Molecular Psychiatry (2005) 10, 719–740
& 2005 Nature Publishing Group All rights reserved 1359-4184/05 $30.00
www.nature.com/mp
REVIEW ARTICLE
Genetic tests of biologic systems in affective disorders
E Hattori1,2, C Liu1, H Zhu1 and ES Gershon1
1
Department of Psychiatry, The University of Chicago, Chicago, IL, USA; 2Laboratory for Molecular Psychiatry, RIKEN Brain
Science Institute (BSI), Wako, Saitama, Japan
To liberate candidate gene analyses from criticisms of inexhaustiveness of examination of
specific candidate genes, or incompleteness in the choice of candidate genes to study for
specific neurobiological pathways, study of sizeable sets of genes pertinent to each putative
pathophysiological pathway is required. For many years, genes have been tested in a ‘one by
one’ manner for association with major affective disorders, primarily bipolar illness. However,
it is conceivable that not individual genes but abnormalities in several genes within a system
or in several neuronal, neural, or hormonal systems are implicated in the functional
hypotheses for etiology of affective disorders. Compilation of candidate genes for entire
pathways is a challenge, but can reasonably be carried out for the major affective disorders as
discussed here. We present here five groupings of genes implicated by neuropharmacological
and other evidence, which suggest 252 candidate genes worth examining. Inexhaustiveness of
gene interrogation would apply to many studies in which only one polymorphism per gene is
analyzed. In contrast to whole-genome association studies, a study of a limited number of
candidate genes can readily exploit information on genomic sequence variations obtained
from databases and/or resequencing, and has an advantage of not having the complication of
an extremely stringent statistical criterion for association.
Molecular Psychiatry (2005) 10, 719–740. doi:10.1038/sj.mp.4001695; published online 31 May 2005
Keywords: mood disorders; candidate genes; association; linkage disequilibrium; biologic
pathways
Identifying susceptibility genes has long been challenging in studies of major affective disorders, as well
as in other common complex diseases such as
schizophrenia, asthma, diabetes, and cardiovascular
diseases. Association study, which typically examines differences in allele frequency of a genetic
marker between cases (affecteds) and controls, remains a major approach of disease gene mapping, and
has been employed to examine possible roles of
candidate genes in the etiology of a disease of interest.
Genomewide linkage analysis, in contrast, does not
limit itself to a particular genomic region, apparently
avoiding the risk of overlooking any genes with poor
or even no information on biological functions. For
more than two decades, linkage mapping has proven
to be remarkably effective to guide researchers to
numerous disease genes, each predisposing to a
Mendelian trait. Also, development of computer
algorithms for model-free linkage analysis has considerably facilitated appropriate genetic dissections of
complex phenotypes with unknown mode of inheritance. In studies of major psychiatric illnesses,
Correspondence: Current address: Dr E Hattori, Laboratory for
Molecular Psychiatry, RIKEN Brain Science Institute, 2-1 Hiro-sawa,
Wako, Saitama 351-0198, Japan.
E-mail: ehattori@brain.riken.jp
Received 29 September 2004; revised 12 April 2005; accepted
2 May 2005
evidence of linkage has led to the detection of
associations of specific genes with illness: dysbindin
1 (DTNBP1)1 on chromosome 6p and neuregulin 1
(NRG1)2 on 8p for schizophrenia, and G72/G30 on 13q
for both schizophrenia3 and bipolar disorder.4 These
genes have been demonstrated to be associated with
schizophrenia and/or bipolar disorder in multiple
independent data sets.5–8 However, model-free linkage analysis is unlikely to detect genes with very
weak effects (modest increase in probability of illness,
given the associated allele). This has led to a
resurgence of association analysis because of its much
higher statistical power, particularly in the studies of
complex diseases, where multiple genes are considered to exert weak effect along with environmental
factors.9
Candidate gene association studies have historically been plagued by nonreplication. A recent
meta-analysis of genes that had a large number of
association studies emphasized possible contribution
of false-negative underpowered studies to inconsistent results, and suggested consistent weak effects of
the genes for serotonin receptor 2A (HTR2A) and
dopamine receptor D3 (DRD3) on susceptibility to
schizophrenia.10 Thus, inconsistencies among reports
may be consequences of what previous studies have
failed to address. Until recently, it has only been
feasible to interrogate a few genes in particular
systems, and these interrogations have often been
Genetic tests of systems in affective disorders
E Hattori et al
720
limited to very few polymorphisms in a limited
number of subjects, because of limitations in the
costs of genotyping, and in the knowledge of the
human genome. Advances in genomics and bioinformatics, in high-throughput genotyping, in statistical
analysis, and in the availability of large samples of
patients with well-defined phenotypes, as well as
comparable numbers of matched controls, can be
expected to enhance the likelihood of detection of
valid associations.
What remains as the most serious concern about the
paradigm of candidate gene association study is its
‘incompleteness’ resulting from ad hoc selection of
candidate genes. A priori hypotheses have to be made
on the primary cause of the disease being studied,
when starting a candidate gene study. For many years,
genes have usually been tested in a ‘one by one’
manner for association with major affective disorders,
primarily bipolar illness. However, it is also conceivable that not individual genes but abnormalities
in several genes within a system or in several
neuronal, neural, or hormonal systems are implicated
in the functional hypotheses for etiology of affective
disorders. Analysis of entire systems examining a
same sample set has only rarely been undertaken,11,12
and examples of definitive success have yet to be
seen. At this time, analysis of a well-chosen and
comprehensive set of candidate genes, with the
support of informatics analysis of the genomic
structure of each gene, may yield successful detection
of specific genes and pathways associated with
illness.
In the following sections, we first discuss the
advantage of a hypothesis-based systematic association study on a limited number of candidate genes in
contrast to the whole-genome association study, and
subsequently demonstrate that, in the case of affective
disorders, compilation of candidate genes pertinent
to each major neurobiological system suggested for
susceptibility can be reasonably carried out. The idea
of testing systems can be generalized to studies of
other common complex diseases.
Systematic candidate gene study vs whole-genome
association study
Association studies are intended to capture linkage
disequilibrium (LD) between genotyped markers and
a disease causal variant, including when the marker
being genotyped happens to be exactly the causal
variant. Recent studies have revealed that chromosomal segments, spanning a few to hundreds of kilobases, can often be represented by only a few
haplotypes because of strong underlying LD that
associates specific alleles at each polymorphic site
in the segment. Such a segment is called a ‘haplotype
block’ and the analysis of a block can be achieved by
typing a small set of SNPs (haplotype tag SNPs or
htSNPs) that are most informative for discriminating
haplotypes.13,14 The International HapMap project15
(see Electronic-Database Information) has just comMolecular Psychiatry
pleted genotyping of a million SNPs in four different
populations with the aim of providing information on
genomic variations including the extent of LD,
haplotype blocks and htSNPs. This modeling of
genomic variations especially favors the idea of
whole-genome LD mapping, which aims to locate
disease susceptibility variants using a set of limited
number of markers across the entire genome.
However, it is open to question how informative the
limited number of SNPs can be in terms of sequence
variations of the genome. First, it is unclear to what
degree the entire genome can be captured by blocklike structures. Since haplotype blocks reflect underlying LD, whose extent varies considerably across the
genome, short blocks may become evident only by
highly dense genotyping. According to a simulation
under a recombination hot-spot model by Wall and
Pritchard,16 even genotyping with a marker density
obtained by resequencing would capture only up to
71% of the entire genome as blocks. Besides, gene
conversion, which is not incorporated into this
model, seems to have generated discrepancies between haplotype block fractions observed in actual
data and predicted by simulations. Gene conversion
can give rise to a ‘hole’ in an LD or a haplotype block,
and a susceptibility variant in such a hole is likely to
be overlooked. Secondly, even when a haplotype
block is evident, it is unclear if markers from the
databases capture sufficient haplotype diversity. For
example, a haplotype with an estimated frequency of
45% may really be a group of three haplotypes each
with a frequency of 15%. This loss of information can
substantially reduce the power of detecting association depending on the frequency of a causal variant.
Selecting the most informative markers not depending on the haplotype block model, as suggested in
Carlson et al,17 would considerably circumvent these
problems. Resequencing of genomic regions of interest will also be necessary (see Electronic-Database
Information for current examples). From the viewpoint of the number of SNPs to be genotyped, these
approaches look feasible for a study of a limited
number of candidate genes, but not for whole-genome
LD mapping or for its gene-focused form.9,18,19
Since the prior probability of association for a
biologic candidate gene can be expected to be
considerably higher than that for a gene randomly
picked up from the genome, candidate gene approach
may benefit from increased statistical power by
analysis controlling ‘false discovery rate’.20 Even in
the conventional statistical tests (eg Bonferroni
procedure) for multiple hypotheses, which control
overall type I error rate, the study of limited number
of candidates derived from a few hypotheses would
not suffer from the complication of a very stringent
statistical criterion for association, because the number of markers would be less than in a whole-genome
LD mapping. Also, a method has recently been
developed to detect a set of associated genes, which
may statistically interact with each other.21 Interpretation of results of this analysis can be more
Genetic tests of systems in affective disorders
E Hattori et al
straightforward when we study multiple genes with
known biologic functions. Thus, whole-genome association study does not replace the candidate gene
approach using a sufficient number of informative
markers, particularly when we are anxious about
missing associations.
Genetic testing of functional systems (pathways) in
major affective disorders
Since the first monoamine hypothesis (Figure 1) of
depression, based on biochemical pharmacology of
antidepressants and reserpine, numerous hypotheses
of dysregulation of functional systems in mood
disorders have been suggested, but so far no consensus has been reached on any primary molecular
mechanism underlying mood disorder susceptibility.
Nonetheless, the choice of several systems (Table 1)
over others for intensive genetic study can be
supported by their relevance to clinical features as
Figure 1 Evolution of ‘monoamine hypothesis’. A major
hypothesis for the biology of depression was developed in
the 1960s, initially proposing that depletion of norepinephrine (NE), and later proposing depletion of serotonin
(5-HT) and dopamine (DA), underlie the illness.100 This
‘monoamine hypothesis’ was proposed because of the
clinical observation that depression often occurs in subjects
taking reserpine, an antihypertensive agent, which depletes
monoamines from the synaptic vesicles. Also, consistent
with the hypothesis was that tricyclic antidepressants and
MAO inhibitors were found to increase synaptic monoamine concentrations. The hypothesis was later modified to
include alterations of monoamine receptor properties so
that it would encompass an explanation for the time
(usually days to weeks) required for an antidepressant to
take clinical effect despite its immediate action to elevate
synaptic monoamine levels.101,102 However, either the
original or modified form of hypothesis has not been
definitively demonstrated so far. Involvement of postsynaptic signaling is now of interest to researchers. A diagram for
NE neurotransmission is shown as an example. The postsynaptic receptors for NE are coupled to guanine nucleotide
binding proteins (G proteins), which transduce
neurotransmitter stimulation to second messenger signaling
systems such as cAMP and phosphoinositide pathways.
Now that numerous components in the NE neurotransmission system have been identified, including metabolic
enzymes, receptors, transporters, and postsynaptic signaling (eg one or more subtypes of G proteins, protein kinase A,
protein kinase C, calcium/calmodulin-dependent protein
kinases), emphasis is being placed on the broader view of
dysregulation in the entire system,103 taking interactions of
each component into account. Components are presented at
the gene level in Table 2. NE: norepinephrine; VMAT:
vesicular monoamine transporters; MAO: monoamine oxidases, COMT: catechol-O-methyltransferase; ax-AR: axadrenerigc receptors; b1-AR: beta-1-adrenergic receptor; G:
G proteins; AC: adenylate cyclases; PL: phospholipases;
PKA: cAMP-dependent protein kinases; PKC: calciumdependent protein kinases; CREB: cAMP-responsive element binding protein; NET: norepinephrine transporter;
DAT: dopamine transporter.
well as by accumulated neurobiological and neuropharmacological findings.
Phenotypic subclasses of the entire spectrum of
affective disorders may have different associations
with the systems in Table 1. However, we need not
assume too much about a specific relationship
between systems and subclasses. When samples from
different types of affective disorders with abundant
clinical records are available, it may be more reasonable to conduct genetic analyses on numerous
phenotypic variables after completion of genotyping.
Such an approach has successfully been employed in
detecting association between the PDE4D gene and
ischemic type stroke in the analysis of all the samples
from broadly defined common forms of stroke.22
The first step of compiling candidate genes in a
given functional hypothesis is to list genes involved
in pathways which represent that hypothesis. GO and
KEGG databases, for example (see Electronic-Database
Information), help overview a set of genes involved in
a particular intracellular pathway. To obtain information on genetic components specifically relevant to
the phenotypes of interest, intensive literature survey
or review is required. There have been a huge number
of reports on specific proteins (sometimes specific
subtypes) altered in post-mortem brains from bipolar
disorder subjects or in brains from rodents treated
with mood stabilizers. Also, animal models and
systematic expression analyses by microarray or
differential display assay provide information on
molecules relevant to mood disorders not only at
the protein level but also at the gene expression level.
721
Tyrosine
(Enzymatic
steps for
synthesis)
VMATs
MAO
NE
α2-ARs
NET and DAT
(Degradation)
α2A-AR
α2C-AR
β1-AR
COMT
G
ATP
MAO
AC
PL
G
α1-ARs
cAMP
Ca++
PKA
CREB
phosphoinositide
pathway
Calmodulin
PKC
Calcium/Calmodulin
dependent protein
kinases
Substrates
(e.g.MARCKS,
Phosphlipase A2)
Regulatory elements
DNA
Molecular Psychiatry
Genetic tests of systems in affective disorders
E Hattori et al
722
Table 1
Major neurobiological/neuropharmacological systems suggested for roles in major affective disorder
Systems
Subsystems or a group of genes to be analyzed
1.
Neurotransmission systems
Monoaminergic neurotransmission (adrenergic, serotonergic
and dopaminergic)
Cholinergic neurotransmission
Amino-acid neurotransmission (GAGAergic and glutamatergic)
Other neurotransmitter or neuromodulator systems
(peptidergic, opioid and others)
2.
A neuroendocrine system
HPA axis
3.
Neurotrophic and growth factor
systems
Neurotrophic/growth factors and shared signaling pathways
4.
Circadian rhythm
Clock genes (eg CLOCK, ARNTL1, ARNTL2, CRY1, CRY2)
Pathways for entrainment to light/darkness cycle and outputs
of suprachiasmatic nucleus (eg ADCYAP1, TGFA, PROK2)
5.
Genes implicated in
pathophysiology of other
diseases relevant to major
affective disorders
Parkinson’s disease genes (eg PARK2, SCNA, UCHL1)
Schizophrenia-related genes (eg NRG1, DTPN1) and genes in
the myelination system (eg MBP, MOG, NRG1)
Genes repeatedly reported to be associated with the
phenotype of interest should be included. In addition,
databases being developed (see GEO and WebQTL in
Electronic-Database Information) allow for retrieving
and analyzing gene expression data according to
researchers’ particular interest, and may contribute
to more extensive compilation of candidate genes in
near future.
There are problems in this approach, though. First,
manual literature mining on which compilation of
candidate genes mostly depends is a tedious procedure. Secondly, there is no completely objective
criterion to determine genes representing each hypothesis. Microarray studies may provide valid quantitative data on difference in expression level of each
gene between bipolar and healthy subjects or between
disease model and wild-type animals. However, such
data may represent secondary effects of illness or
treatment, or species-specific effects. Although a
genetic association strategy can resolve this possibility, it will be a very small fraction of differentially
expressed genes that directly affect susceptibility to
the illness.
Despite these challenges, we have carried out
compilation of a list of candidate genes pertinent
to major hypotheses of major affective disorders
(Table 2).
Neurotransmission systems
The monoamine (adrenergic, dopaminergic, and
serotonergic) neurotransmission systems, which
were the first to be hypothesized as systems whose
derangements cause mood disorders, are offered here
as a detailed example of candidate gene selection in
the neurotransmission systems (see also Figure 1).
Molecular Psychiatry
The genes for tyrosine hydroxylase (TH), which is
a rate-limiting enzyme for dopamine and norepinephrine synthesis, and serotonin transporter
(SLC6A4), which is a pump molecule for reuptake
of synaptic serotonin into the presynaptic nerve
terminal, have been among the most frequently
studied candidates for affective disorders. Abnormalities of these genes can lead to decreased vesicular or
synaptic monoamine levels as predicted by the
original monoamine hypothesis. However, association has not been consistently replicated for any genes
for synaptic components including TH and SLC6A4.23
Genes for synaptic components of monoamine systems, nonetheless, still deserve genetic analysis,
given the possible insufficiency of sample size and
number of markers analyzed in the previous studies.
In the presynaptic nerve terminal, these include genes
for synthetic enzymes (eg TH, DBH, DDC), synaptic
vesicle monoamine transporters (SLC18A1 and
SLC18A2), and reuptake transporters (SLC6A2,
SLC6A3, and SLC6A4), with many being shared
between the three neurotransmitters (Table 2). Monoamines bind to pre- and postsynaptic receptors, of
which numerous subtypes have been found so far.
The list includes seven genes coding for adrenergic
receptors, five for dopaminergic receptors, and 14 for
serotonergic receptors, omitting those with limited
roles in the brain such as beta-2-adrenergic receptor
(ADRB2). Genes for catabolic enzymes bound to
postsynaptic membrane (monoamine oxidases
(MAOA and MAOB) and catechol-O-methyltransferase (COMT)) have also been included in the list.
Abnormality in postsynaptic signaling in bipolar
disorder was first proposed in the phosphoinositide
cycle because it is affected by lithium administration.24,25 Myo-inositol monophosphatase is inhibited
Table 2 Candidate genes pertinent to each putative pathological system: 1. Neurotransmission; 2. A neuroendocrine system; 3. Neurotrophic/growth factor systems; (1–3)
Intracellualr signaling largely shared by 1–3; 4. Circadian rhythm; 5. Genes implicated in the pathophysiology of other disease relevant to major affective disorders
Symbols
Genes
ABCG1
DBH
DRD1
DRD2
DRD3
DRD4
DRD5
NR4A2
1A
1B
1D
1E
1F
2A
2B
2C
3A
3B
4
5A
6
7
MAOA
Monoamine oxidase A
MAOB
Monoamine oxidase B
NURR1
Chromosomal
region
Genomic
size (bp)
Referencesa
104
Norepinephrine
Norepinephrine
Norepinephrine
Norepinephrine
Norepinephrine
Norepinephrine
Norepinephrine
5-HT
5-HT
5-HT
5-HT
5-HT
5-HT
5-HT
5-HT
5-HT
5-HT
5-HT
5-HT
5-HT
5-HT
5-HT
5-HT
5-HT
5-HT
Receptor
Receptor
Receptor
Receptor
Receptor
Receptor
Receptor
Metabolic enzyme
Metabolic enzyme
Metabolic enzyme
Receptor
Receptor
Receptor
Receptor
Receptor
Receptor
Receptor
Receptor
Receptor
Receptor
Receptor
Receptor
Receptor
Receptor
Transporter
8p21.2
5q33.3
20p13
10q25.2
2q11.2
4p16
10q25.3
4p15.32
11p15.1
12q15
5q12.3
6q14.1
1p36.12
6q15
3q11.1
13q14.2
2q37.1
Xq23
11q23.2
11q23.2
5q32
7q36.2
1p36.13
10q23.31
17q11.2
117 256
55 762
27 844
3650
3266
2819
1714
25 691
19 772
93 595
1269
1173
2835
78 988
1101
62 661
15 587
326 074
15 195
41 378
172 618
13 583
14 276
115 268
24 118
5-HT
Dopamine
Dopamine
Dopamine
Dopamine
Dopamine
Dopamine
Dopamine
Others
Metabolic enzyme
Receptor
Receptor
Receptor
Receptor
Receptor
Others
21q22.3
9q34.2
5q35.2
11q23.2
3q13.31
11p15.5
4p16.1
2q24.1
77 974
22 982
3127
65 577
50 200
3400
2032
8250
122
123
124
125
126
127
Multiple
monoaminergic
systems
Multiple
monoaminergic
systems
Multiple
monoaminergic
systems
Metabolic enzyme
7p12.2
102 610
129
Metabolic enzyme
Xp11.3
70 206
130
Metabolic enzyme
Xp11.3
115 765
104
105, 106
106, 107
108
109, 110
111,112
113
114
115
113
116
117
118
119
120
121
128
723
Molecular Psychiatry
DDC
ATP-binding cassette subfamily G member 1
Dopamine beta-hydroxylase precursor
D1 dopamine receptor
D2 dopamine receptor
D3 dopamine receptor
D4 dopamine receptor
D5 dopamine receptor
Nuclear receptor subfamily 4, group A,
member 2
DOPA decarboxylase
HTT
SERT
Subcategories
Genetic tests of systems in affective disorders
E Hattori et al
1. Neurotransmission system
Monoaminergic neurotransmission
ADRA1A
Alpha-1A-adrenergic receptor
ADRA1B
Alpha-1B-adrenergic receptor
ADRA1D
Alpha-1D-adrenergic receptor
ADRA2A
Alpha-2A-adrenergic receptor
ADRA2B
Alpha-2B-adrenergic receptor
ADRA2C
Alpha-2C-adrenergic receptor
ADRB1
Beta-1-adrenergic receptor
QDPR
Quinoid dihydropteridine reductase
TPH1
Tryptophan hydroxylase 1
TPH2
Tryptophan hydroxylase 2
HTR1A
5-Hydroxytryptamine (serotonin) receptor
HTR1B
5-Hydroxytryptamine (serotonin) receptor
HTR1D
5-Hydroxytryptamine (serotonin) receptor
HTR1E
5-Hydroxytryptamine (serotonin) receptor
HTR1F
5-Hydroxytryptamine (serotonin) receptor
HTR2A
5-Hydroxytryptamine (serotonin) receptor
HTR2B
5-Hydroxytryptamine (serotonin) receptor
HTR2C
5-Hydroxytryptamine (serotonin) receptor
HTR3A
5-Hydroxytryptamine (serotonin) receptor
HTR3B
5-Hydroxytryptamine (serotonin) receptor
HTR4
5-Hydroxytryptamine (serotonin) receptor
HTR5A
5-Hydroxytryptamine (serotonin) receptor
HTR6
5-Hydroxytryptamine (serotonin) receptor
HTR7
5-Hydroxytryptamine (serotonin) receptor
SLC6A4
Solute carrier family 6 (neurotransmitter
transporter, serotonin), member 4
Aliases
724
Molecular Psychiatry
Table 2 Continued
Symbols
Genes
Aliases
Tyrosine hydroxylase
COMT
Catechol-O-methyltransferase
SLC6A2
Solute carrier family 6 (neurotransmitter
transporter, noradrenalin), member 2
NET
SLC6A3
Solute carrier family 6 (neurotransmitter
transporter, dopamine), member 3
DAT
SLC18A1
Solute carrier family 18 (vesicular
monoamine), member1
VMAT1
SLC18A2
Solute carrier family 18 (vesicular
monoamine), member2
VMAT2
Cholinergic neurotransmission
CHAT
Choline acetyltransferase
CHRNA3
Cholinergic receptor, nicotinic,
alpha polypeptide 3
CHRNA4
Cholinergic receptor, nicotinic,
alpha polypeptide 4
CHRNA5
Cholinergic receptor, nicotinic,
alpha polypeptide 5
CHRNA6
Cholinergic receptor, nicotinic,
alpha polypeptide 6
CHRNA7
Cholinergic receptor, nicotinic,
alpha polypeptide 7
CHRNB2
Cholinergic receptor, nicotinic,
beta polypeptide 2
CHRNB3
Cholinergic receptor, nicotinic,
beta polypeptide 3
CHRM1
Cholinergic receptor, muscarinic 1
CHRM2
Cholinergic receptor, muscarinic 2
CHRM4
Cholinergic receptor, muscarinic 4
Amino-acid neurotransmission
GABRA1
Gamma-aminobutyric acid (GABA) A receptor,
alpha 1
GABRA2
Gamma-aminobutyric acid A receptor, alpha 2
GABRA3
Gamma-aminobutyric acid A receptor, alpha 3
GABRA5
Gamma-aminobutyric acid A receptor, alpha 5
GABBR1
Gamma-aminobutyric acid B receptor 1
SLC6A1
Solute carrier family 6 (neurotransmitter
transporter, GABA), member 1
Referencesa
Chromosomal
region
Genomic
size (bp)
Metabolic enzyme
11p15.5
7887
131, 132
Metabolic enzyme
22q11
27 047
133
Transporter
16q12.2
46 031
Transporter
5p15.33
52 637
134, 135
Vesicular
transporter
8p21.3
38 346
64
Vesicular
transporter
10q26.11
36 203
136
Metabolic enzyme
Receptor
10q11.23
15q24.3
56 010
25 679
137, 138
Receptor
20q13.33
16 298
137, 138
Receptor
15q24.3
27 806
137, 138
Receptor
8p11.21
15 857
137, 138
Receptor
15q13.3
184 762
137, 138
Receptor
1q22
8827
137, 138
Receptor
8p11.21
39 290
137, 138
Receptor
Receptor
Receptor
11q12.3
7q33
11p11.2
1383
1401
1455
139
44
Multiple
monoaminergic
systems
Multiple
monoaminergic
systems
Multiple
monoaminergic
systems
Multiple
monoaminergic
systems
Multiple
monoaminergic
systems
Multiple
monoaminergic
systems
GABA
Receptor
5q34
50 180
140
GABA
GABA
GABA
GABA
GABA
Receptor
Receptor
Receptor
Receptor
Transporter
4p12
Xq28
15q12
6p22.1
3p25.3
140 186
283 210
34 408
30 856
21 553
141–143
141, 142
141, 142
144
145–147
Genetic tests of systems in affective disorders
E Hattori et al
TH
Subcategories
SLC6A11
GAD65
GAD1
Glutamate decarboxylase 1
GAD67
ABAT
4-Aminobutyrate aminotransferase precursor
GABA-T
GLRA3
GLRB
GRIA1
GRIA2
GRIA3
GRIA4
GRIK1
GRIK2
GRIK3
GRIK4
GRIK5
GRIN1
Glycine receptor, alpha 3
Glycine receptor, beta
Glutamate receptor, ionotropic, AMPA 1
Glutamate receptor, ionotropic, AMPA 2
Glutamate receptor, ionotropic, AMPA 3
Glutamate receptor, ionotrophic
Glutamate receptor, ionotropic kainate 1
Glutamate receptor, ionotropic kainate 2
Glutamate receptor, ionotropic kainate 3
Glutamate receptor, ionotropic kainate 4
Glutamate receptor, ionotropic kainate 5
N-methyl-D-aspartate receptor subunit
zeta 1 (precursor)
N-methyl-D-aspartate receptor subunit 2A
N-methyl-D-aspartate receptor subunit 2B
N-methyl-D-aspartate receptor subunit 2C
N-methyl-D-aspartate receptor subunit 2D
Glutamate receptor, metabotropic 1
Glutamate receptor, metabotropic 2 precursor
Glutamate receptor, metabotropic 3 precursor
Metabotropic glutamate receptor 4
Glutamate receptor, metabotropic 5
Glutamate receptor, metabotropic 6 precursor
Glutamate receptor, metabotropic 7
Metabotropic glutamate receptor 8 precursor
Solute carrier family 1 (neuronal/epithelial
high affinity glutamate transporter,
system Xag), member 1
Solute carrier family 1 (glial high-affinity
glutamate transporter), member 2
Solute carrier family 1 (glial high-affinity
glutamate transporter), member 3
Solute carrier family 1 (high-affinity aspartate/
glutamate transporter), member 6
Solute carrier family 6 neurotransmitter
transporter, glycine), member 9
D-amino-acid oxidase
Serine racemase
SLC6A12
VIAAT
DBI
GRIN2A
GRIN2B
GRIN2C
GRIN2D
GRM1
GRM2
GRM3
GRM4
GRM5
GRM6
GRM7
GRM8
SLC1A1
SLC1A2
SLC1A3
SLC1A6
SLC6A9
DAO
SRR
GABA
Transporter
3p25.3
122230
145
BGT1
GABA
Transporter
12p13.33
23 241
145
GABA
GABA
Transporter
Others
20q11.23
2q14.2
4887
4975
148
Glutamate
GABA
Glutamate
GABA
Glutamate
GABA
Glutamate
Glutamate
Glutamate
Glutamate
Glutamate
Glutamate
Glutamate
Glutamate
Glutamate
Glutamate
Glutamate
Glutamate
Metabolic enzyme
10p12.1
87 894
149
Metabolic enzyme
2q31.1
25 848
149, 150
Metabolic enzyme
16p13.2
109 987
151
Receptor
Receptor
Receptor
Receptor
Receptor
Receptor
Receptor
Receptor
Receptor
Receptor
Receptor
Receptor
4q34.1
4q32.1
5q33.2
4q32.1
Xq25
11q22.3
21q21.3
6q16.3
1p34.3
11q23.3
19q32.2
9q34.3
186 168
95 517
364 262
143 068
304 502
368 731
402 421
669 205
233 113
325 942
64 020
28 919
152
152
153
EAAT3
Glutamate
Glutamate
Glutamate
Glutamate
Glutamate
Glutamate
Glutamate
Glutamate
Glutamate
Glutamate
Glutamate
Glutamate
Glutamate
Receptor
Receptor
Receptor
Receptor
Receptor
Receptor
Receptor
Receptor
Receptor
Receptor
Receptor
Receptor
Transporter
16p13.2
12q13.1
17q25.1
19q13.33
6q24.3
3p21.31
7q21.12
6p21.31
11q14.3
5q35.3
3p26.1
7q31.33
9p24.2
421 920
418 909
18 802
49 262
408 316
9131
220 113
111 816
540 352
16 793
880 272
804 658
96 815
EAAT2
Glutamate
Transporter
11p13
158 150
161–163
EAAT1
Glutamate
Transporter
5p13.2
81 750
161–163
EAAT4
Glutamate
Transporter
19p13.12
22 740
161, 163
GLYT1
Glutamate
Transporter
1p34.1
25 587
164
Glutamate
Glutamate
Metabolic enzyme
Metabolic enzyme
12q24.11
17p13.3
20 831
21 306
3
165
Peptide
Peptide
Transporter
Receptor
20p13
12q14.2
2873
6375
166
166
154
155
156
157
158
158
159
160
161–163
725
Molecular Psychiatry
Other neurotransmitter or neuromodulator systems
AVP
Arginine vasopressin–neurophysin II
AVPR1A
Arginine vasopressin receptor 1A
GAT1
Genetic tests of systems in affective disorders
E Hattori et al
GAD2
Solute carrier family 6 (neurotransmitter
transporter, GABA), member 11
Solute carrier family 6 (neurotransmitter
transporter, betaine/GABA), member 12
Vesicular inhibitory amino-acid transporter
Diazepam binding inhibitor (GABA receptor
modulator, acyl-Coenzyme A binding protein)
Glutamate decarboxylase 2
726
Molecular Psychiatry
Table 2 Continued
Symbols
Genes
Aliases
Subcategories
Cholecystokinin
Peptide
CCKAR
CCKBR
HCRT
Cholecystokinin A receptor
Cholecystokinin B receptor
Orexin precursor
Peptide
Peptide
Peptide
HCRTR1
HCRTR2
NPY
Orexin receptor 1
Orexin receptor 2
Neuropeptide Y
Peptide
Peptide
Peptide
NPY1R
NPY2R
NPY5R
NTS
Neuropeptide Y receptor Y1
Neuropeptide Y receptor Y2
Neuropeptide Y receptor Y5
Neurotensin
Peptide
Peptide
Peptide
Peptide
NTSR1
NTSR2
SST
Neurotensin receptor 1
Neurotensin receptor 2
Somatostatin
Peptide
Peptide
Peptide
TAC1
Tachykinin, precursor 1
Peptide
TACR1
TACR2
TACR3
VIP
Tachykinin receptor 1
Tachykinins receptor 2
Tachykinins receptor 3
Vasoactive intestinal peptide
Peptide
Peptide
Peptide
Peptide
VIPR2
PMCH
Vasoactive intestinal peptide receptor 2
Pro-melanin-concentrating hormone
VPAC2
Peptide
Peptide
GPR24
PDYN
G-protein-coupled receptor 24
Beta-neoendorphin–-dynorphin preproprotein
MCHR1
Peptide
Peptide
OPRD1
Opioid receptor, delta1
OPRK1
Opioid receptor, kappa1
OPRM1
Opioid receptor, kappa1
ADORA1
Adenosine A1 receptor
ADORA2A
Adenosine A2a receptor
ADORA2B
Adenosine A2b receptor
ADORA3
Adenosine receptor A3
2. A neuroendocrine system
Opioid
Opioid
Opioid
Others
Others
Others
Others
Neurotransmitter
Intercellular
signaling
Receptor
Receptor
Neurotransmitter
Intercellular
signaling
Receptor
Receptor
Neurotransmitter
Intercellular
signaling
Receptor
Receptor
Receptor
Neurotransmitter
Intercellular
signaling
Receptor
Receptor
Neurotransmitter
Intercellular
signaling
Neurotransmitter
Intercellular
signaling
Receptor
Receptor
Receptor
Neurotransmitter
Intercellular
signaling
Receptor
Neurotransmitter
Intercellular
signaling
Receptor
Neurotransmitter
Intercellular
signaling
Receptor
Receptor
Receptor
Receptor
Receptor
Receptor
Receptor
Genomic
size (bp)
3p22.1
6802
4p15.2
11p15.4
17q21.2
9025
12 202
1393
167
1p35.2
6p12.1
7p15.3
8074
108 347
417
167
167
168
4q32.2
4q32
4q32
12q21.31
2797
1152
4165
8689
168
168
20q13.33
2p25.1
3q27.3
53 934
12121
1227
169
169
7q21.3
8408
2p12
10q22.1
4q24
6q25.2
150 044
11 498
130 349
8857
7q36.3
12q23.2
116 783
1364
66
52
22q13.2
20p13
3582
15 300
52
171
1p35.3
8q11.23
6q25.2
1q32.1
22q11.23
17p12
1p13.2
51 552
21 771
80 118
76 750
9234
30 980
4689
172
169
170
62
Genetic tests of systems in affective disorders
E Hattori et al
CCK
Referencesa
Chromosomal
region
HPA axis
POMC
Proopiomelanocortin
CRH
Corticotropin-releasing hormone precursor
CRHR1
CRHR2
MC2R
Corticotropin-releasing hormone receptor 1
Corticotropin-releasing hormone receptor 2
Melanocortin 2 receptor (adrenocorticotropic
hormone)
Nuclear receptor subfamily 3, group C,
member 1
Nuclear receptor subfamily 3, group C,
member 2
Melanocortin 4 receptor
Heat shock 70 kDa protein 5 (glucose-regulated
protein, 78 kDa)
Corticosteroid binding globulin precursor
ATP-binding cassette subfamily B member 1
11-Beta-hydroxysteroid dehydrogenase 1
NR3C1
NR3C2
MC4R
HSPA5
SERPINA6
ABCB1
HSD11B1
ACTH
(Glucocorticoid
receptor)
(Mineralocorticoid
receptor)
GRP78
CBG
ABCB1
Neurotrophic/growth factor systems
BDNF
Brain-derived neurotrophic factor
Epidermal growth factor
FGF2
Fibroblast growth factor2
IGF1
Insulin-like growth factor I
TGFB1
Transforming growth factor, beta 1
IGF1R
NTRK2
NTRK3
Insulin-like growth factor 1 receptor precursor
Neurotrophic tyrosine kinase, receptor, type 2
Neurotrophin receptor 3
(1–3) Intracellular
ADCY2
ADCY9
ADRBK2
CREB1
CREM
GNAI2
GNAL
GRK3
7665
8q13.1
2080
17q21.31
7p14.3
18p11.21
51 525
29 697
894
173
Receptor
5q31.3
123 763
174
Receptor
4q31.23
363 604
174
Receptor
Others
18q21.32
9q33.3
999
6478
52
175
Others
Others
Enzyme
14q32.13
7q21.12
19 088
209 617
48 746
55
55
56
Neurotransmitter
Intercellular
signaling
Neurotransmitter
Intercellular
signaling
Neurotransmitter
Intercellular
signaling
Neurotransmitter
Intercellular
signaling
Neurotransmitter
Intercellular
signaling
Receptor
Receptor
Receptor
11p14.1
63 295
39, 60, 61
4q25
99 370
176
4q27
71 528
12q23.2
84 649
62
19q13.2
23561
177
15q26.3
9q21.33
15q25.3
308 747
352 717
379 607
178
cAMP
cAMP
cAMP
cAMP
cAMP
cAMP
cAMP
5p15.31
16p13.3
22q12.1
2q33.3
10p11.21
3p21.31
18p11.21
433 850
150 555
158 971
68 897
84 983
22 633
129 640
179
180
62
35, 179
36, 179
33
181
signaling
signaling
signaling
signaling
signaling
signaling
signaling
62
727
Molecular Psychiatry
signaling largely shared by 1-3
Adenylate cyclase 2
Adenylate cyclase 9
Beta adrenergic receptor kinase 2
CAMP-responsive element binding protein 1
CAMP-responsive element modulator
Guanine nucleotide binding protein
Guanine nucleotide binding protein
(G protein), alpha-activating activity
polypeptide, olfactory type
TrkB
2q23.3
Genetic tests of systems in affective disorders
E Hattori et al
EGF
Neurotransmitter
Intercellular
signaling
Neurotransmitter
Intercellular
signaling
Receptor
Receptor
Receptor
728
Molecular Psychiatry
Table 2
Continued
Symbols
GNAS
PDE4A
PDE4B
PDE4D
PRKACA
RGS20
RGS4
RGS7
PPP1R1B
PPP1R9B
Guanine nucleotide binding protein
(G protein), alpha-stimulating activity
polypeptide 1 (LL)
Phosphodiesterase 4A, cAMP-specific
Phosphodiesterase 4B, cAMP-specific
Phosphodiesterase 4D, cAMP-specific
Protein kinase, cAMP-dependent, catalytic,
alpha
Protein kinase, cAMP-dependent, regulatory,
type II, beta
Regulator of G-protein signaling 20
Regulator of G-protein signaling 4
Regulator of G-protein signaling 7
Protein phosphatase 1, regulatory (inhibitor)
subunit 1B
Protein phosphatase 1 regulatory subunit 9B
CAMK2A
Calcium/calmodulin-dependent protein kinase
II alpha
KCNN3
MARCKS
Calcium-activated potassium channel SK3
Myristoylated alanine-rich protein kinase C
PRKCA
Protein kinase C, alpha
PRKCE
Protein kinase C, epsilon
PLA2G1B
Phospholipase A2, group IB (pancreas)
PLCG1
Phospholipase C, gamma 1
GNB3
Guanine nucleotide-binding protein beta-3
BCL2
B-cell CLL/lymphoma 2
DUSP6
Dual-specificity phosphatase 6
MAP2K2
Mitogen-activated protein kinase kinase 2
MAPK1
Mitogen-activated protein kinase 1
Aliases
DARPP-32
Spinophilin
hSK3
Subcategories
Chromosomal
region
Genomic
size (bp)
Referencesa
cAMP signaling
20q13.32
71 451
33
cAMP
cAMP
cAMP
cAMP
19p13.2
1p31.2
5q11.2
19p13.13
47 837
580 324
615 565
26 045
37, 182
37, 183
184
185, 186
cAMP signaling
7q22.3
116 687
185
cAMP signaling
cAMP signaling
cAMP signaling
cAMP signaling
Calcium signaling
cAMP signaling
Calcium signaling
cAMP signaling
8q11.23
1q23.3
1q43
17q12
78 299
5184
581 608
9699
187
30, 73, 188
179
41
17q21.33
15 179
41, 189
5q33.1
70 277
39, 40, 190
1q22
6q21
162 714
4425
191
192
17q24.1
499 979
192, 193
2p21
532 811
192, 193
12q24.31
5674
194, 195
20q12
38 197
196
12p13.31
7183
197, 198
18q21.33
195 352
199
12q21.33
4023
200
19p13.3
33 805
64, 199
22q11.21
105 092
64, 193
signaling
signaling
signaling
signaling
Calcium signaling
Calcium signaling
Calcium signaling
Phosphoinositide
Calcium signaling
Phosphoinositide
Calcium signaling
Phosphoinositide
Calcium signaling
Phosphoinositide
Neurotrophic
factors
Calcium signaling
Phosphoinositide
Neurotrophic
factors
Calcium signaling
cAMP signaling
Phosphoinositide
Neurotrophic
factors
Neurotrophic
factors
Neurotrophic
factors
Neurotrophic
factors
Genetic tests of systems in affective disorders
E Hattori et al
PRKAR2B
Genes
AKT1
v-Akt murine thymoma viral oncogene
homolog 1
GNAQ
Guanine nucleotide binding protein
(G protein), q
Guanine nucleotide binding protein
(G protein), alpha 11 (Gq class)
Inositol(myo)-1(or 4)-monophosphatase 1
Inositol(myo)-1(or 4)-monophosphatase 2
Inositol polyphosphate-5-phosphatase F
1D-Myo-inositol-trisphosphate 3-kinase A
Inositol-1,4,5-triphosphate-3 kinase B
Phosphatidylinositol (4,5) bisphosphate
Phosphoinositide-3-kinase, class 2, beta
polypeptide
Phosphoinositide-3-kinase, class 3
Phosphatidylinositol 4-kinase, catalytic, alpha
peptide
Phosphatidylinositol-4-phosphate 5-kinase
type
Sac domain-containing inositol phosphatase 3
Synaptojanin 1
GNA11
IMPA1
IMPA2
INPP5F
ITPKA
ITPKB
PIB5PA
PIK3C2B
PIK3C3
PIK4CA
PIP5K2A
KIAA0274
SYNJ1
23 856
42, 43
9q21.2
311 000
201
Phosphoinositide
19p13.3
26 923
201
Phosphoinositide
Phosphoinositide
Phosphoinositide
Phosphoinositide
Phosphoinositide
Phosphoinositide
Phosphoinositide
8q21.13
18p11.21
10q26.12
15q15.1
1q42.12
22q12.2
1q32.1
28 365
49 422
103 044
9624
104 439
11 703
67 707
26, 202
203
203
203
203
203
Phosphoinositide
Phosphoinositide
18q12.3
22q11.21
126 246
131 028
203
203
Phosphoinositide
10p12.2
177 663
203
Phosphoinositide
Phosphoinositide
6q21
21q22.11
134 167
96 978
203
204
BMAL1
11p15.3
109 433
66
BMAL2
1-Dec
12p11.23
3p26.1
87 479
5654
66
66
2-Dec
12p12.1
4886
66
Rev-ErbAalpha
4q12
12q23.3
11p11.2
17q25.3
22q13.1
17p13.1
2q37.3
1p36.23
12q13.3
19q13.33
17q21.1
114 338
102 181
35 768
29 332
25 461
11 913
44 407
60 475
32 260
6642
7933
66, 205
66
66
66
66
66
66
66
206
66
66
3p13
20p12.3
2p13.3
7p11.2
17q25.1
4q35.2
13 406
12 330
106 512
137 918
2549
22 675
73
73
72
72
Pathways for entrainment to light/darkness cycle and outputs of suprachiasmatic nucleus
PROK2
Prokineticin 2
PK2
Clock output
GPR73L1
G-protein-coupled receptor 73-like 1
PKR2
Clock output
TGFA
Transforming growth factor alpha
Clock output
EGFR
ERBB1
Clock output
AANAT
Arylalkylamine N-acetyltransferase
Clock output
MTNR1A
Melatonin receptor 1A
Clock output
Melatonin
Melatonin
207–209
729
Molecular Psychiatry
14q32.33
Genetic tests of systems in affective disorders
E Hattori et al
4. Circadian rhythm
Clock genes
ARNTL
Aryl hydrocarbon receptor nuclear
translocator-like
ARNTL2
Transcription factor BMAL2
BHLHB2
Differentiated embryo chondrocyte expressed
gene
aBHLHB3
Basic helix–loop–helix domain containing,
class B, 3
CLOCK
Clock
CRY1
Cryptochrome 1 (photolyase-like)
CRY2
Cryptochrome 2 (photolyase-like)
CSNK1D
Casein kinase 1, delta isoform 1
CSNK1E
Casein kinase 1 epsilon
PER1
Period 1
PER2
Period 2
PER3
Period 3
TIMELESS
Timeless (Drosophila) homolog
DBP
D site of albumin promoter (albumin D-box)
NR1D1
Nuclear receptor subfamily 1, group D,
member 1
Neurotrophic
factors
Phosphoinositide
Phosphoinositde
730
Molecular Psychiatry
Table 2
Continued
Symbols
MTNR1B
CRX
OPN4
ADCYAP1
ADCYAP1R1
Melatonin receptor 1B
Cone–rod homeobox
Opsin4
Adenylate cyclase-activating polypeptide
Type I adenylate cyclase-activating
polypeptide receptor
Protein-tyrosine kinase fyn
GDP dissociation inhibitor 1
RAB3A, member RAS oncogene family
Neuronal PAS domain protein 2
Chromosomal
region
Genomic
size (bp)
Referencesa
11q14.3
19q13.33
10q23.2
18p11.32
7p14.3
13 160
5524
11 815
5664
43 503
210, 208, 211
66
66
212, 213
69, 212
6q21
Xq28
19p13.11
2q11.2
212 143
6293
7230
175 551
66
66
66
214
6q26
1 379 130
76
4q22.1
22q11.21
5q23.2
4p13
7q31.33
17p11.2
Xp11.3
16p13.3
111 429
8818
151 817
11 518
19 566
1688
24 267
2277
76
76
76
76
76
76
76
76, 79
22q11.21
11q12.1
1p36.23
56 367
16 325
23 544
76
76
215
NUDEL
Cell migration
Cell migration
Cell migration
7q22.1
1q42.2
17p13.1
517 727
399 739
32 293
150
93, 216, 217
217
LIS1
Cell migration
17p13.3
91 953
217, 218
Cell migration
Cell migration
1p35.3
3p26.3
27 328
212 449
219
220
Cell migration
Cell migration
Xq28
11q23.1
13 925
314 048
221, 222
66, 223
Schizophrenia gene
Schizophrenia gene
Schizophrenia gene
Myelination
6p22.3
8p12
22q11
3q26.2
140 167
1 103 459
23 771
13 827
1
2
224
94
Aliases
Melanopsin
PACAP
Subcategories
Clock output
Photoreception
Photoreception
Photoreception
Photoreception
5. Genes implicated in pathophysiology of other diseases relevant to major affective disorders
Genes implicated in the pathophysiology of Parkinson’s disease
PARK2
Parkinson disease (autosomal recessive,
PARK2
juvenile) 2, parkin
PARKIN
SNCA
Synuclein, alpha
PARK1
PNUTL1
Peanut-like 1
CDCREL1
SNCAIP
Synuclein alpha-interacting protein
Synphilin1
UCHL1
Ubiquitin carboxyl-terminal esterase L1
PARK5
GPR37
G-protein-coupled receptor 37
PAELR
UBB
Ubiquitin B precursor
UBE1
Ubiquitin-activating enzyme E1
STUB1
STIP1 homology and U-box containing
CHIP
protein 1
UBE2L3
Ubiquitin-conjugating enzyme E2L 3
UBCH7
UBE2L6
Biquitin-conjugating enzyme E2L 6
UBCH8
PARK7
Parkinson disease (autosomal recessive,
DJ1
early onset) 7 (PARK7)
Genes implicated
RELN
DISC1
NDEL1
PAFAH1B1
PTAFR
CHL1
L1CAM
NCAM1
DTNBP1
NRG1
PRODH
CLDN11
in the pathophysiology of schizophrenia
Reelin
Disrupted in schizophrenia 1
nudE nuclear distribution gene E homolog
like 1
Platelet-activating factor acetylhydrolase,
isoform Ib, alpha subunit 45 kDa
Platelet-activating factor receptor
Cell adhesion molecule with homology to
L1CAM
L1 cell adhesion molecule isoform 1 precursor
Nerural cell adhesion molecule
Dysbindin1
Neuregulin1
Prolin dehydrogenase (oxidase) 1
Oligodendrocyte transmembrane protein
Melatonin
Genetic tests of systems in affective disorders
E Hattori et al
FYN
GDI1
RAB3A
NPAS2
Genes
GALC
MBP
MOG
OLIG1
OLIG2
PLP1
SOX10
TF
The total number of candidate genes: 257; total genomic size: 26 620 628 bp.
a
When a gene has not intensively been studied despite its putative key biologic role in one of the pathways, it may lack a reference. Conversely, when numerous studies
have been conducted, review articles, meta-analyses, and large-scale studies are preferentially given.
94
94
94
94
94
94
94
94
14q31.3
18q23
6p22.1
21q22.11
21q22.11
Xq22.2
22q13.1
3q22.1
Myelination
Myelination
Myelination
Myelination
Myelination
Myelination
Myelination
Myelination
60 484
37 252
15 621
2276
3209
15 706
12 220
32 401
12q13.2
ERBB3
v-Erb-b2 erythroblastic leukemia viral
oncogene
Galactosylceramidase precursor
Myelin basic protein
Myelin oligodendrocyte glycoprotein
Oligodendrocyte transcription factor 1
Oligodendrocyte lineage transcription factor 2
Proteolipid protein 1
SRY (sex determining region Y)-box 10
Transferrin
Myelination
22 605
94
Genetic tests of systems in affective disorders
E Hattori et al
by lithium,24,25 and its coding gene (IMPA2) is a
promising candidate.26,27 Another gene in this pathway, phosphoinositide-3-kinase class 3 (PIK3C3), was
recently reported to be associated with bipolar
disorder and schizophrenia.28 Calcium signaling is
closely linked to the phosphoinositide pathway, and
expression of protein kinase C subtypes (PRKCA and
PRKCE) and its substrate myristoylated alanine-rich
protein kinase C (MARCKS) are reduced in the rat
brain after chronic treatment with lithium.29
Monoamine neurotransmitter receptors, such as
alpha2 and beta-1-adrenergic receptors, are coupled
to G proteins, which, upon stimulation, activate
enzymes in the cAMP-signaling pathway. The gene
for regulator of G-protein signaling 4 (RGS4) was
initially brought into attention by a microarray study
and recently reported to be associated with schizophrenia.30 Selecting candidates based on expression
data also led to the detection of associations of Gprotein-coupled receptor kinase3 (GRK3)31 and other
promising gene32 with bipolar disorder. Altered
expression level of G protein AS and AI2 subunits
(GNAS, GNAI2) in the post-mortem brains from
bipolar or lithium receiving subjects has also been
reported,33 although variants in the former gene are
not apparently associated with bipolar disorder.34
Recent animal studies demonstrated that chronic
administration of antidepressants induces elevation
of cAMP-responsive element binding protein gene
(CREB1) expression-35 and cAMP-responsive element
modulator (CREM)-deficient mice showed emotional
and behavioral changes.36 Also, chronic antidepressant administration increases cAMP phosphodiesterase (PDE4A and PDE4B) expression in rat
frontal cortex.37 A phosphodiesterase inhibitor, rolipram, has been reported to have an antidepressive
effect.38
Further, abnormalities of molecules that overarch
multiple intracellular signaling pathways (eg calcium/
calmodulin-dependent protein kinase II alpha (CAMK2A),39,40 DARPP-32 (PPP1R1B),41 and v-akt murine
thymoma viral oncogene homologs (AKT1),42,43 are
also suggested in psychiatric illnesses.
Roles of other neurotransmission systems including
cholinergic, amino acid (glutamate and GABA) and
peptidergic neurotransmission in bipolar disorder or
related physiological functions such as appetite and
anxiety are also supported by neuropharmacological
findings,44–48 although not detailed here.
731
A neuroendocrine system
The hypothalamic-pituitary-adrenocortical (HPA)
axis has a long history as a stress-response pathway
and has been repeatedly suggested to play a role
in major depressive disorder.49 A recent hypothesis
that elevated levels of cortisol in depressed patients
may contribute to neuronal death and to reduced
dendritic arborizations in hippocampus50–52 seems to
have a potential for elucidating the etiology of mood
disorder. This hypothesis is consistent with the
Molecular Psychiatry
Genetic tests of systems in affective disorders
E Hattori et al
732
previous neuroimaging findings reporting reductions
of hippocampal volume in some mood disorder
subjects.53,54
Obvious candidates based on these formulations
are genes encoding peptide hormones (proopiomelanocortin (POMC) and corticotrophin-releasing hormone precursor (CRH)) and their receptors (MC2R,
MC4R, CRHR1, and CRHR2). In addition, glucocorticoid receptor (NR3C1), which binds to glucocorticoids and then enters the nucleus to enhance or
inhibit gene expression by direct binding to glucocorticoid response elements or by interactions with
other transcriptional factors such as CREB, can be
considered an important signaling component. Other
candidates include genes for heat-shock proteins
such as HSPA5, which associate with the glucocorticoid receptor as chaperones, multidrug-resistant
protein 1 (ABCB1), which pumps out cortisol from
the cell,55 and 11-beta-hydroxysteroid dehydrogenase
1 (HSD11B1), which metabolizes cortisol.56
Neurotrophic factor systems
There has been growing evidence supporting roles of
neurotrophic factors and growth factors, which
regulate neuronal growth, development, survival,
and plasticity, in mood disorders. The gene for
brain-derived neurotrophic factor (BDNF), which is
involved in neuronal survival and arborization in
hippocampus, is an unusually promising candidate.
The expression of BDNF is decreased by stress and
glucocorticoids57 and is increased by chronic antidepressant or electroconvulsive treatment in rat
hippocampus.58,59 Association between BDNF and
bipolar disorder has been replicated in independent
pedigree samples.60,61 Also, the gene for insulin-like
growth factor I (IGF1) would be worth studying based
on its role in neurogenesis and reported altered
expression level in the brains of metamphetaminetreated rats.62 Recently, a requirement was demonstrated for hippocampal neurogenesis for behavioral
effects of antidepressants, consistent with importance
of genes involved in neurogenesis in studies of
depression.63
As in the neurotransmission systems and HPA axis,
genes for molecular components of the neurotrophic
factor system have not been systematically studied for
association with bipolar disorder. Genes such as
NTRK2 and NTRK3 coding for neurotrophic factor
receptors collectively called Trk are candidates as
well as genes for ligands. Among the several intracellular cascade systems activated upon Trk stimulation are phosphoinositide signaling and protein
kinase C pathway, whose components are shared
with neurotransmission systems described above. It
may be challenging to select candidates from the
mitogen-activated protein (MAP) kinase cascade,
another intracellular signaling pathway downstream
of Trk, because of the large number of subtypes for
each protein. However, an expression study on PC12
cells differentiated by nerve growth factor (NGF)
Molecular Psychiatry
showed that lithium administration altered expression of two genes (MAP2K2 and MAPK1) encoding
kinases of this pathway.64
Circadian rhythm
Abnormalities in circadian rhythm are found in
seasonal affective disorder as well as in a fraction of
patients with major depression. The fact that interventions on circadian rhythm such as light therapy
and sleep deprivation can improve the symptoms of
depression or provoke mania might be indicative of
an etiological role of this system.65 The mammalian
circadian pacemaker is located within the suprachiasmatic nucleus (SCN) in the hypothalamus. ‘Clock
genes’ (eg CLOCK, ARNTL (BMAL1), ARNTL2
(BMAL2), PER1, PER2, and PER3) play crucial roles
in generating and regulating circadian rhythm, and
mutations of these genes have already been reported
to cause abnormal circadian locomotion in rodents. In
the past several years, many clock genes have been
identified in various species such as Drosophilae,
fungi, and rodents. As human counterparts or homologues have already described for most clock genes,66
the circadian rhythm system is amenable to genetic
dissection.
Although the clock genes are probably the first
candidates to be studied in the circadian rhythm
system, it is noteworthy that these gene loci did not
show major effect on strain variability in mouse
circadian behavior in a genomewide analysis.67
Studying nonclock genes with suggested roles in
circadian rhythm would also be important. Since the
SCN can be entrained to light/dark cycle, genes
encoding components involved in photoreception in
the retinohypothalamic tract are intriguing candidates. For example, pituitary adenylate cyclaseactivating polypeptide (ADCYAP1) is a major neurotransmitter of this tract as well as glutamate.68 Lack of
either of its receptor genes, ADCYAP1R1 or VIPR2,
leads to abnormal circadian phenotype in rodents.69
In addition, since diurnal rhythmicity in physiological functions and behaviors is eventually affected
in mood disorder, output pathways from the SCN,
including pineal melatonin secretion,70,71 cannot be
omitted. Recently, transforming growth factor-a
(TGFA)72 and prokineticin 2 (PROK2),73 substances
secreted from the SCN to adjacent hypothalamic
areas, have been reported to regulate behavioral
circadian rhythm in mice.
Systems implicated in Parkinson’s disease and
schizophrenia
Other CNS diseases may also provide clues for
susceptibility genes for mood disorders, if they share
etiological mechanisms with mood disorders. Symptoms of depression occur in approximately half of the
subjects with Parkinson’s disease. Although neuropathological changes characteristic of Parkinson’s
Genetic tests of systems in affective disorders
E Hattori et al
disease such as Lewy body formation and demise of
dopaminergic neurons in the substantia nigra are not
generally observed in the post-mortem brains of mood
disorder subjects, depression is reported to be a risk
factor for developing Parkinson’s disease,74,75 suggesting a mechanism shared in part by both illnesses.
Recent studies have revealed that genes playing roles
in the ubiquitin–proteasome pathway cause some
familial forms of Parkinson’s disease (SNCA, PARK2
(PARKIN), UCHL1).76 Several other components that
play crucial roles in this pathway have also been
reported; for example, Parkin-associated endothelin
receptor-like receptor (Pael-R)77 and CDCrel-1
(PNUTL1)78 suggested for one of the substrates for
PARKIN-mediated ubiquitination. Also, a protein
called carboxy-terminus of Hsp70p-interacting protein (CHIP) is known to modulate the function of
PARKIN.79
Also, the hypothesis that one subclass of major
affective disorders shares susceptibility genes in
common with schizophrenia is particularly promising. Genetic epidemiology has provided evidence for
this overlap, primarily in family studies. Gershon et al
observed an excess of major depression and schizoaffective disorder in the relatives of both mood disorder
and schizophrenia probands.80,81 The excess of major
depression in relatives of both mood disorders and
schizophrenia has been a consistent finding.82 Studies from three data sets have addressed the issue of
psychotic mood disorder/schizophrenia overlap. Two
of the data sets found elevated rates of psychotic
mood disorder in relatives of schizophrenic probands,
and vice versa;83–86 the third also suggested shared
liability.87 In addition, some twin studies have found
evidence of shared heritability between psychotic
mood disorder and schizophrenia.88,89 Further, linkage studies of bipolar illness and schizophrenia have
implicated overlapping chromosomal regions, including 10p12–13, 13q31–33, 18p11.2, and 22q11–13,90,91
although not all analyses agree.92 There has been
considerable progress in identifying genes associated
with schizophrenia, particularly in chromosomal
regions where evidence of linkage was suggested.
Among them, the G72/G30 gene locus on 13q33 has
been demonstrated to be associated with both schizophrenia and bipolar disorder.3,4,6–8 The notion of
shared susceptibility gene is also supported by a very
recent association study on DISC1 gene.93 Other
schizophrenia genes such as NRG1 and DTNBP might
also be worth studying for possible association with
mood disorders. In addition, a recent study has
demonstrated convergent expression alterations of
genes involved in myelination in both schizophrenia
and bipolar disorder.94 Such genes would be good
candidates for susceptibility genes shared by major
psychiatric illnesses. Studying these genes implicated in the pathophysiology of schizophrenia
may contribute to eventual reconstruction of the
current diagnostic nosology, and to identification
of new molecular targets with broad therapeutic
spectra.
Prioritizing candidate genes by quantitative trait
loci (QTL) analysis
733
Combining microarray gene expression data and gene
mapping methods to identify genetic determinants of
gene expression (expression phenotypes) has recently
been applied in several species, including mouse and
human.95–97 This has resulted in the successful
identification of QTLs, which control the baseline
expression levels of some genes. We have used this
approach to identify regulators of the expression of
the candidate genes we compiled, in the adult BXD
recombinant inbred mice. We decided to use QTL
mapping data in mouse instead of human, because the
only available human QTL mapping results are from
lymphoblast cell lines, and it has been shown in
mouse that QTLs in brain and hematopoietic stem
cells differ greatly.98 Interval mappings were performed at the WebQTL site (see Electronic-Database
Information), using UTHSC Brain mRNA U74Av2
(Mar04) RMA Orig database. QTL with an empirical
genome-wide P-value less than 0.05 was detected for
six genes, namely HTR2B, HTR4, GRIN2B, PRKCE,
PER3, and BCL2.
We then determined if any of the QTLs is in
syntenic regions to human bipolar linkage findings. If
a cis-acting QTL, for which the QTL is in the target
gene itself, overlaps with bipolar linkage, the target
gene itself merits testing in association studies as
positional candidate for bipolar linkage. If the overlapping QTL is a trans-acting QTL, the regulator at the
QTL is a new candidate gene for association study.
Thus, linkage results to gene expression may point
to new candidate genes and underlying regulatory
pathways for the bipolar linkage. We found that two
QTLs overlap with bipolar linkage regions. A translinked QTL for two genes, HTR4 and BCL2, is
mapped to the same region in mouse genome, and
may thus represent a single linkage. This trans-linked
QTL for the two genes can be divided into four
segments, three of which are in syntenic regions to
bipolar linkage findings at 2q,92 6q,99 and 10q,92
respectively. In addition, a cis-QTL for the gene
PER3 is in syntenic region to bipolar linkage finding
at 1p.92 This suggests that PER3 is a good candidate
for this bipolar linkage. With the identification of
more bipolar linkages and the improvement of QTL
mapping methods, the list of genes with QTLs
overlapping with bipolar linkage will certainly grow.
Requirements for implementation of the systems
genetic approach and future directions
The approach being suggested would benefit from the
feasibility of much denser genotyping compared to
the whole-genome LD mapping. It requires collection
of information on functional importance of polymorphic markers, as well as positions, flanking
sequences, validation status, and allele frequencies.
Since the information is scattered on multiple webbased databases such as those from UCSC Genome
Molecular Psychiatry
Genetic tests of systems in affective disorders
E Hattori et al
734
Bioinformatics, dbSNP, HapMap, and SNP Consortium (see Electronic-Database Information), manual
mining of information can be tedious and sometimes
infeasible. What is needed is a sophisticated informatics system facilitating compilation of pieces of
information from different resources into a single
platform. We might further assign priority of genotyping to each polymorphism according to its potential
functional effect and the degree of LD with other
polymorphisms.
Genotype data obtained by the study of multiple
genes in a biologic system may provide a set of
multiple susceptibility genes either through conventional association analyses or through multilocus
association analyses such as the one developed by
Hoh et al.21 Although the latter may provide a list
of susceptibility genes, in which some of them are
exerting interacting effects, we further need computational modeling, which allows for systems analysis
describing specific relationships between genes and
clinical features. This would provide a basis for
putting genetic results back into biological and
clinical context. The systems listed in Table 1 are
considered more complex in reality than described
above, and it is also possible that interactions
between systems rather than within a system increase
the risk for major affective disorders. For example, a
suggested integral model views multiple systems from
a single perspective of neuronal death/survival.
Hyperfunction of glutamatergic neurotransmission
and HPA axis can lead to neuronal death, whereas
adrenergic/serotonergic neurotransmission and neurotrophic factors favor neuronal survival/arborization
or neurogenesis, with each system interacting with
several others.50–52 The hypothesis-based study described so far is expected to increase the likelihood of
obtaining outputs that can be reasonably interpreted
through the current biological and epidemiological
knowledge of major affective disorders. The systems
functioning conclusions from the genetic outputs,
although, would not necessarily be completely consistent with the current hypothesis-based systems.
The biological meaning of the genetic outputs could
be tested by further research designs such as multiple
gene manipulations in rodents.
Electronic-Database Informaion
Databases for biologic pathways
Gene Ontology (GO) Consortium:
http://www.geneontology.org/
Kyoto Encyclopedia of Genes and Genomes (KEGG)
databases:
http://www.genome.ad.jp/kegg/pathway.html
Databases for genomic information and gene
expression
UCSC Genome Bioinformatics:
http://genome.ucsc.edu/
dbSNP: http://www.ncbi.nlm.nih.gov/SNP/
Molecular Psychiatry
The International HapMap Project:
http://www.hapmap.org/
The SNP Consortium: http://snp.cshl.org/
Gene Expression Omnibus (GEO):
http://www.ncbi.nlm.nih.gov/geo/
WebQTL: http://www.genenetwork.org/
Candidate gene projects involving resequencing
The NIEHS SNPs program:
http://egp.gs.washington.edu/
The Cardiogenomics program:
http://www.cardiogenomics.org
The SeattleSNPs program:
http://pga.gs.washington.edu/
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