Integrated Pathway-Based Approach Identifies
Association between Genomic Regions at CTCF and
CACNB2 and Schizophrenia
Dilafruz Juraeva1.*, Britta Haenisch2,3,4,5., Marc Zapatka6, Josef Frank7, GROUP Investigators`,
iPSYCH-GEMS SCZ working group`, Stephanie H. Witt7, Thomas W. Mu ¨hleisen3,8,9, Jens Treutlein7,
Jana Strohmaier7, Sandra Meier7,10, Franziska Degenhardt3,8, Ina Giegling11,12, Stephan Ripke13,14,
Markus Leber15, Christoph Lange2,16,17, Thomas G. Schulze18, Rainald Mo ¨ssner19, Igor Nenadic20,
Heinrich Sauer20, Dan Rujescu11,12, Wolfgang Maier19, Anders Børglum21,22,23, Roel Ophoff24,25,
Sven Cichon3,8,9,26, Markus M. No ¨then3,8, Marcella Rietschel7", Manuel Mattheisen8,16,17,21,22",
1Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany, 2German Center for Neurodegenerative Diseases (DZNE), Bonn,
Germany, 3Institute of Human Genetics, University of Bonn, Bonn, Germany, 4Federal Institute for Drugs and Medical Devices (BfArM), Bonn, Germany, 5Department of
Psychiatry, University of Bonn, Bonn, Germany, 6Division of Molecular Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany, 7Department of Genetic
Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany, 8Department of Genomics, Life
and Brain Center, University of Bonn, Bonn, Germany, 9Institute for Neuroscience and Medicine (INM-1), Research Centre Juelich, Juelich, Germany, 10National Centre for
Integrated Register-based Research (NCRR), Department of Economics and Business, Aarhus University, Aarhus, Denmark, 11Division of Molecular and Clinical
Neurobiology, Department of Psychiatry, Ludwig-Maximilians-University, Munich, Germany, 12Department of Psychiatry, University of Halle-Wittenberg, Halle, Germany,
13Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America, 14Broad Institute
of MIT and Harvard, Cambridge, Massachusetts, United States of America, 15Institute for Medical Biometry, Informatics, and Epidemiology, University of Bonn, Bonn,
Germany, 16Department of Genomic Mathematics, University of Bonn, Bonn, Germany, 17Department of Biostatistics, Harvard School of Public Health, Boston,
Massachusetts, United States of America, 18Department of Psychiatry and Psychotherapy, University Medical Center Georg-August-Universita ¨t, Go ¨ttingen, Germany,
19Department of Psychiatry, University of Bonn, Bonn, Germany, 20Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany,
21Department of Biomedicine, Aarhus University, Aarhus C, Denmark and Center for Integrated Sequencing, iSEQ, Aarhus, Denmark, 22Lundbeck Foundation Initiative
for Integrative Psychiatric Research, iPSYCH, Aarhus and Copenhagen, Denmark, 23Centre for Psychiatric Research, Aarhus University Hospital, Risskov, Denmark,
24UCLA Center for Neurobehavioral Genetics, Los Angeles, California, United States of America, 25Department of Psychiatry, Rudolf Magnus Institute of Neuroscience,
University Medical Center Utrecht, Utrecht, The Netherlands, 26Department of Medical Genetics, University Hospital Basel, Basel, Switzerland
In the present study, an integrated hierarchical approach was applied to: (1) identify pathways associated with susceptibility to
and (3) annotate the functional consequences of such single-nucleotide polymorphisms (SNPs) in the affected genes or their
regulatory regions. The Global Test was applied to detect schizophrenia-associated pathways using discovery and replication
datasets comprising 5,040 and 5,082 individuals of European ancestry, respectively. Information concerning functional gene-sets
was retrieved from the Kyoto Encyclopedia of Genes and Genomes, Gene Ontology, and the Molecular Signatures Database.
functional processes involved in transcriptional regulation and gene expression, synapse organization, cell adhesion, and
apoptosis. For two genes, i.e. CTCF and CACNB2, evidence for association with schizophrenia was available (at the gene-level) in
both thediscovery studyandpublisheddatafromthePsychiatricGenomicsConsortiumschizophreniastudy.Furthermore,these
genes mapped to four of the 14 presently identified pathways. Several of the SNPs assigned to CTCF and CACNB2 have potential
functional consequences, and a gene in close proximity to CACNB2, i.e. ARL5B, was identified as a potential gene of interest.
Application of the present hierarchical approach thus allowed: (1) identification of novel biological gene-sets or pathways with
potential involvement in the etiology of schizophrenia, as well as replication of these findings in an independent cohort; (2)
regions for schizophrenia.
Citation: Juraeva D, Haenisch B, Zapatka M, Frank J, GROUP Investigators, et al. (2014) Integrated Pathway-Based Approach Identifies Association between
Genomic Regions at CTCF and CACNB2 and Schizophrenia. PLoS Genet 10(6): e1004345. doi:10.1371/journal.pgen.1004345
Editor: Peter Holmans, Cardiff University, United Kingdom
Received August 5, 2013; Accepted March 20, 2014; Published June 5, 2014
Copyright: ? 2014 Juraeva et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This study was supported by the German Federal Ministry of Education and Research (BMBF) through the Integrated Genome Research Network (IG)
MooDS (Systematic Investigation of the Molecular Causes of Major Mood Disorders and Schizophrenia; grant 01GS08144 to MMN and SC, grant 01GS08147 to MR,
grant 01GS08149 to BB), under the auspices of the National Genome Research Network plus (NGFNplus). The research leading to these results has received
funding from the European Community’s Seventh Framework Programme (FP7/2007–2013) under grant agreement nu 279227 (CRESTAR). Further funding came
from the European Union Seventh Framework Programme (FP7/2007–2011) under grant agreement no. 242257 (ADAMS). The Heinz Nixdorf Recall cohort was
established with the support of the Heinz Nixdorf Foundation (Dr G Schmidt, Chairman). MMN is a member of the DFG-funded Excellence Cluster
ImmunoSensation. IN was supported by a Junior Scientist Grant (Rotationsstelle) of IZKF, Jena University Hospital. The funders had no role in study design, data
collection and analysis, decision to publish, or preparation of the manuscript.
PLOS Genetics | www.plosgenetics.org1June 2014 | Volume 10 | Issue 6 | e1004345
Competing Interests: The authors declare no conflict of interest.
* E-mail: firstname.lastname@example.org.
. These authors contributed equally to this work.
" MR, MM, and BB also contributed equally.
` Membership for GROUP Investigators and iPSYCH-GEMS SCZ working group is provided in the Acknowledgments.
Genome-wide association studies (GWAS) have identified
common susceptibility variants for numerous disorders , .
For complex diseases, however, many of the discovered variants
have only a moderate or weak effect on disease risk. Due to
correction for multiple testing and limited sample sizes, GWAS are
likely to miss a fraction of loci with small genetic effect sizes, and
researchers assume that a major fraction of heritability remains
hidden for statistical reasons . One way of overcoming this
problem is to investigate the joint effects of multiple functionally
related genes (e.g. gene-sets or pathways). Pathway-based analysis
of GWAS data increases the power to detect disease related genes
and, potentially, single nucleotide polymorphisms (SNPs) with
small genetic effects. This approach provides valuable biological
insights into the etiology of complex diseases .
At the time of writing, several methods are in use for the
pathway-based analysis of GWAS data , , and pathway
association studies have identified novel candidate genes and
pathways for a range of neuropsychiatric disorders , [7–12].
Various methodological approaches to pathway association
analysis are available. Maciejewski  has described a classifi-
cation for gene-set analysis that is based upon both the statistical
model used and the nature of the underlying hypothesis. This
classification comprises four groups: self-contained, competitive
with sample randomization, competitive with gene randomization,
and parametric. The main advantages of the self-contained and
the competitive with sample randomization tests are twofold.
Firstly, they resemble the underlying biological experiment.
Secondly, the results are amenable to statistical interpretation
While selection of the pathway association method is an
important consideration, the power of a given pathway association
study is also dependent upon other factors. These include the
biological information (i.e. from gene-set and pathway databases)
that is integrated into the model, the use of independent
replication datasets, and the different levels of interpretation,
which extend from the pathway level to the level of SNPs.
As a logical consequence, researchers are now modifying
analytical frameworks in order to increase their power and
potential impact. To achieve this, the present study has applied a
hierarchical approach (see Figure 1). This approach uses three
levels of evidence to unravel novel biological mechanisms with
potential involvement in complex disorders. An advantage of this
approach is that it builds upon previously developed and proven
tools which gain synergistic effects from intersecting three different
levels of evidence, i.e. evidence from the pathway-, gene-, and
SNP-level. To test disease associated gene-sets and pathways, the
Global Test was applied , . To date, this well-established,
self-contained pathway test has mainly been used for gene
expression analyses. Subsequent identification of important risk-
genes within the significant pathways was achieved using FORGE
, while detection of the functional consequences of associated
SNPs, i.e. the SNP function annotation, in the significantly
associated genes was performed using RegulomeDB . As part
of our approach, a well-curated list of pathways and gene-set
collections was integrated, and a reduction in false-positive
findings was sought through the use of large-scale exploratory
and independent replication samples. We applied our approach to
data sets for schizophrenia (SCZ), and provide evidence for new
SCZ risk genes that would otherwise have remained undetected in
the investigated study samples.
Application of the Global Test to the BOMA-UTR (MooDS
SCZ consortium (BOMA)) dataset and independent data from a
Dutch study (UTR), Table 1) yielded 27 pathways that were
significantly associated with SCZ after correction for multiple
testing (False Discovery Rate (FDR),0.05) (Table S1A). Of these,
14 pathways remained significant in the replication dataset. The
replicated pathways are listed in Table 2, together with their
FDRs, nominal p-values, and SNP set sizes. The replicated
pathways include the following: (i) six gene-sets from the
Transcription factor Targets database (dbTFT); (ii) four Gene
Ontology (GO) terms (zinc ion binding, transition metal ion
binding, positive regulation of gene expression, and synapse
organization); (iii) two Kyoto Encyclopedia of Genes and Genomes
(KEGG) pathways (cell adhesion molecules, and apoptosis); (iv)
one gene-set from the Chemical and Genomic Perturbation
database (dbCGP, Kyng DNA damage by UV); and (v) one gene-
set from the microRNA targets database (mir-484 targets). The
Large-scale genetic studies of complex diseases such as
schizophrenia have identified a variety of susceptibility
loci. Since many of the respective variants have only a
weak influence on disease risk, pathophysiological inter-
pretation of the results is problematic. Investigation of the
joint effects of multiple functionally related genes or
pathways increases the power to detect disease related
genes, and provides insights into the etiology of the
disease in question. In the present study, an integrated
hierarchical approach was applied to: (i) identify pathways
associated with complex neuropsychiatric disease schizo-
phrenia (ii) detect potentially affected genes in these
pathways; and (iii) annotate the functional consequences
of genetic markers in the affected genes or their regulatory
regions. Two samples comprising .10,000 individuals of
European ancestry as well as data from the Psychiatric
Genomics Consortium schizophrenia study were exam-
ined. Pathways representing transcriptional regulation and
gene expression, cell adhesion, apoptosis, and synapse
organization showed significant association with schizo-
phrenia. In particular, CTCF, CACNB2, and ARL5B, i.e. genes
involved in chromatin modulation, calcium channel
signaling and membrane transport, respectively, were
highlighted as candidate genes for schizophrenia risk.
Integrated Pathway-Based Approach with Global Test
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gene overlap for each pathway pair is shown in Figure S1. Table
S2 summarizes the redundancy estimates for pathways retrieved
from the same source. A description and a visual depiction of
pathways with similar SNP content in the BOMA-UTR dataset
are provided in Text S1 (section ‘‘Pathway overlap’’) and Figure
S2, respectively. The overall gene and SNP overlap between all
pairs of replicated pathways are provided in Table S3A and
Table S3C, respectively. For the GAIN-MGS dataset, the gene
and SNP overlap information is provided in Table S3B and
Table S3D, respectively. The section ‘‘Subject vs SNP label
permutations’’ in Text S1 and Figure S3 provides a detailed
description of the results of the SNP-label permutation test
coupled with the subject-sampling test.
To visualize the integration of the Global Test application on a
SNP-, a gene- and a pathway level, Circos plots were generated for
the entire genome (Figure 2). These plots illustrate the impact of
those individual SNPs that were annotated to the replicated
pathways (whether overlapping or unique to a specific pathway)
and the associated genes.
A total of 100 genes fulfilled the criteria described in the
Methods section ‘‘Gene-based analysis with Global Test and
FORGE’’, i.e. these genes map to SNPs with a component Global
Test p-value of ,0.001 in the BOMA-UTR dataset. Of these, the
following eight genes were annotated to at least four (up to eight) of
Figure 1. Flowchart for (1) detection and replication of schizophrenia associated pathways and (2) identification of the most
informative genes, and (3) functional annotation of single nucleotide polymorphisms in the genes of interest.
Table 1. Description of individual samples.
SampleAncestry Case (n)Control (n)Platforma
BOMAGerman 1 531 2 168I5, I6Q, IWQ, 
UTRDutch 699642 I5 
GAINEuropean1 1571 364A6
MGSEuropean1 2791 282A6
aPlatforms are: I5, Illumina HumanHap 550; I6Q, Illumina Human610 Quad; IWQ, Illumina Human660W-Quad; A6, Affymetrix Genome-Wide Human SNP Array 6.0.
bPublication reporting individual sample level genotypes for Schizophrenia is listed.
Discovery set: single nucleotide polymorphisms (SNPs) before pruning – 491,393; after pruning – 419,267.
Replication set: SNPs before pruning – 669,059; after pruning – 552,988.
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Table 2. Comparisons of FDRs (BH) and p-values (P) for the BOMA-UTR and the GAIN-MGS data sets for the replicated pathways.
Number of SNPs
dbGO:0008270:zinc ion binding*
dbGO:0046914:transition metal ion binding*
dbGO:0010628:positive regulation of gene
dbKEGG:04514:cell adhesion molecules (cams)
dbCGP:Kyng dna damage by uv
* - Significant pathways identified by more than one pathway analysis method within the BOMA-UTR data set. The test statistics obtained using the alternative algorithms are provided in Table S1B.
Note: FDR – False Discovery Rate; BH – Benjamini-Hochberg.
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the 14 replicated pathways, thus indicating their potential
importance in terms of SCZ risk: FOXP2 (eight pathways);
BCL11A (six pathways); PCDH7 and RPL36P13 (five pathways
respectively); and CACNB2, CTCF, MECOM, and RIMS1 (four
Of the genes that were annotated to the 14 replicated pathways,
the top 100 were then tested in the Psychiatric Genomewide
Association Study Consortium (PGC) data. Of these, significant
results were obtained for 18 genes (see Table S4). The vast
majority of the 18 genes reside on different chromosomes, while
most of the remainder reside on different chromosome arms. It
therefore seems reasonable to assume that they represent
independent signals, which results in a p-value of 0.004 for an
enrichment of SCZ-associated genes among the 100 top genes.
Included in the list of 18 replicated genes are known SCZ
susceptibility genes, such as NRXN1, GRM3, and MMP16. Two of
the eight most frequent genes in the top 14 pathways were also
among the nominally significant genes in the gene-based FORGE
analysis, i.e. CACNB2 (p=8.5761024) and CTCF (p=0.015).
Given the overlap (approx. 1,200 cases) between the PGC sample
(FORGE analyses) and the present discovery sample (component
Global Test), we opted to analyze the PGC dataset without
including our discovery dataset. These analyses generated results
of the same order of magnitude for both genes (CACNB2:
p=0.0090; CTCF: p=0.0320). While CACNB2 showed a trend
towards association in an independent dataset from Denmark
(p=0.0970), thus supporting the strong signal from the PGC data,
CTCF was found to be strongly associated in the same independent
Danish sample (p=0.0075).
Potential functional consequences of SNPs in CTCF
Polyphen-2 predicted that the coding SNPs of interest in CTCF
were ‘‘benign’’, whereas SIFT predicted that they were ‘‘tolerat-
ed’’ (Table S5). Figure 3 illustrates the potential consequences
predicted for SNPs in CTCF and its regulatory regions. These
include SNPs genotyped in the present discovery study and SNPs
identified as their proxies using SNAP. For the latter, only those
that were annotated by RegulomeDB as being (1) likely to affect
DNA binding of the protein and linked to expression of a gene
target, or (2) likely to affect DNA binding, are listed. The complete
functional annotation data for the SNPs of CTCF are provided in
Table S5. All genotyped SNPs annotated to CTCF showed a
significant (component Global Test p-value of #0.05) contribution
to pathway associations. Of these, rs6499137 and rs7191281 were
located at the 39-UTR and the intron of CTCF, respectively. Given
the 20 kb flanking region allowed for assigning the SNPs to a gene,
the other two SNPs were considered to be shared with the
neighboring gene RLTPR. Based on the functional annotation with
(rs6499137) and its proxies were considered to be associated with
the altered expression of the neighboring gene RLTPR (Figure 3,
Table S5). One of the proxies (rs17686899) overlaps with a
number of functional elements, such as open chromatin region,
the binding sites for different transcription factors, and regions
with certain histone modifications across many cell types. This
suggests that the SNP was likely to affect the binding of a number
of transcription factors to the genomic region of this gene. The
respective expression quantitative trait loci (eQTL) information
suggested that the SNP was likely to affect the expression of two
genes, i.e. DUS2L and RLTPR. Among the CTCF-annotated SNPs,
the intronic SNP of CTCF, rs7191281, was one of the top SNPs
(component Global Test p-value of ,0.001) contributing to the
association of CTCF (and the association of the four replicated
pathways containing CTCF). In addition, this SNP had the lowest
p-value in the analyses of the PGC SCZ sample. While no
information concerning functionality was available in the Reg-
ulomeDB database for this intronic SNP of CTCF, its proxy,
rs13334205, was annotated with strong functional consequences.
This proxy SNP was located in the regulatory region of CTCF and
overlapped with the binding site of DNA-binding proteins, such as
EBF1, TCF12, POLR2A, in an open chromatin region (Figure 3,
Potential functional consequences of SNPs in CACNB2
The complete functional annotation data for the SNPs of
CACNB2 are provided in Table S6. The positions of the majority
of the genotyped and the proxy SNPs of CACNB2 overlapped a
motif match to the FOX (FOXP1, FOXJ1, FOXJ2) and GATA
(GATA1, GATA3) family motifs in open chromatin regions. Among
the SNPs mapped to CACNB2, rs12257556 and its proxy
rs4748474 were annotated with the strongest functional conse-
quences. These intronic SNPs were eQTLs for ARL5B, and
overlapped an open chromatin region. The proxy SNPs
rs35803482 and rs7897710 both overlap with the binding sites
of RAD21, SMC3, CTCF, and have a motif match for FOXP1. The
intronic SNP rs2799573 (which was also the most highly
associated SNP of CACNB2 in the PGC data) lies in the binding
region of a number of proteins, such as CDX2, CTCF, JUN, JUND,
MEF2A, RAD21, and SMC3 (Table S6), as identified in the
ENCODE ChIP-seq data across a diverse set of cell types.
SCZ GWAS data analyses
In the present study, a genome-wide pathway association
analysis was performed by means of the Global Test. The analyses
involved well-curated descriptions of 7,350 pathways, and were
carried out on large-scale discovery and replication datasets. A
gene-based analysis of genes with a high contribution to the
significance of the top pathways was then performed using the
Figure 2. (A) Circos plots integrating the Global Test and FORGE analysis and heatmaps for the levels of single nucleotide
polymorphism (SNP)- and gene significance. (B) Inset legend providing information represented by each data ring. Notes: for visibility, the
implicated gene locations were zoomed in upon by up to 1200%. The inset legend image provides information represented by each ideogram. 2
log10of the individual SNP and the gene p-values increase radially outward. The arc of each heatmap wedge maps directly to the location of the SNP
in the genome. The arc width is proportional to the size of the associated gene (plus 20 kb upstream and downstream). Individual SNP p-values for
the BOMA-UTR and the GAIN-MGS data sets are shown as scatterplots on ideograms A and B. The gene p-values for Psychiatric Genetics Consortium
(PGC) datasets are shown as a scatterplot on ideogram C. The significance scores for genes contributing to a pathway significance are shown as
heatmaps on ideograms 1–14. 1 - dbGO:0050808:synapse organization; 2 - dbKEGG:04514:cell adhesion molecules; 3 - dbCGP:Kyng dna damage by
UV; 4 - dbKEGG:04210:apoptosis; 5 - dbGO:0046914:transition metal ion binding; 6 - dbGO:0008270:zinc ion binding; 7 - dbGO:0010628:positive
regulation of gene expression; 8 - dbMIR:gagcctg,mir-484; 9 - dbTFT:v$cebpa 01; 10 - dbTFT::v$hnf4 q6; 11 - dbTFT:v$chop 01; 12 - dbTFT:v$ptf1bea
q6; 13 - dbTFT:v$ciz 01; 14 - dbTFT:v$sox5 01. The darker the red, the higher the contribution of the SNP/gene to the association of the respective
pathway. Comparing the overlapping of important genes in different pathways allows investigation of whether they lie within intersections of those
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SCZ GWAS results of the PGC. Finally, a functional SNP-based
analysis of the top hit genomic regions was conducted. Through
this hierarchical approach, we were able to replicate pathway
findings from previous studies of SCZ and detect novel pathways
and genomic regions with an association to SCZ in the
investigated samples. In the discovery set, we detected evidence
for a significant contribution of 27 pathways. Of these, 14
remained significant in the replication dataset. The 14 replicated
pathways are involved in transcriptional regulation and gene
expression, synapse organization, cell adhesion, and apoptosis.
Previous pathway analyses of SCZ GWAS data have identified
associations with pathways that are mainly involved in processes
critical to synaptic function, neurodevelopment, cell adhesion, the
immune system, the estrogen biosynthetic process, and apoptosis
, , . One of the 14 significant pathways in the present
study, i.e. cell adhesion, was also the most significant pathway in
the study by O’Dushlaine et al. . Jia et al.  reported
nominal significance for the following four pathways: CARM_ER
(CARM1 and Regulation of the Estrogen Receptor); glutamate
metabolism; TNFR1; and TGF beta signaling. Glutamate is
implicated in synaptic neurotransmission, and TGF-beta and
TNFR1 signaling are involved in several cellular processes,
including apoptosis and excitotoxicity. The top hit pathways
‘‘synaptic organization’’ and ‘‘apoptosis’’ from the present study
are thus consistent with the results of Jia et al .
However, the majority of pathways with significant association
to SCZ in the present study are novel, and they are mainly
involved in transcriptional regulation and gene expression. One
reason for the failure of previous pathway-based studies of SCZ to
generate similar findings may have been that they focused mainly
on gene sets from the KEGG and BioCarta databases, whereas we
accessed several pathway databases. These included the GO
database, as well as special gene-set collections on chemical and
genomic perturbations (dbCGP), and transcriptional regulation
such as dbTFT and dbMIR. It should be noted that only few of
our 14 replicated pathways achieved significance in the analysis of
our discovery sample using GRASS , gseaSNP , and
ALIGATOR ; see Text S1 and Table S1C). The difference
in results can be explained by the different assumptions these
alternative pathway approaches rest on.
As part of our hierarchical approach, we aimed to identify
which genes in a particular pathway could be responsible for the
association with SCZ risk. Integration of gene-based analysis
facilitated both the prioritization of potential candidate genes and
more precise formulation of hypotheses concerning the functional
consequences of the potential pathway perturbations (i.e. at the
gene- and SNP-level). In particular, we explored how variants that
emerged as being of importance for our pathway- and gene-based
signals might affect the function and regulation of other genes.
In the gene-based analysis, CACNB2 and CTCF showed the
strongest evidence for association with SCZ in both the present
samples and in those of the PGC. The gene CACNB2 encodes an
auxiliary voltage-dependent L-type calcium-channel subunit that
is mainly expressed in heart and brain tissue . This subunit is
essential for normal surface expression, adequate trafficking, and
functioning of voltage-gated calcium channels . Recently,
CACNB2 was among four loci with genome-wide significance in a
cross-disorder analysis of GWAS data for autism spectrum
disorder, attention deficit-hyperactivity disorder, bipolar disorder,
major depressive disorder, and SCZ . Previously, CACNB2 had
been one of the top hit regions in a GWAS of bipolar disorder I in
a Han Chinese population . Functionally, the calcium channel
beta-2 subunit encoded by CACNB2, together with the calcium
channel alpha(2)/delta subunit, affects the kinetics and expression
Figure 3. RegulomeDB functional annotation for SNPs in CTCF and its regulatory regions. Notes: * genotyped in the BOMA-UTR data set
and sorted by their genomic coordinates. SNPs are within or 20 kb upstream and downstream of CTCF. ** AR FOXA1 USF1 CDX2 HNF4A TRIM28 USF2
TCF4 HDAC2 SP1 BHLHE40. *** KROX SP4 SP1:SP3 HIC1 Zif268 Sp4 Sp1 SP1 Egr. 1 RegulomeDB score: [1f] - likely to affect binding and linked to
expression of a gene target; [2b] - likely to affect binding; [4,5,6] - minimum binding evidence.
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of Ca(V)1.2 (encoded by CACNA1C) . CACNA1C is a well-
established susceptibility gene for bipolar disorder, SCZ, and
major depressive disorder , [28–31]. The RegulomeDB search
of genotyped SNPs and their proxies in CACNB2 resulted in the
detection of the intronic SNPs rs12257556 and rs10764566, and
these were eQTLs for ARL5B. The gene ARL5B encodes a trans-
Golgi network localized small G protein that has been described as
a key regulator of retrograde membrane transport . Altered
ARL5B expression may be involved in the dysregulation of axonal
transport. Interestingly, a previous study found that the transcript
of one of the most widely studied susceptibility genes for SCZ,
DISC1, was an interacting molecule for a motor protein of axonal
transport . It is of note that SNPs (both genotyped and proxies)
at the CACNB2 locus suggested an interplay with our second gene
of interest, i.e. CTCF. Such a connection is also suggested with
RAD21. A substantial body of literature describes an interaction
between RAD21 and CTCF, particularly in neurons , .
Although few data are available on a potential interaction between
CACNB2 and RAD21/CTCF, moderate evidence is available from
several protein-protein interaction databases (data not shown) for
an interplay between CTCF, RAD21, and ARL5B.
CTCF encodes a transcriptional regulator protein with 11
conserved zinc finger domains, and is an important modulator of
conformational changes in chromatin . A recent study of
conditional knockout of the ctcf gene in mice demonstrated that
CTCF was a key regulator of neuronal differentiation, and was
essential for neuronal diversity and functional neural networks
. The authors showed that CTCF was required for appropri-
ate dendritic arborization and synapse formation, since it
controlled clustered protocadherin expression. Previous studies
have shown an association between genetic variation in the
protocadherin gene cluster and SCZ , . Our result adds to
this body of research the finding that transcriptional regulation of
genes essential for neuronal diversity, such as the regulation of
protocadherins by CTCF, may alter synaptic connectivity and thus
contribute to the etiology of SCZ. Intriguingly, evidence from the
majority of CTCF SNPs (both genotyped and proxies) suggested
that the variants influence RLTPR expression (Figure 3). The
RLTPR gene is expressed in several brain regions (EMBL-EBI
ENSG00000159753). The resulting protein has a RGD (Argi-
nine-Glycine-Aspartic acid) motif . This is a universal cell
recognition site of extracellular proteins and interacts with a family
of cell-surface receptors, such as integrins for cell-adhesion
molecules . Together with the replicated KEGG pathway cell
adhesion molecules, this finding strongly supports the hypothesis
that modulation of adhesion, and interactions between cells as well
as cell and the extracellular matrix, are implicated in the etiology
Another top hit gene in the present study was FOXP2, which
was among the top genes in eight of the 14 most implicated
pathways. FOXP2 (forkhead-box P2) is a transcription factor with
an essential role in the development of speech and language
regions in the brain. The fact that SCZ patients often show
language impairments such as reading difficulties  renders
FOXP2 a plausible SCZ candidate gene. Interestingly, a previous
study reported an association between genetic variation in FOXP2
and SCZ in a Han Chinese population . Furthermore, Walker
et al.  identified FOXP2 as an inhibitor of the promoter activity
and protein expression of DISC1. The present study supports the
hypothesis that FOXP2 plays an important role in SCZ on the level
of the transcriptional regulation of target genes.
The association with the apoptosis pathway was driven
predominantly by a SNP which mapped to AKT3. Besides being
detected via the Global Test, this gene was the most significantly
associated gene in the FORGE analysis of the PGC data. AKT3 is
a serin/threonine protein kinase, and is a member of the AKT
family. It is involved in many biological processes, including
apoptosis and cellular proliferation . In a recent study by Diez
et al. , AKT3 was identified as a modulator of the fine
regulation of apoptotic processes and axon growth. Disruption of
AKT3 significantly reduced axon length and viability of neurons
in cell culture . Moreover, AKT3 is the most abundant AKT
member in the brain during neurogenesis. AKT3 controls brain
size, and research has shown that genetic variation (duplication
and point mutation) of AKT3 contributes to hemimegalencephaly
In conclusion, the present study demonstrated that use of
information from databases focusing on cell-regulatory networks
together with information from traditional pathway database
resources can facilitate the identification of susceptibility factors for
the complex neuropsychiatric disease SCZ. Through the applica-
tion of a well-designed hierarchical framework, our study
highlighted the importance of calcium channel signaling, cell
adhesion, and the modulation of transcriptional regulation
implicated in neuronal diversity, neurite growth, and synapse
formation in the etiology of SCZ. In particular, CTCF and
CACNB2 (and possibly ARL5B) were identified as SCZ candidate
Materials and Methods
Each participant provided written informed consent prior to
inclusion and all aspects of the study complied with the
Declaration of Helsinki. The study was approved by the ethics
committees of all study centers. For the German samples, this
comprised the Ethics Committee of the Rheinische Friedrich-
Wilhelms-University Medical School in Bonn, Ethics Committee
‘‘Medizinische Ethik-Kommission II’’ of the University of Heidel-
berg, the Ethics Committee of the Friedrich-Schiller-University
Medical School in Jena, and the Ethics Committee of the Ludwig-
dbGaP were collected using institutional review board-approved
protocols in three studies, i.e. Schizophrenia Genetics Initiative
(SGI), Molecular Genetics of Schizophrenia Part 1 (MGS1), and
Participants from four datasets were included (Table 1). The
discovery set was the BOMA-UTR sample. This consisted of data
from the MooDS SCZ consortium (BOMA) , , and
independent data from a Dutch study (UTR) , and comprised
2,230 SCZ cases and 2,810 controls. The replication set consisted
of the GAIN [dbGaP accession number: phs000021.v2.p1], and
the MGS [dbGaP accession number: phs000167.v1.p1] datasets,
and comprised 2,436 SCZ cases and 2,646 controls . The
BOMA and MGS samples were also used in the PGC SCZ study.
An overlap of 80% existed between the PGC study and the sample
used in the present pathway-based analysis.
Linkage disequilibrium (LD)-based SNP pruning
To accommodate the Global Test’s assumption of indepen-
dence between variables, the SNP set was reduced according to a
variance inflation factor (VIF) and using a sliding window
approach, as implemented in PLINK  (http://pngu.mgh.
harvard.edu/purcell/plink/, version 1.07). A VIF of 100 was used.
The window size was set at 50 SNPs, and was shifted by 5 SNPs at
Integrated Pathway-Based Approach with Global Test
PLOS Genetics | www.plosgenetics.org8June 2014 | Volume 10 | Issue 6 | e1004345
each step. An LD-based pruned set of SNPs (Table 1) was then
considered for mapping to pathways. A detailed description of this
procedure is provided in Text S1 (section ‘‘SNP independence
and LD-based SNP pruning’’) and in Table S7.
For the gene-based analysis, PGC data (https://pgc.unc.edu/
ResultFiles/pgc.scz.2012-04.zip) were used.
Annotation of SNPs to genes
SNPs were annotated with information from dbSNP Build 127.
The ‘‘seq-gene’’ file containing information for annotating the
SNP rs numbers to ENTREZ gene IDs was downloaded from the
NCBI ftp website (BUILD 36.3). SNPs were assigned to a gene if
the SNP was located within the genomic sequence or within 20 kb
of the 59 and 39 ends of the first and last exons in order to account
for important regulatory regions . If a SNP was within a region
shared by more than one gene, it was assigned to all genes (for
details see Text S1).
Pathway and gene-set databases
Selected gene-set collections were accessed from the Molecular
Signatures Database (MSigDB, version 3.0)  website (http://
www.broadinstitute.org/gsea/msigdb). This included the path-
ways from BioCarta (217 pathways), Chemical and Genomic
Perturbations (1,825 gene-sets), Reactome (775 pathways), Micro-
RNA Targets (176 gene-sets), and Transcription Factor Targets
(456 gene-sets). Information concerning GO terms  and
KEGG pathways ,  was obtained from the respective R
packages (3,686 GO terms; GO.db, version 2.5.0; 215 KEGG
pathways; R package KEGG.db, version 2.5.0). At the time of
data retrieval (June, 2011), these repositories were more up-to-date
than the MSigDB database. A total of 7,350 pathways were
included. These were represented by 237 788 (53.7%) of the SNPs
in the BOMA-UTR dataset. Hence 53.7% of SNPs genotyped in
the exploration samples were mapped to pathways. For the SNP
data, SNP effect was coded as an allele dose effect (0, 1, 2).
Detailed information on the pathway information overlap and
redundancy is provided in Text S1 (section ‘‘Choice of pathways
and gene-sets’’) and in Table S2 and Figure S1.
Pathway analysis with the Global Test
For the pathway-based analysis, the Global Test  was used
(R package globaltest, version 5.12.0; Figure 1). The Global Test
takes the individual level GWAS data as an input, and tests
whether the global polymorphism pattern of a group of genes is
significantly associated with the phenotype of interest. To account
for both a potential underlying correlation structure and pathway
and/or gene size, the Global Test with subject sampling was
applied on the basis of 10,000 permutations of case-control status
. To study the impact of pathway and/or gene size in more
detail, a SNP label permutation test was performed (for detailed
information see Text S1, section ‘‘Subject vs SNP label
At the discovery stage of the analysis, less conservative
correction for multiple testing was applied in order to prioritize
the identification of associated pathways. This was a legitimate
approach, since any false positives would be controlled for in the
replication analysis. Multiplicity correction was applied for each
individual collection of pathways/gene-sets. For pathways/gene-
sets retrieved from the KEGG, Reactome, and MSigDB gene-set
collections, the pathway scores were corrected for multiple testing
using the Benjamini-Hochberg method . A pathway was
considered to be significantly associated with the phenotype of
interest (i.e. SCZ) if the false discovery rates from all three of the
following were ,0.05: (i) un-permuted test; (ii) the subject-
sampling test; and (iii) the SNP-label permutation tests. The
resulting list of significant pathways was ranked according to the
false discovery rate obtained from the SNP-label permutation tests.
For the GO terms, correction for multiple testing was performed
using the Focus Level method . A GO term was considered to
be significant if both of the following were ,0.05: (i) the focus level
obtained from the un-permuted test; and (ii) the false discovery
rate obtained from the subject-sampling test. To account for a
gender-specific variance in the perturbed pathways, control for
gender was used as a covariate .
Component Global Test
To estimate the contributions of individual SNPs to a pathway-
or a gene association, the component global test was performed
using the covariates function implemented in the R package globaltest
. Throughout the text, the single SNP p-values obtained using
the Global Test refer to the results obtained using the component
The Global Test with the replication dataset
Only pathways that were significantly associated with SCZ in
the discovery set were followed-up (Figure 1, step 1). All tests in
the follow-up step were performed as described above, with the
exception that all tested pathways were subjected to Benjamini-
Hochberg correction for multiple testing. Possible stratification in
the data was investigated using a multi-dimensional scaling (MDS)
approach. MDS covariates were obtained from PLINK using a
previously described protocol . To correct for the potential
effect of stratification on the association test, the Global Test was
run with four leading MDS dimensions as covariates.
Gene-based analysis with Global Test and FORGE
The aim of the second step (Figure 1, gene-based analysis) was
to identify genes of particular importance to the replicated
pathways. Genes that mapped to one or more of the identified
pathways were analyzed (Figure 1, step 2). First, the component
global test was performed for every individual SNP that was
annotated to the replicated pathways. SNPs with a component
global test p-value of ,0.001 in the BOMA-UTR dataset were
then annotated to genes. These genes are referred to as ‘‘top
genes’’ in the subsequent text. Gene-based analysis of PGC data
for the top genes was then conducted using FORGE  As with
the Global Test, the analyses focused on genomic sequences that
included both the genes themselves and a 20 kb window on either
side of the respective gene to account for important regulatory
regions. Along with the summary statistics of the PGC, genotype
data from the European HapMap 3 samples were used (CEU and
TSI). Details of the program and the test statistic used to calculate
the gene-based p-values (fixed-effects Z score method) are
provided elsewhere . Genes that remained nominally signif-
icant (p,0.05) in both the component global test and the FORGE
analyses were considered for the third step of the analyses (SNP
function annotation). No correction for multiple testing was
performed. However, replication of our most interesting findings
was sought in an independent dataset from Denmark. Detailed
information on these Danish samples is provided elsewhere .
SNP function annotation
The third step (Figure 1, SNP function annotation) focused on
genes identified in step 2. Evidence that SNPs annotated to these
Integrated Pathway-Based Approach with Global Test
PLOS Genetics | www.plosgenetics.org9June 2014 | Volume 10 | Issue 6 | e1004345
genes are implicated in SCZ was sought by investigating the
potential consequences of SNPs in terms of gene regulation or
function. For each gene of interest, we first selected all SNPs that
were annotated to this gene and which had shown evidence for
association with SCZ in the discovery dataset (Global Test, p#
0.05). To account for the relevant information from other
correlated SNPs, we then identified all SNPs from the 1000
genomes project (pilot project)  that showed strong LD with
the associated SNPs (r2.0.8, maximum distance between both
SNPs=500 kb). The webtool SNAP  (Version 2.2) was used.
Each query SNP was included as its own proxy. RegulomeDB 
and Polyphen-2/SIFT ,  were used for the functional
classification of non-coding and coding SNPs, respectively.
27 schizophrenia associated pathways. The values in the cells
indicate the maximum fraction overlap of the genes in a pathway
(listed on y-axis). The corresponding pathway name in the x-axis is
a pathway with the highest overlap (self-overlap is excluded).
The heatmap of the level of gene overlap between the
data are the counts of overlapping implicated single nucleotide
polymorphisms, as detected using the Global Test in the BOMA-
Hierarchical clustering of replicated pathways. The
nucleotide polymorphism-label permutation and subject-sampling
test for all gene-sets.
Comparison of the p-values obtained from the single
BOMA-UTR datasets for top 27 schizophrenia associated
pathways identified by the GlobalTest performed to account for
gender differences, linkage disequilibrium-structure, and gene-set
size, (B) for the independent datasets (BOMA, UTR, GAIN, and
MSG) for the top 27 schizophrenia associated pathways, (C) for
BOMA-UTR dataset for top 14 replicated schizophrenia
associated pathways identified by various analysis methods.
Comparisons of FDRs (BH) and P-values (P) for (A)
pathway databases/gene-set collections.
Comparison of redundancies in the subsets of the 6
pathways in the BOMA-UTR dataset and (B) the GAIN-MGS
dataset. (C) Single nucleotide polymorphisms overlapping between
the 14 replicated pathways in the BOMA-UTR dataset and (D)
the GAIN-MGS dataset.
(A) Genes overlapping between the 14 replicated
values (FORGE analysis), and membership in the SCZ associated
pathways discovered and replicated in the present study. Pathways
in bold also showed an overall association using one of the other
List of schizophrenia (SCZ) associated genes, their p-
three methods (ALIGATOR, GRASS, gseaSNP) applied in the
Potential functional consequences of CTCF associated
Potential functional consequences of CACNB2
remained significant when the test was repeated with varying
degrees of multicollinearity in the data.
The Global Test results for the discovered gene-sets
Description of supplementary results and methods.
We thank two anonymous reviewers, whose comments/suggestions
improved and clarified the manuscript. We are grateful to all of the
patients who contributed to this study. We also thank the probands from
the community-based cohorts of PopGen, KORA, the Heinz Nixdorf
Recall (HNR) study. We thank Rolf Kabbe and Karl-Heinz Grob for
providing IT support. We thank Christine Schma ¨l for her critical reading
of the manuscript. We acknowledge the contribution of Fitnat Buket
Basmanav to the generation of the genome wide association study data sets
analyzed in the present study.
Members of the Genetic Risk and Outcome in Psychosis -
GROUP Investigators (with their main affiliation): Department of
Psychiatry, Rudolf Magnus Institute of Neuroscience, University Medical Center
Utrecht, Utrecht, The Netherlands - Rene ´ S Kahn, Wiepke Cahn
Academic Medical Centre University of Amsterdam, Department of Psychiatry,
Amsterdam, The Netherlands - Don H Linszen, Lieuwe de Haan, Maastricht
University Medical Centre, South Limburg Mental Health Research and Teaching
Network, Maastricht, The Netherlands - Jim van Os, Lydia Krabbendam, Inez
Myin-Germeys, University Medical Center Groningen, Department of Psychiatry,
University of Groningen, Groningen, The Netherlands - Durk Wiersma, Richard
Members of the iPSYCH-GEMS SCZ working group: Centre for
Psychiatric Research, Aarhus University Hospital, Risskov, Denmark - Mors O,
Børglum AD, The Lundbeck Foundation Initiative for Integrative Psychiatric
Research, iPSYCH, Aarhus and Copenhagen, Denmark - Mors O, Mortensen PB,
Pedersen CB, Demontis D, Grove J, Mattheisen M, Børglum AD, National
Centre for Register-based Research, Aarhus University, Aarhus, Denmark - Mortensen
PB, Pedersen CB, Section of Neonatal Screening and Hormones, Statens Serum
Institute, Copenhagen, Denmark - Hougaard DM, Department of Biomedicine and
Centre for Integrative Sequencing, iSEQ, Aarhus University, Aarhus, Denmark -
Demontis D, Grove J, Mattheisen M, Børglum AD, Bioinformatics Research
Centre, Aarhus University, Aarhus, Denmark - Grove J, Department of Genomic
Mathematics, University of Bonn, Bonn, Germany -Mattheisen M, Department of
Biostatistics, Harvard School of Public Health, Boston, USA - Mattheisen M.
Conceived and designed the experiments: DJ BH MZ SC MMN MR MM
BB. Performed the experiments: DJ ML CL SR MZ MM. Analyzed the
data: DJ BH SC MMN MR MM BB. Contributed reagents/materials/
analysis tools: MZ JF SHW TWM JT JS SM FD IG TGS RM IN HS DR
WM AB RO SC MMN MR BB. Wrote the paper: DJ BH SC MMN MR
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