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Comprehensive analysis of tagging sequence variants in DTNBP1 shows no association with schizophrenia or with its composite neurocognitive endophenotypes.

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American Journal of Medical Genetics Part B (Neuropsychiatric Genetics) 147B:1159– 1166 (2008)
Comprehensive Analysis of Tagging Sequence Variants in
DTNBP1 Shows No Association With Schizophrenia or With
Its Composite Neurocognitive Endophenotypes
Kirsten Peters,1 Steven Wiltshire,2,3 Anjali K. Henders,4 Milan Dragović,5 Johanna C. Badcock,5 David Chandler,5
Sarah Howell,5 Chris Ellis,2,3 Sonja Bouwer,1 Grant W. Montgomery,4 Lyle J. Palmer,2,3,5 Luba Kalaydjieva,1
and Assen Jablensky5*
1
Laboratory for Molecular Genetics, Western Australian Institute for Medical Research, University of Western Australia, Nedlands,
Western Australia, Australia
2
Laboratory for Genetic Epidemiology, Western Australian Institute for Medical Research, University of Western Australia, Nedlands,
Western Australia, Australia
3
UWA Centre for Medical Research, University of Western Australia, Nedlands, Western Australia, Australia
4
Genetic Epidemiology Laboratory, Queensland Institute of Medical Research, Brisbane, Queensland, Australia
5
Centre for Clinical Research in Neuropsychiatry, School of Psychiatry and Clinical Neurosciences, University of Western Australia,
Mount Claremont, Western Australia, Australia
In a previous study we identified a relatively
homogeneous subtype of schizophrenia characterized by pervasive cognitive deficit, which was
the exclusive contributor to our findings of linkage to 6p25-p24. The 6p region contains Dysbindin
(DTNBP1), considered to be one of the major
schizophrenia candidate genes. While multiple
studies have reported association between
genetic variation in DTNBP1 and schizophrenia,
the findings have been inconsistent and controversial, leading to recent calls for systematic reexamination and unambiguous evidence of association. To address this, we have undertaken a
comprehensive survey of common genetic variation within DTNBP1 and its association with
schizophrenia, using a HapMap-based genetagging approach. We genotyped 39 tSNPs in a
sample of 336 cases and 172 controls of Anglo-Irish
ancestry, with the phenotype defined as clinical
schizophrenia, and as composite neurocognitive
endophenotypes. Allele and haplotype frequencies, and LD structure in our control sample were
similar to those in other European populations.
Using multivariate generalized linear modeling,
we observed no significant association between
any tSNP and any outcome variable. Association
with haplotypes was examined across the gene
and in the previously associated 50 region. Neither
global haplotype tests, nor specific analysis of
the ‘‘risk’’ haplotype previously reported in an
This article contains supplementary material, which may be
viewed at the American Journal of Medical Genetics website
at http://www.interscience.wiley.com/jpages/1552-4841/suppmat/
index.html.
Grant sponsor: NHMRC; Grant numbers: 404046, 404025;
Grant sponsor: Wind-Over-Water Foundation.
*Correspondence to: Prof. Assen Jablensky, M.D., DMSc,
School of Psychiatry and Clinical Neurosciences, MRF Building
Level 3, 50 Murray Street, Perth 6000, Australia.
E-mail: assen@cyllene.uwa.edu.au
Received 28 November 2007; Accepted 24 January 2008
DOI 10.1002/ajmg.b.30741
Published online 3 March 2008 in Wiley InterScience
(www.interscience.wiley.com)
ß 2008 Wiley-Liss, Inc.
ethnically related population, the Irish highdensity schizophrenia families, showed significant evidence of association with schizophrenia
or with the neurocognitive endophenotypes in
our sample. The framework and results of this
study should facilitate further attempts at reanalysis of DTNBP1, in terms of standardized
approaches to both phenotype definition and
analysis of genetic variation. ß 2008 Wiley-Liss, Inc.
KEY WORDS: schizophrenia; DTNBP1; association; endophenotypes; HapMap
gene-tagging
Please cite this article as follows: Peters K, Wiltshire S,
Henders AK, Dragović M, Badcock JC, Chandler D,
Howell S, Ellis C, Bouwer S, Montgomery GW, Palmer
LJ, Kalaydjieva L, Jablensky A. 2008. Comprehensive
Analysis of Tagging Sequence Variants in DTNBP1
Shows No Association With Schizophrenia or With Its
Composite Neurocognitive Endophenotypes. Am J Med
Genet Part B 147B:1159–1166.
INTRODUCTION
The 6p24-p22 region is now regarded as one of the wellestablished schizophrenia loci (OMIM SCZD3 #600511). It has
been linked to schizophrenia in a number of studies [Schwab
et al., 1995; Straub et al., 1995; Wang et al., 1995] and was
among the few supported by a genome scan meta-analysis
[Lewis et al., 2003]. A candidate gene in the region, Dystrobrevin binding protein 1 (DTNBP1, Dysbindin), was first
identified by Straub et al. [2002] and Van den Oord et al. [2003]
who observed association between DTNBP1 sequence variants
and schizophrenia in a sample of Irish high-density schizophrenia families. Subsequent analyses of nearly 30 independent, mostly case–control samples [Schwab et al., 2003; Van den
Bogaert et al., 2003; Funke et al., 2004; Kirov et al., 2004;
Numakawa et al., 2004; Gornick et al., 2005; Duan et al., 2007;
Tosato et al., 2007; Turunen et al., 2007; Vilella et al., 2007 and
references therein] have led to reports of positive replication
findings in many (but not all) studies. A number of studies
described association between DTNBP1 variation and different measures of cognitive function in patients as well as in
normal subjects [Burdick et al., 2006; Fallgatter et al., 2006;
1160
Peters et al.
Donohoe et al., 2007; Zinkstok et al., 2007], and an effect
of ‘‘high risk’’ variants and/or haplotypes on the clinical
manifestations and progression of the disease [Fanous et al.,
2005; Gornick et al., 2005; DeRosse et al., 2006; Burdick et al.,
2007; Tosato et al., 2007].
While the replication claims have placed Dysbindin among
the major candidate genes conferring susceptibility to schizophrenia [Harrison and Weinberger, 2005; Owen et al., 2005;
Karayiorgou and Gogos, 2006; Riley and Kendler, 2006],
the results of these association studies have been inconsistent
and controversial. Use of limited sets of partially overlapping
markers, and widely divergent findings where associated
alleles and haplotypes differ even between samples derived
from closely related populations [Harrison and Weinberger,
2005; Karayiorgou and Gogos, 2006; Riley and Kendler, 2006]
have made the findings difficult to interpret. A recent metaanalysis [Li and He, 2007] has failed to support association,
and a comprehensive study [Mutsuddi et al., 2006] of normal
variation and linkage disequilibrium (LD) patterns in
DTNBP1 makes it unlikely that the reported disparate positive
findings can be attributed to inter-population differences or to
the existence of multiple rare susceptibility alleles. Dysbindin
has been used as an example of fallible interpretation of the
findings in replication studies [NCI-NHGRI Working Group on
Replication in Association Studies, 2007] and Mutsuddi et al.
[2006] have called for an unambiguous confirmation of the
association, based on a systematic approach.
In a previous study [Hallmayer et al., 2005], we defined two
major schizophrenia subtypes with markedly different neurocognitive profiles and tentatively established a distinct genetic
basis of the cognitive deficit (CD subtype), based on its
exclusive contribution to significant linkage (lod 3.5) to the
6p25-p24 region, where the remaining families with probands
classified as cognitively spared yielded a negative LOD score.
Although DTNBP1 falls outside that interval, it is within the
more broadly defined 6p24-p22 linkage region [Schwab et al.,
1995; Straub et al., 1995; Wang et al., 1995; Lewis et al., 2003]
and a prominent candidate gene demanding investigation in
our sample.
Here we report the results of a comprehensive analysis of
DTNBP1 variation in a case–control association study design.
Our aims were to: (i) Derive a comprehensive set of tagSNPs
(tSNPs) capturing common genetic variation in DTNBP1, and
assess their analytical performance; (ii) Examine the evidence
for association between DTNBP1 variants and schizophrenia,
defined as a clinical diagnostic category; and (iii) Search for
association between DTNBP1 variants and a composite neurocognitive endophenotype, incorporating measures of performance in multiple neurocognitive domains.
MATERIALS AND METHODS
Subjects
The study included a total of 508 individuals (371 male and
137 female) of whom over 75% are of Anglo-Irish descent.
Schizophrenia (SCZ) cases (N ¼ 336, 269 male) included the
93 probands from our previously reported family study
[Hallmayer et al., 2005], whereas the remainder were newly
recruited subjects. The 172 controls (102 male) were recruited
by random sampling from local telephone directories, or among
Red Cross blood donors, with screening for psychopathology
used to exclude individuals with previous diagnosis of
psychotic illness in themselves or in any of their first-degree
relatives. Written informed consent was obtained from all
participating subjects. The study was approved by the Human
Research Ethics Committee of The University of Western
Australia and the North Metropolitan Health Area Ethics
Committee, Perth, Western Australia.
Phenotype Characterization
Clinical diagnosis of affected subjects was based on a videorecorded structured interview with the use of the Schedules
for Clinical Assessment in Neuropsychiatry (SCAN), version
2.0 [Wing et al., 1990], a review of case records, and a
structured developmental history obtained from a key family
member. Research diagnoses were established by consensus
between two senior clinicians, who reviewed independently
the entire diagnostic information, including the videotape
of the SCAN interview, and assigned ICD-10 and DSM-IV
lifetime diagnoses.
All participants (including controls) were administered a
battery of tests assessing several domains of neurocognitive
function, as described in detail [Hallmayer et al., 2005]. In
brief, these included: General cognitive ability: prior or
premorbid IQ (the National Adult Reading Test) and current
IQ (the Shipley Institute of Living Scale); Sustained attention:
visual Continuous Performance Task, degraded-stimulus
version (involving an increased demand on visual encoding)
and identical-pairs version (selectively engaging working
memory); Executive function: The FAS version of the Controlled Oral Word Association Task (assessing effortful
retrieval of stored lexical knowledge); Verbal memory: the
Rey Auditory Verbal Learning Test (assessing immediate and
delayed recall of word lists, retention after distraction, and
errors, generating an index of encoding in verbal memory);
Speed of information processing: Inspection Time task (measuring perceptual encoding and speed of processing, unconfounded by motor-reaction time); Neurobehavioral features: a
structured examination of soft (non-localizing) neurological
signs and the Edinburgh Handedness Inventory of behavioral
lateralization.
A form of latent structure analysis, known as grade of
membership (GoM) analysis [Woodbury et al., 1978; Manton
et al., 1994] was used to integrate the multiple cognitive
measures into a parsimonious number of ‘‘pure types’’ and
assign, to each subject, scores of affinity to each one of several
pure types. This approach identified two distinct neurocognitive profiles, which comprised >90% of the schizophrenia
patients and >23% of their unaffected first-degree relatives: a
cognitive deficit (CD) subtype and a cognitively spared (CS)
subtype. Generalized cognitive deficiency was the most salient
characteristic of the CD subtype, in contrast to mild or patchy
deficits in the CS subtype.
Power of the Study
Power to detect association was determined for each of our
data sets using the Genetic Power Calculator [Purcell et al.,
2003]. For our SCZ/control sample, detectable odds ratios for
the homozygote ranged from 2.7 to 2.15, for allele frequencies
between 0.1 and 0.4. For the CD/control sample, these odds
ratios ranged from 3 to 2.45, and for the CS/control dataset 3.2–
2.6, for the same allele frequency range. These effect sizes are
consistent with those reported in previous DTNBP1 association studies [Schwab et al., 2003; Van den Bogaert et al., 2003;
Van den Oord et al., 2003; Funke et al., 2004; Numakawa et al.,
2004; Williams et al., 2004].
Selection of Polymorphic Markers
SNPs were selected to represent the common genetic
variation in the DTNBP1 genomic structure and 10 kb
upstream and downstream of the gene. We used HapMap
phase II-listed variants with a minor allele frequency >5%,
augmented with coding SNPs from dbSNP, and SNPs
previously reported in association with schizophrenia.
TagSNPs (tSNPs) were identified using the pair-wise option
of Tagger [de Bakker et al., 2005] implemented in Haploview
No Association Between DTNBP1 and Schizophrenia
2
[Barrett et al., 2005], with a threshold of r > 0.8. High Illumina
designability scores served as an additional criterion for the
assembly of the final SNP panel.
Genotyping and Primary Data Analysis
Genotyping was performed on an Illumina BeadStation
using the GoldenGate technology. DNA samples from CEPH
trio 1334 (obtained from the Coriell Cell Repository) served as
internal controls for quality of clustering and reproducibility.
The primary analysis of the genotyping data with the
Illumina BeadStudio software was followed by visual inspection and assessment of data quality and clustering.
Statistical Analyses
Deviations from Hardy–Weinberg equilibrium (P < 0.001)
were examined using PLINK [Purcell et al., 2007]. We used
Haploview [Barrett et al., 2005] to determine the residual LD
between the selected tSNPs and characterize in our control
population the haplotype block structure [Gabriel et al., 2002]
of DTNBP1 and immediate flanking sequence.
Association of DTNBP1 sequence variants with clinically
diagnosed schizophrenia and with neurocognitive endophenotypes was analyzed using generalized linear modeling, implemented in the statistical program SimHap [McCaskie et al.,
2006]. The primary dichotomous outcome variables were
SCZ, CD, and CS, in the respective case/control sample.
The principal explanatory variables were the genotyped
sequence variants in DTNBP1. The SNP genotypes were
coded into three classes (0 ¼ major allele homozygote, 1 ¼
heterozygote, 2 ¼ minor allele homozygote) and analyzed as a
linear (additive, gene-dosage) covariate. Additionally, each
SNP was analyzed as a factored, or codominant, effect, where
the heterozygotes and homozygotes were explicitly modeled; in
instances where there were less than five homozygotes in cases
or controls, the SNP in question was modeled as a dominant
effect only. We corrected for the multiple testing inherent in
this study using the Bonferroni method [Armitage et al., 2002].
Haplotypes were inferred for individuals with ambiguous
phase and haplotype frequencies were estimated using an
expectation–maximization algorithm as implemented in SimHap [McCaskie et al., 2006]. Haplotypes were recoded as
independent factors into three classes (0, 1, or 2), representing
the number of copies of each haplotype in an individual’s
diplotype. In each analysis, we performed a global haplotype
test, where each haplotype is modeled as an additive effect and
examined relative to the same (most abundant) baseline
haplotype. Individual haplotypes of interest were examined
in haplotype-specific tests (under an additive model).
RESULTS
The study included a total of 508 subjects, of whom 336 were
clinically affected, meeting ICD-10 and DSM-IV diagnostic
criteria for schizophrenia. Using a battery of neurocognitive
tests, 155 of the affected individuals were assigned to the
cognitive deficit (CD) subtype of schizophrenia, and 121 to
the cognitively spared (CS) subtype [Hallmayer et al., 2005].
The remainder fell into two residual (non-CD/non-CS)
subtypes: elderly individuals whose cognitive deficit pattern
could be age-related, and a small number of affected subjects
with cognitive function indistinguishable from controls. From
this classification, we constructed three case/control samples
for analysis: schizophrenia (SCZ)/controls, CD/controls, and
CS/controls. The non-CD/non-CS subjects were included only
in the SCZ/controls sample.
Our analytical approach aimed at comprehensive coverage
of the genomic DTNBP1 sequence and of 10 kb upstream and
1161
downstream of the gene. Using resources and criteria likely to
be applied in most future studies (please see Materials and
Methods Section), we constructed a panel of 39 tag SNPs
(tSNPs), capturing a total of 151 common variants across
Dysbindin (Supplementary Material Table I). The resulting
map (Supplementary Material Table I and Fig. 1) shows some
differences with the map proposed in a recent comprehensive
study of normal DTNBP1 variation [Mutsuddi et al., 2006],
due to different selection criteria and genotyping technology
(e.g., 18/42 tSNPs selected in the Mutsuddi et al. study [2006]
are not included in our set: 8 of these are captured by our
tSNPs, 4 have MAF < 5%, and 6 are not listed in HapMap).
The 37 kb region of DTNBP1 (50 region of the gene to IVS4)
where a cluster of SNPs have been studied and found positive,
in different combinations, in European populations [Schwab
et al., 2003; Van den Bogaert et al., 2003; Van den Oord et al.,
2003; Funke et al., 2004; Kirov et al., 2004; Bray et al., 2005;
Gornick et al., 2005; Mutsuddi et al., 2006], was covered in our
study by a total of 12 tSNPs (Fig. 1, Supplementary Material
Table I): rs3213207, rs2619545, rs1011313, rs2619547,
rs2619528, rs2619522, rs1018381, rs1997679, rs909706,
rs9476886, rs2743852, and rs2619538 (ordered here by
chromosomal position). Markers rs2619547, rs909706, and
rs2743852 are not listed in HapMap, but were included in our
study based on previous evidence of association [Funke et al.,
2004; Williams et al., 2004; Gornick et al., 2005]. Three further
SNPs, previously analyzed and found in association, namely
rs2005976, rs760761, and rs1474605 [Van den Bogaert et al.,
2003; Van den Oord et al., 2003], were not included in our set of
tSNPs. The markers rs2005976 and rs760761 could not be
included due to incompatibility with the genotyping technology used (Illumina designability scores of 0). All three markers
are captured by other SNPs genotyped in our study: rs2005976
has been shown previously to be in complete LD with
rs2619528 [Van den Oord et al., 2003], and rs760761 and
rs1474605 is captured by rs2619545 (r2 ¼ 1).
Genotyping using the Illumina GoldenGate technology
resulted in an average call rate of 99.9%. Eight samples with
a call rate <98% were removed from further analysis. Marker
rs11558324 [Gornick et al., 2005], tagging only itself, showed
poor genotyping performance and was also removed. In
addition to the standard analysis with the Illumina BeadStudio software, data quality was assessed by visual inspection. SNP rs3213207, the marker most consistently analyzed in
DTNBP1 association studies in Europeans [Schwab et al.,
2003; Van den Bogaert et al., 2003; Van den Oord et al., 2003;
Funke et al., 2004; Kirov et al., 2004; Bray et al., 2005],
produced ambiguous results in the Illumina genotyping. While
the BeadStudio Genotyping Module software gave it a high
Gentrain score (0.754), visual inspection revealed two heterozygote clusters, possibly resulting from the presence of another
polymorphic position in the same DNA fragment. To test this
possibility and obtain unequivocal genotype assignments, we
sequenced the fragment around rs3213207 in DNA samples
representing the four clusters. This analysis confirmed our
assumption, detecting the presence of rs12527496, located
20 bp away from our target SNP. It also confirmed the
BeadStudio genotype assignment, with samples in both
heterozygote clusters shown to be heterozygous for the
targeted SNP rs3213207, but different in their rs12527496
genotypes. In our design, rs12527496 was captured by
rs3829893, and sequencing analysis confirmed the genotypes
predicted on the basis of LD.
After data cleaning, the final sample for statistical analysis
comprised 329 SCZ cases, including 152 CD and 119 CS, and
171 controls. The final number of tSNPs was 38, capturing
150 variants. No deviation from Hardy–Weinberg equilibrium, examined using PLINK [Purcell et al., 2007], was
observed for any of the 38 tSNPs (P > 0.009) (Supplementary
1162
Peters et al.
Fig. 1. Coverage and LD structure of the 37 kb 50 region of DTNBP1 previously reported to be associated with schizophrenia in European populations.
The order of markers left to right follows nucleotide positions along the chromosome, with the DTNBP1 genomic structure (on minus strand) shown above.
The 37 kb spans the region upstream of DTNBP1 to IVS4. Based on HapMap phase II-listed SNPs, MAF > 5%, and r2 > 0.8, the region was covered by the
12 tSNPs shown above. D0 values were calculated from the genotyping data obtained in the 171 control subjects, using Haploview [Barrett et al., 2005].
The region is split into three haplotype blocks, with Block 2 comprising most previously associated markers (referred to SNPs 4–8 by Mutsuddi et al. [2006]).
[Color figure can be viewed in the online issue, which is available at www.interscience.wiley.com.]
Material Table IIA). The minor allele frequencies observed in
our control sample were very similar to those reported in
HapMap (Supplementary Material Table IIA).
The haplotype block structure [Gabriel et al., 2002] of
DTNBP1 in our control population, as defined by Haploview
[Barrett et al., 2005], comprised eight small blocks of up to 70
kb (Supplementary Material Fig. 1). Blocks 3, 4, and 5 were
located within the large (122 kb, spanning exons 4–8) block
which is shown in HapMap, but was not present in our control
sample of 171 subjects. In the 37 kb spanning the region 50 of
DTNBP1 to IVS4, where most previous association findings in
Europeans cluster [Schwab et al., 2003; Van den Bogaert et al.,
2003; Van den Oord et al., 2003; Funke et al., 2004; Kirov et al.,
2004; Bray et al., 2005], we observed three haplotype blocks of 4
kb (IVS4), 23 kb (IVS4 to IVS1), and 1 kb (IVS1; Fig. 1). The 23
kb block is in the center of the previously associated region;
it is formed by four of our tSNPs (rs2619547, rs2619528,
rs2619522, and rs1018381), capturing two additional SNPs
(rs2005976 and rs760761) that have been analyzed in previous
studies [Schwab et al., 2003; Van den Bogaert et al., 2003; Van
den Oord et al., 2003; Funke et al., 2004].
Within that region, Mutsuddi et al. [2006] selected
six SNPs—rs3213207, rs1011313, rs2005976, rs760761,
rs1018381, and rs2619538 (referred to as tSNP 2, 3, 5, 6, 8,
and 11 respectively in Table III and Figure 1 of Mutsuddi et al.
[2006]), representative of the associated markers and haplotypes in European populations. Mutsuddi et al. [2006]
derived common haplotypes and estimated their frequencies
in the CEU sample. Analysis of the frequencies of these five
haplotypes in our control population showed similar values
(Fig. 2, panel A), except for haplotype 1 which was marginally
less common than haplotype 2. The additional SNPs genotyped
in our sample led to further differentiation, generating 11
haplotypes with frequencies >1% (Fig. 2, panel B). We also
examined the 8-marker haplotypes identified in the study of
Irish high-density schizophrenia families, where association
No Association Between DTNBP1 and Schizophrenia
1163
Fig. 2. Haplotypes in the 37 kb 50 region of DTNBP1 in our control sample and in previous studies of European populations. LD analysis (Fig. 1) revealed
three blocks in the region, therefore the data in this figure do not represent non-recombinant ancestral haplotypes but are shown for the purpose of comparing
population frequencies. SNPs with genotypes inferred based on LD are shown in brackets. * The Illumina assays for these SNPs target the opposite strand; to
allow comparisons with previously reported data, we present the reverse complement alleles. A: 6-marker haplotypes as constructed by Mutsuddi et al.
[2006]. Phylogenetic tree of the common haplotypes as shown in Figure 1 of the Mutsuddi et al. study [2006]. The haplotype frequencies at the bottom have
been obtained from the analysis of 30 CEU trios [Mutsuddi et al., 2006] (in black) and of the 171 unrelated control subjects in the present study (in red).
B: Further haplotype differentiation in this study. The genotyping of additional SNPs results in the differentiation of the two most common 6-marker
haplotypes. The color coding of derivative haplotypes follows that shown in panel A, and the ordering is based on the observed frequencies. The alleles
defining the 6-marker haplotypes are in bold. C: 8-marker haplotypes analyzed in the Irish study of high-density schizophrenia families [Van den Oord et al.,
2003; Riley and Kendler, 2006]. The haplotype frequencies are very similar in our control population, in the Irish schizophrenia families [Van den Oord
et al., 2003], and in the CEU trios [Mutsuddi et al., 2006]. Haplotype 2 corresponds to the Irish high-risk haplotype [Van den Oord et al., 2003; Riley and
Kendler, 2006]. [Color figure can be viewed in the online issue, which is available at www.interscience.wiley.com.]
with Dysbindin was first reported [Van den Oord et al., 2003;
Riley and Kendler, 2006]. The frequencies observed in our
control sample of predominantly Anglo-Irish ancestry were
very similar to those observed in the Irish families [Van den
Oord et al., 2003; Riley and Kendler, 2006], as well as to those in
the CEU sample [Mutsuddi et al., 2006] (Fig. 2, panel C).
After establishing this framework, we analyzed the association of DTNBP1 sequence variants with clinically diagnosed
schizophrenia and with neurocognitive endophenotypes. All
results, reported as odds ratios and 95% confidence limits, are
shown in detail in Supplementary Material Table II. None of
the 38 tSNPs tested under an additive genetic model was
significantly associated with SCZ (P > 0.19), the CD (P > 0.20)
or the CS subtype (P > 0.23). Under a codominant model, SNPs
rs2619545 and rs1997679 (both located within the previously
reported associated region) were nominally associated with
SCZ (P ¼ 0.05 and 0.03, respectively), but were non-significant
after Bonferroni correction for multiple testing (P ¼ 0.86 and
1164
Peters et al.
0.69, respectively). None of the rare SNPs analyzed under a
dominant (instead of codominant) model was significantly
associated with any of the outcomes (P > 0.18).
The results of the haplotype-based association analyses are
presented in Supplementary Material Table III. We performed
the global haplotype test for association with the SCZ, CD, and
CS phenotype using the haplotypes in the eight observed LD
blocks (as shown in Supplementary Material Fig. 1) across
DTNBP1. Significant association was observed for only the
SCZ phenotype with block 1 (P ¼ 0.03), but was non-significant
after Bonferroni correction for multiple testing (P ¼ 0.22). To
obtain data comparable to previous studies, we also performed
the global haplotype test with the 6-marker haplotypes defined
by Mutsuddi et al. [2006] (including our genotyped tSNPs
rs3213207, rs1011313, rs1018381 and rs2619538, and inferred
rs2005976 and rs760761). No significant results were obtained
in this analysis: P ¼ 0.72, P ¼ 0.42, and P ¼ 0.41 respectively
for SCZ, CD, and CS. Finally, based on the similar ethnic
composition of the two samples, we specifically tested the risk
haplotype G-G-A-A-T-G-C-G identified in the Irish schizophrenia families [Van den Oord et al., 2003; Riley and Kendler,
2006] (haplotype 2 in Fig. 2, panel C) for association with
schizophrenia or its subtypes in our sample. No significant
association was found in the haplotype-specific test, between
this haplotype relative to all other haplotypes, and either SCZ,
CD, or CS (all P > 0.14).
DISCUSSION
In this study, based on our previously identified neurocognitive subtypes of schizophrenia and linkage to the short
arm of chromosome 6 [Hallmayer et al., 2005], we aimed at
comprehensive analysis of variation in DTNBP1 and its
association with the disease phenotypes. We relied on widely
used marker selection criteria and genotyping technology to
develop and test an analytical framework that would be
applicable to future studies. Our selection of markers captures
common genetic variants across the gene and its flanking
regions, and results in genotyping data of good quality. While
some of the associated SNPs reported in the literature are not
directly analyzable using the Illumina GoldenGate technology,
they can be captured reliably by tSNPs at very high r2 and D0
values. The allele frequencies and LD pattern observed in our
control sample are in agreement with those reported in public
databases and in a recent detailed study of DTNBP1 [Mutsuddi
et al., 2006]. An exception is the large LD block across exons 4–
8, present in HapMap, which is broken in our larger control
population whose size is larger than the CEU sample. In the 50
region of DTNBP1, harboring most of the reported associated
markers and haplotypes, the LD structure in our sample
resembled closely that in the CEU trios [Mutsuddi et al., 2006].
Haplotype frequencies within the blocks, as well as extending
beyond to cover the commonly associated markers, were in the
range observed in other European populations [Schwab et al.,
2003; Van den Oord et al., 2003; Mutsuddi et al., 2006], with
further differentiation of the two most common haplotypes
resulting from the inclusion of additional SNPs.
Our systematic analysis of association between Dysbindin
sequence variants and schizophrenia produced entirely negative results. We found no evidence of association with any of
the polymorphic markers across the entire gene sequence and
flanking regions or with any of the haplotypes across the gene
or within the previously implicated 50 region. Specific examination of the risk haplotype found in the Irish high-density
family sample [Van den Oord et al., 2003; Riley and Kendler,
2006] also failed to reveal any association in our ethnically
closely related sample.
A number of studies have focused on the association of
Dysbindin with specific cognitive performance tasks in
schizophrenia patients and normal subjects. Although many
results are positive, their interpretation is problematic.
Burdick et al. [2006] found association in schizophrenia
patients and normal controls between a DTNBP1 haplotype
and a factor score for general cognitive ability (g) derived by
principal component analysis of six cognitive measures. The
same DTNBP1 haplotype was also associated with 13.5 IQ
points decline from estimated premorbid to current IQ
[Burdick et al., 2007]. A different risk haplotype was reported
to be associated with impaired prefrontal brain function
[Fallgatter et al., 2006] in 48 healthy German controls and
with better intellectual functioning in a Dutch sample of 52
nuclear families and 31 controls [Zinkstok et al., 2007]. Still
another haplotype was found to affect spatial working memory
and explain 12% of its variance in Irish schizophrenia patients
[Donohoe et al., 2007], while at the same time leading to better
performance in measures of executive function [Donohoe et al.,
2007]. Apart from the diversity of risk haplotypes which
mirrors the general pattern of inconsistency in DTNBP1
association findings, the difficulties in interpreting these
results are compounded by the use of diverse measures and
conflicting findings in the assessment of neurocognitive
function, failing to outline a consistent cognitive profile
associated with DTNBP1.
In addition to a meticulously characterized clinical sample,
we also examined DTNBP1 association with schizophrenia
endophenotypes derived from comprehensive neurocognitive
testing and different measures of cognitive function. As most
neurocognitive tasks typically engage several component
processes, our endophenotype approach relies on searching
for multivariate patterns of dysfunction, rather than for single
isolated deficits. Our definition of the CD subtype incorporates
concurrent deficits in most of the neurocognitive domains
measured in the above studies. One should also note that this
subtype has been shown to be more homogeneous and heritable
than either the clinical category of schizophrenia or the CS
subtype, and to account specifically for our 6p linkage findings
[Hallmayer et al., 2005]. The results of the present study
indicate that the cognitive deficit subtype of schizophrenia is
not associated with variation in DTNBP1.
The genetic data implicating DTNBP1 in susceptibility to
schizophrenia have triggered investigations into the neuronal
functions of Dysbindin. While important functions have
emerged [Numakawa et al., 2004; Weickert et al., 2004;
Kumamoto et al., 2006; Talbot et al., 2006; Camargo et al.,
2007], the genetic evidence remains controversial. Our
analytical framework and the results of this study may
facilitate further attempts at systematic re-analysis of the
involvement of DTNBP1 in schizophrenia susceptibility. In
addition to the need for a uniform comprehensive approach to
association analyses [Mutsuddi et al., 2006; NCI-NHGRI
Working Group on Replication in Association Studies, 2007],
we emphasize the need for a standardized testing of cognitive
function, such as that adopted by the Consortium on the
Genetics of Schizophrenia [Gur et al., 2007] and recently by the
Australian Schizophrenia Research Bank.
ACKNOWLEDGMENTS
We thank all subjects participating in this study, Dr. Brenda
Powell and Dr. Pamela McCaskie for help with the SNP panel
design and the use of SimHap, and Padma Sivadorai for expert
technical assistance. The project was supported by NHMRC
grants 404046 and 404025. LJP was supported by the WindOver-Water Foundation.
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