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Association study of NRG1 DTNBP1 RGS4 G72G30 and PIP5K2A with schizophrenia and symptom severity in a Hungarian sample.

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RESEARCH ARTICLE
Neuropsychiatric Genetics
Association Study of NRG1, DTNBP1, RGS4,
G72/G30, and PIP5K2A With Schizophrenia and
Symptom Severity in a Hungarian Sample
Janos M. Rethelyi,1* Steven C. Bakker,2 Patrıcia Polgar,1 Pal Czobor,1,3 Eric Strengman,4
Peter I. Pasztor,5 Rene S. Kahn,2 and Istvan Bitter1
1
Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
2
Department of Psychiatry, The Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, the Netherlands
3
Nathan Kline Institute for Psychiatric Research, Orangeburg, New York
Complex Genetics Section, Department of Biomedical Genetics, University Medical Center Utrecht, Utrecht, the Netherlands
4
5
Department of Psychiatry, St. John’s Hospital, Budapest, Hungary
Received 19 December 2008; Accepted 29 September 2009
Genetic association studies have yielded extensive but frequently
inconclusive data about genetic risk factors for schizophrenia.
Clinical and genetic heterogeneity are possible factors explaining the inconsistent findings. The objective of this study
was to test the association of commonly incriminated candidate
genes with two clinically divergent subgroups, non-deficit
(SZ-ND) and deficit-schizophrenia (SZ-D), and symptom severity, in order to test for replication of previously reported
results. A homogeneous sample of 280 schizophrenia patients
and 230 healthy controls of Hungarian, Caucasian descent were
genotyped for polymorphisms in schizophrenia candidate
genes NRG1, DTNBP1, RGS4, G72/G30, and PIP5K2A. Patients
were divided into the diagnostic subgroups of SZ-ND and SZ-D
using the Schedule for Deficit Syndrome (SDS), and assessed
clinically by the Positive and Negative Symptom Scale (PANSS).
SNP8NRG241930 in NRG1 and rs1011313 in DTNBP1 were
associated with SZ-ND (P ¼ 0.04 and 0.03, respectively). Polymorphisms in RGS4, G72/G30, and PIP5K2A were neither
associated with SZ-ND nor with SZ-D. SNP8NRG241930
showed association with the PANSS cognitive and hostility/
excitability factors, rs1011313 with the negative factor and SDS
total score, and rs10917670 in RGS4 was associated with the
depression factor. Although these results replicate earlier
findings about the genetic background of SZ-ND and SZ-D
only partially, our data seem to confirm previously reported
association of NRG1 with schizophrenia without prominent
negative symptoms. It was possible to detect associations
of small-to-medium effect size between the investigated
candidate genes and symptom severity. Such studies have the
potential to unravel the possible connection between genetic
and clinical heterogeneity in schizophrenia.
2009 Wiley-Liss, Inc.
Key words: schizophrenia; deficit syndrome; symptom severity; NRG1; DTNBP1
2009 Wiley-Liss, Inc.
How to Cite this Article:
Rethelyi JM, Bakker SC, Polgar P, Czobor P,
Strengman E, Pasztor PI, Kahn RS, Bitter I.
2010. Association Study of NRG1, DTNBP1,
RGS4, G72/G30, and PIP5K2A With
Schizophrenia and Symptom Severity in a
Hungarian Sample.
Am J Med Genet Part B 153B:792–801.
INTRODUCTION
Family and twin studies have consistently demonstrated high
heritability in schizophrenia [Sullivan et al., 2003], a devastating
psychiatric disorder affecting 1% of the population. Schizophrenia
is characterized by heterogeneous clinical symptoms, neurocognitive impairments, and decreased community functioning. Although the evidence for the genetic background of the disorder
is well documented, the search for candidate genes in schizophrenia
has resulted in conflicting findings. Initial reports based on the
positional candidate gene approach have identified several promising genes with biologically plausible functions for the etiopathology of schizophrenia [Harrison and Weinberger, 2005]. However,
many of the positive results found during the initial association
Additional Supporting Information may be found in the online version of
this article.
*Correspondence to:
Dr. Janos M. Rethelyi, Department of Psychiatry and Psychotherapy,
Semmelweis University, 1083 Balassa u. 6., Budapest, Hungary.
E-mail: rethelyi@psych.sote.hu
Published online 24 November 2009 in Wiley InterScience
(www.interscience.wiley.com)
DOI 10.1002/ajmg.b.31049
792
RETHELYI
ET AL.
studies could not be replicated in other populations. Methodological issues proposed to account for the low replicability of results
were insufficient sample size and statistical power, ambiguously or
too widely defined phenotypes, and initial chance findings as a
result of multiple testing [Sullivan, 2007]. Meta-analyses have
demonstrated small but significant associations for several of the
candidate genes [Munafo et al., 2006, 2008; Allen et al., 2008]. The
latest association studies and genome-wide association studies
involving large samples have not confirmed the previously identified candidate genes in schizophrenia [Sanders et al., 2008; Sullivan
et al., 2008], leading to skepticism about the usefulness of association studies in the search for the genetic background of
schizophrenia.
Neuregulin 1 (NRG1) and dysbindin (DTNBP1) were identified
as schizophrenia candidate genes by fine-mapping of linkage
regions 8p13 and 6p22, respectively [Stefansson et al., 2002; Straub
et al., 2002]. The initially uncovered association of these genes with
schizophrenia was replicated in some of the subsequent studies
[Williams et al., 2005]. Other studies, however, yielded negative
findings or positive findings implicating other markers and haplotypes within the same gene, suggesting locus heterogeneity
[Harrison and Law, 2006]. Recent meta-analyses do not support
the association of individual single-nucleotide polymorphisms
(SNPs) in NRG1 with schizophrenia and show a considerable
amount of between-study heterogeneity [Gardner et al., 2006]; but
the haplotype-based analysis does reflect the association, although
not for the originally described Iceland haplotype [Li et al., 2006;
Munafo et al., 2006, 2008]. A recently performed high-resolution
haplotype analysis of DTNBP1 [Mutsuddi et al., 2006] and the
review of Guo et al. [2009] point to major between-study differences in risk allele and haplotype frequencies. Similar to NRG1,
locus heterogeneity and the effect of geographical differences were
proposed as possible explanations. The SZGENE meta-analysis
lends subtle support for the role of DTNBP1 as a candidate gene,
pooled odds ratios for rs1011313[T] (P1325) demonstrated significant association with schizophrenia [Allen et al., 2008].
G72 and G30 are two overlapping genes, often referred to as
D-amino-acid oxidase activator (DAOA) gene. Similar to the previously mentioned ones, it is a positional candidate gene that was
localized during the fine-mapping of the 13q32-33 linkage region,
where several SNPs showed association with schizophrenia
[Chumakov et al., 2002]. Other association studies were inconclusive regarding the association of G72/G30 with schizophrenia, while
meta-analytic data point again to locus heterogeneity [DeteraWadleigh and McMahon, 2006]. The regulator of G-protein signaling 4 (RGS4) gene was first implicated in schizophrenia based on
expression studies that revealed decreased expression of the RGS4
gene in the prefrontal cortex of schizophrenic patients [Mirnics
et al., 2001]. A subsequent association study uncovered a group of
four SNPs that were associated with schizophrenia in three ethnically different samples [Chowdari et al., 2002]. Replication studies
involving this candidate gene vary substantially, including studies
showing the same association pattern or partial replication; one
study failed to detect any association in a Han Chinese sample
[Zhang et al., 2005]. The meta-analysis of Talkowski et al. [2006]
found a significant association for rs951436 (SNP4) in the analysis
including all case–control samples (3,486 cases and 3,755 controls).
793
The same analysis detected no significant association with any of the
SNPs or haplotypes in the family-based data set.
The phosphatidylinositol-4-phosphate 5-kinase, type II, alpha
(PIP5K2A) gene was identified as a schizophrenia candidate gene
based on both functional and positional evidence. Its product is
involved in the phosphoinositide signal transduction system, and it
is also harbored by 10p12, a chromosomal region showing linkage
to schizophrenia and bipolar disorder, although less consistently
than other linkage regions [Stopkova et al., 2003]. Two association
studies have reported several SNPs in PIP5K2A to be associated with
schizophrenia [Stopkova et al., 2005; Schwab et al., 2006]. The
studies of He et al. [2007] and Saggers-Gray et al. [2008] investigated the association of PIP5K2A in populations of non-European
descent with negative results.
Deficit schizophrenia (SZ-D) is a subgroup within schizophrenia
that is characterized by enduring, idiopathic negative symptoms,
including flattened affect, anhedonia, poverty of speech, curbing of
interest, lack of sense of purpose, and diminished social drive
[Carpenter et al., 1988]. These features are continuously present
during periods of clinical stability and are not secondary, that is, not
explainable by depression, anxiety, medication side effect, positive
symptoms, substance abuse, or psychosocial deprivation. Since the
first publication of the original concept a substantial body of
clinical, pharmacologic, neuropsychological, and epidemiologic
evidence has accumulated supporting the construct validity of the
deficit syndrome as a pathophysiologically distinct subgroup within schizophrenia that affects about 15% of first-episode patients and
25–30% of patients with chronic schizophrenia [Kirkpatrick et al.,
2001; Kirkpatrick and Galderisi, 2008]. Patients meeting criteria for
the deficit syndrome initially suffer also from more severe longterm social and occupational disability [Tek et al., 2001] and have
less chance for recovery [Strauss et al., 2008].
Genetic studies targeting the deficit syndrome as a hypothesized
genetically unique subgroup within schizophrenia have provided
further insight into this concept, although with mixed results. A
genetic epidemiological study examining sibling pairs concordant
for core schizophrenia revealed significant correlation of the deficit
status in siblings [Ross et al., 2000]. Bakker et al. [2004, 2007] found
association between non-deficit schizophrenia (SZ-ND) and previously reported risk haplotypes and SNPs of NRG1 and RGS4. No
association was detectable with alleles or haplotypes of DTNBP1
and G72/G30 neither in the SZ-D nor in the SZ-ND groups. The
PIP5K2A gene showed association with both diagnostic subtypes.
In a study enrolling 86 cases and 50 healthy controls, Wonodi et al.
[2006] failed to show the association of the COMT Val158Met SNP
with SZ-D.
Only a few studies have investigated the association of candidate
genes and symptom severity in schizophrenia. A DTNBP1 risk
haplotype has been found to be associated with higher levels of
negative symptomatology [DeRosse et al., 2006]. A study investigating the presence of the DTNBP1 C-A-T risk haplotype
(rs2619539, rs3213207, and rs2619538) in patients of Irish origin
found a significant association with the Positive and Negative
Symptom Scale (PANSS) hostility/excitability factor, and a trend
for negative symptoms [Corvin et al., 2008]. Another study conducted among Korean SZ patients showed a significant association
between PANSS total and positive scores and another DTNBP1
794
haplotype (rs3213207 and rs1011313) incorporating one common
SNP with the previously mentioned C-A-T risk haplotype [Pae
et al., 2008]. Campbell et al. [2008a,b] reported the association of
more severe baseline PANSS total scores in patients participating in
the Clinical Antipsychotic Trials of Intervention Effectiveness
(CATIE) with the markers rs2661319 (SNPRGS4-18) and
rs2842030 in RGS4 and rs10799902 in RGS5. The rs3918346 risk
allele in the D-amino-oxidase gene has been reported to show an
association with the depression/anxiety PANSS score [Corvin et al.,
2007]. Finally, Yue et al. [2007] found the DAOA gene AAG risk
haplotype (rs2391191, rs947267, and rs778294) to be associated
with the PANSS negative, cognitive, and depression factors in Han
Chinese patients. Overall, the reported associations between symptom severity and risk alleles or haplotypes show a substantial
variation among studies and the effect sizes were modest (values
typically fell in the small-to-medium range). Due to the variation of
investigated candidate genes the results are difficult to compare
across studies.
The principal objective of the present study was to investigate the
association of previously reported candidate genes for schizophrenia with SZ-ND and SZ-D in an ethnically homogeneous sample of
Hungarian schizophrenic patients, a population scarcely investigated previously in psychiatric genetic studies. Our primary goal
was the replication of the results of a prior Dutch study. The
original studies of Bakker et al. [2004, 2007] investigated the
association of candidate genes with diagnostic subgroups of schizophrenia that represent the lack or the concentration of negative
symptomatology.
The underlying hypothesis for such an approach is that narrowing the broader phenotype of schizophrenia, that is, decreasing
phenotypic heterogeneity, increases the chances of detecting a
genetic association. In order to account for the phenotypic variation
and heterogeneity further, we complemented the above categorical
approach (i.e., contrasting diagnostic subgroups) with a dimensional one. In particular, we explored the association of the investigated candidate genes with specific symptom clusters and severity
in the patient group. Based on previous results [Bakker et al., 2004,
2007; DeRosse et al., 2006] we hypothesized the association of
NRG1 and RGS4 with SZ-ND, and the association of DTNBP1 with
SZ-D. In addition, in light of the aforementioned findings we
expected that higher level of negative symptomatology would occur
in association with the DTNBP1 risk alleles, and higher positive
symptomatology in association with the NRG1 and RGS4 risk
alleles.
METHODS
Sample Collection, Clinical, and
Psychopathological Assessment
Inpatients and outpatients with a DSM-IV diagnosis [American
Psychiatric Association, 1994] of schizophrenia (n ¼ 284) were
enrolled in the study after written informed consent from two
major psychiatric centers in Budapest, Hungary (Department of
Psychiatry and Psychotherapy, Semmelweis University and Szent
Janos Hospital, Psychiatry Unit). Criteria for exclusion were severely disorganized behavior that prevented patient cooperation
AMERICAN JOURNAL OF MEDICAL GENETICS PART B
and precluded testing, and severe comorbidity, such as neurological
disorders, head trauma, mental retardation, or substance abuse. All
patients underwent clinical testing; a subsample of 266 patients
participated also in neuropsychological testing (results not presented in this study). The DSM-IV diagnosis for schizophrenia
excluding schizoaffective disorder was validated using the MINI 5.0
Neuropsychiatric Interview [Balazs et al., 1998]. The Hungarian
version of the Schedule for the Deficit Syndrome (SDS) was
obtained for all patients [Kirkpatrick et al., 1989], out of whom
154 met the criteria for the deficit syndrome. The SDS assesses
enduring negative symptoms (flattened affect, anhedonia, poverty
of speech, curbing of interest, lack of sense of purpose, diminished
social drive) based on the present condition and the history of the
patient. Following the original instructions, patients were assigned
to the SZ-D group if at least two of these symptoms were present in
an enduring manner, and all other underlying causes could be ruled
out. The PANSS [Kay et al., 1987] was administered by trained staff
members. Healthy controls (n ¼ 238) were recruited from the
employees of Semmelweis University (Budapest, Hungary) and
outpatients of the Department of Internal Medicine after screening
for psychiatric disorders. The study was approved by the Hungarian
National Scientific and Ethical Committee (ETT TUKEB) and the
Semmelweis University Institutional Ethical Board. DNA samples
of 280 schizophrenic (127 non-deficit and 153 deficit) patients and
230 controls were available; these subjects represented the target
population for this study. Basic demographic and clinical data
describing the two diagnostic subgroups and the control group are
summarized in Table I. Chi-square and General Linear Model
(GLM) analysis showed differences between the groups in terms
of age, level of education, duration of illness, and psychopathology
scores.
Marker Selection
The selection of SNPs was based on the marker selection and results
of prior Dutch reports of Bakker et al. [2004, 2007], and the existing
literature on candidate genes (mentioned earlier). In the case of
SNPs forming haplotype blocks in the Dutch sample, one of them
was omitted (rs760761, rs2619522, rs3916967, and rs1421292).
rs746203 (hCV9591220) was omitted because of the lack of
any association in the original sample. Additional SNPs of
potential importance (SNP8NRG221132, SNP8NRG241930,
SNP8NRG243177, and rs909706) were added to the set of markers
in accordance with recent studies, in order to increase compatibility
with these results [Burdick et al., 2007; Stefanis et al., 2007].
Genotyping
Genotyping was performed based on the TaqMan 50 -exonuclease
allelic discrimination assay, using the ABI Prism 7900HT Sequence
Detection System (Applied Biosystems, Foster City, CA). Polymerase chain reaction (PCR) for each SNP was performed with 5 ng of
DNA, 2.0 ml of ABI Taqman Universal Mastermix, 1.9 ml of water,
and 0.1 ml of 20 SNP assay (ABI). The 5 ml reactions were
performed in a 384-well plate (ABI). The DNA samples were PCR
amplified using the following conditions: 2 min hold at 50 C,
10 min denaturation step at 95 C, 50 cycles of 92 C for 15 sec, and
RETHELYI
ET AL.
795
TABLE I. Clinical and Demographic Characteristics of the SZ Subgroups and the Control Group
Age (SD)
Sex (M:F, %)
Level of education (E:S:H ratio, %)b
Education (years, SD)c
Age at onset (SD)
Duration of illness (SD)
No. of hospitalizations (mean, SD)
Inpatient:outpatient ratio (%)
SDS total score (SD)
PANSS total (SD)
PANSS positive (SD)
PANSS negative (SD)
PANSS general (SD)
SZ-ND (n ¼ 127)
SZ-D (n ¼ 153)
Control (n ¼ 230)
F/x2a
P-value
36.2 (11.2)
38.8 (12.0)
39.9 (15.0)
3.18
0.04
59:68 (46%:54%)
71:82 (46%:54%)
98:132 (42%:58%)
1.06
0.58
38:54:35 (30%:43%:27%) 56:81:16 (36%:53%:11%) 52:115:63 (23%:50%:27%) 23.9 <0.0001
13.4 (3.1)
11.9 (2.8)
13.9 (3.2)
12.49 <0.0001
28.4 (9.5)
28.8 (9.8)
—
0.12
0.73
7.5 (8.2)
10.1 (9.5)
—
5.34
0.021
5.3 (4.9)
6.9 (6.0)
—
5.63
0.02
81:46 (64%:36%)
118:35 (77%:23%)
—
7.94
0.02
10.7 (2.0)
16.6 (2.9)
—
381.10 <0.0001
72.0 (14.7)
83.5 (14.0)
—
44.90 <0.0001
17.9 (4.9)
17.6 (4.9)
—
0.14
0.71
17.1 (4.6)
22.4 (4.3)
—
100.10 <0.0001
37.0 (8.4)
43.4 (8.1)
—
42.38 <0.0001
a
Results of GLM and c2 statistics and corresponding levels of significance.
For completed education, E ¼ elementary, S ¼ secondary, H ¼ higher education.
c
Available for 158 patients and 205 controls.
b
60 C for 1 min. The plates were scanned utilizing the Allelic
Discrimination End-Point Analysis on the ABI Prism 7900HT
Sequence Detection System. The allelic discrimination data were
analyzed by the AutoCall algorithm of the SDS v2.1 software (ABI).
To ensure reliability, all PCR plates contained 16 blind duplicate
samples.
Statistical Methods
The Statistical Analysis System for Windows (version 9.1; SAS
Institute, Cary, NC) was used for statistical analyses. Genotype
frequencies in controls were checked for Hardy–Weinberg equilibrium (HWE) using a SAS macro calculating the 2 ln Q and chisquare test statistics for the hypothesis of HWE and the corresponding observed levels of significance. Allele frequencies were
calculated and cross-tabulated for the deficit and non-deficit group,
the total schizophrenia group, and controls. Association of alleles
with disease groups was tested using the Generalized Linear Model
(GENMOD) analysis, where alleles of interest served as a dependent
variable (in separate analyses), and disease group was used as an
independent variable; the likelihood ratio statistics determined
from the model tested the association. Statistical significance was
determined for the whole model, and for the subgroup comparisons, using model contrasts. All P-values are two-tailed and
represent comparison-wise type 1 error rates, with no adjustment
for multiple testing. Major haplotypes were calculated, and their
association with schizophrenia and the subgroups was analyzed
using the Unphased software (version 3.0.12) [Dudbridge, 2008].
Linkage disequilibrium (LD) analyses and comparison of LD results
with HapMap data [The International HapMap Consortium, 2005]
were performed using the Haploview 4.1 software [Barrett et al.,
2005].
The association of symptom severity with SNPs was tested using
the GLM analysis. In the GLM model, genotypes (in separate
analyses) were the independent variables (risk allele carriers vs.
non-carriers). The PANSS total score, the five PANSS factors,
identified by Varimax rotation principal component analysis,
and the SDS total score were applied as dependent variables; age
and sex were added as covariates to the analyses. The identified
PANSS factors (positive, negative, hostility/excitability, cognitive,
depression) were in accordance with the literature [Lindenmayer
et al., 1995] both in terms of item composition and explained
variance. The item composition of the five factors is listed in
Supplementary Material Table I. Similar to previous reports, the
total variance explained by the five factors was 57.3%, indicating the
assay sensitivity of clinical testing. In the GLM analyses testing
the association of PANSS positive and negative factors the opposite
factors (positive or negative, depending on analysis) were included
as covariates to distinguish primary and secondary negative symptoms. Indicators of symptom severity were compared between
genotypes by effect size estimation [Cohen, 1988]. By definition,
healthy controls were omitted from the analyses of symptom
severity. The analysis of continuous variables, rather than categorical variables, holds the advantage of an increased statistical power
to as much as 50% [Selvin, 2004].
RESULTS
Genotyping Results: General Information and
Intermarker Linkage Disequilibrium (LD)
The investigated candidate genes were genotyped altogether for
14 SNPs. The rate of successful genotyping was above 97% for all
SNPs, except for rs2619528 in DTNBP1, where 90.2% of the samples
were genotyped. All markers except for rs2619539 were in HWE,
defining the P-value for HWE at the level of 0.001 (Supplementary
Material Table II). This marker was omitted from further analyses.
Patterns of LD between the SNPs for the whole sample are shown
in Figure 1. In NRG1 SNP8NRG221533 and SNP8NRG243177
composed a haplotype block (D0 ¼ 0.90, r2 ¼ 0.70). SNPs rs3213207
796
AMERICAN JOURNAL OF MEDICAL GENETICS PART B
FIG. 1. NRG1 and DTNBP1 intermarker linkage disequilibrium
measured with D0 . D0 values were calculated from the genotyping
data obtained from the entire sample, using Haploview.
and rs2619528 in DTNBP1 (D0 ¼ 0.951, r2 ¼ 0.549) and rs10917670
and rs2661319 in RGS4 (D0 ¼ 0.879, r2 ¼ 0.546) were in close LD.
There was hardly any LD between rs2391191 and rs3918342 in
G72/G30.
SZ-D subgroups, and the entire schizophrenia sample (SZ).
Resulting P-values of likelihood tests indicate statistical significance
comparing the control sample and the total schizophrenia sample,
and contrast between the control sample and both the SZ-ND and
SZ-D subgroups.
For NRG1, the SNP8NRG241930[G] marker was associated with
SZ-ND (P ¼ 0.04), but not with SZ-D, nor with the total SZ sample.
No additional SNP in NRG1 showed an association with SZ, or the
subgroups. The two-marker haplotypes NRG221132–241930 and
NRG241930–243177 both were associated with SZ-ND as single
markers (P ¼ 0.03 and 0.04, respectively), although the overall
association of the haplotypes did not reach the level of statistical
significance.
For DTNBP1, rs1011313[C] showed association with the SZ-ND
group (P ¼ 0.03) but not with the SZ-D group or the entire SZ
sample. The association approached significance for rs909706 in the
total SZ sample (P ¼ 0.069). Analysis of two-marker haplotypes
yielded a significant association between rs909707–rs3213207 and
the total SZ sample (P ¼ 0.05).
Neither of the markers nor the two-marker haplotype in RGS4
was associated with SZ-ND or SZ-D, or the pooled SZ sample. For
G72/G30, we detected a marginally significant association between
rs2391191 and SZ-D (P ¼ 0.07), otherwise there was no association
between rs3918342 or the haplotype with any of the groups. For
PIP5K2A, the tested marker rs10828317 was not associated with any
of the disease subgroups.
Association of the Investigated SNPs and
Haplotypes With Schizophrenia, Non-Deficit and
Deficit-Schizophrenia
Association of Investigated Genotypes With
Symptom Severity
Table II summarizes allele frequencies of all selected markers, and
major haplotype frequencies in the control sample, the SZ-ND and
Results of the GLM analyses showed that the association of
psychopathological data (PANSS factor and total scores, and the
TABLE II. Association of Investigated SNPs and Haplotypes With Schizophrenia, SZ-ND and SZ-D
Schizophrenia
Gene
NRG1
DTNBP1
Haplo
RGS4
G72/G30
PIP5K2A
Marker/dbSNP
NRG221132
NRG221533
NRG241930
NRG243177
Haplo
rs1011313
rs909706
rs3213207
rs2619528
rs909706–rs3213207
rs10917670
rs2661319
Haplo
rs2391191
rs3918342
Haplo
rs10828317
Allele
C
C
G
C
Cont.
87.8
35.6
65.9
60.5
SZ-ND
89.7
39.0
73.6
57.1
C
C
C
C
CG
A
C
88.6
54.6
13.4
81.1
41.5
42.8
47.1
93.3
60.3
11.4
81.5
48.8
43.3
48.4
A
C
41.1
43.8
40.8
44.6
C
29.1
25.2
P
0.43
0.39
0.04
0.38
n.s.
0.03
0.15
0.43
0.90
0.17
0.88
0.74
n.s.
0.93
0.83
n.s.
0.27
SZ-D
87.8
36.1
66.5
63.2
90.8
60.6
10.2
82.1
49.3
48.3
44.0
34.9
49.3
27.3
Cont., control group (n ¼ 230); SZ-ND, non-deficit schizophrenia (n ¼ 127); SZ-D, deficit-schizophrenia (n ¼ 153); SZ: all schizophrenics (n ¼ 280).
P
0.98
0.90
0.87
0.45
n.s.
0.32
0.11
0.16
0.74
0.09
0.11
0.40
n.s.
0.07
0.16
n.s.
0.60
SZ
88.67
37.4
69.7
60.4
91.9
60.5
10.8
81.8
49.1
46.1
46.0
37.6
47.3
26.4
P
0.66
0.58
0.21
0.97
n.s.
0.08
0.069
0.19
0.77
0.05
0.27
0.72
n.s.
0.24
0.29
n.s.
0.34
RETHELYI
ET AL.
SDS total score) with genetic variables approached statistical
significance. Findings are summarized below in this section in
order to characterize the effect sizes for the pertinent associations for subsequent meta-analytic investigations. For NRG1,
SNPNRG241930[G] was associated with the cognitive (d ¼
0.48) and hostility/excitability factor (d ¼ 0.37) scores, and the
PANSS total score (d ¼ 0.21). Notably, the association was
opposite to the direction expected by meta-analytic data. For
DTNBP1, rs1011313[T] was associated marginally with the negative factor score (d ¼ 0.28) and the SDS total score (d ¼ 0.21), while
rs909706[T] (d ¼ 0.23), rs3213207[T] (d ¼ 0.26), and rs2619528[T] (d ¼ 0.23) showed association with the positive factor score.
rs10917670[G] in RGS4 showed a statistically significant association (d ¼ 0.31) with the PANSS depression factor score. The
observed associations resulted in low (d ¼ 0.2–0.4) or medium
(d ¼ 0.4–0.5) effect sizes. Results on symptom severity are summarized in Table III.
DISCUSSION
The primary objective of this study was to replicate the results of the
original association study of Bakker et al. [2004, 2007], focusing on
negative symptomatology, in an ethnically similar, however, geographically distinct population of schizophrenic patients. The
original study took place in the Netherlands and was limited to
patients with Dutch, Caucasian origin. Patients fulfilling criteria for
SZ-D were overrepresented. The replication phase followed the
original study in most aspects, that is, Hungarian schizophrenic
patients, exclusively with Caucasian ethnic background were
enrolled in the study, and the proportion of SZ-D patients was
similar to that in the original study. The large proportion of SZ-D
patients in the Hungarian sample is explicable by the patient
characteristics at the study sites, two inpatient units involved in
the treatment of chronic, severely ill patients. The number of
healthy controls was less in the replication phase; however, it is
important to note that the control group in the Hungarian study
was composed by individually recruited healthy subjects, instead of
bloodbank controls. The SDS was used for clinical assessment in
both studies. Validation of the clinical diagnosis of patients was
done with different diagnostic interviews, the Comprehensive
Assessment of Symptoms and History (CASH) [Andreasen et al.,
1992] in the prior study, and the MINI 5.0 Neuropsychiatric
Interview in the replication study; however, both instruments have
been shown to be reliable. The use of the SDS to differentiate
patients according to the concentration of negative symptomatology was the core concept of both studies. The common approach
was based on the assumption that genetic variation may be
decreased by the reduction of phenotypic variation, by investigating
homogenous subgroups, and thus making the detection of genetic
association more probable.
The importance of this replication study is underscored by the
fact that Hungarian schizophrenia patients have only been studied
in a few psychiatric genetic studies [Schwab et al., 2003; Szekeres
et al., 2004]. Accumulating association study findings in schizophrenia point to the importance of locus heterogeneity, a phenomenon that can be better understood by replicating studies in
different regions and populations [Straub and Weinberger, 2006].
797
The original reports of Bakker et al. found SNP8NRG221533 in
NRG1 and rs10917670 in RGS4 to be associated with SZ-ND,
furthermore rs10828317 in PIP5K2A to be associated with both
SZ-ND and SZ-D [Bakker et al., 2004, 2007]. In the Hungarian
sample, we detected association of NRG1 similarly only with SZ-ND
(P ¼ 0.04), although with another variant, SNP8NRG241930[G].
This marker has been shown to be associated with schizophrenia in
earlier studies as well [Petryshen et al., 2005], although the pooled
odds ratio estimate of all published association studies was not
statistically significant (OR ¼ 0.96; 95% CI: 0.88–1.04); it approached statistical significance if the meta-analysis was restricted
to only the Caucasian studies (OR ¼ 0.93; 95% CI: 0.85–1.03)
[Allen et al., 2008]. As in the Dutch sample, allele frequencies in
SZ-D were almost indistinguishable from those of controls. Several
lines of evidence indicate that NRG1 is involved in psychosis
phenotypes with relative preservation of affect and better prognosis,
such as SZ-ND, bipolar disorder with mood-incongruent psychotic
features, or schizotypal personality traits [Green et al., 2005; Lin
et al., 2005]. Both the original study and the replication are
consistent with these earlier findings. It is conceivable that across
populations different SNPs are associated with a nearby genetic
variant that is associated with psychotic traits without prominent
negative symptoms. The distribution of allelic frequencies across
groups and the direction of effect sizes for SNP8NRG221533,
the common NRG1 marker in the two studies were opposite
(Supplementary Material Table III).
Marker rs1011313[C] in DTNBP1 was associated with SZ-ND,
and the two-marker haplotype composed by rs909706 and
rs3213207 was associated with SZ. The SZGene meta-analysis
demonstrated significant pooled ORs for rs1011313[T] in Caucasian samples (OR ¼ 1.23; 95% CI: 1.07–1.40), thus the distribution
of allelic frequencies in the majority of previous studies was opposite [Allen et al., 2008]. A possible explanation for the contradictory
result is that the C allele of rs1011313 is a protective allele that is
associated with reduced risk for negative symptoms and SZ-D in
schizophrenia patients.
We found no association of RGS4 and PIP5K2A markers with
any of the subgroups, and an association approaching significance
for rs2391191 in G72/G30 with SZ-D. The results observed in the
Hungarian study are at variance with the original study, where
RGS4 was associated with SZ-ND and PIP5K2A with both groups. It
should be noted, however, that the observed allelic variations fall in
the range of previous meta-analytic data and are similar in direction
in the two samples (Supplementary Material Table III). In addition,
the meta-analysis of the combined (the Netherlands and
Hungarian) sample resulted in a low, however, statistically significant effect size for rs10828317[T] in PIP5K2A (h ¼ 0.14; 95% CI:
0.25 to 0.04). Furthermore, we only studied a limited number of
SNPs that showed association in previous studies. Association of
other SNPs in the Hungarian population can therefore not be ruled
out yet. It is important to note that while our investigation might
have been underpowered to detect associations with a small effect
size, neither study could identify a specific association of the deficit
syndrome with the investigated candidate genes. It is conceivable
that SZ-D is associated with other markers, or other sources of
genetic variation, as suggested by recent research about the role of
copy number variations in schizophrenia [Vrijenhoek et al., 2008].
PANSS negative factor
PANSS hostility/excitability
factor
PANSS cognitive factor
Marker/dbSNP
221132[C]a
221533[C]
241930[G]
243177[T]
rs1011313[T]
rs909706[T]
rs3213207[T]a
rs2619528[T]
rs10917670[G]
rs2661319[C]
rs2391191[A]
rs3918342[T]
rs10828317[T]
Risk all.
carrier (%)
79.5
62.3
89.6
63.8
15.5
62.5
78.8
34.7
79.2
68.6
61.4
76.6
90.9
Risk all.
carrier
10.5
10.4
10.5
10.5
10.5
10.5
10.5
10.2
10.3
10.5
10.6
10.4
10.5
Risk allele
non-carrier
10.3
10.6
10.4
10.5
10.5
10.5
10.4
10.7
11.3
10.6
10.3
10.7
10.8
Effect size
(Cohen d)
0.06
0.06
0.03
0.00
0.00
0.00
0.03
0.16
0.31
0.03
0.09
0.09
0.09
PANSS depression factor
Comparisons were calculated between risk allele homozygotes and other genotypes due to low case numbers.
a
PIP5K2A
G72/G30
RGS4
DTNBP1
Gene
NRG1
Gene
NRG1
Risk all.
carrier
77.9
78.1
78.0
78.0
79.9
78.4
78.4
77.6
78.4
78.3
78.6
77.6
79.3
Risk allele
non-carrier
80.0
78.9
81.2
78.7
78.1
78.0
77.8
78.5
79.5
78.5
78.0
79.8
79.2
Effect size
(Cohen d)
0.14
0.05
0.21
0.05
0.12
0.03
0.04
0.06
0.07
0.01
0.04
0.14
0.01
PANSS total score
Risk all.
carrier
13.7
13.9
13.9
13.7
14.6
13.8
13.9
13.8
14.0.
13.8
13.9
13.7
13.9
Risk allele
non-carrier
14.0
14.2
14.5
14.1
13.8
14.1
13.8
13.8
13.9
14.3
14.0
14.5
14.3
SDS total score
Effect size
(Cohen d)
0.17
0.06
0.16
0.09
0.21
0.08
0.03
0.00
0.03
0.13
0.05
0.19
0.10
Risk all. Risk all. Risk allele Effect size Risk all. Risk allele Effect size Risk all. Risk allele Effect size Risk all. Risk allele Effect size
Marker/dbSNP carrier (%) carrier non-carrier (Cohen d) carrier non-carrier (Cohen d) carrier non-carrier (Cohen d) carrier non-carrier (Cohen d)
221132[C]a
79.5
14.7
14.7
0.00
21.9
22.9
0.16
10.1
10.5
0.11
15.6
16.3
0.17
221533[C]
62.3
14.8
14.8
0.00
22.1
22.1
0.00
10.2
10.2
0.00
15.6
16.1
0.12
241930[G]
89.6
14.7
14.7
0.00
22.1
21.8
0.05
10.1
11.4
0.37
15.6
17.5
0.48
243177[T]
63.8
14.7
14.7
0.00
22.1
22.1
0.00
10.2
10.2
0.00
15.5
16.2
0.17
DTNBP1 rs1011313[T]
15.5
14.1
14.8
0.16
23.5
21.8
0.28
10.4
10.2
0.06
16.0
15.7
0.07
rs909706[T]
62.5
15.1
14.1
0.23
21.7
22.7
0.16
10.4
9.8
0.17
15.8
15.7
0.02
78.8
14.9
13.8
0.26
22.0
22.0
0.00
10.2
10.0
0.06
15.6
16.4
0.20
rs3213207[T]a
rs2619528[T]
34.7
14.0
15.0
0.23
22.3
21.9
0.07
9.7
10.4
0.20
16.3
15.5
0.20
RGS4
rs10917670[G]
79.2
14.7
14.9
0.05
21.9
22.2
0.05
10.3
10.4
0.03
15.9
15.9
0.00
rs2661319[C]
68.6
14.8
14.6
0.05
21.8
22.5
0.11
10.3
10.1
0.06
15.8
15.7
0.02
G72/G30 rs2391191[A]
61.4
14.8
14.6
0.05
21.8
22.3
0.08
10.2
10.2
0.00
15.9
15.7
0.05
rs3918342[T]
76.6
14.6
14.9
0.07
21.9
22.5
0.10
10.0
10.6
0.17
15.6
15.9
0.07
PIP5K2A rs10828317[T]
90.9
14.7
15.1
0.09
22.1
22.3
0.03
10.2
9.9
0.09
15.7
15.9
0.05
PANSS positive factor
TABLE III. Association of the Investigated Genotypes With PANSS Factor and Total Scores and the SDS Total Score
798
AMERICAN JOURNAL OF MEDICAL GENETICS PART B
RETHELYI
ET AL.
Caution must be exercised while interpreting the above findings.
As mentioned earlier, the results are not adjusted for multiple
comparisons, neither for the number of SNPs in one candidate gene
nor for all investigated associations. The results would not withstand this correction, and therefore can only be interpreted in the
context of meta-analytic data. However, based on our a priori
calculations our sample has the power to detect associations of the
magnitude that were observed in the Netherlands sample. Practical
issues explaining the low sample size were that we restricted the
recruitment of patients to two clinical centers and we sought to have
a sample where the proportion of deficit patients is higher.
Using factor analysis we identified five PANSS factors that served
as indicators of symptom clusters and severity. It should be
emphasized that the factor structure of the PANSS yielded by our
factor analyses was consistent with earlier results and pointed to
high assay sensitivity of the clinical assessment. Testing the relationship of the investigated SNPs with PANSS total and factor
scores revealed several associations. The positive factor was associated with four markers in DTNBP1. Negative factor scores and the
SDS total score were significantly higher in carriers of the rs1011313
risk allele. The risk haplotype of DTNBP1 has been shown to be
associated with higher levels of negative symptomatology and worse
cognitive decline in schizophrenia [Funke et al., 2004; DeRosse
et al., 2006; Burdick et al., 2007]. Moreover, Pae et al. [2008]
observed an association of a two-marker haplotype containing
rs1011313 with total PANSS scores. Unfortunately, the originally
described DTNBP1 risk haplotype cannot be investigated in
our sample, since data on rs1018381 (P1578), the marker most
significantly associated with SZ in the original sample, are not
available.
The association of the cognitive and the hostility/excitability
factors, and the PANSS total score approached significance with
SNP8NRG241930[G], the marker that was associated significantly
with the SZ-ND group. Notably, the sign of association for the
cognitive factor was negative, which is consistent with the finding
that SZ-D patients display a higher level of cognitive impairment
compared to SZ-ND patients. It is noteworthy that the depression
factor was associated significantly with rs10917670[G] in RGS4,
which showed no association with the SZ sample or the subgroups.
Although rs10917670 was not included in the study of Campbell
et al. [2008a], in that sample rs2661319 (SNP18) was significantly
associated with the PANSS total score.
Overall, the magnitude of the observed associations was similar
to those reported in earlier studies, representing effect sizes in the
small-to-medium rage. The interpretation of these findings is
difficult, since similar to results of prior investigations, the associations that we found were modest, and in some cases were at
variance with comparable previous studies. It should be noted that
despite the fact that the factor-analytic technique identifies basic
underlying symptoms of schizophrenia and allows for an efficient
compression of psychopathological data, PANSS factors are still
likely to reflect complex phenotypes, incorporating both trait- and
state-related symptoms. Thus, prospective studies focusing on
temporally stable symptom clusters may be needed to unravel the
connection between symptomatological and genetic heterogeneity
in schizophrenia. A recently published study successfully identified
two SNPs in genes not implicated earlier for schizophrenia that were
799
associated with lifetime positive and disorganized symptom
severity [DeRosse et al., 2008].
To summarize, our study demonstrated the feasibility of defining
particular patient groups with specific characteristics for genetic
association studies. Our data are suggestive of an association of
NRG1 with SZ-ND and lend support to the idea that the power of
genetic studies may be increased by analyzing disease subtypes. In
addition, we tested the association of psychopathological clusters
with the investigated candidate genes, resulting in several associations of interest. These novel approaches have the potential of
clarifying the connection between multiple genetic factors and
clinical heterogeneity in schizophrenia and might ultimately lead
to the identification of further candidate genes, or other sources
of genetic variation that play a role in the etiopathology of
schizophrenia.
ACKNOWLEDGMENTS
The authors are indebted to Vikt
oria Szab
o and Adrien St€
undl, who
helped in preparing DNA samples; furthermore, to Beatrix Mersich,
Krisztina Magyar, Agnes
Fabian, and Krisztina Jarvas for their
assistance in clinical data collection. We also wish to thank the
R
help of Csilla Boly
os, Marta Farkas, Zita Murai, Eva
ozsav€
olgyi,
Tamas Kurimay, Henrik Horvath, and Gabor Gazdag in recruiting
patients and healthy controls to the study. We are grateful to Roel
Ophoff for his advice and support at all stages of the study.
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