Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Open Access

Peer-reviewed

Research Article

Association of ARHGAP18 polymorphisms with schizophrenia in the Chinese-Han population

  • Weiyun Guo ,

    Contributed equally to this work with: Weiyun Guo, Yaqi Cai

    Affiliations College of Life Science and Technology, Xinxiang Medical University, Xinxiang, China, Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China

    ⨯
  • Yaqi Cai ,

    Contributed equally to this work with: Weiyun Guo, Yaqi Cai

    Affiliations Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China, Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China

    ⨯
  • Hongxing Zhang,

    Affiliations Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China, Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China, Department of Psychology, Xinxiang Medical University, Xinxiang, China

    ⨯
  • Yongfeng Yang,

    Affiliations Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China, Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China

    ⨯
  • Ge Yang,

    Affiliations Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China, Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China

    ⨯
  • Xiujuan Wang,

    Affiliations Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China, Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China

    ⨯
  • Jingyuan Zhao,

    Affiliation Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China

    ⨯
  • Juntang Lin,

    Affiliations College of Life Science and Technology, Xinxiang Medical University, Xinxiang, China, Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China, Institute of Anatomy I, Friedrich Schiller University Jena, Jena, Germany

    ⨯
  • Jinfu Zhu,

    Affiliations Institute of Anatomy I, Friedrich Schiller University Jena, Jena, Germany, Department of Psychology, Xinxiang Medical University, Xinxiang, China

    ⨯
  • Wenqiang Li ,

    lwq781603@163.com (WL); lvx928@126.com (LL)

    Affiliations Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China, Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China

    ⨯
  • Luxian Lv

    lwq781603@163.com (WL); lvx928@126.com (LL)

    Affiliations Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China, Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China

    ⨯

Abstract

Numerous developmental genes have been linked to schizophrenia (SZ) by case-control and genome-wide association studies, suggesting that neurodevelopmental disturbances are major pathogenic mechanisms. However, no neurodevelopmental deficit has been definitively linked to SZ occurrence, likely due to disease heterogeneity and the differential effects of various gene variants across ethnicities. Hence, it is critical to examine linkages in specific ethnic populations, such as Han Chinese. The newly identified RhoGAP ARHGAP18 is likely involved in neurodevelopment through regulation of RhoA/C. Here we describe four single nucleotide polymorphisms (SNPs) in ARHGAP18 associated with SZ across a cohort of >2000 cases and controls from the Han population. Two SNPs, rs7758025 and rs9483050, displayed significant differences between case and control groups both in genotype (P = 0.0002 and P = 7.54×10−6) and allelic frequencies (P = 4.36×10−5 and P = 5.98×10−7), respectively. The AG haplotype in rs7758025−rs9385502 was strongly associated with the occurrence of SZ (P = 0.0012, OR = 0.67, 95% CI = 0.48–0.93), an association that still held following a 1000-times random permutation test (P = 0.022). In an independently collected validation cohort, rs9483050 was the SNP most strongly associated with SZ. In addition, the allelic frequencies of rs12197901 remained associated with SZ in the combined cohort (P = 0.021), although not in the validation cohort alone (P = 0.251). Collectively, our data suggest the ARHGAP18 may confer vulnerability to SZ in the Chinese Han population, providing additional evidence for the involvement of neurodevelopmental dysfunction in the pathogenesis of schizophrenia.

Citation: Guo W, Cai Y, Zhang H, Yang Y, Yang G, Wang X, et al. (2017) Association of ARHGAP18 polymorphisms with schizophrenia in the Chinese-Han population. PLoS ONE 12(4): e0175209. https://doi.org/10.1371/journal.pone.0175209

Editor: Xiang Yang Zhang, University of Texas Health Science Center at San Antonio Cancer Therapy and Research Center at Houston, UNITED STATES

Received: February 21, 2017; Accepted: March 22, 2017; Published: April 6, 2017

Copyright: © 2017 Guo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: This work was supported by the following: (1) the National Natural Science Foundation of China (to WL, 81201040; to YY, U1404811; to HZ, 81571315), http://www.nsfc.gov.cn/. (2) the Natural Science Foundation of Henan (to WL, 122300413212; to WG, 162102310488; to J. Zhu, 132102310164), http://www.hnkjt.gov.cn/. (3) the Scientific Research Fund of Xinxiang Medical University (to WL, ZD2011-20; to YY, 2013QN102), http://www.xxmu.edu.cn/. (4) the Open Fund of Henan Key lab of Biological Psychiatry (to WG, ZDSYS2014002). (5) the support project for the Disciplinary group of Psychology and Neuroscience to Xinxiang Medical University, http://www.haedu.gov.cn/. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Schizophrenia (SZ) is among the most severe and difficult to treat psychiatric disorders due to variable expression of psychotic symptoms, mood deregulation, and cognitive dysfunction[1]. There is also considerable heterogeneity in disease heritability, implying that SZ arises from a complex interaction of multiple genetic susceptibility factors; thus, there is no unified pathogenic model [2]. Compelling evidence points to disturbances in neurodevelopment during the prenatal and early postnatal periods that impact brain maturation during adolescence and early adulthood, ultimately leading to the delayed emergence of psychiatric symptoms[3]. It is thus believed that different allelic combinations of neurodevelopmental genes (haplotypes) may predispose individuals to SZ and other major psychiatric disorders [4, 5].

Neurodevelopmental abnormalities may result from prenatal immune activation [6], alimentary deficiency [7], and genetic factors. Indeed, multiple genetic factors have been linked to schizophrenia susceptibility[8–10], including genes associated with Rho GTPase signaling pathways[11]. Rho GTPase activating proteins (RhoGAPs) are a large protein family containing approximate 80 members that stimulate GTP hydrolysis, thereby turning the GTP-bound active form of Rho into a inactive GDP-bound form[12, 13]. The Rho family GTPase play a critical role in many aspects of neuronal development, including neurite outgrowth[14, 15], neuronal differentiation [14], axon guidance[15–17], and synaptic formation and maintenance[18,19]. Likewise, many RhoGAP proteins have been linked to neurodevelopmental processes and related disabilities. For example, oligophrenin-1 encodes a RhoGAP involved in X-linked mental retardation [20, 21]. Recently, dysfunction of RhoGAPX-1 and ARHGAP6 has been implicated in a wide range of developmental defects seen in microphthalmia with linear skin defects syndrome[22, 23], while srGAP1, srGAP2, and srGAP3 have been linked to mental retardation, schizophrenia, and seizures[24]. Considering the functional redundancy of many RhoGAP proteins, these findings suggest that additional family members are also involved in pathological conditions related to aberrant neurodevelopment.

ARHGAP18 is a newly identified RhoGAP capable of regulating RhoA and RhoC activities in a cell type-specific context [25, 26]. Although the functions of ARHGAP18 in the central nervous system are presently unknown, recent studies have shown a potential correlation between genetic polymorphisms of ARHGAP18 and the occurrence of schizophrenia. Through the combinatorial use of a genome-wide screening and neuroimaging, single nucleotide polymorphisms (SNPs) within ARHGAP18 were associated with schizophrenia [27, 28]. However, these studies were based on Western populations and not validated in an independent case−control study. Herein, we evaluated the association of ARHGAP18 polymorphisms and schizophrenia in a large Chinese Han population of SZ patients and matched controls.

Materials and methods

Subjects

All participants were recruited from northern Henan Province and had four biological grandparents of Han Chinese ancestry. The Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders-Fourth Edition IV (DSM-IV) (1994) Axis I Disorders was used to exclude individuals with a history of severe medical complications (such as diabetes, cardiovascular disease, hypertension), organic brain diseases, concomitant major psychiatric disorders, and/or substance dependence. The discovery cohort consisted of 528 patients (264 males and 264 females; mean age: 27.32 ± 8.03 years old) and 528 healthy controls matched for sex ratio, age, and ethnicity (264 males and 264 females; mean age: 27.73 ± 8.01 years old). The validation cohort consisted of 860 patients (430 males and 430 females; mean age: 28.34 ± 9.25 years old) and 860 healthy matched controls (430 males and 430 females; mean age: 29.58 ± 7.29 years old). For each patient, the diagnosis of SZ was confirmed by at least two psychiatrists according to the DSM-IV criteria for paranoid SZ. All healthy volunteers were recruited from Xinxiang Medical University, Xinxiang city, and surrounding communities and villages by posters in Physical Examination Center and hospitals in towns and counties. Any individual with a personal or family history of mental or neurological diseases was excluded. The controls were well matched to the patient group for gender ratio (1:1 for both groups), age (F = 0.621, P = 0.464), and ethnicity (all unrelated, living in North Henan Province, and with all biological grandparents of Chinese-Han ancestry).

Written informed consent was obtained for all participants. The study was approved by the ethics committee of the Second Affiliated Hospital of Xinxiang Medical University.

Genotyping

A peripheral blood sample was drawn from each subject into vacutainer tubes containing the anticoagulant ethylenediaminetetraacetic acid. Genomic DNA was extracted from leukocytes using the RelaxGene Blood DNA System (Tiangen Biotech., Beijing, China). In the discovery stage, the genotypes of 35 SNPs in ARHGAP18 were evaluated using the Illumina GoldenGate assay on a BeadStation 500G Genotyping System (Illumina, Inc., San Diego, CA, USA) according to the manufacturer’s instructions.

Validation of specific SNPs, including rs9483050, rs7758025, rs12197901, and rs9492347, was performed using the TaqMan genotyping method according to the manufacturer’s protocol, with allelic discrimination and analysis performed on an ABI Prism 7900 Sequence Detection System (Applied Biosystems, Foster City, CA, USA). The ABI Taqman probe sequences are listed in S1 Table. To evaluate the quality of genotyping, 5% of the samples were randomly selected and re-genotyped. The genotyping consistency rate was more than 98%.

Bioinformatics analyses

All genotype data were examined for cluster separation using Illumina quality scores generated by the software. Poorly performing SNPs as defined by a GenTrain score < 0.4 or a cluster separation score < 0.6 were excluded. SNPs were further excluded if controls were not in Hardy—Weinberg equilibrium. As a genotyping quality control, four SNPs were genotyped in duplicate in 100 samples by DNA sequencing.

Genotypes and allele frequencies in SZ and control subjects were compared using the Haploview V4.1 program with Bonferroni correction to exclude type I errors (including from other SNPs in the same GoldenGate 384 assay relevant to a different experimental design). Hardy—Weinberg equilibrium was also evaluated using this program,. The standardized measures of linkage disequilibrium (LD) coefficients (D′), haplotype frequency, haplotype block, and haplotype association were assessed using Haploview V4.1.

Allele and genotype counts were compared by the Pearson chi-square test. A power analysis was performed using the Genetic Power Calculator[29]. Genotyping data (include other SNPs in the same GoldenGate 384 assay relevant to a different experimental design) were analyzed using the Markov chain Monte Carlo algorithm in Structure 2.3 [30] to generate population stratification assignments for all individuals. Odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated to evaluate the effect of different alleles and haplotypes on SZ risk. The haplotype frequencies were estimated using the expectation maximization (EM) algorithm.

Results

We selected a total of thirty-five SNPs in ARHGAP18 for genotypic distribution analysis in 528 patients with schizophrenia and 528 healthy controls. All SNPs evaluated demonstrated a minor allele frequency greater than 5% in the studied samples. Power analysis revealed that the total sample size (n = 1056) had the power (0.86) to detect a small (r = 0.1–0.23) effects and the power (1.00) to detect both medium(r = 0.24–0.36) and large(r > 0.37) effects on genotype distributions. The genotype and allele frequencies of these SNPs in patients and controls are shown in Table 1.

thumbnail
Download:
Table 1. Genotype and allele frequencies of thirty-two SNPs in the ARHGAP18 gene of schizophrenia patients and controls.

https://doi.org/10.1371/journal.pone.0175209.t001

The genotype and allelic frequencies of two SNPs, rs7758025 and rs9483050 displayed significant differences between the case and control groups (rs7758025: genotype P = 0.0002; allele P = 4.36×10-5; rs9483050: genotype P = 7.54×10-6; allele P = 5.98×10-7). In addition, rs9492347 genotype frequency was associated with schizophrenia (P = 0.011) as was rs12197901 allelic frequency (P = 0.022). The genotypic distribution of these four SNPs did not demonstrate significant deviations from Hardy−Weinberg equilibrium in the control group.

We next performed LD analysis using pairs of SNPs to further analyze the haplotype structure. As shown in Fig 1, Table 2, the LD plot consisted of thrity-five SNPs. Haplotypes GG and GA in the LD block rs7758025−rs9385502 showed minimal difference between the case and control groups (P = 0.175 and P = 0.232, respectively), while haplotype AG was strongly associated with schizophrenia (P = 0.0012, OR = 0.67, 95% CI = 0.48−0.93). These associations remained following a 1000-times random permutation test (P = 0.022). Haplotype CG in the LD block rs11753915−rs9483050 was also associated with schizophrenia (P = 9.6×10−6, OR = 0.58, 95% CI = 0.44−0.78) even after Bonferroni correction (P = 0.0001).

thumbnail
Download:
Table 2. Haplotype analysis among SZ and controls.

https://doi.org/10.1371/journal.pone.0175209.t002

thumbnail
Download:
Fig 1. LD structure and the D′ values for the 31 Single Nucleotide Polymorphisms (SNPs).

https://doi.org/10.1371/journal.pone.0175209.g001

We then re-tested the four ARHGAP18 SNPs associated with schizophrenia in an independent validation cohort of 860 patients and 860 controls. In stage 2, the ample size gets a high power (0.93). As shown in Table 3, both genotype and allelic frequencies of rs7758025 and rs9483050 SNPs displayed strong associations with schizophrenia in the validation cohort. Of note, rs9483050 appeared most strongly associated with the disease state across the two cohorts. In addition, the allelic frequency of rs12197901 remained associated with schizophrenia in the combined analysis (P = 0.021), although not in the validation cohort alone (P = 0.251). The trends observed for the association of rs9492347 with SZ were not confirmed in the validation cohort or combined analysis.

thumbnail
Download:
Table 3. SNP association analysis for ARHGAP18 in stage 2 and combined sample set.

https://doi.org/10.1371/journal.pone.0175209.t003

Discussion

Herein, we describe associations between ARHGAP18 polymorphisms and schizophrenia in a Chinese Han population. In stage one, we screened SNPs in ARHGAP18 from GWAS data, and discovered four SNPs, rs7758025, rs9483050, rs9492347 and rs12197901, associated with schizophrenia. In stage two, we validated our findings in an independent cohort and demonstrated that rs9483050 is strongly associated with schizophrenia. Our data suggest that allelic variation in the ARHGAP18 gene may confer vulnerability to SZ in the Chinese Han population, providing additional evidence for the involvement of disrupted neurodevelopmental signals in disease pathogenesis.

Although the detailed molecular events during SZ progression remain elusive, it is widely accepted that abnormalities in early brain development caused by inherited genetic variants alter critical developmental and maturational processes, resulting in eventual emergence of disabling psychoses[31–33]. A plethora of genes have polymorphisms associated with SZ [9, 34–38]. Nevertheless, due to complexity of epidemiology, including ethnicity and disease subtype, our understanding of disease heritability remains limited and it is likely that many more SZ susceptibility genes remain to be identified.

ARHGAP18 (6q22.33) lies within a previously reported SZ candidate region (SCR) identified by Lerer et al [39], in an ethnically homogeneous family-based Arab—Israeli sample [27, 28]. There are 134 genes in this risk region, of which several contain SNPs enriched in sporadic SZ cases, such as dystrobrevin-binding protein 1 (DTNBP1) and laminin alpha-2 (LAMA2). Given the large genomic distance spanned and the difference in localization of linkage peaks, it is possible that this region harbors additional SZ susceptibility genes, one of which may be ARHGAP18. In fact, there are several reports on the involvement of ARHGAP18 SNPs in human diseases, including SZ. Recurrent chromosomal imbalances affecting the ARHGAP18 locus were observed in six of nine patients with neurofibromatosis type 1[40]. Also, Potkin et al. used brain activation pattern during a working memory task as a quantitative trait to interpret GWAS data and identified ARHGAP18 as a SZ risk gene [27]. Specifically, they found six SNPs within ARHGAP18, rs12664247, rs4509146, rs11154490, rs2051632, rs17469847 and rs10484284, with statistically significant relationships in both the discovery and validation cohorts.

Herein, we used large discovery and validation cohorts to identify the rs9492347, rs7758025, and rs9483050 SNPs of ARHGAP18 as susceptibility loci for SZ. Our results are intriguing in several ways. First, these SNPs were not associated with SZ in GWASs interrogating ARHGAP18, although those studies did not use the sample SNP panel studied here. Also, the number of included samples in our study is much higher than previous studies on ARHGAP18 SNPs. Moreover, our study extends the risk of SZ conferred by ARHGAP18 SNPs from Western populations to the Eastern Han population.

ARHGAP18 encodes one of approximately 80 RhoGAP proteins [12, 13]. As a RhoGAP, ARHGAP18 mainly serves as a molecular switch for controlling the balance between active and inactive Rho proteins to regulate Rho-mediated signaling pathways. Rho GTPases, a protein family composed of 22 members in mammals, are known as important modulators of the actin cytoskeleton influencing neuronal morphology and migration [41, 42]. In addition, Rho GTPases are also reportedly involved in the regulation of growth factor-linked signal pathways.

Among members of the Rho GTPase family, RhoA is the main molecule responding to ARHGAP18 regulation. ARHGAP18-knockdown cells demonstrated impaired cell spreading, premature formation of stress fibers, and sustained activation of RhoA upon cell attachment, resulting in inhibition of cell migration [25, 26]. Although the neurodevelopmental function of ARHGAP18 has not been elucidated, numerous studies have suggested a critical role for RhoA in neurogenesis and maturation [43–46]. Abolishing RhoA activity in the postnatal stage led to major changes in density and absolute number of neurons in the somatosensory cortex [47]. Also, deletion of RhoA from neural progenitor cells in mice resulted in abnormal locomotor behavior [48, 49]. Of note, all three identified SNPs are located in the intron region, which is common for most top hit SZ-associated SNPs revealed by GWASs. The presence of these intronic SNPs may regulate ARHGAP18 mRNA splicing in a trans-acting manner, thereby leading to malfunction of the ARHGAP18−RhoA axis in neurodevelopment.

Conclusion

In summary, our study provides novel data suggesting an association between ARHGAP18 and SZ susceptibility. Replication studies in different ethnic populations, particularly in patients with defined SZ phenotypes, and more samples, are required to confirm the role of ARHGAP18 variants in SZ.

Supporting information

S1 Table. Taqman probe sequences of four SNPs.

https://doi.org/10.1371/journal.pone.0175209.s001

(DOC)

Acknowledgments

The authors thank the patients, their families, and the healthy volunteers for their participation, and the physicians who helped us collect clinical data and blood samples in the Second Affiliated Hospital of Xinxiang Medical University.

Author Contributions

  1. Conceptualization: WG WL.
  2. Data curation: YC HZ YY.
  3. Formal analysis: GY J. Zhao.
  4. Funding acquisition: J. Zhu WL LL.
  5. Investigation: WG YY.
  6. Methodology: WG GY.
  7. Project administration: WG HZ WL.
  8. Resources: XW J. Zhao.
  9. Software: HZ YY.
  10. Supervision: YC LL.
  11. Validation: WG YC WL LL.
  12. Visualization: HZ WL LL.
  13. Writing – original draft: WG YC.
  14. Writing – review & editing: JL WL.

References

  1. 1. Messias EL, Chen CY, Eaton WW. Epidemiology of schizophrenia: review of findings and myths. The Psychiatric clinics of North America. 2007;30(3):323–38. pmid:17720026
  2. 2. Hu X, Zhang J, Jin C, Mi W, Wang F, Ma W, et al. Association study of NRXN3 polymorphisms with schizophrenia and risperidone-induced bodyweight gain in Chinese Han population. Progress in neuro-psychopharmacology & biological psychiatry. 2013;43:197–202.
  3. 3. Brown AS, Begg MD, Gravenstein S, Schaefer CA, Wyatt RJ, Bresnahan M, et al. Serologic evidence of prenatal influenza in the etiology of schizophrenia. Archives of general psychiatry. 2004;61(8):774–80. pmid:15289276
  4. 4. Fatemi SH, Folsom TD. The neurodevelopmental hypothesis of schizophrenia, revisited. Schizophrenia bulletin. 2009;35(3):528–48. pmid:19223657
  5. 5. Owen MJ, O'Donovan MC, Thapar A, Craddock N. Neurodevelopmental hypothesis of schizophrenia. The British journal of psychiatry: the journal of mental science. 2011;198(3):173–5.
  6. 6. Bauman MD, Iosif AM, Smith SE, Bregere C, Amaral DG, Patterson PH. Activation of the maternal immune system during pregnancy alters behavioral development of rhesus monkey offspring. Biological psychiatry. 2014;75(4):332–41. pmid:24011823
  7. 7. Anjos T, Altmae S, Emmett P, Tiemeier H, Closa-Monasterolo R, Luque V, et al. Nutrition and neurodevelopment in children: focus on NUTRIMENTHE project. European journal of nutrition. 2013;52(8):1825–42. pmid:23884402
  8. 8. Li W, Song X, Zhang H, Yang Y, Jiang C, Xiao B, et al. Association study of RELN polymorphisms with schizophrenia in Han Chinese population. Progress in neuro-psychopharmacology & biological psychiatry. 2011;35(6):1505–11.
  9. 9. Yue WH, Wang HF, Sun LD, Tang FL, Liu ZH, Zhang HX, et al. Genome-wide association study identifies a susceptibility locus for schizophrenia in Han Chinese at 11p11.2. Nature genetics. 2011;43(12):1228–31. pmid:22037552
  10. 10. Rapoport JL, Giedd JN, Gogtay N. Neurodevelopmental model of schizophrenia: update 2012. Molecular psychiatry. 2012;17(12):1228–38. pmid:22488257
  11. 11. Datta D, Arion D, Corradi JP, Lewis DA. Altered expression of CDC42 signaling pathway components in cortical layer 3 pyramidal cells in schizophrenia. Biological psychiatry. 2015;78(11):775–85. pmid:25981171
  12. 12. Tcherkezian J, Lamarche-Vane N. Current knowledge of the large RhoGAP family of proteins. Biology of the cell / under the auspices of the European Cell Biology Organization. 2007;99(2):67–86.
  13. 13. Peck J, Douglas Gt, Wu CH, Burbelo PD. Human RhoGAP domain-containing proteins: structure, function and evolutionary relationships. FEBS letters. 2002;528(1–3):27–34. pmid:12297274
  14. 14. Rosario M, Franke R, Bednarski C, Birchmeier W. The neurite outgrowth multiadaptor RhoGAP, NOMA-GAP, regulates neurite extension through SHP2 and Cdc42. The Journal of cell biology. 2007;178(3):503–16. pmid:17664338
  15. 15. Brouns MR, Matheson SF, Settleman J. p190 RhoGAP is the principal Src substrate in brain and regulates axon outgrowth, guidance and fasciculation. Nature cell biology. 2001;3(4):361–7. pmid:11283609
  16. 16. Foletta VC, Brown FD, Young WS 3rd. Cloning of rat ARHGAP4/C1, a RhoGAP family member expressed in the nervous system that colocalizes with the Golgi complex and microtubules. Brain research Molecular brain research. 2002;107(1):65–79. pmid:12414125
  17. 17. Hallam SJ, Goncharov A, McEwen J, Baran R, Jin Y. SYD-1, a presynaptic protein with PDZ, C2 and rhoGAP-like domains, specifies axon identity in C. elegans. Nature neuroscience. 2002;5(11):1137–46. pmid:12379863
  18. 18. Zhang H, Macara IG. The PAR-6 polarity protein regulates dendritic spine morphogenesis through p190 RhoGAP and the Rho GTPase. Developmental cell. 2008;14(2):216–26. pmid:18267090
  19. 19. Lamprecht R, Farb CR, LeDoux JE. Fear memory formation involves p190 RhoGAP and ROCK proteins through a GRB2-mediated complex. Neuron. 2002;36(4):727–38. pmid:12441060
  20. 20. Billuart P, Bienvenu T, Ronce N, des Portes V, Vinet MC, Zemni R, et al. Oligophrenin-1 encodes a rhoGAP protein involved in X-linked mental retardation. Nature. 1998;392(6679):923–6. pmid:9582072
  21. 21. Zanni G, Saillour Y, Nagara M, Billuart P, Castelnau L, Moraine C, et al. Oligophrenin 1 mutations frequently cause X-linked mental retardation with cerebellar hypoplasia. Neurology. 2005;65(9):1364–9. pmid:16221952
  22. 22. Prakash SK, Paylor R, Jenna S, Lamarche-Vane N, Armstrong DL, Xu B, et al. Functional analysis of ARHGAP6, a novel GTPase-activating protein for RhoA. Human molecular genetics. 2000;9(4):477–88. pmid:10699171
  23. 23. Schaefer L, Prakash S, Zoghbi HY. Cloning and characterization of a novel rho-type GTPase-activating protein gene (ARHGAP6) from the critical region for microphthalmia with linear skin defects. Genomics. 1997;46(2):268–77. pmid:9417914
  24. 24. Ma Y, Mi YJ, Dai YK, Fu HL, Cui DX, Jin WL. The inverse F-BAR domain protein srGAP2 acts through srGAP3 to modulate neuronal differentiation and neurite outgrowth of mouse neuroblastoma cells. PloS one. 2013;8(3):e57865. pmid:23505444
  25. 25. Chang GH, Lay AJ, Ting KK, Zhao Y, Coleman PR, Powter EE, et al. ARHGAP18: an endogenous inhibitor of angiogenesis, limiting tip formation and stabilizing junctions. Small GTPases. 2014;5(3):1–15.
  26. 26. Maeda M, Hasegawa H, Hyodo T, Ito S, Asano E, Yuang H, et al. ARHGAP18, a GTPase-activating protein for RhoA, controls cell shape, spreading, and motility. Molecular biology of the cell. 2011;22(20):3840–52. pmid:21865595
  27. 27. Potkin SG, Macciardi F, Guffanti G, Fallon JH, Wang Q, Turner JA, et al. Identifying gene regulatory networks in schizophrenia. NeuroImage. 2010;53(3):839–47. pmid:20600988
  28. 28. Potkin SG, Turner JA, Fallon JA, Lakatos A, Keator DB, Guffanti G, et al. Gene discovery through imaging genetics: identification of two novel genes associated with schizophrenia. Molecular psychiatry. 2009;14(4):416–28. pmid:19065146
  29. 29. Purcell S, Cherny SS, Sham PC. Genetic Power Calculator: design of linkage and association genetic mapping studies of complex traits. Bioinformatics. 2003;19(1):149–50. pmid:12499305
  30. 30. Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics. 2000;155(2):945–59. pmid:10835412
  31. 31. Cannon TD. How Schizophrenia Develops: Cognitive and Brain Mechanisms Underlying Onset of Psychosis. Trends in cognitive sciences. 2015;19(12):744–56. pmid:26493362
  32. 32. Sakurai T, Gamo NJ, Hikida T, Kim SH, Murai T, Tomoda T, et al. Converging models of schizophrenia—Network alterations of prefrontal cortex underlying cognitive impairments. Progress in neurobiology. 2015;134:178–201. pmid:26408506
  33. 33. Voineskos AN. Genetic underpinnings of white matter 'connectivity': heritability, risk, and heterogeneity in schizophrenia. Schizophrenia research. 2015;161(1):50–60. pmid:24893906
  34. 34. Loh PR, Bhatia G, Gusev A, Finucane HK, Bulik-Sullivan BK, Pollack SJ, et al. Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance-components analysis. Nature genetics. 2015;47(12):1385–92. pmid:26523775
  35. 35. Ripke S, O'Dushlaine C, Chambert K, Moran JL, Kahler AK, Akterin S, et al. Genome-wide association analysis identifies 13 new risk loci for schizophrenia. Nature genetics. 2013;45(10):1150–9. pmid:23974872
  36. 36. Lee SH, DeCandia TR, Ripke S, Yang J, Schizophrenia Psychiatric Genome-Wide Association Study C, International Schizophrenia C, et al. Estimating the proportion of variation in susceptibility to schizophrenia captured by common SNPs. Nature genetics. 2012;44(3):247–50. pmid:22344220
  37. 37. Shi Y, Li Z, Xu Q, Wang T, Li T, Shen J, et al. Common variants on 8p12 and 1q24.2 confer risk of schizophrenia. Nature genetics. 2011;43(12):1224–7. pmid:22037555
  38. 38. Schizophrenia Psychiatric Genome-Wide Association Study C. Genome-wide association study identifies five new schizophrenia loci. Nature genetics. 2011;43(10):969–76. pmid:21926974
  39. 39. Lerer B, Segman RH, Hamdan A, Kanyas K, Karni O, Kohn Y, et al. Genome scan of Arab Israeli families maps a schizophrenia susceptibility gene to chromosome 6q23 and supports a locus at chromosome 10q24. Molecular psychiatry. 2003;8(5):488–98. pmid:12808429
  40. 40. Asai A, Karnan S, Ota A, Takahashi M, Damdindorj L, Konishi Y, et al. High-resolution 400K oligonucleotide array comparative genomic hybridization analysis of neurofibromatosis type 1-associated cutaneous neurofibromas. Gene. 2015;558(2):220–6. pmid:25562418
  41. 41. Ridley AJ. Rho GTPase signalling in cell migration. Current opinion in cell biology. 2015;36:103–12. pmid:26363959
  42. 42. McCormack J, Welsh NJ, Braga VM. Cycling around cell-cell adhesion with Rho GTPase regulators. Journal of cell science. 2013;126(Pt 2):379–91. pmid:23547086
  43. 43. Azzarelli R, Pacary E, Garg R, Garcez P, van den Berg D, Riou P, et al. An antagonistic interaction between PlexinB2 and Rnd3 controls RhoA activity and cortical neuron migration. Nature communications. 2014;5:3405. pmid:24572910
  44. 44. Katayama K, Imai F, Campbell K, Lang RA, Zheng Y, Yoshida Y. RhoA and Cdc42 are required in pre-migratory progenitors of the medial ganglionic eminence ventricular zone for proper cortical interneuron migration. Development. 2013;140(15):3139–45. pmid:23861058
  45. 45. Li J, Anton ES. Rnd-ing up RhoA activity to link neurogenesis with steps in neuronal migration. Developmental cell. 2011;20(4):409–10. pmid:21497751
  46. 46. Kitaoka Y, Kitaoka Y, Kumai T, Lam TT, Kuribayashi K, Isenoumi K, et al. Involvement of RhoA and possible neuroprotective effect of fasudil, a Rho kinase inhibitor, in NMDA-induced neurotoxicity in the rat retina. Brain research. 2004;1018(1):111–8. pmid:15262212
  47. 47. Sanno H, Shen X, Kuru N, Bormuth I, Bobsin K, Gardner HA, et al. Control of postnatal apoptosis in the neocortex by RhoA-subfamily GTPases determines neuronal density. The Journal of neuroscience: the official journal of the Society for Neuroscience. 2010;30(12):4221–31.
  48. 48. Mulherkar S, Liu F, Chen Q, Narayanan A, Couvillon AD, Shine HD, et al. The small GTPase RhoA is required for proper locomotor circuit assembly. PloS one. 2013;8(6):e67015. pmid:23825607
  49. 49. Katayama K, Leslie JR, Lang RA, Zheng Y, Yoshida Y. Left-right locomotor circuitry depends on RhoA-driven organization of the neuroepithelium in the developing spinal cord. The Journal of neuroscience: the official journal of the Society for Neuroscience. 2012;32(30):10396–407.