zai clement c 200806 phd thesis

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GENE ASSOCIATION STUDIES OF SCHIZOPHRENIA & TARDIVE DYSKINESIA By Clement Zai A thesis submitted in conformity with the requirements For the degree of Doctor of Philosophy Institute of Medical Science University of Toronto © Copyright by Clement C. Zai (2008)

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Page 1: Zai Clement C 200806 PhD Thesis

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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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

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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

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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

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(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

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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

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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

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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

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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

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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

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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.

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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 ++++ ++ ++ ++ +

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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).

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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).

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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

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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.

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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

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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

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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

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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

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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

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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

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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

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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).

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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

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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

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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

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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

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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,

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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).

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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

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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.

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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

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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).

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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

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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.

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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

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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.

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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

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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.

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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

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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

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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

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(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.

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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

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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).

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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

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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).

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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-

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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.

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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.

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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

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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

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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.

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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

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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.

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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

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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

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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

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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

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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

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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.

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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

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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),

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and post-hoc analyses of the continuous variable were carried out using univariate general linear

model in SPSS.

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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

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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).

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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

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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.

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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.

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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.

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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.

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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.

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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 0.05-0.98

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.

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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*

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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.

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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 -

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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 -

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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

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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)

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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.

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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

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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

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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,

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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).

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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.

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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.

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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

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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

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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.

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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.

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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,

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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

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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

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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

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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%

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power to detect the differences in AIMS scores observed among the C939T genotypes (Glantz,

1992). To detect such differences, a sample size of at least 120 per genotype group will be

needed for future studies.

The DRD2 gene is unlikely to be the only genetically determined factor for TD, as other

genes have been associated with TD as well. Genetic studies have identified DRD3 to be

reproducibly associated with TD (Steen et al, 1997; Basile et al, 1999; Segman et al, 1999;

Lovlie et al, 2000; Liao et al, 2001; Garcia-Barcelo et al, 2001; Lerer et al, 2002). Studies in

other genes such as HTR2A, HTR2C, CYP1A2, and manganese superoxide dismutase require

further investigation (Hori et al, 2000; Basile et al, 2001; Segman et al, 2000; Segman et al,

2001; Schulze et al, 2001; Tan et al, 2001). As nearly all antipsychotics target more than one

receptor, it is likely that TD is not related to one receptor gene, but rather it is a polygenic

condition with each gene contributing a small proportion of the risk to the disorder. Gene-gene

interaction studies may help in identifying and clarifying pathways that contribute to TD. TD

risk is also likely to be influenced by several environmental factors (Müller et al, 2004), and

acquiring this information will help immensely in increasing the statistical power and limiting

the effects of potential confounders in genetic studies of TD.

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Figure 9. Schematic diagram of the DRD2 gene with its exons and introns. The positions of the 12 polymorphisms used for the present study are indicated within the gene.

5’ 3’

1 2 3 4 5 6 7 8 1 kb ~15kb ~34kb ~5kb

A-241G

-141C Ins/Del

rs4648317

rs1125394

rs1079598

TaqID

C957T

C939T

rs2242591

rs2242593

rs2242592 TaqIA

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Table 13. Statistical analyses on demographics (gender, age) as well as total AIMS scores and TD diagnoses with genotypes of the 12 polymorphisms in DRD2.

TD DRD2 markers N (F/M) Age (years) Total AIMS score +/- SD (N) Yes No

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).

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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).

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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.

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Keywords: Schizophrenia, tardive dyskinesia, gene association, polymorphism, Protein

Kinase B PKB/AKT1, Dopamine Receptor DRD2, Abnormal Involuntary Movement

Scale (AIMS)

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7.1 ABSTRACT

Tardive dyskinesia (TD) represents a potentially irreversible motor side effect

associated with chronic antipsychotic exposure. Dopamine neurotransmission system

changes have been implicated, and a number of studies have focused on the association of

dopamine system gene polymorphisms and TD; for example, we recently found an

association between polymorphisms in the dopamine D2 receptor gene DRD2 and TD.

The small odds ratio, though, suggests additional factors are involved in the

etiopathology of TD. All antipsychotic drugs block the D2 receptors AKT1 acts

downstream of the D2 receptor, and all antipsychotic drugs block the D2 receptor to some

degree. Haloperidol has been shown to alter AKT1 activity. Although AKT1 has been

identified as a candidate gene for schizophrenia, it has not been investigated in TD.

Thus, in the present study, we examined eight polymorphisms spanning the AKT1 gene

and their association with TD in our Caucasian (N=193) and African-American (N=30)

samples. AKT1 polymorphisms and haplotypes were not significantly associated with TD

occurrence or severity as measured by AIMS (Abnormal Involuntary Movement Scale).

However, interaction analysis showed a significant interaction between rs6275 of DRD2

and rs3730358 of AKT1 (p<1X10-5). Taken together, the present study suggests that the

interaction of DRD2 and AKT1 is involved in TD development, though further studies are

required.

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7.2 INTRODUCTION

Tardive dyskinesia (TD) is a motor side effect linked to chronic antipsychotic

treatment that affects between 16 and 43% of SCZ patients treated with conventional or

typical antipsychotics (Tarsy and Baldessarini, 2006). Older age, female gender, and

African American ethnicity have all been suggested to increase the risk and severity of

TD (Kane et al, 1988; Woerner et al, 1998; Smith and Dunn, 1979; van Os et al, 1997;

Jeste et al, 2000). The use of alcohol, tobacco, and recreational drugs can further the risk

of TD (Menza et al, 1991; Bailey et al, 1997; Olivera et al, 1990). As a class the newer,

‘atypical’ antipsychotics have been linked to a diminished risk of motor side effects, but

this advantage has been tempered by substantially higher costs, questionable clinical

superiority, and their own substantial side effects in the form of weight gain and

metabolic disturbances (Lieberman et al, 2005). As a result, there has been a renewed

interest in typical antipsychotic use once again. This, in combination with the fact that

atypicals are not devoid of TD liability, requires that we continue in our pursuit to better

understand TD and those at risk for this potentially irreversible side effect.

Though the etiopathology of TD remains elusive, a number of mechanisms have

been proposed. TD has been postulated to arise from a hypersensitivity of dopamine

receptors secondary to chronic antipsychotic exposure, resulting in excessive function of

dopamine in the central nervous system (CNS). This theory is compatible with the fact

that all marketed antipsychotics block dopamine receptors, albeit to varying degrees

(Tarsy and Baldessarini, 1977; Klawans et al, 1980; Gerlach and Casey, 1988; Abilio et

al, 2003).

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Family studies have suggested a genetic component underlying TD development

(Müller et al, 2001), and several studies have investigated the role of dopaminergic

system genes in TD. The association between DRD3 Ser9Gly and TD (Badri et al, 1996;

Steen et al, 1997) has been confirmed by two meta-analyses (Lerer et al, 2002; Bakker et

al, 2006), while our laboratory recently analyzed twelve DRD2 polymorphisms in TD and

found that haplotypes containing rs6275 and rs6277 were associated with TD (Zai et al,

2007a). A recent meta-analysis conducted by our laboratory (Zai et al, 2007b) also

confirmed the association between DRD2 Taq1A and TD, detected initially by Chen et al

(1997a). Nonetheless, the odds ratios obtained from the meta-analyses ranged from 1.1

to 1.5, supporting the notion that multiple genetic factors influence TD risk. Most TD

genetic studies thus far have analyzed genes that directly affect dopamine metabolism or

dopamine receptor function, but none have investigated the role of gene products that

transduce signals downstream of dopamine receptors.

AKT1, also known as protein kinase B (PKB), is a serine/threonine kinase that is

involved in numerous signaling pathways (Nicholson and Anderson, 2002). It is

important in the regulation of neuronal plasticity (Kim and Chung, 2002; Wang et al,

2003) and synaptic transmission (Beaulieu et al, 2005). Moreover, it has been linked to

both SCZ and dopamine function. Specifically, the AKT1 gene, located at 14q32.32, has

been associated with SCZ in some studies (Emamian et al, 2004; Ikeda et al, 2004;

Schwab et al, 2005; Bajestan et al, 2006), but not others (Ohtsuki et al, 2004; Ide et al,

2006; Liu et al, 2006; Turunen et al, 2007). Beaulieu et al (2004) found that

pharmacological or genetic upregulation of dopamine neurotransmission decreased the

activating phosphorylation of AKT1; the decrease was blocked or attenuated by

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raclopride, a D2/D3 receptor antagonist, and not by SCH23390, a D1 receptor antagonist.

The results were corroborated by the observation that activating phosphorylation of

AKT1 was increased in mice deficient in D2 or D3 (Beaulieu et al, 2007), or by

haloperidol treatment in mice (Emamian et al, 2004). The results suggest that AKT1 acts

downstream of D2 receptors and may be relevant in dopamine related disorders including

schizophrenia and TD.

In the present study, we tested for an association between the AKT1 gene and TD

in our samples of Caucasian and African American schizophrenia patients using eight

polymorphisms spanning the AKT1 gene (Emamian et al, 2004; Schwab et al, 2005;

Table 17, Figure 11). The polymorphisms were selected because they were used

previously in schizophrenia genetic studies and they have high minor allele frequencies.

The linkage disequilibrium chart showing D’ values on the eight polymorphisms is also

provided as a reference in designing future genetic studies (Table 4). In addition, because

AKT1 acts downstream of D2, we also tested for an interaction between DRD2 and AKT1

polymorphisms in TD.

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7.3 PATIENTS AND METHODS

7.3.1 Subjects

The sample for this study is largely the same as the one used in Zai et al (2007a).

Subjects were recruited from four clinical sites in North America: Center for Addiction

and Mental Health in Toronto, Ontario (Remington, N=92); Case Western Reserve

University in Cleveland, Ohio (Meltzer, N=69); Hillside Hospital in Glen Oaks, New

York (Lieberman, N=50); University of California at Irvine, California (Potkin, N=12).

Subjects were selected based on their diagnoses for SCZ or schizoaffective Disorder

according to DSM-III-R or DSM-IV (APA, 2000). All patients had undergone at least

one year of treatment with typical or atypical antipsychotic treatment, and the presence of

TD was evaluated using the Abnormal Involuntary Movement Scale (AIMS) (Guy, 1976;

Schooler and Kane, 1982) or the modified Hillside Simpson Dyskinesia Scale (HSDS) for

the 50 patients from the Hillside Hospital (Basile et al, 1999).

In all, 223 SCZ patients were studied. Of this sample, 193 are Caucasians, with

76 of these subjects positive for the diagnosis of TD. The remaining 30 are African-

Americans, of which 11 are TD-positive. Because of the small sample size, the African-

Americans were used only in the allele frequency association tests.

7.3.2 Gene polymorphism analysis

Genomic DNA was purified from whole blood samples using a non-enzymatic

method previously described (Lahiri and Nurnburger, 1991). The 10µL Polymerase

Chain Reactions were performed on 20ng genomic DNA using ABI TaqMan genotyping

assays under the following conditions: 95oC for 10min, followed by 50 cycles of 92oC for

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15sec and 60oC for 1min. The rs numbers for the markers and their relative positions in

the AKT1 gene are given in Table 17 and Figure 11. Genotypes were determined using

Allelic Discrimination software in the ABI Prism® 7500 Sequence Detection System

(Applied Biosystem, Foster City, CA, USA).

7.3.3 Statistics

Statistical analyses of individual polymorphisms were conducted using the SPSS

program v14.0. The χ2 test was used for fit of genotypes to Hardy-Weinberg equilibrium

and to test for gender differences. The association of genotype frequencies with age and

AIMS was assessed using one-way ANOVA. Where the Levene Test for Homogeneity

of Variance was violated, the Kruskal-Wallis test was performed. The differences in

allele and genotype frequencies between patients with and without TD were analyzed by

the χ2 test. For contingency tables with at least one expected cell count of less than five,

Fisher Exact Tests were performed (http://home.clara.net/sisa/fiveby2.htm). Haplotype

analyses and linkage disequilibrium calculations were conducted using the UNPHASED

v2.402 (Dudbridge et al, 2003) and Haploview v3.2 (Barrett et al, 2005) Programs

respectively. Gene-gene interaction analysis was performed using HELIXTREE

(GoldenHelix), and post-hoc analyses of the continuous variable (AIMS) were carried out

using univariate general linear model in SPSS.

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7.4 RESULTS

7.4.1 Sample Characteristics

The genotype distributions of all the AKT1 gene polymorphisms in the Caucasian

and African American samples did not differ significantly from the Hardy-Weinberg

equilibrium (p>0.10). There was a significant difference in genotype frequencies of

rs2498784 in males versus females (Table 18), while age was not found to be associated

with any of the eight polymorphisms. We assume that the association with sex is

spurious; nonetheless, we included sex as a covariate in the ANCOVA for this marker.

7.4.2 Association analysis of individual polymorphisms with TD occurrence and AIMS

In our Caucasian sample, we did not find a significant association between any of

the eight AKT1 polymorphisms tested and TD, although we found a trend for allele 1 (G)

of rs10149779 to be under-represented in the TD-positive group (p=0.08; Tables 18, 19).

Next, we tested for an association between genotype frequencies and AIMS scores, but

did not find a significant association with any of the eight polymorphisms. Using the

Haploview program, strong evidence for linkage disequilibrium was found between

rs3803304 and rs2494731, prompting us to test for association between TD and two-

marker haplotypes across AKT1 (Figure 12). None of the two-marker haplotypes were

associated with TD or AIMS scores (data not shown). For the African-American sample,

preliminary results indicated a significant association between TD and rs2494738, as well

as a trend for the adjacent rs3730358 (Table 19).

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7.4.3 Significant interaction between DRD2 and AKT1 polymorphisms in TD

Using the Caucasian sample, we analyzed the interaction between the 12 DRD2

polymorphisms reported previously (Zai et al, 2007a) and the eight AKT1 polymorphisms

in this study. Due to the large number of pairwise comparisons, we used the Bonferroni

correction to account for multiple testing. Results are summarized in Figure 13. We

found DRD2 rs6275 to interact with AKT1 rs3730358 (Bonferroni p=0.007), and to

confirm the findings, we conducted a two-way ANOVA under the univariate general

linear model analysis option using SPSS with AIMS as the dependent variable and with

rs3730358 and rs6275 as fixed factors. A graphical representation of the interaction is

given in Figure 14. The interaction between rs3730358 and rs6275 was significant

(p<0.001). Because the assumption of equal variances for ANOVA was violated by the

significant Levene’s Test, we conducted the same analysis using ranked AIMS as

dependent variable in place of AIMS. The results were equally significant (p=0.008).

More detailed analysis showed that the average total AIMS scores of subjects with

genotype 2/2 (T/T) at rs6275 were higher than subjects with genotype 1/2 (C/T), and

those with genotype 1/2 (C/T) were higher than those with genotype 1/1 (C/C), as

reported previously {linear regression: r=0.244, F=9.978, p(1,158)=0.002} (Zai et al,

2007a). While the same association was observed at rs6275 in patients with genotype 1/1

(C/C) at rs3730358 {linear regression: r=0.409, F=21.529, p(1,107)<0.001}, those of

carriers with allele 2 (T) at rs3730358 did not follow the trend at rs6275. Instead, carriers

of allele 2 (T) at rs3730358 had similar total average AIMS scores among the three

rs6275 genotype groups {linear regression: r=0.061, F=0.156, p(1,42)=0.695}. When we

examined rs6275 and rs3730358 in TD, we found a significant under-representation of

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the 1/1 (C/C) genotype at rs6275 in TD-positive patients with 1/1 (C/C) genotype at

rs3730358 (p=0.006), but not in TD-positive patients carrying at least one copy of allele 2

(T) at rs3730358 (p=0.99).

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7.5 DISCUSSION

This is the first reported genetic association study between AKT1 and TD. While

the results are not significant for AKT1 by itself in our Caucasian sample, we found that

DRD2 rs6275 interacts with AKT1 rs3730358 in TD. Specifically, allele 2 (T) at

rs3730358 restricts the genotypic effects of rs6275 on TD occurrence and severity. This

finding is in agreement with the strong evidence that AKT1 is a downstream signal

transducer of the D2 receptor (Beaulieu et al, 2007). While this positive gene-gene

interaction result is intriguing, the methods for gene-gene interaction analyses are not

well established, thus these results should therefore be considered preliminary.

Nevertheless, the genetic interaction reported herein survived Bonferroni correction and

encourages further investigations into the molecular mechanisms underlying this

association.

We recently reported an association of rs6275 and an adjacent functional

polymorphism rs6277 with TD (Zai et al, 2007a). While rs6277 genotypes are associated

with D2 expression (Duan et al, 2003; Hirvonen et al, 2004), they do not appear to

interact strongly with AKT1 polymorphisms to affect AIMS score (Figure 3). rs6275

does not appear to be associated with D2 expression (Duan et al, 2003); nevertheless, it

may influence RNA splicing at exon 6, thus changing the ratio of long and short D2

isoforms. AKT1 haplotypes containing the C allele at rs3730358 have been associated

with altered AKT1 expression (Emamian et al, 2004). Interaction analysis using

haplotypes may provide valuable information about the regions of biological interest

within DRD2 and AKT1, but larger samples will be required for such extensive

examinations.

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The lack of association between AKT1 and TD in our Caucasian sample could be

a true negative result; alternatively, the polymorphisms may only have a small

contributing effect to the risk for TD, and the sample size in the current study provides

insufficient power to detect the small difference in AIMS observed among the genotypes.

Under reasonable assumptions (α=0.05, risk allele frequency=0.3), the current sample

has 82.1% power to detect a risk ratio for TD of as low as 2.0. The association between

rs2494738 and TD in our African-American sample, as well as the rs6275-rs3730358

interaction requires replication in larger samples.

The present study has several limitations. First, not all relevant clinical data were

available for our study. These include medication history (antipsychotics, dose,

duration), schizophrenia disease history (onset, clinical subtypes, severity), and co-

morbidities. Medications taken by patients for other adverse effects (e.g., parkinsonism,

anxiety) could mask TD (Shale and Tanner, 1996; Egan et al, 1997; Glazer, 2000).

Conversely, environmental risk factors such as smoking have been identified as a risk

factor for TD and could contribute to our findings, though we do not have such

information available for our entire sample. Finally, different manifestations (e.g., facial

versus truncal) of the TD phenotype could have different genetic contributions. We

evaluated total AIMS scores because our sample sizes would have been too small to have

enough power to detect an association with specific subtypes of TD. On the other hand,

TD is likely less sensitive to design-related variables than presumably more complex

phenotypes such as antipsychotic response.

Meta-analyses support the association of DRD2 (Zai et al, 2007b), DRD3 (Lerer

et al, 2002; Bakker et al, 2006), HTR2A (Lerer et al, 2005), and CYP2D6 (Patsopoulos et

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al, 2005) with TD. Studies of other genes such as HTR2C, CYP1A2, and manganese

superoxide dismutase are preliminary and require replication to confirm their potential

association with TD (Basile et al, 2000; Segman et al, 2000; Schulze et al, 2001; Hori et

al, 2000). As all antipsychotics target more than one receptor and behavioural

phenotypes are complex, it is likely that TD is a polygenic condition with each gene

contributing a small proportion of the risk. TD risk is also likely to be influenced by

many environmental factors (Menza et al, 1991; Bailey et al, 1997; Olivera et al, 1990).

Acquiring this additional information for future samples will help in limiting effects of

potential confounds in genetic studies of TD and, in doing so, increase the predictive

value of future genetic tests for TD. The present study encourages further investigations

of the interactions among genes along signaling pathways in deciphering the

pathophysiology of TD and other complex genetic diseases.

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Figure 11. Schematic diagram of the AKT1 gene with its exons and introns. The darker color indicates the coding region. The positions of the eight polymorphisms used for the present study are indicated within the gene.

5’ 3’

1 2 3 4 5 6 7 8 9 10 11 12 13 14 1 kb

rs2498784

rs1130214

rs2494746

rs10149779

rs2494738

rs3730358

rs2494731

rs3803304

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Table 17. ABI Assays-on-Demand with information on their corresponding AKT1 polymorphisms used in the present study. VIC and FAM are fluorescent dyes that are conjugated onto probes specific for the corresponding alleles of each polymorphism. Assay-on-Demand

Allele 1 FAM

Allele 2 VIC

Polymorphism Name(s)

Location with respect to AKT1 gene

References

rs2498784 C T SNP1a, G-5983A 5’ Schwab et al, 2005 rs1130214 G T SNP2, C-754A Intron 1 Emamian et al,

2004 rs2494746 G C G1261C Intron 2 rs10149779 C T SNP2a, G7894A Intron 2 Schwab et al, 2005 rs2494738 C T G12294A Intron 2 rs3730358 C T SNP3, G12573A,

G3+18A Intron 3 Emamian et al,

2004 rs3803304 G C G19834C,

G11+69C Intron 11

rs2494731 G C G21300C Intron 12

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Table 18. Statistical analyses on demographics (sex, age) as well as total AIMS scores and TD occurrence with each of the eight AKT1 polymorphisms. DRD2 markers N (M/F) Age (years) Total AIMS score TD (Yes/No) rs2498784 1/1 (C/C)

1/2 (C/T) P

162(116/46) 27(11/16) 3(1/2) 0.002*

38.04+/-10.14 37.63+/-8.84 29.67+/-8.74 0.353

6.28+/-7.90 5.50+/-4.85 0.00+/-0.00 0.177#

63/99 13/14 0/3 0.266*

rs1130214 1/1 (G/G) 1/2 (G/T) 2/2 (T/T) P

111(68/43) 59(44/15) 17(12/5) 0.201

37.81+/-10.13 37.27+/-9.46 38.71+/-10.90 0.863

5.58+/-7.36 7.04+/-7.91 6.38+/-7.58 0.544

38/73 28/31 7/10 0.239

rs2494746 1/1 (G/G) 1/2 (G/C) 2/2 (C/C) P

149(106/43) 31(16/15) 4(2/2) 0.060*

38.05+/-10.08 37.97+/-9.05 27.25+/-8.62 0.101

6.20+/-7.82 4.58+/-4.37 3.67+/-6.35 0.749#

58/91 12/19 1/3 1.000*

rs10149779 1/1 (C/C) 1/2 (C/T) 2/2 (T/T) P

113(70/43) 57(41/16) 16(11/5) 0.417

37.53+/-10.09 38.12+/-9.68 37.75+/-10.98 0.936

5.52+/-7.35 7.79+/-7.98 6.00+/-7.46 0.239

39/74 29/28 7/9 0.116

rs2494738 1/1 (C/C) 1/2 (C/T) 2/2 (T/T) P

172(115/57) 13(8/5) 1(1/0) 0.842*

37.73+/-10.02 39.31+/-9.40 21.00 0.211

6.04+/-7.58 2.91+/-2.66 8.00 0.498#

66/106 4/9 1/0 0.526*

rs3730358 1/1 (C/C) 1/2 (C/T) 2/2 (T/T) P

131(84/47) 55(39/16) 1(0/1) 0.221*

38.31+/-9.91 37.25+/-9.94 25.00 0.345

6.04+/-7.87 6.11+/-6.51 7.00 0.991

51/80 22/33 1/0 0.540*

rs3803304 1/1 (G/G) 1/2 (G/C) 2/2 (C/C) P

10(6/4) 76(51/25) 102(67/35) 0.873*

31.60+/-9.12 38.13+/-9.84 38.02+/-10.06 0.137

3.90+/-3.70 7.10+/-8.00 5.81+/-7.59 0.369

4/6 31/45 40/62 0.969*

rs2494731 1/1 (G/G) 1/2 (G/C) 2/2 (C/C) P

80(53/27) 88(60/28) 22(13/9) 0.722

38.01+/-10.21 38.57+/-9.89 33.95+/-9.12 0.149

6.00+/-7.98 6.41+/-7.56 5.84+/-6.07 0.931

32/48 32/56 12/10 0.298

* With at least 1 expected cell count <5; Fisher Exact Test was used. # Variances among comparisons groups differ significantly; Kruskal-Wallis test was used. Bolded numbers indicate 0.05<p<0.10; bolded and italicized numbers indicate p<0.05.

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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.

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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).

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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

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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

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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’

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Zai et al VIII. Discussion

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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.

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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

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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

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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.

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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

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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.

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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

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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

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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.

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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

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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).

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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

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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.

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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

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or inactivated to explore the biological effects caused by the genetic variations and their

interactions.

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CHAPTER 9

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