Case–case genome-wide association analysis shows markers
differentially associated with schizophrenia and bipolar
disorder and implicates calcium channel genes
David Curtisa, Anna E. Vinea, Andrew McQuillinb, Nicholas James Bassb,
Ana Pereirab, Radhika Kandaswamyb, Jacob Lawrenceb, Adebayo Anjorinb,
Khalid Choudhuryb, Susmita R. Dattab, Vinay Purib, Robert Krasuckib,
Jonathan Pimmb, Srinivasa Thirumalaib, Digby Questedb
and Hugh M.D. Gurlingb
Objective There are theoretical reasons why comparing
marker allele frequencies between cases of different
diseases, rather than with controls, may offer benefits. The
samples may be better matched, especially for background
risk factors common to both diseases. Genetic loci may
also be detected which influence which of the two diseases
occurs if common risk factors are present.
Method We used samples of UK bipolar and
schizophrenic cases that had earlier been subject to
genome-wide association studies and compared marker
allele frequencies between the two samples. When these
differed for a marker, we compared the case sample allele
frequencies with those of a control sample.
Results Eight markers were significant at P value of less
than 10–5. Of these, the most interesting finding was for
rs17645023, which was significant at P value of less than
10–6and which lies 36kb from CACNG5. Control allele
frequencies for this marker were intermediate between
those for bipolar and schizophrenic cases.
Conclusion The application of this approach suggests
that it does have some merits. The finding for
CACNG5, taken together with the earlier implication
of CACNA1C and CACNA1B, strongly suggests a key
role for voltage-dependent calcium channel genes
in the susceptibility to bipolar disorder and/or
schizophrenia. Psychiatr Genet 21:1–4? c 2011 Wolters
Kluwer Health | Lippincott Williams & Wilkins.
Psychiatric Genetics 2011, 21:1–4
Keywords: association, bipolar disorder, calcium channel, schizophrenia
aCentre for Psychiatry, Barts and the London School of Medicine and
Dentistry andbDepartment of Mental Health Sciences, Molecular Psychiatry
Laboratory, Windeyer Institute of Medical Sciences, University College London,
Correspondence to Professor David Curtis, MD, PhD, Adult Psychiatry, Royal
London Hospital, London E11BB, UK
Tel: +44 207 3777729; fax: +44 207 3777316;
Received 23 December 2009 Revised 22 March 2010
Accepted 8 May 2010
The traditional design for an association study is, of
course, to compare marker genotypes of a sample of cases
affected with a disease against those of an unaffected
control sample. However, there are a number of reasons
to propose that there could be benefits from comparing
cases suffering from one disease against cases with a
One possibility to consider is that the samples might be
better matched. If the two diseases were similar in terms
of their epidemiology then the patients suffering from
either disease might be similar in terms of genetic and
nongenetic background. Typically, unaffected controls
will have been recruited from a nonclinical setting and
might differ in subtle ways from a patient sample. In con-
trast, samples of patients suffering from different diseases
might be similar to each other in a number of respects.
If they are recruited from the same clinical setting
they might be similar in terms of their geographical
background, their social class, and factors influencing
presentation to services. If the two diseases share risk
factors, which might be genetic or nongenetic, the two
patient samples might be well matched for such risk
factors and this would be expected to confer an advantage
when seeking to identify the genetic risk factors specific
to one or other disease. Having well-matched samples
reduces background noise and is expected to increase
both the power and specificity of the association studies.
In the case of bipolar disorder and schizophrenia, evi-
dence has been presented to support the hypothesis that
both share genetic risk factors (Purcell et al., 2009).
Arguably, comparing samples of both would, to some
extent, control for the presence of this shared component
and would enhance the ability to detect genes of major
A very extreme example of the possible advantage
conferred by comparing samples of patients with diff-
erent diseases is that there might be a common genetic
0955-8829 ? c 2011 Wolters Kluwer Health | Lippincott Williams & Wilkins
variant that did not usually produce any increased risk of
one or other disease but that did influence which of the
two diseases an individual was likely to develop if
a sufficient number of other risk factors, shared by both
diseases, were present. Thus, one might postulate that
there were a number of factors that combined to produce
a high probability that a patient would develop some form
of psychotic illness but there might also be a common
variant that would influence whether this illness took the
form of schizophrenia or bipolar disorder. If one studied
a sample of cases of either illness compared with
the controls there might be only a minor difference in
frequency of this variant, whereas if samples of cases of
the two illnesses were compared this difference might
become far more striking and more easily detectable.
Arguably, another benefit of the case–case approach is
that it may become possible to identify markers speci-
fically associated with one or other disease as opposed to
the less specific shared risk factors. For example, if it
were the case that both coronary artery disease and type 2
diabetes were associated with obesity then in a case–
control study of one disease, one would not know whether
a gene that showed association influenced the specific
molecular pathogenesis of the disease in question or
whether it exerted its effect more indirectly, for example,
by influencing obesity. However, if cases of both diseases
were compared then one could conclude that any
associated marker pointed to an effect specific to one or
other disease, and such information might be more
helpful in elucidating the related molecular pathology.
The corollary of this is that the case–case method would
have an important disadvantage, namely, that it would be
expected to fail to identify any genetic variants that
might influence susceptibility to both diseases simulta-
neously. Thus, it could at best be seen as complementary
to the case–control approach and certainly could not
claim to replace it.
We have recruited samples of cases with bipolar disorder
and schizophrenia and a control sample and these have
been subjected to genome-wide association studies
(Ferreira et al., 2008; Sklar et al., 2008; Purcell et al.,
2009). In the case of bipolar disorder, these provided
some evidence to implicate MYO5B, CACNA1C, and
ANK3, whereas the study of schizophrenia implicated
markers in the human leukocyte antigen region. Addi-
tional association studies using these samples have
implicated a number of genes as being involved in both
diseases including BRD1, DISC1, and DAOA (Bass et al.,
2009; Hennah et al., 2009; Nyegaard et al., 2009) whereas
separately the schizophrenia sample has been reported
to show association with epsin 4 and PCM1 (Pimm et al.,
2005; Datta et al., 2008) and the bipolar sample with
P2RX7 (McQuillin et al., 2009). Here, we report our
application of a case–case analysis of these samples to
throw some light on the possible value of this approach.
The research has received UK NHS Multicentre Re-
search Ethics Committee approval from the London
Metropolitan Multicentre Research Ethics Committee.
The samples used consisted of 506 patients with bipolar 1
disorder, 523 patients with schizophrenia, and 505
controls. The patients were recruited on the basis of
having European, non-Jewish ancestry and at least three
grandparents who were from the UK with the fourth
possibly coming from a different European country as
defined before the 2004 enlargement. All patients were
interviewed using the lifetime version of the Schizo-
phrenia and Affective Disorders Schedule and were
assigned a Research Diagnostic Criteria diagnosis. The
patients with bipolar disorder and controls were geno-
typed using the Affymetrix 500K array (Affymetrix, Santa
Clara, California, USA) whereas the patients with
schizophrenia were genotyped using the Affymetrix
Genome-Wide Human SNP Array 5.0 (Affymetrix) and
both sets of cases were shown to be genetically well
matched to the control sample (ISC, 2008; Sklar et al.,
There were 302482 markers for which genotypes were
available for both sets of cases. Both sets of cases had
been shown earlier to be genetically well matched to the
control sample. For these markers, allele frequencies were
compared between bipolar disorder and schizophrenia
using a two-by-two w2test. For markers significant at
P value of less than 10–5the allele frequencies of each
case sample were also compared with those of the control
sample. The positions of the nearby genes were obtained
from the UCSC Human Genome Browser.
Table 1 shows genotype counts for all markers significant
at P value of less than 10–5. It should go without saying
that any or all of these results could have occurred by
chance, given the number of markers tested and also the
fact that this is a secondary analysis, the original case–
control analysis being primary. With this proviso, arguably
the most interesting finding is that obtained for
rs17645023, which is significant at P=10–6.1. It can be
seen that the allele frequencies in the control sample
are intermediate between those in the bipolar and
schizophrenia samples, meaning that neither case–control
comparison produces similar levels of statistical signifi-
cance. This marker lies between CACNG5 and CACNG4,
which are the genes coding for subunits of a voltage-
dependent calcium channel. It is 36kb from CACNG5 and
79kb from CACNG4.
There are a number of other markers that show more
marked differences in allele frequencies between cases
of bipolar disorder and schizophrenia than between either
sample of cases and controls, comprising rs11210359,
rs1795648, rs6459804, and rs6459806. The last two of
2011, Vol 21 No 1
these are in complete linkage disequilibrium with each
other and are located in PTPRN2, the gene for a receptor-
type protein tyrosine phosphatase. Rs1795648 is in
ERC2, which is thought to be involved in the organization
of the cytomatrix at the nerve terminals active zone,
which regulates neurotransmitter release.
Two markers, rs17075286 and rs1203847, show differ-
ences in frequencies between both the case samples, but
show a similar effect when the schizophrenia sample is
compared with the controls. Neither of these markers was
reported to be significant at P value of less than 10–5in
the original genome-wide association studies of schizo-
phrenia because the results were reported for the com-
bined samples obtained from several different centers
rather than individually for the sample of UK patients
collected at University College London.
Finally, rs7065696 shows some difference in allele fre-
quencies between schizophrenia cases and controls
(P=10–4.6), but the difference is more marked when
both sets of cases are compared (P=10–6.4). Although
this difference in statistical significance may seem note-
worthy, in fact it is accounted for by a very small difference
between the bipolar counts and the control counts. This
marker is in PHF8, the gene for PHD finger protein 8.
With regard to CACNA1B and CACNA1C, which have
been implicated earlier and which code for subunits of
the same voltage-dependent L-type calcium channel as
contains the subunits coded by CACNG5 and CACNG4,
no markers were significant at P value of less than 0.01 in
the case–case analysis.
Comparing allele frequencies between the two sets of
cases has drawn attention to a number of markers that
would not have been picked up by case–control com-
parisons using these samples. Of course, we cannot be
sure that any of the results represents a genuine effect
and determining this will involve genotyping additional
markers in the implicated regions and carrying out tests
in additional samples. Nevertheless, we feel that the
findings are of some interest.
CACNA1C has been implicated earlier in susceptibility
to bipolar disorder (Ferreira et al., 2008) (Keers et al.,
2009). It codes for a a subunit of a voltage-dependent
L-type calcium channel. The related voltage-dependent
calcium channel gene, CACNA1B, has also been impli-
cated in susceptibility to schizophrenia (Moskvina et al.,
2009). Here we show that there is a difference in allele
frequencies between bipolar and schizophrenic cases for
rs17645023, which lies 36kb from CACNG5, coding for
a l unit of the same type of calcium channel. As we have
argued earlier, (Curtis et al., 2007) when genome-wide
association studies are carried out it makes sense to
accord more prominence to findings relating to markers
that relate to candidate genes than to anonymous markers
with similar P values. Hence, we regard this result as
possibly indicating a real effect of variation at CACNG5
in modifying the susceptibility to bipolar disorder and/or
If they indicate a real effect, findings such as that
obtained for rs17645023, in which the allele frequency
difference is more marked for the case–case compari-
son than the case–control comparison, may be suggestive
of variants that exert their effect in the presence
of background risk factors that produce an overall
increased susceptibility to psychotic illness. In contrast,
obtaining findings that are more significant for the case–
case comparison may simply reflect chance variation in
the allele frequencies between the control sample and
one of the case samples, thus exaggerating a real but
relatively small effect. It would not be possible to
elucidate this further without carrying out additional
This study shows some of the potential benefits of
carrying out comparisons between samples of cases
of different but related diseases. Arguably, the finding
of association with a marker close to CACNG5 when
taken with the earlier evidence implicating CACNA1C
and CACNA1B makes a compelling case that abnormal-
ities of voltage-dependent calcium channel genes may
represent important risk factors for bipolar disorder and/
Markers significant at P value of less than 0.0001 in comparison between schizophrenia and bipolar disorder cohorts
Genotype counts Minus log (p)
Marker ChromosomePosition SchizophreniaBipolar disorderControls
55571760 63 217 242
15751019574 254 195 112 264 130 89 248 168
157510483 75 253 195 113 263 130 89 248 168
64917033 14 158 331
53974054 43 32 447
171 55 205 241
0 13 483
27 185 290 49 195 263
0 7 484
4 29 4810 3 486
31 208 246 25 180 288
0 34 4511 39 449
Comparison of schizophrenia and bipolar disorder Curtis et al.
Acknowledgements Download full-text
Supported by grants G9623693N and G0500791 from the
UK Medical Research Council, the Neuroscience Re-
search Charitable Trust, and by a research lectureship
from the Priory Hospitals. Anna E. Vine was supported by
the Medical Research Council grant number G0802366.
Support for genotyping was from NIMH, Neuroscience
MH067288. The authors thank the Manic Depression
Fellowship (MDF) and their members for help with the
collection of patient samples. They also thank the Inter-
national Schizophrenia Consortium (ISC) for their help
in genotyping the schizophrenia cases of University
grants MH062137 and
G0802366 from the UK Medical Research Council, grants
MH062137 and MH067288 from NIMH, the Neuro-
science Research Charitable Trust and by a research
lectureship from the Priory Hospitals.
by grants G9623693N,G0500791and
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