An exploratory association study of the influence of dysbindin and neuregulin polymorphisms on brain morphometry in patients with schizophrenia and healthy subjects from South India

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An exploratory association study of the influence of dysbindin and neuregulin polymorphisms on brain morphometry in patients with schizophrenia and healthy subjects from South India Priyadarshini Thirunavukkarasu a,b, Anupa A. Vijayakumari a,b, John P. John a,b,c,*, Harsha N. Halahalli a,e,1, Pradip Paul b,d, Somdatta Sen b,d, Meera Purushottam b,d, Sanjeev Jain b,d a

Multimodal Brain Image Analysis Laboratory (MBIAL), NIMHANS, Bangalore 560029, India Department of Psychiatry, NIMHANS, Bangalore 560029, India c Department of Clinical Neuroscience, NIMHANS, Bangalore 560029, India d Molecular Genetics Laboratory, NIMHANS, Bangalore 560029, India e Department of Neurophysiology, NIMHANS, Bangalore 560029, India b

A R T I C L E I N F O

A B S T R A C T

Article history: Received 19 December 2013 Received in revised form 9 April 2014 Accepted 13 April 2014 Available online xxx

Multiple genetic risk variants may act in a convergent manner leading on to the pathophysiological alterations of brain structure and function in schizophrenia. We examined the effect of polymorphisms of two candidate genes that mediate glutamatergic signaling, viz., dysbindin (rs1011313) and neuregulin (rs35753505), on brain morphometry in patients with schizophrenia (N = 38) and healthy subjects (N = 37) from South India. Patients with schizophrenia showed trend-level (p < 0.001 uncorrected, 20 voxel extent correction) volumetric reductions in multiple brain regions when compared to healthy control subjects. Trend-level volumetric differences were also noted between homozygotes of the risk allele (AA) of the neuregulin (NRG1) polymorphism and heterozygotes (AG), as well as homozygotes of the risk allele (CC) of the dysbindin (DTNBP1) polymorphism and heterozygotes (TC), irrespective of diagnosis. Moreover, an additive effect of the risk alleles on brain morphometry was also noted. These preliminary findings highlight the possible influence of polymorphisms of risk genes on brain morphometry in schizophrenia. ß 2014 Elsevier B.V. All rights reserved.

Keywords: NRG1 DTNBP1 Voxel based morphometry Gray matter volumes

1. Introduction Structural abnormalities in multiple brain regions have been reported in patients with schizophrenia (Shenton et al., 2010). The most consistently replicated of these morphometric findings are

* Corresponding author at: Multimodal Brain Image Analysis Laboratory (MBIAL), Neurobiology Research Center (NRC), National Institute for Mental Health and Neurosciences (NIMHANS), PB No. 2900, Dharmaram P.O., Hosur Road, Bangalore 560 029, Karnataka, India. Tel.: +91 80 26995329; fax: +91 80 26564822/26564492. E-mail addresses: [email protected] (P. Thirunavukkarasu), [email protected] (A.A. Vijayakumari), [email protected], [email protected] (J.P. John), [email protected] (H.N. Halahalli), [email protected] (P. Paul), [email protected] (S. Sen), [email protected] (M. Purushottam), [email protected] (S. Jain). 1 Current address: Department of Physiology, K.S. Hegde Medical Academy, Nitte University, Mangalore 575018, Karnataka, India.

reduced whole brain volumes (Harvey et al., 1993; Lim et al., 1996) and increased ventricular volumes (Gaser et al., 2004). However, no regional brain abnormality has been consistently replicated across different samples of patients with schizophrenia (Ioannidis, 2011). This observed variation in brain imaging studies may be due to the influence of confounding factors such as age, medication, illness chronicity, sample heterogeneity, variability of statistical thresholds used etc. More importantly, genetic risk for schizophrenia is likely to be mediated by multiple risk variants acting in a convergent manner (Harrison and Weinberger, 2005); the differential effects of these genetic risk variants on brain morphometry may be a major source of variability of findings across association studies of patients with schizophrenia and healthy subjects. Schizophrenia is a polygenic condition wherein specific structural abnormalities owing to the influence of multiple genes have been reported (van Haren et al., 2008). The glutamatergic

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Please cite this article in press as: Thirunavukkarasu, P., et al., An exploratory association study of the influence of dysbindin and neuregulin polymorphisms on brain morphometry in patients with schizophrenia and healthy subjects from South India. Asian J. Psychiatry (2014), http://dx.doi.org/10.1016/j.ajp.2014.04.002

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2. Methods

- onset schizophrenia (N = 38) were recruited from the out-patient department of NIMHANS. The diagnosis of schizophrenia was arrived at using DSM-IV criteria (Diagnostic Statistical Manual for Mental Disorders-Fourth edition) based on the consensus of a research psychiatrist who conducted a semi-structured interview and a trained research assistant who used the Mini International Neuropsychiatric Interview (MINI) Plus (Sheehan et al., 1998). The baseline severity of schizophrenia psychopathology was evaluated using the Positive and Negative Syndrome Scale (PANSS) (Kay et al., 1987) by two trained raters who had established good inter-rater reliability. The history of exposure to antipsychotics was ascertained by interviewing the patient and relative/s, and corroborated from available medical records. Twenty three of the thirty eight patients were not on neuroleptics, of which 16 were drug naı¨ve at the time of recruitment into the study. The remaining patients (N = 15) were on antipsychotics, the cumulative doses of which were converted to ‘risperidone equivalents’ (Woods, 2003; Kroken et al., 2009; Taylor et al., 2009) (Table 1). Patients who did not meet criteria for any other Axis I disorder, including substance dependence (other than nicotine) as per MINI-Plus, with an age of first onset of psychotic symptoms at or after 17 years of age and a duration of illness less than or equal to 5 years were recruited into the study. Healthy subjects (N = 37) with no history of neurological or psychiatric illnesses, matched for age, gender and education were recruited. The healthy comparison subjects were ascertained to be free from Axis I or II psychiatric disorders using the MINI-Plus. Current use/abuse of psychotropic drugs as well as history of psychiatric illness in first-degree relatives in the healthy comparison subjects were ruled out by an unstructured clinical interview. The presence of any unstable medical/neurological condition was ruled out in both groups of subjects using an unstructured clinical interview, detailed physical examination and baseline laboratory investigations. Written informed consent was obtained from all the participants prior to enrolment into the study. The demographic and clinical characteristics of the study samples are given in Table 1.

2.1. Study sample

2.2. Magnetic resonance imaging acquisition

The study was carried out at the National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India, with approval from the Institute Ethics Committee. Subjects with recent

MRI scans were acquired on Philips Achieva 3.0T scanner using a SENSE-8 head coil. T1 anatomical images were acquired with Magnetization Prepared Rapid Gradient Echo (MPRAGE) sequence

hypothesis has recently come to occupy a central position amongst the various theories of schizophrenia pathophysiology (Moghaddam and Javitt, 2012). Genes primarily involved in neurogenesis and/or synaptic transmission have shown significant association with schizophrenia as well as with brain morphometric variations. Several genes mediating glutamatergic neurotransmission are involved in various regulatory functions in schizophrenia (Moghaddam, 2003). For example, genes mediating glutamatergic signaling such as neuregulin (NRG1) and dysbindin (DTNBP1) genes have been linked to risk for schizophrenia by candidate gene studies (Munafo` et al., 2006; Straub et al., 2002). In a recent study, Agim et al. (2013) using independent datasets from three genome wide association studies with modest sample sizes, showed an association of the NRG1 haplotypes with genetic risk for schizophrenia. Interestingly, certain polymorphisms of these genes have been linked with brain morphometric variations (Barnes et al., 2012; Narr et al., 2009; Sprooten et al., 2009; Tognin et al., 2011). We attempted to examine the effect of DTNBP1 (rs1011313) and NRG1 (rs35753505) polymorphisms on brain morphometry in patients with schizophrenia and healthy subjects recruited from South India. From amongst several SNPs of the NRG1 and DTNBP1genes that have been reported to be significantly associated with schizophrenia (Funke et al., 2004; Yang, 2012), we chose the, above-mentioned SNPs of DTNBP1 and NRG1 genes, as they have been shown by previous haplotype analysis and association studies to be core markers of schizophrenia (Bray et al., 2005; Kukshal et al., 2013; Nawaz et al., 2013; Pae et al., 2008; Prata et al., 2009). Surprisingly, however, no previous study has examined the relationship between the above SNPs and brain volumes. An additive model was also used to investigate the cumulative effect of risk alleles of these polymorphisms on regional gray matter (GM) morphometry. We hypothesized that the risk alleles of the above gene polymorphisms will have differential effects on brain volume, individually as well as additively.

Table 1 Demographic and clinical variables of study samples. Characteristics

Gender Male Female Age, years: mean (SD) Education:Formal education, years: mean (SD) Diagnosis, N Paranoid schizophrenia Undifferentiated schizophrenia Schizophreniform disorder Positive and Negative Syndrome Scale (PANSS), psychopathology score: mean (SD) Positive Negative Global Total Age at onset of illness, years: mean (SD) Duration of illness, months: mean (SD) Medication statusa Antipsychotic naive/free, N On medication, N Life-time cumulative neuroleptic exposure in risperidone equivalent dosages (mg)a: mean (SD) a

Participants (N = 75) Control group (N = 37)

Schizophrenia group (N = 38)

30 7 26.46 (5.942) 13.35 (3.691)

27 11 27.47 (7.759) 12.45 (3.064) 26 7 5 14.94 (5.918) 14.29 (5.292) 26.19 (7.377) 55.38(13.929) 25.50 (7.914) 23.53 (16.975) 16/7 15 1285.8453 (1959.06995)

Life-time cumulative neuroleptic exposure expressed in risperidone equivalents (mg); SD-standard deviation.

Please cite this article in press as: Thirunavukkarasu, P., et al., An exploratory association study of the influence of dysbindin and neuregulin polymorphisms on brain morphometry in patients with schizophrenia and healthy subjects from South India. Asian J. Psychiatry (2014), http://dx.doi.org/10.1016/j.ajp.2014.04.002

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with a resolution of 1 mm  1 mm  1 mm. Field of view was 256 mm  256 mm and acquisition matrix was 256  256. The echo time, TE was 3.8 ms and repetition time, TR was 8.2 ms. Flip angle was 88 with a sense factor of 3.5. All scans were inspected for motion artifacts and gross pathology by a neuroradiologist. 2.3. Voxel-based morphometric (VBM) analysis Image processing and analysis were carried out using the VBM8 toolbox (Christian Gaser’s VBM8 toolbox; http://dbm.neuro.unijena.de/) of Statistical Parametric Mapping version 8 (www.fil.ion.ucl.ac.uk/spm) running under Matlab R2012a. The images were segmented into gray matter, white matter, and cerebrospinal fluid. A high-dimensional spatial normalization with DARTEL (Diffeomorphic Anatomical Registration Through Exponentiated Lie algebra) was used to iteratively register the gray and white matter images with their average, in order to improve the accuracy of the intersubject registration. The final group-specific DARTEL templates were then registered to the MNI space using the standard Montreal Neurological Institute 152 (MNI152) template. The above spatially normalized images are then smoothed with a Gaussian kernel of 8 mm full-width-at-half-maximum (Ashburner and Friston, 2005). The total gray matter (GM), white matter (WM) and cerebrospinal (CSF) fluid volumes were generated from the VBM analysis. The total brain volumes (TBV) were calculated as sum of GM and WM volumes. Using the VBM toolbox, analysis of covariance (ANCOVA) within the framework of general linear model (GLM) was used to compare between phenotypes (schizophrenia vs. healthy subjects); genotype-wise sub-groups irrespective of phenotype (NRG1: AA vs. AG; DTNBP1: CC vs. TC); and sub-groups based on presence of homozygosity of risk alleles of both candidate genes vs. others (see below). Age, gender and TBV were used as nuisance covariates. We have chosen to include TBV as a co-variate since reduced whole brain volumes have been most consistently reported in schizophrenia. Thus, including TBV as co-variate adjusts for global atrophy and looks for regionally-specific changes (http://www.fil.ion.ucl.ac.uk/mgray/Presentations/VBM.ppt). The threshold for definitive between-group differences in the phenotype-wise and genotype-wise comparisons was set a priori as p < 0.05 (FDR corrected). Trend-level between-group differences are reported at an uncorrected p < 0.001 significance threshold with an extent threshold (k) of 20 voxels. There is no universal agreement regarding the voxel extent threshold to be set for reducing the Type I error while reporting trend-level uncorrected results. A substantial number of studies have used an extent threshold of 10 voxels (e.g., Liu et al., 2013; Xiao et al., 2013), but we have chosen to use a somewhat more conservative extent threshold of 20 voxels, similar to the convention followed in some previous reports (e.g., Fusar-Poli et al., 2013; Molina et al., 2011). 2.4. Genotyping Venous blood sample of approximately 10 ml was collected from all the study participants. The DNA was extracted from the

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blood samples by modified salting out method (Miller et al., 1988). The SNP genotyping at rs1011313 of DTNBP1 gene was performed using commercially available taqman SNP assays from Life Technologies on 7500 Real time PCR system (Life technologies). The NRG1 polymorphism (rs35753505) was genotyped using 7500 Real time PCR system (Life technologies). Based on Krug et al. (2008), the following primers and FAM/JOEN probes were used for the SNP detection:forward primer 50 -TTTAAGGCATCAGTTTTCAATAGCTTTTTTATGT-30 ; reverse primer 50 -AGACAGATGTCTCAAGAGACTGGAA-30 ; 50 -/56-FAM/CATGTATCTTTATTTTACCAAAT/3IABkFQ/-30 ; 50 -/56-JOEN/ CATGTATCTTTATTTTGCCAAAT/3IABkFQ/-30 . The genotype and allelic frequency distributions of DTNBP1 (rs1011313) and NRG1 (rs35753505) polymorphisms were calculated and found to be in Hardy–Weinberg’s Equilibrium. To begin with, we examined the effect of diagnosis (schizophrenia versus healthy subjects) on brain morphometry using VBM at a significance threshold of FDR p < 0.05. We then used a full factorial model in VBM to examine the relationship between diagnosis (schizophrenia versus healthy) and each of the candidate genotypes (NRG1: AA vs. AG; DTNBP1: CC vs. TC) as well as their interactions (NRG1  DTNBP1). Since there were no significant results that emerged from these analyses at the a priori decided stringent threshold of FDR p < 0.05, we report here the trend-level effects of diagnosis (schizophrenia versus healthy), NRG1 genotypes (AA vs. AG), DTNBP1 (CC vs. TC) as well as the additive effects of the risk alleles of the above genes (homozygotes for risk alleles of both gene polymorphisms vs. others) on regional brain volumes. The effect of the risk alleles on brain morphometry was examined irrespective of diagnosis, since there were no significant (at FDR p < 0.05) diagnosis  genotype effects as was evident from the full-factorial general linear model (GLM) analysis (see above), nor was there a significant (at FDR p < 0.05) effect of diagnosis (schizophrenia vs. healthy subjects) on brain volumes. Since the number of subjects homozygous for the non-risk alleles of both NRG1 and DTNBP1 were too low [NRG1: GG (N) = 8; DTNBP1: TT (N) = 2] group comparisons were carried out only between subjects homozygous for the risk alleles versus subjects heterozygous for the risk alleles of both the polymorphisms [NRG1: AA (N = 32) versus AG (N = 35);DTNBP1: CC (N = 47) versus TC (N = 26)]. Furthermore an additive model was used to investigate the association of the two risk alleles with regional GM differences i.e. individuals homozygous for risk alleles of both the polymorphisms, AA of NRG1 polymorphism and CC of DTNBP1 polymorphism (N = 19) versus others (N = 56). 3. Results The frequency distribution of the genotypes and alleles of the NRG1 (rs35753505) and DTNBP1 (rs1011313) polymorphisms in the schizophrenia and healthy control samples are given in Table 2. There was no significant difference observed in the frequency distribution of genotypes for either polymorphism between patients and controls. As mentioned before, VBM comparisons of gray matter volumes (GMV) between patients with schizophrenia (N = 38) and healthy

Table 2 Frequency distribution of genotypes and alleles in subjects with NRG1 and DTNBP1 polymorphisms. Gene

Polymorphism

NRG1

rs35753505

DTNBP1

Subjects

Genotype frequency (%)

Allele frequency

Pearson Chi-Square

Asymptotic significance – 2sided, p value

Patients (N = 38) Healthy subjects (N = 37)

AA 18 (47.4) 14 (37.8)

AG 15 (39.5) 20 (54.1)

GG 5 (13.2) 3 (8.1)

A 0.67 0.65

G 0.33 0.35

1.701

0.427

Patients (N = 38) Healthy subjects (N = 37)

CC 28 (73.7) 19 (51.4)

TC 10 (26.3) 16 (43.2)

TT – 2 (5.4)

C 0.87 0.46

T – 0.27

5.0961

0.078

rs1011313

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Fig. 1. Statistical parametric t-maps of two sample random effects (RFX) analysis showing (a) greater gray matter volumes and (b) reduced gray matter volume in individuals with risk genotype AA for NRG1 polymorphism, rs35753505 (N = 32) in comparison to individuals with genotype AG (N = 35) at a significance threshold of p < 0.001 uncorrected and an extent threshold of 20 voxels. Total brain volume (TBV), age, and gender were entered in the analysis as nuisance co-variates. Image as per neurological convention (image left is subject’s left).

control subjects (N = 37) did not yield significant regional volumetric differences at the p < 0.05 FDR corrected threshold. At the p < 0.001 uncorrected threshold, trend-level GM volumetric reductions were noted in bilateral parahippocampal gyri (BA35), bilateral culmen, left middle frontal gyrus (BA10), left inferior frontal gyrus (BA47), left posterior cingulate gyrus (BA29), left medial temporal lobe, left inferior occipital gyrus (BA18), left declive, left uvula and right middle temporal gyrus (BA21). TBV, age and gender were entered in the above analyses as nuisance covariates. A GLM full-factorial analysis testing the interaction between diagnosis and genotype did not yield significant (FDR p < 0.05) results. Therefore, we examined the effect of risk alleles of the above gene polymorphisms on GMV, irrespective of phenotype [i.e. for NRG1 (rs35753505): AA vs AG; for DTNBP1 (rs1011313): CC vs. TC] with age, gender and TBV as co-variates using VBM analysis. Similar to the result of the phenotype-wise comparison, genotypewise morphometric comparisons also yielded only trend-level

volumetric differences. Individuals with the risk genotype AA (N = 32) of the rs35753505 polymorphism of NRG1 gene showed a trend toward increased GMV in bilateral cingulate gyri (BA32), right precentral gyrus (BA4) and left cuneus (BA18) (Fig. 1a and Table 3) and reduced GMV in tuber of left cerebellar vermis (X coordinate = 3.67, Y coordinate = 68, Z coordinate = 25.2; Z score = 3.405123) (Fig. 1b), in comparison to those with the AG genotype (N = 35). Individuals with the risk genotype CC of the rs1011313 polymorphism of DTNBP1 gene (N = 47) showed a trend toward reduced GMV of right parahippocampal gyrus (X coordinate = 28.32, Y coordinate = 15.58, Z coordinate = 14.29; Z score = 3.818175) in comparison to those with the TC genotype (N = 26). No genotype-diagnosis interaction effects on GMV was observed with the NRG1 polymorphism. A trend toward an interaction effect of the DTNBP1 polymorphism with diagnosis was noted in the full factorial analysis in left inferior occipital gyrus

Table 3 Two-sample random effects (RFX) analysis: Individuals with risk genotype AA for NRG1 polymorphism, rs35753505 (N = 32) vs those with AG genotype (N = 35). The brain regions that showed trend toward greater regional gray matter volume at a significance threshold of p < 0.001 uncorrected and an extent threshold of 20 voxels, are listed along with their co-ordinates in Talairach space. Total brain volume (TBV), age, and gender were entered in the analysis as co-variates. X coordinate

Y coordinate

Z coordinate

Brain regions

Z score

Brodmann area

0.1 2.81 37.22 2.82

22.29 16.3 17.59 93.9

32.07 35.6 49.18 8.85

Left cingulate gyrus Right cingulate gyrusa Right precentral gyrus Left cuneus

3.640389 3.478281 3.520391 3.402416

32 32 4 18

a

2 clusters significant at p < 0.001 uncorrected; cluster with higher Z-score listed.

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Fig. 2. Statistical parametric t-map of two sample random effects (RFX) analysis showing greater gray matter volumes in individuals who are homozygous for both risk alleles (A and C of NRG1 and DTNBP1 respectively) (N = 19) in comparison to others (N = 56) (irrespective of diagnosis) at a significance threshold of p < 0.001 uncorrected and an extent threshold of 20 voxels. Total brain volume (TBV), age, and gender were entered in the analysis as nuisance co-variates. Image as per neurological convention (image left is subject’s left).

(BA17), left insula (BA13), bilateral superior temporal gyrus (BA22), right inferior parietal lobule (BA40) and right middle frontal gyrus (BA6) at p < 0.001 (k = 20 voxels) (Supplementary Fig. 1 and Supplementary Table 1). However, post hoc VBM comparisons between CC and TC genotypes separately in healthy and the schizophrenia samples did not reveal any evidence for a specific effect of the DTNBP1 polymorphism in patients with schizophrenia. A full-factorial analysis of interaction between NRG1 polymorphism and DTNBP1 polymorphism revealed regional GM differences in right fusiform gyrus (BA20), left postcentral gyrus (BA3) and left cerebellar tonsil at p < 0.001 (k = 20 voxels) (Supplementary Fig. 2 and Supplementary Table 2). Moreover, using the additive model described above, those individuals who were homozygous for the risk alleles of both polymorphisms (A and C) (N = 19) irrespective of diagnosis, showed a trend toward

increased GMV in left precuneus (BA19), right precentral gyrus (BA4), right caudate and left parahippocampal gyrus (BA35) in comparison to others (N = 56) (Fig. 2 and Table 4). 4. Discussion We explored the individual as well as interactional effects of risk variants of two candidate genes (NRG1 and DTNBP1) that mediate glutamatergic signaling on brain morphometry in schizophrenia and healthy subjects. The results of our study indicate that the above risk alleles at rs1011313 of DTNBP1 and rs35753505 of NRG1 have differential effects individually as well as additively on brain morphometry, irrespective of phenotype. Many previous studies have reported regional volumetric reductions in patients with recent-onset (Steen et al., 2006) and

Please cite this article in press as: Thirunavukkarasu, P., et al., An exploratory association study of the influence of dysbindin and neuregulin polymorphisms on brain morphometry in patients with schizophrenia and healthy subjects from South India. Asian J. Psychiatry (2014), http://dx.doi.org/10.1016/j.ajp.2014.04.002

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Table 4 Two-sample random effects (RFX) analysis: Individuals who were homozygous for the risk alleles of NRG1 polymorphism rs35753505 (A) and DTNBP1 polymorphism rs1011313 (C) (N = 19) vs others (N = 56). The brain regions that showed trend toward greater regional gray matter volume at a significance threshold of p < 0.001 uncorrected and an extent threshold of 20 voxels, are listed along with their co - ordinates in Talairach space. Total brain volume (TBV), age, and gender were entered in the analysis as covariates. X coordinate

Y coordinate

Z coordinate

Brain regions

Z score

Brodmann area

33.59 39.96 7.35 14.85

68.01 17.87 6.71 28.73

33.75 51.91 3.69 8.15

Left precuneus Right precentral gyrus Right caudate Left parahippocampal gyrus

4.108576 3.752989 3.468421 3.454443

19 4 – 35

chronic (Shenton et al., 2010) schizophrenia. However, in a systematic meta-review, on structural brain alterations in schizophrenia, Shepherd et al. (2012) reported only limited high quality evidence and a large volume of low quality evidence supporting gray or white matter changes in schizophrenia. As mentioned in the introduction, variability in the frequencies of the risk alleles and genotypes across samples, along with other confounding factors could explain the inconsistency of morphometric findings of previous studies. In the present study, we observed morphometric differences between patients with recent-onset schizophrenia and healthy control subjects only at a trend-level (p < 0.001 uncorrected, k = 20 voxels), when age, gender and TBV were included as co-variates in the analysis. However, such trend-level volumetric differences were noted even when the overall sample was divided into subgroups based on their genotype with reference to the NRG1 and DTNBP1 polymorphisms. Individuals homozygous for the risk allele A of NRG1gene, irrespective of diagnosis, showed a trend (p < 0.001 uncorrected, k = 20) toward increased regional GMV in bilateral cingulate gyri, right precentral gyrus and left cuneus and decreased GMV in tuber of left cerebellar vermis. Similarly, individuals homozygous for the risk allele C of DTNBP1 gene, irrespective of diagnosis, showed a trend toward decreased regional GMV of the right parahippocampal gyrus (p < 0.001 uncorrected, k = 20). Even though the NRG1 polymorphism rs35753505 has been reported to be a core marker of schizophrenia (Kukshal et al., 2013; Nawaz et al., 2013; Prata et al., 2009), there have surprisingly been no previous studies that have looked at brain morphometric changes associated with this polymorphism in healthy or schizophrenia subjects. Previous studies have reported decreased regional brain volumes associated with the rs6994992 polymorphism (Barnes et al., 2012; McIntosh et al., 2008). Therefore ours is the initial report of a trend toward increased regional brain volumes associated with the rs35753505 polymorphism. Nevertheless, this polymorphism was also found to be associated with reductions in brain volume in an isolated region in the left cerebellar vermis. The involvement of NRG1 gene in brain development (Addington et al., 2007; Barnes et al., 2012) could potentially explain the regional volumetric differences (both increases and reduction) noted in individuals who were homozygous for the risk allele of the NRG1 polymorphism rs35753505. Similarly, the role of dysbindin in the cytoskeletal organization of growth cones of hippocampal neurons (Kubota et al., 2009) might mediate the observed influence of the DTNBP1 risk variant (rs1011313) on parahippocampal GMV. A trend for interactional effects between NRG1 and DTNBP1 was noted on full-factorial analysis. The additive effect of the above risk alleles on brain morphometry was an increase in regional GMV in the left precuneus, left parahippocampal gyrus, right precentral gyrus and right caudate, perhaps as a result of the more extensive increase in GMV associated with the AA genotype of NRG1 as mentioned above. The results of this exploratory study may be considered as preliminary, given the fact that the volumetric changes reported here are only at a trend level (p < 0.001 uncorrected, k = 20 voxels).

Moreover, the sample sizes in the present study were only modest (schizophrenia = 38; healthy subjects = 37). Nevertheless, the above trend-level differences constitute the initial evidence for morphometric changes associated with the NRG1 polymorphism rs35753505 and the DTNBP1 polymorphism rs1011313, which need replication in larger samples. Thus we conclude that trend-level brain morphometric differences may be seen not just across phenotypes (schizophrenia vs. healthy control subjects), but also across different sub-groups based on genotypes (AA vs. AG; CC vs. TC; A and C homozygotes vs. others). It is interesting to note that risk alleles of polymorphisms of two genes mediating glutamatergic neurotransmission showed differences in their extent and direction of effects on brain morphometry, with the AA genotype of NRG1 associated with comparatively widespread volumetric increases and circumscribed volumetric reductions, while the CC genotype of DTNBP1 was associated only with a circumscribed volumetric reduction in the right parahippocampal gyrus. The additive effects of multiple risk genes of small effect (Girard et al., 2012) on brain morphometry could explain the inconsistency of the reported volumetric findings across different samples of patients with schizophrenia. Therefore future studies should aim at modeling the interactions of multiple risk genes that influence brain morphometry using in silico approaches like Brain Cloud (http://www.libd.org/braincloud) and GeneMania (http://www.genemania.org). These applications utilize expression datasets [e.g., Gene Expression Omnibus (accession GSE30272); dbGaP (accession phs000417.v1.p1)] or evidence from previous studies for modeling gene-gene and gene-protein interactions. Such approaches could characterize the quantitative interactions between multiple genes and identify the expression quantitative trait loci (eQTL) that regulate gene expression in different brain regions. Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at http://dx.doi.org/10.1016/j.ajp.2014.04.002.

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Please cite this article in press as: Thirunavukkarasu, P., et al., An exploratory association study of the influence of dysbindin and neuregulin polymorphisms on brain morphometry in patients with schizophrenia and healthy subjects from South India. Asian J. Psychiatry (2014), http://dx.doi.org/10.1016/j.ajp.2014.04.002