Skip to main content
PMC home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2017 Jun 28.
Published in final edited form as: Schizophr Res. 2016 Apr 19;174(1-3):29–34. doi: 10.1016/j.schres.2016.03.031

Association study of MiRSNPs with schizophrenia, tardive dyskinesia and cognition

Jibin John a, Triptish Bhatia b, Prachi Kukshal a, Puneet Chandna c, Vishwajit L Nimgaonkar d, Smita N Deshpande b, BK Thelma a,*
PMCID: PMC5487370  NIHMSID: NIHMS861995  PMID: 27106592

Abstract

MicroRNAs (miRNAs) bind to 3′UTRs of genes and negatively regulate their expression. With ~50% of miRNAs expressing in the brain, they play an important role in neuronal development, plasticity, cognition and neurological disorders. Conserved miRNA targets are present in > 60% genes in humans and are under evolutionary pressure to maintain pairing with miRNA. However, such binding may be affected by genetic variant(s) in the target sites (MiRSNPs), thereby altering gene expression. Differential expression of a large number of genes in postmortem brains of schizophrenia (SZ) patients compared to controls has been documented. Thus studying the role of MiRSNPs which are underinvestigated in SZ becomes attractive. We systematically selected 35 MiRSNPs with predicted functional relevance in 3′UTRs of genes shown previously to be associated with SZ, genotyped and tested their association with disease, using independent discovery and replication samples (total n = 1017 cases; n = 1073 controls). We also explored genetic associations with two sets of quantitative traits, namely tardive dyskinesia (TD) and cognitive functions disrupted in SZ in subsets of the study cohort. In the primary analysis, a significant association of MiRSNP rs7430 at PPP3CC was observed with SZ in the discovery and the replication samples [discovery: P = 0.01; OR (95%CI) 1.24 (1.04–1.48); replication: P = 0.03; OR (95%CI) 1.20 (1.02–1.43)]. In the exploratory analyses, five SNPs were nominally associated with TD (P values 0.04–0.004). Separately, 12 SNPs were associated with one or more of the eight cognitive domains (P values 0.05–0.003). These associations, particularly the SNP at PPP3CC merit further investigations.

Keywords: MiRSNP, Schizophrenia, Tardive dyskinesia, Cognition, Association study, 3′-Untranslated region (3′-UTR)

1. Introduction

Genetic heterogeneity combined with variable phenotypic expressivity in schizophrenia (SZ) has made it difficult to dissect the pathogenesis of this common, complex neuropsychiatric disorder. Of the 60 to 83% heritability estimated for this disorder (Cannon et al., 1998; Lichtenstein et al., 2009), a major proportion remains to be explained (Lee et al., 2012). Recent genome wide association studies (GWAS) have identified several common SNPs as risk factors, supporting the polygenic inheritance of SZ (Purcell et al., 2009). However, ~90% of the associated variants from GWAS are localized to non-coding regions of the genome – a situation similar to other genetically complex disorders. Though the functional significance of these associated variants remains obscure, some may influence gene expression (Richards et al., 2012). Differential expression of a number of genes involved in neurodevelopment, neurotransmitter mediated signaling pathways, immune response and other signaling pathways has been observed in both blood/lymphoblastoid cell lines and postmortem brain samples of SZ patients compared with suitable controls (Harrison, 1999; Harrison and Weinberger, 2005; Michel et al., 2012; Schmitt et al., 2011). Variation in gene expression is controlled by both cis and trans acting loci. Further, polymorphisms at these loci can also influence gene expression (Kim et al., 2012b). The majority, ~69–80% of these variants act in a cell type dependent manner (Dimas et al., 2009), but the mechanism by which they affect gene expression in health and disease is largely unknown.

Approximately half of all protein coding genes in mammals are thought to be under the regulation of microRNAs (miRNAs) (Krol et al., 2010), which are ~22 nucleotide long single stranded non coding RNA molecules, that negatively regulate gene expression (Schanen and Li, 2010). Most of the cellular processes are directly or indirectly influenced by miRNAs by base pairing to 3′UTR regions of target mRNA genes and lead to either mRNA cleavage or translation repression based on miRNA-target mRNA complementarity (Nilsen, 2007). miRNA-mRNA base pairing mediated regulation of gene expression can be influenced by several factors, including chromosomal abnormalities encompassing miRNA genes (Calin and Croce, 2006), their epigenetic modifications (Fabbri et al., 2013), genetic variants in the miRNA and their targets and proteins involved in miRNA processing (Ryan et al., 2010). Roughly 50% of all known miRNAs are expressed in mammalian brains (O’Carroll and Schaefer, 2013). Spatio-temporal and sex related expression patterns of miRNAs in brains of humans are observed and the target genes of these miRNAs are mainly associated with various neurodevelopmental processes/disorders and transcriptional regulation (Ziats and Rennert, 2013). Conserved miRNA targets are present in >60% of protein coding genes in humans and are also under evolutionary pressure to maintain pairing with miRNA (Friedman et al., 2009). Such binding may be affected by ins/del variations or SNPs located in the target site of the miRNAs (MiRSNPs), which could create a new binding site or abolish an existing one or may modulate the strength of binding. Thus, MiRSNPs can affect gene expression and may have influence on phenotypic variability and risk of various diseases (Chen et al., 2008).

Regulatory functions of miRNAs in nervous system range from all stages of neuronal development to plasticity, cognitive functions and several neurological disorders and disease (Fiore et al., 2011). miRNAs are differentially expressed in brains of SZ patients compared to controls (Beveridge and Cairns, 2011). Similarly, differential expression of genes involved in miRNA processing among cases and controls has also been reported (Zhou et al., 2012).

Furthermore, association of SNPs in miRNAs with SZ have been reported (Beveridge and Cairns, 2011). On the other hand, MiRSNPs have also been identified among SNPs reported in Chinese SZ GWAS and brain eQTLs (Liu et al., 2012). Association of MiRSNPs has also been reported in an independent candidate gene based association study in a Chinese population (Gong et al., 2013). This association though modest, reiterates the likely role of MiRSNPs in SZ pathogenesis. With this background, we primarily aimed at investigating the association of MiRSNPs in well investigated candidate genes with SZ in genetically distinct north Indian cohorts. Several of these genes are also reported to be associated with tardive dyskinesia (TD), an iatrogenic disorder observed in a subset of SZ patients and to a lesser extent in cognition. Further, the role of MiRSNPs in TD or in cognition has not been reported. Therefore, we also explored the association of the selected SNPs with these two quantitative traits in the study cohort.

2. Materials and methods

2.1. Sample recruitment and diagnostic assessment

Recruitment of the participants with SZ and controls (adult/cord blood) and inclusion criteria have been previously described (Kukshal et al., 2013; Tiwari et al., 2007, 2005a). Briefly, patients diagnosed with SZ or schizoaffective disorder conforming to DSM IV criteria were consented and were recruited from PGIMER-Dr. RML Hospital, New Delhi. All participants were assessed using the Hindi version of the Diagnostic Interview for Genetic Studies (DIGS) and the Family Interview for Genetic Studies (FIGS) (Deshpande et al., 1998; Nurnberger et al., 1994). Controls in the study comprised of adults and cord blood samples. Written informed consent was obtained from all the adult participants. Cord blood controls were collected from anonymous discarded placenta, with institutional ethical committee clearance and approval from the participating hospitals. The study was approved by the institutional ethical committees of all participating institutions.

The participants were divided into two groups: a discovery sample composed of 496 patients and 522 controls and a replicative sample composed of 521 patients and 551 controls (457 were cord blood controls). Two different subsets of the discovery cohort were used for association with TD and cognition.

2.2. Assessment of tardive dyskinesia

A subset of SZ patients assessed for TD at the time of recruitment of the participants for genetic analysis, were included in this study (Tiwari et al., 2005b). Briefly, TD was assessed using the Abnormal Involuntary Movement Scale (AIMS). A total of 91 SZ patients were diagnosed with TD (denoted as TD-Y Mean AIMS score 6.19 ± 3.38) and 161 were without TD (TD-N). 26 of the 91 TD-Y cases received first generation antipsychotic medications (duration of schizophrenia 11.8 ± 8.67 years; mean AIMS score 6.54 ± 3.6), 23 received atypical antipsychotic drugs (duration of schizophrenia 6.86 ± 5.08 years; mean AIMS score 5.69 ± 3.17) and 42 were treated with both sets of antipsychotic drugs at different times during their illness (duration of schizophrenia 10.40 ± 8.40 years; mean AIMS score 6.24 ± 3.31). Although the data on the class of drugs that was received by the patients was available, no reliable information on drug dosage could be documented as detailed in our previous published papers on TD (Tiwari et al., 2005a, 2005b). Research diagnostic criteria (a total of two mild or at least one moderate or higher rating in any of the symptoms) was used for classifying SZ patients as tardive dyskinesia positive or tardive dyskinesia negative and only this status was used for association analysis.

2.3. Neurocognitive assessment

Cognitive assessment was performed using the Hindi version of University of Pennsylvania Computerized Neurocognitive Battery (Penn CNB) as described in our previous studies (Bhatia et al., 2012; Kukshal et al., 2013). The Penn CNB estimates variation in eight selected cognitive domains known to be impaired among patients with SZ, namely abstraction and mental flexibility, attention, face memory, spatial memory, working memory, spatial ability, sensorimotor and emotional processing. It was administered to a subset of SZ cases (n = 152; 96 males and 56 females; mean age 31 ± 9.54 years) and adult controls (n = 292; 181 male and 111 female; mean age 38 ± 14.17 years) at the time of recruitment. The Penn CNB estimates accuracy and speed for each cognitive domain. As they are correlated, only the accuracy indices were used for the genetic analyses. The Penn CNB data of participants is automatically stored at CNB site at University of Pennsylvania. Normalized Z scores retrieved from the repository at Penn CNB for the eight selected cognitive domains namely abstraction and mental flexibility, attention, face memory, spatial memory, working memory, spatial ability, sensorimotor and emotional processing were used for association testing.

2.4. Selection of MiRSNPs

As mentioned earlier, this study was aimed at investigating the association of MiRSNPs in known SZ candidate genes with SZ, TD and cognition using case-control approach. MiRSNPs to be included in the study were prioritised as detailed below.

  • 100 top ranked candidate genes from schizophrenia gene resource (Jia et al., 2010) (http://bioinfo.mc.vanderbilt.edu/SZGR) were selected. In addition, since a considerable number of candidate gene based association studies in SZ as well as TD and to some extent cognition, are from the dopaminergic pathway, genes from this pathways were also included.

  • 611 3′UTR SNPs from dbSNP (http://www.ncbi.nlm.nih.gov/snp) with minor allele frequency (MAF) >0.01 (since the focus of the study is to investigate the role of common variants) which were distributed over 92 of the selected genes mentioned above were selected. In the remaining genes, the MAF of 3′UTR SNPs was <0.01 and therefore not included for further analysis.

  • 254 MiRSNPs from among the 611 SNPs listed above were identified (Barenboim et al., 2010) (http://cbdb.nimh.nih.gov/microsniper).

  • The binding strength of miRNA to its target mRNA is usually measured by minimal Gibbs free energy (MFE) (Rusinov et al., 2005). In general, the greater the MFE, the higher the strength of miRNA-mRNA pairing, with a related negative impact on gene expression. The MFE difference between variant and wild type alleles were calculated using RNA Hybrid software (Rehmsmeier et al., 2004) (http://bibiserv.techfak.uni-bielefeld.de/rnahybrid) and MiRSNPs showing ΔE difference ≥ 1.5 KCal/mol between the alleles, were prioritized.

  • Co-expression of target gene and miRNA(s) in brain tissues is an essential requirement for pairing between miRNA and its target site in 3′UTR. Accordingly, expression of miRNA in brain tissue was checked using microRNA.org (http://www.microrna.org/microrna/home.do) and 59 SNPs from 46 genes that bind to at least one miRNA expressed in the brain were selected.

  • Of these, a total of 35 MiRSNPs that could be genotyped reliably by MassARRAY® System by Agena Bioscience™ were finally included in the study (Supplementary Table 1). Of note, miRNA 137 was reported to be significantly associated with SZ based on a genome-wide association study (Ripke et al., 2011). However, this miRNA does not have any binding site in the 3′UTR of the gene that we prioritised for the study.

2.5. Genotyping

All participants provided 5 ml each of venous blood for genetic analysis. Genomic DNA was extracted using conventional phenol chloroform method, routinely used in the laboratory. All the MiRSNPs (N =35) were assayed using MassARRAY® System at a commercial facility (Aceprobe Technologies, India). SNP(s) associated with SZ in discovery cohort were genotyped in the replication cohort using PCR-RFLP. Briefly, forward and reverse primers were designed using Primer3 software and used for PCR amplification. Amplified products were genotyped by RFLP, using Tail restriction enzyme (Supplementary Table 2) and resolved on 2% agarose gel.

2.6. Statistical analysis

Hardy Weinberg equilibrium (HWE) for each SNP genotyped was checked using PLINK. All SNPs in HWE (p > 0.001) were included in the association analyses. Associations of MiRSNPs with SZ or TD were evaluated using Trend test in PLINK (http://pngu.mgh.harvard.edu/~purcell/plink/). The power of the combined study sample was calculated using Quanto software (Gauderman, 2006). Linear regression analysis to determine the association between the SNPs and different cognitive domains, using the normalized accuracy scores was performed separately by SPSS ver. 14.

3. Results

Demographic details of the study cohort are provided in Table 1.

Table 1.

Demographic details of the sample.

Study cohort
Discovery
Replication
Samples (total) Cases (496) Controls (522) Cases (521) Controls (551)a
Males 279 319 291 273
Females 217 203 230 278
Mean age (range in years) 30.47 ± 9.97 (14 to 66) 39.34 ± 9.97 (15 to 60) 30.14 ± 8.76 (15 to 60) 33.13±10.51 (19 to 71)
Open in a new tab
a

457 cord blood controls; mean age calculated for adult controls only

3.1. Genotyping quality control

All the 35 SNPs were in HWE (p > 0.001).rs17036056 and rs7124665 were monomorphic and were excluded from the study. Individuals with genotype failure of ≥ 3SNPs were discarded from the study. Thus, a total of 490 cases and 518 controls and 33 SNPs were included for association analysis. Each MassARRAY® assay plate included a CEPH sample as a known positive control, one negative control and a duplicate sample as plate control. All these samples produced concordant results.

3.2. Tests of genetic associations

3.2.1. Primary analyses: associations with SZ

Of the 33 SNPs tested, only one MiRSNP namely rs7430 from PPP3CC showed significant association with SZ [χ2 = 5.85; P = 0.01; OR (95% CI) 1.24 (1.042–1.48)] in the discovery cohort. The association was also detected in the replication sample [χ2 = 4.54; P = 0.03; OR (95% CI) 1.20 (1.02–1.43)] and in combined analysis of two cohorts [χ2 = 10.38; P = 0.001; OR (95% CI) 1.22 (1.08–1.38)] with >80% power.

3.2.2. Exploratory analyses with tardive dyskinesia

Five MiRSNPs one each from MTHFR, SCN1A, PIP4K2A, CLDN5 and GCLM showed allelic association with TD (Table 2). The power of the analyses ranged from 36 to 96%.

Table 2.

MiRSNPs associated with tardive dyskinesia among SZ patients.

Gene SNP χ2 P OR (95% CI) Binding MiRNAs
SCN1A rs10497275 8.50 0.004 0.42 (0.23–0.77) hsa-miR-1286
GCLM rs17881908 5.53 0.02 5.56 (1.11–27.89) hsa-miR-582-3p
MTHFR rs4846049 5.19 0.02 1.54 (1.06–2.24) hsa-miR-555
PIP4K2A rs10734041 4.70 0.03 0.26 (0.08–0.92) hsa-miR-602
CLDN5 rs756654 3.90 0.04 1.53 (1.0–2.41) hsa-miR-486-3p
Open in a new tab

3.2.3. Exploratory analyses with cognitive domains

Nominal association of MiRSNPs from 12 candidate genes was observed with one or more measures of the cognitive domains assessed in this study (Table 3). There was no overlap between the miRNAs which were binding to each of these target genes (Table 3).

Table 3.

MiRSNPs associated with accuracy index of cognitive domains.

GENE SNP-id Face memory Emotion Working memory Spatial memory Sensory motor and emotional processing Attention Abstraction and mental flexibility Spatial ability Binding MiRNAs
PIP4K2A rs10734041 0.009 0.03 – – – – – – hsa-miR-602
EGR3 rs11136094 – 0.04 0.001 – – – – – hsa-miR-940
COMT rs165728 – 0.05 – 0.05 – – – – hsa-mir-654-5P
DISC1 rs16856322 – – – – 0.03 0.03 – – hsa-miR-409-3p
NRG1 rs17731664 – – – – – – 0.04 – hsa-miR-641
hsa-miR-3617-5p
hsa-miR-199a-3p
hsa-miR-208a
hsa-miR-936
hsa-miR-199b-3p
hsa-miR-128
SCN1A rs1813502 – – – – – – – 0.03 hsa-miR-1286
MAOB rs2072745 – – – – – – – 0.02 hsa-mir-635
SYN2 rs2289708 – 0.005 – – – – – – hsa-miR-1908
CHGB rs2821 – – 0.003 – – – – – hsa-miR-577
MTHFR rs4846049 – – – – – – – 0.01 hsa-miR-555
IL1RN rs9005 – – 0.02 0.03 – 0.003 0.05 – hsa-miR-1538
hsa-miR-138-5p
CACNAIB rs9414688 – 0.02 0.002 – – 0.001 0.003 hsa-miR-760
hsa-miR-627
hsa-miR-760
Open in a new tab

4. Discussion

A few studies have reported on the association of regulatory SNPs at miRNA binding sites with risk for SZ but none have been reported in relation to quantitative traits such as TD and cognition. Of the 33 MiRSNPs from 32 candidate genes tested in this study rs7430 from PPP3CC was associated with SZ in two independent samples. PPP3CC codes for calcineurin catalytic γ subunit (CNA), which is a calcium-dependent serine/threonine protein phosphatase involved in the downstream regulation of dopaminergic signal transduction (Greengard, 2001), Long Term Depression (LTD) (Mulkey et al., 1994) and bidirectional plasticity (Mulkey et al., 1994; Zeng et al., 2001). Decreased hippocampal expression of this gene in SZ has been reported (Eastwood et al., 2005). Furthermore, this gene is located at chromosome 8p21.3, one of the chromosomal regions linked to SZ in early family based studies (Gerber et al., 2003). SNPs at this gene have also been reported to be associated with SZ in different populations (Horiuchi et al., 2007; Liu et al., 2007). Further, rs7430, the associated SNP is localized to the putative binding sites of two miRNAs, namely hsa-miR-662 and hsa-miR-657. The SNP is predicted to have functional impact, as in silico analysis indicates differential binding affinity of its wild type and variant alleles (Supplementary Table 1).

The five MiRSNPs associated with TD were localized to different genes. The strongest association was observed with the MiRSNP in SCN1A, that encodes the alpha subunit of voltage gated sodium channel NaV1.1 and it is essential for generation and propagation of action potential in nerve cells. However, the exact role of this gene in the pathogenesis of TD is uncertain. On the other hand, GCLM codes for the first rate-limiting enzyme in glutathione synthesis and its decreased expression in SZ patient fibroblasts (Tosic et al., 2006) and in peripheral blood of SZ patients (Che et al., 2009) has been reported. A number of studies have reported that oxidative stress is involved in the pathophysiology of TD (Thelma et al., 2007), providing a testable mechanism underpinning the genetic association. MTHFR, a commonly investigated candidate gene in SZ is essential for the conversion of homocysteine to methionine. In TD positive cases a higher homocysteine level is reported (Lerner et al., 2005), thus the nominal association of MiRSNP rs4846049 in MTHFR in our study may also have a functional basis. The observed association of MiRSNP rs10734041 in PIP4K2A is consistent with a prior reported association with TD in a Caucasian ancestry sample (Fedorenko et al., 2014). Further, PIP4K2A is involved in G-protein coupled receptor mediated signaling and may provide protection against apoptosis and the stress response (van den Bout and Divecha, 2009).

A total of 12 MiRSNPs associated with one or more of cognitive domains (Table 3) are from strong SZ candidate genes, which are directly or indirectly involved in neurotransmitter signaling processes and these are individually discussed below. Of these, MiRSNPs from MTHFR, PIP4K2A and SCN1A associated with TD and described in the preceding section were also seen to be associated with one or more cognitive domains. Of the associated genes, CACNA1B encodes the pore-forming subunit of an N-type voltage-dependent calcium channel, which facilitates Ca2+ entry, thus promoting neurotransmitter release and synaptic transmission (Catterall, 2000). CACNA1A a paralog of this gene has been implicated for various cognitive functions (Alonso et al., 2008). IL1RN codes for Interleukin-1 receptor antagonist (IL-1RA) which inhibits the activities of interleukin 1, alpha (IL1A) and interleukin 1, beta (IL1B), and modulates a variety of IL-1 related immune and inflammatory responses. Low levels of IL-1RA expression in the prefrontal cortex of SZ patients has been reported (Toyooka et al., 2003) and elevated serum levels of IL-1RA in bipolar disorder patients were associated with impaired cognitive function (Lotrich et al., 2014). It is also known that post-operative cognitive declines can be prevented by intracisternal administration of il-1ra in aged rats (Barrientos et al., 2012).

PIP4K2A, another gene found to be associated with cognition is involved in phosphoinositide signal transduction pathway and consequently modulates AKT activity. AKT signaling pathway is known to be associated with cognitive impairments (Shu et al., 2013). Expression of PIP4K2A mRNA was also reported to be significantly increased in lymphocyte cell lines derived from schizophrenia patients (Saggers-Gray et al., 2011). Early Growth Response3 (EGR3) is an early gene transcription factor, expressed throughout the brain (Beckmann and Wilce, 1997), playing an important role in neuronal development, synaptic plasticity, learning and memory processes (O’Donovan et al., 1999) It is also involved in the regulation of NMDA receptor levels in cortical neurons (Kim et al., 2012a) and various cognitive processes like learning and memory (Poirier et al., 2008). EGR3 transcript is known to be down regulated in the prefrontal cortex of SZ patients (Yamada et al., 2007). Notably different laminar patterns of COMT mRNA expression in pyramidal neurons has been observed in SZ cases compared to controls (Matsumoto et al., 2003). Association of COMT with various cognitive functions has been reported (Simpson et al., 2014). DISC1 is a major risk gene in SZ with an important role in neuronal maturation, proliferation, migration, positioning, differentiation, dendritic growth, axonal outgrowth, and synaptic plasticity through its direct or indirect interactions with various signaling pathways in brain including AKT, GABA, GSK3β, WNT, and NMDA-R (Wu et al., 2013). Its role in cognition has been documented (Burdick et al., 2005; Thomson et al., 2013). Differential expression of NRG1 transcripts was observed in postmortem brains of SZ patients compared to controls (Hashimoto et al., 2004). This gene is also reported to be associated with cognition (Alfimova et al., 2011; Kukshal et al., 2013). Various studies in mice have shown that NRG1 is associated with various cognitive functions and social behavior (Kato et al., 2010; O’Tuathaigh et al., 2007). SCN1A has been discussed in the preceding section on TD. Knock down of this gene in basal forebrain region in rats induces cognitive impairments without seizure (Bender et al., 2013). As for MAOB, an important gene from the dopaminergic pathway, animal studies have reported its role in various cognitive functions (Singh et al., 2013). SYN2 codes for neuronal phosphoproteins associated with the cytoplasmic surface of synaptic vesicles, and are implicated in synaptogenesis and the modulation of neurotransmitter release, suggesting a potential role in several neuropsychiatric diseases (Lee et al., 2005). Decreased expression of SYN2 is reported in brains of SZ patients (Mirnics et al., 2000) SYN2 knockout mice demonstrate deficits in cognition and neuronal loss and gliosis during senescence (Corradi et al., 2008). CHGB encodes a tyrosine-sulfated secretory protein abundant in peptidergic endocrine cells and neurons and may serve as a precursor for regulatory peptides. Differential expression of this gene is reported in SZ postmortem brain samples compared to controls (Chu and Liu, 2010). MTHFR is associated with TD and has been discussed in TD section. This gene is also known to be associated with cognition (Ford et al., 2012) and higher homocysteine levels are reported with cognitive dysfunction (Lerner et al., 2005).

In summary, we report on plausible associations between MiRSNPs associated with SZ and TD. Other associations with cognitive domains were also noted. All the associations merit further investigations.

Supplementary Material

1

Acknowledgments

We are thankful for the financial assistance from Council for Scientific and Industrial Research (CSIR), New Delhi, India for JRF and SRF to Jibin John. We are also grateful for the sample collection by trained and dedicated staff at Dr RML hospital, New Delhi; DNA isolation by Mrs Anjali Dabral, Department of Genetics, University of Delhi, South Campus; and sequencing facilities at Central Instrumentation Facility, University of Delhi, South Campus, New Delhi.

DSK-PDF (BL/13-14/0404) to Dr Prachi Kukshal from UGC, New Delhi, India. We gratefully acknowledge infrastructure support provided by the University Grants Commission (UGC), New Delhi, through Special Assistance Programme and Department of Science and Technology, New Delhi, through FIST and DU-DST PURSE programmes to the Department of Genetics, UDSC.

Role of funding sources

This work was supported in part by grants #BT/PR2425/Med13/089/2001 to BKT and SND, from the Department of Biotechnology; JC BOSE fellowship #SR/S2/JCB-44/2011 to BKT from the Department of Science and Technology, Government of India; and Tri National Training Program in Psychiatric Genetics #D43TW008302 to VLN from NIH, USA. All funding sources had no further role in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication.

Appendix A. Supplementary data

Supplementary data to this article can be found online at http://dx.doi.org/10.1016/j.schres.2016.03.031.

Footnotes

Contributors

Prof. B.K. Thelma, Prof. S. N. Deshpande and Prof. V. L. Nimgaonkar designed the study and wrote the protocol. Mr Jibin John prioritized and genotyped the SNPs in the replication phase, performed statistical analysis and wrote the first draft of the manuscript. Dr Puneet Chandna contributed to the genotyping. Prof. S. N. Deshpande and her team provided the research samples and Dr. T. Bhatia did the cognitive analysis and Dr. Prachi Kukshal contributed to the statistical analysis. All authors contributed to and have approved the final manuscript.

Conflict of interest

The authors declare that there are no conflicts of interest in relation to the subject of this study.

References

  1. Alfimova MV, Abramova LI, Aksenova EV, Golubev SA, Frolova LF, Ganisheva TK, Shemiakina TK, Orlov VA, Golimbet VE. The association between the NRG1 gene polymorphism and cognitive functions in patients with schizophrenia and healthy controls. Zh Nevrol Psikhiatr Im S S Korsakova. 2011;111(6):53–57. [PubMed] [Google Scholar]
  2. Alonso I, Marques JM, Sousa N, Sequeiros J, Olsson IA, Silveira I. Motor and cognitive deficits in the heterozygous leaner mouse, a Cav2.1 voltage-gated Ca2+ channel mutant. Neurobiol Aging. 2008;29(11):1733–1743. doi: 10.1016/j.neurobiolaging.2007.04.005. [DOI] [PubMed] [Google Scholar]
  3. Barenboim M, Zoltick BJ, Guo Y, Weinberger DR. MicroSNiPer: a web tool for prediction of SNP effects on putative microRNA targets. Hum Mutat. 2010;31(11):1223–1232. doi: 10.1002/humu.21349. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Barrientos RM, Hein AM, Frank MG, Watkins LR, Maier SF. Intracisternal interleukin-1 receptor antagonist prevents postoperative cognitive decline and neuroinflammatory response in aged rats. J Neurosci. 2012;32(42):14641–14648. doi: 10.1523/JNEUROSCI.2173-12.2012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Beckmann AM, Wilce PA. Egr transcription factors in the nervous system. Neurochem Int. 1997;31(4):477–510. doi: 10.1016/s0197-0186(96)00136-2. discussion 517–476. [DOI] [PubMed] [Google Scholar]
  6. Bender AC, Natola H, Ndong C, Holmes GL, Scott RC, Lenck-Santini PP. Focal Scn1a knockdown induces cognitive impairment without seizures. Neurobiol Dis. 2013;54:297–307. doi: 10.1016/j.nbd.2012.12.021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Beveridge NJ, Cairns MJ. MicroRNA dysregulation in schizophrenia. Neurobiol Dis. 2011;46(2):263–271. doi: 10.1016/j.nbd.2011.12.029. [DOI] [PubMed] [Google Scholar]
  8. Bhatia T, Agarwal A, Shah G, Wood J, Richard J, Gur RE, Gur RC, Nimgaonkar VL, Mazumdar S, Deshpande SN. Adjunctive cognitive remediation for schizophrenia using yoga: an open, non-randomized trial. Acta Neuropsychiatr. 2012;24(2):91–100. doi: 10.1111/j.1601-5215.2011.00587.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Burdick KE, Hodgkinson CA, Szeszko PR, Lencz T, Ekholm JM, Kane JM, Goldman D, Malhotra AK. DISC1 and neurocognitive function in schizophrenia. Neuroreport. 2005;16(12):1399–1402. doi: 10.1097/01.wnr.0000175248.25535.f6. [DOI] [PubMed] [Google Scholar]
  10. Calin GA, Croce CM. MicroRNAs and chromosomal abnormalities in cancer cells. Oncogene. 2006;25(46):6202–6210. doi: 10.1038/sj.onc.1209910. [DOI] [PubMed] [Google Scholar]
  11. Cannon TD, Kaprio J, Lonnqvist J, Huttunen M, Koskenvuo M. The genetic epidemiology of schizophrenia in a Finnish twin cohort. A population-based modeling study Arch. Gen Psychiatry. 1998;55(1):67–74. doi: 10.1001/archpsyc.55.1.67. [DOI] [PubMed] [Google Scholar]
  12. Catterall WA. Structure and regulation of voltage-gated Ca2+ channels. Annu Rev Cell Dev Biol. 2000;16:521–555. doi: 10.1146/annurev.cellbio.16.1.521. [DOI] [PubMed] [Google Scholar]
  13. Che R, Tang W, Shao L, Zhao Q, Huang K, Tang R, Li Z, Zhao X, Ji W, Zhou G, Feng G, He L, Shi Y. Association and expression studies of glutamate-cysteine ligase modifier (GCLM) and schizophrenia in the Chinese Han population. Psychiatr Genet. 2009;19(5):279–280. doi: 10.1097/YPG.0b013e32832cec76. [DOI] [PubMed] [Google Scholar]
  14. Chen K, Song F, Calin GA, Wei Q, Hao X, Zhang W. Polymorphisms in microRNA targets: a gold mine for molecular epidemiology. Carcinogenesis. 2008;29(7):1306–1311. doi: 10.1093/carcin/bgn116. [DOI] [PubMed] [Google Scholar]
  15. Chu TT, Liu Y. An integrated genomic analysis of gene-function correlation on schizophrenia susceptibility genes. J Hum Genet. 2010;55(5):285–292. doi: 10.1038/jhg.2010.24. [DOI] [PubMed] [Google Scholar]
  16. Corradi A, Zanardi A, Giacomini C, Onofri F, Valtorta F, Zoli M, Benfenati F. Synapsin-I- and synapsin-II-null mice display an increased age-dependent cognitive impairment. J Cell Sci. 2008;121(Pt 18):3042–3051. doi: 10.1242/jcs.035063. [DOI] [PubMed] [Google Scholar]
  17. Deshpande SN, Mathur MN, Das SK, Bhatia T, Sharma S, Nimgaonkar VL. A Hindi version of the diagnostic interview for genetic studies. Schizophr Bull. 1998;24(3):489–493. doi: 10.1093/oxfordjournals.schbul.a033343. [DOI] [PubMed] [Google Scholar]
  18. Dimas AS, Deutsch S, Stranger BE, Montgomery SB, Borel C, Attar-Cohen H, Ingle C, Beazley C, Gutierrez Arcelus M, Sekowska M, Gagnebin M, Nisbett J, Deloukas P, Dermitzakis ET, Antonarakis SE. Common regulatory variation impacts gene expression in a cell type-dependent manner. Science. 2009;325(5945):1246–1250. doi: 10.1126/science.1174148. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Eastwood SL, Burnet PW, Harrison PJ. Decreased hippocampal expression of the susceptibility gene PPP3CC and other calcineurin subunits in schizophrenia. Biol Psychiatry. 2005;57(7):702–710. doi: 10.1016/j.biopsych.2004.12.029. [DOI] [PubMed] [Google Scholar]
  20. Fabbri M, Calore F, Paone A, Galli R, Calin GA. Epigenetic regulation of miRNAs in cancer. Adv Exp Med Biol. 2013;754:137–148. doi: 10.1007/978-1-4419-9967-2_6. [DOI] [PubMed] [Google Scholar]
  21. Fedorenko OY, Loonen AJ, Lang F, Toshchakova VA, Boyarko EG, Semke AV, Bokhan NA, Govorin NV, Aftanas LI, Ivanova SA. Association study indicates a protective role of phosphatidylinositol-4-phosphate-5-kinase against tardive dyskinesia. Int J Neuropsychopharmacol. 2014;18(6) doi: 10.1093/ijnp/pyu098. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Fiore R, Khudayberdiev S, Saba R, Schratt G. MicroRNA function in the nervous system. Prog Mol Biol Transl Sci. 2011;102:47–100. doi: 10.1016/B978-0-12-415795-8.00004-0. [DOI] [PubMed] [Google Scholar]
  23. Ford AH, Flicker L, Hankey GJ, Norman P, van Bockxmeer FM, Almeida OP. Homocysteine, methylenetetrahydrofolate reductase C677T polymorphism and cognitive impairment: the health in men study. Mol Psychiatry. 2012;17(5):559–566. doi: 10.1038/mp.2011.18. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Friedman RC, Farh KK, Burge CB, Bartel DP. Most mammalian mRNAs are conserved targets of microRNAs. Genome Res. 2009;19(1):92–105. doi: 10.1101/gr.082701.108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Gauderman WMJ. QUANTO 1.1: a computer program for power and sample size calculations for genetic-epidemiology studies. 2006 http://hydra.usc.edu/gxe (webcite)
  26. Gerber DJ, Hall D, Miyakawa T, Demars S, Gogos JA, Karayiorgou M, Tonegawa S. Evidence for association of schizophrenia with genetic variation in the 8p21.3 gene, PPP3CC, encoding the calcineurin gamma subunit. Proc Natl Acad Sci U S A. 2003;100(15):8993–8998. doi: 10.1073/pnas.1432927100. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Gong Y, Wu CN, Xu J, Feng G, Xing QH, Fu W, Li C, He L, Zhao XZ. Polymorphisms in microRNA target sites influence susceptibility to schizophrenia by altering the binding of miRNAs to their targets. Eur Neuropsychopharmacol. 2013;23(10):1182–1189. doi: 10.1016/j.euroneuro.2012.12.002. [DOI] [PubMed] [Google Scholar]
  28. Greengard P. The neurobiology of slow synaptic transmission. Science. 2001;294(5544):1024–1030. doi: 10.1126/science.294.5544.1024. [DOI] [PubMed] [Google Scholar]
  29. Harrison PJ. The neuropathology of schizophrenia. A critical review of the data and their interpretation Brain. 1999;122(Pt 4):593–624. doi: 10.1093/brain/122.4.593. [DOI] [PubMed] [Google Scholar]
  30. Harrison PJ, Weinberger DR. Schizophrenia genes, gene expression, and neuropathology: on the matter of their convergence. Mol Psychiatry. 2005;10(1):40–68. doi: 10.1038/sj.mp.4001558. image 45. [DOI] [PubMed] [Google Scholar]
  31. Hashimoto R, Straub RE, Weickert CS, Hyde TM, Kleinman JE, Weinberger DR. Expression analysis of neuregulin-1 in the dorsolateral prefrontal cortex in schizophrenia. Mol Psychiatry. 2004;9(3):299–307. doi: 10.1038/sj.mp.4001434. [DOI] [PubMed] [Google Scholar]
  32. Horiuchi Y, Ishiguro H, Koga M, Inada T, Iwata N, Ozaki N, Ujike H, Muratake T, Someya T, Arinami T. Support for association ofthe PPP3CC gene with schizophrenia. Mol Psychiatry. 2007;12(10):891–893. doi: 10.1038/sj.mp.4002019. [DOI] [PubMed] [Google Scholar]
  33. Jia P, Sun J, Guo AY, Zhao Z. SZGR: a comprehensive schizophrenia gene resource. Mol Psychiatry. 2010;15(5):453–462. doi: 10.1038/mp.2009.93. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Kato T, Kasai A, Mizuno M, Fengyi L, Shintani N, Maeda S, Yokoyama M, Ozaki M, Nawa H. Phenotypic characterization of transgenic mice overexpressing neuregulin-1. PLoS One. 2010;5(12):e14185. doi: 10.1371/journal.pone.0014185. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Kim JH, Roberts DS, Hu Y, Lau GC, Brooks-Kayal AR, Farb DH, Russek SJ. Brain-derived neurotrophic factor uses CREB and Egr3 to regulate NMDA receptor levels in cortical neurons. J Neurochem. 2012a;120(2):210–219. doi: 10.1111/j.1471-4159.2011.07555.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Kim S, Cho H, Lee D, Webster MJ. Association between SNPs and gene expression in multiple regions of the human brain. Transl Psychiatry. 2012b;2:e113. doi: 10.1038/tp.2012.42. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Krol J, Loedige I, Filipowicz W. The widespread regulation of microRNA biogenesis, function and decay. Nat Rev Genet. 2010;11(9):597–610. doi: 10.1038/nrg2843. [DOI] [PubMed] [Google Scholar]
  38. Kukshal P, Bhatia T, Bhagwat AM, Gur RE, Gur RC, Deshpande SN, Nimgaonkar VL, Thelma BK. Association study of neuregulin-1 gene polymorphisms in a North Indian schizophrenia sample. Schizophr Res. 2013;144(1–3):24–30. doi: 10.1016/j.schres.2012.12.017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Lee HJ, Song JY, Kim JW, Jin SY, Hong MS, Park JK, Chung JH, Shibata H, Fukumaki Y. Association study of polymorphisms in synaptic vesicle-associated genes, SYN2 and CPLX2, with schizophrenia. Behav Brain Funct. 2005;1:15. doi: 10.1186/1744-9081-1-15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Lee SH, DeCandia TR, Ripke S, Yang J, Sullivan PF, Goddard ME, Keller MC, Visscher PM, Wray NR. Estimating the proportion of variation in susceptibility to schizophrenia captured by common SNPs. Nat Genet. 2012;44(3):247–250. doi: 10.1038/ng.1108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Lerner V, Miodownik C, Kaptsan A, Vishne T, Sela BA, Levine J. High serum homocysteine levels in young male schizophrenic and schizoaffective patients with tardive parkinsonism and/or tardive dyskinesia. J Clin Psychiatry. 2005;66(12):1558–1563. doi: 10.4088/jcp.v66n1210. [DOI] [PubMed] [Google Scholar]
  42. Lichtenstein P, Yip BH, Bjork C, Pawitan Y, Cannon TD, Sullivan PF, Hultman CM. Common genetic determinants of schizophrenia and bipolar disorder in Swedish families: a population-based study. Lancet. 2009;373(9659):234–239. doi: 10.1016/S0140-6736(09)60072-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Liu YL, Fann CS, Liu CM, Chang CC, Yang WC, Hung SI, Yu SL, Hwang TJ, Hsieh MH, Liu CC, Tsuang MM, Wu JY, Jou YS, Faraone SV, Tsuang MT, Chen WJ, Hwu HG. More evidence supports the association ofPPP3CC with schizophrenia. Mol Psychiatry. 2007;12(10):966–974. doi: 10.1038/sj.mp.4001977. [DOI] [PubMed] [Google Scholar]
  44. Liu C, Zhang F, Li T, Lu M, Wang L, Yue W, Zhang D. MirSNP, a database of polymorphisms altering miRNA target sites, identifies miRNA-related SNPs in GWAS SNPs and eQTLs. BMC Genomics. 2012;13:661. doi: 10.1186/1471-2164-13-661. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Lotrich FE, Butters MA, Aizenstein H, Marron MM, Reynolds CF, 3rd, Gildengers AG. The relationship between interleukin-1 receptor antagonist and cognitive function in older adults with bipolar disorder. Int J Geriatr Psychiatry. 2014;29(6):635–644. doi: 10.1002/gps.4048. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Matsumoto M, Weickert CS, Beltaifa S, Kolachana B, Chen J, Hyde TM, Herman MM, Weinberger DR, Kleinman JE. Catechol O-methyltransferase (COMT) mRNA expression in the dorsolateral prefrontal cortex of patients with schizophrenia. Neuropsychopharmacology. 2003;28(8):1521–1530. doi: 10.1038/sj.npp.1300218. [DOI] [PubMed] [Google Scholar]
  47. Michel M, Schmidt MJ, Mirnics K. Immune system gene dysregulation in autism and schizophrenia. Dev Neurobiol. 2012;72(10):1277–1287. doi: 10.1002/dneu.22044. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Mirnics K, Middleton FA, Marquez A, Lewis DA, Levitt P. Molecular characterization of schizophrenia viewed by microarray analysis of gene expression in prefrontal cortex. Neuron. 2000;28(1):53–67. doi: 10.1016/s0896-6273(00)00085-4. [DOI] [PubMed] [Google Scholar]
  49. Mulkey RM, Endo S, Shenolikar S, Malenka RC. Involvement of a calcineurin/inhibitor-1 phosphatase cascade in hippocampal long-term depression. Nature. 1994;369(6480):486–488. doi: 10.1038/369486a0. [DOI] [PubMed] [Google Scholar]
  50. Nilsen TW. Mechanisms of microRNA-mediated gene regulation in animal cells. Trends Genet. 2007;23(5):243–249. doi: 10.1016/j.tig.2007.02.011. [DOI] [PubMed] [Google Scholar]
  51. Nurnberger JI, Jr, Blehar MC, Kaufmann CA, York-Cooler C, Simpson SG, Harkavy-Friedman J, Severe JB, Malaspina D, Reich T. Diagnostic interview for genetic studies. Rationale, unique features, and training NIMH Genetics Initiative. Arch Gen Psychiatry. 1994;51(11):849–859. doi: 10.1001/archpsyc.1994.03950110009002. discussion 863–844. [DOI] [PubMed] [Google Scholar]
  52. O’Carroll D, Schaefer A. General principals of miRNA biogenesis and regulation in the brain. Neuropsychopharmacology. 2013;38(1):39–54. doi: 10.1038/npp.2012.87. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. O’Donovan KJ, Tourtellotte WG, Millbrandt J, Baraban JM. The EGR family of transcription-regulatory factors: progress at the interface of molecular and systems neuroscience. Trends Neurosci. 1999;22(4):167–173. doi: 10.1016/s0166-2236(98)01343-5. [DOI] [PubMed] [Google Scholar]
  54. O’Tuathaigh CM, Babovic D, O’Sullivan GJ, Clifford JJ, Tighe O, Croke DT, Harvey R, Waddington JL. Phenotypic characterization of spatial cognition and social behavior in mice with ‘knockout’ of the schizophrenia risk gene neuregulin 1. Neuroscience. 2007;147(1):18–27. doi: 10.1016/j.neuroscience.2007.03.051. [DOI] [PubMed] [Google Scholar]
  55. Poirier R, Cheval H, Mailhes C, Garel S, Charnay P, Davis S, Laroche S. Distinct functions of egr gene family members in cognitive processes. Front Neurosci. 2008;2(1):47–55. doi: 10.3389/neuro.01.002.2008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Purcell SM, Wray NR, Stone JL, Visscher PM, O’Donovan MC, Sullivan PF, Sklar P. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature. 2009;460(7256):748–752. doi: 10.1038/nature08185. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Rehmsmeier M, Steffen P, Hochsmann M, Giegerich R. Fast and effective prediction of microRNA/target duplexes. RNA. 2004;10(10):1507–1517. doi: 10.1261/rna.5248604. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Richards AL, Jones L, Moskvina V, Kirov G, Gejman PV, Levinson DF, Sanders AR, Purcell S, Visscher PM, Craddock N, Owen MJ, Holmans P, O’Donovan MC. Schizophrenia susceptibility alleles are enriched for alleles that affect gene expression in adult human brain. Mol Psychiatry. 2012;17(2):193–201. doi: 10.1038/mp.2011.11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Ripke S,AR,K,KS, Levinson DF,SP, Holmans PA, Lin DY, Duan J, Ophoff RA, Andreassen OA, Scolnick E, Cichon S, St Clair D, Corvin A, Gurling H, Werge T, Rujescu D, Blackwood DH, Pato CN, Malhotra AK, Purcell S, Dudbridge F, Neale BM, Rossin L, Visscher PM, Posthuma D, Ruderfer DM, Fanous A, Stefansson H, Steinberg S, Mowry BJ, Golimbet V, De Hert M, Jönsson EG, Bitter I, Pietiläinen OP, Collier DA, Tosato S, Agartz I, Albus M, Alexander M, Amdur RL, Amin F, Bass N, Bergen SE, Black DW, Børglum AD, Brown MA, Bruggeman R, Buccola NG, Byerley WF, Cahn W, Cantor RM, Carr VJ, Catts SV, Choudhury K, Cloninger CR, Cormican P, Craddock N, Danoy PA, Datta S, de Hann L, Demontis D, Dikeos D, Djurovic S, Donnelly P, Donohoe G, Duong L, Dwyer S, Fink-Jensen A, Freedman R, Freimer NB, Friedl M, Georgieva L, Giegling I, Gill M, Glenthøj B, Godard S, Hamshere M, Hansen M, Hansen T, Hartmann AM, Henskens FA, Hougaard DM, Hultman CM, Ingason A, Jablensky AV, Jakobsen KD, Jay M, Jurgens G, Kahn RS, Keller MC, Kenis G, Kenny E, Kim Y, Kirov GK, Konnerth H, Konte B, Krabbendam L, Krausucki R, Lasseter VK, Laurent C, Lawrence J, Lencz T, Lerer FB, Liang KY, Lichtenstein P, Lieberman JA, Linszen DH, Lönnqvist J, Loughland CM, Maclean AW, Maher BS, Maier W, Mallet J, Malloy P, Mattheisen M, Mattinsgsdal M, McGhee KA, McGrath JJ, McIntosh A, McLean DE, McQuillin A, Melle I, Michie PT, Milanova V, Morris DW, Mors O, Mortensen PB, Moskvina V, Muglia P, Myin-Germeys I, Nertney DA, Nestadt G, Nielsen J, Nikolov I, Nordentroft M, Norton N, Nöthen MM, O’Dushlaine CT, Olincy A, Olsen L, O’Neill FA, Ørntoft T, Owen MJ, Pantelis C, Papadimitriou G, Pato MT, Peltonen L, Petursson H, Pickard B, Pimm J, Pulver AE, Puri V, Quested D, Quinn EM, Rasmussen HB, Réthelyi JM, Ribble R, Rietschel M, Riley BP, Ruggeri M, Schall U, Schulze TG, Schwab SG, Scott RJ, Shi J, Sigurdsson E, Silverman JM, Spencer CC, Stefansson K, Strange A, Strengman E, Stroup TS, Suvisaari J, Tereniuis L, Thirumalai S, Thygesen JH, Timm S, Toncheva D, van den Oord E, van Os J, van Winkel R, Veldink J, Walsh D, Wang AG, Wiersma D, Wildenauer DB, Williams HJ, Williams NM, Wormley B, Zammit S, Sullivan PF, O’Donovan MC, Daly MJ, Gejman PV. Genome-wide association study identifies five new schizophrenia loci. Nat Genet. 2011;43(10):969–976. doi: 10.1038/ng.940. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Rusinov V, Baev V, Minkov IN, Tabler M. MicroInspector: a web tool for detection of miRNA binding sites in an RNA sequence. Nucleic Acids Res. 2005;33:W696–W700. doi: 10.1093/nar/gki364. Web Server issue. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Ryan BM, Robles AI, Harris CC. Genetic variation in microRNA networks: the implications for cancer research. Nat Rev Cancer. 2010;10(6):389–402. doi: 10.1038/nrc2867. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Saggers-Gray L, Wildenauer DB, Schwab SG. Expression of PIP4K2A in lymphocyte cell lines from a sample of schizophrenia patients with previous evidence for association. Schizophr Res. 2011;130(1–3):295–296. doi: 10.1016/j.schres.2011.02.010. [DOI] [PubMed] [Google Scholar]
  63. Schanen BC, Li X. Transcriptional regulation of mammalian miRNA genes. Genomics. 2010;97(1):1–6. doi: 10.1016/j.ygeno.2010.10.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Schmitt A, Hasan A, Gruber O, Falkai P. Schizophrenia as a disorder of disconnectivity. Eur Arch Psychiatry Clin Neurosci. 2011;261(Suppl 2):S150–S154. doi: 10.1007/s00406-011-0242-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Shu Y, Zhang H, Kang T, Zhang JJ, Yang Y, Liu H, Zhang L. PI3K/Akt signal pathway involved in the cognitive impairment caused by chronic cerebral hypoperfusion in rats. PLoS One. 2013;8(12):e81901. doi: 10.1371/journal.pone.0081901. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Simpson EH, Morud J, Winiger V, Biezonski D, Zhu JP, Bach ME, Malleret G, Polan HJ, Ng-Evans S, Phillips PE, Kellendonk C, Kandel ER. Genetic variation in COMT activity impacts learning and dopamine release capacity in the striatum. Learn Mem. 2014;21(4):205–214. doi: 10.1101/lm.032094.113. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Singh C, Bortolato M, Bali N, Godar SC, Scott AL, Chen K, Thompson RF, Shih JC. Cognitive abnormalities and hippocampal alterations in monoamine oxidase A and B knockout mice. Proc Natl Acad Sci U S A. 2013;110(31):12816–12821. doi: 10.1073/pnas.1308037110. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Thelma BK, Tiwari AK, Deshpande SN, Lerer B, Nimgaonkar VL. Genetic susceptibility to tardive dyskinesia in chronic schizophrenia subjects: role of oxidative stress pathway genes. Schizophr Res. 2007;92(1–3):278–279. doi: 10.1016/j.schres.2006.12.019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Thomson PA, Parla JS, McRae AF, Kramer M, Ramakrishnan K, Yao J, Soares DC, McCarthy S, Morris SW, Cardone L, Cass S, Ghiban E, Hennah W, Evans KL, Rebolini D, Millar JK, Harris SE, Starr JM, Macintyre DJ, McIntosh AM, Watson JD, Deary IJ, Visscher PM, Blackwood DH, McCombie WR, Porteous DJ. 708 Common and 2010 rare DISC1 locus variants identified in 1542 subjects: analysis for association with psychiatric disorder and cognitive traits. Mol Psychiatry. 2013 doi: 10.1038/mp.2013.68. [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Tiwari AK, Deshpande SN, Rao AR, Bhatia T, Lerer B, Nimgaonkar VL, Thelma BK. Genetic susceptibility to tardive dyskinesia in chronic schizophrenia subjects: III. Lack of association of CYP3A4 and CYP2D6 gene polymorphisms. Schizophr Res. 2005a;75(1):21–26. doi: 10.1016/j.schres.2004.12.011. [DOI] [PubMed] [Google Scholar]
  71. Tiwari AK, Deshpande SN, Rao AR, Bhatia T, Mukit SR, Shriharsh V, Lerer B, Nimagaonkar VL, Thelma BK. Genetic susceptibility to tardive dyskinesia in chronic schizophrenia subjects: I. Association of CYP1A2 gene polymorphism. Pharm J. 2005b;5(1):60–69. doi: 10.1038/sj.tpj.6500282. [DOI] [PubMed] [Google Scholar]
  72. Tiwari AK, Deshpande SN, Lerer B, Nimgaonkar VL, Thelma BK. Genetic susceptibility to tardive dyskinesia in chronic schizophrenia subjects: V. Association of CYP1A2 1545C > T polymorphism. Pharm J. 2007;7(5):305–311. doi: 10.1038/sj.tpj.6500415. [DOI] [PubMed] [Google Scholar]
  73. Tosic M, Ott J, Barral S, Bovet P, Deppen P, Gheorghita F, Matthey ML, Parnas J, Preisig M, Saraga M, Solida A, Timm S, Wang AG, Werge T, Cuenod M, Do KQ. Schizophrenia and oxidative stress: glutamate cysteine ligase modifier as a susceptibility gene. Am J Hum Genet. 2006;79(3):586–592. doi: 10.1086/507566. [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Toyooka K, Watanabe Y, Iritani S, Shimizu E, Iyo M, Nakamura R, Asama K, Makifuchi T, Kakita A, Takahashi H, Someya T, Nawa H. A decrease in interleukin-1 receptor antagonist expression in the prefrontal cortex of schizophrenic patients. Neurosci Res. 2003;46(3):299–307. doi: 10.1016/s0168-0102(03)00093-2. [DOI] [PubMed] [Google Scholar]
  75. van den Bout I, Divecha N. PIP5K-driven PtdIns(4.5)P2 synthesis: regulation and cellular functions. J Cell Sci. 2009;122(Pt 21):3837–3850. doi: 10.1242/jcs.056127. [DOI] [PubMed] [Google Scholar]
  76. Wu Q, Li Y, Xiao B. DISC1-related signaling pathways in adult neurogenesis of the hippocampus. Gene. 2013;518(2):223–230. doi: 10.1016/j.gene.2013.01.015. [DOI] [PubMed] [Google Scholar]
  77. Yamada K, Gerber DJ, Iwayama Y, Ohnishi T, Ohba H, Toyota T, Aruga J, Minabe Y, Tonegawa S, Yoshikawa T. Genetic analysis of the calcineurin pathway identifies members of the EGR gene family, specifically EGR3, as potential susceptibility candidates in schizophrenia. Proc Natl Acad Sci U S A. 2007;104(8):2815–2820. doi: 10.1073/pnas.0610765104. [DOI] [PMC free article] [PubMed] [Google Scholar]
  78. Zeng H, Chattarji S, Barbarosie M, Rondi-Reig L, Philpot BD, Miyakawa T, Bear MF, Tonegawa S. Forebrain-specific calcineurin knockout selectively impairs bidirectional synaptic plasticity and working/episodic-like memory. Cell. 2001;107(5):617–629. doi: 10.1016/s0092-8674(01)00585-2. [DOI] [PubMed] [Google Scholar]
  79. Zhou Y, Wang J, Lu X, Song X, Ye Y, Zhou J, Ying B, Wang L. Evaluation of six SNPs of MicroRNA machinery genes and risk of schizophrenia. J Mol Neurosci. 2012;49(3):594–599. doi: 10.1007/s12031-012-9887-1. [DOI] [PubMed] [Google Scholar]
  80. Ziats MN, Rennert OM. Identification of differentially expressed microRNAs across the developing human brain. Mol Psychiatry. 2013 doi: 10.1038/mp.2013.93. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1
NIHMS861995-supplement-1.docx (18.5KB, docx)

RESOURCES