A total of 480 patients (schizophrenia, n=287; ASDs, n=193) and 296 healthy control subjects were recruited. The schizophrenia group comprised 189 males (mean age, 42.0±10.9 years) and 98 females (42.8±10.8) and the ASD group consisted of 177 males (15.4±4.8) and 16 females (14.1±3.1). The control group comprised 142 males (39.7±5.7) and 154 females (33.4±6.3). Patients were enrolled among participants who visited the Departments of Neuropsychiatry in the East-West Neomedical Center and Kyung Hee Medical Center (Seoul, Korea). Patients were diagnosed with schizophrenia and ASDs by at least two well-trained psychiatrists, according to the Diagnostic and Statistical Manual of Mental Disorders (4th edition), using available historical information from interviews and clinical records. Subjects enrolled into the control group were assessed as mentally healthy through a general health examination program. Participants with any neurological diseases and psychiatric disorders were excluded.

In the analysis of clinical symptoms of schizophrenia and ASDs, detailed clinical features were confirmed by cliniclans. Schizophrenia patients were classified into 4 subgroups, using the operation criteria checklist for psychotic illness (OPCRIT): poor concentration, persecutory delusion, auditory hallucination and affective disturbances. ASD patients were divided into 2 subgroups, according to the scores of the Childhood Autism Rating Scale (CARS), using a cut-off score of 30 to diagnose ASDs: <37 and ≥37 scores (Table I). The ASD group contained 12 Asperger’s disorder (11 males/1 female), 34 atypical autism (30 males/4 females) and 147 autistic disorder (136 males/11 females) cases. The present study was conducted in accordance with the guidelines of the Helsinki Declaration and was approved by the Ethics Review Committee of Medical Research Institute, Kyung Hee University Medical Center. Informed consent was obtained from each subject. If the patients were incommunicable, informed consent was obtained from their parents or guardians.

In our case-control study, the mean ages of the schizophrenia and ASDs groups were different. It was extremely difficult to obtain controls similar to the onset age of ASDs. To avoid the possibility of subjects with undetected or subclinical ASDs, healthy subjects aged >20 years old were included in the control group. Moreover, adult subjects were used as controls, in case of infant or child diseases (21,22). The gender ratios of the schizophrenia and ASDs groups were also different. Gender ratios of schizophrenia and autism are well-known to be ~1:1 (male:female) and 9:1 (male:female), respectively. Therefore, statistical data in this study were analyzed following adjustment for age and gender as covariables.

Two SNPs were selected (rs3755724, −55C/T and rs17035945, 3′-untranslated region) (21). These two SNPs also belong to the intronic SNPs (iSNPs) of SYN2. We searched common SNPs with minor allele frequency (MAF) >0.05, using the SNP database of the National Center for Biotechnology Information (http://www.ncbi.nlm.nih.gov/SNP, BUILD 135). However, no common SNPs were identified in the coding region of TIMP4. Genomic DNAs were extracted from peripheral blood using the Roche DNA Extraction kit (Roche Diagnostics, Indianapolis, IN, USA). PCR was conducted using the primers for each SNP as described previously (21): rs3755724 (sense, 5′-AGAGGCAGACAGAATTA CAACAGGCA-3′; antisense, 5′-ACATGACAGAGTCTCC AGTGAGAAGG-3′, 439 bp) and rs17035945 (sense, 5′-CCT GAAGATCAAGCCAGTTCTCC-3′; antisense, 5′-GGAGAGG TAGTACCTATTCTGAG-3′, 660 bp). The genotypes of the two examined SNPs were determined by direct sequencing (Macrogen, Seoul, Republic of Korea). SeqManII software (DNASTAR, Inc., Madison, WI, USA) was used to analyze the sequencing data.

Hardy-Weinberg equilibrium (HWE) was calculated using SNPStats (http://bioinfo.iconcologia.net/index.php). SNPStats, SNPAnalyzer Pro (ISTECH, Inc., Goyang, Korea) and SPSS 18.0 (SPSS, Inc., Chicago, IL, USA) programs were used to obtain the odds ratios (ORs), 95% confidence intervals (CIs) and P-values. The linkage disequilibrium (LD) block was evaluated using Haploview version 4.2 (Daly Lab, Cambridge, MA, USA) and Gabriel’s method (23). Multiple logistic regression models were performed to analyze the genetic data: codominant1 (major allele homozygotes vs. heterozygotes), codominant2 (major allele homozygotes vs. minor allele homozygotes), dominant (major allele homozygotes vs. heterozygotes + minor allele homozygotes), recessive (major allele homozygotes + heterozygotes vs. minor allele homozygotes), overdominant (major allele homozygotes + minor allele homozygotes vs. heterozygotes) and log-additive (major allele homozygotes vs. heterozygotes vs. minor allele homozygotes) (24). If the number of subjects was <5, the P-value was reevaluated by Fisher’s exact test. P<0.05 was considered to indicate a statistically significant difference.

The genotype and allele frequencies of the two examined SNPs in schizophrenia are shown in Table II. The rs3755724 and rs17035945 SNPs were not associated with the development of schizophrenia. In the analysis of clinical symptoms, the genotype frequency of rs3755724 was significantly different between schizophrenia without poor concentration and schizophrenia with poor concentration (P=0.044 in the codominant2 model and P=0.041 in the log-additive model). The allele frequency of rs3755724 was also associated with schizophrenia with poor concentration (P=0.043). The C allele frequency of rs3755724 was lower in the poor concentration (+) group (38.9%) than in the poor concentration (−) group (47.5%; Table III). However, these correlations disappeared following Bonferroni’s correction. The two tested SNPs were not correlated with the other clinical phenotypes of schizophrenia, including persecutory delusion, auditory hallucination and affective disturbances (data not shown). These results suggest that rs3755724 and rs17035945 are not associated with schizophrenia in the Korean population.

In Table IV, the genotype and allele frequencies of rs3755724 and rs17035945 did not contribute to the susceptibility of ASDs. As shown in Table V, these two SNPs were not different between the two subgroups according to the CARS scores (ASDs with <37 and ASDs with ≥37). Next, we analyzed 177 males with ASDs as the number of females with ASDs was relatively small (n=16). The rs3755724 and rs17035945 SNPs were not associated with male ASDs (data not shown). Finally, we evaluated 136 males with autistic disorder, while excluding females with autistic disorder (n=11), Asperger’s disorder (n=12) and atypical autism (n=34). The two tested SNPs did not correlate with the susceptibility and severity of male autistic disorder (data not shown). These results suggest that rs3755724 and rs17035945 are not associated with ASDs in the Korean population.

The two examined SNPs were in Hardy-Weinberg equilibrium (P>0.05, data not shown). Haploview version 4.2 was used to evaluate the LD block between rs3755724 and rs17035945. The LD block in the control group was not made (D’=0.644 and r²=0.085). Thus, we did not analyze the haplotypes of these two SNPs.

The power of the sample size was estimated using a genetic power calculator (http://pngu.mgh.harvard.edu/~purcell/gpc). The conditions of the sample power were α=0.05, risk=2-fold and no. of cases=80% power. In schizophrenia, the sample power of each SNP was 0.89 for rs3755724 (n=218) and 0.90 for rs17035945 (n=214). In ASDs, the sample power of each SNP was 0.89 for rs3755724 (n=150) and 0.82 for rs17035945 (n=185). Therefore, the number of cases was sufficient to obtain statistical confidence.