CNV: ASD and SCZ
Running head: CNVS IN AUTISM AND SCHIZOPHRENIA
Rare Copy Number Variants Shared in Autism and Schizophrenia*
Coppes, L.
*Masters Thesis, Universiteit Utrecht, Utrecht, The Netherlands
1
CNV: ASD and SCZ
2
Abstract:
Recently the large scale gathering of copy number variant (CNV) data from
patients with schizophrenia (SCZ) and autism (ASD) has yielded new genetic data
through which we can examine these disorders. One of the repercussions has been a
suggested similarity between autism and schizophrenia, in that they may share some
genetic aetiology (Corvin, 2010). While some CNVs have been shown to overlap
between ASD and SCZ (Toro et al., 2010), many rare CNVs have been discarded due to
low sample numbers. In this study we provide a comprehensive overview of all CNVs
associated with ASD and SCZ, including rare CNVs and examine the genes found in the
CNVs. Ultimately I provide an extensive overlap of the genes found in CNVs detected in
ASD and SCZ. Furthermore, we present some trends in our data by examining the gene
ontology and expression of the overlapped CNVs. Using a literature search in PubMed,
recently published CNV data was accumulated from 2008 onwards for ASD and SCZ,
including rare CNVs previously ignored. This yielded a list of 939 and 852 genes for
ASD and SCZ respectively, of which 130 were shared. While the trends in the data are
difficult to pin point, some suggested shared molecular functions, processes and cellular
components are provided with supporting background literature. These include, but are
not limited to, a discussion of neurodevelopment and cell adhesion processes. What this
study illustrates is a potentially shared genetic aetiology between ASD and SCZ. This
converges with a broader hypothesis suggesting that many psychiatric disorders share
genetic variants which lay a framework for later psychiatric dysfunction (Craddock &
Owen, 2010). This study also provides new genetic data to examine the relationship
between ASD and SCZ.
Keywords: autism, schizophrenia, copy number variant, cell adhesion molecule, GABA
receptor, acetylcholine receptor, cell junction, genetics, genetic variants, genetic
aetiology
CNV: ASD and SCZ
3
Introduction:
Psychiatric disorders have been, and continue to be separated by symptomatic
differences, as significant behavioural differences exist between psychiatric disorders.
Autism spectrum disorders (ASD) has been characterised by behaviours such as
impairments in social interaction and the expression of repetitive behaviours. This is in
contrast with schizophrenia (SCZ) which has been characterised by two sets of
symptoms, both positive and negative. Positive symptoms include hallucinations and
delusions, whereas negative symptoms involve a lack of motivation and emotion. The
influence of genetics is unquestionable in both disorders as ASD has a heritability
estimate of 90% (Bailey et al., 1995) and SCZ has a heritability estimate of 80%
(Sullivan, Kendler, & Neale, 2003). While it seems that these two psychiatric diseases
diverge, due to the different symptoms, new data suggests that they may be more similar
than perceived as a number of specific copy number variants (CNVs) are shared between
ASD and SCZ patients (Corvin, 2010). It should also be noted that the similarities
between ASD and SCZ is not a novel idea, as Kanner initially suggested that ASD was a
subtype of SCZ (Kanner, 1943), although we later went on to think of ASD and SCZ as
independent and unrelated (Rutter, 1975). The idea that ASD and SCZ share some
commonality is re-emerging as it seems that ASD and SCZ share a number of genetic
variants (Corvin, 2010), and consequently neurobiological pathways.
These variants are not exclusively shared by ASD and SCZ, as it seems that these
variants are also involved in the progression of many other psychiatric disorders,
including bi-polar disease, epilepsy, attention-deficit/hyperactivity disorder (ADHD) and
intellectual disability. This has lead to the suggestion that there is an underlying genetic
similarity between different psychiatric disorders, which lays a groundwork for
neurological dysfunction (Craddock & Owen, 2010). The groundwork essentially
predisposes one to an increased likelihood of psychiatric disorder, whereas environmental
influence, and likely other genetic variants and mutations, play a role in guiding one to a
specific psychiatric disorder. This breakthrough in psychiatric research is in part due to
CNV: ASD and SCZ
4
large sample screens for rare copy number variants (CNV), the genetic culprits of interest
in this paper.
CNVs are portions of DNA with copy-number differences when comparing
multiple genomes (Feuk, Carson, & Sherer, 2008). These structural variants may infringe
on a gene through its coding region, promoter region or any region essential to genetic
expression. The disruption manifests itself as a dosage-dependant gain or loss of function
due to deletions or duplications of the copy-numbers (Toro et al., 2010), and when certain
critical genes are altered the potential for grave neuropsychiatric disorders increases.
Another type of genetic alteration that has been examined are single nucleotide
polymorphisms (SNP). While there is unquestionable utility in examining and
understanding SNPs, it would seem that utilizing CNVs is certainly as important. First of
all, CNVs are reported to have a larger nucleotide content than CNVs, suggesting a large
wealth of genetic data and potential influence over phenotypes (Pang et al., 2010; Redon
et al., 2006, Stranger et al., 2007). Secondly, SNPs provide only a locus of interest
within a gene, sometimes appearing outside of the protein coding region and having little
downstream effect. On the other hand CNVs mark a causal gene by interrupting or
duplicating it potentially affecting gene expression or gene dosage (Shelling & Ferguson,
2006). While SNPs have provided critical information to understand psychiatric disease,
it would seem that examining CNVs is required to develop a more complete framework.
Recently large samples have been screened for CNVs in both SCZ (Stefansson et
al., 2008; 2009) and ASD (Pinto et al., 2010). These studies, along with their
predecessors, have provided some integral insight into the genetics of these disorders.
While a number of significant genetic candidates are elucidated, some challenges must be
faced to address the breadth of genetic variants observed in SCZ and ASD. This includes
the rarity of certain genetic abnormalities, as a number of genetic variants are found in
extremely limited number of patients, often even a single patient referred to as a private
mutation (Stefansson et al., 2008). This has caused some rare CNVs to remain outside of
previous analyses, CNVs that may be harbouring prime genetic culprits for SCZ and/or
ASD. Another issue is addressing a lack of replicable data, to reliably illustrate the
CNV: ASD and SCZ
5
implication of a rare CNV and the manifestation of the psychiatric disorder in question.
Although progress is being made as some deletions have been replicated for ASD
(Kumar et al., 2008; Marshall et al., 2008; Weiss et al., 2008; Mefford et al., 2009) and
SCZ (International Schizophrenia Consortium, 2008; Stefansson et al., 2008; Wilson et
al., 2006; Vrijenhoek et al., 2008; Xu et al., 2008). Finally, and perhaps most important
for this paper, is the overlap of similar genetic abnormalities in different psychiatric
diseases that have emerged from these studies.
Genetic variants that breach a specific psychiatric disorder and show significant
associations in multiple neurodevelopment disorders have lead to the suggestion that
these disorders may share a common genetic aetiology, at least in part. For example,
Corvin (2010) examined a number of genome-wide association studies (GWAS) for
genetic alterations found in bipolar disease, SCZ and ASD. They found that cell
adhesion molecules (CAM), such as CNTNAP2 and NRXN1 are often disrupted in all
three of these patient groups. They go on to suggest that CAMs likely underlie
morphological abnormalities present in psychiatric disorders by altering
neurodevelopment, a suggested function of CAMs. Toro et al. (2010) found similar
results examining genetic variants associated to ASD and SCZ, among other disorders.
CAMs like NRXN1 and CNTNAP2 showed a higher CNV burden in SCZ and ASD.
CAMs seem to play a significant role in SCZ and ASD, as their appearance seems
scattered amongst much CNV research. Two CAMs likely implicated in brain
development and synapse formation are of particular interest, NRXN1 and CNTNAP2.
NRXN1 has been associated with ASD (Wiśniowiecka-Kowalnik et al., 2010; Bucan et
al., 2009; Glessner et al., 2009; Kumar & Christian, 2009; Marshal et al., 2008; Kim et
al., 2008) and SCZ (Rujescu et al., 2009; Kirov et al., 2009; Corvin, 2010); CNTNAP2
has also been associated with ASD (Burbach & van der Zwaag, 2009; Kumar &
Christian, 2009) and SCZ (Burbach & van der Zwaag, 2009; Friendman et al., 2010 ).
Moreover NRXN1 has been associated with ADHD (Bradley et al., 2010) and intellectual
disability (Ching et al., 2010), and CNTNAP2 has been associated with bipolar disease
(Wang, Lui & Aragam, 2010). These genetic similarities point to the potential for shared
aetiology between not only ASD and SCZ, but a plethora of psychiatric disorders,
CNV: ASD and SCZ
6
potentially through the pathways in which CAMs such as NRXN1 and CNTNAP2 are
players.
However it would seem that this is just the tip of the iceberg, as many rare CNVs,
such as private mutations, have yet to be examined across psychiatric disorders. This
issue was mentioned earlier in the introduction, and as such all CNVs, including private
CNVs, will be included in this paper. Moreover, few comparisons of CNV data for SCZ
and ASD have been done to date. While it seems certain that SCZ and ASD do share
some genetic variants, such as NRXN1 or CNTNAP2 (Corvin, 2010), a deeper
examination of this overlap is crucial to understanding the relationship between SCZ and
ASD. Finally, SCZ and ASD are not exclusively related, as the shared variants include
psychiatric disorders like bipolar disease, epilepsy and ADHD. This has lead to a
proposed model by Craddock and Owen (2010) suggesting that psychiatric disorders
share genetic variants that lay a foundation for psychiatric dysfunction. While the
relationship between SCZ and ASD remains unclear, what seems to be emerging is
shared genetic infringements by CNVs are causing us to reconsider the relationship
between SCZ, ASD and many other psychiatric disorders.
Newly published literature has started illustrating how ASD, SCZ and other
psychiatric disorders share specific genetic variants. However, this genetic overlap
remains mostly undefined, partially due the large number of rare CNVs, or private
mutations, discussed in the introduction. As such, our goal is to examine the entire
breadth of shared CNVs in SCZ and ASD, specifically elucidating rare genetic variants
shared in ASD and SCZ. We also clustered the affected genes in terms of genetic
ontology and examined the expression analysis across different brain regions and over
time. This would allow us to not only present a list of shared genetic variants between
SCZ and ASD, but hopefully characterise the shared variants based on molecular
function, biological process, cellular component and expression patterns. While the
relationship between ASD and SCZ will not be characterised based on the results, we
hope to provide a comprehensive overview of genetic variants to be examined more
CNV: ASD and SCZ
7
closely within ASD and SCZ, elucidating a wealth of genetic abnormalities upon which
the overlap between SCZ and ASD can be furthered and understood.
Methods:
Procedure
1) Assembly of gene lists:
A search using PubMed was applied to acquire recently published large scale CNV data
for ASD and SCZ. No special consideration was given when selecting published CNV
data other than utilising the most recent data, published from 2008 until now. Using the
publically available supplementary materials provided from the selected papers, an
overview of genes found in CNVs was created for both SCZ and ASD independently.
All disease selective genetic variants were included in our assembly to assure that a
comprehensive overview was compiled. Therefore, some CNVs may be rare and found
in only one individual, while other CNVs are found significantly more often in cases
rather than controls. Also the resulting gain or loss of function due to the CNVs was not
taken into account, as any altered gene expression was considered. The two compiled
data sets were superposed and the genes were examined for the presence of CNVs in both
populations.
2) Gene ontology analysis:
A further analysis of gene ontology was also pursued. The genes found in CNVs in both
ASD and SCZ were clustered based on molecular function, cellular component and
biological process using the protein analysis through evolutionary relationships
(PANTHER) classification system gene expression analysis tool (pantherdb.org, 2010).
It allowed us to examine if there were functions, processes or cellular components that
were significantly overrepresented in our list of shared CNVs. This was done using the
list of shared CNVs between SCZ and ASD and comparing it too a reference list of the
homo-sapiens genome provided by PANTHER (pantherdb.org, 2010). Based on the
reference list and the total number of genes in the shared list, an estimate of the expected
CNV: ASD and SCZ
8
genes for each molecular function, biological process or cellular component in the
matched list was calculated. The actual number of genes found for each molecular
function, biological process and cellular component were compared to the estimate to
determine the significance of over/under representation. A detailed methodology
explaining the bioinformatic algorithms involved can be found in Thomas et al. (2003)
and Mi et al. (2005). To provide more meaningful results general groupings such as
“binding” were ignored to highlight more specific groupings such as “protein or nucleic
acid binding.” This was achieved by removing grouping terms with more than 5000
genes from the reference list associated with it.
3) Expression Analysis:
Finally the lists were also sent for an expression analysis by Sigrid Swagemakers and
Peter van der Spek at Erasmus UMC Rotterdam with data from Gene Logic Inc.
(Gaithersburg, MD). An analysis was done for shared CNVs between SCZ and ASD, all
CNVs for both SCZ and ASD individually, and non-shared CNV for both SCZ and ASD
individually. Heat maps were created representing higher and lower than average
expression, with red and blue coloring respectively. The expression analysis was done
across different ages and different brain regions.
Data Sources
While no special consideration was given to selecting published CNV data other then
how recently it was published, a list of the selected studies is provided:
Autism: Pinto et al. (2010); van der Zwaag et al. (2009); Christian et al. (2008)
Schizophrenia: Magri et al. (2010); Kirov et al. (2009); Lee et al. (2010); Xu et al.
(2009); Crespi, Stead, & Elliot (2010); Tam et al. (2010); Glessner et al. (2010);
Stefansson et al. (2009).
It should be noted that some of the CNV data was acquired by different procedures, based
on the methodology provided by the original authors of the data. While aware of the
potential problematic impact, no correction for this was pursued in this research as the
procedures used across the research remain fairly arbitrary from one research group to
another. We hope only to compare previously presented CNV data that is selective to
CNV: ASD and SCZ
9
ASD and SCZ, acquired and analyzed according to the paper from which the data was
drawn.
Results:
A list of CNVs was made for SCZ and ASD, with a total of 852 and 939 genes in each
list respectively. These lists were compared to identify the CNVs present in both SCZ
and ASD populations. There were 130 CNVs that appeared both in SCZ and ASD (see
Table 1). Therefore of the 852 CNVs found in SCZ, 15.3% showed overlap with ASD,
and of the 939 CNVs found in ASD, 13.8% showed overlap with SCZ. This suggests
that there are similar CNVs in SCZ and ASD.
Table 1. CNVs Shared in Schizophrenia and Autism
Pinto et
al.
(2010)
“known”
ASD
genes
Pinto et al.
(2010)
“candidate”
ASD genes
Pinto et
al. (2010)
rare
CNVs,
Tam et al.
(2010)
ANKRD12
Glessner et al.
(2010)
International
Schizophrenia
Consortium
(2008)
PARK2
NUDT3,
GPC6
EHHADH,
ATP2C2,
GPC6
PRIM2A,
FAM19A2
Crespi et al.
(2010)
TBX1
SHANK3,
TBX1
PRKAG2
GPR89A
GRM7,
ERBB4,
MLL3
UBE2L3
van der
Christian et al.
Zwaag et al. (2008) AGRE
(2009) ASD abnormalities
susceptibility
genes
MKRN3
RAB23,
PRIM2A
LARGE
SNAP29,
SERPIND1
RTN4R,
COMT,
SEPT5
DGCR14, GSCL, C25A1,
HIRA, CLTCL1,
MRPL40, CDC45L,
SEPT5, CLDN5, TBX1,
GP1BB, GNB1L,
C22orf29, COMT,
XNRD2, ARCF,
C22orf25, HTF9C,
RANBP1 , ZDHHC8,
TN4R, DGCR6L,
SCARF2, NF74,
KLHL22, PCQAP ,
RPM1, KLF13
RTNR4,
SHANK3,
COMT,
TRPM1, KLF13,
KCTD13, SEZ6L2,
TAOK2, CDC95,
CNV: ASD and SCZ
SEPT5
Xu et al.
(2009)
Lee et al.
(2010)
Kirov et al.
(2009)
Magri et al.
(2010)
UBE3A,
TBX1
NRXN1
PARK2,
CNTN4
C4orf45
CSMD1
PRKAG2
RXRA
TPPP,
FHIT,
GABRB3,
GABRA5
TBC1D5,
CAMK1D,
ABCA13,
DSC3
ZC3HAV1,
TTC26,
MPV17L,
C16orf45,
DSG3
NKD2,
VIPR2,
GSC2,
SNAP29,
SERPIND1,
RTN4R,
COMT,
SEPT5
B3GAT1
NIPA1,
CYFIP1,
NRXN1,
NIPA2
10
DOC2A, FAM57B,
ALDOA, PPP4C, TBX6
YPEL3, MAPK3,
GDPD3, DGCR14,
PMP22, GSCL
SLC25A1, CLTC1 ,
HIRA, UFD1, MRPL40,
CLDN5, CDC45L,
SEPT5, TBX1,
GNB1L,c22orf29,COMT
TXNRD2,ARVCF,
DGCR8, c22orf25,
RANBP1, HTF9C,
RTNR4, ZDHHC8
DGCR6L
CNTN4
OPTN, MCM10,
C10orf49, PHYH,
SEPHS1, CDRT15
HS3ST3B1, PMP22,
FLJ45831, TEKT3,
DGCR2, TSSK2,
DGCR14, SLC25A1,
MRPL40, CDC45L,
UFD1L, SEPT5
CLDN5, TBX1, GP1BB
GNB1L, C22orf29,
COMT, TXNRD2,
ARVCF, DGCR8,
c22orf25, RANBP1,
HTF9C, RTNR4,
ZDHHC8, DGCR6L,
ZNF74, SCARF2,
KLHL22, ALG10,
MAGEL2, MKRN3,
C15orf2, NDN, SNURF,
SNRPN, ATP10A,
UBE3A, GABRB3,
GABRA5, GABRG3,
KLF13, TRPM1
NIPA2, KLF13, RPM1,
TUBGCP5, CYFIP1,
C16orf45
In order to characterise the genes that emerged from the list of shared CNVs in SCZ and
ASD, the genes were further analysed for gene ontology, specifically molecular function
(see figure 1), biological process (see figure 2) and cellular component (see figure 3). To
focus on the most significant biological processes, molecular functions and cellular
components, only significantly represented clusters (p<0.05), are presented in this paper.
CNV: ASD and SCZ
11
For molecular function this included 10 different processes which involved 60 genes, of
which some genes were duplicates involved in multiple processes. For molecular
function this included 21 different functions which involved 247 genes, of which many
genes were duplicates. Finally, cellular component included 3 different components
which involved only 13 genes, of which some genes were duplicates. These duplicates
occur because genes participate in multiple functions, processes and across various
cellular components within the body. For an overview of the genes associated with each
molecular function, biological process and cellular component see the supplementary
materials provided (see supplementary 1-3).
Figure 1. Molecular Function clustering
Acetylcholine receptor activity
GABA receptor activity
Cytoskeleton protein binding
Transferase activity
Lipid transporter activity
Ligand-gated ion channel activity
Methyltransferase activity
Receptor activity
Calcium ion binding
This illustrates the different molecular functions that were associated with the CNVs
shared by ASD and SCZ.
CNV: ASD and SCZ
12
Figure 2. Biological Process clustering
developmental process
oxygen and reactive oxygen species metabolic
process
anion transport
mesoderm development
cell adhesion
protein metabolic process
skeletal system development
neurological system process
meiosis
cell cycle
cellular calcium ion homeostasis
cell-cell adhesion
system process
homeostatic process
lipid metabolic process
cellular component morphogenesis
anatomical structure morphogenesis
synaptic transmission
cell communication
lipid transport
This illustrates the different biological processes that were associated with the CNVs
shared by ASD and SCZ.
Figure 3. Cellular Component clustering
Cell junction
Plasma membrane
Microtubule
This illustrates the different cellular components that were associated with the CNVs
shared by ASD and SCZ.
CNV: ASD and SCZ
13
In order to provide some overview of significance, this data is also presented in
decreasing order of significance for each gene ontology clustering. This was done for
biological process (see table 2), however only the top 10 processes are shown as 21
processes showed significance (p<0.05), and thus a cut off was made to make the data
more presentable. All 21 biological processes can be seen in figure 2 or supplementary
material 2. This data was also presented for molecular function (see table 3) and cellular
component (see table 4). For a comprehensive list of the genes associated with each
molecular function, biological process and cellular component see the supplementary
materials (supplementary 1-3). Some notable biological processes include the
developmental process, cell adhesion and neurological system processes. Some notable
functions include receptor activity, specifically acetylcholineterase receptor activity and
GABA receptor activity, and transferase activity, specifically methytransferase. Finally,
the only cellular components in the matched list were cell junctions, microtubules and
plasma membranes. This suggests that there are groups of CNVs with a heavy burden in
ASD and SCZ, unfortunately the amount of significant data makes it very difficult to
point out only a few culprits.
Table 2. CNV disrupted genes shared by Autism and Schizophrenia, clustered by
Biological Process.
Biological
Process
# of genes
# of genes
in Reference in ASD &
SCZ match
list
Developmental 3001
28
process
Oxygen and
63
3
reactive
oxygen species
metabolic
process
Anion
150
4
transport
Mesoderm
1528
16
development
Cell adhesion
1333
14
Protein
3240
27
metabolic
Expected #
of genes
Increase (+)
or Decrease
(-)
Significance
(P value)
16.58
+
0.00314
0.35
+
0.00531
0.83
+
0.00989
8.44
+
0.00990
7.36
17.90
+
+
0.0153
0.0166
CNV: ASD and SCZ
process
Skeletal
system
development
Neurological
system process
Meiosis
Cell cycle
490
7
2.71
+
0.0193
1954
18
10.80
+
0.0212
192
1840
4
17
1.06
10.17
+
+
0.0223
0.0244
14
Table 3. CNV disrupted genes shared by Autism and Schizophrenia, clustered by
Molecular Function.
Molecular
Function
# of genes
in
Reference
Expected #
of genes
Increase (+) Significance
or Decrease (P value)
(-)
47
# of genes
in ASD &
SCZ
match list
3
Acetylcholine
receptor activity
GABA receptor
activity
Cytoskeleton
protein binding
Transferase
activity
Lipid transporter
activity
Ligand-gated ion
channel activity
Methyltransferase
activity
Receptor activity
Calcium ion
binding
Ligase activity
0.26
+
0.00235
47
3
0.26
+
0.00235
396
7
2.19
+
0.00334
1593
16
8.80
+
0.0143
92
3
0.51
+
0.0148
112
3
0.62
+
0.0246
132
3
0.73
+
0.0373
1808
454
16
6
9.99
2.51
+
+
0.0403
0.0406
613
7
3.39
+
0.0540
Table 4. CNV disrupted genes shared by Autism and Schizophrenia, clustered by Cellular
Component.
Cellular
Component
# of genes in
Reference
Cell junction 121
Plasma
131
membrane
# of genes in
ASD & SCZ
match list
4
4
Expected #
of genes
0.67
0.72
Increase (+)
or Decrease
(-)
+
+
Significance
(P value)
0.00472
0.00622
CNV: ASD and SCZ
Microtubule
Intracellular
348
1192
5
11
1.92
6.59
+
+
15
0.0444
0.0656
The CNVs shared by both SCZ and ASD were further analysed for expression in various
regions of the human brain (see figure 4) and during various stages of development (see
figure 5), with a complete overview provided in the supplementary materials (see
supplementary 4).
Figure 4: The expression of CNVs shared by autism and schizophrenia across various
brain regions
CNV: ASD and SCZ
16
Figure 5: The expression of CNVs shared by autism and schizophrenia from 0.1 to 83
years of age
Globally what was observed is that both SCZ and ASD have many genes with higher
than average expression when individuals are 2 years or younger. However, SCZ has far
more genes with higher than average expression when individuals are older, over 65
years of age. In the list of matched CNV disrupted genes between SCZ and ASD we see
predominantly higher than average expression during early development. This points to
developmentally early shared genetic dysfunction in ASD and SCZ, suggesting that these
genes may lay the groundwork for later psychiatric dysfunction in life.
As an overwhelming amount of data was provided the results are referred to in the
discussion for specific CNV disrupted genes of interest. The expression data results
referred to in the discussion include the early expression patterns of genes with receptor
activity, such as GABRB3, GABRA5 and GABRG3; genes responsible for cell adhesion,
such as NRXN1, SEZ6L2, DSC3, SCARF2, DSG3, RTN4R, CSMD1, ERBB4 and
CNV: ASD and SCZ
17
CNTN4; and finally genes expressed in cell junctions, such as CLDN5 and ARVCF. The
details of this expression data will be presented in the discussion for these individual
genes, in an attempt to characterise some of the gene ontology clusters.
Discussion:
The aim of the paper was to examine the overlap of CNVs between ASD and
SCZ, specifically attempting to provide a broad overview of all the genes found in CNVs
detected between ASD and SCZ, including rare CNVs. As hypothesized there were
many CNVs shared by SCZ and ASD alluding to a shared genetic aetiology. This is not
the first example of shared genetic dysfunction in ASD and SCZ, as a number of studies
have indicated significant overlap in NRXN1, CNTNAP2 and other cell adhesion
molecules (Corvin et al. 2010; Toro et al. 2010). Moreover, as mentioned in the
introduction, the genetic overlap between psychiatric disorders doesn’t include only ASD
and SCZ, but extends to include disorders such as epilepsy, bipolar, ADHD and
intellectual disability (Craddock & Owen, 2010). We also examined the clustering of
genes based on genetic ontology to preliminarily characterise the genes found in CNVs
shared between SCZ and ASD. However, the amount of significant data acquired makes
it difficult to point out specific culprits as having a larger effect on ASD and SCZ
development. The same can certainly be said for expression analysis, as a detailed
conclusion seems impossible given the amount of data. Having said this, including
previously published data may allow us to examine certain clusters and expression
patterns that might critical to the development of ASD and SCZ, such as CAMs and early
neurodevelopment.
As indicated the results illustrate genetic commonality in two disorders that show
high levels of genetic influence and heritability. Along with the expression analysis,
illustrating that many shared CNVs between SCZ and ASD have higher than average
expression during early development, a suggestion could be made. It could be that genes
that normally show higher than average expression during early development and are
found in CNVs detected SCZ and ASD, are laying a neural groundwork leading to a high
CNV: ASD and SCZ
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susceptibility for psychiatric disorders. It is through this early dysfunction that ASD and
SCZ are related, and perhaps also related to a plethora of other psychiatric disorders.
While no single CNV can be suggested as a prime culprit, this study does suggest that
CNVs may burden early neurodevelopment and lay the groundwork for psychiatric
dysfunction. What emerges is that two disorders presently examined, diagnosed and
treated differently might actually be progressing through shared neurobiological
pathways.
While clustering the genes seems to provide little conclusive evidence, due to the
amount of data, we can examine some convergence with previous literature in an attempt
to comment on what processes, functions and components might be dysfunctional in
ASD and SCZ. Beginning with molecular function, some interesting comments can
certainly be made. We see that genes associated with receptor activity have a high CNV
burden in ASD and SCZ, specifically GABA and acetylcholine receptors. The genes for
GABA and acetylcholine receptor activity were GABRG3, GABRB3, GABRA5 all of
which show significantly higher expression in early development when introducing the
details of our expression results. The most notable may be GABARA5 with significantly
higher than average expression before 2 years of age. GABA receptors have previously
been discussed in SCZ, and in fact GABAergic treatment is also being investigated very
closely (Coyle et al., 2010). Moreover, Wassef, Baker & Kochan (2003) illustrated
abnormalities in GABA receptors in a SCZ population, and along with the significance of
GABA receptor activity in our results may point towards the GABA receptor as a culprit
in SCZ progression. GABA is also present in ASD research, as reduced GABA(A)
receptors were found posterior cingulate cortex and fusiform gyrus in ASD (Oblak,
Gibbs, & Blatt, 2010). In our results we also see that acetylcholine receptor activity is
highly burdened by CNVs in SCZ and ASD. In previous literature, a decrease in
nicotinic receptors were found in the cerebral cortex of patients with ASD (Lee et al.,
2002; Ray et al., 2005) and in SCZ lowered levels alpha7 nicotinic acetylcholine receptor
have been found (Perl et al., 2003). Moreover both ASD and SCZ research is examining
treatments targeting acetylcholine receptors (Lipiello, 2006; Hajos & Rogers, 2010).
It
would seem that examining genes found in CNVs with GABA and acetylcholine receptor
CNV: ASD and SCZ
19
functionality may provide a new means to understanding the dysfunction in GABA and
acetylcholine receptors seen in ASD and SCZ. Moreover, the significantly high
expression in early development suggests that these genes may be worth examining as
potential culprits that might lay the groundwork for psychiatric disorders such as SCZ
and ASD.
The same examination was done for biological process clustering. The most
interesting process was cell adhesion, as it plays a central role in neurodevelopment. This
specifically includes cell-cell adhesion, whose dysfunction has previously been
considered as a shared culprit between ASD and SCZ (Toro et al., 2010). The genes
found in CNVs that were included in our list were NRXN1, SEZ6L2, DSC3, SCARF2,
DSG3, RTN4R, CSMD1, ERBB4 and CNTN4. It is also interesting that all of these
genes show significantly higher than average expression in early development, most only
up to 2 or 3 years of age, when introducing the details of our expression results.
However, RTN4R and NRXN1 show moments of higher than average expression into
adolescence, up to 18 years of age. As these genes show higher than average early
expression they may be involved in laying the groundwork for later psychiatric disorders,
such as SCZ and ASD. Moreover, as NRXN1 and other CAMs have previously been
shown to play a significant role in SCZ and ASD, to continue examining CAMs is likely
a fruitful endeavour.
Finally we examined the cellular components that were found to be significantly
burdened by CNVs in ASD and SCZ. These were cell junctions, plasma membrane and
microtubules. The importance of cell junctions is particularly interesting as CAMs play a
role in the maintenance of cell junctions. Therefore the dysfunction of genes in the cell
junction component may support the dysfunction of CAMs, an accepted culprit in ASD
and SCZ. The genes found in cell junctions in our data set include CLDN5, DSC3,
DSG3 and ARVCF. As DSC3 and DSG3 were also included in cell adhesion clustering
for biological process, and both show higher than average early expression up to 2.5
years of age, when introducing the details of our expression results. This would suggest
that they would be key genes to investigate more closely. The association of CAMs with
CNV: ASD and SCZ
20
cell junction genes, specifically DSC3 and DSG3, may elucidate the role of CAMs in
SCZ and ASD. Moreover, these genes once again show higher than average expression
in early development, supporting the hypothesis that early developmental problems may
lay the groundwork for a multitude of psychiatric disorders, including SCZ and ASD.
To summarize the clustering of genes found in CNVs detected SCZ and ASD, it
would seem that the complexity of these disorders comes to the forefront, as many
processes and functions are associated with these genes. Hopefully some clarity is
brought to the results through the aid of previous literature, but these results will only be
fully comprehended when a more in-depth investigation of these CNVs is done. It would
seem that CAMs are of much interest in the progression of ASD and SCZ, and the
dysfunction of CAMs is likely linked to the development of ASD and SCZ. Moreover,
cell junctions may be of interest based on the overwhelming data suggesting CAM
dysfunction as critical to ASD and SCZ and the association of cell junctions and CAMs.
Finally the GABA and acetylcholine receptor activity is significantly burdened by CNVs
in ASD and SCZ, and the genes involved show very early expression in development.
Although these results are presented as the key trends in our data based on the previous
literature, much of our data remains to be examined and is potentially very useful.
While ASD and SCZ may seem like traditionally different disorders, this is not in
fact completely true. ASD was initially considered a subtype of SCZ by Kanner in the
early 1900’s (Kanner, 1943). ASD was later considered as distinct and separate from
SCZ (Rutter, 1975) the prevailing clinical perception we still have today. More recently,
Crespi et al. (2010) have re-introduced a relationship between SCZ and ASD reminiscent
of Bleuler, although certainly different. Based on the examination of seven statistically
significant CNV loci in ASD and SCZ they have suggested that ASD and SCZ may be
diametrically opposite disorders. ASD tended to exhibit up-regulation of pathways due
to loss of function mutations in negative regulators, contrasted with SCZ which tended to
exhibit reduced pathway activation (Crespi et al., 2010). When examining this in
conjunction with general brain size differences in ASD and SCZ, enhanced and reduced
brain size respectively, it seems that ASD and SCZ may in fact be opposite ends of a
CNV: ASD and SCZ
21
developmental spectrum. One should note that genes such as NRXN1 were in fact
identically disrupted in ASD and SCZ in terms of functionality. Therefore the
relationship is likely not a straight forward dichotomy, but in fact something more
complex than that. While we have yet to examine the dosage dependant gain-loss of
function of the CNVs discussed in this paper, there is a convergence of our results with
Crespi et al. (2010) in that shared genetic aetiology is presented. It would seem safe to
say that SCZ and ASD have a significant overlap in CNVs, however the relationship
within our results needs to be examined, specifically the gain-loss of function.
While the overlap is presented and discussed in detail, one should note that the
majority of CNVs weren’t overlapping between ASD and SCZ (see figure 6).
Figure 6: This is a visual representation of CNV overlap between SCZ, ASD and other
psychiatric disorders, illustrating that while overlap exists the majority of the CNVs are
outside of this overlap. It also illustrates that different types of overlap exists, CNVs
shared by many psychiatric disorders including ASD and SCZ (black), CNVs shared by
ASD and SCZ only (red), CNVs shared by either SCZ or ASD and other psychiatric
disorders (green and blue respectively), and finally CNVs exclusive to a psychiatric
CNV: ASD and SCZ
22
disorder such as ASD or SCZ (white). Based on our study SCZ showed 15.3% overlap
with ASD, and conversely ASD showed 13.8% overlap with SCZ
The CNVs that weren’t shared between ASD and SCZ likely represent two groups of
genes, one group that is exclusive to SCZ or ASD development and the second group that
overlaps with other psychiatric disorders. As mentioned, the overlap between psychiatric
disorders doesn’t end with SCZ and ASD, but we see that many psychiatric disorders
share some genetic aetiology (Craddock & Owen, 2010). Moreover, many of the CNVs
shared by SCZ and ASD are likely also shared by many other psychiatric disorders.
As mentioned this research follows a trend of recent studies examining the
overlap of various psychiatric disorders including, but not limited to, SCZ, ASD, ADHD,
bipolar disorder and epilepsy. As more evidence surfaces illustrating that psychiatric
disorders have underlying genetic similarities there will come a time that a new model
will be applied to understanding psychiatric disorders in general. A proposed system has
been made by Craddock and Owen (2010) in which shared genetic variants, along with
environmental pressures, effect a number of neural systems and ultimately are expressed
as clinical symptoms associated with psychiatric disorders (see figure 7).
CNV: ASD and SCZ
23
Figure 7: Proposed model between genotype variation and differing phenotype
manifestation – a simplification (Craddock & Owen, 2010)
This model illustrated how genetic variants (asterisks) influence different biological
systems (blue arrows) and ultimately affect different neural modules (blue ovals). Also,
environmental influences can shape different modules to express different clinical
syndromes across the different domains on psycho-pathology.
We see that a genetic variant (asterisks in figure 7) can have a number of influences over
various biological systems, something that was also noted in the overwhelming data of
the gene ontology analysis. It is here were genetic variants may lay the groundwork for
later psychiatric dysfunction. The biological systems in turn influence a number of
neural modules, it may also be here that environmental pressures channels the influence
of the genetic variant towards a more specific neural module and ultimately a specific
phenotype. Finally the neural modules influence a variety of psychopathological
spectrums, which illustrate not only the spectrum of associated behaviours, but also the
overlap between different domains of psychopathology often present in clinical practice.
CNV: ASD and SCZ
24
It is this potential for broad influence that gives CNVs the capability of emitting different
phenotypes from similar genotype differences.
Examining the model proposed by Craddock & Owed (2010) in conjunction with
our results we see how the model offers a potential method through which shared CNVs
ultimately lead to different clinical symptoms. This is illustrated by genetic variants
having a broad effect over various different neural modules, neural modules responsible
for different symptoms such as mood and cognition. This effect is most obvious in our
gene ontology data, as a number of genes have multiple functions and therefore affect
multiple neural modules. The model also includes the need for other influences, both
genetic and environmental, ultimately guiding an individual towards a specific
psychiatric disorder are also taken into account. This includes the genetic variation not
shared between psychiatric disorders, with repercussions on neural modules leading to
specific symptoms, and environmental influence specifically altering the neural modules
as the express specific symptoms. While this model is certainly a simplification of the
entire process, it would seem to offer a number of benefits in understanding the data both
in this paper and across the literature examining CNV overlap between psychiatric
disorders.
There are a number of limitations of which the reader should be cautioned when
examining these results. Most notable is the lack of statistical significance when
overlapping the CNVs between SCZ and ASD. This choice was made consciously as
many rare CNVs, which were one of the main areas of focus for this paper, occur in such
low levels compared to the sample sizes involved that they are immediately discarded.
When this occurs there in no reassurance that these rare CNVs don’t play a significant
role in the progression of the disorder, and these CNVs are therefore just as critical to
examine. This certainly means that some of the genetic overlap between SCZ and ASD
presented in this paper is not actually overlapping, but the breadth of overview offered in
this paper compensates for this fact. Another limitation to address is the different origins
of the data, as we collected CNV data across diverse literature. While aware that most of
the time CNVs are found using different methodologies in different papers, one of the
CNV: ASD and SCZ
25
primary goals of this paper was to increase the breadth of examined overlap between
ASD and SCZ. As such we decided to compile data from across the field, regardless of
the methods used. Moreover, we focused on including all rare CNVs found in patient
groups presented in these papers, not only significant CNVs. Due to this there was no
need to standardize the methodology of CNV discovery, as we are truly interested in any
potential rare CNV. These limitations should not be underestimated or neglected, but the
trade off for a more thorough examination of SCZ and ASD overlap was the primary goal
of this paper.
As SCZ and ASD are currently treated and discussed as different psychiatric
disorders, this paper will hopefully challenge us to re-examine the relationship between
SCZ and ASD more carefully. We have seen that there is some shared genetic aetiology
between SCZ and ASD, and other literature further illustrates this overlap across a variety
of psychiatric disorders. While psychiatric disorders clearly differ based on clinical
symptoms, we should not assume that this means that the mechanisms responsible for
these symptoms are also exclusively different between psychiatric disorders. In fact, this
shared genetic aetiology is suggesting that there are similar mechanisms responsible for
many psychiatric disorders. While the clinical symptoms differ, the process of
developing these symptoms might be similar, and therefore treatment and a general
understanding of psychiatric disorders requires an examination of this shared overlap
between psychiatric disorders. This may ultimately lead to a new overview of psychiatric
disorders, how they progress and how they are interrelated. The list of CNVs presented
should open the possibility to examine the significance of CNVs and their influence on
ASD and SCZ more closely. While a large amount of information is delivered in this
paper the question still remains, which of these CNVs could be culprits in the progression
of SCZ and ASD. Some suggestions have been made, such as DSG3 and DSC3
associated with cell junction and CAMs, or SCARF2 associated with CAMs and showing
early expression during development. Also GABA and acetylcholine receptor activity,
specifically GABRA5, GABRB3 and GABRG3 showed a significant CNV burden in
ASD and SCZ and also showed early expression during development. However, this is
just the beginning of potential research to better understand the relationship between SCZ
CNV: ASD and SCZ
26
and ASD, and the relationship of all psychiatric disorders in general, based on the list of
shared CNVs between SCZ and ASD. The purpose of this paper was to provide a
comprehensive overview of CNVs in ASD and SCZ, the next step is to take the
individual genes and examine them more closely. When are they expressed during
development? Are they producing a gain or loss of function? Is the gain or loss of
function the same in both ASD and SCZ, or rather do they oppose each other as
suggested by Crespi et al. (2010)? The continuous dissection of the genetic influences
shared by ASD and SCZ will be crucial not only in understanding and treating these
respective disorders, but will likely play a huge role in our understanding of psychiatric
disorders in general. This shared overlap is part of a larger trend in research examining
the genetic overlap among virtually all psychiatric disorders, and may one day drastically
alter our understanding of psychiatric disorders, their individual progression and their
shared dysfunctions.
CNV: ASD and SCZ
27
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CNV: ASD and SCZ
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Supplementary Materials:
Supplementary 1: Significant molecular functions and their associated CNV disrupted
genes
Genes involved
Significance
(p value)
Acetylcholine
GABRG3, GABRA5, GABRB3
0.00235
GABRG3, GABRA5, GABRB3
0.00235
Cytoskeleton
TEKT3, TSSK2, TPPP, TUBGCP5, KLHL22,
0.0061
protein binding
CAMK1D, ARVCF
Tranferase
TSSK2, PRIM2A, MAPK3, HS3ST3B1, SEPHS1,
activity
TAOK2, HTF9C, B#GAT1, LARGE, ALG10, MLL3,
receptor activity
GABA receptor
activity
0.0143
OCA2, ERBB4
Lipid transporter
SEZ6L2, NRXN1, CSMD1
0.0246
GABRG3, GABRA5, GABRB3
0.0373
HTF9C, MLL3, COMT
0.0373
GRM7, SEZ6L2, NRXN1, SCARF2, TRPM1,
0.0403
activity
Ligand-gated ion
channel activity
Methyltransferas
e activity
Receptor activity
GABRG3, GABRA5, RTNR4, CSMD1, VIPR2,
GABRB3, ERBB4
Calcium ion
binding
TSSK2, DSC3, SCARF2, DSG3, OCA2, PPP4C,
0.0406
CNV: ASD and SCZ
36
Supplementary 2: Significant biological processes and their associated CNV disrupted
genes
Genes involved
Significance
(p value)
Developmental
UBE3A, TEKT3, TSSK2, PMP22, CLDN5,
process
SCARF2, NRXN1, DGCR13, RTRN4, TPPP,
0.00314
MAPK3, VIPR2, TUBGCP5, KLHL22, TBC1D5,
OCA2, HERC2, PARK2, PHYH, GSC2, ERBB4,
TBX6, CNTN4, CAMK1D, KLF13, TBX1
Oxygen and oxygen
TXNRD2, PMP22, MPV17L
0.00531
Anion transport
GABRG3, GABRA5, GABRB3, OCA2
0.00989
Mesoderm
UBE3A, TSSK2, NRXN1, SCARF2, RTN4R,
0.00990
development
MAPK3, HERC2, VIPR2, CA2, TBX6, GSC2,
reactive species
metabolic process
ERBB4, CNTN4, KLF13, TBX1
Cell adhesion
SEZ6L2, NRXN1, SCARF2, DSC3, RTN4R,
0.0153
CSMD1, DSG3, GPC6, VIPR2, ERBB4, CNTN4,
ARVCF, NDN
Protein metabolic
UBE3A, YPEL3, UBE2L3, TSSK2, MKRN3,
process
SEZ6L2, PRKAG2, NRXN1, CSMD1, MAPK3,
0.0166
HS3ST3B1, KLHL22, OCA2, TAOK2, HERC2,
PARK2, LARGE, ALG10, ERRBB4, EHHADH,
PPP4C, CNTN4, CAMK1D, SERPIND1,
MRPL40, UFD1L
Skeletal system
UBE3A, NRXN1, SCARF2, RTN4R, HERC2,
development
GSC2
Neurological system
GRM7, TSSK2, CLTCL1PMP22, NRXN1,
process
SCARF2, DOC2A, GABRG3, GABRA5, VIPR2,
KLHL22, RAB23, PMP22, OCA2, GABRB3,
CAMK1D, RXRA, COMT, GNB1L
0.0193
0.0212
CNV: ASD and SCZ
Meiosis
TSSK2, OCA2, PPP4C, CAMK1D
0.0223
Cell cycle
CDC45L, TSSK2, MKRN3, PMP22, PRIM2A,
0.0244
SEPT5, MAPK3, TUBGCP5, RAB23, OCA2,
ERBB4, CNTN4, PPP4C, CAMK1D, RXRA,
HIRA, NDN
Cellular calcium
ATP2C2, ATP10A
0.0328
SEZ6L2, DSC3, NRXN1, SCARF2, DSG3,
0.0333
homeostasis
Cell-cell adhesion
RTN4R, CSMD1, ERBB4, CNTN4
System process
GRM7, TSSK2, CLTCL1, PMP22, NRXN1,
0.0347
SCARF2, DOC2A, GABRG3, GABRA5, VIPR2,
KLHL22, RAB23, OCA2, GABRB3, CNTN4,
CAMK1D, RXRA, COMT, GNB1L
Homeostatic process
ATP2C2, ATP10A, VIPR2
0.0446
Lipid metabolic
YPEL3, GDPD3, ABCA13, SLC25A1, PRKAG2,
0.0455
process
ATP2C2, NUDT3, PHYH, EHHADH, RXRA,
ATP10A
Cellular component
TEKT3, TSSK2, PMP22, CLDN5, TPPP,
morphogenesis
DGCR13, TUBGCP5, KLHL22, TBC1D5,
0.0460
CAMK1D
Anatomical structure
TEKT3, TSSK2, PMP22, CLDN5, TPPP,
morphogenesis
DGCR13, TUBGCP5, KLHL22, TBC1D5,
0.0460
CAMK1D
Synaptic
CLTCL1, DOC2A, NRXN1, GABRG3,
transmission
GABRA%, GABRB3, COMT
Cell communication
GRM7, TSSK2, CLTCL1, MKRN3, SEZ6L2,
PRKAG2, SCARF2, NRXN1, SHANK3, DOC2A,
DSC3, CYFIP1, CSMD1, GABRG3, GABRA5,
RTN4R, DSG3, MAPK3, HS3ST3B1, VIPR2,
RAB23, OCA2, PARK2, ERBB4, GABRB3,
PPP4C, CNTN4, CAMK1D, COMT, RXRA
0.0471
0.0477
37
CNV: ASD and SCZ
Lipid transport
ABCA13, SLC25A, ATP2C2, ATP10A
38
0.0481
Supplementary 3: Significant cellular components and their associated CNV disrupted
genes
Genes involved
Significance (p
value)
Cell junction
CLDN5, DSC3, DSG3, ARVF
0.00472
Plasma membrane
CLDN5, DSC3, DSG3, ARVF
0.00622
Microtubule
TEKT3, TSSK2, TPPP, TUBGCP5, CAMK1D
0.0444
Supplementary 4: Expression Analysis
See attached PowerPoint for expression data overview.
-Slides 2-4: expression analysis for shared CNVs between ASD and SCZ across
brain regions.
-Slides 5-7: expression analysis for shared CNVs between ASD and SCZ over
time.
-Slide 8: expression analysis for all CNVs in ASD over time
-Slide 9: expression analysis for all CNVs in ASD across brain regions
-Slide 10: expression analysis for CNVs in SCZ across brain regions
-Slide 11: expression analysis for CNVs in SCZ over time
-Slide 12: expression analysis for CNVs only in SCZ over time
-Slide 13: expression analysis for CNVs only in SCZ across brain regions
-Slide 14: expression analysis for CNVs only in ASD over time
-Slide 15: expression analysis for CNVs only in ASD across brain regions