The B1 allele in intron 1 has been associated with decreased D2
binding in vivo (Jönsson et al, 1999b; Ritchie and Noble, 2003), as has T957 (Hirvonen et al,
2004). The latter has also been reported to decrease DRD2 mRNA stability in vitro (Duan et al,
2003). The TaqIA polymorphism has received much attention recently as it was discovered to
reside in an overlapping gene, ANKK1, and cause a non-synonymous coding polymorphism
(Neville et al, 2004). The A1 allele has been associated with reduced D2 levels in several studies
(Noble et al, 1991; Thompson et al, 1997; Pohjalainen et al, 1998; Jönsson et al, 1999b; Ritchie
and Noble, 2003). Laruelle and coworkers (1998), however, did not find a significant
association between TaqIA and D2 expression levels, but this negative finding could be the result
of population stratification as subjects with four different ethnic backgrounds were included.
DRD2 has been previously associated with schizophrenia. Several polymorphisms,
including the –141C Ins, A2, and B2 variants, as well as their haplotypes, have been reported to
be positively associated or over-transmitted in various schizophrenia samples (Arinami et al,
1997; Breen et al, 1999; Inada et al, 1999; Jönsson et al, 1999a; Golimbet et al, 1998; Dubertret
et al, 2001). However, other investigators did not replicate the findings, suggesting that further
studies are required (Hori et al, 2001a; Stober et al, 1998; Suzuki et al, 2000; Tallerico et al,
1999). DRD2 is one of the first genes to be tested in TD genetic studies. In all, ten studies have
been conducted on DRD2 and TD. Specifically, the A-241G, –141C Ins/Del, TaqIB, TaqID,
C939T, and TaqIA polymorphisms have been analyzed for association with TD (Chen et al,
1997a; Inada et al, 1997; 1999; Hori et al, 2001a, b; Kaiser et al, 2002; Chong et al, 2003a;
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Segman et al, 2003; Lattuada et al, 2004; de Leon et al, 2005, Srivastava et al, 2006). Chen et al
(1997a) initially detected an association between the TaqIA marker and TD particularly in
female schizophrenia patients. However, the DRD2 association with TD could not be replicated
in most other studies.
In the present study, we tested for the presence of an association between the DRD2 gene
and TD using both continuous (AIMS) and dichotomous (TD occurrence) measures in relatively
large samples of Caucasian and African-American patients with SCZ using 12 polymorphisms
spanning the DRD2 gene: A-241G, -141C Ins/Del, TaqID, C939T, C957T, TaqIA, rs4648317,
rs1125394, rs1079598, rs2242591, rs2242592, and rs2242593 all of which have recently been
used in a detailed analysis of DRD2 in clozapine response (Hwang et al, 2005).
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5.3 PATIENTS AND METHODS
5.3.1 Subjects
Subjects were recruited from four clinical sites in North America: Center for Addiction
and Mental Health in Toronto, Ontario (Dr. G Remington, N=94); Case Western Reserve
University in Cleveland, Ohio (Dr. HY Meltzer, N=77); Hillside Hospital in Glen Oaks, New
York (Dr. JA Lieberman, N=49); University of California at Irvine, California (Dr. SG Potkin,
N=12). Subjects were selected based on their diagnoses of SCZ or Schizoaffective Disorder
according to DSM-III-R or IV (APA, 1994). All patients had undergone cumulatively at least
one year of treatment with typical antipsychotics. For the Meltzer, Lieberman, and Potkin
samples, the presence or absence of tardive dyskinesia was evaluated before any atypical
antipsychotic administration, while patients were on mixed typical and atypical antipsychotics
when TD was evaluated in the Remington sample. The presence of TD was assessed using the
Abnormal Involuntary Movement Scale (AIMS) or the modified Hillside Simpson Dyskinesia
Scale (HSDS) for 49 patients recruited from the Hillside Hospital (Guy, 1976; Schooler and
Kane, 1982; Basile et al, 1999). The seven body area items and the overall global item of HSDS
match those of AIMS, thus the assessment for the presence of TD was the equivalent for all four
sites. All four clinicians (GR, HYM, JAL, SGP) are highly experienced in TD severity
measurements of which the consistency was further enhanced by exchange visits across sites.
In all, 232 patients were studied. 202 of them were European Caucasians, of which 80 were
positive for the occurrence of TD. The remaining 30 were African-Americans, of which 11 were
TD-positive. AIMS scores were available for 197 patients (171 Caucasians and 26 African
Americans). Because of small sample size, the African-Americans were only used in the test for
allele frequency association with TD.
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5.3.2 Gene polymorphism analysis
Genomic DNA was purified from whole blood samples using high-salt method
previously described (Lahiri and Nurnburger, 1991). 10µL Polymerase Chain Reactions on 20ng
genomic DNA were performed using TaqMan allele-specific assays with the following
conditions: 95oC 10min, followed by 50 cycles of 92oC 15sec, 60oC 1min. Genotyping was done
after the subjects completed the follow-up in which all laboratory staff was blind to the AIMS
scores. The A-241G, –141C Ins/Del, TaqID, C939T, and TaqIA polymorphisms have been used
in previous TD studies. The C957T polymorphism was studied because of its functional
significance and its high minor allele frequency (Hirvonen et al, 2004; Duan et al, 2003). The
remaining polymorphisms, rs4648317, rs1125394, rs1079598, rs2242591, rs2242592, and
rs2242593, were selected based on their position within the DRD2 gene and the presence of
sufficiently high minor allele frequencies (Hwang et al, 2005). The assays with their
corresponding polymorphisms and locations are shown in Figure 9. Genotypes were determined
using the ABI Prism® 7000 Sequence Detection System with the Allelic Discrimination program
within the ABI software (Applied Biosystems, Foster City, CA). All ambiguous genotypes were
retyped, and if they remained ambiguous, they were taken out of the analysis.
5.3.3 Statistics
Statistical analyses were conducted using the Statistical Package for the Social Sciences
version 10.0.7, Haploview version 3.2, and UNPHASED version 2.402 (SPSS, 2000; Barrett et
al, 2005; Dudbridge, 2003; Hwang et al, 2005). Odds ratio calculations were conducted using
Program 2BY2 version 2 written by Jurg Ott. Genotype frequency distribution was tested for
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fitness to Hardy-Weinberg equilibrium using Haploview. The association of genotype
frequencies with age and AIMS scores was assessed using ANOVA, and where the variances of
AIMS scores among genotypes differed significantly using the Levene Test for Homogeneity of
Variance, AIMS scores were examined with the Kruskal-Wallis test on SPSS. Gender
differences in genotype frequencies were assessed using the χ2 test on SPSS. The differences in
allele and genotype frequencies between patients with and without TD were analyzed by χ2 test.
For contingency tables with at least one expected cell count of less than five, two-tailed Fisher’s
Exact Tests were performed (URL: http://home.clara.net/sisa/fiveby2.htm). Haplotype analyses
and linkage disequilibrium calculations were conducted using UNPHASED and Haploview,
respectively.
ETHICAL CONSIDERATIONS
The scientific work described in the present paper complies with the current laws of
Canada and the US, as well as the ethical standards established in the 1964 Declaration of
Helsinki. Informed consent was obtained before subjects’ participation, and this study was
approved by the Ethics Committee of the Centre for Addiction and Mental Health.
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5.4 RESULTS
5.4.1 Sample Characteristics
The genotype distributions of all the DRD2 gene polymorphisms in the European
Caucasian samples did not differ significantly from the Hardy-Weinberg equilibrium (p>0.10).
rs1125394 was found not to be in Hardy-Weinberg equilibrium in our African-American sample
(p<0.01). A significant increase in frequency of TD was found in females (p=0.009), while no
significant association was found between gender and genotype frequencies (Table 1). We also
found a significant positive correlation between AIMS scores and age (r=0.206; p=0.007).
5.4.2 Association study of individual polymorphisms with TD occurrence and AIMS
With our Caucasian sample, we found the C957T allele frequencies to be significantly
associated with TD occurrence (p=0.0135). Specifically, the T957 allele appears to be under-
represented in TD-positive patients compared to TD-negative patients (ORT957=0.59 [CI: 0.39-
0.90]; Table 14). We also found a significant association for the C allele that appears to be
present in a lower proportion of TD-positive patients compared to TD-negative patients
(p=0.0085, ORC939=0.56 [CI: 0.36-0.86]; Table 14). The genotype frequencies of the C957T and
C939T polymorphisms were also found to be significantly associated with TD (p=0.013,
ORTT957=0.30 [CI: 0.13-0.69]; p=0.022, ORCC939=0.42 [CI: 0.23-0.79]; Table 13). We further
tested for differences in average AIMS scores among genotypes with ANOVA. C957T and
C939T showed the same trend as for TD analyses, with the differences being suggestive for
C939T (p=0.150 and p=0.071, respectively; Table 13). Upon close inspection with student t-
tests, we found patients homozygous for C939 to have significantly lower total AIMS scores
than patients carrying at least one copy of T939 (p=0.001). Patients homozygous for T957 had
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significantly lower total AIMS scores than patients carrying at least one copy of C957 (p=0.044).
rs2242592 also showed significant or suggestive association with TD at the allelic and genotypic
levels. The rs2242592 findings could be due to generally high linkage disequilibrium observed
in the 3’ region of DRD2 (Figure 10). The other polymorphisms analyzed did not show
significant associations with TD either for allele, genotype, or AIMS scores analyses.
5.4.3 Association study of haplotypes with TD diagnosis and AIMS
Using the Haploview program with linkage disequilibrium block defined by Gabriel et al
(2002), strong evidence for linkage was found between C939T and C957T, prompting us to test
for association between TD and two-marker haplotypes across DRD2 (Figure 9). rs2242593 and
Taq1A were also found to be in linkage disequilibrium in our sample. COCA-PHASE analysis
within UNPHASED revealed an association for the C939T-C957T haplotype and TD diagnosis
(global p=0.021; Table 15). Specifically, the C939-T957 haplotype appeared to occur less often
while the T939-C957 haplotype appeared more frequently in TD-positive patients (p=0.007,
ORC939-T957=0.56 [CI: 0.37-0.86]; p=0.013, ORT939-C957=1.72 [CI: 1.11-2.65], respectively). QT-
PHASE analysis within the UNPHASED program using two-marker haplotypes also showed
significant association of haplotypes containing C957T and C939T polymorphisms with AIMS
scores (p=0.0087; Table 15). Patients carrying the C939-T957 haplotype had significantly lower
average AIMS scores than other haplotypes, while those carrying the T939-C957 had
significantly higher average AIMS scores (p=0.017 and p=0.0013, respectively).
For the African-American sample, preliminary results indicated a marginally significant
association between TD and alleles of the C957T (p=0.047), with the T957 allele appearing to be
protective (ORT957=0.22 [CI: 0.04-1.08]). The linkage disequilibrium chart showing D’ values
Clement Zai V. DRD2 and Tardive Dyskinesia
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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%
Clement Zai V. DRD2 and Tardive Dyskinesia
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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.
Clement Zai V. DRD2 and Tardive Dyskinesia
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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
1/1 (A/A) 1/2 (A/G)
161(54/107) 28(8/20)
37.71+/-9.46 37.25+/-11.72
5.53+/-7.18 (131) 7.56+/-8.75 (27)
59 12
102 16
A-241G
P 0.605 0.820 0.200 0.531 1/1 (Del/Del) 1/2 (Ins/Del) 2/2 (Ins/Ins)
2(1/1) 32(10/22) 150(49/101)
38.00+/-1.41 39.06+/-10.76 37.14+/-9.76
6.50+/-6.36 (2) 5.71+/-7.84 (28) 5.66+/-7.20 (123)
1 14 54
1 18 96
-141C Ins/Del
P 0.927* 0.608 0.987 0.554* 1/1 (C/C) 1/2 (C/T) 2/2 (T/T)
143(51/92) 38(8/30) 4(2/2)
37.83+/-10.16 37.92+/-8.56 26.25+/-4.35
5.78+/-7.21 (124) 5.32+/-7.61 (28) 5.00+/-7.07 (2)
56 12 2
87 26 2
rs4648317
P 0.144* 0.066 0.947 0.581* 1/1 (A/A) 1/2 (A/G) 2/2 (G/G)
135(42/93) 49(17/32) 6(2/4)
37.76+/-10.36 38.31+/-8.55 39.50+/-10.56
6.34+/-8.04 (116) 4.97+/-5.89 (38) 5.60+/-6.69 (5)
52 18 4
83 31 2
rs1125394
P 0.907* 0.878 0.988! 0.372* 1/1 (C/C) 1/2 (C/T) 2/2 (T/T)
6(3/3) 48(16/32) 138(45/94)
36.33+/-11.73 38.83+/-8.52 37.34+/-10.33
6.20+/-6.65 (5) 4.74+/-5.88 (39) 6.34+/-7.96 (118)
4 16 55
2 32 84
rs1079598
P 0.684* 0.632 0.514! 0.277* 1/1 (C/C) 1/2 (C/T) 2/2 (T/T)
50(15/35) 83(30/53) 56(15/41)
37.42+/-9.35 37.43+/-9.89 37.21+/-10.30
6.31+/-8.00 (45) 5.22+/-6.44 (64) 6.16+/-8.34 (49)
20 33 17
30 50 39
TaqID
P 0.485 0.991 0.946! 0.467 1/1 (C/C) 1/2 (C/T) 2/2 (T/T)
80(27/53) 85(29/56) 22(5/17)
37.51+/-8.69 38.08+/-9.78 34.36+/-11.21
3.80+/-4.91 (69) 6.69+/-7.46 (68) 9.85+/-11.77 (20)
21 38 11
59 47 11
C939T
P 0.573 0.263 0.071! 0.022 1/1 (C/C) 1/2 (C/T) 2/2 (T/T)
44(10/34) 102(38/64) 43(14/29)
36.59+/-10.94 38.21+/-9.32 37.95+/-9.09
7.29+/-9.42 (38) 5.84+/-6.62 (82) 3.89+/-5.86 (38)
20 43 8
24 59 35
C957T
P 0.229 0.646 0.150! 0.013 1/1 (A/A) 1/2 (A/G) 2/2 (G/G)
126(40/86) 51(16/35) 8(4/4)
37.49+/-10.32 37.45+/-8.53 38.50+/-11.16
6.14+/-8.06 (107) 5.03+/-5.99 (40) 5.14+/-5.90 (7)
46 18 5
80 33 3
rs2242591
P 0.602* 0.960 0.705 0.357* 1/1 (C/C) 1/2 (C/T) 2/2 (T/T)
17(3/14) 89(31/58) 79(29/54)
35.18+/-12.60 37.69+/-9.80 38.02+/-9.08
7.44+/-10.36 (16) 6.99+/-7.94 (72) 4.37+/-5.85 (70)
8 40 24
9 49 59
rs2242592
P 0.355 0.547 0.135! 0.070 1/1 (A/A) 1/2 (A/G) 2/2 (G/G)
131(42/89) 50(15/35) 6(3/3)
37.63+/-10.23 37.98+/-8.71 36.33+/-11.73
6.05+/-7.97 (111) 5.27+/-5.92 (40) 6.20+/-6.65 (5)
47 20 4
84 30 2
rs2242593
P 0.588* 0.923 0.842 0.291* 1/1 (T/T) 1/2 (C/T) 2/2 (C/C)
6(2/4) 72(27/45) 111(32/79)
38.33+/-10.35 38.47+/-9.07 36.63+/-10.38
6.12+/-8.16 (5) 4.93+/-5.88 (60) 5.20+/-6.87 (93)
3 25 40
3 47 71
TaqIA
P 0.446* 0.458 0.960! 0.781*
* 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).
Clement Zai V. DRD2 and Tardive Dyskinesia
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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.
Haplotype P-value (TD+/-) P-value (AIMS)
A-241G – -141C I/D 0.358 0.326 -141C I/D – rs4648317 0.616 0.924 rs4648317 – rs1125394 0.771 0.726 rs1125394 – rs1079598 0.869 0.362 rs1079598 – TaqID 0.423 0.609 TaqID – C939T 0.0569 0.00655
C939T – C957T 0.0206 0.00868
C957T – RS2242591 0.0339 0.0628
rs2242591 – rs2242592 0.0341 0.131 rs2242592 – rs2242593 0.0148 0.122 rs2242593 – TaqIA 0.536 0.629 Bolded numbers indicate 0.05<p<0.10; bolded and italicized numbers indicate p<0.05.
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CHAPTER 6
META-ANALYSIS OF TWO DOPAMINE D2 RECEPTOR GENE POLYMORPHISMS
WITH TARDIVE DYSKINESIA IN SCHIZOPHRENIA PATIENTS
Published in Molecular Psychiatry
Clement C. Zai(1,2), Vincenzo De Luca(1,2), Rudi Hwang(1), Aristotle Voineskos(1), Daniel J.
Müller(1,3), 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, Charité Campus Mitte,
Berlin, Germany
Mr. Zai performed all the bibliographical searches for DRD2 gene association studies of tardive
dyskinesia. He performed the meta-analysis under the guidance of Dr. Vincenzo De Luca, and
wrote the manuscript.
RUNNING TITLE: Two Dopamine D2 Gene Polymorphisms in Tardive Dyskinesia, a Meta-
analysis
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6.1 INTRODUCTION
Tardive dyskinesia (TD) is a potentially irreversible movement disorder related to
chronic antipsychotic medication exposure. The etiology is unknown, though genetic factors are
likely contributors (Müller et al, 2001). Although the dopamine D2 receptor has been recognized
as the main target for antipsychotics, studies investigating potential involvement of D2 gene
(DRD2) polymorphisms in TD have yielded mixed results (Zai et al, 2007a). Of these
polymorphisms, TaqIA and –141C Ins/Del have been studied most extensively.
The –141C Del allele has been reported to decrease DRD2 promoter activity in vitro,
while two in vivo studies showed the Del allele to be associated with either an increase in striatal
D2 binding or no significant difference (Zai et al, 2007a). The TaqIA polymorphism was
recently discovered to reside in the coding region of an overlapping gene coding for a
Serine/Threonine kinase, and causes an amino acid change (Zai et al, 2007a). However, the A1
(T) allele has been associated with reduced D2 levels in most reported studies (Zai et al, 2007a).
Clement Zai VI. DRD2-Tardive Dyskinesia meta-analysis
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6.2 METHODS
6.2.1 Publication Search
We carried out a computer search on the National Library of Medicine’s PubMed online
search engine database for all papers published up to December 2006 on “tardive dyskinesia”
and “DRD2”. In all, 12 genetic association studies were found reporting on TD and DRD2. Six
included the number of patients with and without TD and genotypes for TaqIA, while five
included the number of TD-positive and TD-negative patients with genotypes for the –141C
Ins/Del polymorphism (Zai et al, 2007a; Chen et al, 1997a; Inada et al, 1999; Hori et al, 2001a;
Chong et al, 2003a; Segman et al, 2003; Liou et al, 2006). All subjects were selected based on
their diagnoses of Schizophrenia or Schizoaffective Disorder, according to DSM-III-R or IV,
using case records with or without patient interviews. The presence of TD was assessed using
the Abnormal Involuntary Movement Scale (AIMS), or the modified Hillside Simpson
Dyskinesia Scale (HSDS) in the case of 49 patients from our study as described previously (Zai
et al, 2007a). TD ratings were performed once for the majority of the studies. Quantitative
AIMS scores were not available for most subjects and were not included in the analyses.
6.2.2 Sample Description
In all, 1256 schizophrenia patients were genotyped for TaqIA. 507 of them were positive
for the diagnosis of TD. 897 schizophrenia patients were genotyped for –141C Ins/Del, of which
328 were TD-positive.
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6.2.3 Analysis
We analysed the data using the Stata Release 8 statistical software package (StataCorp.
2003. Stata Statistical Software: Release 8. College Station, TX: StataCorp LP). The odds ratios
and standard errors of TaqIA and –141C I/D for TD from the individual studies were calculated
using the “metan” command, with the pooled odds ratio and standard error calculated under the
random effects model. The possible effects of ethnicity, age, and gender ratio on heterogeneity
amongst the studies were assessed by meta-regression analysis using the “metareg” command.
We tested for publication bias using the “metabias” command.
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6.3 RESULTS AND DISCUSSION
Compared to TD-negative patients, TD-positive patients had a higher A2 allele frequency
(p=0.003), with an effect size of 1.30 (95% CI: 1.09-1.55), and higher A2/A2 genotype
frequency (p=0.001), with an effect size of 1.50 (95% CI: 1.17-1.92). The –141C Ins/Del alleles
and genotypes were not associated with TD. There was no evidence of heterogeneity amongst
the studies or publication bias for TaqIA or –141C Ins/Del (p>0.1). Ethnicity, gender ratio, or
age did not contribute to the results observed for TaqIA (p>0.1). Results are summarized in
Table 16.
Despite heterogeneity amongst studies in terms of TD assessment, results from the
present meta-analysis suggest that TaqIA is associated with TD and are in agreement with two of
the previous studies (Chen et al, 1997a; Liou et al, 2006). The mixed results in other studies
could be attributed to the lower A1 allele frequencies observed in European Caucasians
compared to East Asians, as well as small sample sizes. The results reported here could be
affected by potential confounding factors, including tobacco and substance use, antipsychotic
dose, and years of antipsychotic exposure, information that was not available for most of the
studies. False-positive results from multiple testing are possible, but association between TaqIA
and TD from the present meta-analysis would have survived correction for testing two markers.
The relatively low OR is consistent with the idea of contributions of multiple genetic variants in
complex phenotypes, with DRD3 Ser9Gly as an example of another genetic risk factor for TD
(Bakker et al, 2006). Nonetheless, the present study, the first meta-analysis of DRD2
polymorphisms in TD, encourages further examination of the role of TaqIA in TD.
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Table 16. Summary for meta-analysis of DRD2 TaqIA and –141C Ins/Del polymorphisms.
SNP Study OR2
(allele)
95% CI
(allele)
Wt.
%
OR22
(genotype)
95% CI
(genotype)
Wt.
%
Inada 1999 0.60 0.29-1.21 19 0.58 0.26-1.33 24 Hori 2001a 1.52 0.79-2.91 22 1.88 0.88-3.99 27 Segman 2003 1.06 0.37-3.01 9 1.06 0.36-3.13 16 Liou 2006 0.92 0.55-1.56 32 1.52 0.25-9.28 6 Zai 2006a 0.73 0.36-1.45 19 0.71 0.34-1.52 27
-141C
Ins/Del
(CC,
Ins)
Overall 0.92 0.67-1.25 100 0.99 0.61-1.59 100
Chen 1997a 1.37 0.89-2.09 17 2.13 1.13-4.02 15 Hori 2001a 1.17 0.72-1.89 13 1.46 0.62-3.45 8 Chong 2003a* 1.28 0.92-1.78 28 1.32 0.82-2.11 28 Segman 2003 1.40 0.71-2.76 7 1.57 0.72-3.44 10 Liou 2006 1.57 1.10-2.24 24 1.87 1.10-3.20 22 Zai 2006a 0.95 0.57-1.57 12 1.01 0.55-1.84 17
TaqIA
(C,
A2)
Overall 1.30 1.09-1.55 100 1.50 1.17-1.92 100
*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.
Clement Zai VII. DRD2 & AKT1 in Tardive Dyskinesia
Page 141
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).
Clement Zai VII. DRD2 & AKT1 in Tardive Dyskinesia
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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
Clement Zai VII. DRD2 & AKT1 in Tardive Dyskinesia
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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
Clement Zai VII. DRD2 & AKT1 in Tardive Dyskinesia
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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.
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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.
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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).
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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
suicide attempts warrants independent replication.
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
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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
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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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