Evaluation of a Susceptibility Gene for Schizophrenia:
Genotype Based Meta-Analysis of RGS4
Polymorphisms from Thirteen Independent Samples
Michael E. Talkowski, Howard Seltman, Anne S. Bassett, Linda M. Brzustowicz, Xiangning Chen,
Kodavali V. Chowdari, David A. Collier, Quirino Cordeiro, Aiden P. Corvin, Smita N. Deshpande,
Michael F. Egan, Michael Gill, Kenneth S. Kendler, George Kirov, Leonard L. Heston, Pat Levitt,
David A. Lewis, Tao Li, Karoly Mirnics, Derek W. Morris, Nadine Norton, Michael C. O’Donovan,
Michael J. Owen, Christian Richard, Prachi Semwal, Janet L. Sobell, David St Clair, Richard E. Straub,
B.K. Thelma, Homero Vallada, Daniel R. Weinberger, Nigel M. Williams, Joel Wood, Feng Zhang,
Bernie Devlin, and Vishwajit L. Nimgaonkar
Background: Associations between schizophrenia (SCZ) and polymorphisms at the regulator of G-protein signaling 4 (RGS4) gene
have been reported (single nucleotide polymorphisms [SNPs] 1, 4, 7, and 18). Yet, similar to other SCZ candidate genes, studies have
been inconsistent with respect to the associated alleles.
Methods: In an effort to resolve the role for RGS4 in SCZ susceptibility, we undertook a genotype-based meta-analysis using both
published and unpublished family-based and case-control samples (total n ? 13,807).
Results: The family-based dataset consisted of 10 samples (2160 families). Significant associations with individual SNPs/haplotypes
were not observed. In contrast, global analysis revealed significant transmission distortion (p ? .0009). Specifically, analyses suggested
overtransmission of two common haplotypes that account for the vast majority of all haplotypes. Separate analyses of 3486 cases and
3755 control samples (eight samples) detected a significant association with SNP 4 (p ? .01). Individual haplotype analyses were not
significant, but evaluation of test statistics from individual samples suggested significant associations.
Conclusions: Our collaborative meta-analysis represents one of the largest SCZ association studies to date. No individual risk factor
arose from our analyses, but interpretation of these results is not straightforward. Our analyses suggest risk due to at least two common
haplotypes in the presence of heterogeneity. Similar analysis for other putative susceptibility genes is warranted.
Key Words: RGS4, schizophrenia, meta-analysis, association, poly-
ing candidate genes, and replicate studies have detected associ-
ations at a number of these genes. Yet, the majority of replication
studies have been inconsistent with respect to the associated
alleles, haplotypes, and conferred risks. For example, the neu-
regulin 1 locus (NRG1) has recently received considerable atten-
tion in replication studies. An initial risk haplotype was identified
in an Icelandic population by Stefansson et al (2002) and in a
second sample from Scotland (Stefansson et al 2003). At least
fforts to identify genetic risk factors for schizophrenia
(SCZ; Mendelian Inheritance in Man [MIM] ) using
linkage and association studies have yielded several excit-
eight successive studies have attempted to replicate these initial
results in European (Williams et al 2003; Corvin et al 2004;
Petryshen et al 2005), Chinese (Yang et al 2003; Tang et al 2004;
Zhao et al 2004; Li et al 2004), and Japanese (Iwata et al 2004)
populations. Only some of these replicate studies have detected
associations, and few have detected associations with SCZ from
identical alleles, single nucleotide polymorphisms (SNPs), and
haplotypes as the original findings. Similarly, complex patterns of
associations with reference to replicate studies have been ob-
served following the initial associations with SCZ described by
Straub et al (2002) for the dystrobrevin binding protein 1 gene
(DTNBP1) (Schwab et al 2003; Morris et al 2003; Van Den
Bogaert et al 2003; Tang et al 2003; van den Oord et al 2003b;
Williams et al 2004a). Thus, even for these two widely
From the Departments of Human Genetics, Psychiatry, Neuroscience, and
Cardiff, Wales, United Kingdom; Department of Psychiatry (APC, MG,
DWM), Institute of Molecular Medicine, Trinity College, Dublin, Ireland;
Behavior Genetics, Virginia Commonwealth University, Richmond, Vir-
ginia; Department of Psychiatry (QC, HV), University of Sao Paulo, Sao
Paulo, Brazil; Department of Psychiatry (SND), Dr. Ram Manoher Lohia
Hospital, New Delhi, India; Department of Human Genetics (PS, BKT),
University of Delhi South Campus, New Delhi, India; Department of
of Psychiatry (ASB), University of Toronto and Center for Addiction &
Mental Health, Toronto, Ontario, Canada; Zilkha Neurogenetic Institute
University of Southern California, Los Angeles, California; Department of
Psychiatry (DAC, TL), Institute of Psychiatry, London, United Kingdom;
Genes, Cognition, and Psychosis Program (MFE, RES, DRW), National
Institute of Mental Health, Bethesda, Maryland; Vanderbilt Kennedy
Center for Research on Human Development and Department of Phar-
of Psychiatry (DAC, TL), West China Hospital, Sichuan University, PR
China; Department of Mental Health (DSC, FZ), University of Aberdeen,
Foresterhill, Aberdeen, United Kingdom; and Department of Psychiatry
(LLH), University of Washington, Seattle, Washington.
Address reprint requests to Vishwajit L. Nimgaonkar, M.D., Ph.D., University of
Pittsburgh – WPIC, Departments of Psychiatry and Human Genetics, 3811
Received May 2, 2005; revised November 8, 2005; accepted February 8,
BIOL PSYCHIATRY 2006;60:152–162
© 2006 Society of Biological Psychiatry
investigated genes, interpretation of replicate studies is diffi-
Several factors, including the overestimation of risk in the
initial study, disparate power amongst replicate studies, pheno-
typic heterogeneity, and population heterogeneity need to be
considered (Ioannidis et al 2001). Furthermore, in large genes
such as DTNBP1 and NRG1, adequate coverage of all variants
across the gene is often challenging.
Meta-analysis might be a means of resolving disparate results.
For example, pooling samples from several studies could pro-
duce greater power than individual studies or might amplify
trends for association in small individual studies. Meta-analyses
have been reported for a number of candidate genes for schizo-
phrenia, including COMT, DRD2, DRD3, NOTCH4, and SLC6A3,
to name a few (Glatt et al 2003a, 2003b, 2005; Dubertret et al
1998; Jonsson et al 2003, 2004; Gamma et al 2005). In addition to
the limitations outlined above, these analyses typically assessed
only one variant, used only published data, and predominantly
involved case-control designs, not family-based designs. Argu-
ably, it would be better to analyze the original data than
published summary statistics from individual studies. Therefore,
we conducted meta-analysis involving individual genotype data
across all available samples at the regulator of G-protein signal-
ing 4 locus (RGS4).
The regulator of G-protein signaling 4 locus is a member of
guanine triphosphate (GTP)ase-activating proteins that regulate
the timing and duration of G-protein-mediated receptor signaling
through neurotransmitter receptors that have been implicated in
the pathophysiology or treatment of SCZ (De Vries et al 2000).
Expression of RGS4 transcript, but not other RGS family mem-
bers, was reduced in the cortex of postmortem brain samples
from patients with SCZ (Mirnics et al 2001). A recent study in
normal individuals found a dense distribution of RGS4 messen-
ger RNA (mRNA) in most cortical layers examined (Erdely et al
2004). The regulator of G-protein signaling 4 locus is localized to
1q23.3 at 160.2 Mb.
Chowdari et al (2002) first conducted association and linkage
analyses of RGS4 using family-based and case-control samples. A
panel of 13 SNPs was evaluated in independently ascertained
samples from Pittsburgh, the National Institutes of Mental Health
(NIMH) Collaborative Genetics Initiative, and New Delhi. In both
US samples, transmission distortion of individual alleles and
haplotypes was observed at four SNPs, denoted SNPs 1, 4, 7, and
18. However, the associated alleles and haplotypes differed. The
overtransmitted haplotypes were G-G-G-G in the Pittsburgh
sample and A-T-A-A in the NIMH and Indian samples. Curiously,
these risk haplotypes were the two most common in the popu-
lation, with estimated frequencies of .44 and .39, respectively.
Case-control comparisons in the US sample did not reveal
significant associations (Chowdari et al 2002).
Following these initial results, four independent studies have
been reported (Figure 1), all of which analyzed these same four
SNPs. Using a large case-control sample from Cardiff, United
Kingdom, Williams et al (2004b) detected significant associations
with the T and A alleles at SNPs 4 and 18 but not the 4 SNP
haplotype. A case-control study from Dublin, Ireland revealed
significant associations at SNPs 1 and 7, as well as multiple
haplotypes (Morris et al 2004). Associations were found with the
same alleles (G) reported in the Pittsburgh sample when their
sample was restricted to a narrow diagnosis of SCZ. The first
family-based replicate study utilized multiply affected pedi-
grees from Ireland and revealed an association with the G
allele at SNP 18 (Chen et al 2004). There was also significant
overtransmission of the G-G-G haplotype at SNPs 1, 4, and 18
to probands, similar to the Pittsburgh families. Analysis of a
sample from Brazil did not yield significant case-control
differences, although modest overtransmission of the G allele
at SNP 18, as well as the G-G haplotype at SNPs 7 and 18 was
reported (Cordeiro et al 2005). A linkage study provided
evidence for linkage with SCZ near RGS4 at 1q21-22 (Brzus-
towicz et al 2000), but recently presented follow-up investi-
gations have not suggested associations with RGS4 in this
same sample (Brzustowicz et al 2004).
The published data thus suggest an association with SCZ at
RGS4. Similar to other susceptibility candidates, however, the
results suggest complex associations that are difficult to interpret.
Thus, we sought a comprehensive evaluation of all studies of
this gene. We reasoned that analyses of multiple samples
might lend clarity, for example, by identifying false-positive
results or through highlighting modest effects by the amassing
of large samples. We analyzed SNPs 1, 4, 7, and 18 (Chowdari
et al 2002). These SNPs have been reported in all previous SCZ
studies at this gene, and recent analyses indicate they are
substantially correlated (r2? .8) with 75% of all common
polymorphisms spanning the gene (Chowdari et al, unpub-
lished data, 2005). In addition to studying cases and unrelated
control samples, we ascertained family-based samples to
mitigate against confounding due to population stratification.
To reduce the effects of publication bias, namely a bias toward
publishing significant associations, we sought genotype data
from all published reports as well as any known ongoing
investigations. The use of individual genotypes allowed a
wide variety of SNP and haplotype analyses.
Methods and Materials
We organized data from independent investigators worldwide
who had either published peer-reviewed manuscripts or ab-
stracts at scientific meetings regarding RGS4 associations with
schizophrenia or schizoaffective disorder (SCZA) (Chowdari et al
2002; Williams et al 2004b; Morris et al 2004; Chen et al 2004;
Cordeiro et al 2005). We also contacted investigators known to
organization of RGS4 and the four SNPs investigated are shown. Previously
reported studies that detected significant associations with allele compris-
ing each haplotype are listed.
M.E. Talkowski et al
BIOL PSYCHIATRY 2006;60:152–162 153
be conducting association studies on SCZ susceptibility genes to
identify any additional datasets.
Assays were conducted independently at each site, and
assay methods varied (Table 1) (Chen et al 1999). Four SNPs
were assayed, namely SNPs 1 (rs10917670), 4 (rs951436), 7
(rs951439), and 18 (rs2661319) of RGS4. Single nucleotide
polymorphisms 1, 4, and 7 span 849 bases in the 5’ upstream
region of the gene. Single nucleotide polymorphism 7 is 5.46
kilobase (kb) from the transcription start site for exon 1. Single
nucleotide polymorphism 18 is in the first intron. All four SNPs
span 6.935 kb (Figure 1).
Quality Control for Genotype Assays
Quality control measures varied across sites (Table 1). Sites 1,
2, 3, 5, and 10 used known homozygous and heterozygous
positive control samples generated by sequencing individuals
from the Pittsburgh sample. At site 4, SNP 1 and SNP 7 were
genotyped in duplicate for all individuals. Site 6 used a semiau-
tomatic procedure and all genotypes were scored automatically
using a script as described (van den Oord et al 2003a). For sites
8, 12, and 13, interplate and intraplate duplicate testing of known
DNA samples was performed. At site 10, all genotypes were
performed twice and read, blind to affected status, primarily
using an automated genotype option. For site 7, initial genotype
calls were conducted automatically by pyrosequencing software,
and duplicate genotypes were generated for 6% of the sample for
Comparison of Genotype Assay Methods
Error estimates for five genotyping methods were con-
ducted independently by investigators at Pittsburgh and Dub-
lin, including 1) resequencing (Pittsburgh); 2) SNaPshot assay
Table 1. Meta-Analysis Site and Sample Information
Sample Investigators/Sample Ethnicity
Family-Based SamplesCase-Control Samples
Sample Collection and
Familiesn Trios CasesControl Subjects
151 463151 151127 DSM-IV SNaPshot
2 269912 26900 DSM-IVFP
3 39146 3900 DSM-IIIRSNaPshot
4000 711708 DSM-IV FP/RFLPg
5 49 14749271 576DSM-IVFP
6 26713374600 DSM-IVFP-TDI
7 2575 2500 DSM-IIIR Pyrosequencing
8N609 2,036609 609157 DSM-IIIR CASP
9N293 90529300 DSM-IV FP
N 182609 82 612704DSM-IIIRTaqman
000 299645 DSM-IIIR SNaPshot
12276 1180153255 242 DSM-IVTaqman
13N000580 596 DSM-IIIRCASP
Total2,1607,8101,716 3,488 3,755
RFLP, restriction fragment length polymorphisms; SNP, single nucleotide polymorphism; RGS4, regulator of G-protein signaling 4; SCZ, schizophrenia;
NIMH, National Institute of Mental Health; FP, fluorescence polarization; FP-TDI, fluorescence polarization with template directed dye terminator incorpora-
tion; CASP, competitive allele specific polymerase chain reaction.
aReported associations and associated alleles for SNPs 1–4–7–18, respectively, at RGS4.
bReported in narrow SCZ subtype.
cSamples 1 and 11 have been expanded from initial populations for the current analyses.
dNational Institute of Mental Health Collaborative Genetics Initiative.
eDue to known ethnic admixture within population, ethnicity for some individuals is unclear. All individuals recoded as “Brazilian.”
fThe majority of the population is Caucasian, however individuals of other ethnicities exist and have been identified for analyses.
gFP assay used to type SNPs 4, 7, 18. RFLP method used for SNP1.
hDescribed in Chowdari et al 2002.
iDescribed in Williams et al 2004b.
jDescribed in Morris et al 2004.
kDescribed in Chen et al 2004.
lDescribed in Cordeiro et al 2005.
154 BIOL PSYCHIATRY 2006;60:152–162
M.E. Talkowski et al
(Pittsburgh, Dublin); 3) single strand conformational polymor-
phism method (SSCP) (Pittsburgh); 4) restriction fragment
length polymorphisms method (RFLP) (Pittsburgh); and 5)
Taqman assay (Dublin).
Family-based and case-control association analyses were con-
ducted separately, using individual SNPs as well as haplotypes.
For both sets of analyses, samples from individual sites were
analyzed first, followed by the pooled datasets. We tested for
Hardy-Weinberg equilibrium among cases, control samples, and
parents using the GENEPOP software, version 1.31 (see Appendix 1
for web addresses to all software packages). Mendelian inconsis-
tencies were evaluated using PEDCHECK software (O’Connell and
Family-Based Associations. We tested individual SNPs and
haplotypes for linkage and association using the transmission
disequilibrium test (TDT) (GENEHUNTER software) (Kruglyak et
al 1996), followed by analysis of extended pedigrees with a
generalization of the TDT, the family-based association test
(FBAT). Transmission distortion of all haplotypes was assessed
using global tests of association available in TRANSMIT (version
2.5.2) (Clayton and Jones 1999) and FBAT software (Laird et al
2000). TRANSMIT was implemented to conduct bootstrap testing
using 10,000 bootstrap samples.
Evaluation of Individual Samples:The pooled data can be
influenced by sites with larger samples, and pooled data may
obscure associations if risk is conferred by different alleles or
haplotypes in individual samples, as has been previously re-
ported for RGS4. Hence, significance tests were performed on
the data from each site and on data amalgamated over sites. To
test whether the distribution of site-specific p-values deviated
from the uniform distribution expected under the null hypothe-
sis, we performed a simple test first described by R.A. Fisher.
Under the null hypothesis, two times the negative log of a
p-value is distributed as a ?2; for the sum of N independent tests,
the sum is distributed as a ?2
Cladistic Analysis:We performed cladistic analyses using
EHAP software (Seltman et al 2003). Conceptually, this approach
uses the evolutionary relationship among sampled haplotypes to
structure tests of association in case-control designs or differen-
tial transmission in family-based designs (Seltman et al 2001,
2003). This methodology takes into account the uncertainty in
phase determination rather than using only the most likely
phase. To assure no recombination between generations, EHAP
evaluates possible recombination events and generates an alert if
recombination is detected.
When multilocus genotypes are compatible with multiple
haplotype pairs (configurations), EHAP computes statistics on
the basis of the joint probability of phenotype and haplotype
configurations. For our analyses, we eliminated multilocus geno-
types as uninformative if they were consistent with too many
haplotype configurations (?10) or if haplotypes were rare rela-
tive to sample size. Also, we connected nodes (haplotypes) only
if they were separated by a single mutational step or two steps if
the node was not connected to the rest of the cladogram (or
network). Permutation with score testing was employed for tests
of association as incorporated in EHAP.
Case-Control Associations. Genotype comparisons for indi-
vidual samples were evaluated using the Armitage trends test
(SAS software, SAS Institute Inc., Cary, North Carolina). Haplo-
type frequencies were estimated using PHASE software, version
2.0.2 (Stephens et al 2001; Stephens and Donnelly 2003), and
case-control differences were evaluated with an estimation max-
imization algorithm using an omnibus likelihood ratio test with
SNPEM software (Fallin et al 2001).
For cladistic analyses, a haplotype relative risk model imple-
mented as a general linear model was used. All case-control studies
were analyzed using both measured haplotype analysis (MHA)
permutation and permutation of the overall (full cladogram vs.
single collapsed node) test. Each of these was performed with both
likelihood ratio and score testing. Good agreement of p-values was
found for all four analyses, and only likelihood ratio, overall
permutation method results are reported here.
Regression analyses were performed successively for each SNP.
The dependent variable was case/control status, and the indepen-
dent variables were SNP genotype and site of ascertainment. As a
measure of heterogeneity between samples ascertained across sites,
we examined the interaction between site of ascertainment and SNP
genotype on case status. This model is likely to account for
heterogeneity between varying ethnic groups (Caucasian, Indian,
Chinese, and “Brazilian”), as well as heterogeneity introduced
between sites ascertaining similar ethnic groups (e.g., by genotyp-
ing variation or unknown admixture). These analyses were con-
ducted in the entire sample, the Caucasian only sample (six
samples), and the Caucasians ascertained in European countries
(three samples). Based on these results, haplotype associations
were assessed by pooling the Caucasian samples (six samples, 5596
individuals). Similar to the family sample, we also evaluated statis-
tical distributions from individual studies as described above, fol-
lowed by analysis of pooled samples.
We examined population dispersion amongst the Caucasian
samples (see Weir and Hill 2002 for review) by the standardized
measure of variation among subpopulations first put forth by
Wright (1950). As an estimator of ?, or the degree of allele
sharing identical by descent between populations, we used
FSTAT software (Goudet 1994, 1995) to obtain the unbiased
estimate of Fstas described by Weir and Cockerham (1984).
We analyzed published as well as unpublished data. At the
time of our analyses, the majority of the unpublished data were
ongoing studies intended for peer-reviewed publication on
completion, so formal analyses of publication bias would not be
fruitful. Nevertheless, it is possible that we were more likely to
become aware of an unpublished dataset due to some common
feature, e.g., if they were positive. It is also possible that datasets
not showing a significant association were not submitted for
publication as quickly as those with significant associations.
Thus, we evaluated our results using the published and unpub-
lished datasets separately.
Thirteen groups submitted individual genotype data, with two
declinations. Six provided unpublished genotype data (samples
7, 8, 9, 10, 12, 13) (Table 1). Two of the previously reported
samples were enlarged (Chowdari et al 2002; Morris et al 2004)
and are reported here. In sum, genotypes for 13,807 individuals
were obtained (Table 1). Most probands were diagnosed with
schizophrenia or schizoaffective disorder (DSM III or DSM IV
criteria). Nine cases (?.002% of the total sample) had other
diagnoses: psychosis NOS (n ? 6), schizoid personality disorder
(n ? 1), and schizotypal personality disorders (n ? 2).
M.E. Talkowski et al
BIOL PSYCHIATRY 2006;60:152–162 155
Parents of probands and available family members were
ascertained at 10 sites. The dataset included probands with both
available parents (case-parent trios, “trios,” n ? 1716 families), as
well as probands with one available parent (n ? 444 families).
The latter group also included families with multiple affected
and/or unaffected siblings and relatives. Thus, the entire family
dataset incorporated 7810 individuals from 2160 families (“ex-
tended family sample”).
Eight sites ascertained unrelated control individuals (Table 1).
The recruitment of control individuals varied and included both
screened and unscreened individuals with respect to psychiatric
illness (Chowdari et al 2002; Cordeiro et al 2005; Egan et al 2000;
Morris et al 2004; Williams et al 2004b). A total of 3755 control
individuals were available. They were compared with 3486
cases, including 2242 persons without available relatives and
1244 probands from the family-based samples (one proband per
family was randomly selected when multiple affected individuals
were available from the same pedigree). In this sample, 77.28%
(n ? 5596) reported Caucasian ancestry and were recruited from
six sites (samples 1, 4, 10, 11, 12, and 13) (Table 1).
Overall, 5.4% of genotypes from the case-control sample were
unavailable (10.9%, 4.1%, 3.2%, 3.3% for SNPs 1, 4, 7, 18, respec-
tively). Genotypes were unavailable in the family dataset for 10.9%,
10.2%, 10.3%, and 8.4% of samples for SNPs 1, 4, 7, and 18,
respectively, excluding sample 9. Sample 9 typed a subset of
samples for all SNPs and subsequently genotyped all samples based
on the LD patterns, resulting in missing data rates of 23.1%, 28.8%,
90.7%, and 0% at SNPs 1, 4, 7, and 18, respectively.
Comparison of Genotype Accuracy Rates
At Pittsburgh, no discrepancies were detected in genotypes of
72 individuals between the SNaPshot assay and the sequencing
method at all four SNPs. Hence, the SNaPshot method was used
as a reference. The SNaPshot and RFLP methods were compared
for SNPs 1 and 7, and unacceptably high discrepancy rates were
noted (SNP 1: 7.51%, n ? 493 samples; SNP 7: 3.95%, n ? 506
samples). SNaPshot and SSCP methods were compared at SNPs 4
and 18, where lower discrepancy rates were observed (.59%, n ?
507, and .79%, n ? 509, respectively). Due to the low concor-
dance rates between assays, all data submitted for meta-analysis
from samples 1, 3, and 5 were genotyped using the SNaPshot
assay. At Dublin, 48 individuals were compared using the
SNaPshot assay and Taqman assay for all SNPs. No discrepancies
were detected between these two methods.
We estimated LD for all locus pairs for each sample using
EMLD software. Analyses were performed on control samples or
parents of probands from trio samples. Table 2 provides LD
information for all samples and all SNP combinations.
We found seven transmissions inconsistent with Mendelian
inheritance among 2160 families across all four SNPs. No sample
had more than three non-Mendelising families. Genotypes for
these individuals were set to null. To evaluate the distribution of
genotypes amongst the parent populations included in the
analyses, we assessed Hardy-Weinberg Equilibrium (HWE) in the
parents at each sample for each of the four SNPs. We found only
the parents of the Indian sample deviated significantly from HWE
at SNP1 (p ? .01). This rate is about what we would expect by
chance. No recombination events were detected by EHAP.
Analysis of Individual Sample Data. Single nucleotide poly-
morphism and/or haplotype based associations were observed
in four samples (p ? .055), all of which have been previously
reported: samples 2, 3 (Chowdari et al 2002); sample 6 (Chen et
al 2004); and sample 5 (Cordeiro et al 2005). Cladistic analyses
revealed associations at two of these samples (samples 3 and 6).
Global tests of association of all haplotypes using FBAT were
significant for samples 2 (p ? .02), 3 (p ? .002), and 6 (p ? .007)
(Chowdari et al 2002; Chen et al 2004).
For individual SNPs, the distribution of sample-specific
p-values was consistent with a uniform, but the p-values for
global haplotype tests showed greater mass toward small
p-values (?2? 47.4, df ? 20, p ? .0005). In fact, p-values were
less than .5 for all tests. This conclusion was not altered by
removing sample 3, the most significantly associated sample (?2
? 34.95, df ? 18, p ? .009). Inspection of Table 3 shows
deviation from expected haplotype transmissions for one of the
two common haplotypes at most samples (p ? .2 for 8 of the 10
Table 2. Linkage Disequilibrium Across All Samples
Locus Pairs (D’/r2)
SampleEthnicity 1&4 1&71&184&7 4&187&18
LD is provided for each locus pair. D= values are given first, followed by the r2values.
LD, linkage disequilibrium.
156 BIOL PSYCHIATRY 2006;60:152–162
M.E. Talkowski et al
When evaluating individual haplotype transmissions, i.e.,
TDT analyses, the G-G-G-G haplotype was overtransmitted in
four samples (samples 1, 5, 6, 10 only: 110 transmitted haplotypes,
79 not tranmsitted haplotypes) and the A-T-A-A haplotype was
overtransmitted in four other samples (samples 2, 3, 8, 12 only: 110
transmitted haplotypes, 79 nontranmsitted haplotypes). Consistent
with disparate haplotypes being overtransmitted amongst different
samples in Table 3, cladistic analyses across all trio datasets did not
detect a significant individual risk haplotype (p ? .289).
test analyses were conducted for individual SNP/haplotype
transmissions using the case-parent trios (n ? 1716), and analy-
ses of the extended pedigree datasets were performed using
FBAT (n ? 2160 families; 7810 individuals, mean of 3.62
individuals/pedigree). Of note, there were more extended ped-
igrees than trios from samples 6, 10, and 12 (Chen et al 2004)
(Table 1). Significant transmission distortion of individual SNPs
was not noted for TDT analyses of the trio sample (numbers of G
alleles transmitted/not transmitted, T/NT: SNP 1, 588/584, p ?
.91; SNP 4, 554/559, p ? .88; SNP 7, 499/484, p ? .63; SNP 18,
639/616, p ? .52). Similar results were obtained from FBAT
analyses of the extended pedigree dataset (p ? .4 for all SNPs).
Consistent with published frequency estimates, we find two
common haplotypes in our population, namely G-G-G-G
(42.4%) and A-T-A-A (38.9%). The frequency of the next com-
mon haplotype, G-T-G-A, was 8.2%. All other haplotypes had a
frequency of 5% or less. Analyses of individual haplotype
transmissions using the TDT suggested no significant distortions
for any of the common haplotypes when they were analyzed
individually (G-G-G-G: 246/243 [p ? .89]; A-T-A-A: 273/244 [p ? .2];
G-T-G-A: 97/100 [p ? .83]). Individual haplotype analyses also did
not reveal significant associations in the extended pedigrees (G-G-
G-G, p ? .18; A-T-A-A, p ? .39; G-T-G-A, p ? .72).
By contrast, global tests of transmission distortion for all
haplotypes revealed significant associations in the trio as well
as the extended family samples using TRANSMIT software
(trios: ?2? 33.63, df ? 15, p ? .003; extended family samples:
?2? 37.3, df ? 15, p ? .001). Similar results were detected
using the FBAT permutation test (whole marker results: trios,
p ? .010; entire dataset, p ? .0006). Bootstrap testing was carried
out using transmit software, and the global results from 10,000
bootstrap samples indicated significant transmission distortion
(p ? .0019).
We evaluated the discrepancy in significance values between
the initial analysis of individual haplotypes and the subsequent
global tests. Inspection of the results of global tests suggested
overtransmission of the two common haplotypes at the expense
of other haplotypes. We examined this result in two ways. 1) We
assessed the impact of excluding rare haplotypes using the
extended family sample. When the global tests were restricted to
haplotypes with a frequency greater than 1% (6 haplotypes) or
5% (3 haplotypes), significant associations persisted (p ? .0007
and p ? .01, respectively). Bootstrap testing (10,000 samples)
was also carried out on these restricted haplotypes, and the
results remained unaltered (global significance for haplotypes
greater than 1% and 5% ? .0006 and .012, respectively). 2) We
constructed specific contrasts using EHAP software. When either
of the two common haplotypes was individually contrasted
against a bin encompassing all other haplotypes, significant
transmission distortion was not detected; however, when the two
most common haploytpes were combined and contrasted against
all other haplotypes combined, significant overtransmission of
the common haplotypes was detected (p ? .004). Thus, the initial
significant p-values for the global tests are due to overtransmis-
sion of the G-G-G-G and A-T-A-A haplotypes at the expense of
all other haplotypes.
To determine if the global analyses were influenced by ethnic
variation, we conducted separate global tests of the Caucasian (n ?
1233 families) and non-Caucasian (n ? 925) families. In the
the global test of transmission distortion as in the entire sample,
although it was not quite significant (p ? .08). Consistent with our
observations in the entire dataset, results were significant when
restricting the haplotypes to those greater than 1% (p ? .003) or
5% frequency (p ? .03) in the Caucasian sample. All global
analyses were significant in the non-Caucasian families (all
haplotypes, p ? .002; haplotypes ? 1%, p ? .01; haplotypes ?
5%, p ? .05).
We assessed HWE in the cases and control samples from
individual samples (eight samples, two groups) for all four SNPs
(64 tests), and found deviations in two samples (p ? .01; control
samples in sample 12 at SNP 7 and control samples from sample
8 at SNP 4), roughly equal to what is expected by chance.
A reasonable concern is whether these samples are too
heterogenous with respect to ancestry. Naturally, the samples
with Asian, African, and Amerindian ancestry would be expected
to differ somewhat from the samples of European ancestry;
but do the six samples of European ancestry differ substan-
tially? To assess this question, we examined their degree of
divergence, as estimated by Fst. Estimated Fstwas .001 and
.002 overall for cases and control samples, respectively, and
individual loci showed similar estimates (cases: .001, .000,
.003, .000; control samples: .001, .001, .003, .001 for SNPs 1, 4,
7, and 18, resectively). Fstranges from 0 to 1, and the estimate
for these RGS4 SNPs are consistent with the frequent obser-
vation of little heterogeneity among samples of European
ancestry (Morton 1992; Devlin and Roeder 1999; Devlin et al
2001; Chakraborty 1993).
Analysis of Individual Sample Data. Significant associations
with individual SNPs were noted in three samples (Sample 4:
Table 3. Family-Based Analyses: Haplotype Transmission Results
Family-based transmission distortion for the two most common haplo-
types in individual samples. Results show overtransmitted haplotype and
corresponding individual haplotype p-value.
FBAT, family-based association test; TDT, transmission disequilibrium
ap-values are results of haplotype analyses (FBAT software) with ex-
tended pedigrees. All other p-values are single haplotype TDT results.
bGlobal p-values for individual samples are whole marker results gener-
ated using the FBAT permutation test (100,000 permutations).
M.E. Talkowski et al
BIOL PSYCHIATRY 2006;60:152–162 157
SNPs 4 and 18, Sample 8: SNP4, Sample 13: SNPs 1, 7, and 18;
Table 4). All significant associations were observed with alleles
constituting the A-T-A-A haplotype. Global tests incorporating all
four SNP haplotypes (i.e., SNPEM omnibus likelihood ratio tests)
detected associations in three samples (p ? .05; samples 8, 12,
and 13; Table 5). Cladistic analyses revealed haplotype associa-
tions at two of these samples (samples 8, 12) but only a trend at
sample 13 (p ? .10).
The distribution of p-values for sample specific tests deviated
significantly from a uniform for two of the SNPs, SNP 4 (?2?
31.59, df ? 16, p ? .01) and SNP 7 (?2? 29.2, df ? 16, p ? .02),
while the other two SNPs showed similar but not significant
deviations (SNP 1 [p ? .07]; SNP 18 [p ? .09]). Similar to the
family sample, sample-specific global tests of association using
the four SNP haplotypes suggested a significant deviation from a
uniform distribution for all samples (?2? 49.6, df ? 16, p ?
.002), as well samples of Caucasian ancestry only (?2? 29.7,
df ? 12, p ? .002) (Table 5). However, in this case, the
conclusion was not robust to removing the most significantly
associated sample (sample 8 excluded: ?2? 18.75, df ? 14, p ?
.17). Cladistic analyses suggested a modest trend for association
(p ? .11) across all samples.
Analyses of All Samples. Regression analyses indicated sig-
nificant effects of SNP genotype on case status for SNPs 4 (?2?
10.75, df ? 3, p ? .01) and 7 (?2? 7.91, df ? 3, p ? .05) across
all samples. No associations were observed when analyses were
restricted to Caucasian samples.
The interaction of SNP genotype and site of ascertainment on
case status suggested significant heterogeneity for SNPs 1 (?2?
34.95, df ? 21, p ? .001), 4 (?2? 41.9, df ? 21, p ? .004), and
18 (?2? 37.7, df ? 21, p ? .01) across all samples, SNP 1 only
in the Caucasian samples (six samples; p ? .003), and no
heterogeneity in Caucasians ascertained in European countries
(three samples). Thus, heterogeneity is present across all sam-
Table 4. Case-Control Analyses: SNP-Based Results
Results for the SNP-based analyses from individual samples.
Samples are grouped by ethnicity.
SNP, single nucleotide polymorphism; CI, confidence interval.
aFrequencies are given with reference to the G allele at each SNP.
bp-values are trends test results from genotype data.
cOdds ratio, associated allele, and 95% confidence intervals (CI) for significant associations.
158 BIOL PSYCHIATRY 2006;60:152–162
M.E. Talkowski et al
ples, but is diminished when restricting analyses to the Caucasian
Analysis of Pooled Data for Haplotypes. Due to observed
heterogeneity amongst all samples, haplotype analyses were
conducted by pooling the case-control samples of Caucasian
ancestry (n ? 5596). These analyses did not yield significant
associations with any of the individual haplotypes or global tests
of association across all haplotypes (4 SNP omnibus likelihood
ratio: p ? .51). We also designed specific contrasts using EHAP
software to determine associations with the two most common
haplotypes, similar to analyses in the family sample. When the
two common haplotypes were combined and contrasted against
all other haplotypes combined, a modest association was
detected (frequency of two common haplotypes/frequency of
rare haplotypes; cases: .834/.166, control samples: .817/.183,
p ? .09).
Six of the seven published datasets detected significant or
marginally significant associations at the SNP or haplotype level
(samples 1, 2, 3, 4, 6, 11). We ascertained six unpublished
case-control datasets. We detected associations in case-control
analyses at the SNP (samples 8, 13) or haplotype level (sample
12) in three of the six additional samples. No associations were
detected in any of the five unpublished family-based samples
included in these analyses.
Genetic analyses of complex diseases are fraught with chal-
lenges because we often know so little about how to control for
the genetic and environmental sources of heterogeneity. For this
reason, when studies attempting to replicate initial associations
of complex disorders produce different results, it is difficult to
interpret their significance. Recently, a number of candidates
for schizophrenia susceptibility genes have emerged from the
analyses of linkage-defined positional candidates, some of
which have been motivated by other biological information
such as gene expression. In addition, there have been studies
replicating these initial findings, in a sense, but the interpre-
tation of these results is often obscure. In fact, most often
neither the associated alleles nor the associated haplotypes
are consistent across these studies (Shirts and Nimgaonkar
2004). Here, we investigate the results for one of the genes
recently described as a positional and functional SCZ suscep-
tibility candidate, RGS4. Unlike many of the other candidates,
RGS4 is a relatively small gene in which patterns of LD have
been investigated and associations reported for a limited
number of SNPs. Yet, like the other candidates, studies of the
association between SCZ and RGS4 alleles and haplotypes
have been plagued by inconsistency. In this report, we
performed meta-analysis in an attempt to understand these
Our goal was to elicit greater evidence for, or against, RGS4 as
a gene containing variations affecting susceptibility to SCZ.
Moreover, if the evidence was positive, then we hoped the
analyses would elucidate exactly what factors generate suscep-
tibility. Our results are compatible with at least two risk variants
conferring susceptibility to SCZ, specifically both the common
haplotypes of the four alleles in which associations have been
previously reported at this gene.
Our family-based analyses detected significant transmission
distortion incorporating all haplotypes. These observations were
made using two different software programs, making it unlikely
that they are due to idiosyncrasies in analytic software. The
results could also not be attributed to deviations from Hardy-
Weinberg Equilibrium in the parent population. We scrutinized
these results and conducted additional analyses, all of which
suggest that overtransmission of both of the two most common
haplotypes appears to be the most parsimonious explanation for
the results of the global tests.
Evaluation of the distribution of test statistics from individual
samples also supported family-based associations, even after the
most significant sample was excluded. These analyses would be
particularly persuasive if consistent deviations from expected
distributions were detected across multiple studies, rather than
being attributable to few studies with large effect sizes. Indeed,
inspection of transmission distortions at each sample revealed
modest deviations from expectations in the global tests for most
samples (Table 3).
Case-control analyses appear to support this conclusion. No
individual risk haplotype was detected in cladistic analyses or
assessment of the pooled Caucasian sample. Instead, the distri-
bution of test statistics suggested associations with global haplo-
type tests across samples. If transmission to affected offspring
was biased toward the two most common haplotypes, one would
expect to detect this effect in a sufficiently powered case-control
sample. We investigated this hypothesis in the Caucasian sample.
Our results showed nonsignificant patterns similar to those of the
family-based analyses of association with both common haplo-
types compared with all other haplotypes. However, the differ-
Table 5. Case-Control Analyses: Four SNP Haplotype Results by Sample
Estimated haplotype frequencies in cases and control subjects from individual samples.
Samples are grouped by ethnicity.
SNP, single nucleotide polymorphism.
ap-values represent EM algorithm estimations of SNP haplotypes omnibus likelihood ratio tests of association for all haplotypes.
M.E. Talkowski et al
BIOL PSYCHIATRY 2006;60:152–162 159
ences between case and control haplotype frequencies were
relatively small (?2%).
Collectively, our analyses point toward a modest association
resulting from overtransmission of both of the common haplo-
types to SCZ cases at the expense of other haplotypes. There are
a number of possible explanations for the observed results,
including biological, statistical, molecular, and population phe-
Is there a biologically plausible explanation why two com-
mon haplotypes, accounting for greater than 80% of all haplo-
types, are overtransmitted to individuals with SCZ? Arguably, the
simplest explanation is that the liability locus or loci remain
undetected and are found more commonly (or exclusively) on
these two haplotypes. Certainly recurrent mutations or recombi-
nations that transfer liability alleles between haplotypes are likely
to involve these common haplotypes. Our results could account
for at least two different possibilities in such a scenario: allelic
(intragenic) heterogeneity or the contribution of multiple indi-
vidual loci to susceptibility. Similar results could also be obtained
by the presence of a single, rare susceptibility variant occurring
against the background of both common haplotypes. Evaluation
of these explanations would require comprehensive sequencing
through RGS4 and its surrounding regions in many individuals.
The nebulous nature of what constitute the important elements
for expression of RGS4 complicates this analysis. On the other
hand, expression assays using RGS4 alleles and haplotypes are
reasonably straightforward and of interest in light of past analy-
ses (Mirnics et al 2001; Erdely et al 2004).
It is also possible, although more difficult to defend, that the
haplotypes themselves have an impact on SCZ susceptibility.
Notably, SNPs 1, 4, and 7 lay within the 5’ upstream region of the
gene, and the haplotypes investigated here span the first exon of
RGS4. The potential effect of these, or unknown variants as
discussed above, on promoter activity and/or transcription is
intriguing, given our results. The significance of our findings
could also be rooted in phenotypic subgroups for which RGS4
may modulate expression of the disease phenotype. Seeking
clinical subfeatures that may be significantly impacted by func-
tional changes related to RGS4 could provide insight into the
biological role of this gene on SCZ susceptibility.
Statistical phenomena may also contribute to our findings.
One of the curious observations from the past studies of RGS4 is
that one common haplotype would appear to be overtransmitted
in one sample and yet the other common haplotype would
appear to be overtransmitted in the next sample tested. Is this
phenomenon compatible with our results, which suggest that
both common haplotypes are overtransmitted? Recent simulation
studies suggest that even when the liability locus is amongst the
loci tested within a gene, the liability locus often does not
produce the maximum test statistic (Roeder et al 2005). Instead,
other loci in substantial LD with the liability locus yield the
maximum test statistic. Moreover, haplotype analyses carry sim-
ilar challenges, as simulations have shown that in the presence of
a liability haplotype, multiple patterns of haplotype associations
can be found (Seltman et al 2001). Seltman et al (2001) concluded
that in many instances, cladograms and measured haplotype
analyses, such as those conducted herein, can provide greater
insight into what haplotype bears risk alleles. However, if the
scenario revealed by our analyses is, in fact, true, then cladistic
analyses are unlikely to yield much insight.
By our analysis plan, we first performed global tests of
association using bootstrap testing and permutation tests; if these
tests were significant, we then would explore the data to
determine what was generating the significant findings. In fact,
our global tests were significant, and our conclusions are based
on our subsequent exploratory analyses. It is noteworthy, how-
ever, that even if we were to correct for our exploratory analyses
by a conservative Bonferroni-type correction for the number of
SNPs (4), common haplotypes (6), and study designs tested (2,
family-based and case-control), our results would still exceed the
significance threshold (p ? .001) for significant transmission
It is possible that the overtransmission of the two common
haplotypes results from technical issues of little interest to the
genetics of SCZ, such as population heterogeneity or molecular
analysis. Due to the use of transmission tests, confounding due to
population heterogeneity is of little concern. We conducted
analyses to assess population heterogeneity in our case-control
sample, and our results suggest heterogeneity is relatively minor
across samples of European ancestry. However, technical molec-
ular issues could explain the result. Due to the retrospective
nature of the analyses, uniform quality control in genotyping
measures could not be imposed. Notably, we scrutinized quality
control and found that rigorous checks were used in genotyping
assays. Still, it is well known that genotyping errors can mimic
biased transmissions (see Gordon et al 1999; Mitchell et al 2003
for review), and that bias is most likely to present itself as the
overtransmission of common alleles/haplotypes. Countering this
concern, somewhat, are shared observations: case-control anal-
yses suggest similar overrepresentation of common haplotypes
in SCZ cases, and rarer haplotypes showed similar frequencies in
the singleton cases that could not be evaluated for Mendelian
transmission (or in the control samples) than in the family-based
probands that did have Mendelian checks. Still, potential con-
founds due to assay variation could impact on our results and
In summary, we report a meta-analysis of RGS4 polymor-
phisms with schizophrenia. Genotype data from 13,807 individ-
uals were analyzed collaboratively by 13 independent groups. To
our knowledge, this is the largest such study to date in SCZ
research. Future studies may require sequencing across the risk
haplotypes in a large number of patients. Similar methodology to
that presented here may help resolve some of the other contro-
versial associations reported for psychiatric and nonpsychiatric
genetically complex disorders.
This work was supported by grants from the National Institute
of Mental Health (NIMH) (MH56242 and MH53459 to VLN),
(K02 MH070786 to KM), (MH62440 to LMB), the Indo-US Project
Agreement (#N-443-645 to VLN and BKT), an NIMH Conte
Center for the Neuroscience of Mental Disorders (MH051456 to
DAL), The Intramural Research Program of NIMH (DRW, RES,
MFE), United Kingdom MRC (MJO, GK, MCO, and NMW),
Science Foundation of Ireland (MG, DWM, APC), The National
Science Foundation of China (TL), Welcome Trust (TL and
DAC), NARSAD (TL), The Schizophrenia Research Fund (TL and
DAC), GSK (FZ and DS), The Canadian Institutes of Health
Research (ASB), The Bill Jefferies Schizophrenia Endowment
Fund (ASB), Canada Research Chair in Schizophrenia Genetics
(ASB), NARSAD (ASB), and Janssen Research Foundation
funded family recruitment in Bulgaria.
We thank Shawn Wood for his help with the cladistic analyses.
Brzustowicz LM, Hodgkinson KA, Chow EW, Honer WG, Bassett AS (2000):
Location of a major susceptibility locus for familial schizophrenia on
chromosome 1q21-q22. Science 288:678–682.
160 BIOL PSYCHIATRY 2006;60:152–162
M.E. Talkowski et al
Brzustowicz LM, Simone J, Mohseni P, Hayter JE, Hodgkinson KA, Chow EW,
ceptibility to the CAPON region of chromosome 1q22. Am J Hum Genet
methods, and implications. In: Majumder PP, editor. Human Population
Genetic. New York: Plenum Press, 189–206.
Chen X, Dunham C, Kendler S, Wang X, O’Neill FA, Walsh D, et al (2004):
phrenia in Irish high density families. Am J Med Genet B Neuropsychiatr
nucleic acid analysis. Genome Res 9:492–498.
Clayton D, Jones H (1999): Transmission/disequilibrium tests for extended
Cockerham CC, Weir BS (1986): Estimation of inbreeding parameters in
Cordeiro Q, Talkowski ME, Chowdari KV, Wood J, Nimgaonkar V, Vallada H
(2005): Association and linkage analysis of RGS4 polymorphisms with
schizophrenia and bipolar disorder in Brazil. Genes Brain Behav 4:45–50.
Confirmation and refinement of an ’at-risk’ haplotype for schizophrenia
suggests the EST cluster, Hs.97362, as a potential susceptibility gene at
the Neuregulin-1 locus. Mol Psychiatry 9:208–213.
Devlin B, Roeder K (1999): Genomic control for association studies. Biomet-
Devlin B, Roeder K, Bacanu S-A (2001): Unbiased methods for population-
based association studies. Genet Epidemiol 21:273–284.
Dubertret C, Gorwood P, Ades J, Feingold J, Schwartz JC, Sokoloff P (1998):
Meta-analysis of DRD3 gene and schizophrenia: Ethnic heterogeneity
and significant association in Caucasians. Am J Med Genet 81:318–322.
Egan MF, Goldberg TE, Gscheidle T, Weirich M, Bigelow LB, Weinberger DR
(2000): Relative risk of attention deficits in siblings of patients with
Erdely HA, Lahti RA, Lopez MB, Myers CS, Roberts RC, Tamminga CA, et al
Fallin D, Cohen A, Essioux L, Chumakov I, Blumenfeld M, Cohen D, et al
frequencies: Application to APOE locus variation and Alzheimer’s dis-
ease. Genome Res 11:143–151.
Gamma F, Faraone SV, Glatt SJ, Yeh YC, Tsuang MT (2005): Meta-analysis
morphism of the dopamine transporter gene. Schizophr Res 73:55–58.
Glatt SJ, Faraone SV, Tsuang MT (2003a): Association between a functional
catechol O-methyltransferase gene polymorphism and schizophrenia:
Meta-analysis of case-control and family-based studies. Am J Psychiatry
Glatt SJ, Faraone SV, Tsuang MT (2003b): Meta-analysis identifies an associ-
ation between the dopamine D2 receptor gene and schizophrenia. Mol
Glatt SJ, Wang RS, Yeh YC, Tsuang MT, Faraone SV (2005): Five NOTCH4
Evidence from meta-analyses. Schizophr Res 73:281–290.
Gordon D, Matise TC, Heath SC, Ott J (1999): Power loss for multiallelic
lated data. Genet Epidemiol 17(suppl 1):S587–S592.
Goudet J (1995): Fstat version 1.2: a computer program to calculate Fstatis-
tics. J Heredity 86:485–486.
Ioannidis JP, Ntzani EE, Trikalinos TA, Contopoulos-Ioannidis DG (2001):
Replication validity of genetic association studies. Nat Genet 29:306–
al (2003): Dopamine D3 receptor gene Ser9Gly variant and schizophre-
nia: Association study and meta-analysis. Psychiatr Genet 13:1–12.
analysis of the dopamine D3 receptor gene (DRD3) Ser9Gly variant and
schizophrenia. Psychiatr Genet 14:9–12.
Kruglyak L, Daly MJ, Reeve-Daly MP, Lander ES (1996): Parametric and non-
parametric linkage analysis: A unified multipoint approach. Am J Hum
Laird N, Horvath S, Xu X (2000): Implementing a unified approach to family
based tests of association. Genet Epidemiol 19(suppl 1):536–542.
Li T, Stefansson H, Gudfinnsson E, Cai G, Liu X, Murray RM, et al (2004):
Identification of a novel neuregulin 1 at-risk haplotype in Han schizo-
risk haplotype. Mol Psychiatry 9(7):698–704.
Mirnics K, Middleton FA, Stanwood GD, Lewis DA, Levitt P (2001): Disease-
in schizophrenia. Mol Psychiatry 6:293–301.
Mitchell AA, Cutler DJ, Chakravarti A (2003): Undetected genotyping errors
Morris DW, McGhee KA, Schwaiger S, Scully P, Quinn J, Meagher D, et al
Morton NE (1992): Genetic structure of forensic populations. Proc Natl Acad
O’Connell JR, Weeks DE (1998): PedCheck: A program for identification of
genotype incompatibilties in linakge analysis. Am J Hum Genet 63:259–
Petryshen TL, Middleton FA, Kirby A, Aldinger KA, Purcell S, Tahl AR, et al
physiology. Mol Psychiatry 10(4):366–374, 328.
locus tests to detect gene/disease associations. Genet Epidemiol 28(3):
Schwab SG, Knapp M, Mondabon S, Hallmayer J, Borrmann-Hassenbach M,
Albus M, et al (2003): Support for association of schizophrenia with
genetic variation in the 6p22.3 gene, dysbindin, in sib-pair families with
linkage and in an additional sample of triad families. Am J Hum Genet
Seltman H, Roeder K, Devlin B (2001): Transmission/disequilibrium test
meets measured haplotype analysis: Family-based association analysis
guided by evolution of haplotypes. Am J Hum Genet 68:1250–1263.
Seltman H, Roeder K, Devlin B (2003): Evolutionary-based association anal-
ysis using haplotype data. Genet Epidemiol 25:48–58.
Stefansson H, Sarginson J, Kong A, Yates P, Steinthorsdottir V, Gudfinnsson
Stephens M, Donnelly P (2003): A comparison of bayesian methods for
haplotype reconstruction from population genotype data. Am J Hum
Stephens M, Smith NJ, Donnelly P (2001): A new statistical method for
haplotype reconstruction from population data. Am J Hum Genet 68:
Straub RE, Jiang Y, MacLean CJ, Ma Y, Webb BT, Myakishev MV, et al (2002):
mouse dysbindin gene, is associated with schizophrenia. Am J Hum
within 5’ end of the Neuregulin 1 gene are genetically associated with
schizophrenia in the Chinese population. Mol Psychiatry 9:11–12.
Tang JX, Zhou J, Fan JB, Li WX, Shi YY, Gu NF, et al (2003): Family-based
Van Den Bogaert A, Schumacher J, Schulze TG, Otte AC, Ohlraun S,
Kovalenko S, et al (2003): The DTNBP1 (dysbindin) gene contributes to
M.E. Talkowski et al
BIOL PSYCHIATRY 2006;60:152–162 161
schizophrenia, depending on family history of the disease. Am J Hum Download full-text
van den Oord EJ, Jiang Y, Riley BP, Kendler KS, Chen X (2003a): FP-TDI SNP
scoring by manual and statistical procedures: A study of error rates and
types. Biotechniques 34:610–616, 618–620, 622 passim.
van den Oord EJ, Sullivan PF, Jiang Y, Walsh D, O’Neill FA, Kendler KS, et al
(2003b): Identification of a high-risk haplotype for the dystrobrevin
binding protein 1 (DTNBP1) gene in the Irish study of high-density
schizophrenia families. Mol Psychiatry 8:499–510.
Weir BS, Hill WG (2002): Estimating F-statistics. Annu Rev Genet 36:721–750.
Williams NM, Preece A, Morris DW, Spurlock G, Bray NJ, Stephens M, et al
(2004a): Identification in 2 independent samples of a novel schizophre-
nia risk haplotype of the dystrobrevin binding protein gene (DTNBP1).
Williams NM, Preece A, Spurlock G, Norton N, Williams HJ, McCreadie RG, et
al (2004b): Support for RGS4 as a susceptibility gene for schizophrenia.
Williams NM, Preece A, Spurlock G, Norton N, Williams HJ, Zammit S, et al
schizophrenia. Mol Psychiatry 8:485–487.
Wright S (1950): Genetical structure of populations. Nature 166:247–249.
Yang JZ, Si TM, Ruan Y, Ling YS, Han YH, Wang XL, et al (2003): Association
study of neuregulin 1 gene with schizophrenia. Mol Psychiatry 8:
based association study of the neuregulin1 gene and schizophrenia.
Appendix 1. Electronic Database Information
Online Mendelian Inheritance in Man (OMIM), http://www.
GENEHUNTER: http://www.fhcrc.org/ labs/kruglyak
162 BIOL PSYCHIATRY 2006;60:152–162
M.E. Talkowski et al