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The impact of the metabotropic glutamate receptor and other gene family interaction networks on autism

Nature Communications volume 5, Article number: 4074 (2014) | Download Citation

Abstract

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 neurologically normal controls, to find potentially druggable genetic targets. We find significant enrichment of structural defects (P≤2.40E−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 (P≤3.83E−23, 2.5-fold enrichment), as is the calmodulin 1 (CALM1) gene interaction network (P≤4.16E−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.

Introduction

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 behaviour1. ASDs are four times more common in boys2,3, and the most recent prevalence estimates across the United States range from 1%4 to 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 studies8,9,10, 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 population17,18,19. 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 therapeutic 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 signalling24,25,26,27 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 schizophrenia31,32,33,34,35,36, two neuropsychiatric disorders that are highly coincident with the ASDs. Also, the vast majority of significant defective genes identified from recent whole-exome sequences belong to gene families17,18,19.

Many studies have found defective genetic networks in the ASDs21,23,37,38,39,40 (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 schizophrenia31,32,33,34,35,36, 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 ~\n70% 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 (P≤0.001) in the larger European (63.3 versus 54.5 Kb, respectively) and African-derived (70.4 versus 48.0 Kb, respectively) populations.

Table 1: Distribtion of CNVs across samples and estimated ancestry.

We then searched for pan-ethnic CNV regions (CNVRs) discovered in the European-derived data set (4,602 cases versus 4,722 controls; P≤0.0001 by Fisher’s exact test) and replicated in an independent ASD data set of African ancestry (312 cases versus 4,169 controls; P≤0.001 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/Angelman 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 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.

Figure 1: Significance of CNVRs by GWAS of ASDs in European-derived or African-derived populations.
Figure 1

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 (P≤0.0001) that replicated in Africans (P≤0.001) 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.

Table 2: Significant copy-number variable regions.

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 data49,50,51 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 neurotransmission. 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 European-derived ASD cases (265/4,602) versus only 3% of ethnically matched controls (153/4,722), a 1.8-fold enrichment of frequency (PFisher ≤2.40E−09). By 1,000 random network permutations, we found this excess of enrichment across cases in the mGluR pathway to also be statistically significant (Pperm ≤0.05). 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 significance 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).

Table 3: Top gene family interaction networks discovered.
Figure 2: Enrichment of optimal CNVRs across mGluR network of genes.
Figure 2

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.

Many large studies of CNVs implicate genes within the glutamatergic signaling pathway in the aetiology of the ASDs21,23,37,38,39,40, 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 (PFisher ≤3.83E−23, enrichment=2.53, Pperm ≤0.042) 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 (P≤4.06E−11, OR=2.04) and UBE3A (1.50E−06, OR=inf) 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 (PFisher≤2.96E−17, enrichment=2.3, Pperm ≤0.008, and the SWI/SNF related, matrix associated, actin-dependent regulator of chromatin, subfamily c (SMARCC) GFIN (PFisher ≤1.22E−09, enrichment=1.9, Pperm ≤0.035). The POU5F family of genes encodes for transcription factors containing a POU homeodomain, 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 (PFisher ≤3.12E−23, enrichment=1.6, Pperm ≤0.040). 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 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 (Pfisher ≤4.16E−04, enrichment=14.37, Pperm ≤0.002), 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 Ca2+-mediated coordination of a large number of enzymes, ion channels and other proteins including kinases and phosphatases; 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.

Figure 3: Enrichment of optimal CNVRs across CALM1 network.
Figure 3

The first degree-directed interaction network defined by CALM1 is shown.

Table 4: Most significant individual gene interaction networks ranked by permutation testing.

Among other highly ranked first degree gene interaction networks were the nuclear receptor co-repressor 1 (NCOR1; Pfisher ≤1.11E−06, enrichment=13.37, Pperm ≤0.004) and BCL2-associated athanogene 1 (BAG1; Pfisher ≤2.18E−04, enrichment=15.40, Pperm ≤0.014) 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 neurodegenerative 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 comprehensive, 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 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 Children’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 discrimination 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 platform 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 ~\n50% of controls were re-used from previously published 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 ≥0.9) across 140 K non-correlated SNPs. Ethnicity of samples was inferred by a supervised k-means classification (k=3) of the first 10 eigenvectors estimated by principal component analysis across the same subset of 140 K non-correlated 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 algorithm66, 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 ≥95%) across SNPs, low s.d. of normalized intensity (s.d. ≤0.3), low absolute genomic wave artefacts (|GCWF| ≤0.02) and low numbers of CNVs called (#CNVs ≤100). 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 sets49,50,51 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 N is 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=0.65, specificity=1.00, NPV=1.00 and PPV=0.88. For duplications, our sensitivity=0.68, specificity=0.99, NPV=0.94 and PPV=0.91.

Additional information

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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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 provided 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 Philadelphia; 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 information

Author notes

    • Suma Jacob

    Present address: Institute of Translational Neuroscience, Department of Psychiatry, University of Minnesota, Minneapolis, Minnesota 55455, USA;

    • Joseph D. Buxbaum

    Present address: Department of Psychiatry, Rush University Medical Center, Chicago, Illinois 60612, USA;

    • Latha Soorya

    Present address: Department of Psychiatry, Kaiser Permanente, San Francisco, California 94118, USA;

    • Sven Bölte

    Present address: Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada V6T 2A1;

    • Anthony J. Bailey

    Present address: Office of the President, Tufts University, Medford, Massachusetts 02155, USA;

    • Anthony P. Monaco

    Present address: Department of Epidemiology & Biostatistics, Case Western Reserve University, Cleveland, Ohio 44106, USA;

    • Jonathan L. Haines

    Present address: Hospital for Sick Children, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, Canada M5G1L7

Affiliations

  1. The Center for Applied Genomics, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania 19104, USA

    • Dexter Hadley
    • , Zhi-liang Wu
    • , Charlly Kao
    • , Akshata Kini
    • , Alisha Mohamed-Hadley
    • , Kelly Thomas
    • , Lyam Vazquez
    • , Haijun Qiu
    • , Frank Mentch
    • , Renata Pellegrino
    • , Cecilia Kim
    • , John Connolly
    • , Joseph Glessner
    •  & Hakon Hakonarson
  2. Department of Pediatrics, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania 19104, USA

    • Hakon Hakonarson
  3. Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA;

    • Dalila Pinto
    • , Latha Soorya
    • , Alexander Kolevzon
    •  & Joseph D. Buxbaum
  4. Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA;

    • Dalila Pinto
    • , Latha Soorya
    • , Alexander Kolevzon
    •  & Joseph D. Buxbaum
  5. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA;

    • Dalila Pinto
    •  & Joseph D. Buxbaum
  6. The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA;

    • Dalila Pinto
    •  & Joseph D. Buxbaum
  7. The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA;

    • Dalila Pinto
  8. Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA;

    • Dalila Pinto
    • , Alexander Kolevzon
    •  & Joseph D. Buxbaum
  9. INSERM, U1130, 75005 Paris, France;

    • Catalina Betancur
  10. CNRS, UMR 8246, 75005 Paris, France;

    • Catalina Betancur
  11. Sorbonne Universités, UPMC Université Paris 6, Neuroscience Paris Seine, 75005 Paris, France;

    • Catalina Betancur
  12. Program in Genetics and Genome Biology, The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Ontario, Canada M5G1L7;

    • Andrew D. Paterson
    •  & Stephen W. Scherer
  13. Discipline of Psychiatry, School of Medicine, Trinity College Dublin, Dublin 8, Ireland;

    • Alison Merikangas
    • , Nadia Bolshakova
    • , Richard Anney
    • , Sean Brennan
    • , Michael Gill
    •  & Louise Gallagher
  14. Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania 15213, USA;

    • Lambertus Klei
    • , Nancy Minshew
    •  & Bernie Devlin
  15. Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, 3584CX Utrecht, The Netherlands;

    • Jacob A.S. Vorstman
    • , Maretha V. De Jonge
    •  & Herman van Engeland
  16. Department of Psychiatry and Behavioural Neurosciences, Offord Centre for Child Studies, McMaster University, Hamilton, Ontario, Canada L8S 4K1;

    • Ann Thompson
    •  & Peter Szatmari
  17. National Children's Research Centre, Our Lady's Children's Hospital, Dublin 12, Ireland;

    • Regina Regan
    • , Tiago R. Magalhaes
    •  & Jillian P. Casey
  18. Academic Centre on Rare Diseases, School of Medicine and Medical Science, University College Dublin, Dublin 4, Ireland;

    • Regina Regan
    • , Tiago R. Magalhaes
    • , Judith Conroy
    • , Jillian P. Casey
    • , Andrew Green
    •  & Sean Ennis
  19. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK;

    • Alistair T. Pagnamenta
    •  & Anthony P. Monaco
  20. Instituto Nacional de Saúde Doutor Ricardo Jorge, 1649-016 Lisboa, Portugal;

    • Bárbara Oliveira
    • , Catarina T. Correia
    • , Inês C. Conceição
    •  & Astrid M. Vicente
  21. Center for Biodiversity, Functional & Integrative Genomics, Faculty of Sciences, University of Lisbon, 1749-016 Lisboa, Portugal;

    • Bárbara Oliveira
    • , Catarina T. Correia
    • , Inês C. Conceição
    •  & Astrid M. Vicente
  22. McLaughlin Centre, University of Toronto, Toronto, Ontario, Canada M5S1A1;

    • Stephen W. Scherer
  23. Department of Neurology and Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, California 90095, USA;

    • Daniel H. Geschwind
  24. John 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;

    • John Gilbert
    • , Michael Cuccaro
    •  & Margaret Pericak-Vance
  25. Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Goethe-University, 60528 Frankfurt am Main, Germany;

    • Eftichia Duketis
    • , Andreas G. Chiocchetti
    • , Fritz Poustka
    • , Sven Bölte
    •  & Christine M. Freitag
  26. Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA;

    • Gerard D. Schellenberg
  27. Vanderbilt Brain Institute, Center for Human Genetics Research and Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, Tennessee 37232, USA;

    • Jonathan L. Haines
    •  & James S. Sutcliffe
  28. Children's University Hospital Temple Street, Dublin 1, Ireland;

    • Judith Conroy
  29. Department of Pharmacy and Biotechnology, University of Bologna, 40126 Bologna, Italy;

    • Elena Bacchelli
    •  & Elena Maestrini
  30. Department of Pediatrics, University of Alberta, Edmonton, Alberta, Canada T6B 2H3;

    • Lonnie Zwaigenbaum
  31. School of Education, University of Birmingham, Birmingham B15 2TT, UK;

    • Kerstin Wittemeyer
  32. Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford OX3 7JX, UK;

    • Simon Wallace
    •  & Anthony J. Bailey
  33. Unité de Recherche Interdisciplinaire Octogone, Centre d'Etudes et de Recherches en Psychopathologie, Toulouse 2 University, 31058 Toulouse, France;

    • Bernadette Rogé
  34. Autism Research Unit, The Hospital for Sick Children, Toronto, Ontario, Canada M5G 1X8;

    • Wendy Roberts
  35. Unidade de Neurodesenvolvimento e Autismo do Serviço do Centro de Desenvolvimento da Criança and Centro de Investigação e Formação Clinica, Pediatric Hospital, Centro Hospitalar e Universita�rio de Coimbra, 3000-602 Coimbra, Portugal;

    • Susana Mouga
    • , Frederico Duque
    • , Cátia Café
    • , Joana Almeida
    • , Guiomar Oliveira
    •  & Catarina Correia
  36. University Clinic of Pediatrics and Institute for Biomedical Imaging and Life Science, Faculty of Medicine, University of Coimbra, 3000-354 Coimbra, Portugal;

    • Susana Mouga
    • , Frederico Duque
    • , Guiomar Oliveira
    • , Joana Almeida
    • , Cátia Café
    •  & Catarina Correia
  37. Department of Pediatrics, Vanderbilt University, Nashville, Tennessee 37232, USA;

    • Susan G. McGrew
  38. Weill Cornell Medical College/New York Presbyterian Hospital Teachers College, New York, New York 10065, USA;

    • Catherine Lord
  39. FondaMental Foundation, 94010 Créteil, France;

    • Marion Leboyer
    • , Richard Delorme
    •  & Thomas Bourgeron
  40. INSERM U955, Psychiatrie Génétique, 94010 Cre�teil, France;

    • Marion Leboyer
  41. Université Paris Est, Faculté de Médecine, 94010 Créteil, France;

    • Marion Leboyer
  42. Assistance Publique-Hôpitaux de Paris, Henri Mondor-Albert Chenevier Hospital, Department of Psychiatry, 94010 Créteil, France;

    • Marion Leboyer
  43. Institute of Health and Society, Newcastle University, Newcastle upon Tyne NE1 4LP, UK;

    • Ann S. Le Couteur
  44. Institute for Juvenile Research and Department of Psychiatry, University of Illinois at Chicago, Chicago, Illinois 60608, USA;

    • Suma Jacob
    • , Stephen Guter
    •  & Edwin H. Cook
  45. Institute of Brain, Behaviour and Mental Health, University of Manchester, Manchester M139PL, UK;

    • Jonathan Green
  46. Manchester Academic Health Sciences Centre, Manchester M13 9NT, UK;

    • Jonathan Green
  47. National Centre for Medical Genetics, Our Lady�s Children�s Hospital, Dublin 12, Ireland;

    • Andrew Green
    •  & Sean Ennis
  48. Gillberg Neuropsychiatry Centre, University of Gothenburg, 41119 Gothenburg, Sweden;

    • Christopher Gillberg
  49. Discipline of Genetics, Faculty of Medicine, Memorial University of Newfoundland, St John�s, Newfoundland, Canada A1B 3V6;

    • Bridget A. Fernandez
  50. Institut Pasteur, Human Genetics and Cognitive Functions Unit, 75015 Paris, France;

    • Richard Delorme
    •  & Thomas Bourgeron
  51. CNRS URA 2182 Genes, Synapses and Cognition, Institut Pasteur, 75015 Paris, France;

    • Richard Delorme
    •  & Thomas Bourgeron
  52. Assistance Publique-Ho�pitaux de Paris, Robert Debre� Hospital, Department of Child and Adolescent Psychiatry, 75019 Paris, France;

    • Richard Delorme
  53. Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina 27710, USA;

    • Geraldine Dawson
  54. University Paris Diderot, Sorbonne Paris Cite�, 75013 Paris, France;

    • Thomas Bourgeron
  55. Kings College London, Institute of Psychiatry, London SE5 8AF, UK;

    • Patrick F. Bolton
  56. South London & Maudsley Biomedical Research Centre for Mental Health, London SE5 8AF, UK;

    • Patrick F. Bolton
  57. Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, Washington 98195, USA;

    • Raphael Bernier
  58. Paediatric Neurodisability, King�s Health Partners, King�s College London, LondonWC2R 2LS, UK;

    • Gillian Baird
  59. Bloorview Research Institute, University of Toronto, Toronto, Ontario, Canada M4G 1R8;

    • Evdokia Anagnostou
  60. Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, Washington 98195-9460, USA;

    • Ellen M. Wijsman
  61. Department of Biostatistics, University of Washington, Seattle, Washington 98195-9460, USA;

    • Ellen M. Wijsman
  62. Battelle Center for Mathematical Medicine, The Research Institute at Nationwide Children�s Hospital, Columbus, Ohio 43205, USA;

    • Veronica J. Vieland
  63. Dalla Lana School of Public Health, Toronto, Ontario, Canada M5T 3M7;

    • Andrew D. Paterson
  64. Institute of Neuroscience, Newcastle University, Newcastle upon Tyne NE2 4HH, UK;

    • Jeremy R. Parr
  65. Institute of Psychiatric Research, Department of Psychiatry, Indiana University School of Medicine, Indianapolis, Indiana 46202, USA;

    • John I. Nurnberger
  66. Department of Medical and Molecular Genetics and Program in Medical Neuroscience, Indiana University School of Medicine, Indianapolis, Indiana 46202, USA;

    • John I. Nurnberger
  67. Division of Molecular Genome Analysis, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany;

  68. Center for Applied Genomics, The Children�s Hospital of Philadelphia, Philadelphia, Pennsylvania 19104, USA;

    • Sabine M. Klauck
  69. Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA;

    • Hakon Hakonarson
  70. Department of Psychiatry, Division of Child and Adolescent Psychiatry, University of Miami Miller School of Medicine, Miami, Florida 33136, USA;

    • Hakon Hakonarson
  71. Utah Autism Research Program, University of Utah Psychiatry Department, Salt Lake City, Utah 84108, USA;

    • Susan E. Folstein
  72. Stella Maris Clinical Research Institute for Child and Adolescent Neuropsychiatry, 56128 Calambrone, Pisa, Italy;

    • Hilary Coon
  73. Stanford University Medical School, Department of Psychiatry, Stanford, California 94305, USA;

    • Agatino Battaglia
  74. Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA;

    • Joachim Hallmayer

Consortia

  1. AGP Consortium

    List of participants and their affiliations appear at the end of the paper.

Authors

  1. Search for Dexter Hadley in:

  2. Search for Zhi-liang Wu in:

  3. Search for Charlly Kao in:

  4. Search for Akshata Kini in:

  5. Search for Alisha Mohamed-Hadley in:

  6. Search for Kelly Thomas in:

  7. Search for Lyam Vazquez in:

  8. Search for Haijun Qiu in:

  9. Search for Frank Mentch in:

  10. Search for Renata Pellegrino in:

  11. Search for Cecilia Kim in:

  12. Search for John Connolly in:

  13. Search for Joseph Glessner in:

  14. Search for Hakon Hakonarson in:

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 genotyping 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

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Hakon Hakonarson.

Supplementary information

PDF files

  1. 1.

    Supplementary Tables

    Supplementary Tables 1-3

Text files

  1. 1.

    Supplementary Data 1

    Experimental PCR validation of CNV predictions.

About this article

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DOI

https://doi.org/10.1038/ncomms5074

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