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ARTICLE
Received 5 Dec 2013 |Accepted 7 May 2014 |Published 13 Jun 2014
The impact of the metabotropic glutamate
receptor and other gene family interaction
networks on autism
Dexter Hadley1, Zhi-liang Wu1, Charlly Kao1, Akshata Kini1, Alisha Mohamed-Hadley1, Kelly Thomas1,
Lyam Vazquez1, Haijun Qiu1, Frank Mentch1, Renata Pellegrino1, Cecilia Kim1, John Connolly1,
AGP Consortium*, Joseph Glessner1& Hakon Hakonarson1,2
Although multiple reports show that defective genetic networks underlie the aetiology of
autism, few have translated into pharmacotherapeutic opportunities. Since drugs compete
with endogenous small molecules for protein binding, many successful drugs target large
gene families with multiple drug binding sites. Here we search for defective gene family
interaction networks (GFINs) in 6,742 patients with the ASDs relative to 12,544 neuro-
logically normal controls, to find potentially druggable genetic targets. We find significant
enrichment of structural defects (Pr2.40E 09, 1.8-fold enrichment) in the metabotropic
glutamate receptor (GRM) GFIN, previously observed to impact attention deficit hyperactivity
disorder (ADHD) and schizophrenia. Also, the MXD-MYC-MAX network of genes, previously
implicated in cancer, is significantly enriched (Pr3.83E 23, 2.5-fold enrichment), as is the
calmodulin 1 (CALM1) gene interaction network (Pr4.16E 04, 14.4-fold enrichment),
which regulates voltage-independent calcium-activated action potentials at the neuronal
synapse. We find that multiple defective gene family interactions underlie autism, presenting
new translational opportunities to explore for therapeutic interventions.
DOI: 10.1038/ncomms5074 OPEN
1The Center for Applied Genomics, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania 19104, USA. 2Department of Pediatrics, University of
Pennsylvania School of Medicine, Philadelphia, Pennsylvania 19104, USA. Correspondence and requests for materials should be addressed to H.H.
(email: hakonarson@email.chop.edu).
*List of participants and their affiliations appear at the end of the paper.
NATURE COMMUNICATIONS | 5:4074 | DOI: 10.1038/ncomms5074| www.nature.com/naturecommunications 1
&2014 Macmillan Publishers Limited. All rights reserved.
The autism spectrum disorders (ASDs) represent a group of
highly heritable childhood neuropsychiatric disorders
characterized by a variable phenotypic spectrum of
neurodevelopmental deficits of impaired socialization, reduced
communication and restricted, repetitive, or stereotyped beha-
viour1. ASDs are four times more common in boys2,3, and the
most recent prevalence estimates across the United States range
from 1%4to 2%5, although a recent study reported a prevalence
as high as 2.6% in a general school-aged population in South
Korea6. The ASDs have an estimated heritability as high as 90%7
based on data on monozygotic twin concordance studies8–10,
whereas recent estimates of the sibling recurrence risk range from
19% to 22%11,12.
Despite being highly heritable, the vast majority of family
studies suggest that the ASDs do not segregate as a simple
Mendelian disorder, but rather display clinical and genetic
heterogeneity consistent with a complex trait13. Indeed, recent
studies estimate that the ASDs may comprise up to 400 distinct
genetic and genomic disorders that phenotypically converge14,15.
Common variants such as single-nucleotide polymorphisms seem
to contribute to ASD susceptibility, but, taken individually, their
effects appear to be small16. However, there is increasing evidence
that the ASDs can arise from rare or ‘private’ highly penetrant
mutations that segregate in families but are less generalizable to
the general population17–19. Many genes implicated thus far,
which are involved in chromatin remodelling, metabolism,
mRNA translation and synaptic function, seem to converge in
common pathways or genetic networks affecting neuronal and
synaptic homeostasis16.
Such remarkable phenotypic and genotypic heterogeneity when
coupled to the private nature of mutations in the ASDs has
hindered identification of new genetic risk factors with thera-
peutic potential. However, it is noteworthy that many of the
rare gene defects implicated in the ASDs belong to gene families.
For instance, rare defects impacting multiple members of both
the post-synaptic neuroligin (NLGN) gene family20 as well as
their pre-synaptic neurexin molecular-interacting partners21,22
have long been reported in patients with ASDs. In addition, a
number of other defective gene families with important
functional roles have subsequently been well-characterized
including ubiquitin conjugation23, gamma-aminobutyric acid
receptor signalling24–27 and cadherin/protocadherin cell
junction proteins28 in the brain. Furthermore, multiple defects
in voltage-gated calcium channels have been found in
schizophrenia29, and a defective network of metabotropic
glutamate (GRM) receptor signalling was found in both
ADHD30 and schizophrenia31–36, two neuropsychiatric dis-
orders that are highly coincident with the ASDs. Also, the vast
majority of significant defective genes identified from recent
whole-exome sequences belong to gene families17–19.
Many studies have found defective genetic networks in the
ASDs21,23,37–40 (see ref. 16 for review), and we complement these
in this work by uncovering new networks and implicating specific
defective gene families that may be enriched for novel potential
therapeutic targets. Drug-binding sites on proteins usually exist
out of functional necessity33, and gene families derive from gene
duplication events that present additional binding sites for a given
drug to exert its effects. Most successful drugs achieve their
activity by competing for a binding site on a protein with an
endogenous small molecule41; therefore, many successful
pharmacologic gene targets are within large gene families.
Indeed, nearly half of the pharmacologic gene targets fall into
just six gene families: G-protein-coupled receptors (GPCRs),
serine/threonine and tyrosine protein kinases, zinc
metallopeptidases, serine proteases, nuclear hormone receptors
and phosphodiesterases41. Moreover, many large gene families
are localized to pre- and post synaptic neuronal terminals to
coordinate the highly complex and evolutionarily conserved
process of neurotransmission42, which is thought to be
compromised to varying degrees in the autistic brain43.
Therefore, we hypothesize that we may select more druggable
targets for the ASDs by enriching for defective interaction
networks defined by gene families.
Here we perform a large genome-wide association study
(GWAS) of structural variants that disrupt gene family protein
interaction networks in patients with autism. We find multiple
defective networks in the ASDs, most notably rare copy-number
variants (CNVs) in the metabotropic glutamate receptor
(mGluR) signalling pathway in 5.8% of patients with the ASDs.
Defective mGluR signalling was found in both ADHD30 and
schizophrenia31–36, two common neuropsychiatric disorders that
are highly coincident with the ASDs. Furthermore, we find other
attractive candidates such as the MAX dimerization protein
(MXD) network that is implicated in cancer, and a Calmodulin 1
(CALM1) gene interaction network that is active in neuronal
tissues. The numerous defective gene family interactions we find
to underlie autism present many novel translational opportunities
to explore for therapeutic interventions.
Results
To identify and comprehensively characterize defective genetic
networks underlying the ASDs, we performed a large-scale
genome association study for copy-number variation (CNVs)
enriched in patients with autism. By combining the affected cases
from previously published large ASD studies21,23,28,44 with more
recently recruited cases from the Children’s Hospital of
Philadelphia, we executed one of the largest searches for rare
pathogenic CNVs in ASDs to date. In sum, 6,742 genotyped
samples from patients with the ASDs were compared with those
from 12,544 neurologically normal controls recruited at The
Children’s Hospital of Philadelphia (CHOP).
These cases were each screened by neurodevelopmental
specialists to exclude patients with known syndromic causes for
autism. Genotyping was performed at CHOP for the vast
majority of the ASD cases as well as all the controls. After
cleaning the data to remove sample duplicates and performing
standard QC for CNVs, we first inferred the continental ancestry
of 5,627 affected cases and 9,644 disease-free controls using a
training set defined by populations from HapMap 3 (ref. 45) and
the Human Genome Diversity Panel46 (Table 1). Using this QC
criteria, we estimated that the sensitivity and specificity of calling
CNVs is B70% and 100%, respectively, across 121 different
genomic regions assayed by PCR (Methods). Across all
ethnicities, there was an increased burden of CNVs in cases
versus controls, a statistically significantly difference (Pr0.001)
in the larger European (63.3 versus 54.5 Kb, respectively) and
African-derived (70.4 versus 48.0 Kb, respectively) populations.
We then searched for pan-ethnic CNV regions (CNVRs)
discovered in the European-derived data set (4,602 cases versus
4,722 controls; Pr0.0001 by Fisher’s exact test) and replicated in
an independent ASD data set of African ancestry (312 cases
versus 4,169 controls; Pr0.001 by Fisher’s exact test) with
subsequent measurement of overall significance across the entire
multi-ethnic discovery cohort (5,627 cases versus 9,644 controls)
for maximal power (Fig. 1, Table 2). On the basis of these
selection criteria, two large well-known ASD risk loci emerged
that harboured multiple duplications in the Prader Willi/Angel-
man syndrome (15q11–13) critical region, and multiple deletions
were detected in the DiGeorge syndrome (22q11) critical region,
albeit notably smaller than the 22q11 deletion syndrome. A third
locus harbouring deletions in poly ADP-ribose polymerase family
ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms5074
2NATURE COMMUNICATIONS | 5:4074 | DOI: 10.1038/ncomms5074| www.nature.com/naturecommunications
&2014 Macmillan Publishers Limited. All rights reserved.
8 (PARP8) on chromosome 5q11 was also discovered. PARP8
was previously identified as associated with the ASDs in a Dutch
population47, but it has not previously been described for its pan
ethnic distribution across European-derived and African-derived
populations.
We examined the genetic interaction networks derived from
gene families with members localized to the the Prader Willi/
Angelman syndrome (15q11-13) critical region, the DiGeorge
syndrome (22q11) critical region, and the novel PARP8 (5q11)
region using a method previously applied to ADHD30; however,
hardly any of the most significant genes harbouring significant
CNVRs clustered within gene families. Consequently, we
broadened our search for gene family interaction networks
(GFINs) and searched the entire genome for GFINs with CNVs
enriched in autism. For every gene family, we defined a GFIN as
the genetic interaction network spawned by its multiple
duplicated members. We used standard HUGO48 gene names
to define 1,732 GFINs across which we searched for enrichment
of network defects associated with the ASDs. However, because
there is an a priori excess of CNV burden in ASD cases over
disease-free controls (Table 1), larger GFINs are expected to
display significant enrichment of case defects by virtue solely of
their increased size and complexity. Therefore, for each GFIN, we
used a network permutation test of case enrichment across 1,000
random sets of networked genes to control for the GFIN size and
complexity. With this approach, we robustly identified network
defects associated with the ASDs by minimizing statistical artefact
derived from any a priori excessive CNV burden in cases over
controls, as well as other unknown biases that may be inherent in
the human interactome data49–51 that we mined.
Out of 1,732 GFINs, we used the network permutation test to
rank 1,557 GFINs with defined CNVs for enrichment of genetic
defects in the ASDs. Among the top GFINs (Table 3) was the
metabotropic glutamate receptor (mGluR) pathway defined by
the GRM family of genes that impacts glutamatergic neuro-
transmission. The GRM family contains eight members, all of
which were defined in the human interactome to cumulatively
spawn a GFIN of 279 genes (Fig. 2). Across this GFIN for
the GRM family of genes, we found CNV defects in 5.8% of
Table 1 | Distribtion of CNVs across samples and estimated
ancestry.
Continental ancestry Case Control Total
Europe
Number of samples 4,602 4,722 9,324
*CNV burden (Kb) 63.3 54.5
Africa
Number of samples 312 4,169 4,481
*CNV burden (Kb) 70.4 48.0
America
Number of samples 485 276 761
CNV burden (Kb) 59.1 58.4
Asia
Number of samples 201 350 551
CNV burden (Kb) 56.1 54.1
Other
Number of samples 27 127 154
CNV burden (Kb) 51.5 49.4
All Ethnicities
Number of samples 5,627 9,644 15,271
*CNV burden (Kb) 63.0 51.7
CNV ¼copy-number variation. The table shows the distribution of cases, controls and CNV
coverage across estimated continental ancestry. For groups of cases and controls across
estimated ancestries, the table lists the numbers of subjects that passed quality control and their
group-wise CNV burden, defined as the average span of CNVs in Kb for each group.
*Statistically significant (Pr0.01 by PLINK permutation test) differences in CNV burden are
marked with an asterix(*).
35
30
25
20 5q11.1
15q12–13.1
22q11.22
22q11.22
15q12–13.1
5q11.1
15
–log(P)–log(P)
10
5
0
6
5
4
3
2
1
0
123456789
Chr
Africa
Europe
10 11 12 13 14 15 16 17 18 19 20 21 22
123456789
Chr
10 11 12 13 14 15 16 17 18 19 20 21 22
Figure 1 | Significance of CNVRs by GWAS of ASDs in European-derived or African-derived populations. The Manhattan plots show the log10
transformed P-value of association for each CNVR along the genome. Adjacent chromosomes are shown in alternating red and blue colours. The regions
discovered in Europeans (Pr0.0001) that replicated in Africans (Pr0.001) are highlighted with black arrows labelled by chromosome band. GWAS of
4,634 cases versus 4,726 controls in Europeans is shown on top and GWAS of 312 cases versus 4,173 controls in Africans is shown below.
NATURE COMMUNICATIONS | DOI: 10.1038/ncomms5074 ARTICLE
NATURE COMMUNICATIONS | 5:4074 | DOI: 10.1038/ncomms5074| www.nature.com/naturecommunications 3
&2014 Macmillan Publishers Limited. All rights reserved.
European-derived ASD cases (265/4,602) versus only 3% of
ethnically matched controls (153/4,722), a 1.8-fold enrichment of
frequency (P
Fisher
r2.40E 09). By 1,000 random network
permutations, we found this excess of enrichment across cases
in the mGluR pathway to also be statistically significant (P
perm
r0.05). In addition, 69.2% (124/181) of the informative genes
within our mGluR network showed an excess of CNVs among
cases. However, the component genes that harbour the most
significant CNVRs contributing to this overall network signifi-
cance reveal that the duplicated mGluR genes themselves (GRM1,
GRM3, GRM4, GRM5, GRM6, GRM7 and GRM8) fail to achieve
significance individually, although there is a trend for an excess
of CNV defects across a specific subset of mGluR receptors
(GRM1, GRM3, GRM5, GRM7, GRM8) that is unique to cases
(Supplementary Table 1).
Many large studies of CNVs implicate genes within the
glutamatergic signaling pathway in the aetiology of the
ASDs21,23,37–40, and SNP52,53 and CNV duplications54 of
GRM8 have been reported in association with the ASDs before
in humans. Moreover, a recent functional study demonstrated
that in mouse models of tuberous sclerosis and fragile X, two
different forms of syndromic autism, the autistic phenotype was
ameliorated by modulation of GRM5 in opposite directions for
each syndrome, which suggests that GRM5 functional activity is
central in defining the axis of synaptopathophysiology in
syndromic autism55. Our GRM network findings implicate rare
defects in mGluR signalling also contribute to the ASDs outside
of fragile X and tuberous sclerosis, and we posit that functional
mGluR synaptopathophysiology may be initiated from many
dozens if not hundreds of defective genes within the mGluR
pathway that may account for as much as 6% of the
endophenotypes of the ASDs (Table 3).
In addition, we recently demonstrated the importance of
mGluRs in ADHD30,56, a highly co-incident neuropsychiatric
disorder within the autism spectrum. However, in contrast to
ADHD where defects within the mGluR receptors themselves
(GRMs) were among the most significant copy-number defects
contributing to the overall network significance, we found that in
the ASDs defects of component GRMs contributed only modestly
to the overall significance of the mGluR pathway. Nonetheless,
the defects within GRM1, GRM3, GRM5, GRM7 and GRM8 that
we identified as unique to cases and thus enriched are the same
GRMs we identified as being pathogenic in ADHD and may
impact glutamatergic signalling.
Among the most highly ranked GFINs by permutation testing,
the MAX dimerization protein (MXD) GFIN (P
Fisher
r3.83E 23,
Table 2 | Significant copy-number variable regions.
CNVR Genes Bands Size (Kb) No. of
SNP
No. of
Case
No. of
Control
All Europe Africa
P-value OR P-value OR P-value OR
del ZNF280B 22q11.22 53.4 13 130 0 2.56E 57 Inf 1.94E 33 Inf 3.34E 04 Inf
del *PARP8 5q11.1 47.7 8 70 8 2.76E 22 15.1 3.84E 13 12.0 2.69E 06 40.9
dup *GABRB3 15q12 49.0 20 28 0 7.60E 13 Inf 1.50E 06 Inf 3.34E 04 Inf
dup *GABRG3 15q12 135.3 13 27 1 3.72E 11 Inf 1.60E 05 19.5 3.34E 04 Inf
dup *HERC2 15q13.1 84.4 2 24 0 4.12E 11 Inf 6.17E 06 Inf 3.34E 04 Inf
CNVR ¼copy-number variable region; OR ¼odds ratio. The table shows CNVRs distinguishing cases from controls significant across both European-derived populations (Pr0.0001 by Fisher’s exact
test) and African-derived populations (Pr0.001). For each CNVR, the table lists the type (del or dup), the closest gene impacted, the chromosomal band, the approximate size of the defect (Kb), the
number of contributing SNPs, the numbers of affected cases and controls, as well as P-value and odds ratio (OR) from Fisher’s exact test for across all populations, and subsets of European-derived and
African-derived populations.
*Genes with an asterix (*) harbour CNVRs that disrupt their exons of directly, while those without the asterix are located in the genomic region around the intergenic CNVRs.
Table 3 | Top gene family interaction networks discovered.
Gene family Enriched genes Cases Controls Gene Network Association
Name Size No. Frequency No. Frequency No. Frequency P
fisher
Enrichment P
perm
BRF 2 242/326 0.742 567 0.123 370 0.078 3.30E 13 1.65 0.040
CCL 24 108/144 0.75 231 0.05 129 0.027 5.62E 09 1.88 0.008
CCNT 2 183/254 0.72 613 0.133 381 0.081 1.10E 16 1.75 0.007
ELAVL 4 108/156 0.692 327 0.071 152 0.032 6.87E 18 2.3 0.043
ERCC 7 263/369 0.713 836 0.182 560 0.119 7.67E18 1.65 0.035
GRM 8 124/181 0.685 265 0.058 153 0.032 2.40E 09 1.82 0.043
GTF2H 5 152/223 0.682 391 0.085 233 0.049 3.21E 12 1.79 0.049
KIAA 106 268/373 0.718 988 0.215 647 0.137 3.12E 23 1.72 0.045
KPNA 7 256/367 0.698 560 0.122 369 0.078 1.26E 12 1.63 0.028
MXD 3 52/64 0.813 366 0.08 156 0.033 3.83E 23 2.53 0.042
POU5F 2 94/130 0.723 293 0.064 131 0.028 2.96E 17 2.38 0.041
RAD 7 218/309 0.706 535 0.116 339 0.072 9.68E 14 1.7 0.042
SAP 4 111/150 0.74 274 0.06 151 0.032 9.61E 11 1.92 0.040
SMAD 8 845/1,225 0.69 1,782 0.387 1,424 0.302 1.81E 18 1.46 0.039
SMARCC 2 106/147 0.721 239 0.052 131 0.028 1.22E 09 1.92 0.043
SMC 5 88/120 0.733 336 0.073 176 0.037 1.71E 14 2.03 0.034
The table shows significant gene family interaction networks (GFINs) by network permutation testing (P
perm
r0.05) enriched for CNV defects across at least 5% of cases. The table lists the name and
size of gene family tested, the number and frequency of network genes enriched in the second degree geneinteraction network, the number and frequency of cases harbouring defects across the network,
the number and frequency of controls harbouring defects across the network, the significance of association by Fisher’s exact test, the enrichment of CNV defects in cases, and the significance of that
enrichment by 1,000 random network permutations.
ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms5074
4NATURE COMMUNICATIONS | 5:4074 | DOI: 10.1038/ncomms5074 | www.nature.com/naturecommunications
&2014 Macmillan Publishers Limited. All rights reserved.
enrichment ¼2.53, P
perm
r0.042) was the most enriched. The
MXD family of genes encode proteins that interact with MYC/
MAX network of basic helix-loop-helix leucine zipper (bHLHZ)
transcription factors that regulate cell proliferation, differentiation
and apoptosis (MIM 600021)57; MXD genes are important
candidate tumour suppressor genes as the MXD-MYC-MAX
network is dysregulated in various types of cancer58. Interestingly
an epidemiological link between autism and specific types of
cancer has been reported59, and anticancer therapeutics were
recently shown to modulate ASD phenotypes in the mouse
through regulation of synaptic NLGN protein levels60. Within the
component genes contributing to the MXD GFIN significance,
duplications in PARP10 (Pr4.06E 11, OR ¼2.04) and UBE3A
(1.50E 06, OR ¼inf) are the most significantly enriched
(Supplementary Table 2). It is notable that we found PARP8 as
significant across ethnicities as described earlier (Table 2), and we
previously described the importance of structural defects in
UBE3A in the ASDs23.
Other notable significant GFINs uncovered were POU
class 5 homeobox (POU5F) GIFN (P
Fisher
r2.96E 17,
enrichment ¼2.3, P
perm
r0.008, and the SWI/SNF related,
matrix associated, actin-dependent regulator of chromatin,
subfamily c (SMARCC) GFIN (P
Fisher
r1.22E 09,
enrichment ¼1.9, P
perm
r0.035). The POU5F family of genes
encodes for transcription factors containing a POU home-
odomain, and their role has been demonstrated in embryonic
development, especially during early embryogenesis, and it is
necessary for embryonic stem cell pluripotency. Component
genes of the SMARCC gene family are members of the SWI/SNF
family of proteins, whose members display helicase and ATPase
activities and which are thought to regulate transcription of
certain genes by altering the chromatin structure around those
genes. Most interestingly, the KIAA family of genes ranked
among the top GFINs (P
Fisher
r3.12E 23, enrichment ¼1.6,
P
perm
r0.040). KIAA genes have been identified in the Kazusa
cDNA sequencing project61 and are predicted from novel large
human cDNAs; however, they have no known function.
We also hypothesized that some component members of gene
families may contribute disproportionately to the significance of a
GFIN because they are highly connected to interacting gene
partners that are enriched for CNV defects in ASD. Therefore, we
decomposed the 1,732 gene families into their 15,352 component
SHANK1
HOMER1
ABI3
C1orf116
STX12
PHKG2
PTH2R
PHKA2
PHKA1
PYGM
RYR2
CALM1
PHKB
GRIK1
PDE1C
ADD1
PYGL
RYR1
HOMER3
GRIA1
AGAP2
ADD2
PIK3CA
ADCY1
PDE1B
ITPR1
ERP44
PLCG2
SCTR
PRKCA
LYN
PDE6G
GLP1R
GRM5
GLP2R
AQP1
LRP2BP
PICK1
ASIC2
GRB7
SACS
GRM7
PRLHR
ASIC1
PGM1 GNB2L1
EGFR
MAPT
RGS2
CIC
IQGAP2
SLC6A3
APTX
EFNB1
SOCS6
ATXN7L3
CALM2
ERBB2
SNCA
SYK
SRC
FYN
PIK3R1 CALB2
TUBA4A
LTA
LINC00324
TUBG1
PXN
KIAA1683
SLC2A1
BTBD2 CAMK4
GAPDH
CAMK2B
GRIK3
GP1BA
TPI1
F3
TGM2
FURIN
FLNA
PRDX1
TJP1
PSEN1
ALDOA
MARK4
DYNLL1
ARL15
OPRD1
ARRB1
MTNR1B
ARHGAP24
ADA
GNAI3
GNAI2
ARRB2
DRD2
ITGB1
CDC42
ITGB7
NFKBIA
DCN
G6PD
MAP4
MAPK1
SELE
DRD3
ADRBK1
GRM1
ADRB2
CAMK1
RHOA
PLCB3
CNP
GRM8
TLR10
MTNR1A
GNAQ
TBXA2R
VIPR1
CHRM3
CA8
HTR2C
F2RL3
ADRA2C
F2RL2
GRM2 F2R
CRHR1
GNAO1
GRM6
CACNA1B GNA15
RGS12
PLCB1
CHRM2
GNAI1
ADRA2A
CXCR2
CNR1
ADORA1
NPY2R
CCNB1
MC4R
BTK
RALA
GRM4
HTR2A
FPR1
ADRA1B
BDKRB1
BDKRB2
PSMD13
DSTN
PSME1
PCDHA4
TXN
SHBG
RIF1
LNX2
TK1
MAGEA2B
CMPK1
RRM1
PSMD1
PSMA1
PSMC1
PSMD6
PSAT1
SET
TYMS
PPP2R1A
PCBP1
MRPS16
ECHS1
GLRX3
STRAP
RPN2
SNRPB2 NAA15
SARS
PCBP3
TNIK
ACAT1
HNRNPA3
GSN
CACYBP
BCAP31
VHL
NUDC
NANS
RANBP1
RCC2
UCHL1
PPIH
GOT1
DHCR7 TEAD3
RCC1
RAP2A
FKBP3
PRPSAP1
KIAA0090
CNPY2
MRPL14
TBCA
SEPT4
PCMT1
TFAM
EIF3M
ACAT2
PRMT1
PDCD5
NMI
RAB2A
SORD
MTHFD1
S100A6
APP
SDC3
LRRC59
SIAH1
DISC1
FSCN1
RUVBL2
ACP1
SETD4
ANXA2
TRAF2
TUBA3C
NCK1
CALM3
RPLP2
UBQLN4
YWHAQ
SOCS7
COPB2
EIF3H
BTG2
CHGB
PSMD11
C7orf25
HTT
ACTB
STAU1
GRM3
CYCS
MYC
IMPDH2
RPS14
ACTR2
SERPINB9
QRICH2
HBXIP
MX1
RPA2
PAFAH1B3
TUBB
LAMA4
TCP1
MYO6
GRB2
HSP90AB1
DLST
ZAP70
Figure 2 | Enrichment of optimal CNVRs across mGluR network of genes. Nodes of the network are labelled with their gene names, with red and
green representing deletions and duplications, respectively, while grey nodes lack CNV data. Dark and light colours represent enrichment in cases
and controls, respectively. The genes defining the network are shown as diamonds, while all other genes are shown as circles. Blue lines indicate evidence
of interaction.
NATURE COMMUNICATIONS | DOI: 10.1038/ncomms5074 ARTICLE
NATURE COMMUNICATIONS | 5:4074 | DOI: 10.1038/ncomms5074 | www.nature.com/naturecommunications 5
&2014 Macmillan Publishers Limited. All rights reserved.
duplicated genes of which 1,218 had defined networks with data
to test for significance by genome-wide network permutation.
The calmodulin 1 (CALM1) gene interaction network ranked
highest by network permutation testing of case enrichment for
CNV defects across 1,000 random gene networks (Fig. 3, Table 4)
and represents a novel and attractive candidate gene for the
ASDs. Across the CALM1 network, we found CNV defects in 14/
4,618 cases versus only 1/4726 controls (P
fisher
r4.16E 04,
enrichment ¼14.37, P
perm
r0.002), and these defects were
distributed such that 90% (9/10) of genes that harboured CNVs
in the CALM1 interactome were enriched in cases. Closer
inspection of the most significant CNVR contributing to the
CALM1 network significance (Supplementary Table 3) revealed
that no single gene was significant on its own; instead, with the
exception of only one gene (PTH2R), each contributing CNVR
tagged highly penetrant rare defects unique to cases. Calmodulin
is the archetype of the family of calcium-modulated proteins of
which nearly 20 members have been found. Calmodulin contains
149 amino acids that define four calcium-binding domains used
for Ca2þ-mediated coordination of a large number of enzymes,
ion channels and other proteins including kinases and phospha-
tases; its functions include roles in growth and cell cycle
regulation as well as in signal transduction and the synthesis
and release of neurotransmitters [MIM 114180]57.
Among other highly ranked first degree gene interaction
networks were the nuclear receptor co-repressor 1 (NCOR1;
P
fisher
r1.11E 06, enrichment ¼13.37, P
perm
r0.004) and
BCL2-associated athanogene 1 (BAG1; P
fisher
r2.18E 04,
enrichment ¼15.40, P
perm
r0.014) networks. NCOR1 is a
transcriptional coregulatory protein that appears to assist nuclear
receptors in the downregulation of DNA expression through
recruitment of histone deacetylases to DNA promoter regions; it
is a principal regulator in neural stem cells51. The oncogene BCL2
is a membrane protein that blocks the apoptosis pathway, and
BAG1 forms a BCL2-associated athanogene and represents a link
between growth factor receptors and antiapoptotic mechanisms.
The BAG1 gene has been implicated in age-related neuro-
degenerative diseases, including Alzheimer’s disease62,63.
In summary, given the private nature of mutations in the
ASDs, considering the cumulative contributions of rare highly
penetrant genetic defects boosts our power to discover and
prioritize significant pathway defects. As a result, our compre-
hensive, unbiased analytical approach has identified a diverse set
of specific defective biological pathways that contribute to the
underlying aetiology of the ASDs. Among GFINs robustly
enriched for structural defects, the most enriched was that of
the MXD family of genes that has been implicated in cancer
pathogenesis58, thereby providing concrete genetic defects to
explore the reported coincidence of specific cancers with the
ASDs59. The most highly ranked component duplicated gene
interaction network involves defects in CALM1 and its multiple
interacting partners that are important in regulating voltage-
independent calcium-activated action potentials at the neuronal
synapse. Moreover, we found significant enrichment for
defects within the GFIN for GRM that defines the mGluR
pathway that has previously been shown to be defective in other
neuropsychiatric diseases29,30. While specific mGluR gene family
members have been shown to underlie syndromic ASDs55, our
findings suggest that rare defects in mGluR signalling also
contribute to idiopathic autism across the entire GFIN for GRM
genes.
Consequently, in addition to specific neuronal pathways that
are expected to be defective in the ASDs like those defined by
GRM and CALM duplicate genes, we implicate completely novel
Table 4 | Most significant individual gene interaction networks ranked by permutation testing.
Gene Family Member Enriched Genes Cases Controls Gene Network Association
No. Frequency No. Frequency # Frequency P
fisher
Enrichment P
perm
AKAP13 7/7 1.00 16 0.0035 1 0.0002 1.14E 04 16.43 0.012
BAG1 7/7 1.00 15 0.0032 1 0.0002 2.18E 04 15.40 0.014
CALM1 9/10 0.90 14 0.0030 1 0.0002 4.16E 04 14.37 0.002
CASP6 16/17 0.94 46 0.0100 6 0.0013 2.96E 09 7.91 0.012
GTF2H3 23/26 0.88 42 0.0091 8 0.0017 3.66E 07 5.41 0.009
MAP3K5 11/12 0.92 34 0.0074 4 0.0008 2.02E 07 8.76 0.012
NCOR1 9/10 0.90 26 0.0056 2 0.0004 1.11E 06 13.37 0.004
PARP1 5/5 1.00 5 0.0011 0 0.0000 2.95E 02 inf 0.012
PTPN13 6/6 1.00 9 0.0019 0 0.0000 1.75E 03 inf 0.007
TCEA1 22/26 0.85 39 0.0084 7 0.0015 5.94E 07 5.74 0.009
The table lists the name and gene family member tested, the number and frequency of network genes enriched, the number and frequency of cases harbouring defects, the number and frequency of
controls harbouring defects, and the significance of association by Fisher’s exact test, the odds ratio of the effect size, and the significance of association by random permutation of network while
controlling for number of genes tested.
ADD2
SCTR
IQGAP2
VIPR1
ADD1
C4orf3
PHKA1
ADCY1
PDE1C
PDE1B
CALM1
PTH2R
GLP2R
PHKG2
PYGL
PHKA2
GLP1R
PHKB
CRHR1
PYGM
GRB7
Figure 3 | Enrichment of optimal CNVRs across CALM1 network. The first
degree-directed interaction network defined by CALM1 is shown.
ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms5074
6NATURE COMMUNICATIONS | 5:4074 | DOI: 10.1038/ncomms5074 | www.nature.com/naturecommunications
&2014 Macmillan Publishers Limited. All rights reserved.
biological pathways such as the MXD pathway specific forms of
which may be associated with the ASDs59. Given the unmet need
for better treatment for neurodevelopmental diseases64, the
functionally diverse set of defective genetic interaction networks
we report presents attractive genetic biomarkers to consider for
targeted therapeutic intervention in ASDs and across the
neuropsychiatric disease spectrum.
Methods
Ethics statement.The research presented here has been approved by the Chil-
dren’s Hospital of Philadelphia IRB (CHOP IRB#: IRB 06-004886). Some patients
and their families were recruited through CHOP outreach clinics. Written
informed consent was obtained from the participants or their parents using IRB
approved consent forms prior to enrollment in the project. There was no dis-
crimination against individuals or families who chose not to participate in the
study. All data were analysed anonymously and all clinical investigations were
conducted according to the principles expressed in the Declaration of Helsinki.
Sample processing.The majority of cases (5,049 of 6,742) and all controls
(12,544) were genotyped with genome-wide coverage using the Infinium II plat-
form across various iterations of the HumanHap BeadChip with 550 K, 610 K,
660 K and 1 M markers by the Center for Applied Genomics at The Children’s
Hospital of Philadelphia (CHOP). There were 1,693 cases genotyped by the AGP
consortium. All cases and B50% of controls were re-used from previously pub-
lished large ASD studies21,23,28,44. All cases were diagnosed by ADI-R/ADOS and
fulfilled standard criteria for ASDs. Duplicate samples were removed by selecting
unique samples with the best quality (based on genotyping statistics used to QC
samples) from clusters defined by single linkage clustering of all pairs of samples
with high pairwise identity by state measures (IBS Z0.9) across 140 K non-
correlated SNPs. Ethnicity of samples was inferred by a supervised k-means
classification (k¼3) of the first 10 eigenvectors estimated by principal component
analysis across the same subset of 140 K non-correla ted SNPs. We used HapMap 3
(ref. 45) and the Human Genome Diversity Panel46 samples with known
continental ancestry to train the k-means classifier implemented by the R Language
for Statistical Computing65.
CNV inference and association.We called CNVs with the PennCNV algo-
rithm66, which combines multiple values, including genotyping fluorescence
intensity (Log R Ratio), population frequency of SNP minor alleles (B-allele
frequency) and SNP spacing into a hidden Markov model. The term ‘CNV’
represents individual CNV calls, whereas ‘CNVR’ refers to population-level
variation shared across subjects. Quality control thresholds for sample inclusion in
CNV analysis included a high call rate (call rate Z95%) across SNPs, low s.d. of
normalized intensity (s.d. r0.3), low absolute genomic wave artefacts (|GCWF|
r0.02) and low numbers of CNVs called (#CNVs r100). Genome-wide
differences in CNV burden, defined as the average span of CNVs, between cases
and controls and estimates of significance were computed using PLINK67. CNVRs
were defined based on the genomic boundaries of individual CNVs, and the
significance of the difference in CNVR frequency between cases and controls was
evaluated at each CNVR using Fisher’s exact test.
Gene family interaction networks definition and association.We extended our
previous work on ADHD30 here to rank all GFINs by a network permutation test.
Specifically, using merged human interactome data from three different yeast two
hybrid generated data sets49–51 accessed through the Human Interactome
Database68, we defined the directed second-degree gene interaction network for all
gene families here just as we did for the sole metabotropic glutamate receptor gene
family network in ADHD. Specifically, here we use GFIN to refer to these gene
family-derived interaction networks. In sum, we found 2,611 gene families with at
least two members based on official HUGO48 gene nomenclature, and generated
1,732 GFINs using. For 1,557 GFINs with defined CNVs, we calculated an odds ratio
of cumulative network enrichment over all genes harbouring CNVs within the
network. Moreover, for each GFIN, we quantified its enrichment by a permutation
test of 1,000 second-degree gene interaction networks derived from a random set of N
genes, where Nis the number of members of a given gene family. Because the CNVs
we are focused on are so rare, we are relatively underpowered to achieve significance
by permutation testing after correcting for multiple GFIN tests. However, we report
all GFINs in the manuscript in order of their nominal/marginal significance.
Experimental validation of CNVs.Significant CNVRs that we identified were
validated using commercially available qPCR Taqman probes run on the ABI
GeneAmp 9700 system from Life Technology. Supplementary Data 1 lists 251
reactions that we tested using 121 different genomic probes across 85 different
samples for which DNA was available. For deletions, our sensitivity ¼0.65,
specificity ¼1.00, NPV ¼1.00 and PPV ¼0.88. For duplications, our
sensitivity ¼0.68, specificity ¼0.99, NPV ¼0.94 and PPV ¼0.91.
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Acknowledgements
We thank all study participants and their families. We thank all the staff at the Center for
Applied Genomics at CHOP for their invaluable contributions to recruitment of study
subjects and genotyping of samples. We also gratefully acknowledge the resources pro-
vided by the AGRE Consortium and their participating families, and by the Autism
Genome Project (AGP) Consortium and their participating families. The study was
funded by an Institutional Development Fund from The Children’s Hospital of Phila-
delphia; The Margaret Q Landenberger Foundation; The Lurie Family Foundation; The
Kubert Estate Fund and by U01HG005830. AGRE is a program of Autism Speaks and is
at present supported, in part, by grant 1U24MH081810 from the National Institute of
Mental Health to C.M. Lajonchere (PI) and formerly by grant MH64547 to D.H.
Geschwind (PI). AGRE-approved academic researchers can acquire the data sets from
AGRE at http://www.agre.org. There were 1,693 cases of the full AGP data sets that were
genotyped by the AGP consortium. The full AGP data sets are made available from
dbGaP at http://www.ncbi.nlm.nih.gov/gap. The remaining 5,049 cases and all 12,544
controls were all genotyped by the Center for Applied Genomics at the Children’s
Hospital of Philadelphia.
Author contributions
D.H., Z.W., C.K., J.C., J.G. and H.H. conceived the study. D.H., A.K., K.T., F.M., and
H.Q. performed computational analyses. A.M.H., L.V., R.P., and C.K. performed geno-
typing and experimental validation. H.H. and AGP consortium coordinated sample
recruitment. D.H., C.K., Z.W., and H.H. interpreted the results. D.H. and H.H. wrote the
manuscript. All authors read, edited and approved the final manuscript
Additional information
Supplementary Information accompanies this paper at http://www.nature.com/
naturecommunications
Competing financial interests: The authors have no competing financial interests.
Reprints and permission information is available online at http://npg.nature.com/
reprintsandpermissions/
How to cite this article: Hadley, D. et al. The impact of the metabotropic glutamate
receptor and other gene family interaction networks on autism. Nat. Commun. 5:4074
doi: 10.1038/ncomms5074 (2014).
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ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms5074
8NATURE COMMUNICATIONS | 5:4074 | DOI: 10.1038/ncomms5074 | www.nature.com/naturecommunications
&2014 Macmillan Publishers Limited. All rights reserved.
Dalila Pinto1,2,3,4,5,6, Alison Merikangas11, Lambertus Klei12, Jacob A.S. Vorstman13, Ann Thompson14,
Regina Regan15,16, Alistair T. Pagnamenta17,Ba
´rbara Oliveira18,19, Tiago R. Magalhaes15,16, John Gilbert22,
Eftichia Duketis23, Maretha V. De Jonge13, Michael Cuccaro22, Catarina T. Correia18,19, Judith Conroy16,26,
Ine
ˆs C. Conceic¸a
˜o18,19, Andreas G. Chiocchetti23, Jillian P. Casey15,16, Nadia Bolshakova11, Elena Bacchelli27,
Richard Anney11, Lonnie Zwaigenbaum28, Kerstin Wittemeyer29, Simon Wallace30, Herman van Engeland13,
Latha Soorya1,2,75, Bernadette Roge
´31, Wendy Roberts32, Fritz Poustka23, Susana Mouga33,34, Nancy Minshew12,
Susan G. McGrew35, Catherine Lord36, Marion Leboyer37,38,39,40, Ann S. Le Couteur41, Alexander Kolevzon1,2,6,
Suma Jacob42,43, Stephen Guter42, Jonathan Green44,45, Andrew Green16,46, Christopher Gillberg47,
Bridget A. Fernandez48, Frederico Duque33,34, Richard Delorme37,49,50,51, Geraldine Dawson52,Ca
´tia Cafe
´33,
Sean Brennan11, Thomas Bourgeron37,49,50,53, Patrick F. Bolton54,55,SvenBo
¨lte23,77, Raphael Bernier56,
Gillian Baird57, Anthony J. Bailey30,78, Evdokia Anagnostou58, Joana Almeida33, Ellen M. Wijsman59,60,
Veronica J. Vieland61, Astrid M. Vicente18,19, Gerard D. Schellenberg24, Margaret Pericak-Vance22,
Andrew D. Paterson10,62, Jeremy R. Parr63, Guiomar Oliveira33,34, Joana Almeida33,34,Ca
´tia Cafe
´33,34,
Susana Mouga33,34, Catarina Correia33,34, John I. Nurnberger64,65, Anthony P. Monaco17,79, Elena Maestrini27,
Sabine M. Klauck67, Hakon Hakonarson68,69, Jonathan L. Haines25,80, Daniel H. Geschwind21,
Christine M. Freitag23, Susan E. Folstein70, Sean Ennis16,46, Hilary Coon71, Agatino Battaglia72,
Peter Szatmari14,81, James S. Sutcliffe25, Joachim Hallmayer73, Michael Gill11, Edwin H. Cook42,
Joseph D. Buxbaum1,2,3,4,6,74, Bernie Devlin12, Louise Gallagher11, Catalina Betancur7,8,9, Stephen W. Scherer10,20
1Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA; 2Department of Psychiatry,
Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA; 3Department of Genetics and Genomic Sciences, Icahn School of Medicine at
Mount Sinai, New York, New York 10029, USA; 4The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York,
New York 10029, USA; 5The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA;
6Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA; 7INSERM, U1130, 75005 Paris, France; 8CNRS, UMR
8246, 75005 Paris, France; 9Sorbonne Universite
´s, UPMC Universite
´Paris 6, Neuroscience Paris Seine, 75005 Paris, France; 10Program in Genetics and
Genome Biology, The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Ontario, Canada M5G1L7; 11Discipline of Psychiatry, School of
Medicine, Trinity College Dublin, Dublin 8, Ireland; 12Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania 15213,
USA; 13Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, 3584CX Utrecht, The Netherlands; 14Department of
Psychiatry and Behavioural Neurosciences, Offord Centre for Child Studies, McMaster University, Hamilton, Ontario, Canada L8S 4K1; 15National Children’s
Research Centre, Our Lady’s Children’s Hospital, Dublin 12, Ireland; 16Academic Centre on Rare Diseases, School of Medicine and Medical Science, University
College Dublin, Dublin 4, Ireland; 17Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK; 18Instituto Nacional de Sau
´de
Doutor Ricardo Jorge, 1649-016 Lisboa, Portugal; 19Center for Biodiversity, Functional & Integrative Genomics, Faculty of Sciences, University of Lisbon, 1749-
016 Lisboa, Portugal; 20McLaughlin Centre, University of Toronto, Toronto, Ontario, Canada M5S1A1; 21Department of Neurology and Center for Autism
Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, California 90095, USA; 22John P. Hussman
Institute for Human Genomics, Dr John T. Macdonald Foundation Department of Human Genetics, University of Miami School of Medicine, Miami, Florida
33136, USA; 23Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Goethe-University, 60528 Frankfurt am Main, Germany;
24Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA; 25Vanderbilt Brain
Institute, Center for Human Genetics Research and Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, Tennessee 37232,
USA; 26Children’s University Hospital Temple Street, Dublin 1, Ireland; 27Department of Pharmacy and Biotechnology, University of Bologna, 40126 Bologna,
Italy; 28Department of Pediatrics, University of Alberta, Edmonton, Alberta, Canada T6B 2H3; 29School of Education, University of Birmingham, Birmingham
B15 2TT, UK; 30Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford OX3 7JX, UK; 31Unite
´de Recherche Interdisciplinaire Octogone,
Centre d’Etudes et de Recherches en Psychopathologie, Toulouse 2 University, 31058 Toulouse, France; 32Autism Research Unit, The Hospital for Sick
Children, Toronto, Ontario, Canada M5G 1X8; 33Unidade de Neurodesenvolvimento e Autismo do Servic¸o do Centro de Desenvolvimento da Crianc¸a and
Centro de Investigac¸a˜o e Formac¸a˜o Clinica, Pediatric Hospital, Centro Hospitalar e Universita
´rio de Coimbra, 3000-602 Coimbra, Portugal; 34University
Clinic of Pediatrics and Institute for Biomedical Imaging and Life Science, Faculty of Medicine, University of Coimbra, 3000-354 Coimbra, Portugal;
35Department of Pediatrics, Vanderbilt University, Nashville, Tennessee 37232, USA; 36Weill Cornell Medical College/New York Presbyterian Hospital
Teachers College, New York, New York 10065, USA; 37FondaMental Foundation, 94010 Cre
´teil, France; 38INSERM U955, Psychiatrie Ge
´ne
´tique, 94010
Cre
´teil, France; 39Universite
´Paris Est, Faculte
´de Me
´decine, 94010 Cre
´teil, France; 40Assistance Publique-Ho
ˆpitaux de Paris, Henri Mondor-Albert Chenevier
Hospital, Department of Psychiatry, 94010 Cre
´teil, France; 41Institute of Health and Society, Newcastle University, Newcastle upon Tyne NE1 4LP, UK;
42Institute for Juvenile Research and Department of Psychiatry, University of Illinois at Chicago, Chicago, Illinois 60608, USA; 43Present address: Institute of
Translational Neuroscience, Department of Psychiatry, University of Minnesota, Minneapolis, Minnesota 55455, USA; 44Institute of Brain, Behaviour and
Mental Health, University of Manchester, Manchester M139PL, UK; 45Manchester Academic Health Sciences Centre, Manchester M13 9NT, UK; 46National
Centre for Medical Genetics, Our Lady’s Children’s Hospital, Dublin 12, Ireland; 47Gillberg Neuropsychiatry Centre, University of Gothenburg, 41119
Gothenburg, Sweden; 48Discipline of Genetics, Faculty of Medicine, Memorial University of Newfoundland, St John’s, Newfoundland, Canada A1B 3V6;
49Institut Pasteur, Human Genetics and Cognitive Functions Unit, 75015 Paris, France; 50CNRS URA 2182 Genes, Synapses and Cognition, Institut Pasteur,
75015 Paris, France; 51Assistance Publique-Ho
ˆpitaux de Paris, Robert Debre
´Hospital, Department of Child and Adolescent Psychiatry, 75019 Paris, France;
52Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina 27710, USA; 53University Paris Diderot,
Sorbonne Paris Cite
´, 75013 Paris, France; 54Kings College London, Institute of Psychiatry, London SE5 8AF, UK; 55South London & Maudsley Biomedical
NATURE COMMUNICATIONS | DOI: 10.1038/ncomms5074 ARTICLE
NATURE COMMUNICATIONS | 5:4074 | DOI: 10.1038/ncomms5074 | www.nature.com/naturecommunications 9
&2014 Macmillan Publishers Limited. All rights reserved.
Research Centre for Mental Health, London SE5 8AF, UK; 56Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle,
Washington 98195, USA; 57Paediatric Neurodisability, King’s Health Partners, King’s College London, London WC2R 2LS, UK; 58Bloorview Research Institute,
University of Toronto, Toronto, Ontario, Canada M4G 1R8; 59Division of Medical Genetics, Department of Medicine, University of Washington, Seattle,
Washington 98195-9460, USA; 60Department of Biostatistics, University of Washington, Seattle, Washington 98195-9460, USA; 61Battelle Center for
Mathematical Medicine, The Research Institute at Nationwide Children’s Hospital, Columbus, Ohio 43205, USA; 62Dalla Lana School of Public Health,
Toronto, Ontario, Canada M5T 3M7; 63Institute of Neuroscience, Newcastle University, Newcastle upon Tyne NE2 4HH, UK; 64Institute of Psychiatric
Research, Department of Psychiatry, Indiana University School of Medicine, Indianapolis, Indiana 46202, USA; 65Department of Medical and Molecular
Genetics and Program in Medical Neuroscience, Indiana University School of Medicine, Indianapolis, Indiana 46202, USA; 66Division of Molecular Genome
Analysis, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; 67Center for Applied Genomics, The Children’s Hospital of Philadelphia,
Philadelphia, Pennsylvania 19104, USA; 68Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
19104, USA; 69Department of Psychiatry, Division of Child and Adolescent Psychiatry, University of Miami Miller School of Medicine, Miami, Florida 33136,
USA; 70Utah Autism Research Program, University of Utah Psychiatry Department, Salt Lake City, Utah 84108, USA; 71Stella Maris Clinical Research Institute
for Child and Adolescent Neuropsychiatry, 56128 Calambrone, Pisa, Italy; 72Stanford University Medical School, Department of Psychiatry, Stanford,
California 94305, USA; 73Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA; 74Present address:
Department of Psychiatry, Rush University Medical Center, Chicago, Illinois 60612, USA; 75Present address: Department of Psychiatry, Kaiser Permanente,
San Francisco, California 94118, USA; 76Present address: Department of Women’s and Children’s Health, Center of Neurodevelopmental Disorders (KIND),
Karolinska Institutet, 11330 Stockholm, Sweden; 77Present address: Department of Psychiatry, University of British Columbia, Vancouver, British Columbia,
Canada V6T 2A1; 78Present address: Office of the President, Tufts University, Medford, Massachusetts 02155, USA; 79Present address: Department of
Epidemiology & Biostatistics, Case Western Reserve University, Cleveland, Ohio 44106, USA; 80Present address: Hospital for Sick Children, Centre for
Addiction and Mental Health, University of Toronto, Toronto, Ontario, Canada M5G1L7
ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms5074
10 NATURE COMMUNICATIONS | 5:4074 | DOI: 10.1038/ncomms5074 | www.nature.com/naturecommunications
&2014 Macmillan Publishers Limited. All rights reserved.
