Association study of Neuregulin-1 gene polymorphisms in a north Indian
Prachi Kukshala,b, Triptish Bhatiac, A.M. Bhagwatb, Raquel E. Gurd, Ruben C. Gurd, Smita N. Deshpandec,
Vishwajit L. Nimgaonkare, B.K. Thelmaa,⁎
aDepartment of Genetics, University of Delhi South campus, Benito Juarez Road, New Delhi − 110 021, India
bC.B. Patel Research Centre, Vile Parle (West), Mumbai, India
cDepartment of Psychiatry, Dr. RML Hospital, New Delhi – 110 001, India
dDepartment of Psychiatry, Neuropsychiatry Section, University of Pennsylvania, Philadelphia, PA, USA
eDepartment of Psychiatry and Human Genetics, Western Psychiatric Institute and Clinic, University of Pittsburgh School of Medicine and Graduate School of Public Health, 3811 O'Hara
Street, Pittsburgh, PA 15213, USA
a b s t r a c t a r t i c l e i n f o
Received 31 August 2012
Received in revised form 20 November 2012
Accepted 17 December 2012
Available online 26 January 2013
SNP and microsatellite markers
Background: Neuregulin-1 (NRG1) gene polymorphisms have been proposed as risk factors for several common
disorders. Associations with cognitive variation have also been tested. With regard to schizophrenia (SZ) risk,
studies of Caucasian ancestry samples indicate associations more consistently than East Asian samples,
suggesting heterogeneity. To exploit the differences in linkage disequilibrium (LD) structure across ethnic
groups, we conducted a SZ case–control study (that included cognitive evaluations) in a sample from the
north Indian population.
Methods: NRG1 variants (n=35 SNPs, three microsatellite markers) were initiallyanalyzed among cases (DSM IV
region inNorth India. Nominally significant associations with SZwere next analyzed inrelation to neurocognitive
Results: Three variants and one microsatellite showed allelic association with SZ (rs35753505, rs4733263,
type 221121 (rs35753505-rs6994992-rs1354336-rs10093107-rs3924999-rs11780123) showed (p=0.0004)
(uncorrected) associations with emotion processing and attention at rs35753505 and rs6994992, respectively.
Conclusions: Suggestive associations with SZ and SZ-related neurocognitive measures were detected with two
SNPs from the NRG1 promoter region in a north Indian cohort. The functional role of the alleles merits further
© 2013 Elsevier B.V. All rights reserved.
Schizophrenia (MIM 181500, SZ) is a common, lifelong disorder
with a life time prevalence of 0.8% among Indian adults (Faraone et
al., 2002; Saha et al., 2005). The relatively high heritability of SZ has
motivated intensive gene mapping efforts (Shirts and Nimgaonkar,
2004; Talkowski et al., 2007; Chen et al., 2009; Talkowski et al., 2010;
Greenwood et al., 2012). Meta-analysis of 32 genome-wide linkage
studies of SZ suggested linkage on chromosome 8p (16–33 Mb)
(Ng et al., 2009) for 22 European-ancestry samples. Recently, genome-
wide association studies (GWAS) have identified several relatively
common single nucleotide polymorphisms (SNPs) that are associated
with SZ (Potkin et al., 2009; Shi et al., 2009; McClay et al., 2010; Ripke
et al., 2011; Shi et al., 2011).
Several studies have focused on the signaling protein NRG1 and its
receptor ERRB4. A variety of NRG1 isoforms (estimated n=30) are pro-
duced by alternative splicing(Tan et al., 2007; Liu et al., 2011). They are
expressed in varying proportions at relatively high levels in a variety of
peripheral tissues as well as the brain. In the brain, NRG1 is considered
to be a pleiotropic growth factor with an integral role in its develop-
ment, organization, and function (Li et al., 2006). NRG1 plays key roles
in several neurotransmitter systems, including(N-methyl-D-aspartate),
acetylcholine, as well as gamma-Aminobutyric acid (Fischbach and
Rosen, 1997; Ozaki et al., 1997; Rieff et al., 1999; Cameron et al., 2001).
Several NRG1 SNPs have been reported to be associated with SZ,
albeit at nominal levels of significance (Farrer et al., 2001; Falls,
2003a,b; Hashimoto et al., 2004; Bertram et al., 2005; Harrison and
Schizophrenia Research 144 (2013) 24–30
⁎ Corresponding author. Tel.: +91 11 24118201; fax: +91 11 24112761.
E-mail address: firstname.lastname@example.org (B.K. Thelma).
0920-9964/$ – see front matter © 2013 Elsevier B.V. All rights reserved.
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Weinberger, 2005; Gardner et al., 2006). Stefansson et al. (2002) first
reported linkage to a locus on Chromosome 8 in an Icelandic sample and
subsequently a replicated association witha 7-marker risk haplotype in a
Scottish sample (Stefansson et al., 2003). Meta-analysis of 26 published
case–control and family-based association studies showed association of
SNP8NRG221132, 420M9-1395, 478B14-848 and suggested population
stratification for SNP8NRG221533 (Gong et al., 2009). Another meta-
analysis of 13 studies reported association with six markers between
two adjacent, but distinct haplotypes blocks in Caucasian and Asian
ancestry samples (Li et al., 2006). Another group found non-signficant
association of SNP8NRG221533 after taking study design and ancestry
into account (Munafo et al., 2006, 2008). Only one study has been
reported from South Asia. This study from Pakistan investigated two
SNPs in 100 cases and 70 adult controls. It suggested nominal association
with the exonic SNP rs3924999 (Naz et al., 2011).
Impairment in several cognitive domains has been reported in SZ
(Heinrichs et al., 1997; Goldberg and Green, 2002; Buchanan et al.,
2005; Snitz et al., 2006; Gur et al., 2007; Reichenberg and Harvey,
2007; Barch and Smith, 2008; Ranganath et al., 2008; Tandon et al.,
2009; Yokley et al., 2012). NRG1 SNPs may also be associated with cog-
nitive dysfunction, particularly attention (Yokley et al., 2012); spatial
memory and social behavior (O'Tuathaigh et al., 2007). The NRG1
SNP8NRG221533 (rs35753505) has most widely been evaluated in
relation to cognition (Kurnianingsih et al., 2011). A role for NRG1 in SZ
has also been supported by animal studies using NRG1 and ErbB4
mutant mice (Gerlai et al., 2000; Stefansson et al., 2002; Bao et al., 2003;
Corfas et al., 2004; Steinthorsdottir et al., 2004; Gu et al., 2005; Rimer et
al., 2005), which exhibit behaviors similar to those of established rodent
models of SZ (Lipska, 2004).
NRG1 polymorphisms have been proposed as risk factors for several
other common disorders, including Alzheimer's disease (Chaudhury et
al., 2003; Go et al., 2005); epilepsy (early myoclonic encephalopathy;
(Raj et al., 2001), multiple sclerosis (Cannella et al., 1999; Viehover et al.,
2001), bipolar disorder (Thomson et al., 2007; Goes et al., 2008; Prata et
(Garcia-Barcelo et al., 2009; Tang et al., 2011).
transmitter function. With regard to SZ risk, the results from Caucasian
ancestry samples appear to be more consistent whereas the results
from the Asian samples are variable, suggesting locus heterogeneity. In
order to exploit the differences in LD structure across ethnic groups,
we investigated a north Indian population using a case–control design.
NRG1 SNP associations with cognitive variation were further tested in
a sub-group of this sample.
2.1. Recruitment and diagnostic assessment
The recruitment and assessment of the sample has been described in
prior studies (Bhatia et al., 2008). Briefly, patients with a clinical diagno-
sis of SZ or schizoaffective disorder were referred from the outpatient
department of Dr. Ram Manohar Lohia Hospital, as well as other private
and public psychiatric facilities in Delhi, India. All patients (n=1007)
were assessed using the Hindi versions of the Diagnostic Interview for
Genetic Studies (DIGS) and the Family Interview for Genetic Studies
(FIGS) (Nurnberger et al., 1994; Deshpande et al., 1998; http://
wwwgrb.nimh.nih.gov/gi.html). This information was synthesized with
available medical records and presented to board certified psychiatrists
who assigned consensus diagnoses.
The control samples (n=1019) included non-psychotic adults
(n=521) who were recruited from the same communities in which
the patients resided. Care was taken not to include multiple related
mation was obtained with the use of a semi-structured questionnaire
and care was taken to avoid recruitment of 1st and 2nd degree related-
ness in our case–control cohort. We also included a control group com-
prising neonatal blood samples from live births at Lok Nayak Hospital,
New Delhi; this group could not therefore be screened for psychotic ill-
natal controls to evaluate for psychotic illness in the parents and other
first or second degree relatives. Neonatal blood was not taken if any
family member was reported to have psychotic illness. No information
apart from gender was provided about these anonymous samples.
All participants (except the neonatal control samples) provided
written informed consent. Written informed consent was obtained
from mothers for the neonatal sample. The study was approved by
Institutional Ethical Committee at Dr. Ram Manohar Lohia (RML)
Hospital, New Delhi,and the Institutionalreviewboard at the University
of Pittsburgh, USA.
2.2. Cognitive evaluation
The Hindi version of the Penn Cognitive Neuropsychiatric Battery
(n=256) of participants comprisingcases (n=116) and adult controls
(n=140). The following cognitive domains were assessed: abstraction
memory, spatial ability, sensorimotor and emotional processing. The
CNB evaluates accuracy, speed and efficiency for each domain. As
these indices are correlated for any one domain, we analyzed the accu-
racy measures for parsimony.
2.3. Selection of polymorphisms
A total of 35 SNPs and three microsatellite markers were tested. We
selected markers based on prior reported associations and based on
local LD (r2>0.8; Indian, GIH data in Hapmap, www.hapmap.org). We
also focused on SNPs in exonic regions and in the 5′ sequences, the latter
because it has been proposed that regulatory variants in NRG1 may be
particularly involved in pathogenesis (Law et al., 2006).
2.4. Genotype assays
extension reaction chemistry in the MALDI-TOF mass spectrometry plat-
form (www.sequenom.com/iplex/) using iPLEX® Gold reagents. An ABI
3730 machine was used for fragment analysis of the fluorescent labeled
microsatellite markers. Quality checks were performed by using dupli-
cates and CEPH samples in each plate.
2.5. Statistical analysis
Hardy Weinberg equilibrium (HWE) was examined for each SNP. All
SNPs conforming to HWE estimates (p>0.01) were included in the asso-
ciation analyses. LD values (r2) were estimated for the genotyped data
using the Tagger algorithm in Haploview version 4.1 (Barrett et al.,
2005; http://www.broad.mit.edu/mpg/haploview/). Heterogeneity be-
was also tested using Haploview software. Case–control associations for
individual SNPs were evaluated using the Trends test in PLINK (http://
pngu.mgh.harvard.edu/~purcell/plink/). Associations with microsatellite
markers were assessed using CLUMP software (Sham and Curtis, 1995;
http://www.smd.qmul.ac.uk/statgen/dcurtis/software.html). SNPs which
showed association either for allelic or model-wise tests were included
for haplotype analysis, using PLINK and UNPHASED (Dudbridge, 2003,
2008). Power was estimated using Quanto software (Gauderman and
Morrison., 2006; http://hydra.usc.edu/gxe/).
Multivariateanalyses were used totest associationsbetweenindivid-
for Social Sciences (SPSS Version 16, http://hydra.usc.edu/gxe). Linear
P. Kukshal et al. / Schizophrenia Research 144 (2013) 24–30
regression analyses were conducted separately for each cognitive do-
main to test associations between cognitive variables and two SZ associ-
the outcome variables and genotypes for individual SNPs, gender and
diagnosis were used as covariates for these analyses.
3.1. Demographic data
Menconstituted 56.8% of the cases and 55.74% of the controls.There
were no significant case–control differences with regard to gender in
the two groups. There was a significant difference in the ages of the
cases and the controls (mean±standard deviation, SD; adult controls:
43.03±14.0; cases: 29.9±8.95).
3.2. Quality control for genotype assays
All the SNPs were in HWE (p>0.01). Of the 2044 participants in
the study, genotypes from individuals with less than 90% genotype
calls were excluded from all analysis (n=18). Therefore, a total of
2026 participants were analyzed (n=1007 cases, n=1019 controls).
Overall, the call rate was over 97% for the SNPs and over 95% for the
3.3. LD patterns
LD between pairs of SNPs was estimated for the control individuals
using r2values (Supplementary Fig. 1). Overall, the patterns of LD
resembled those observed in Caucasian ancestry individuals (www.
hapmap.org). The SNPs genotyped were generally not in tight LD with
the following notable exceptions: rs6988339 and rs10691392 (r2=
0.9) and rs6994992 and rs4733263 (r2=0.97).
3.4. Case–control comparisons
The adult and the neonatal control samples did notdiffer significantly
with regard to genotype or allele frequencies for any of the polymor-
phisms (Supplementary Table I). Test of heterogeneity performed for
each SNP did not show significant differences between the two groups
of controls (Supplementary Table I). Since the distribution of the poly-
morphisms was comparable in the two groups, the control groups were
pooled for all further analysis.
Three polymorphisms were nominally associated with SZ risk
(pb0.05 uncorrected for multiple comparisons, Table 1; Supplementary
Table II) of which rs6994992 and rs4733263 are in LD: rs35753505
(p=0.04; OR=1.15(95% confidence intervals, CI, 1.01–1.31), rs473
3263 (p=0.04; OR=1.14(95% CI, 1.01–1.31)), rs6994992 (p=0.026;
OR=1.15(95% CI, 1.02–1.3)),but nonewithstoodBonferronicorrections.
One microsatellite marker 420_M9-1395 (p=0.016) located in 5′
region also showed nominal association (Table 1; Supplementary
Table II). Four more SNPs showed genotypic association. TT genotype
of rs3924999 a Val>Leu missense polymorphism in exon 11 and GG
genotype in rs11780123 in 3′ region showed association (0.02 and
0.01 respectively) under a Dominant model. TT genotype of rs1354336
and rs10093107 showed association (0.009 and 0.008 respectively)
under Recessive model (Table 1).
3.5. Haplotypic association
Using the 6 associated SNPs in linkage equilibrium namely
rs35753505, rs6994992, rs1354336, rs10093107, rs3924999 and
rs11780123 (Table 1), two to six SNP sliding window haplotypes
(frequency>5%) were constructed and global p values were tabulated
(Table 2a). Out of 74 haplotypes constructed using Plink, 18 haplotypes
were significantly different. A five-marker haplotype comprised of
0.0004) and a six-marker haplotype with rs35753505–rs6994992–
rs1354336–rs10093107–rs3924999–rs11780123 (p=0.0004) remained
significant after Bonferroni corrections (alpha value 0.05/74=0.0006;
Table 2b). Notably, most of the associations were driven by the two
promoter SNPs rs35753505 and rs6994992 (Tables 2a and 2b;
Supplementary Table II).
3.6. Cognitive variables
Computerized neurocognitive data were available for cases and adult
controls (n=256). In this group, there were no significant gender differ-
p=4.3×10−24) than those of cases (31.0, SD 9.32). Therefore, the cogni-
tive measures were adjusted for age. We analyzed eight neurocognitive
domains, namely abstraction and mental flexibility, attention, face mem-
ory, spatial memory, working memory, spatial ability, sensorimotor and
emotion processing (Gur et al., 2007). Of the three allelic associated
SNPs only rs35753505 and rs6994992 were used for further cognitive
sion analysis, an association between rs35753505 and emotion process-
ing was noted (p=0.031). At rs6994992, an association with attention
was noted (p=0.047; Table 3). There was no significant interaction be-
tween SNP genotype and case–control status at either locus (data not
Association of NRG1with SZ.
MAF Trends test DominantRecessiveAdditive
SNP BPMAFca FcoCHISQp(df=1)CHISQp(df=1)CHISQ p(df=1) CHISQp(df=2)
BP: genomic location (base pairs). MA: Minor allele; MAF: Minor allele frequency; Fca: Minor allele frequency in cases; Fco: Minor allele frequency in unaffected control aliases:⁎
SNP8NRG221533; ** SNP8NRG243177; # Microsatellite Marker rs4733263 and rs6994992 are in LD (r2=0.9).
Significant p values (pb0.05) are marked in bold.
P. Kukshal et al. / Schizophrenia Research 144 (2013) 24–30
3.7. Power analysis
an OR of 1.5 or greater, for SNPs having minor allele frequencies (MAF)
greater than 5%, assuming alpha=0.05, uncorrected for multiple
Three SNPs namely rs35753505, rs4733263 and rs6994992 showed
modest allelic association and four addition SNPs rs1354336, rs100
93107,rs3924999 and rs11780123namely showedgenotypic (dominant
or recessive) association and one microsatellite marker (420M9-1395)
showed modest allelic association with SZ in our north Indian sample
(Table 1), though it should be noted that the associations did not remain
significant following Bonferoni corrections for multiple comparisons.
rs6994992 was also reported to be associated with SZ in Caucasian sam-
ples (Hall et al., 2006; Law et al., 2006). Notably, the risk allele at this
SNP (T) is associated with increased type IV NRG1 messenger RNA levels
(Law et al., 2006), lower prefrontal (and temporal) activation and devel-
opment of psychotic symptoms in individuals at high risk for SZ (Hall et
al., 2006). This SNP maps upstream of the NRG1 type IV 5′-exon
(Steinthorsdottir et al., 2004; Law et al., 2006; Tan et al., 2007; Shamir
2006). The other associated polymorphisms may also have functional
effects, as associations with cortical volumes have been reported at
420_M9-1395 (Addington et al., 2007) and rs6994992 (Mata et al.,
2009, 2010). 420M9-1395 and rs35753505 may influence brain develop-
ment (Addington et al., 2007). In addition, reduction of white matter
fractional anisotropy was associated withrs35753505 in the anterior cin-
gulum (Wang et al., 2009; Kurnianingsih et al., 2011). As the associated
polymorphisms are localized to the 5′ region, it is possible that variation
in the promoter region of NRG1 elevates risk for SZ. Indeed, post-
of SZ patients (Harrison and Law, 2006), as well as the hippocampal
cortical white matter myelination in the frontal lobe (Konrad and
Winterer, 2008). NRG1variants likely modulate brain activation during
episodic memory processing in key areas for memory encoding and
retrieval, with SZ risk alleles showing hyper activation in areas associated
with elaborate encoding strategies (Krug et al., 2010). Exonic SNP
rs3924999, a missense variant present in NRG1 (Val>Leu in exon 11)
increased the risk of schizophrenia (Walss-Bass et al., 2006). Genotypic
association of this SNP has been observed for antisaccades and smooth
pursuit eye movements (Schmechtig et al., 2010) and lower prepulse
inhibition, an endophenotype of schizophrenia (Hong et al., 2008). This
derlying visuospatial sensorimotor transformations, a mechanism that
has been found to be impaired in patients with schizophrenia and their
We also found significant 5- and 6-marker haplotypic association
withstanding Bonferroni correction (Table 2b) and the associations
seems to be primarily attributable to the promoter SNPs rs6994992
(Tables 2a and 2b). However, a significantly associated truncated
Sliding window haplotype analysis of nominally associated SNPs.
Name Map information2-mhap 3-mhap 4-mhap 5-mhap6-mhap
2-mhap: Globalp-values of successive two marker haplotypes generated using UNPHASED.
Data are presented in the same format for each adjacent pair of markers down this column.
3-mhap: Global p-values of three marker haplotypes generated using UNPHASED. Data
Similarly, 4-, 5- and 6-mhap denote haplotypes incorporating the respective number of
Haplotypes with a frequency lower than 5% were not included in the analysis;
Significant p values (pb0.05) are marked in bold.
Significant haplotypes of associated SNPs.
SNPsHaplotype Freq.OR chi Sq.P
2 SNP window
3 SNP window
4 SNP window
5 SNP window
6 SNP window
2211210.08 1.75 12.60
Freq: Frequency of Haplotypes (>5%);
Allele 2 in haplotypes represent minor allele.
⁎ Haplotypes significant after Bonferroni corrections (alpha value 0.05/74=0.0006).
P. Kukshal et al. / Schizophrenia Research 144 (2013) 24–30
haplotype (Table 2b) suggests contributions of the 3′ marker
(rs11780123) also. Of note, functional imaging studies have report in-
tragenic epistasis between 5′ and 3′ markers in NRG1 (Nicodemus et
al., 2010; Moon et al., 2011).
The associations between rs35753505 and rs6994992 and cognitive
ples; e.g., (Yokley et al., 2012) and (O'Tuathaigh et al., 2007), though
there are some reports of non-significant associations (Crowley et al.,
2008). In healthy participants, rs35753505 was not associated with
working memory or task performance (Krug et al., 2008; Kircher et al.,
2009a), but was associated with semantic verbal fluency (Kircher et
al., 2009b) and sustained attention (Stefanis et al., 2007). rs6994992,
originally identified as part of the so-called deCODE haplotype, could
be specifically related to disruption of normal frontal and temporal
lobe function, premorbid intelligence levels and the emergence of psy-
chotic symptoms (Harrison and Law, 2006; Li et al., 2006). Individuals
with theTT genotype at this SNP also had reduced white matter density
andstructural connectivity (McIntosh et al., 2008), impaired frontal and
temporal lobe activation (Hall et al., 2006), and cognition (Hall et al.,
2006;Stefanis et al.,2007; Sprootenetal., 2009),includingreducedspa-
tial working memory capacity (Stefanis et al., 2007) and emotion pro-
cessing (Keri and Kelemen, 2008).
There are some limitations in the present study. First, several associ-
ations did not withstand Bonferroni corrections for multiple compari-
sons. Thus, the effects of NRG1 polymorphisms in this, the largest
is a modest (~1%) probability that some of the neonatal controls will be
diagnosed with SZ in later life (estimated n=approximately 5). Such
misdiagnosis would tend to diminish observed associations. Finally,
population substructure as a potential source for the association could
not be evaluated in the sample using Principle Components Analysis
(PCA) or Multi Dimensional Scaling (MDS), as ancestry informative or
genome wide markers were not evaluated.
In conclusion, nominal associations with SZ were noted with three
NRG1 polymorphisms. Two of the associated SNPs were also associat-
ed with cognitive variation in the combined case–control sample.
These associations are consistent with prior reports, predominantly
in Caucasian samples. As the associated polymorphisms and haplo-
types are localized to the 5′NRG1 sequences, they may reflect subtle
in the brain, as well as functional studies of the associated polymor-
phisms are warranted.
Supplementary data to this article can be found online at http://
Role of funding source
We received financial support for this project from National Institutes of Health (under
Training Program for Psychiatric Genetics in India, Grant# 5D43 TW006167-02). Additional
NIH support for VLN is acknowledged through grant MH66263.
Prof. B.K. Thelma and Prof. V. L. Nimgaonkar designed the study and wrote the protocol.
Prof. Deshpande and her team provided the research samples; Prof. R.E. Gur and Prof. R.C.
Gur provided the neurocognitive battery and Dr. Triptish Bhatia did the cognitive analysis.
Dr. A. M. Bhagwat is the Ph D supervisor for Prachi Kukshal. Prachi Kukshal managed the
literature searches did the genetic analysis and wrote the first draft of the manuscript.
All authors contributed to and have approved the final manuscript.
Conflict of Interest
The authors have no conflicts of interest to declare.
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Significant associations between cognitive variables and NRG1 SNPs.
Outcome variable Covariates Unstandardized
t p value95% confidence
interval for B
BStd. errorB LowerUpper
Emotion processing (Constant)
Significant p values (pb0.05) are marked in bold.
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