Over the past decade, research on the genetic variants underlying susceptibility to autism and autism spectrum disorders (ASDs) has focused on linkage and candidate gene studies. This research has implicated various chromosomal loci and genes. Candidate gene studies have proven to be particularly intractable, with many studies failing to replicate previously reported associations. In this paper, we investigate previously implicated genomic regions for a role in ASD susceptibility, using four cohorts of European ancestry. Initially, a 384 SNP Illumina GoldenGate array was used to examine linkage at six previously implicated loci. We identify linkage approaching genome-wide suggestive levels on chromosome 2 (rs2885116, MLOD=1.89). Association analysis showed significant associations in MKL2 with ASD (rs756472, P=4.31 × 10−5) and between SND1 and strict autism (rs1881084, P=7.76 × 10−5) in the Finnish and Northern Dutch populations, respectively. Subsequently, we used a second 384 SNP Illumina GoldenGate array to examine the association in seven candidate genes, and evidence for association was found in RELN (rs362780, P=0.00165). Further increasing the sample size strengthened the association with RELN (rs362780, P=0.001) and produced a second significant result in GRIK2 (rs2518261, P=0.008). Our results strengthen the case for a more detailed study of the role of RELN and GRIK2 in autism susceptibility, as well as identifying two new potential candidate genes, MKL2 and SND1.
Autism, a neuropsychiatric disorder with an onset before 3 years of age, is characterised by impaired reciprocal communication and social interaction, as well as by restricted and stereotyped patterns of interests and behaviour. The definition can be further broadened to include atypical autism, Asperger's syndrome and pervasive developmental disorder not otherwise specified (PDD-NOS), to create a class of conditions collectively referred to as autism spectrum disorders (ASDs) (OMIM %209850).
ASDs affect approximately 0.6–1.2% of the general population,1, 2 with a marked excess of boys compared with girls of ∼4:1.3, 4 Multiple lines of evidence have shown that autism has a large genetic component. The prevalence of ASDs is increased to 2–8% among siblings of affected individuals,4, 5 and the concordance rates increase from 0% in same-sex dizygotic twins to 36–60% in monozygotic pairs.6, 7 Thus, the heritability of autism is ∼90%, making it the most heritable of the childhood onset neuropsychiatric disorders.7
Despite the obvious importance of genetic factors in autism development, the search for genes underlying susceptibility has met with limited success. A large number of linkage studies have been conducted and have identified possible susceptibility loci on multiple chromosomes.8 Although there is not total concordance between the different studies, certain regions, such as those on chromosomes 2, 3, 7, 11, 16, 17 and 19, have been implicated multiple times. Candidate gene studies have been used as an alternative approach for identifying variants increasing susceptibility to autism. Between 1995 and 2008, nearly 200 genes were investigated for association with autism, with more than 80 reported with nominally positive results. However, even the most frequently associated genes, such as RELN (reelin),9 have negative replications reported.10 Recently, the first genome-wide association study for ASDs has been published, implicating a number of genes and genomic regions, most significantly the area between cadherin genes CDH9 (cadherin 9) and CDH10 (cadherin 10) on chromosome 5p14.1. However, in this study, only a single SNP reached genome-wide significance after correction for multiple testing, confirming the difficulties involved in the identification of common variants contributing to ASD susceptibility.11
We attempted to refine our understanding of the genetics of autism by investigating previously reported linkage and candidate gene results in our European populations. Our collaboration includes groups from the International Molecular Genetic Study of Autism Consortium (IMGSAC), Paris Autism Research International Study (PARIS) and Finland. These three groups have performed whole-genome linkage screens for autism in their respective family collections,12, 13, 14, 15 with the six highest LOD scores on chromosomes 2q, 3q, 6q, 7q, 16p and 17q. In addition, IMGSAC and PARIS are members of the Autism Genome Project (AGP) consortium, which recently conducted a genome-wide linkage study using Affymetrix (Santa Clara, CA, USA) 10k arrays, including a large number of samples from these two consortia.2 To refine previously identified linkage peaks, we chose SNPs from the Affymetrix 10k array that tag variation within these top six loci and genotyped them in additional IMGSAC, PARIS and Finnish families. These results were analysed in combination with overlapping SNPs from the AGP study for IMGSAC and PARIS samples in a meta-analysis of linkage. Furthermore, we genotyped these SNPs in trios from Finnish and Northern Dutch isolated populations to identify regions of extended linkage disequilibrium (LD) within these loci.
Second, we attempted to identify or replicate associations of autism and ASD with seven key candidate genes. These were NOSTRIN (nitric oxide synthase trafficker),16 GRIK2 (glutamate receptor, ionotropic, kainite 2),17, 18 RELN,9 PRKCB1 (protein kinase C, beta),19, 20 SLC6A4 (solute carrier family 5 (neurotransmitter transporter, serotonin), member 4),21, 22 SHANK3 (SH3 and multiple ankyrin repeat domains 3)2, 23 and ASMT (acetylserotonin O-methyltransferase).24
The individuals included in this study came from four European based collections/populations: IMGSAC, PARIS, Finland and Northern Holland. Assessment and selection criteria have been described previously for IMGSAC,14 PARIS,25 Finnish12 and the Northern Dutch22 probands. All affected individuals from the IMGSAC, PARIS and Northern Dutch cohorts had ADI-R26 and/or ADOS27 assessments. The Finnish probands were identified by ICD-10 and DSMIV criteria diagnosed by experienced clinicians. General summary phenotypic information, where available, is provided (Supplementary Table 1). For Illumina GoldenGate (Illumina, San Diego, CA, USA) genotyping, the samples were either genomic DNA with a concentration of 60–100 ng/μl (∼26.4%) or whole-genome amplified DNA (GenomiPhi v2 (GE Healthcare, Amersham, UK)), with an estimated concentration of 200 ng/μl (∼73.6%). For Sequenom iPLEX (Sequenom, San Diego, CA, USA)-based genotyping, samples were diluted to 10 ng/μl for genomic DNA, or to 1:10 (∼20 ng/μl) for whole-genome-amplified DNA.
SNP selection and genotyping
Separate Illumina GoldenGate 384 SNP arrays were designed for linkage and candidate gene studies.
For the linkage array, a subset of SNPs from the Affymetrix 10k v2 SNP array located within previously identified regions of linkage on chromosomes 2q, 3q, 6q, 7q, 16p and 17q were identified. Data for these SNPs were downloaded for the HapMap Phase II (release 21) CEU samples. Those SNPs tagging variation in these regions with a minimum allele frequency (MAF) of ≥0.05 and r2≥0.8 were selected for genotyping using the Tagger program in Haploview v4.0.28 In addition, four SNPs on the X chromosome were included to confirm the sex of new individuals. A total of 93 multiplex families were genotyped (44 IMGSAC, 16 PARIS and 33 Finnish). An additional 248 families (66 Finnish singletons, 182 Northern Dutch trios, the majority from singleton families) were also genotyped to examine the extent of LD within these regions in relatively isolated populations. In total, 1127 individuals from 341 families were genotyped on this array (Supplementary Table 2).
For the candidate gene array, SNPs within and 5 kb 5′ and 3′ of NOSTRIN, GRIK2, RELN, PRKCB1, SLC6A4, SHANK3 and ASMT (NCBI build 35) were downloaded for the HapMap Phase II (release 21) CEU samples, and those tagging variation in these regions with a MAF ≥0.05 and r2≥0.8 using the Tagger program in Haploview v4.028 were chosen. For SNPs tagging more than 10 others, a second ‘safety net’ SNP was chosen to capture identical variation, accounting for 34 SNPs on the array. Additional SNPs of interest were also chosen from the published literature. Samples genotyped on the array consisted of trios of one randomly chosen affected individual, and both parents, where available, from multiplex families. In total, 1144 samples from 389 families (284 IMGSAC, 72 PARIS and 33 Finnish) were genotyped (Supplementary Table 3).
Genotyping for both arrays was performed using the standard protocols for GoldenGate assays (Illumina).
There is an overlap of the samples genotyped in this study with those used to replicate recent genome-wide association results.11, 29 However, in the latter studies, the SNPs chosen were to replicate individual signals, in contrast to our haplotype-tagging approach to investigate association across specific candidate genes.
Genotypes were called using BeadStudio software (Illumina) with manual editing of clusters. Mendelian inheritance errors were identified and removed using PedCheck.30 SNPs with poor clustering or >10 Mendelian errors, and samples with a genotyping success rate <80% or >10 Mendelian errors, were excluded from further analysis. All SNPs were tested for Hardy–Weinberg equilibrium using the BeadStudio software (Illumina).
Before analysing linkage data, genotypes for 262 IMGSAC and 49 PARIS families genotyped by the AGP using the Affymetrix 10k v2 SNP array were downloaded from the AGP database (http://davinci.tcag.ca/agp_freeze2/). Data from the 384 SNPs used in this study were combined with those we generated to create an extended cohort of 397 families (301 IMGSAC, 64 PARIS, 32 Finnish). Data were formatted using MEGA2 before linkage analysis using the ASPEX package (ftp://lahmed.stanford.edu/pub/aspex/). Parent-of-origin analyses were performed using the sex_split option of the ASPEX package. LOD scores ≥1.9 and ≥3.3 were considered to be evidence of suggestive and significant linkage, respectively.31
Association analyses were performed using the transmission disequilibrium test (TDT), which is robust against population stratification. The candidate gene association data were analysed for each individual population genotyped and the results were combined in a meta-analysis. We estimated odds ratios and the respective SE from transmission frequencies.32 Meta-analysis was performed by pooling all data using an inverse variant approach.33 For all association analyses, we considered each gene or region examined as a separate hypothesis. Therefore, we determined a Bonferroni-corrected significance threshold by dividing the nominal significance threshold (P=0.05) by the number of SNPs tested at that locus. Reducing the LD threshold from r2=0.8 to r2=0.2 does not significantly reduce the number of independent SNPs, and therefore the Bonferroni correction could be applied. For each linkage region in which we examined association, we further divided the significance threshold by 2 because these loci were tested for association with both ASD and strict autism.
The number and density of SNPs on either array were insufficient to perform a comprehensive analysis for the presence of copy number variants at the loci investigated.
The five most significant SNPs from the candidate gene array were genotyped in an additional 282 IMGSAC singleton families and in 188 Northern Dutch trios, the majority of whom were also from singleton families. Sequences flanking the SNPs were obtained (http://www.Biomart.com) and iPLEX assays were designed (http://www.realSNP.com). Genotyping was performed using the Sequenom iPlex platform (Sequenom), with standard protocols. Power calculations for the replication sample were performed using the Genetic Power Calculator for discrete trait TDT (GPC) (http://pngu.mgh.harvard.edu/~purcell/gpc).34 Parameters used for the GPC were 0.17% for disease prevalence, perfect LD between tested marker and disease allele, an additive model and a type 1 error rate of 0.05.
Both Illumina GoldenGate 384 SNP genotyping arrays performed well. From the linkage array, a total of 379 SNPs and 1112 samples survived quality control, with a mean genotyping success rate of >99% for the retained samples (Supplementary Table 2). For the candidate gene array, after quality control, 354 SNPs and 1127 samples remained for analysis, resulting in a mean genotyping success rate of >99% after quality control (Supplementary Table 3).
The combination of our data with those from the AGP study2 resulted in a final sample set of 397 families (301 IMGSAC, 64 PARIS and 32 Finnish). Multipoint linkage analysis was performed on the sibpairs. The largest signal obtained was on chromosome 3, with the peak at rs2862479 (MLOD=1.5) (Figure 1). The Finnish cohort is a relatively genetically isolated population, suggesting that it may contain different susceptibility loci to either the IMGSAC or PARIS cohorts. Therefore, we repeated the analysis excluding the 32 Finnish families, which decreased the signal on chromosome 3 (peak MLOD=0.79, rs2862479). A signal approaching suggestive levels of linkage31 was observed on chromosome 2 in the remaining families (peak MLOD=1.89, rs2885116) (Figure 1).
It has previously been shown that parent-of-origin effects may have an important role in ASD susceptibility.35 Parent-of-origin analysis for our entire data set was performed, resulting in an increased peak signal on chromosome 3 for maternal alleles, with the zenith shifting 5′ from rs2862479 to rs4129157 (MLOD=1.78). The analysis was repeated excluding the Finnish subset of individuals, resulting in an increased signal on chromosome 2 spanning four SNPs (rs726032, rs726033, rs1374431 and rs2885116), each with an LOD score of 1.67 for the maternal alleles.
Association analysis in published regions of linkage
Two sets of trios (62 Finnish and 179 Northern Dutch after quality control), mainly consisting of singleton families, were genotyped on the linkage array. Analysis of the LD patterns of the SNPs for these populations showed no significant difference from that of the CEU samples in the HapMap data (data not shown). Therefore, a TDT test was carried out using these data for both strict definition autism and ASD, including an additional 33 Finnish multiplex families we had genotyped (Finnish strict N=80, ASD N=95; Northern Dutch strict N=99, ASD N=179) (Figure 2). In total, 375 SNPs in the linkage regions survived quality control (chromosome 2 N=142, chromosome 3 N=22, chromosome 6 N=55, chromosome 7 N=100, chromosome 16 N=21, chromosome 17 N=35). The Bonferroni-corrected significance thresholds for each locus were as follows: chromosome 2 P≤0.000176, chromosome 3 P≤0.00114, chromosome 6 P≤0.000455, chromosome 7 P≤0.00025, chromosome 16 P≤0.00119 and chromosome 17 P≤0.00074. Association was found on chromosome 7 with a SNP within the gene SND1 (Staphylococcal nuclease and tudor domain containing 1) in Northern Dutch individuals with strictly defined autism (rs1881084, P=7.76 × 10−5), and to a lesser extent in the wider Northern Dutch ASD cohort (P=0.001838), but this association was not present in the Finnish population. A significant association was observed on chromosome 16 for rs756472 in the gene MKL2 (MKL/myocardin-like 2) in the Finnish population, for both strict autism and ASD (strict autism, P=2.46 × 10−4; ASD, P=4.31 × 10−5). However, this association was not observed in the Northern Dutch population.
After identifying these associations, analysis was performed on the largest individual cohort of samples from IMGSAC (N=301). Although nominal associations were found on chromosomes 2, 6, 7, 16 and 17, none of these were with rs1880184 or rs756472.
Candidate gene array
A TDT was performed for each cohort genotyped on the candidate gene array for both strict autism and ASD. These results were combined for the 382 families in total by performing a pooled meta-analysis (Figure 3). In total, excluding safety net SNPs, 328 SNPs within the candidate genes survived quality control (NOSTRIN N=15, GRIK2 N=105, RELN N=118, PRKCB1 N=72, SLC6A4 N=7, SHANK3 N=9, ASMT N=2). The Bonferroni-corrected significance thresholds for each locus were as follows: NOSTRIN P≤0.00333, GRIK2 P≤0.000476, RELN P≤0.000424, PRKCB1 P≤0.000694, SLC6A4 P≤0.00714, SHANK3 P≤0.00556 and ASMT P≤0.025. The two strongest associations, obtained for both strict autism and ASD, were with rs362780 in RELN (strict autism P=0.00165, ASD P=0.00165) and rs2518261 in GRIK2 (strict autism P=0.00955, ASD P=0.00170). However, neither of these associations passed the thresholds corrected for multiple testing.
The majority of trios genotyped on the candidate gene array were from the IMGSAC population (N=279). It was of interest that the strongest association obtained in this cohort was for rs11074601 in PRKCB1 (P=0.00596). However, this SNP did not reach our corrected significance threshold.
Replication of candidate gene association
To replicate the results for the five SNPs showing the strongest association with ASD in the meta-analysis, rs2518256 (P=0.01399), rs2518261 (P=0.00170), rs362780 (P=0.00165), rs9925126 (P=0.01260) and rs11074601 (P=0.01693) (Table 1), additional Northern Dutch (N=188) and IMGSAC families (N=276) were genotyped using the Sequenom iPLEX platform. Power calculation analysis showed that our replication sample should give sufficient power (>78%) to replicate the results of these five SNPs. However, no significant associations were observed in the individual replication populations (data not shown) or when combined in the meta-analysis (Table 1).
A TDT meta-analysis of all available data from the original candidate gene array and the replication set was performed in a total of 853 families genotyped (IMGSAC N=560, Northern Dutch N=188, Finnish N=33, PARIS N=72). Significant associations were obtained for rs25185261 (GRIK2 P=0.008) and rs362780 (RELN P=0.001) (Table 1). However, although the association observed for rs362780 increased, it failed to reach the corrected significance threshold.
Although there is considerable evidence for a strong genetic component underlying autism susceptibility, there has been slow progress towards identifying risk loci involved with a large degree of confidence. Studies have struggled to replicate positive results for linkage regions and individual candidate genes. We have investigated a set of the most commonly implicated linkage regions and candidate genes for significance in autism cohorts of European descent.
The six regions of the genome we investigated had previously been identified by members of our consortium as showing linkage with autism or ASD. Our results failed to identify loci with LOD scores reaching the levels required for evidence of linkage. A peak was found on chromosome 2 fractionally below the Lander and Kruglyak31 cutoff for suggestive evidence of linkage. This result was obtained only after the subset of Finnish families, accounting for ∼8% of our total available sample, had been removed from the analysis. The increase in signal from chromosome 2 is encouraging and we take this to strengthen support for an autism susceptibility locus on this chromosome, which has been implicated in a number of previous studies. Our results localise the peak with LOD ≥1.5 distal to the area of the region genotyped, between SNPs rs2161994 and rs1861896. Although not directly under the strongest point of linkage, it is interesting to note that the gene ZNF804A lies in the proximal region of this peak. A SNP in this gene has recently been found to be strongly associated with schizophrenia and bipolar disorder.36 Gain-of-copy number CNVs containing this gene have also been identified in three affected individuals in a whole-genome study by the AGP,2 and in addition, a case of autism with a translocation affecting chromosome 2q32.1 has been reported.37, 38 Our parent-of-origin analysis indicated that this signal is being driven mainly by maternal inheritance. This is in contrast to a study by Lamb et al,35 which found an approximately equal contribution of alleles from both parents. In addition, our results indicate that the linkage observed on chromosome 3 is also maternal, although the top LOD score (rs4129157, LOD =1.78) does not reach suggestive levels. These results hint at a role for imprinting at these loci. Imprinting is known to be of importance in ASD, as shown by a significant number of cases being due to inheritance of maternal duplications of the 15q11–13 locus, which includes imprinted genes.39
Association analysis of SNPs in regions of previously reported linkage was performed in the Finnish and Northern Dutch populations. The SNPs had been chosen to tag variation within the regions examined with r2>0.8. Therefore, although the SNP coverage was not dense, it should be sufficient to examine association. Our results identified two SNPs with associations surviving Bonferroni correction. The strongest association was with rs756472 on chromosome 16, a SNP located in the gene MKL2, in the Finnish cohort. To our knowledge, this SNP has only been investigated once previously for its role in autism, with no association found.40 A SNP on chromosome 7, rs1881084, was also found to be associated in the Northern Dutch cohort. This SNP lies within intron 7 of SND1, a highly conserved transcriptional coactivator encoding one of the proteins comprising the RNA-induced silencing complex.41 Interestingly, it has been found that the localisation of E-cadherin changes from the cell membrane to the cytoplasm of mouse cells with high levels of snd1 protein.41 CNVs in protocadherin genes PCDH942 and PCDH1043 have been implicated in autism, in addition to PCDH8 in schizophrenia.44 Cadherins also mediate cell–cell neural interactions and may have an important part in neural development.41 Therefore, SND1 may be a good candidate for further study of its role in autism development.
Moreover, the presence of the leucine-rich repeat (LRR) gene LRRC4 (LRR containing 4) within intron 16 of SND1 must also be noted. Suggestive association was found by Wang et al11 in two LRR genes, LRRC1 (LRR containing 1) and LRFN5 (LRR and fibronectin type III domain containing 5), and recent work in our own group has found association with two further LRR genes, LRRTM3 (LRR transmembrane neuronal 3) and LRRN3 (LRR neuronal 3), the latter association also being found in the Northern Dutch cohort. However, it should be noted that two SNPs located between LRRC4 and rs1881084 failed to show any association in the Northern Dutch cohort (rs1419970 P=0.0506; rs178733 P=0.713). In addition, no association with either SND1 or MKL2 was found in the IMGSAC families. As both Finnish and Dutch cohorts are from relatively isolated populations, this finding may indicate the importance of particular genes or variants for autism and ASD susceptibility in different populations. Alternatively, it may represent differences in assessment of the various cohorts, or the distribution of particular phenotypes in their members (Supplementary Table 1).
In keeping with the trend of many such studies on autism, our candidate gene analysis found no associations that survived Bonferroni correction in the original set of samples genotyped. Similar to the linkage analysis, this finding may be due to heterogeneity within our populations. In addition, it may be that, although the results do not pass strict correction for multiple testing, they may represent, along with other studies, individual genes that are a part of the same network contributing to ASD susceptibility. However, we did identify several interesting nominal associations that, given the stringency of the Bonferroni method, were of sufficient interest to warrant further investigation. TDT analysis of additional European samples for the five most significant SNPs genotyped also failed to find association. However, when a meta-analysis was performed for the combined set of original and replication samples, two significant associations were observed, one with rs362780 in RELN and the other with rs2518261 in GRIK2.
RELN is located in the region of linkage for autism on chromosome 7,14 and has been repeatedly studied as a candidate gene for autism. Evidence has been published supporting9 and rejecting10 the association of RELN with autism (reviewed by Freitag8). Functionally, RELN is considered as a good candidate gene because of its involvement in neuronal migration, and mice lacking its expression share regions of brain alteration with autistic individuals.9 The association of this gene in our initial and extended populations adds evidence implicating RELN in ASD.
GRIK2 is also an interesting candidate because of its location in a region of linkage for autism on 6q21.15 Moreover, glutamatergic neurons originate in brain regions implicated in autism, and glutamate antagonists can cause symptoms similar to those of autism.45 Four studies have specifically looked for association between GRIK2 and autism, with three finding positive evidence17, 18, 46 and one study failing to do so.47
In their recent genome-wide association paper, Wang et al11 report top associations (P<0.01) for 26 candidate genes. Their reported SNPs include 10, the closest gene to which is GRIK2, and of these, two SNPs lie within GRIK2 itself (rs4839797, intron 1, P=0.003978 and rs2782908, intron 13, P=0.005692). Although we did not tag either of these SNPs in our array, it is of interest that our significant association with GRIK2 also occurs in intron 1 of the gene. Our findings add to the cumulative case for a role for this gene in autism susceptibility.
Our data further implicate the chromosome 2 linkage region, as well as RELN and GRIK2, in autism susceptibility. Despite the difficulties in confirming these findings, further studies of these loci are warranted. We have also identified MKL2 and SND1 as potentially interesting new candidates for further investigation.
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We thank the families who have participated in the study and Chris Allan at the WTCHG core genomics facility for technical support. This paper includes data generated by the Autism Genome Project (AGP) (see supplementary information for full AGP authorship list.) The AGP gratefully acknowledges the contributions of the families who participated in this study. Current support for the AGP comes mainly from grants from Autism Speaks (USA), Genome Canada (Canada), the Health Research Board (HRB; Ireland), the Hilibrand Foundation (USA) and the Medical Research Council (MRC; UK). Funding: This work was funded by EC 6th FP AUTISM MOLGEN (grant number LSHM-CT-2005–512158) and was also supported by the Wellcome Trust (grant number 075491/Z/04).
The authors declare no conflict of interest.
Supplementary Information accompanies the paper on European Journal of Human Genetics website
Members of the EU Autism MOLGEN Consortium in alphabetical order are as follows:
Reija Alen1, Elena Bacchelli2, Anthony Bailey3, Gillian Baird4, Agatino Battaglia5, Catalina Betancur6, Annelies de Bildt7, Francesca Blasi2, Sven Bölte8, Patrick Bolton9, Thomas Bourgeron10, Karen Brøndum-Nielsen11, Simona Carone2, Pauline Chaste10, Andreas Chiocchetti12, Eftichia Duketis13, Christelle Durand10, Herman van Engeland14, Penny Farrar15, Sabine Feineis-Matthews13, Bärbel Felder12, Kostas Francis3, Jeanne Fremolle16, Carina Gillberg17, Christopher Gillberg17, Hany Goubran-Botros10, Demetrious Haracopos11, Evelyn Herbrecht13, Richard Holt15, Gemma Honeyman3, Jasmin Honold13, Renske Houben14, Aislinn Hutchison3, Roberta Igliozzi5, Torben Isager11, Irma Järvelä1, Maria Johansson17, Maretha de Jonge14, Sabine M. Klauck12, Anne Koivisto1, Hanna Komu1, Marion Leboyer18, Ann Le Couteur19, Justin Lowen3, Elena Maestrini2, Carine Mantoulan16, Jonas Melke10, Helen McConachie19, Ruud Minderaa7, Anthony Monaco15, Erik Mulder7, Taina Nieminen-von Wendt1, Ilona Nummela1, Gudrun Nygren17, Geeta Pakalapati12, Katerina Papanikolaou20, Barbara Parrini5, Lennart Pederson11, Liz Pellicano3, Catherine Pienkowski16, Judith Ponsford3, Annemarie Poustka12, Fritz Poustka13, Maria Rastam17, Karola Rehnström1, Katy Renshaw3, Bernadette Rogé16, Dorothea Ruehl13, Michael Rutter4, Susan Sarenius1, Gabriele Schmötzer13, Claudia Schuster12, Henrik Anckarsater17, Raffaella Tancredi5, Maïté Tauber16, John Tsiantis20, Nora Uhlig13, Raija Vanhala1, Simon Wallace3, Lennart von Wendt1, Kerstin Wittemeyer3, Tero Ylisaukko-oja1.
1Helsinki University Central Hospital, Helsinki, Finland.
2University of Bologna, Alma Mater Studorium, Bologna, Italy.
3Department of Psychiatry, University of Oxford, Oxford, UK.
4Newcomen Centre, Kings College London, London, UK.
5Stella Maris Clinical Research Institute for Child and Adolescent Neuropsychiatry, Pisa, Italy.
6INSERM U952, CNRS UMR7224, UPMC Univ Paris 06, Paris, France.
7Stichting Universitaire en Algemene Kinder – en Jeugdpsychiatrie Noord – Nederland (Accare)/University Medical Center Groningen, University Center Child and Adolescent Psychiatry Groningen, Groningen, The Netherlands.
8Department of Child and Adolescent Psychiatry, Central Institute of Mental Health, Mannheim, Germany.
9Department of Child and Adolescent Psychiatry, Kings College London, London, UK.
10Institut Pasteur, Paris, France.
11Center for Autisme, Herlev, Denmark.
12Division of Molecular Genome Analysis, German Cancer Research Center (DKFZ), Heidelberg, Germany.
13Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Johann Wolfgang Goethe-University, Deutschordenstr. 50, 60528, Frankfurt/Main, Germany.
14Department of Child and Adolescent Psychiatry, University Medical Centre Utrecht, Utrecht, The Netherlands.
15Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.
16Universite de Toulouse le Mirail., Toulouse, France.
17Department of Child and Adolescent Psychiatry, Goteborg University, Goteborg, Sweden.
18INSERM U955; AP-HP, Henri Mondor-Albert Chenevier Hospital, Department of Psychiatry; Université Paris 12, Faculty of Medicine, Créteil, France.
19Department of Child and Adolescent Psychiatry, Sir James Spence Institute, University of Newcastle, Royal Victoria Infirmary, Newcastle, UK.
20Department of Child Psychiatry, Medical School, National and Kapodistrian University of Athens, Athens, Greece.
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