Original Article | Published:

Common genetic variants on 1p13.2 associate with risk of autism

Molecular Psychiatry volume 19, pages 12121219 (2014) | Download Citation


Autism is a highly heritable neurodevelopmental disorder, and known genetic variants, mostly rare, account only for a small proportion of cases. Here we report a genome-wide association study on autism using two Chinese cohorts as gene discovery (n=2150) and three data sets of European ancestry populations for replication analysis of top association signals. Meta-analysis identified three single-nucleotide polymorphisms, rs936938 (P=4.49 × 10−8), non-synonymous rs6537835 (P=3.26 × 10−8) and rs1877455 (P=8.70 × 10−8), and related haplotypes, AMPD1-NRAS-CSDE1, TRIM33 and TRIM33-BCAS2, associated with autism; all were mapped to a previously reported linkage region (1p13.2) with autism. These genetic associations were further supported by a cis-acting regulatory effect on the gene expressions of CSDE1, NRAS and TRIM33 and by differential expression of CSDE1 and TRIM33 in the human prefrontal cortex of post-mortem brains between subjects with and those without autism. Our study suggests TRIM33 and NRAS-CSDE1 as candidate genes for autism, and may provide a novel insight into the etiology of autism.


Autism (OMIM 209850) is a childhood neurodevelopmental disorder characterized by impairment in language communication, social interaction and responsiveness, and restricted and repetitive patterns of interest or behavior.1 The disorder presents clinically during the first 3 years of life and is about three times more common in boys than in girls. The prevalence has increased from no more than 5 per 10 000 individuals throughout the 1980s to 1 in 50 school-age children in the United States according to a recent report in 2013,2 although the estimate may be partly due to a change in the practices of diagnosis and ascertainment. The fact that autism concordance in MZ twins approaches 92% in contrast to 10% in DZ twins suggests a strong genetic basis.3

Evidence from different cases supports the fact that chromosomal abnormality contributes to the risk for autism,4 and linkage studies and cytogenetic analysis have led to identification of several novel candidate genes including neurexins (NRXNs) and neuroligins (NLGNs). Through the genomic linkage scan, significant linkages have been reported on 2q31, 3q and 7q (22, 34).5, 6 Several regions of interest, including 13q, 16p, 17q, 1p13.2, 1q31.1, 5p13, 8q24, 15q, 19p and Xq, although not consistently, have been suggested in more than one study sample. The majority of these regions, except 1p13.2 and 2q32, have been reported with chromosomal abnormality.6

Several genome-wide studies have been carried out to identify the genetic variants associated with risk for autism or autistic spectrum disorders. Copy number variations (CNVs) including several large recurrent deletions or duplications have been found disrupting either single gene or a chromosomal region containing multiple genes, and the best established autism-associated CNVs include 7q11.23, 15q11–13, 16p11.2 and 22q11.2 loci, and NRXN1, CNTN4, NLGNs and SHANK3 genes.7, 8, 9, 10, 11, 12, 13 Recently conducted exome-sequencing studies suggest that hundreds of de novo mutations have some role in the development of autism, and solid evidence implicates a few specific genes (CHD8, KATANAL2, SCN2A, NTNG1).14, 15, 16, 17 Those structural variants or de novo mutations, many of which are high penetrant or altering protein but individually rare, together account for a limited proportion of the genetic risk for autism. In contrast, only a modest number of common variants have been reported at CDH9-CDH10, SEMA5A and MACROD2 loci through genome-wide association (GWA) studies.12, 18, 19

Autism ‘with marked phenotypic heterogeneity’ is etiologically multifactorial. Hypothesizing that multiple common variants collectively or interacting with environmental factors account for a certain proportion of risk for autism,20, 21 we performed a GWA analysis of two independent cohorts of a Chinese population sample, and top signals were replicated in three data sets of European ancestry populations. Meta-analysis of single-nucleotide polymorphisms (SNPs) and haplotypes showing a consistent trend of association in the five cohorts identified three SNPs and haplotypes associated with autism at genome-wide significance. Significant cis-acting regulatory effects and differential expression between autism cases and controls were found for NRAS-CSDE1 and TRIM33, providing molecular evidence for these genetic associations. Our findings provide a novel insight into the genetic etiology of autism.

Materials and methods

Study design and subjects

Subjects used for genome-wide gene discovery included one cohort of case–parent triad families (n=870 subjects) and one cohort of cases with autism and unrelated controls (n=1280 subjects) in the Chinese population. Autism probands were diagnosed independently by two experienced psychiatrists using the DSM-IV-TR (American Psychiatric Association, 2000) criteria for autism. The diagnostic procedure also included an assessment using a series of tools—neurological examination, mental status examination, the Childhood Autism Rating Scale (CARS) and Autism Behavior Checklist (ABC). Subjects with Asperger’s syndrome, Rett syndrome and pervasive developmental disorder not otherwise specified (PPD-NOS), such as fragile-X syndrome, were excluded from this study. All participating individuals provided written informed consent.

Three other cohorts of an autism family-based European ancestry population were used to validate the findings of discovery, and a meta-analysis of five cohorts was carried out to obtain combined evidence for association. These cohorts included samples from the Autism Genetic Research Exchange (AGRE), the Simons Foundation for Autism Research Initiative (SAFRI) Base and the Autism Genome Project (AGP). The sample recruitments of the three European cohorts have been described elsewhere,9, 13, 18, 19 and additional sample descriptions are also provided in the Supplementary Method.

DNA extraction, genotyping and quality control

Whole genomic DNA was extracted from the whole blood of all autism patients and their parents as well as from unrelated normal controls in the two Chinese cohorts using standard proteinase K digestion and the phenol–chloroform method. All autism patients and their parents as well as 110 unrelated controls were genotyped for SNP across the whole genome using Illumina HumanHap CNV370K BeadChip at the State Key Laboratory of Medical Genetics (SKLMG) in Changsha, China. An additional 1000 unrelated controls were genotyped using Illumina HumanHap 610 Quad BeadChip at the Genotyping Core Facility at the State Key Laboratory Incubation Base for Dermatology in Hefei, China. All SNPs in the HumanHap CNV370 are covered in the HumanHap 610 Quad BeadChip.

Genome-wide quality control was applied to each individual cohort. Individuals with missing SNP call rate >2% were excluded; SNPs were zeroed out if Mendelian errors were >5% and individuals were removed if Mendelian errors were >3% from the analysis. Sample duplication and cryptic relatedness were examined by the identity-by-state analysis of genotypes in autosomes. One of each related pair (IBD-sharing coefficients >0.10) was excluded. Individuals with sex error were identified, based on the heterozygote of the X chromosome, and excluded. For better utilization of the two Chinese cohorts, genome-wide imputation using IMPUTE222 and HapMap CHB data as reference was performed to increase the genotyping coverage. Identity-by-state and multidimensional scaling analyses were used to examine genetic heterogeneity in each cohort and to remove genetic outliers.

The genotyping and quality control of the three European cohorts have been described elsewhere.9, 18, 19 In brief, AGRE samples were genotyped using Illumina 550K, and Simons Foundation for Autism Research Initiative (SFARI) and AGP samples were typed using Illumina 1M chips. The same quality control procedure was applied to the existing data sets before the analysis.

Genetic association analysis

We first performed the transmission disequilibrium test analysis of the case–parent triad cohort and logistic regression analyses for the case–control cohort from China using PLINK23; the genetic model was also assessed for the case–control data. Because either of the two Chinese cohorts alone may not be able to provide enough power according to our power analysis (Supplementary Method), we set α=0.10 for both the case–parent triad and the case–control cohort. Individual SNPs passed the threshold and combined analysis showed nominal significance of association (P<0.01) were included for meta-analysis with SNPs that showed the same direction of transmitted allele in three additional family cohorts of European ancestry. The combined P-values were calculated using Stouffer’s Z-score method for meta-analysis (Supplementary Method).

Haplotype analysis

As the GWA study was designed to find common genetic variants, the functional variants that link the genetic association with human diseases may at least partly lie in the nearby linked chromosomal region.24 To search for such possible functional SNPs, a haplotype analysis was carried out in each of five cohorts for SNPs that were genotyped in the two Chinese cohorts in a chromosomal region around the top association signals. This haplotype analysis was also used to confirm the single SNP association, rather than as a discovery analysis. The analysis was performed using the sliding window approach, followed by meta-analysis of haplotype association. Haplotype-based transmission disequilibrium test association (‘--hap-tdt’) and haplotype-based case–control association (‘--hap-assoc’) analyses were performed using PLINK for family-based and case–control samples, respectively, and haplotype frequency was estimated based on all samples in each individual cohort using PLINK (‘--hap-freq’). Although replication and meta-analysis of multiple cohorts were used to reduce possible false positives, associations of the final haplotypes identified at each locus with autism were also assessed by permutation tests to further correct for multiple testing. The permutation test was performed using FBAT for each of the four family-based cohorts and using PLINK for the case–control cohort separately, followed by a calculation of combined P-value. SNP functional prediction and genomic characteristics were assessed using bioinformatics tools developed on the basis of HapMap data.25

Post-mortem brain expression analysis

Both cis-association of genetic variants with mRNA expression and differential expression were examined for the genome-wide-associated SNPs and loci in post-mortem human brains. The cis-association analysis was performed on the basis of genotype and gene expression data from the post-mortem human prefrontal cortex of 224 postnatal subjects (109 Caucasians and 115 African Americans). The sample was collected at the Clinical Brain Disorders Branch of National Institute of Mental Health of NIH, and details of genotyping and the experiment have been described elsewhere (http://braincloud.jhmi.edu).26 General linear regression analysis was performed to test for the association of SNP genotype and the mean level of gene expression while adjusting for age, sex, post-mortem interval (in h), RNA integrity number and race. Differential gene expression was analyzed for the genome-wide-associated loci in the frontal cortex of human brains from 16 cases with autism and 16 controls. Instead of assessing each probe separately and to avoid multiple testing issues, we tested for the mean difference in gene expression between cases and controls for nine probes in the genome-wide-associated loci simultaneously using multivariate analysis of variance while adjusting for covariates (e.g., sex, age, RNA integrity number and post-mortem interval). The sample collection, original data quality control and data processing methods have been described elsewhere.27


Genome-wide associations

We first performed transmission disequilibrium test of genome-wide SNPs in one cohort of cases with autism and their parents (n=825 subjects) and logistic regression analysis in one cohort of autism cases and unrelated normal controls (n=1120 subjects) in the Chinese population sample (Table 1). No genome-wide significant association (P<5 × 10−08) was found in either cohort (Supplementary Figures S1 and S2), and evidence for population stratification in the case–control cohort was minimal (Lambda GC=1.017). Combined P-values of SNPs showing consistent trend of association (P<0.1) in both cohorts suggested strong association signals (P<10 × 10−7) for rs6537825 and rs10858046 at TRIM33, for rs1877455 and rs4839385 at BCAS2 and for rs2268697 and rs926938 at AMPD1-NRAS (Supplementary Table S1). Although none of these SNPs reached genome-wide significance, all were genotyped and located at a linkage region (1p13.2) previously indicated for autism.6 We did not find any CNVs in either of the Chinese cohorts, although some normal structural variants have been reported in this region.

Table 1: Study sample characteristics and genotyping by cohort

Replication analysis of three European ancestry cohorts

To control false-positive findings and gain statistical power to detect genetic association, we performed a replication analysis of those top association signals in two Chinese cohorts combined (P<0.01), followed by a meta-analysis with SNPs showing consistent trend of association in three additional data sets of European ancestry (Table 1). Several top signals discovered in the Chinese samples were replicated (P<0.05) in at least one of the three European ancestry data sets, and the associations appeared weaker than in the Chinese population. However, the meta-analysis suggests an association with autism at, or very close to, genome-wide significance with three SNPs: rs926938 (P=4.49 × 10−8) at AMPD1-NRAS, rs6537835 (P=3.26 × 10−8) in TRIM33 and rs1877455 (P=8.70 × 10−8) (Table 2). Whereas rs6537835 is non-synonymous and rs926938 is located upstream of AMPD1 and downstream of NRAS, rs1877455 is an intronic variant of DENND2C between TRIM33 and BCAS2 loci.

Table 2: Association signal (P<10 × 10−5) P-values and meta-analysis of five independent cohorts (genotyped SNPs only)

Although these three genome-wide-associated SNPs are located in the linkage disequilibrium (LD) block of 1p13.2, a different LD structure is noted between Chinese and European ancestry populations (Figure 1). According to the HapMap data in the combined Han Chinese and Japanese populations (CHBJPT), the genome-wide-associated SNP, rs926938 at AMPD1-NRAS, is in moderate LD (r2>0.563 and D′=1) with two others—rs6537835 at TRIM33 and rs1877455 at TRIM33-BCAS2, in which the latter two SNPs are in strong LD with each other (r2>0.908 and D′=1). All three SNPs are common in Chinese populations (minor allele frequency (MAF), 0.38–0.45). In contrast, in the European ancestry population (CEU), rs926938 is in less LD (r2<0.11 and D′=1) with both rs6537835 and rs1877455, but the latter two SNPs showed the same strong LD (r2>0.908, D′=1) as in Asian populations (CHBJPT). It is worth noting that rs926938 is similarly common (MAF=0.50) in both populations, but rs6537835 and rs1877455 have much lower allele frequency (MAF=0.08) in the European ancestry population, which explains the weaker genetic association observed in the European ancestry samples.

Figure 1
Figure 1

Regional association plot of negative logarithm of P-values with recombination in the 1p13.2 region. Linkage disequilibrium calculation is based on the genome-wide-associated SNP rs926938. (a, top panel) Based on HapMap data in Han Chinese and Japanese (CHBJPT) populations. (b, lower panel) Based on HapMap data in the European population. Triangle=meta-analysis P-values of SNPs, which shows consistent direction of association in five cohorts; circle=genotyped SNPs; and diamond=imputed SNPs in the Chinese triad cohort, which were not all replicated in the same direction for meta-analysis of five cohorts. CEU, European ancestry population.

Meta-analysis of single SNP associations in five cohorts also showed association signals (P<10 × 10−5) that do not reach genome-wide significance at the 1p13.2 region (e.g., DENND2C, CSDE1, SYCP1) and at PLA3G4A, EXTL3, PICALM, NEDD1 and MYOM1 loci (Table 2). For those SNPs imputed in the two Chinese cohorts, meta-analyses did not find any GWAs with autism.

Functional haplotype for autism

We conducted a fast search for possible functional variants that may be in LD with the top association signals through haplotype analysis of 112 SNPs in the chromosomal region of 500 kb around the associated SNP rs926938. The haplotype analysis was also used to define the LD block spanned by the three associated SNPs, and was performed using up to 10-SNP sliding window approach in each of five sample cohorts.

Meta-analysis identified multiple haplotypes associated with autism at, or close to, genome-wide significance (P<10 × 10−8). Interestingly, three most significant haplotypes were observed exactly around the three autism-associated SNPs (Table 3). According to further bioinformatics analyses in both populations,25 the majority of SNPs involved with these three haplotypes are functional, but they were not detected as strong signals owing to lower MAF (e.g., 10489525 in CSDE1, P=5.51 × 10−6). The first haplotype, AGG, was observed at rs926938 with rs8453 and rs10489525, and it spans the AMPD1-NRAS-CSDE1 locus (P=9.33 × 10−8). While SNPs rs926938 and rs10489525 are transcription-factor binding sites, rs8453 is conserved and appears with a high regulation potential (score=0.35); both rs8453 and rs10489525 are located in CSDE1. The second haplotype, AGTTGTCCA, at rs6537825–rs11582563–rs11585926–rs11589568–rs7511633–rs6661053–rs11102800–rs3827735–rs11102807 was located at TRIM33 (P=4.06 × 10−8). In the E3 ubiquitin ligase TRIM33, rs6537825—the non-synonymous SNP associated with autism—causes the substitution of threonine for isoleucine. It should further be noted that these SNPs involved with the haplotype are in LD (r2>0.50) with several other functional SNPs in TRIM33 in HapMap data (Supplementary Tables S2 and S3), particularly in the CHB population. Finally, the third haplotype, TCT, involved rs10858047–rs11587400–rs1877455 at the TRIM33-BCAS2 locus (P=8.36 × 10−8). Although the SNP rs1877455 was the genome-wide-associated SNP with autism, the other two were functional. The rs11587400 is conserved and may have high regulation potential (score=0.45), and rs10858047 is also conserved and is in LD with several functional SNPs, including rs222493, a transcription-factor-binding site, and rs222493, an miRNA-binding site, at TRIM33-BCAS2 (r2=0.88 and D′=0.94).

Table 3: Top associated haplotypes and frequency at three genome-wide-associated loci from the meta-analysis of five cohorts

The permutation test confirmed that all three final haplotypes, AGG of rs926938–rs8453–rs10489525 at AMPD1-NRAS-CSDE1 (permutation P=4.4 × 10−5), AGTTGTCCA of rs6537825–rs11582563–rs11585926–rs11589568–rs7511633–rs6661053–rs11102800–rs3827735–rs11102807 at TRIM33 (permutation P=3.9 × 10−5) and TCT of rs10858047–rs11587400–rs1877455 at the TRIM33-BCAS2 locus (permutation P=7.7 × 10−6), were unlikely associated with autism by chance (Supplementary Table S4).

Cis-acting regulatory effects and differential expression in human brain

To find evidence of molecular mechanism for these genetic associations, we examined the cis-acting regulatory effect on mRNA gene expression in post-mortem human brains. As the true causal variants were not clear and statistical power to detect significant association with gene expression may be a concern when MAF is low and sample size is not large, genetic association with gene expression analysis was performed for all genes and SNPs that were involved in the three haplotypes associated with autism. Gene expression analyses of three SNPs, rs926938–rs8453–rs10489525, with each of three genes, AMPD1-NRAS-CSDE1, found that rs8453 was significantly associated with the gene regulations of CSDE1 (P=0.0009) and NRAS (P=0.0561), and that rs10489525, was significantly associated with the gene regulations of CSDE1 (P=0.0187) and NRAS (P=0.0751) (Figure 2). Although we did not observe significant association of rs6537825 with the expression of TRIM33, two common variants, rs11102800 and rs11102807 (MAF, 0.43–0.48), that formed an autism-associated haplotype with rs6537825 appear associated with TRIM33 expression (P<0.054). The analysis showed that disease-risk-associated alleles (rs8453, rs11102800 and rs11102807) appear associated with lower expressions of CSDE1, NRAS and TRIM33. Unfortunately, we did not find a significant association (P>0.20) of the autism-implicated SNP, rs1877455, and two others (rs10858047 and rs11587400) on the haplotype, with the expressions of TRIM33 and BCAS2 (Supplementary Table S5).

Figure 2
Figure 2

The cis-acting regulatory associations of genome-wide autism-associated loci at NRAS-CSDE1 and TRIM33 in postnatal human brains (n=224). Plots were based on post hoc least-square estimates of mean by genotype from multiple linear regression models adjusted for covariates of sex, age (at brain sample collection), RNA integrity number and post-mortem intervals (in h).

The genome-wide-associated loci were differentially expressed in the post-mortem human frontal cortex between subjects with and those without autism. On the basis of the exact F-test for multivariate analysis of variance, we found an overall significant differential expression (P=0.0332) of the nine probes in the six loci spanned by the genome-wide-associated SNPs (Supplementary Figure S3). Specifically, significant differential gene expressions were observed at TRIM33 (P=0.0423), NRAS (P=0.0463) and BCAS2 (P=0.0155), and close to a significant level of differential expression (P=0.057) at CSDE1 (Supplementary Tables S6 and S7). This is worth noting given that the sample size (16 cases and 16 controls) is not large, and we also understand that MAF of the disease-associated SNPs is common in Chinese but much lower in the European ancestry population. More importantly, the differentially expressed genes in human brains were consistent with that where the cis-acting regulatory effects we found were on TRIM33, NRAS and CSDE1 in an independent brain sample of normal subjects. Our findings also confirmed a finding in a recent study on post-mortem brain expression that TRIM33, in the prefrontal cortex, is differentially regulated (P=0.0049, fold change=−1.6) in human brains between autism and normal subjects.28

Significant differential expression of TRIM33 (P=0.025) and NRAS (P=0.0099) was also observed in the temporal cortex in a smaller number of samples comprising 10 autism cases and 11 normal controls (Supplementary Table S6).


Through GWA analysis of two cohort Chinese samples and replication analysis of top signals in three cohort European ancestry samples from previous GWA studies, our study revealed that multiple common genetic variants at AMPD1-NRAS-CSDE1, TRIM33 and TRIM33-BCAS2 are associated with autism, and all these loci are located in an LD block of 1p13.2 linkage region, previously reported as being linked to autism in multiple independent studies. In a whole genomic linkage scan of 147 autism-affected American sib pairs, using 362 microsatellite markers Risch et al.29 reported the top signal (maximum multipoint LOD score 2.15) with microsatellite marker D1S1675.29 This marker maps to within 1 Mb of TRIM33, which is the nearest gene. In a linkage study of 17 multiplex Finish families designed to replicate 10 previously reported linkage regions, a top linkage peak was observed with marker D1S1675, although none of the 10 loci were replicated.30 Later, a high-density SNP genome-wide linkage scan in a large extended pedigree of seven individuals affected with autism also suggested 1p13.2 for autism (nonparametric linkage score=2.34, P=0.0094).31 On the basis of our study, several genes in this linkage region could be potential candidates for autism susceptibility.

TRIM33, an E3 ubiquitin ligase, is the most likely candidate. Not only have mutations in ubiquitin E3 ligases been reported as being associated with other neuropsychiatric disorders such Angelman syndrome—a disorder with many features that overlap with autism (UBE3A),32 Charcot–Marie–Tooth disease (LRSAM1),33 Juvenile onset Parkinson’s disease (PARK2)34 and X linked-mental retardation (CUL4B)35, 36—but also CNVs not found in controls affecting ubiquitin pathways, including UBE3A and PARK2, 8, 10 have been found enriched in autism patients. The SNP in TRIM33, rs6537825, showing the strongest association with autism, is non-synonymous and causes an isoleucine-to-threonine substitution, and has significant cis-acting regulatory effect on TRIM33 expression in the brain. This and other functional polymorphisms may slightly alter the function of TRIM33 and make the ubiquitin–proteasome system less efficient. Finally, TRIM33 was differentially expressed in human brains between autism patients and normal subjects, and was reported as one of the top loci associated with differential expressions across the whole genome in an independent sample of post-mortem human brains.28

In overlapping genes related to environmentally responsive, toxicogenomics and human immune and inflammatory response with 5300 genes in autism-linked regions, Herbert et al.37 proposed NRAS as a candidate gene for potential gene–environment interactions in autism. However, we believe that CSDE1, or together with NRAS, is a likely candidate gene for autism. While a GWA was observed at functional SNP rs926938 between AMPD1 and NRAS, this and two other functional SNPs, rs8453 and rs1048952 in CSDE1, formed a haplotype associated with autism at genome-wide significance. One of the functional SNPs in CSDE1, rs1048952, also showed as an association signal in multiple cohorts, and combined together in five cohorts it was just below the genome-wide significance. Moreover, two functional SNPs, rs8453 and rs1048952, have a cis-acting regulatory effect on CSDE1 and NARS, both of which were differentially expressed in the frontal or temporal cortex of human brains between autism cases and normal controls. This provides further molecular evidence for CSDE1 and NRAS as candidate genes for autism. A recent whole exome sequencing also identified that de novo loss-of-function mutation in CSDE1 causes autism.16

Through this study, we also demonstrated the importance of further fine-mapping association analyses of genome-wide-associated loci through haplotype analysis in finding causal or functional variants that are often rare but are more likely to contribute to the GWA. The GWA study is designed to detect association with common genetic variants; however, in some cases the associations are difficult to understand owing to the lack of known functions for the associated SNPs. Some have argued by means of a simulation study that multiple rare mutations may account for the common genetic association through ‘synthetic association’.24 Although others may have an alternative argument against it, our study may provide an empirical evidence of the importance of fine-mapping association around the top signal. For example, one of our associated SNPs, rs926938, was found at AMPD1-NRAS; through haplotype analysis, we found a haplotype that minor allele of rs926938 formed with major alleles of two others in CSDE1 associated with autism at genome-wide significance. Further downstream analyses of brain expressions tend to support CSDE1 as a candidate gene in autism; unfortunately, the latter two SNPs were not detected as genome-wide-associated signals through a single SNP association analysis owing to lower MAF unless a haplotype analysis was performed.

Given that very few common genetic variants are found to be associated with autism, our study may provide novel insight into the parthenogenesis of common genetic variants and autism or other neuropsychiatric disorders.


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We are grateful to all the children with autism, their families and to the normal controls who participated in this study. We thank Autism Speaks for sharing resources from the Autism Genetic Resources Exchange (AGRE), the Simons Foundation for Autism Research Initiative (SFARI) for providing data from the Simons Simplex Collection (SCC), the NIH GWAS Data Repository (AGP data set: phs000267.v1.p1) and the Contributing Investigator(s) who contributed the phenotype and genotype data from his/her original studies. We also thank Mr Tianzhang Ye, Dr Carlo Colantuoni and Dr Joel E Kleinman for assisting in accessing the brain expression data and Dr Elizabeth Sherman for comments. The research was supported by the National Basic Research Program of China (2012CB517900, 2011CB510002), the National Natural Science Foundation of China (81330027, 81161120544), the National Alliance for Research on Schizophrenia and Depression (NARSAD) Award (17616 to LZ) and Intramural Research Program funding from the National Institute of Mental Health, The National Institutes of Health in the United States.

Author information

Author notes

    • K Xia
    • , H Guo
    •  & Z Hu

    These authors contributed equally to this work.


  1. State Key Laboratory of Medical Genetics, Central South University, Changsha, Hunan, China

    • K Xia
    • , H Guo
    • , Z Hu
    • , Y Peng
    • , Z Xiong
    • , Q Pan
    • , Z Long
    • , X Li
    • , W Li
    • , X Xu
    • , L Lu
    • , Y Liu
    • , Y Hu
    • , D Tian
    • , L Long
    • , L Zhang
    • , Y Pan
    • , J Chen
    • , H Peng
    • , Q Liu
    • , W Su
    • , L Wu
    • , D Liang
    • , H Dai
    • , X Yan
    • , Y Feng
    • , B Tang
    • , J Li
    • , J Xia
    • , Z Zhang
    •  & J Zhao
  2. School of Biological Science and Technology, Central South University, Hunan, China

    • K Xia
    • , H Guo
    • , Z Hu
    • , Y Peng
    • , Z Xiong
    •  & J Li
  3. Division of Intramural Research Programs, National Institute of Mental Health, The National Institutes of Health, Bethesda, MD, USA

    • H Guo
    • , D St Clair
    •  & F Zhang
  4. Mental Health Institute, The Second Xiangya Hospital, Central South University, Hunan, China

    • G Xun
    • , Y He
    • , J Ou
    • , Y Liu
    • , X Li
    • , X Luo
    •  & J Zhao
  5. Department of Psychiatry, Yale University, New Haven, CT, USA

    • L Zuo
    •  & X Luo
  6. State Key Laboratory Incubation Base of Dermatology, Anhui, China

    • K Wang
    • , L Sun
    •  & X Zhang
  7. Department of Pediatrics, No. 3 Hospital of the Sun Yat-sen University, Guangdong, China

    • X Zou
  8. The Xiangya Hospital, Central South University, Hunan, China

    • X Yan
    • , Y Feng
    •  & B Tang
  9. University of Aberdeen, Royal Cornhill Hospital, Aberdeen, UK

    • Z Miedzybrodzka
    •  & D St Clair
  10. The Lieber Institute for Brain Development, Johns Hopkins University Medical Campus, Baltimore, MD, USA

    • F Zhang


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Competing interests

The authors declare no conflict of interest.

Corresponding authors

Correspondence to K Xia or J Zhao or F Zhang.

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Supplementary Information accompanies the paper on the Molecular Psychiatry website (http://www.nature.com/mp)

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