close

Вход

Забыли?

вход по аккаунту

?

Cannabinoid receptor 1 gene (CNR1) and susceptibility to a quantitative phenotype for hebephrenic schizophrenia.

код для вставкиСкачать
American Journal of Medical Genetics Part B (Neuropsychiatric Genetics) 147B:279 –284 (2008)
Cannabinoid Receptor 1 Gene (CNR1) and Susceptibility to a
Quantitative Phenotype for Hebephrenic Schizophrenia
Iván Chavarrı́a-Siles,1,2 Javier Contreras-Rojas,1,2 Elizabeth Hare,1 Consuelo Walss-Bass,1
Paulina Quezada,1 Albana Dassori,1 Salvador Contreras,1 Rolando Medina,1 Mercedes Ramı́rez,1
Rodolfo Salazar,2 Henriette Raventos,2 and Michael A. Escamilla1*
1
Department of Psychiatry, Psychiatric Genetics Research Center, University of Texas Health Science Center at San Antonio,
San Antonio, Texas
2
Centro de Investigación en Biologı´a Celular y Molecular, Universidad de Costa Rica, San Jose´, Costa Rica
Functional alterations of components of the endogenous cannabinoid system, in particular of
the cannabinoid receptor 1 protein (CB1), are
hypothetical contributors to many of the symptoms seen in schizophrenia. Variants within
the cannabinoid receptor 1 gene (CNR1) have
been shown to be directly associated with
the hebephrenic form of schizophrenia in a
Japanese population. This finding, however, has
yet to be replicated. In the present study we
sought to study the same (AAT)n-repeat microsatellite of the CNR1 gene which showed association to hebephrenic schizophrenia in Japan,
and to investigate whether this microsatellite
showed association to a hebephrenic type of
schizophrenia in a family-based association study
in a population of the Central Valley of Costa Rica.
The Lifetime Dimensions of Psychosis Scale and a
best estimate consensus process were utilized
to identify subjects with schizophrenia who
had an elevated lifetime dimensional score for
negative and disorganized symptoms, which we
used as a proxy for ‘‘hebephrenia.’’ Using the
Family Based Association Test we found association of these hebephrenic subjects and the
(AAT)n-repeat marker of the CNR1 (multi-allelic
P ¼ 0.0368). Our hypothesis that an association
with the (AAT)n-repeat marker of CNR1 would
not be found with the more general type of
schizophrenia was also confirmed. Schizophrenic
subjects with prominent lifetime scores for
disorganization and negative symptoms (dimension for hebephrenia) are associated with the
CNR1 gene and present a type of symptomatology
that resembles chronic cannabinoid-induced
psychosis. The current finding points to the
possibility of different genetic and pathophysiologic mechanisms underlying different types of
schizophrenia.
ß 2008 Wiley-Liss, Inc.
Grant sponsor: NIH Research; Grant number: D43 TW06152.
*Correspondence to: Michael A. Escamilla, Department of
Psychiatry, University of Texas Health Science Center at San
Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78229.
E-mail: escamillam@uthscsa.edu
Received 15 March 2007; Accepted 20 June 2007
DOI 10.1002/ajmg.b.30592
ß 2008 Wiley-Liss, Inc.
KEY WORDS: CNR1; hebephrenic schizophrenia; central Valley of Costa
Rica; disorganization; negative
symptoms
Please cite this article as follows: Chavarrı́a-Siles I,
Contreras-Rojas J, Hare E, Walss-Bass C, Quezada P,
Dassori A, Contreras S, Medina R, Ramı́rez M, Salazar R,
Raventos H, Escamilla MA. 2008. Cannabinoid Receptor
1 Gene (CNR1) and Susceptibility to a Quantitative
Phenotype for Hebephrenic Schizophrenia. Am J Med
Genet Part B 147B:279–284.
INTRODUCTION
Schizophrenia (SC) is a debilitating psychiatric disease
which affects 1% of the population worldwide [Kessler et al.,
1994], and is thought to be caused in part by genetic factors
[Gottesman, 1994]. The cannabinoid receptor 1 gene (CNR1),
which encodes the cannabinoid receptor 1 protein (CB1), a
G-coupled protein receptor that controls neuronal activity, is
an attractive candidate gene for SC, based on genetic mapping
studies as well as several experimental and clinical findings.
The CNR1 gene is located on chromosome 6q14–15, a region
which has been designated as the Schizophrenia 5 locus
(OMIM603175). CB1 is one of the most abundant receptors in
the human brain, distributed mainly in the cortex, hippocampus, basal ganglia, and cerebellum. In all these structures,
CB1 has a presynaptic localization which could be related to its
role in synaptic neurotransmission [Pazos et al., 2005]. Due to
the distribution of the CB1 in GABAergic and glutametergic
synapses, its activation produces a local net effect of hyperpolarization of the presynaptic neuron that leads to a general
inhibitory effect [Howlett et al., 2004; Rodriguez de et al.,
2005]. CB1 receptors also co-localize with dopamine D1/D2
receptors in the brain; this co-localization could influence the
activity of the dopamine signaling in psychotic related
disorders [Rodriguez De et al., 2001]. CNR1 knockout mice
have been proposed as animal models for SC because they
exhibit D2 dopamine receptor hyperactivation and their
behavioral alterations mimic symptoms in SC [Fritzsche,
2001]. Several clinical studies indicate that cannabis abuse
may have psychomimetic effects in previously non-psychotic
subjects and, in schizophrenic patients, cannabis abuse can
worsen symptoms and result in liability to relapse [Ujike
and Morita, 2004]. Most recently, Solowij and Michie [2007]
have summarized the similarities and common neurophysiology underlying the cognitive dysfunction associated with
long time cannabis use and the cognitive dysfunction seen in
schizophrenia.
280
Chavarrı́a-Siles et al.
Despite several lines of genetic and biological evidence
that support the hypothesis that alterations of the CB1 may
contribute to SC there is conflicting evidence regarding
whether polymorphisms in or near the CNR1 gene are
associated with SC. All previous studies have utilized case/
control designs, which are vulnerable to false positive or negative results due to population stratification. For the phenotype
of SC, two previous studies; one in the Chinese population [Tsai
et al., 2000] and the other in the French Caucasian population
[Leroy et al., 2001], showed no association between polymorphisms in CNR1 and the phenotype of SC; while two
studies, one study in the Spanish population [Martinez-Gras
et al., 2006] and one in the Japanese population [Ujike et al.,
2002] showed association of the (AAT)n CNR1 microsatellite
with SC. In the Japanese study, this microsatellite was
strongly associated with the hebephrenic type of SC.
In the present study, we utilized family-based association
analyses to investigate whether a hebephrenic type of
schizophrenia would be associated with the CNR1 gene
in the Central Valley of Costa Rican population (CVCR).
Hebephrenic schizophrenia derives from a description of one
subtype of what Eugen Bleuler called ‘‘the schizophrenias.’’ We
hypothesized that a hebephrenic type of schizophrenia most
closely approximated the type of cognitive dysfunction associated with the use of cannabinoids [Solowij and Michie, 2007],
and that this type of schizophrenia would thus be most likely to
show association to CNR1. We also hypothesized that CNR1
gene would not be associated to the broader phenotype of SC in
our sample, as the association would be diluted when using the
more diverse set of phenotypes classified under the broad
DSM-IV classification of Schizophrenia [APA, 1994].
METHODS
Sample Population
All participants were recruited in accordance with the
principles of the Declaration of Helsinki and with approval from
the Institutional Review Boards of the University of Costa Rica
and the University of Texas Health Science Center at San
Antonio. Families were recruited through assessment of a proband, who was recruited from psychiatric hospitals and clinics in
the Central Valley of Costa Rica; all probands had a history of at
least one hospitalization with discharge diagnosis of SC
according to the ICD 9 or ICD 10 system [Montero et al., 2002].
Diagnostic Procedures
The original sample consisted of 490 subjects with diagnosis
of SC (476 families, 1,246 subjects) All probands and any other
family member with a history of psychosis picked up by the
Family Interview for Genetic Studies (FIGS) were interviewed
by a local psychiatrist using the Spanish version of the
Diagnostic Interview for Genetics Studies (DIGS) [Nurnberger
et al., 1994]. In addition, at least one healthy relative was
interviewed using the Spanish version of the FIGS to gather
additional information about each affected subject. All available medical records were also gathered for each affected
subject (here ‘‘affected’’ refers to a subject with a history of
psychosis). For each affected subject, all three sources of
information (DIGS, FIGS, and medical records) were analyzed
by two bilingual psychiatrists (best estimate raters) blind to
the previous history of the subject and to the family relationships between subjects. The best estimating team arrived at
a lifetime consensus diagnosis using DSM-IV criteria, as
described in previous studies of the CVCR [Escamilla et al.,
1996, 1999]. After this process, only 244 affected subjects had
consensus DSM-IV diagnoses of schizophrenia (Table I). We
also were able to obtain a diagnosis of abuse or dependence of
different substances in these 244 subjects using the DSM-IV
criteria (Table II). Previous history of substance abuse or
dependence was not an exclusion criteria for this study. Each
affected subject was also scored by each best estimate rater
for lifetime dimensions of psychosis (LDPS), using the scale
developed by Levinson et al. [2002]. The LDPS creates a
profile of the lifetime characteristics of each case based on
retrospective ratings, encompassing dimensions of positive
symptoms, Shneiderian symptoms, depressive, manic, negative, and disorganized symptoms. A consensus score for each
dimension of psychosis was arrived at through averaging of the
two individual raters’ scores. Since the hebephrenic subtype
of SC reported on by Ujike et al. [2002] utilized the ICD-10
classification, and a hebephrenic subtype is not defined per se
by the DSM-IV, we utilized the LDPS to define which subjects
with a DSM-IV consensus diagnosis of SC scored highly on
disorganized and negative symptoms over their lifetime. The
ICD-10, used by Ujike et al. in their study, characterizes the
hebephrenic type of schizophrenia largely based on predominance of disorganized, and negative symptoms [World Health
Organization, 2003]. As noted in Ujike et al. [2002], ‘‘hebephrenic type schizophrenia is characterized by predominant
negative symptoms such as blunt affect, disorganized thought,
and deterioration of personality.’’ ‘‘Deterioration of personality’’ is not specifically mentioned in the ICD-10 classification
of hebephrenic schizophrenia, but can perhaps be implied
in the context of a person with prominent negative and
disorganized behaviors and communication skills.
Defining Schizophrenic Subjects
With Hebephrenia
As the DSM-IV, which we used to make categorical diagnoses
in our study, does not have a category for ‘‘hebephrenia,’’ we
utilized the LDPS scale to create a quantitative ‘‘hebephrenia’’
score for each affected subject, with the intention to analyze
persons who scored high on this trait separately from the
overall sample. To obtain a quantitative trait score for
‘‘hebephrenia,’’ we used all of the items for rating negative,
and disorganized symptoms in the LDPS; we multiplied
severity, and duration score for the items N-1 (Blunted,
restricted affect), N-2 (Poverty of speech), D-1 (Formal thought
disorder) and D-2 (Bizarre behavior), and then summed these
four products. The overall score for the ‘‘LDPS-hebephrenia’’
dimension could range from 0 to 64 points. For the 244
schizophrenic subjects these scores were normally distributed
(Shapiro–Wilk Statistic ¼ 0.99) with a mean of 29.1 and
standard deviation of 13.9.
For our study we arbitrarily chose subjects who scored 30 or
higher on this ‘‘hebephrenic’’ dimension, as those who had
hebephrenia as a prominent component of their illness. As
duration of the symptoms is an important factor in the
‘‘hebephrenia’’ scores (each dimension is scored by multiplying
severity of the symptom with duration of the symptom), we also
TABLE I. Demographics of the Sample
Sample (diagnostic method)
Schizophrenia—hospital discharge diagnosis (ICD10)
SC-best-estimation (DSM-IV)
Hebephrenic-SC (quantitative trait)
Families (N)
Subjects (N)
Affected (N)
Age (mean)
Sex (M/F, %)
476
237
66
1246
724
231
490
244
66
44.83
37.12
36.54
58/42
70/30
79/21
CNR1 and Susceptibility to Hebephrenic Schizophrenia
TABLE II. Sustance Abuse and Dependence Among Subjects
With DSM-IV Diagnosis of Schizophrenia (N ¼ 244)
Abuse
Drug
Alcohol
Cannabis
Cocaine
Heroine
LSD
Opiates
Inhalants
N
15
20
8
0
1
0
6
Dependence
%
6
8
3
0
0.4
0
2
N
%
45
14
8
0
2
0
3
18
5
3
0
0.8
0
1
limited our association study of the ‘‘hebephrenic’’ type to those
between age 25 and 45. Subjects who are much younger or older
than this range (by virtue of their age) would be biased to score
lower (for young persons) or higher (for older persons) on
the hebephrenia score, compared to those in the range of 25–
45 years of age.
As a secondary analysis, we used a quantitative analysis test
to investigate whether the quantitative trait of ‘‘hebephrenia,’’
as defined by us using the LDPS scale, showed association to
the CNR1 gene. Only the 148 SC subjects who were between
age 25 and 45 were included in this quantitative association
analysis.
Genotyping
Genomic DNA was extracted from blood samples using a
Puregene DNA purification kit (Gentra, Minneapolis, MN).
DNA from 725 subjects (244 subjects with SC and their
relatives) was genotyped for the (AAT)n repeat polymorphism
using the fluorescently labeled designed primers 50 GAAAGCTGCAAGAGCCC30 and 50 TTTTCCTGTGCTGCCAGGG30
(Applied Biosystems, Foster City, CA). Standard PCR was
performed using GeneAmp PCR system 9700 (Applied Biosystems). Amplified fragments were analyzed on the ABI
3100 Genetic Analyzer (Applied Biosystems) and genotypes
were assigned using GeneMapper v3.5 (Applied Biosystems).
Two individuals blind to the diagnosis scored each genotype
separately. Discrepancies were discussed with review of the
peaks of the original run to obtain a final genotype.
All genotypes were checked for mendelian errors using the
program INFER in PEDSYS (http://www.sfbr.org/software/
pedsys/pedsys.html). If mendelian errors were found, genotype
diagrams for these families were reviewed and, if necessary,
re-genotyped. Any families which still showed non-mendelian
inheritance for the microsatellite were not included in further
analyses.
Statistical Analysis
The genotypes were analyzed for Hardy–Weinberg disequilibrium using the PEDSTATS program among unrelated
individuals (Wigginton and Abecasis, 2005). All association
analyses were performed using the Family Based Association Test (FBAT) (http://www.biostat.harvard.edu/fbat/
default.html), with the following settings: model additive, test
bi-allelic (provides asymptotic P values of the Z score function,
which looks at the transmitted alleles to affected offspring) and
multi-allelic, and minimum size 10 (only alleles that were
present in at least 10 informative families were tested for
association). This program provides empirical P values for
association studies and allows for multiple affected subjects
and any available relatives to be included in the analyses.
Affected subjects were defined as being between the ages of
281
25 and 45, and having a consensus DSM-IV diagnosis of
Schizophrenia and a LDPS-Hebephrenia score of 30 or higher.
Transmitted alleles (those going from parents to the affected
subjects) were compared to non-transmitted alleles (those not
transmitted from parents to affected subjects). When both
parents were not available, additional siblings of the affected
subjects were genotyped to permit inference of the parental
alleles using the program INFER in PEDSYS. For the
statistical analysis of whether a ‘‘hebephrenic’’ type of
schizophrenia was associated with CNR1, we only analyzed
affected subjects with ages ranging from 25 to 45 years old at
the time of the interview, to obtain a more homogeneous group
and avoid biases for the duration of the symptoms (as
mentioned before the LDPS incorporates both severity and
duration in determining the score of a lifetime dimension).
The LDPS-hebephrenia scores were used as a quantitative
trait to test for association with the CNR1 gene using FBAT.
The program computes both bi-allelic tests and multi-allelic
tests of association for microsatellite markers with quantitative traits, using the following commands in FBAT: trait LDPS;
offset 0.000; model additive; test bi-allelic, and multiallelic; minsize 10; min_freq 0.000; P 1.000.
RESULTS
Genotyping of the AAT-repeat in our sample revealed nine
different alleles, with product sizes ranging from 219 to 243 bp
at 3 bp intervals. This is the same number of alleles found by
Ujike et al. [2002] in the Japanese population. The family
structure of our sample consisted of 126 complete trios,
118 families with only one parent (for these families additional
siblings were used to reconstruct the genotypes of the missing
parents); 661 subjects were successfully genotyped with a
genotyping completion rate of 0.91 (230 affected subjects,
431 relatives); in addition 29 genotypes were inferred for
missing parents using siblings genotypes. The genotypes distribution were within the expected values of Hardy–Weinberg
equilibrium (chi-squared ¼ 12.6078; P value ¼ 0.2464). Out of
the 244 subjects with consensus diagnosis of schizophrenia
only 148 were in the age range between 25 and 45 years old and
66 subjects satisfied the criteria for ‘‘hebephrenia’’: DSM-IV
consensus diagnosis of schizophrenia with ‘‘LDPS -hebephrenia’’ scores of 30 or higher (Mean: 40.3, SD: 8.45). The cutoff
point of 30 points is the percentile 58 of the normal distribution
of the scores. This cutoff point was arbitrarily chosen and was
the only one used in the present analyses. The vast majority
of these 66 subjects were classified as undifferentiated or
disorganized subtype at the time of the consensus diagnosis
using the DSM-IV (Table III).
In order to test if this score can be used as a quantitative trait
for association analysis, we analyzed the normal distribution of
the scores in the subjects with age range between 25 and 45
(N ¼ 148); the scores were normally distributed (Shapiro–Wilk
Statistic ¼ 0.99) with a mean of 28.6 and a standard deviation
of 13.4. Only the 148 subjects between the ages of 25 and 45
(plus their relatives) underwent quantitative trait analyses for
the hebephrenia trait.
TABLE III. DSM-IV Diagnoses for the Hebephrenic Subjects
According to the Lifetime Dimensions of Psychosis Scale
(LDPS) Score
DSM-IV diagnoses
Schizophrenia undifferentiated
Schizophrenia disorganized
Schizophrenia paranoid
Schizophrenia residual
Schizophrenia NOS
N ¼ 66
%
37
13
7
2
7
56
20
10
4
10
282
Chavarrı́a-Siles et al.
Using FBAT we found association of the ‘‘hebephrenic’’ type
of SC and the (AAT)n-repeat marker of the CNR1 (multiallelic P value of 0.0368) (Table IV). None of the individual
alleles showed positive evidence of association with hebephrenic schizophrenia, although allele 8 showed a trend towards
positive association (bi-allelic P value ¼ 0.06333, Z value ¼
1.805). Allele 4 showed a negative association to the hebephrenic subjects (bi-allelic P value ¼ 0.0243, Z value ¼ 2.251)
(Table I).
Using FBAT to test for association in all 244 subjects with
consensus DSM-IV diagnosis of SC (broad phenotype), we
found no association with the (AAT)n-repeat polymorphism
(multi-allelic P value ¼ 0.238), although allele 8 showed a trend
towards positive association with schizophrenia (bi-allelic
P value ¼ 0.0710, Z value ¼ 1.805) and allele 4 showed a
negative trend towards association with schizophrenia
(bi-allelic P value ¼ 0.069, Z value ¼ 18.18).
After confirming that the trait we used had a normal
distribution in the age group 25–45, we used quantitative
FBAT analysis to test if this trait was associated to the CNR1
(AAT)n polymorphism in this group (N ¼ 148) (Table V). The
LDPS-hebephrenia trait was significantly associated with
the (AAT)n-repeat marker of the CNR1 gene (multi-allelic
P value ¼ 0.0282). Independently only allele 8 showed
positive evidence of association with the trait (bi-allelic
P value ¼ 0.0100, Z ¼ 2.573) and allele 4 showed a trend to
negative association with the trait (bi-allelic P value ¼ 0.0606,
Z ¼ 1.876)
DISCUSSION
Available biological and clinical evidence supports the
hypothesis that CNR1 may be one of the genes which
contributes to the pathophysiology of SC. The findings of Ujike
et al. [2002] that genetic variation near the CNR1 gene might
be associated with hebephrenic schizophrenia, whose predominant symptoms in the tenth revision of the International
Classification of Diseases (ICD-10) include negative and
disorganized symptoms [World Health Organization, 2003]
support this hypothesis.
The hebephrenic subtype of SC is a distinct and more
quantifiable phenotype than the broader category of SC
described in the DSM-IV; it is characterized by predominant
negative symptoms and disorganization. As early as Bleuler
[1911] in an attempt to classify ‘‘the schizophrenias’’ into more
homogeneous groups, which he believed would one day be
subdivided into their ‘‘natural subdivisions,’’ followed the work
of Kraepelin and others and defined this type as having, among
other qualities, ‘‘dull’’ emotions, ‘‘blunted’’ affect, ‘‘slackening’’
of attention, and disorganized emotions, and behaviors. The
hebephrenic type of SC is a distinct subtype of schizophrenia
[Jabs et al., 2002; World Health Organization, 2003] which is
no longer in the DSM-IV, but which is still included in the ICD10. The patients in our sample were initially diagnosed using
the DSM-IV criteria, making direct comparison with studies
that use ICD-10 classification impossible. Nevertheless, in the
present study, the Lifetime Dimensions of Psychosis Scale
(LDPS) [Levinson et al., 2002] was scored for each subject;
with this scale different symptoms can be scored to obtain
quantitative phenotypes for genetic studies, and, as in the
case of the present study, to describe different ‘‘subtypes’’ of
schizophrenia, which can be analyzed for association to specific
genes.
Our study indirectly confirms (we used an alternative
method to diagnose hebephrenia) the previous report of Ujike
et al. that the (AAT)n microsatellite, located in the 30
untranslated region of the CNR1, is associated with hebephrenic schizophrenia. In both of these studies, the (AAT)n
microsatellite was significantly associated with this particular
phenotype. As classically defined [Bleuler, 1954] this type of
schizophrenia is characterized by predominant negative
symptoms and disorganization, which resembles what is seen
in animal models of cannabinoid exposure [Viveros et al., 2005]
and in chronic cannabinoid-induced psychosis [Halikas et al.,
1971]. The current study is also the first study of the CNR1
gene and schizophrenia to control for potential stratification by
utilizing family based association analyses. In sum, the current
study provides clear evidence that supports the finding of Ujike
et al.’s [2002] that variation in the CNR1 gene confers risk for a
hebephrenic type of schizophrenia.
Our secondary hypothesis, that the same polymorphic
marker from the CNR1 gene would not show association to
the more general phenotype of SC, was also supported, as the
global test of association was not significant, despite the fact
that the affected sample of SC subjects (N ¼ 244) was almost
four times as large as the sample of SC subjects who scored high
on a lifetime dimension of hebephrenia (N ¼ 66). Our results
when testing the SC phenotype, suggest that inclusion of other
types of SC (other than the hebephrenic type) may dilute the
power to find association of SC with the CNR1 gene. Out of
four previous studies testing for association between SC as a
general phenotype and the CNR1 gene, only one [Ujike et al.,
2002] found significant association for the (AAT)n microsatellite (after correcting for multiple testing), and in this
latter study, the association was driven primarily by the
hebephrenic subtype of SC. The specific alleles associated
with hebephrenia in our sample were not the same as in the
Japanese study of Ujike et al., although the number of alleles
for the AAT repeat were the same in both populations. The fact
that different alleles are associated in these two populations
might indicate that the specific allele is not the causal polymorphism for the trait, but that rather it is in linkage disequilibrium with a disease causing variant. If this were the case,
this may reflect either different variants in the gene associated
TABLE IV. Association Analysis of CNR1 (AAT)n-Repeat Marker With Hebephrenic
Schizophrenia (HSC) and Schizophrenia (SC) in the Central Valley of Costa Rica population
HSC (N ¼ 66)
(AAT)-repeat
a
Frequency
4
5
6
7
8
Multiallelic P:
0.233
0.053
0.179
0.204
0.255
Alleles
P value <0.05 are in bold.
a
Only informative alleles are shown.
Z value
P value
2.251
0.0243
1.732
0.0832
1.460
0.1444
0.115
0.9080
1.857
0.0633
P ¼ 0.0368
SC (N ¼ 244)
Z value
P value
1.818
0.0690
0.573
0.5668
0.115
0.9086
0.164
0.8697
1.805
0.0710
P ¼ 0.2382
CNR1 and Susceptibility to Hebephrenic Schizophrenia
TABLE V. Association Analysis of CNR1 (AAT)n-Repeat Marker
With the LDPS-Hebephrenia Quantitative Trait in the Central
Valley of Costa Rica population
LDPS-Hebephrenia trait
(N ¼ 148b)
(AAT)-repeat (allelesa)
Z value
P Value
4
5
6
7
8
Multi-allelic P:
1.876
0.952
1.905
0.358
2.573
0.060666
0.340885
0.056825
0.720442
0.010084
0.028279
P value <0.05 are in bold.
a
Only informative alleles are shown.
b
Only subjects in age range 25–45 were used for this analysis.
with the disease (independent variants which arose by
mutation in the two different populations) or the effect of
recombination between this marker and a shared disease
causing variant, which occurred at some point during the
historical divergence of the two populations. The study by
Martinez-Gras et al. [2006] did not present corrected (for
multiple testing) association results for the (AAT)n microsatellite analyses, although they did show a strong negative
association of one particular allele (allele 4) with the phenotype
of Schizophrenia. In the current study, we show a trend
towards significance for that particular allele being negatively
associated with SC (P ¼ .069, Table I). Interestingly, in our
sample, allele 4 is also negatively associated with the
hebephrenic type of SC (P ¼ .024, Table I). Since the CVCR
population shares common ancestry with the Spanish population [Escamilla et al., 1996], it is possible that both populations
share particular genetic variants in the CNR1 gene which
contribute to schizophrenia or hebephrenic schizophrenia in
particular, which are in tight linkage disequilibrium with the
(AAT)n microsatellite. In such a situation, the two populations
might share common alleles in this microsatellite which
predict either increased or decreased risk for schizophrenic
illnesses.
As a secondary analysis we tested the trait ‘‘LDPShebephrenia’’ for those subjects in the age range 25–45 for
association with the CNR1 in order to confirm if this trait can
be used as a quantitative trait for association studies. The
results confirm the association of the trait with the (AAT)n
repeat marker (multi-allelic P value ¼ 0.028); allele 8 which
previously showed a positive trend towards association with
SC and the hebephrenic type of SC, is positively associated
with the trait (bi-allelic P value ¼ 0.010); and allele 4 showed a
trend toward negative association with the trait (bi-allelic
P value ¼ 0.060), which is in relation to what we found for the
phenotype hebephrenia in the CVCR population.
Our clinical results also illuminate the complexity of the
schizophrenic phenotype, as seen in clinical practice. Out of
over 400 subjects being treated for Schizophrenia in the public
health system of Costa Rica (by ICD-10 criteria, which includes
a schizoaffective type of schizophrenia), only approximately
half of these subjects met DSM-IV criteria for SC after a best
estimate consensus process. We have also shown that subjects
with a major lifetime component of what in the past would
have been considered hebephrenia are now classified with a
variety of diagnostic types under the DSM-IV, predominantly
undifferentiated and disorganized (Table II). Since subtypes of
schizophrenia may change over the course of a subject’s life
[Kendler et al., 1985], lifetime dimensional ratings, such as
those obtained by the LDPS, may play a special role in better
characterizing biological types of schizophrenia. Furthermore,
283
our data suggest that dimensions, rather than categories
of schizophrenic subtypes, might prove especially useful in
teasing out which genes underlie what Eugen Bleuler termed
‘‘the Schizophrenias.’’
As pointed out previously by Martinez-Gras et al.; although
the (AAT)n-repeat CNR1 polymorphism is associated with (in
our study) hebephrenic schizophrenia, this polymorphism may
not be the functional polymorphism directly responsible for
this association. It is more likely that this polymorphism is in
linkage disequilibrium with other polymorphisms in the CNR1
gene, which may directly cause psychopathologic changes
contributing to schizophrenia [Martinez-Gras et al., 2006].
Targeted sequencing and association analyses are a logical
next step to identify direct causal variants of the CNR1 gene.
The present study has several limitations. First, we used an
alternative approach to define which subjects had a phenotype
of ‘‘hebephrenic’’ schizophrenia, which differs from the diagnostic procedure used in the original study by Ujike et al. [2002]
(ICD-10 classification of hebephrenic schizophrenia). Since the
ICD-10 classification is based on clinical observation, we were
able to use the LDPS retrospectively to obtain similar clinical
information. Second, as this was the first time the LDPS has
been used to try to define a ‘‘hebephrenic’’ type of schizophrenia, we used our best judgement to define procedures and cutoff
points for defining this phenotype. Clearly other cutoff points
could be selected, and ‘‘best fit’’ post hoc analyses may
eventually be useful to find the best fit for defining a particular
phenotype associated to the CNR1 gene. Such analyses,
however, are beyond the scope of the present study, which
was focused primarily on testing whether a ‘‘hebephrenic’’ type
of schizophrenia was associated with the CNR1 gene.
All in all, our current findings suggest that different genetic
and pathophysiologic mechanisms may underlie different
forms of SC, and that these different forms may be quantifiable
using dimensional rating systems, in addition to classical
categorical systems.
ACKNOWLEDGMENTS
This project was supported by NIH Research Grant # D43
TW06152 funded by the Fogarty International Center, The
National Institute on Drug Abuse and the National Institute of
Mental Health. We are indebted to the patients and family
member who participated in this study. We also want to thank
the psychiatry departments of hospitals and clinics in Costa
Rica that collaborated in this project (in particular Hospital
Nacional Psiquiatrico in Pavas). We thank the personnel of the
CIBCM at the University of Costa Rica for their assistance
collecting the sample.
REFERENCES
American Psychiatric Association. 1994. Diagnostic and statistical manual
of mental disorders, 4th ed. Washington, DC: American Psychiatric
Press.
Bleuler E. 1911. Originally published in German as Dementia Praecox oder
die Gruppe der Schizophrenien. English version. 1950.Dementia
Praecox or the Group of Schizophrenias. New York: International
Universities Press.
Bleuler M. 1954. The concept of schizophrenia. Am J Psychiatry 111:382–
383.
Escamilla MA, Spesny M, Reus VI, Gallegos A, Meza L, Molina J, Sandkuijl
LA, Fournier E, Leon PE, Smith LB, Freimer NB. 1996. Use of linkage
disequilibrium approaches to map genes for bipolar disorder in the Costa
Rican population. Am J Med Genet 67:244–253.
Escamilla MA, McInnes LA, Spesny M, Reus VI, Service SK, Shimayoshi N,
Tyler DJ, Silva S, Molina J, Gallegos A, Meza L, Cruz ML, Batki S,
Vinogradov S, Neylan T, Nguyen JB, Fournier E, Araya C, Barondes SH,
Leon P, Sandkuijl LA, Freimer NB. 1999. Assessing the feasibility of
284
Chavarrı́a-Siles et al.
linkage disequilibrium methods for mapping complex traits: An initial
screen for bipolar disorder loci on chromosome 18. Am J Hum Genet 64:
1670–1678.
Fritzsche M. 2001. Are cannabinoid receptor knockout mice animal models
for schizophrenia? Med Hypotheses 56:638–643.
Gottesman II. 1994. Schizophrenia epigenesis: Past, present, and future.
Acta Psychiatr Scand Suppl 384:26–33.
Halikas JA, Goodwin DW, Guze SB. 1971. Marihuana effects. A survey of
regular users. JAMA 217:692–694.
Howlett AC, Breivogel CS, Childers SR, Deadwyler SA, Hampson RE,
Porrino LJ. 2004. Cannabinoid physiology and pharmacology: 30 years
of progress. Neuropharmacology 47:345–358.
Jabs BE, Verdaguer MF, Pfuhlmann B, Bartsch AJ, Beckmann H. 2002. The
concept of hebephrenia over the course of time with particular reference
to the Wernicke-Kleist-Leonhard School. World J Biol Psychiatry 3:200–
206.
Kendler KS, Gruenberg AM, Tsuang MT. 1985. Subtype stability in schizophrenia. Am.J.Psychiatry 142:827–832.
Kessler RC, McGonagle KA, Zhao S, Nelson CB, Hughes M, Eshleman S,
Wittchen HU, Kendler KS. 1994. Lifetime and 12-month prevalence of
DSM-III-R psychiatric disorders in the United States. Results from the
National Comorbidity Survey. Arch Gen Psychiatry 51:8–19.
Leroy S, Griffon N, Bourdel MC, Olie JP, Poirier MF, Krebs MO. 2001.
Schizophrenia and the cannabinoid receptor type 1 (CB1): Association
study using a single-base polymorphism in coding exon 1. Am J Med
Genet 105:749–752.
Levinson DF, Mowry BJ, Escamilla MA, Faraone SV. 2002. The Lifetime
Dimensions of Psychosis Scale (LDPS): Description and interrater
reliability. Schizophr Bull 28:683–695.
Martinez-Gras I, Hoenicka J, Ponce G, Rodriguez-Jimenez R, JimenezArriero MA, Perez-Hernandez E, Ampuero I, Ramos-Atance JA, Palomo
T, Rubio G. 2006. (AAT)n repeat in the cannabinoid receptor gene, CN
R1:Association with schizophrenia in a Spanish population. Eur Arch
Psychiatry Clin Neurosci 256:437–441.
Montero AP, Balderas T, Pereira M, Lizano A, Medina R, Contreras S,
Dassori A, Katz I, Levinson D, Garbarz J, Escamilla MA, Raventos H.
2002. Resultados de la categorización de una muestra de pacientes con
diagnostico de egreso de esquizofrenia. Acta Medica Costarricense 44:
74–78.
Nurnberger JI Jr, Blehar MC, Kaufmann CA, York-Cooler C, Simpson SG,
Harkavy-Friedman J, Severe JB, Malaspina D, Reich T. 1994.
Diagnostic interview for genetic studies. Rationale, unique features,
and training. NIMH Genetics Initiative. Arch Gen Psychiatry 51:849–
859.
Pazos MR, Nunez E, Benito C, Tolon RM, Romero J. 2005. Functional
neuroanatomy of the endocannabinoid system. Pharmacol Biochem
Behav 81:239–247.
Rodriguez De FF, Gorriti MA, Bilbao A, Escuredo L, Garcia-Segura LM,
Piomelli D, Navarro M. 2001. Role of the endogenous cannabinoid
system as a modulator of dopamine transmission: Implications for
Parkinson’s disease and schizophrenia. Neurotox Res 3:23–35.
Rodriguez de FF, Del A I, Bermudez-Silva FJ, Bilbao A, Cippitelli A, Navarro
M. 2005. The endocannabinoid system: Physiology and pharmacology.
Alcohol Alcohol 40:2–14.
Solowij N, Michie P. 2007. Cannabis and cognitive dysfunction: Parallels
with endophenotypes of schizophrenia? J Psychiatr Neurosci 32:30–52.
Tsai SJ, Wang YC, Hong CJ. 2000. Association study of a cannabinoid
receptor gene (CNR1) polymorphism and schizophrenia. Psychiatr
Genet 10:149–151.
Ujike H, Morita Y. 2004. New perspectives in the studies on endocannabinoid and cannabis: Cannabinoid receptors and schizophrenia. J
Pharmacol Sci 4:376–381.
Ujike H, Takaki M, Nakata K, Tanaka Y, Takeda T, Kodama M, Fujiwara Y,
Sakai A, Kuroda S. 2002. CNR1, central cannabinoid receptor gene,
associated with susceptibility to hebephrenic schizophrenia. Mol
Psychiatry 7:515–518.
Viveros MP, Llorente R, Moreno E, Marco EM. 2005. Behavioural and
neuroendocrine effects of cannabinoids in critical developmental
periods. Behav Pharmacol 16:353–362.
Wigginton JE, Abecasis GR. 2005. PEDSTATS: Descriptive statistics,
graphics and quality assessment for gene mapping data. Bioinformatics
21:3445–3447.
World Health Organization. 2003. International Statistical Classification of
Diseases Then Revision 2nd ed. Geneva: World Health Organization.
Документ
Категория
Без категории
Просмотров
2
Размер файла
78 Кб
Теги
cannabinoid, phenotypic, cnr1, genes, hebephrenic, receptov, susceptibility, schizophrenia, quantitative
1/--страниц
Пожаловаться на содержимое документа