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Year: 2012

Schizophrenia risk polymorphisms in the TCF4 gene interact with smokingin the modulation of auditory sensory gating

Quednow, Boris B

Abstract: Several polymorphisms of the transcription factor 4 (TCF4) have been shown to increase therisk for schizophrenia, particularly TCF4 rs9960767. This polymorphism is associated with impairedsensorimotor gating measured by prepulse inhibition–an established endophenotype of schizophrenia.We therefore investigated whether TCF4 polymorphisms also affect another proposed endophenotypeof schizophrenia, namely sensory gating assessed by P50 suppression of the auditory evoked potential.Although sensorimotor gating and sensory gating are not identical, recent data suggest that they sharegenetic fundamentals. In a multicenter study at six academic institutions throughout Germany, weapplied an auditory P50 suppression paradigm to 1,821 subjects (1,023 never-smokers, 798 smokers)randomly selected from the general population. Samples were genotyped for 21 TCF4 polymorphisms.Given that smoking is highly prevalent in schizophrenia and affects sensory gating, we also assessedsmoking behavior, cotinine plasma concentrations, exhaled carbon monoxide, and the Fagerström Test(FTND). P50 suppression was significantly decreased in carriers of schizophrenia risk alleles of the TCF4polymorphisms rs9960767, rs10401120rs, rs17597926, and 17512836 (P < 0.0002-0.00005). These geneeffects were modulated by smoking behavior as indicated by significant interactions of TCF4 genotype andsmoking status; heavy smokers (FTND score 4) showed stronger gene effects on P50 suppression thanlight smokers and never-smokers. Our finding suggests that sensory gating is modulated by an interactionof TCF4 genotype with smoking, and both factors may play a role in early information processing deficitsalso in schizophrenia. Consequently, considering smoking behavior may facilitate the search for geneticrisk factors for schizophrenia.

DOI: https://doi.org/10.1073/pnas.1118051109

Posted at the Zurich Open Repository and Archive, University of ZurichZORA URL: https://doi.org/10.5167/uzh-68521Journal ArticlePublished Version

Originally published at:Quednow, Boris B (2012). Schizophrenia risk polymorphisms in the TCF4 gene interact with smokingin the modulation of auditory sensory gating. Proceedings of the National Academy of Sciences of theUnited States of America, 109(16):6271-6276.DOI: https://doi.org/10.1073/pnas.1118051109

Schizophrenia risk polymorphisms in the TCF4 geneinteract with smoking in the modulation of auditorysensory gatingBoris B. Quednowa,b,1,2, Jürgen Brinkmeyerc,d,1, Arian Mobascherc,d,e, Michael Nothnagelf, Francesco Mussoc,Gerhard Gründerg, Noah Savarya, Nadine Petrovskyb, Ingo Frommannb, Leonhard Lennertzb, Katja N. Spreckelmeyerg,Thomas F. Wienkerh, Norbert Dahmene, Norbert Thueraufi, Marion Clepcei, Falk Kieferj, Tomislav Majick,Rainald Mössnera, Wolfgang Maierb,l, Jürgen Gallinatk, Amalia Diaz-Lacavam, Mohammad R. Toliatm, Holger Thielem,Peter Nürnbergm, Michael Wagnerb,l, and Georg Wintererd,m

aExperimental and Clinical Pharmacopsychology, Clinic for Affective Disorders and General Psychiatry, University Hospital of Psychiatry Zurich, 8032 Zurich,Switzerland; bDepartment of Psychiatry and Psychotherapy, University of Bonn, 53105 Bonn, Germany; cDepartment of Psychiatry, Heinrich-HeineUniversity Düsseldorf, 40629 Düsseldorf, Germany; dInstitute of Neurosciences and Medicine, Helmholtz Research Center Juelich, 52428 Jülich, Germany;eDepartment of Psychiatry, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; fInstitute of Medical Informatics and Statistics, Christian-AlbrechtsUniversity Kiel, 24105 Kiel, Germany; gDepartment of Psychiatry and Psychotherapy, Rheinisch-Westfälische Technische Hochschule Aachen University,52062 Aachen, Germany; hInstitute of Medical Biometry, Informatics and Epidemiology, University of Bonn, 53105 Bonn, Germany; iDepartment of Psychiatry,Friedrich-Alexander University Erlangen-Nuernberg, 91054 Erlangen, Germany; jDepartment of Addictive Behavior and Addictive Medicine, CentralInstitute of Mental Health, 68159 Mannheim, Germany; kDepartment of Psychiatry, Charité University Hospital, Campus Mitte, 10117 Berlin, Germany;lGerman Center for Neurodegenerative Diseases, 53105 Bonn, Germany; and mCologne Center for Genomics, Cologne University, 50931 Cologne, Germany

Edited by Leslie Lars Iversen, University of Oxford, Oxford, United Kingdom, and approved February 17, 2012 (received for review November 3, 2011)

Several polymorphisms of the transcription factor 4 (TCF4) have

been shown to increase the risk for schizophrenia, particularly

TCF4 rs9960767. This polymorphism is associated with impaired

sensorimotor gating measured by prepulse inhibition—an estab-

lished endophenotype of schizophrenia. We therefore investigated

whether TCF4 polymorphisms also affect another proposed endo-

phenotype of schizophrenia, namely sensory gating assessed by

P50 suppression of the auditory evoked potential. Although sen-

sorimotor gating and sensory gating are not identical, recent data

suggest that they share genetic fundamentals. In a multicenter

study at six academic institutions throughout Germany, we applied

an auditory P50 suppression paradigm to 1,821 subjects (1,023

never-smokers, 798 smokers) randomly selected from the general

population. Samples were genotyped for 21 TCF4 polymorphisms.

Given that smoking is highly prevalent in schizophrenia and af-

fects sensory gating, we also assessed smoking behavior, cotinine

plasma concentrations, exhaled carbon monoxide, and the Fager-

ström Test (FTND). P50 suppression was significantly decreased in

carriers of schizophrenia risk alleles of the TCF4 polymorphisms

rs9960767, rs10401120rs, rs17597926, and 17512836 (P < 0.0002–

0.00005). These gene effects were modulated by smoking behavior

as indicated by significant interactions of TCF4 genotype and

smoking status; heavy smokers (FTND score ≥4) showed stronger

gene effects on P50 suppression than light smokers and never-

smokers. Our finding suggests that sensory gating is modulated

by an interaction of TCF4 genotype with smoking, and both factors

may play a role in early information processing deficits also in

schizophrenia. Consequently, considering smoking behavior may

facilitate the search for genetic risk factors for schizophrenia.

single nucleotide polymorphism | intermediate phenotype | nicotine |gene–environment interaction

Recent large genomewide association studies (GWAS) iden-tified and consistently confirmed that common variants of

the transcription factor 4 (TCF4) gene contribute to the risk ofschizophrenia (1–3). In these analyses, two single nucleotidepolymorphisms (SNPs) located in the intron of the TCF4 gene onchromosome 18q21.1 (rs9960767, rs17512836) and an intragenicSNP near the TCF4 gene (rs4309482) have shown the strongestassociation with the disease (1–3). TCF4 is a class I basic helix–loop–helix (bHLH) protein involved in the control of neuronaland glial progenitor cells, which are important for the devel-opment of the mammalian central nervous system (CNS) (4, 5).

The exact role of TCF4 in the brain and the functional activityof these nonsynonymous TCF4 variants on the level of gene ex-pression are not yet fully understood (4, 6). A recent postmortemstudy suggested that the rs9960767 SNP is neither functional noraffects mRNA expression in the adult human brain, indicatingthat TCF4mutations may exert their effects on expression throughposttranscriptional effects or exclusively in a developmental con-text (e.g., by gene–environment interactions) (7).In a translational animal study, it was initially shown that

transgenic mice moderately overexpressing TCF4 in the post-natal brain display profound reductions in sensorimotor gatingas measured by prepulse inhibition (PPI) of the acoustic startleresponse (8), which is an established translational endopheno-type of schizophrenia (9). Consequently, we recently investigatedthe impact of the TCF4 rs9960767 SNP on PPI in humans andfound that the schizophrenia risk allele C of this SNP wasstrongly associated with reduced PPI in two independent samplesof healthy volunteers and schizophrenia spectrum patients (10).Auditory sensory gating, i.e., P50 suppression of the auditory

evoked potential (AEP) is another measure of gating function.P50 gating is regarded as a useful endophenotype of schizophre-nia and is conceptually related to, albeit not equivalent with,sensorimotor gating as measured by PPI (9). In the classical au-ditory conditioning-testing P50 paradigm (11), pairs of identicalauditory stimuli (conditioning stimulus S1 and test stimulus S2)are presented at an interstimulus interval (ISI) of 500 ms, whereasthe cortical response to these stimuli is assessed via electroen-cephalography (EEG). Normal subjects usually have a suppressedP50 response to the second stimulus. Source localization studiesand intracerebral electrophysiological recordings suggest thatthe generators of sensory gating are in the hippocampus, insula,lateral temporal, temporo-parietal, and prefrontal cortex (PFC)

Author contributions: B.B.Q., J.B., A.M., F.M., G.G., N.D., F.K., R.M., W.M., J.G., M.W., and

G.W. designed research; B.B.Q., J.B., A.M., F.M., G.G., N.P., I.F., L.L., K.N.S., T.F.W., N.D.,

N.T., M.C., F.K., T.M., A.D.-L., M.R.T., H.T., P.N., M.W., and G.W. performed research; M.R.T.

contributed new reagents/analytic tools; B.B.Q., J.B., M.N., N.S., I.F., R.M., M.W., and G.W.

analyzed data; and B.B.Q., J.B., M.N., R.M., M.W., and G.W. wrote the paper.

The authors declare no conflict of interest.

This article is a PNAS Direct Submission.

1B.B.Q. and J.B. contributed equally to this work.

2To whom correspondence should be addressed. E-mail: [email protected].

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.

1073/pnas.1118051109/-/DCSupplemental.

www.pnas.org/cgi/doi/10.1073/pnas.1118051109 PNAS Early Edition | 1 of 6

NEUROSCIENCE

(12–14). Auditory sensory gating has been conceptualized as animportant preattentive filter function that protects cognitive pro-cesses from potentially interfering and irrelevant information (15).Accordingly, P50 suppression has been related to attentionalperformance, working memory, and behavioral inhibition (16–19).Schizophrenia patients display diminished P50 suppression

(20–25), even if some studies failed to find differences betweenpatients and controls (26–29). However, meta-analyses confirmedmoderate to large effects of sensory gating deficits in schizo-phrenia across studies (30–32). The P50 suppression was sug-gested as an endophenotype of schizophrenia (9) because it isheritable (33–35), it is reduced in subjects with schizotypal per-sonality disorder and in unaffected relatives of schizophreniapatients (36–38), and decreased P50 suppression levels arepresent in early stages of schizophrenia (39, 40). Moreover, P50suppression deficits have been repeatedly associated to SNPs ofthe α7 nicotinic acetylcholine receptor subunit gene (CHRNA7)(41), whereas the initially reported impact of catechol-O-meth-yltransferase (COMT) gene variations on P50 suppression (42)was not replicated in three following studies (43–45). Despite thefact that schizophrenia patients display both sensorimotor andsensory gating abnormalities, and although both measures areprocedurally similar, they are usually not correlated and might beregulated by distinct neuronal pathways (46–49). However, a re-cent analysis from the Consortium on the Genetics of Schizo-phrenia (COGS) suggested that both gating measures likelyshare some genetic basis: PPI and P50 suppression were bothinfluenced by variations in CHRNA7, the neuregulin-1 (NRG1)gene, and the ionotropic glutamate receptor δ2-subunit gene(GRID2) (45, 50, 51). In the COGS study, the TCF4 gene wasnot investigated and, thus, the impact of the TCF4 SNPs onschizophrenia risk and sensorimotor gating (PPI) warrants alsoan investigation of its influence on P50 suppression.Because recent GWAS findings identified several TCF4 poly-

morphisms as genetic risk factors for schizophrenia (1–3), humancarriers of the TCF4 rs9960767 risk allele C showed reducedgating abilities in the PPI paradigm (10), and auditory sensorygating is an established endophenotype of schizophrenia (9), wehypothesized that healthy carriers of TCF4 schizophrenia riskpolymorphisms would also display reduced auditory sensory gat-ing as reflected in a decreased P50 suppression. Given that riskgene variants for schizophrenia (including those identified inTCF4) are common in the general population and because heri-table variation of endophenotype measures is also present insubjects not affected with schizophrenia (33–35), one can studythe possible association of risk variants with putative endophe-notypes in healthy subjects. This approach offers a number ofadvantages over classical case-control studies: (i) confoundingeffects of illness and treatment are ruled out, (ii) larger samplesstratified for the presence or absence of possible environmentalmoderators can be studied in a limited time, and (iii) indepen-dence of conventional, phenotypic diagnostic criteria. Thus, pos-itive findings for single gene variants in healthy controls can helpto discover biologically valid knowledge about previously un-known mechanisms linked to this gene (52). To test our hypoth-esis, we assessed a large and genetically homogeneous sample of1,821 volunteers exclusively of German ancestry with an estab-lished auditory sensory gating paradigm in the frame of a multi-center study at six academic institutions throughout Germany. Allparticipants were genotyped for the 20 most significant TCF4SNPs from the recent GWAS of Ripke et al. (ref. 1; http://www.broadinstitute.org/mpg/ricopili/; window 1.5Mb, threshold P <

1.0 × 10−4) and for the most significant TCF4 SNP rs9960767reported by Stefansson et al. (2) and Steinberg et al. (3). Giventhat smoking is a critical confounding factor with regard to theP50 suppression (53) and because schizophrenia patients arefrequently heavy smokers (54), we additionally investigated theimpact of smoking severity as a possible mediating factor on TCF4gene effects on P50 suppression.

Results

One of the 21 TCF4 SNPs was monomorphic (rs17509991) and,therefore, excluded from further analyses. All other SNPs werein Hardy–Weinberg equilibrium (HWE) (Table S1) and partly instrong linkage disequilibrium (LD) (Fig. S1).Unless otherwise stated, we compared SNP risk allele carriers

against noncarriers throughout (10). Percent P50 suppressionwas significantly affected by 4 of the 20 TCF4 SNPs (Bonferroni-corrected threshold P < 0.0024 for 21 markers) and smokingstatus, whereas there was also a significant genotype–smokinginteraction in each marker (Table 1). Given that the rs17597926and the rs17512836 SNP showed almost perfect linkage dis-equilibrium (LD, r2 = 0.99), only data of the rs17597926 SNP arefurther presented. Remaining pairwise relationships showedmoderate LD between SNPs (Fig. 1). In each of the significantSNPs, carriers of the respective schizophrenia risk alleles dis-played reduced auditory sensory gating (Fig. 2A). The significantinteractions of the factors genotype and smoking status indicatedthat the genotype effects were different among never, light, andheavy smokers (Fig. 2B). Heavy smokers [Fagerström Test ofNicotine Dependence (FTND) score ≥ 4] consistently showedstronger TCF4 gene effects on P50 suppression (Cohen’s d =0.63–1.04) than light smokers (d = 0.27–0.43) and never-smokers(d = −0.04 to 0.06]. Bonferroni post hoc tests of the main effectof smoking status replicated the finding shown in an overlappingsample of the present population (53): In all tests, heavy smokersdisplayed lower P50 suppression levels than light smokers (P =0.014–0.006; d = 0.34–0.59) and never-smokers (P = 4.4 × 10−5

to 5.5 × 10−6; d = 0.55–0.76), whereas light smokers and never-smokers did not significantly differ (P = 0.070–0.482; d = 0.17–0.21). The effect for the repeated factor electrode position alonewas not significant (all tests: F < 1.2), but it interacted with ge-notype in the analyses of rs9960767 [F(1,1812) = 7.10; P = 0.008;ηp

2 = 0.004] and rs10401120 [F(1,1799) = 6.94; P = 0.008; ηp2 =

0.004], indicating that the TCF4 genotype effect was more evi-dent at the frontal electrode (Fz) (Fig. S2). Further interactionsbetween factors and covariates were not significant. Finally,the covariate age (P = 4.1 × 10−14 to 7.7 × 10−15, ηp

2 = 0.033–0.035) revealed a significant effect, whereas the impact of sex,study center, cotinine plasma level, and longitude/latitude ofstudy center was not significant (all tests: F < 1.25). As shownbefore (53), sensory gating increases with age (see also correla-tion analyses in SI Discussion). Longitude and latitude were in-cluded in the analysis to control for subtle population differencesbetween the sampling sites. Because only SNP rs9960767 presentsa reasonable number of rare homozygotes (n > 10), the analysisof this marker was repeated in a threefold genotype factor design.However, the genotype effects and genotype–smoking interac-tions remained largely the same (Fig. S3).The genotype groups of the significant SNPs did not differ re-

garding any demographic data, smoking parameters, or psycho-metric scales (Table S2). Thus, the effects observed because ofrisk allele carrier-ship could not be explained by demographicalstratification effects.As shown in Table S3, the four TCF4 SNPs showing significant

association with P50 suppression did not significantly affect theamplitude of the P50 AEP neither on the response to the firstclick (S1) nor to the second click (S2). Analyses of the differenceand the ratio of P50 amplitudes as further measures of auditorysensory gating revealed the same effects as in the analysis ofpercent P50 suppression. Again, the TCF4 genotype effect onP50 suppression was most pronounced at the Fz electrode.Correlations of P50 parameters with demographic and smokingvariables are shown in SI Discussion and Table S4.To assess the association between the reduced levels of P50

suppression and TCF4 risk allele carrier-ship, odds ratios (OR)were calculated. If clinical criteria of one and two SDs wereapplied to define low P50 suppression phenotypes, all of thesephenotypes showed significant associations with the respective

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four TCF4 risk-alleles within the total sample (range OR = 1.81–2.58) and within heavy smokers (range OR = 3.21–7.60) but notwithin the never-smokers (range OR = 1.23–1.75) (Table S5). Ateach criterion, the odds ratios were highest in heavy smokers andlowest in never-smokers, whereas light smokers were interme-diate (range OR = 2.44–2.78). Moreover, there was no associ-ation of the four significant TCF4 SNPs with the smokingphenotype (Table S6).

Discussion

In an endophenotype-based association study approach, we in-vestigated the impact of TCF4 schizophrenia risk SNPs on anestablished psychophysiological endophenotype of schizophrenia.We could demonstrate that carrier-ship of risk alleles at fourTCF4 SNPs (rs9960767, rs17512836, rs17597926, and rs10401120)were significantly associated with reduced auditory sensory gat-ing as measured by P50 suppression of the AEP. Noteworthy,two of these SNPs (rs9960767, rs17512836) were among the top

markers associated with schizophrenia with genomewide signifi-cance in recent large GWAS meta-analyses (1, 2), whereas SNPrs17597926 was the marker in the TCF4 gene showing the secondsmallest P value in one of these studies (1). Markers rs17512836and rs17597926 were in strong LD (r2 = 0.99), thus representingessentially the same association signal. Further pairwise LDrelationships were moderate (r2 = 0.38–0.64). Interestingly, thegenotypic effects of each SNP were strongly modulated bysmoking behavior with only smokers showing reliable TCF4-P50suppression associations, whereas the genetic effect was small ornot present in never-smokers. Moreover, genotype–smoking in-teractions were dose-related because TCF4 SNP genotype effectsamplified with increasing smoking severity.TCF4 belongs to a subfamily of bHLH transcriptional regu-

lators that recognizes the Ephrussi-box (E-box) binding site onthe DNA that usually lies upstream of a gene in a promoter region(55). At early developmental stages, E-box transcription factorssuch as TCFE2a, TCF12, and TCF4 show widespread expressionthroughout the brain, but only TCF4 displays sustained expres-sion in the adult brain of mice, which is most prominent in thecerebellum, hippocampus, and cortex (8, 56). TCF4-null knock-out mice die in the first 24 h after birth and display brain-stem abnormalities (4, 57). Haploinsufficiency of the TCF4 genein humans causes the Pitt-Hopkins syndrome—an autosomal-dominant neurodevelopmental disorder characterized by severemental and motor retardation, microcephaly, epilepsy, facialdysmorphisms, and intermittent hyperventilation—reflecting thatTCF4 is critical for the development of the mammalian CNS(6, 55). Thus, the respective TCF4 polymorphisms might have asubtle impact on brain development that contributes to gatingabnormalities and that increases the risk for schizophrenia.The present results are in line with our previous findings that

the schizophrenia risk allele C of the TCF4 rs9960767 SNP isassociated with diminished sensorimotor gating as measured byPPI (8, 10). Although PPI and P50 suppression are not correlatedand might be regulated—at least in part—by distinct neuronalmechanisms (46–49), recent work suggests that they neverthelessmight share some common genetic pathways (41, 45, 50, 51).However, the modifying influence of smoking on the effect ofTCF4 shown here was not present in our previous investigation

Table 1. The effects of TCF4 SNP risk allele carrier-ship and smoking status on percent P50 suppression of the auditory evoked

potential (averaged across electrode positions Fz and Cz)

TCF4

genotype

Position on

chromosome

18q21.1

Allele frequency number, %

Total

n

Schizohrenia

risk allele*

Genetic

model†

Effect on P50 suppression

Common

homocygotes Heterozygotes

Rare

homocygeotes

Factor

genotype

(df = 1)

Factor

Smoking

(df = 2)

Interaction

smoking–

genotype

(df = 2)

rs9960767‡ Intron

53155002

AA 1,610,

88.4%

AC 199,

10.9%

CC 12,

0.7%

1,821 C AA vs. AC+CC F = 16.7;

P = 4.5 × 10−5;

ηp2= 0.010

F = 11.7;

P = 8.7 × 10−6;

ηp2= 0.013

F = 7.1;

P = 0.001;

ηp2= 0.008

rs10401120 Intron

53192498

CC 1,647,

90.9%

CT 156,

8.6%

TT 9,

0.5%

1,812 T CC vs. CT+TT F = 15.9;

P = 6.9 × 10−5;

ηp2= 0.009

F = 10.9;

P = 1.9 × 10−5;

ηp2= 0.013

F = 5.8;

P = 0.003;

ηp2= 0.007

rs17597926§ Intron

53205938

GG 1,723,

94.6%

GA 95,

5.2%

AA 3,

0.2%

1,821 A GG vs. GA+AA F = 14.5;

P = 1.4 × 10−4;

ηp2= 0.008

F = 9.5;

P = 7.9 × 10−5;

ηp2= 0.011

F = 4.9;

P = 0.008;

ηp2= 0.006

rs17512836{ Intron

53194961

TT 1,721,

94.6%

TC 95,

5.2%

CC 3,

0.2%

1,819 C TT vs. TC+CC F = 13.9;

P = 2.0 × 10−4;

ηp2= 0.008

F = 9.2;

P = 1.1 × 10−4;

ηp2= 0.011

F = 4.4;

P = 0.013;

ηp2= 0.005

*According to Ripke et al. (1) and Stefansson et al. (2).†ANCOVA with genotype (twofold), and smoking status (threefold) as fixed factors, electrode position (twofold) as repeated factor, and age, sex, study cite,

cotinine plasma level, and longitude and latitude of the study centers as covariates.‡Most significant TCF4 SNP in the schizophrenia GWAS of Stefansson et al. (2).§Second most significant TCF4 SNP in the schizophrenia GWAS of Ripke et al. (2).{Most significant TCF4 SNP in the schizophrenia GWAS of Ripke et al. (1).

Fig. 1. Pairwise linkage disequilibrium (LD), as measured by r2, of four TCF4

SNPs that were significantly associated with percent P50 suppression.

Quednow et al. PNAS Early Edition | 3 of 6

NEUROSCIENCE

on TCF4 gene effects on PPI (10). The previously investigatedsamples may have been too small and underpowered to reliablyexamine the effects of smoking as a mediating factor on theTCF4 gene effects on PPI. The potentially modifying effect ofsmoking on TCF4 gene effects on PPI (and other schizophreniaendophenotypes) should therefore be investigated in largersamples. Finally, the TCF4 rs9960767 genotypic effect on PPIdisplayed a much stronger effect size (Cohen’s d = 0.90) than themean effect on P50 suppression (d = 0.18–0.20). This differencemay be explained either by the “winner’s curse”—which meansthat estimations of the genetic effect based on novel associationfindings tend to be upwardly biased (58)—by the fact that P50suppression usually displays a less beneficial retest reliabilitycompared with PPI (21, 59), or by a differential impact of TCF4on the underlying neural systems (or a mixture of these factors).Interestingly, TCF4 genotype effects on P50 suppression were

more evident at the Fz than on the vertex electrode (Cz). Pre-vious studies reported that the PFC substantially contributeseither to the sensory gating process per se (13) or at least to thegeneration of the P50 amplitude (14). Additionally, data froma recent EEG source localization study suggest that the sensorygating deficit of schizophrenia patients could be explained bydysfunction of the dorsolateral PFC (60). Thus, TCF4 mutations

(in combination with smoking) might have a specific impact onPFC function in schizophrenia.How can the unexpected smoking–genotype interaction re-

garding P50 suppression be elucidated? There are at least twopossible scenarios: The first is a hidden gene–gene interaction: Inthis model, TCF4 SNPs interact with a hidden gene (or genes) sothat only the presence of two or more risk alleles is associatedwith both smoking severity and P50 suppression, whereas theTCF4 SNPs were exclusively associated with P50 suppression butnot with smoking. Promising candidates for the “hidden” SNPsmay lie in the CHRNA3-CHRNA5-CHRNB4 gene cluster codingfor α3, α5, and β4 nicotinic acetylcholine receptor (nAChR)subtypes. SNPs from this gene cluster were reliably associatedwith smoking behavior (61–63), and also with sensorimotorgating (PPI) (64) and cognitive performance (65). SNPs of theCHRNA7 would also be interesting candidates because of thereported associations with P50 suppression and smoking behav-ior (41, 66, 67).The second, and maybe more appealing, explanation for the

present result pattern could be a gene–environment interaction,in which smoking represents a long-lasting and ongoing envi-ronmental influence. This interpretation would be in line withthe suggestion of Williams et al. (7) that the TCF4 schizophreniarisk alleles may exert their effects on expression exclusively in adevelopmental context because postmortem data of this studysuggested that SNP rs9960767 is neither functional nor affectsmRNA expression in the adult human brain. Furthermore, evi-dence accumulates that risk SNPs might have significant ex-pression effects on other genes but not on the one in which theyare located (68). However, at this point, we can only hypothesizewhich neurobiological mechanisms might underlie this smoking–TCF4 interaction on P50 suppression: (i) Smoking-induced plas-ticity of brainstem nAChR (69–72) in concert with subtle neuro-developmental changes in pontine nuclei induced by TCF4 genevariations (4) may affect P50 suppression. (ii) TCF4-inducedchanges in the noradrenaline system (6, 55) might interact withnicotine-induced changes of α7 nAChR function (73) when mod-ulating P50 suppression. (iii) Nicotine may be involved in the meth-ylation of DNA sequences leading to an epigenetic change of theexpression of the TCF4 gene (or other genes interacting withTCF4) with functional consequences on early information pro-cessing (74, 75).Several studies have demonstrated that CHRNA7 promoter

variations are associated with schizophrenia and affect P50suppression (41). The present study adds TCF4 mutations ascomplementary genetic factors (or more exactly a TCF4–smok-ing interaction) to the population variation in P50 suppressionand it has been shown that also TCF4 gene variations increasethe risk for schizophrenia (1–3). However, it should be noticedthat the explained variances of P50 suppression by the factorsTCF4 genotype (0.8–1.0%), smoking (1.1–1.3%), and age (3.3–3.5%) as well as of the TCF4–smoking interaction (0.07–0.08%)were rather small, reflecting that either many other geneticfactors might be involved or that P50 suppression may also havea strong state-dependent part as it was suggested (33).The question arises whether effects of population stratification

might have influenced our results because we have not typed astandard panel of ancestry-informative SNPs to control for strat-ification effects. However, we aimed to build a genetically highlyhomogeneous sample of subjects with an ancestry exclusivelyfrom Germany, and we randomly selected our participants fromthe general population to avoid possible stratification effects. Ad-ditionally, the European autosomal gene pool was recently foundto be rather small, especially in northern and middle Europesubpopulations (76). Nevertheless, this study has shown a conti-nent-wide correlation between geographic and genetic distancealong the north-south axis and, to a lesser extent, also along theeast-west axis (76). Therefore, we included longitude and latitudeof the study centers as covariates in our analyses to control for

B

26.0

28.0

30.0

32.0

34.0

36.0

AA

n=1610

AC+CC

n=211

CC

n=1643

CT+TT

n=165

GG

n=1723

GA+AA

n=98

rs9960767 rs10401120 rs17597926

%P

50

su

pp

res

sio

n*** *** ***A

20.0

22.0

24.0

26.0

28.0

30.0

32.0

34.0

36.0

38.0

AA

n=1610

AC+CC

n=211

CC

n=1643

CT+TT

n=165

GG

n=1723

GA+AA

n=98

rs9960767 rs10401120 rs17597926

%P

50

su

pp

res

sio

n

heavy smoker

light smoker

never smoker

Fig. 2. The effects of three TCF4 SNPs (rs9960767, rs10401120, and rs17597926,

which was in almost complete linkage disequilibrium with rs17512836) on

percent P50 suppression of the auditory evoked potential (means and SEM;

adjusted for age, sex, study site, cotinine plasma level, and longitude and

latitude of the study center) averaged across the electrode positions Fz and Cz

(A), and stratified according to smoking behavior (never-smokers: n = 1,023,

light smokers: n = 466, and heavy smokers n = 332) (B). ***P < 2.0 × 10−4.


even subtle population stratification, which did not change ourresults. We also explored regional differences in TCF4 SNP ge-notype frequencies between study centers located in three areas ofGermany (North, Midwest, and South), but we found no signifi-cant differences between these regions (Fig. S4). Lastly, the TCF4SNP allele frequencies of the present sample fitted with the fre-quencies of the European HapMap data (CEU), and none of theinvestigated SNPs deviated from the HWE. Thus, the strong as-sociation of TCF4 with auditory sensory gating is likely not ex-plainable by population stratification effects.In conclusion, our results suggest that the schizophrenia risk

alleles of TCF4 variants interact with smoking behavior withregard to auditory sensory gating, which is an established endo-phenotype of schizophrenia. We hypothesized that this findingcould be interpreted as a gene–environment interaction withplausible neurobiological explanations. If smoking behaviorstrongly modulates the TCF4 SNP effects on a proposed endo-phenotype of schizophrenia, it might also modulate the risk forschizophrenia itself. We therefore suggest the investigation ofpotential moderating effects of dimensional and binary measuresof smoking behavior on genetic risk factors of schizophrenia. Incase-control association studies, stratification for smoking be-havior may add power to yield stronger gene effects. Moreover,it should be further explored whether nicotine use itself mightenhance the risk for schizophrenia as indicated by longitudinalstudies showing that beyond cannabis and alcohol use, earlyconsumption of tobacco increases the risk for psychosis (77, 78).Finally, an extended endophenotype including electrophysio-logical gating measures such as PPI or P50 suppression, smokingbehavior, and risk genes such as TCF4may be suitable as an earlyindicator for a developing psychosis.

Materials and Methods

Participants. The study was carried out in the framework of the German

multicenter study: Nicotine: Physiological and Molecular Effect in the CNS

(53, 79). All subjects were randomly selected from the local general pop-

ulation of seven cities across Germany (Aachen, Berlin, Bonn, Düsseldorf,

Erlangen, Mainz, and Mannheim) via official residents’ registers and con-

tacted by letter with an invitation to participate in the study. Overall, n =

56,350 subjects were contacted, of whom n = 4,760 responded by phone and

completed an initial prescreening interview. Healthy subjects of German

origin who met the inclusion/exclusion criteria (SI Discussion) were invited

for a final screening investigation, which included a lifetime smoking history

assessment, a medical examination, a standardized psychiatric interview

(SCID-I), a drug urine screening, exhaled carbon-monoxide (COHb) mea-

surement, and blood for routine clinical laboratory tests and genotyping. n =

2,442 subjects were finally included in the study. Across study sites, n = 468

subjects had either no electroencephalography (EEG) assessment (e.g., the

Mannheim center did not apply EEG) or displayed an insufficient EEG data

quality, whereas n = 238 subjects either did not provide blood samples, their

DNA was not recoverable, or the genotyping failed, leaving a final sample

of n = 1,821 participants (Table S2). The sample consisted of 1,023 never-

smokers (lifetime smoking <20 cigarettes), 466 light smokers FTND score <4),

and 332 heavy smokers (FTND ≥4).

The study was approved by the ethics committees of each study site’s

local university and was conducted according to the declaration of Helsinki.

Written informed consent was obtained from all subjects.

Study Procedures.Across all study sites, the studywas performed on the day of

study inclusion according to the same standard operating procedure in-

cluding regular monitoring with strict adherence to a fixed time table. After

inclusion, subjects provided extensive demographical and smoking-related

information and answered a battery of questionnaires on smoking-related

behavior (for details, see SI Discussion), drug/alcohol consumption habits,

and personality (79). Afterward, a neuropsychological test battery and an

EEG investigation were performed (79). The EEG was obtained between 1:30

AM and 2:30 PM in all subjects. Subsequently, venous blood was obtained to

determine cotinine plasma levels and for genotyping. In smoking subjects,

the EEG was recorded between 1 and 3 h after the last cigarette smoked ad

libitum. Genotyping information is given in the SI Discussion.

P50 Auditory Double Click Paradigm. Subjects were instructed to keep their

eyes closed throughout the EEG experiment. Five minutes of continuous

resting EEG was recorded before the P50 auditory double-click paradigm,

which was based on the work of Adler et al. (80). One hundred pairs of two

250 sinus tones (clicks) of 2,000 Hz (50-ms duration including rise and fall

time) were administered binaurally via headphone at a 50-dB sound pres-

sure level. A fixed ISI of 500 ms was chosen between paired clicks S1 and S2.

Between pairs of clicks, the intervals were (pseudo)randomized varying

between 5 and 9 s (mean 7 s). Subjects were instructed to stay awake and to

listen to the tones. EEG data acquisition and analysis is described in SI Dis-

cussion. The amplitudes of the event-related P50 potential (filtered at 10–45

Hz, 12 dB) were calculated across electrodes by automatically locating the

most positive peak in the respective time window. The P50 response was the

most positive peak between 48 and 68 ms after stimulus onset (both after S1

and S2) (24). The P50 amplitude was measured relative to the preceding

negativity. For the analysis of P50 suppression, the percent P50 suppression

([1 − S2/S1] × 100), the difference of P50 amplitudes (S1 − S2), and the ratio

of P50 amplitudes (S2/S1) was used. Low value of the percent and the dif-

ference suppression as well as high values of the ratio indicate weak in-

hibition of the second click (i.e., no gating or filtering) or low P50 amplitude

after the first click. Fz and Cz electrodes were analyzed because previous

studies have shown that P50 suppression measured at these electrodes dis-

criminate best between schizophrenia patients and controls (23, 24, 81).

Details of the statistical analysis are given in the SI Discussion.

ACKNOWLEDGMENTS. The present work was funded within the frameworkof the German Research Foundation (Deutsche Forschungsgemeinschaft;DFG) national priority Program SPP1226 Nicotine: Physiological and Molecu-lar Effect in the CNS (Wi1316/6-1 andWi1316/9-1) coordinated by G.W. B.B.Q.is supported by Swiss National Science Foundation Grant PP00P1_123516.M.W. is supported by German Research Foundation Grant WA 737/7. W.M.and R.M. are supported by European Union Grant FP7-Health-F4-2009-242257-ADAMS and by Bundesministeriums für Bildung und Forschung(BMBF) Grants Psychotherapie von Positivsymptomatik psychotischer Störun-gen (POSITIVE) 01GV0907 and NGFN+ MooDs PNM-01GS08146-3. M.N. issupported by the German Federal Ministry for Education and Researchthrough National Genome Research Network Grant 01GS0809.

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Supporting Information

Quednow et al. 10.1073/pnas.1118051109

SI Discussion

It should be noted that Steinberg et al. (1) recently analyzed theTCF4 SNP rs9960767 in a large metaanalysis including theGöttingen Research Association for Schizophrenia (GRAS) sam-ple, which mainly consists of schizophrenia patients and controlsof German ancestry. Although the populationwide significancewas replicated for this SNP, no significant effect was found in theGRAS sample. However, these contrary results might possibly beexplained by a lack of power because the Steinberg study finallyincludes 18,206 cases and 42,536 controls compared with 1,041cases and 1,144 controls in the GRAS sample (2). Furthermore,in a previous meta-analysis of schizophrenia GWAS samples, aGerman subsample from Bonn showed a significant associationwith rs9960767, whereas another German subsample from Mu-nich at least revealed a strong trend for this marker (3).Nevertheless, these mixed results point to the fact that the

TCF4 gene effects on schizophrenia are quite small and do nothave predictive value (2). However, a multivariate phenotype-based association analysis of the GRAS sample revealed an in-teresting trend for an association between TCF4 rs9960767 anda dimensional schizophrenia phenotype that combines positive,negative, and cognitive symptoms (2). In concert with the presentdata this demonstrates that the (endo)phenotype-based ap-proach could boost the power of a genetic study compared withthe approach of classical categorization of the mental illness byusing the current diagnostic manuals as used in classical case-control studies (4).

Patricipants. Inclusion and exclusion criteria of the study are listedbelow (for further details, see ref. 5):Inclusion criteria.

i) Age: 18–65 y.ii) Smoking status: current smoker (also occasional smoker with

a minimum of seven cigarettes per week/one cigarette perday) or never-smoker (max. 20 cigarettes per lifetime).

iii) All grandparents of subjects were required to be born inGermany or in a country adjacent to Germany.

iv) Mother-tongue level German.v) Each participant must have been personally contacted by

letter (via official local residents’ registers).

Exclusion criteria.

i) Former smoker.ii) Alcohol- or substance abuse within previous six months

(DSM-IV).iii) Lifetime diagnosis of alcohol- or substance dependence

(DSM-IV).iv) Other DSM-IV axis-1 psychiatric diagnosis within previous

6 mo.v) Non-German origin (see inclusion criteria).vi) No mother-tongue level German.vii) Serious vision or hearing impairment.viii) Pregnancy.ix) CNS-relevant medication within previous 6 mo.x) CNS-relevant (neurological/medical) illnesses lifetime.

Genotyping. DNA for genotyping was isolated from EDTA acidanticoagulated blood by using the FlexiGene DNA Kit (Qiagen).The TCF4 SNP (rs9960767) was analyzed by a Taqman assay(Applied Biosystems). PCR amplification was performed by using10 ng of genomic DNA on the GeneAmp 9700 thermal cycler,

and fluorescence intensity was read on the 7900HT SequenceDetection System (Applied Biosystems). The other 20 TCF4

SNPs (rs4131791, rs4309482, rs8092679, rs9951150, rs11152369,rs11874716, rs11875348, rs12966547, rs17511376, rs17512836,rs17594526, rs17596974, rs17597926, rs9646596, rs9953930,rs10401120, rs17594665, rs17594721, rs17596267) were analyzedby applying the iPlex SNP genotyping assay (Sequenom) followedby mass spectrometry (MALDI-TOF) on a MassArray AnalyzerCompact (Sequenom). Ten nanograms of genomic DNA wereused per multiplex reaction. Briefly, following a locus-specificPCR, a primer extension reaction was performed by using mass-modified ddNTPs. After desalting, the products were spottedonto a chip matrix and analyzed by MALDI-TOF. Clusteringand genotype calling was performed by using the MassARRAYTyper v4.0 software (Sequenom). For quality control, the completesample was genotyped twice. Concordance among duplicate sam-ples was 100%.The minor allele frequencies of every investigated TCF4 SNP

in the total sample (n = 2,204) were comparable with theHapMap-CEU population available at the National Center forBiotechnology Information Web site (www.hapmap.org), and allwere distributed in accordance with the Hardy–Weinberg equi-librium (HWE). The call rates for each SNP were >99.5%(Table S1). Pairwise linkage disequilibrium (LD) relationships ofall SNPs (the monomorphic SNP rs17509991 was excluded) areshown in Fig. S1.EEG Data Acquisition and Analysis.EEG data were recorded either byusing a 32-channel EEG system from Brain Products (BrainAmpDC; used in three study sites) or a Neuroscan (Synamps-2). A 32-channel EEG cap (EasyCap) was used at all study sites. Sys-tematic inspection across centers revealed no equipment-de-pendent site effects on P50 (6). Twenty-nine scalp electrodeswere distributed according to the extended 10-20 system. Twoelectrodes were placed at the outer canthi and above the lowerorbital portion of the orbicularis oculi muscle to detect hori-zontal and vertical eye movements, respectively. One electrodewas attached to the subjects left wrist for recording the elec-trocardiogram (ECG). Data were recorded relative to the Czreference. Sampling rate of the analog data were 250 Hz. Abandpass filter of 0.53–45 Hz and a 50-Hz notch filter were ap-plied. Impedance at all recording electrodes was <10 kW.EEG data analysis was conducted with the software package

Brain Vision Analyzer (Brain Products). Voltage changes of >70μV were classified as artifacts and were not considered for fur-ther analysis. Artifact detection was performed semiautomaticallywith visual control. The continuous EEG data were rereferencedto common average and segmented into 700-ms epochs (−200 mspre- to 500 ms poststimulus), baseline-corrected, and averaged.Questionnaires. For the estimation of verbal intellectual perfor-mance, the Mehrfachwahl-Wortschatz-Intelligenztest (MWT-B)was used (7). To assess the severity of nicotine dependence, weapplied the Fagerstöm Test for Nicotine Dependence (FTND) (8)and the Questionnaire on smoking urges (QSU) (9). Alcohol usepatterns were evaluated by the Alcohol Use Disorder Identifica-tion Test (AUDIT) (10). A German version of the NEO-FiveFactor Inventory (NEO-FFI) was used to measure basic person-ality dimensions (11). Finally, number of cigarettes smoked perday (CPD) was assessed and pack years corrected for abstinentperiods were estimated [for details, see Brinkmeyer et al. (6)].Statistical Analysis. Based on our previous (4) and the present re-sults, we did a priori- and post hoc-power analyses, respectively,for the genotype effect by using G*Power 3.0.3 (12). A priori,

Quednow et al. www.pnas.org/cgi/content/short/1118051109 1 of 10

when we assumed an effect size of Cohen’s d = 0.90 (4) anda Bonferroni-corrected α error of P = 0.0024 (21 SNPs), 147subjects were needed to detect an genotype effect between twogroups with a power of 99% (critical F = 9.55). Post hoc, ata significance level of P = 0.0024, the actual effect sizes of theTCF4 genotype effects were smaller than in our previous study(4): rs9960767, d = 0.20; rs10401120, d = 0.19; rs17597926, d =0.18. However, the actual power to detect TCF4 genotype effectswas good: rs9960767, 89.4%; rs10401120, 84.5%; rs17597926,78.6% (critical F = 9.24).All further statistical analyses were performed by using the

PASW 18.0 software package (SPSS Inc.). The demographic data,questionnaires, and smoking variables were analyzed by one-wayanalysis of variance (ANOVA) with the exception of frequencydata. Frequency data were analyzed by using χ

2 tests or Fisher’sexact test where applicable. For the primary dependent variableof percent P50 suppression at Fz and Cz a 2*2*3 (electrodeposition*genotype*smoking status) general linear model (GLM)repeated-measures analysis of covariance (ANCOVA) was cal-culated. The two fixed factors were genotype (three- or twofold,corresponding to a general genotypic risk model or a modelfocusing on the carrier-ship of the risk allele, respectively) andsmoking status (threefold: never-smokers, light smokers, heavysmokers), because smoking status was shown to have a strongimpact on P50 suppression (6). If the minor allele frequencywas <5% or <20 subjects of the minor allele homozygotes wereavailable, we compared carriers of the schizophrenia risk allelewith homozygous carriers of the non-risk allele in a two-groupdesign as applied before (4). Age, sex, and study site were in-troduced as covariates because they were shown to influence P50suppression (6, 13). Cotinine plasma level was introduced asa covariate to control for acute nicotine effects (14). Moreover,we introduced decimal longitude and latitude of the study cen-ters as covariates to control for potential effects of populationstratification because it was shown that genetic and geographicdistance was correlated along the north-south axis and, to alesser extent, also along the east-west axis (15). All other P50parameters were analyzed by one-way ANCOVA with smoking,age, sex, and study site as covariates. Confirmatory statisticalcomparisons were carried out at a significance level set at P <

0.05 (two-tailed). In the primary ANCOVA analyses, the Bon-ferroni-corrected significance level for genotype effects was setat P < 0.0024 (21 SNPs). In correlation analyses (according to

Pearson or Spearman where appropriate) the statistical thresh-old was set at P < 0.00007 (two-tailed) after Bonferroni cor-rection. Effect size calculations between two groups refer toCohen’s d (12). Linkage disequilibrium was estimated by usingHaploView (16). Odds ratios (OR) were calculated according toBland and Altman (17).Correlations with P50 Parameters. Correlation analyses wereBonferroni-corrected (threshold P < 7.1 × 10−5). Given thatincreasing age was strongly correlated with higher P50 suppres-sion across all parameters (difference, ratio, percent) at bothelectrodes (r = 0.138–0.179, P = 3.6 × 10−9 to 1.2 × 10−14) andalso with elevated S1 P50 amplitude at Cz (r = 0.094, P = 5.7 ×

10−5), partial correlations correcting for age were conducted forall further correlation analyses.High verbal IQ was associated with increased S1 amplitudes

at both electrodes (Fz: r = 0.100, P = 4.9 × 10−5; Cz: r = 0.117,P = 2.2 × 10−6). Smoking status (threefold) was associated withS1-S2 difference (Fz: r = 0.107, P = 5.0 × 10−6; Cz: r = 0.100,P= 1.9 × 10−5) and S1 amplitudes (Fz: r= 0.155, P= 3.1 × 10−11;Cz: r = 0.130, P = 2.8 × 10−8), indicating lower P50 suppressionand decreased S1 amplitudes with increasing smoking severity.Associations of P50 parameters with dimensional smoking vari-ables were calculated only within smokers (Table S4). HigherFTND and QSU sores, more CPDs, higher CO concentration inrespiratory air, and increased pack years were associated withsmaller S1 amplitudes. Moreover, there were negative correla-tions between S1-S2 differences and FTND sores (and similartrends to further smoking parameters), confirming that increasedsmoking behavior was associated with decreased P50 suppres-sion, when measured by S1-S2 difference (Table S4).Higher S1 P50 amplitudes were strongly related to greater P50

suppression across all parameters but the percent (Fz: r = 0.408,P = 8.1 × 10−74; Cz: r = 0.338, P = 9.1 × 10−50) and ratio P50suppression measures (Fz: r = −0.408; Cz: r = −0.338) were lessaffected by S1 amplitude than the difference scores (Fz: r =0.701, P = 1.1 × 10−268; Cz: r = 0.68, P = 1.5 × 10−249).Moreover, percent and ratio P50 suppression were highly cor-related with S1-S2 difference at each electrode (all r > 0.90). Thepercent P50 suppression score was less affected by effects of age,smoking, and S1 amplitude in comparison with the S1-S2 dif-ference score, confirming its applicability for the analysis of ge-netic effects.

1. Steinberg S, et al.; Irish Schizophrenia Genomics Consortium; GROUP; Wellcome Trust

Case Control Consortium 2 (2011) Common variants at VRK2 and TCF4 conferring risk

of schizophrenia. Hum Mol Genet 20:4076–4081.

2. Papiol S, et al. (2011) Dissociation of accumulated genetic risk and disease severity in

patients with schizophrenia. Transcult Psychiatry 1:e45.

3. Stefansson H, et al.; Genetic Risk and Outcome in Psychosis (GROUP) (2009) Common

variants conferring risk of schizophrenia. Nature 460:744–747.

4. Quednow BB, et al. (2011) The schizophrenia risk allele C of the TCF4 rs9960767

polymorphism disrupts sensorimotor gating in schizophrenia spectrum and healthy

volunteers. J Neurosci 31:6684–6691.

5. Lindenberg A, et al. (2011) The German multi-centre study on smoking-related

behavior-description of a population-based case-control study. Addict Biol 16:638–653.

6. Brinkmeyer J, et al. (2011) P50 sensory gating and smoking in the general population.

Addict Biol 16:485–498.

7. Lehrl S (1999) Mehrfachwahl-Wortschatz-Intelligenztest (MWT-B) (Hogrefe, Göttingen,

Germany), 4th Ed.

8. Heatherton TF, Kozlowski LT, Frecker RC, Fagerström KO (1991) The Fagerström Test

for Nicotine Dependence: A revision of the Fagerström Tolerance Questionnaire. Br J

Addict 86:1119–1127.

9. Müller V, Mucha RF, Ackermann K, Pauli P (2001) Die Erfassung des Cravings bei

Rauchern mit einer deutschen Version des “Questionaire on Smoking Urges” (QSU-G).

Z Klin Psychol Psychother 30:164–171.

10. Babor TF, Higgins-Biddle JC, Saunders JB, Monteiro MG (2001) The Alcohol Use

Disorders Identification Test (World Health Organization, Geneva), 2nd Ed.

11. Costa PT, McCrae RR (1992) Revised NEO Personality Inventory (NEO PI-R) and NEO

Five Factor Inventory. Professional Manual (Psychol Assess Resour, Odessa, FL).

12. Faul F, Erdfelder E, Lang AG, Buchner A (2007) G*Power 3: A flexible statistical power

analysis program for the social, behavioral, and biomedical sciences. Behav Res

Methods 39:175–191.

13. Hetrick WP, et al. (1996) Gender differences in gating of the auditory evoked

potential in normal subjects. Biol Psychiatry 39:51–58.

14. Adler LE, Hoffer LD, Wiser A, Freedman R (1993) Normalization of auditory physiology

by cigarette smoking in schizophrenic patients. Am J Psychiatry 150:1856–1861.

15. Lao O, et al. (2008) Correlation between genetic and geographic structure in Europe.

Curr Biol 18:1241–1248.

16. Barrett JC, Fry B, Maller J, Daly MJ (2005) Haploview: Analysis and visualization of LD

and haplotype maps. Bioinformatics 21:263–265.

17. Bland JM, Altman DG (2000) Statistics notes. The odds ratio. BMJ 320:1468.


Fig. S1. Pairwise linkage disequilibrium (LD) of 20 TCF4 SNPs, as measured by r2. Several markers were in strong LD. SNPs that were significantly associated

with sensory gating are marked with a red frame.


rs9960767

20.0

22.0

24.0

26.0

28.0

30.0

32.0

34.0

36.0

38.0

AA

n=1610

AC+CC

n=211

AA

n=1610

AC+CC

n=211

zCzF

%P

50 s

up

pre

ssio

nheavy smoker

light smoker

never smoker

rs10401120

20.0

22.0

24.0

26.0

28.0

30.0

32.0

34.0

36.0

38.0

CC

n=1647

CT+TT

n=165

CC

n=1647

CT+TT

n=165

zCzF

%P

50 s

up

pre

ssio

n

heavy smoker

light smoker

never smoker

Fig. S2. The effects of the TCF4 SNPs rs9960767 (Upper) and rs10401120 (Lower) on percent P50 suppression of the auditory evoked potential (means and

SEM; adjusted for age, sex, study site, and cotinine plasma level, and longitude and latitude of the study center) across the electrode positions Fz and Cz. The

repeated factor electrode position interacted with the factor genotype [rs9960767: F(1,1812) = 7.10, P = 0.008, ηp2 = 0.004; rs10401120: F(1,1799) = 6.94, P = 0.008,

ηp2 = 0.004], reflecting that the TCF4 genotype effect was more evident at the Fz electrode.


20.0

22.0

24.0

26.0

28.0

30.0

32.0

34.0

36.0

38.0

40.0

AA

n=1610

AC

n=199

CC

n=12

heavy smoker

light smoker

never smoker

n=7

n=1

n=4

Fig. S3. The effects of TCF4 rs9960767 genotype on mean percent P50 suppression of the auditory evoked potential (Fz and Cz; means adjusted for age, sex,

study site, and smoking) stratified according to smoking behavior [never smokers: n = 1,023 (lifetime smoking < 10 cigarettes), light smokers: n = 466 (Fa-

gerström score < 4), and heavy smokers n = 332 (TND ≥ 4)]. A 2*3*3 (electrode position*genotype*smoking status) repeated-measures ANCOVA with age, sex,

study site, cotinine plasma level as well as longitude and latitude of the study centers as covariates revealed a significant main effects of genotype [P = 3.1 × 10−4,

ηp2 = 0.009] and smoking [P = 0.015, ηp

2 = 0.005] and a significant interaction of the factors smoking and genotype [P = 0.007, ηp2 = 0.008].


MMainz

n = 423

Bonn

n = 211

Düsseldorf

n= 333

Aachen

n = 198

Berlin

n = 311

Erlangen

n = 366

North Germany:

rs9960767: AA: 90.7%, AC: 9.0%, CC: 0.3%

rs10401120: CC: 93.5%, CT: 6.1%, TT: 0.3%

rs17597926: GG: 96.1%, GA: 3.5%, AA: 0.3%

rs17512836: TT: 96.1%, TC: 3.6%, CC: 0.3%

Mid-West Germany:

rs9960767: AA: 89.9%, AC: 9.3%, CC: 0.8%

rs10401120: CC: 91.7%, CT: 7.8%, TT: 0.5 %

rs17597926: GG: 94.9%, GA: 5.1%, AA: 0.0%

rs17512836: TT: 95.0%, TC: 5.0%, CC: 0.0%

South Germany:

rs9960767: AA: 87.2%, AC: 12.3%, CC: 0.4%

rs10401120: CC: 90.2%, CT: 9.5%, TT: 0.3%

rs17597926: GG: 94.0%, GA: 5.9%, AA: 0.2%

rs17512836: TT: 93.9%, TC: 5.9%, CC: 0.2%

Statistics:

rs9960767: Chi2=6.97, p=.14

rs10401120: Chi2=4.60, p=.33

rs17597926: Chi2=4.60, p=.33

rs17512836: Chi2=4.93, p=.30

Mannheim

n = 362*

*No EGG measurment

Fig. S4. TCF4 genotype frequencies across three regional clusters of study centers. TCF4 genotype frequencies did not significantly differ between North,

Midwest, and South Germany. Germany map: Wikipedia/Creative Commons License.


Table S1. Minor allele frequencies, tests on deviation from Hardy–Weinberg equilibrium (HWE),

and call rates for all investigated TCF4 SNPs in the total sample (n = 2,204)

Name Position ObsHET PredHET HWE Call rate, % MAF MAF-HapMap CEU Alleles

rs10401120* Intron 0.08 0.09 0.08 99.6 0.05 0.06 C:T

rs11152369 Intron 0.05 0.05 0.40 99.9 0.03 0.03 A:C

rs11874716 Intergenic 0.50 0.49 0.49 100.0 0.43 0.42 T:G

rs11875348 Intergenic 0.31 0.31 0.92 99.9 0.19 0.23 C:A

rs12966547 Intergenic 0.50 0.49 0.46 99.8 0.43 0.42 G:A

rs17511376 Intron 0.06 0.07 0.20 100.0 0.03 0.03 C:T

rs17512836*,† Intron 0.05 0.05 0.47 99.8 0.03 0.03 C:T

rs17594526 Intron 0.05 0.05 0.12 99.9 0.03 0.03 C:T

rs17594665 Intron 0.05 0.05 0.37 99.6 0.03 0.03 G:A

rs17594721 Intron 0.05 0.05 0.40 99.6 0.03 0.03 A:G

rs17596267 Intron 0.06 0.07 0.20 99.5 0.03 0.03 T:A

rs17596974 Intron 0.08 0.08 1.00 99.8 0.04 0.05 C:T

rs17597926* Intron 0.05 0.05 0.47 100.0 0.03 0.04 G:A

rs4131791 Intergenic 0.50 0.49 0.48 99.9 0.43 0.42 C:T

rs4309482‡ Intergenic 0.50 0.49 0.43 100.0 0.43 0.42 A:G

rs8092679 Intron 0.06 0.06 0.25 99.7 0.03 0.04 G:A

rs9646596 Intron 0.05 0.05 0.39 99.6 0.03 0.04 G:A

rs9951150 Intergenic 0.49 0.49 0.75 99.5 0.44 0.45 A:G

rs9953930 Intergenic 0.31 0.31 0.76 99.6 0.19 0.24 T:C

rs9960767*,§ Intron 0.11 0.11 0.17 99.7 0.06 0.07 A:C

HWE, the P value for deviation from Hardy-Weinberg equilibrium; MAF, minor allele frequency; MAF-HapMap-

CEU, minor allele frequency in the HapMap-CEU sample; ObsHET, observed heterozygosity; PredHet, predicted

heterozygosity.

*SNPs significantly associated with sensory gating.†Most significant TCF4 SNP in the schizophrenia GWAS of Ripke et al. (1).‡Second most significant TCF4 SNP in the schizophrenia GWAS of Ripke et al. (1).§Most significant TCF4 SNP in the schizophrenia GWAS of Stefansson et al. (2) and Steinberg et al. (3).


Table S2. Demographic data, smoking parameters, and psychometric scales of healthy volunteers grouped according to their TCF4

genotype

Measurements Risk allele carrier Nonrisk allele carrier Total F/χ2 df/dferr P ηp2

TCF4 rs9960767 genotype, CC+AC/AA

n 211 (11.6%) 1,610 (88.4%) 1,821 (100%) — — — —

Age 34.7 (0.85) 36.2 (0.33) 36.0 (0.32) 2.40 1/1,820 0.12 0.001

Years of school education 11.7 (0.11) 11.8 (0.04) 11.8 (0.04) 0.33 1/1,820 0.57 0.000

Verbal IQ* 109.9 (1.07) 109.7 (0.34) 109.7 (0.33) 0.03 1/1631 0.87 0.000

Percent male 40.8% 42.6% 42.4% 0.26 1 0.61 —

Percent smokers (never-smoker/light

smoker/heavy smoker)

56.9/25.1/18.0% 56.1/25.7/18.3% 56.2/25.6/18.2% 0.05 1 0.98 —

Current cigarettes smoked per day (CPD) 5.8 (0.63) 6.2 (0.25) 6.1 (0.23) 0.25 1/1820 0.62 0.000

Pack years†,‡ 12.7 (1.42) 14.4 (0.56) 14.2 (0.52) 1.12 1/766 0.29 0.001

Fagerström Test for Nicotine Dependence 1.28 (0.15) 1.28 (0.06) 1.28 (0.05) 0.00 1/1820 0.98 0.000

Questionnaire on smoking urges† 104.7 (4.58) 100.2 (1.58) 100.7 (1.49) 0.93 1/766 0.34 0.001

Exhaled CO, parts per million 6.2 (0.59) 6.9 (0.25) 6.8 (0.23) 0.95 1/1820 0.33 0.001

Cotinine plasma concentration, ng/mL 52.7 (7.38) 50.8 (2.42) 51.1 (2.31) 0.07 1/1820 0.80 0.000

Alcohol Use Disorder Identification Test 4.1 (0.23) 4.0 (0.08) 4.0 (0.08) 0.13 1/1820 0.71 0.000

NEO-FFI Neuroticism 1.5 (0.06) 1.5 (0.03) 1.5 (0.03) 0.00 1/1820 0.96 0.000

Extraversion 2.6 (0.16) 2.6 (0.04) 2.6 (0.04) 0.19 1/1820 0.67 0.000

Openness to experience 2.6 (0.17) 2.6 (0.05) 2.6 (0.05) 0.00 1/1820 0.97 0.000

Agreeableness 2.8 (0.14) 2.8 (0.05) 2.8 (0.05) 0.00 1/1820 0.97 0.000

Conscientiousness 2.9 (0.16) 2.9 (0.05) 2.9 (0.05) 0.00 1/1820 0.95 0.000

TCF4 rs10401120 genotype, CT+TT/CC

n 165 (9.1%) 1,647 (90.9%) 1,812 (100%) — — — —

Age 35.1 (0.96) 36.1 (0.34) 36.0 (0.32) 0.80 1/1807 0.37 0.000

Years of school education 11.7 (0.12) 11.8 (0.04) 11.8 (0.04) 1.05 1/1807 0.31 0.000

Verbal IQ* 109.3 (1.17) 109.8 (0.34) 109.7 (0.33) 0.20 1/1624 0.66 0.000

Percent male 42.8% 42.3% 42.4% 0.01 1 0.92 —



61.4/24.1/14.5% 55.7/25.7/18.6% 56.3/25.5/18.2% 2.43 1 0.30 —


Pack years†,‡ 12.1 (1.61) 14.3 (0.54) 14.1 (0.52) 1.31 1/759 0.25 0.002







Extraversion 2.7 (0.20) 2.6 (0.03) 2.6 (0.04) 0.64 1/1807 0.42 0.000


Agreeableness 2.9 (0.18) 2.8 (0.05) 2.8 (0.05) 0.09 1/1807 0.76 0.000


TCF4 rs17597926 genotype GA+AA/GG

n 98 (5.4%) 1,723 (94.6%) 1,821 (100%) — — — —

Age 35.6 (1.27) 36.0 (0.33) 36.0 (0.32) 0.10 1/1820 0.37 0.000

Years of school education 11.7 (0.16) 11.8 (0.04) 11.8 (0.04) 0.58 1/1820 0.45 0.000

Verbal IQ* 110.8 (1.56) 109.7 (0.34) 109.7 (0.33) 0.58 1/1619 0.45 0.000

Percent male 39.8% 42.5% 42.3% 0.27 1 0.60 —



62.2/26.5/11.2% 55.8/25.7/18.5% 56.2/25.7/18.1% 3.43 1 0.18 —


Pack years†,‡ 13.0 (2.03) 14.2 (0.54) 14.1 (0.52) 0.21 1/760 0.64 0.000







Extraversion 2.8 (0.34) 2.5 (0.04) 2.6 (0.04) 1.74 1/1820 0.19 0.001


Agreeableness 3.0 (0.30) 2.8 (0.05) 2.8 (0.05) 1.20 1/1820 0.27 0.001


Means and SEs of means in parentheses, sex and smoking status in frequency data.

*Verbal IQ data were only available for 89.6% of the total sample.†Only obtained in smokers.‡Corrected by intermittent periods of life-time abstinence and accounting for variable cigarette consumption.


Table S3. Auditory evoked potentials and P50 suppression parameters at the Fz and Cz electrode of 1,821 healthy

volunteers grouped according to their TCF4 genotype

TCF4 genotype Variable Risk allele carrier Nonrisk allele carrier F df/dferr P ηp2

rs9960767 (AC+CC/AA) S1 Fz 7.05 (0.07) 7.14 (0.03) 1.48 1/1815 0.22 0.001

Cz 6.93 (0.06) 6.98 (0.02) 0.45 1/1815 0.50 0.000

S2 Fz 4.81 (0.06) 4.72 (0.02) 2.67 1/1815 0.11 0.002

Cz 4.55 (0.05) 4.48 (0.02) 1.89 1/1815 0.17 0.001

S1-S2* Fz 2.06 (0.08) 2.39 (0.03) 15.9 1/1815 7.1 × 10−5 0.009

Cz 2.25 (0.07) 2.48 (0.02) 10.1 1/1815 0.001 0.006

S2/S1* Fz 0.71 (0.008) 0.67 (0.003) 18.9 1/1815 1.4 × 10−5 0.011

Cz 0.67 (0.007) 0.65 (0.003) 11.2 1/1815 0.001 0.006

rs10401120 (CT+TT/CC) S1 Fz 7.00 (0.08) 7.15 (0.03) 2.92 1/1802 0.09 0.002

Cz 6.9 (0.07) 6.98 (0.02) 0.57 1/1802 0.45 0.000

S2 Fz 4.79 (0.06) 4.72 (0.02) 1.19 1/1802 0.28 0.001

Cz 4.54 (0.06) 4.48 (0.02) 0.85 1/1802 0.36 0.000

S1-S2* Fz 1.98 (0.09) 2.39 (0.03) 17.6 1/1802 2.9 × 10−5 0.010

Cz 2.20 (0.08) 2.47 (0.02) 11.7 1/1802 0.001 0.007

S2/S1* Fz 0.72 (0.010) 0.67 (0.003) 18.1 1/1802 2.2 × 10−5 0.010

Cz 0.68 (0.008) 0.65 (0.003) 10.6 1/1802 0.001 0.006

rs17597926 (GA+AA/GG) S1 Fz 6.97 (0.11) 7.14 (0.03) 2.44 1/1815 0.12 0.001

Cz 6.89 (0.09) 6.98 (0.02) 1.01 1/1815 0.31 0.001

S2 Fz 4.81 (0.08) 4.73 (0.02) 0.94 1/1815 0.33 0.001

Cz 4.55 (0.07) 4.48 (0.02) 0.79 1/1815 0.37 0.000

S1-S2* Fz 1.91 (0.13) 2.38 (0.03) 13.0 1/1815 3.2 × 10−4 0.008

Cz 2.11 (0.10) 2.47 (0.02) 11.5 1/1815 0.001 0.007

S2/S1* Fz 0.73 (0.014) 0.67 (0.003) 14.2 1/1815 1.7 × 10−4 0.008

Cz 0.69 (0.011) 0.65 (0.003) 12.0 1/1815 0.001 0.007

Means and SEs of means in parentheses; adjusted for smoking status, age, sex, study site, cotinine plasma level, and longitude and

latitude of the study centers. Significant effects are in bold.

*Smoking status as fixed-factor.

Table S4. Age-corrected partial correlations of P50 amplitude (S1) and P50 suppression

parameters with smoking variables

Variable FTND score QSU CPD Pack years Exhaled CO Cotinine level

S1 amplitude Fz −0.180 −0.128 −0.161 −0.144 −0.192 −0.071

Cz −0.168 −0.146 −0.157 −0.130 −0.171 −0.047

S1-S2 Fz −0.152 −0.087 −0.125 −0.109 −0.139 −0.063

Cz −0.169 −0.122 −0.140 −0.123 −0.128 −0.057

S2/S1 Fz 0.117 0.045 0.083 0.081 0.095 0.046

Cz 0.140 0.083 0.095 0.093 0.074 0.050

%P50 Fz −0.117 −0.045 −0.083 −0.081 −0.095 −0.046

Cz −0.140 −0.083 −0.095 0.093 −0.074 −0.050

Variables only in smokers, Bonferroni-corrected threshold P < 7.1 × 10−5. Bold values are significant at P <

7.1 × 10−5. CPD, cigarettes per day; FTND, Fagerström Test for Nicotine Dependence; QSU, Questionnaire on

smoking urges.


Table S5. Association of low P50 suppression of the auditory evoked potential with the presence of the schizophrenia risk alleles of TCF4

polymorphisms in the general population

TCF4 genotype

Criterion for low

P50 levels* Samples Total n Low P50, n Risk-allele, n

Low P50 +

risk-allele, n Odds ratio 95% CI P†

rs9960767 (risk-allele C) 1 SD Never-smokers 1,023 124 120 17 1.228 0.707–2.131 0.274

Light smokers 466 51 53 11 2.442 1.166–5.117 0.019

Heavy smokers 332 49 38 12 3.206 1.490–6.896 0.004

Total sample 1,821 224 211 40 1.813 1.244–2.643 0.002

2 SD Never-smokers 1,023 41 120 6 1.305 0.537–3.172 0.347

Light smokers 466 15 53 4 2.983 0.915–9.730 0.079

Heavy smokers 332 19 38 6 4.053 1.441–11.400 0.013

Total sample 1,821 75 211 16 2.157 1.217–3.823 0.010

rs10401120 (risk-allele T) 1 SD Never-smokers 1,020 124 101 16 1.414 0.799–2.501 0.151

Light smokers 463 51 40 8 2.209 0.957–5.010 0.059

Heavy smokers 329 48 24 8 3.313 1.331–8.242 0.013

Total sample 1,812 223 165 32 1.834 1.212–2.776 0.004

2 SD Never-smokers 1,020 41 101 6 1.595 0.654–3.890 0.213

Light smokers 463 15 40 3 2.777 0.750–10.283 0.131

Heavy smokers 329 19 24 4 3.867 1.174–12.741 0.040

Total sample 1,812 75 165 13 2.186 1.175–4.067 0.015

rs17597926 (risk-allele A) 1 SD Never-smokers 1,023 124 61 10 1.459 0.720–2.953 0.193

Light smokers 468 50 26 5 2.101 0.755–5.842 0.132

Heavy smokers 330 47 11 5 5.496 1.606–18.810 0.011

Total sample 1,821 221 98 20 1.942 1.163–3.242 0.011

2 SD Never-smokers 1,023 41 61 4 1.754 0.604–5.093 0.224

Light smokers 468 15 26 2 2.750 0.587–12.885 0.200

Heavy smokers 330 18 11 3 7.600 1.829–31.587 0.017

Total sample 1,821 74 98 9 2.579 1.244–5.347 0.016

*Refers to a mean percent P50 suppression across Fz and Cz electrodes of 34.28% (±9.37 SD) in the present population.†Fisher’s exact test (one-tailed). Significant effects are in bold.

Table S6. Frequencies of TCF4 polymorphisms significantly associated with auditory sensory gating in 1023/1020 never-smokers and 798/

792 smokers with suitable P50 suppression data

TCF4 genotype Controls/cases Allele 1, % Allele 2, %

Pair of Allele 1,

% (n)

Both alleles,

% (n)

Pair of

Allele 2, % (n) Armitage’s Trend Test

rs9960767 Controls (n = 1,023) 94.0 6.0% 88.3 (903) 11.0 (113) 0.7 (7) Odds ratio = 0.967/1.034

χ2 = 0.05 P = 0.82(Alleles A=1 and C=2) Smokers (n = 798) 94.1 5.9 88.6 (707) 10.8 (86) 0.6 (5)

Total (n = 1,821) 94.0 6.0 88.4 (1,610) 10.9 (199) 0.7 (12)

rs10401120 Controls (n = 1,020) 94.9 5.1 91.9 (919) 7.6 (96) 0.5 (5) Odds ratio = 0.846/1.183

χ2 = 1.50 P = 0.22(Alleles C=1 and T=2) Smokers (n = 792) 95.9 4.1 90.1 (728) 9.4 (60) 0.5 (4)

Total (n = 1,812) 95.3 4.7 90.9 (1,647) 8.6 (156) 0.5 (9)

rs17597926 Controls (n = 1,023) 97.0 3.0 94.0 (962) 5.8 (59) 0.2 (2) Odds ratio = 0.775/1.290

χ2 = 1.57 P = 0.21(Alleles G=1 and A=2) Smokers (n = 798) 97.7 2.3 95.4 (761) 4.5 (36) 0.1 (1)

Total (n = 1,821) 97.3 2.7 94.6 (1,723) 5.2 (95) 0.2 (3)

None of these TCF4 polymorphisms were associated with the smoking phenotype.


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