Impact of Interacting Functional Variants in COMT on RegionalGray Matter Volume in Human Brain

Robyn Honea+,1,4, Beth A. Verchinski+,1, Lukas Pezawas1,2, Bhaskar S. Kolachana1,Joseph H. Callicott1, Venkata S. Mattay1, Daniel R. Weinberger1,*, and Andreas Meyer-Lindenberg1,3

1 Genes, Cognition and Psychosis Program, National Institute of Mental Health; Division of IntramuralResearch; National Institutes of Health; Department of Health and Human Services, Bethesda, 20892, USA

3Central Institute for Mental Health, J5, 68159 Mannheim, Germany

AbstractBackground—Functional variants in the catechol-O-methyltransferase (COMT) gene have beenshown to impact cognitive function, cortical physiology and risk for schizophrenia. A recent studyshowed that previously reported effects of the functional val158met SNP (rs4680) on brain functionare modified by other functional SNPs and haplotypes in the gene, though it was unknown if theseeffects are also seen in brain structure.

Methods—We used voxel-based morphometry to investigate the impact of multiple functionalvariants in COMT on gray matter volume in a large group of 151 healthy volunteers from the CBDB/NIMH Genetic Study of Schizophrenia.

Results—We found that the previously described rs4680 val risk variant affects hippocampal anddorsolateral prefrontal (DLPFC) gray matter volume. In addition, we found that this SNP interactswith a variant in the P2 promoter region (rs2097603) in predicting changes in hippocampal graymatter volume consistent with a nonlinear effect of extracellular dopamine.

Conclusions—We report evidence that interacting functional variants in COMT affect gray matterregional volume in hippocampus and DLPFC, providing further in vivo validation of the biologicalimpact of complex genetic variation in COMT on neural systems relevant for the pathophysiologyof schizophrenia and extending observations of nonlinear dependence of prefrontal neurons onextracellular dopamine to the domain of human brain structure.

IntroductionCatechol-O-methyltransferase (COMT) is a major enzyme degrading catecholamines,especially dopamine (DA) (Bertocci et al., 1991; Grossman et al., 1992). The COMT gene islocated on chromosome 22q11.22-23 and consists of two promoters and six exons whichencode both the membrane-bound (MB-COMT) and soluble (S-COMT) forms of COMT. Of

*Address correspondence to: Daniel R. Weinberger, Director: Genes, Cognition and Psychosis Program, National Institute of MentalHealth; IRP; NIMH, NIH, Rm 4S-235, Bethesda, MD 20892, USA, Phone: 301-402-7564, Fax: 301-480-7795, email: E-mail:Daniel.weinberger@mail.nih.gov.+These authors contributed equally to this work2current address: Universitätsklinik für Psychiatrie, Vienna, Austria4current address: University of Kansas Medical Center, Kansas City, KSPublisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customerswe are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resultingproof before it is published in its final citable form. Please note that during the production process errors may be discovered which couldaffect the content, and all legal disclaimers that apply to the journal pertain.

NIH Public AccessAuthor ManuscriptNeuroimage. Author manuscript; available in PMC 2010 March 1.

Published in final edited form as:Neuroimage. 2009 March 1; 45(1): 44–51. doi:10.1016/j.neuroimage.2008.10.064.

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the two confirmed isoforms, MB-COMT is predominantly expressed in the central nervoussystem at neuronal dendritic processes throughout the cortex, cerebellum, amygdala, putamen,thalamus, spinal cord, and hippocampus (Hong et al., 1998; Masuda et al., 2003). COMTappears to be concentrated in the extrasynaptic spaces of the prefrontal cortex and hippocampus(Deutch and Roth, 1990; Matsumoto et al., 2003a; Matsumoto et al., 2003b). Since prefrontaldopamine transporters are scarce, COMT is thought to play a key role in clearing dopamine inthe prefrontal cortex, which has been demonstrated directly by a two- to threefold increase inbaseline frontal dopamine in male COMT knockout mice (Gogos et al., 1998). Anevolutionarily recent functional single nucleotide polymorphism (SNP) in COMT results in theamino acid substitution of valine (val) with methionine (met) at codon 158 of MB-COMT(rs4680, (Savitz et al., 2006)). This substitution leads to a significant (approximately 40%)decrease in enzymatic activity in the brain and lymphocytes (Chen et al., 2004a) of the met-allele compared to the val-containing polypeptide. Consequently, met-carriers are presumablyassociated with a higher level of prefrontal extra-cellular dopamine (Chen et al., 2004b;Lachman et al., 1996). A large body of work has demonstrated an impact of this genetic varianton neural function related to cognitive and affective processing. Several studies have shownthat met/met homozygous individuals have increased PFC signal to noise (Egan et al., 2001;Mattay et al., 2003; Meyer-Lindenberg et al., 2005) and improved performance on prefrontallydependent cognitive tasks like working memory, while those with the high activity val-allelehave relatively poorer performance and “inefficient” dorsolateral prefrontal function (Bilderet al., 2002; Bilder et al., 2004; Egan et al., 2001; Malhotra et al., 2002; Mattay et al., 2003;Meyer-Lindenberg et al., 2005; Savitz et al., 2006). Sub-optimal DA activity in the prefrontalcortex could contribute to some of the cognitive abnormalities found in schizophrenia (Meyer-Lindenberg et al., 2002; Meyer-Lindenberg and Weinberger, 2006; Weinberger, 1999b),namely working memory, perserverative errors on the Wisconsin Card Sorting Test (WCST),verbal fluency, and IQ (Bearden et al., 2004; Egan et al., 2001; Rosa et al., 2004). The COMTval/met polymorphism is also associated with functional variation in hippocampal processingof emotional stimuli (Drabant et al., 2006; Heinz and Smolka, 2006) and of episodic memory(Bertolino et al., 2004).

COMT is located in a region implicated in schizophrenia by linkage studies (Owen et al.,2004) as well as the 22q11.2 deletion syndrome, which is associated with strongly increasedrisk for psychosis (Sanders et al., 2005; Yan et al., 1998; Zinkstok and van Amelsvoort,2005). However, despite the large body of convergent evidence implicating COMT val158metin brain function, the strength of association with manifest psychiatric illness has been variable,and the attributable risk low (Fan et al., 2005; Munafo et al., 2005). One reason for thisdiscrepancy could be that additional loci within COMT have an effect on gene function,ultimately affecting enzyme activity and adding complexity to the functional and clinicalimplications of COMT variation. Independent functional effects of three genetic variants inCOMT have indeed been demonstrated. A cis functional variant (rs2097603) linked upstreamin the P2 promoter, driving transcription of the predominant form of COMT in the brain (MB-COMT), affects COMT activity in lymphocytes and post-mortem brain tissue (Chen et al.,2004a). Another variant in the 3′ untranslated region (rs165599), highly associated withschizophrenia in a large sample of Israelis of Ashkenazi descent (Shifman et al., 2002), wasfound to differentially affect expression of rs4680 alleles in human brain tissue (Bray et al.,2003), possibly through altering an miRNA binding site (Barenboim, Lipska and Weinberger,unpublished results). A further synonymous variant has been identified only 60 nucleotidesfrom val/met and impacts COMT translation through an alteration of mRNA secondarystructure (Nackley et al., 2006). Furthermore, recent studies show that genetic variants inCOMT may functionally interact with regard to brain function in healthy humans (Meyer-Lindenberg et al., 2006), suggesting a possible impact on other neural parameters such as brainstructure.

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The relationship between brain structure and the COMT val158met variant, studied in isolation,is unclear. Two studies reported no associations between genotype and brain volume in healthycontrols (Ho et al., 2005; Zinkstok et al., 2006), and two reported genotype effects in patientswith schizophrenia (Ohnishi et al., 2006) or subjects at risk for psychosis (McIntosh et al.,2006a). All of these earlier studies have involved small samples and none have investigatedstructural correlates of other functional SNPs or haplotypes in COMT.

The current study sought to investigate the impact of several COMT polymorphisms on graymatter volume in healthy subjects using optimized voxel-based morphometry (VBM). VBMhas been useful in identifying alterations in gray matter (GM) related to genetic variation brain-derived neurotrophic factor (BDNF), 5-HTTLPR, and DISC1 (Callicott et al., 2005; Pezawaset al., 2005; Pezawas et al., 2004). We utilized a previously described regression-basedapproach to probe the influence of probabilistic haplotype variation in COMT on brain structure(Meyer-Lindenberg et al., 2006). We investigated a 2-SNP haplotype composed of rs4680 anda P2 promoter region SNP (rs2097603) and a 3-SNP haplotype of rs2097603 – rs4680 –rs165599, previously found to have a statistically strong impact on prefrontal function (Meyer-Lindenberg et al., 2006) and risk for schizophrenia (Nicodemus et al., 2007). Our large samplealso allowed us to control for occult genetic stratification in genes such as BDNF, accountingfor a potential confound in interpreting dopaminergic affects on the brain (Meyer-Lindenbergand Weinberger, 2006; Savitz et al., 2006).

We report evidence that interacting functional variants in COMT affect gray matter regionalvolume in hippocampus and DLPFC, providing further in vivo validation of the biologicalimpact of complex genetic variations within COMT on brain structure.

Methods and MaterialsSubjects

Subjects were selected from a larger population after careful screening (Egan et al., 2001),ensuring they were free of any current or lifetime history of psychiatric or neurological illness,psychiatric treatment, and drug or alcohol abuse. Only Caucasians of European ancestry werestudied to avoid stratification artifacts. Subjects gave written informed consent and participatedin the study according to the guidelines of the National Institute of Mental Health InstitutionalReview Board. For customized template creation, a sample of 171 subjects meeting thesecriteria was used (aged 19-61; mean, 32.36 ± 9.43); from these, all scans from subjects withavailable COMT genotype data were used for the VBM analysis (72 male; 79 females; agerange 19-61) (Table 1).

Genotyping and haplotype constructionCOMT genotype frequencies were in Hardy-Weinberg equilibrium. Genotyping for the threeSNPs studied here, (1) P2 promoter SNP (rs2097603), (2) val158met SNP (rs4680) and (3) 3′flanking SNP (rs165599) were performed as described previously using the Taqman 5′endonuclease assay (Chen et al., 2004a). There were no significant differences in age, gender,IQ, handedness, or years of education between genotype groups for the val158met SNP or theP2 promotor SNP. There was a significant difference in IQ for the 3′ flanking SNP genotypegroups A/A and G/G in IQ (p<.024), thus IQ was included as a nuisance covariate in all imaginganalyses. Linkage disequilibrium between markers was determined by GOLD (Abecasis andCookson, 2000). We constructed likely 2- and 3-SNP haplotypes in our samples by use ofPHASE 2.1 (Stephens and Donnelly, 2003). Since we had 151 subjects, only haplotypes withestimated frequency > 3% were used in subsequent analysis to ensure we had sufficientnumbers of scans per haplotype. Frequencies are shown in Table 2.

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Structural image processing and analysesThree-dimensional volumes of T1-weighted SPGR images were acquired on a 1.5T GE scannerwith a voxel resolution of 0.975 × 0.975 × 1.5 mm3 (TR/TE/NEX 24/5/1; flip angle 45°; matrixsize 256 × 256; FOV 24 × 24 cm; 124 sagittal slices). Data were analyzed on a Linuxworkstation (Red Hat 8.0) using MATLAB 6.51SP1 (Mathworks, Natick, MA) using SPM2(http://www.fil.ion.ucl.ac.uk/spm/; Wellcome Department of Imaging Neuroscience, London,UK). Additional imaging software packages used were Analysis of Functional NeuroImages(AFNI) (Cox, 1996), and Surface Mapping with AFNI (SUMA) (Argall et al., 2005).

Template creation was based on a sample of 171 healthy control subjects recruited as describedabove. An iterative procedure was used to make the customized template optimally suited forour sample, as described previously (Pezawas et al., 2004).

Structural imaging data were preprocessed using AFNI to set the origins followed by optimizedVBM, performed in SPM2, using the non-uniformity correction option to correct for imageinhomogeneities (Ashburner and Friston, 2000; Good et al., 2001). Relative volume wasassessed by correcting for volume changes made during the normalization transformation andimages were modulated by multiplication with the determinant of the Jacobian matrix fromthe spatial normalization function. An integration of modulated gray matter voxel valuesresulted in estimates for total gray matter volume. The normalized segmented images weresmoothed using a 10mm FWHM isotropic Gaussian kernel.

The normalized and smoothed gray matter images were analyzed using the General LinearModel as implemented in SPM2. For the three single SNP analyses we used a multipleregression model that included age, second-order polynomial age expansions, gender, and IQas nuisance covariates of no interest to assess genetic affects on absolute volumes. In additionwe ran an identical analysis, also adding total gray matter volume in the design matrix to assessgenetic affects on relative volumes. Voxel-by-voxel t-tests were used at the level of the wholebrain to determine global differences in gray matter volumes based on COMT val158met,grouping alleles as val/val homozygotes, val/met heterozygotes, and met/met homozygotes. Inaddition, for the P2 Promotor SNP (rs2097603) and the 3′ flanking SNP (rs165599) globaldifferences in gray matter volume were tested between the AA, AG, and GG allele carriers.

SPM2 was also used for an adapted haplotype trend regression model. We tested for theassociation between both absolute and relative gray matter volumes and the estimatedhaplotype frequencies with the same nuisance covariates as before (42). Each of the haplotypecombinations (for both the 2SNP and 3SNP haplotypes) were contrasted within the analysisfor significant global gray matter variation.

Previous data has demonstrated the influence of genetic variation in COMT on the function ofDLPFC (i.e. Brodmann's areas (BA) 9, 10, 45, and 46) and hippocampus (Bertolino et al.,2006; Drabant et al., 2006; Harrison and Weinberger, 2005; Smolka et al., 2005), so we useda region of interest (ROI) approach to specifically probe effects in these areas. The choice ofthe a-priori DLPFC ROI was based on the maximum effect in several studies and consisted ofBA 9, 10, 45 and 46 (Callicott et al., 2003; Egan et al., 2001). ROIs were made based on theWFU PickAtlas (http://www.fmri.wfubmc.edu; Advanced Neuroscience Imaging ResearchCore, Wake Forest University, Winston-Salem, NC, USA), selecting the above mentionedareas with a dilation of 1 mm. Gray matter volume changes were assessed statistically usingt-contrasts after small-volume correction for these ROIs in each of the 3 single SNP analyses,as well as the two haplotype analyses. Family-wise error rate estimations were used to correctfor multiple comparisons across voxels, where a corrected probability of p < 0.05 wasconsidered significant.

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ResultsSingle SNP analyses

We found gray matter volume differences in both medial temporal and frontal structures basedon the val158met SNP. Specifically, at the whole-brain level, we found significant decreasesin volume in a cluster of voxels including the left hippocampus and parahippocampal gyrus inval-allele carriers and val homozygotes relative to met homozygotes (p<.05 FWE corrected,Figure 1a). Within our hippocampal ROI, we identified significant bilateral hippocampalreductions in val-carriers (p<.01 FWE corrected MNI coordinates (-24, -12, -27) and (24, -13,-26)). In the reverse contrast (val/val > val/met > met/met), our a-priori ROI in DLPFC(including BA 9, 10, 45, and 46) showed a trend for volume reductions in the met-carriers (p<.068 FWE corrected, Figure 1b). No additional voxels showed significant association at thewhole-brain level.

Independent analysis of the P2 promoter and 3′ flanking SNPs revealed no significant effectof genetic variation for either SNP alone on brain structure. This included investigation ofabsolute and relative volumes at the whole brain level as well as in our hippocampal and DLPFCregions of interest.

2-SNP & 3-SNP Haplotype analysesAnalysis of the 2-SNP haplotype, composed of the P2 promoter SNP and val158met SNP,revealed a significant effect (in ROI) for variation of val158met when contrasted on the P2promoter-A background, that is, the P2-A allele & val/val (A-val) > P2-A allele & met-carriers(A-met) (p < 0.06, whole-brain corrected, p < 0.01, corrected in hippocampal ROI, (-34, -14,-13), Figure 2). There was also a trend for an effect of variation in the hippocampal ROI in thecontrast of the P2-G allele & met-carriers (G-met) > P2-G allele & val-carriers (G-val). Whenordered by putative genetic variation of enzymatic activity (Meyer-Lindenberg et al., 2006),such that the highest activity was for A-val, then G-val (effect of reduced expression atpromoter SNP in val-carriers), then A-met and finally G-met (corresponding to the met-carrierson each of the two P2 promoter backgrounds), a nonlinear U-shape relationship of haplotypeeffects was found in absolute volumes, where both A-met and G-val were associated with lowerhippocampal volumes than A-val and G-met haplotypes (Figure 3).

The 3-SNP haplotype analysis, consisting of the P2 promoter, val158met, and the 3′ flankingSNP (rs2097603-rs4680-rs165599), did not uncover significant effects in either hippocampusor DLPFC. However, based on previous findings with this haplotype in a functional MRIdataset, we investigated effects in BA 45, corresponding to the locale of our previousobservation of maximal functional effects of this gene (Meyer-Lindenberg et al., 2006); thisyielded a non-significant trend that was congruent with this earlier study (A-val-A had lessgray matter volume than A-val-G, p < 0.15, corrected in BA 45 ROI).

DiscussionOur results show an impact of COMT genetic variation on brain structure that is characterizedby allelic variation in both the val158met SNP and a 2-SNP haplotype consisting of val158metand the P2 promoter SNP. In our study, the functional COMT val158met polymorphismsignificantly influenced hippocampal cortical volume. While the individual analysis of the P2promoter and 3′ SNPs did not reveal a significant effect on brain structure, the 2-SNP haplotyperevealed the complexity of the effects of multiple variants on gray matter volume, similar toprevious results on brain function (Meyer-Lindenberg et al., 2006). Our data implies thatcomplex genetic variation in COMT may impact gray matter volume, possibly throughneurotrophic or neurotoxic effects of varying levels of DA.

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Previous work involving val158met and brain structure has resulted in conflicting results.Interestingly, Kates et al. found a COMT by gender interaction on the DLPFC in children withvelo-cardio-facial syndrome (Kates et al., 2006). A recent paper studying young healthy adultsfound no group differences in regional gray matter density as a function of genotype (Zinkstoket al., 2006), indicating that regional volume measures, as opposed to density measures, maybe more sensitive indicators of genotype-related alterations. Issues related to reducedsensitivity may also underlie a negative result by Ho and colleagues, who found no effect ofthis genotype on total frontal gray matter in patients and controls, but did not study regionalvolume change (Ho et al., 2005). In a group of subjects at high risk for psychosis, McIntoshand coworkers (McIntosh et al., 2006a) saw increased volume in met homozygotes relative toval homozygotes in the anterior cingulate. No relevant effect was observed in this location inour study, nor in the studies by Zinstock et al and Ho et al. It appears possible that the at-riskgroup showed different genotype effects because they are expected have more interactinggenetic variants predisposing them to psychosis, which might impact on expression of theneural phenotype (Harrison and Weinberger, 2005). This interpretation could also apply to theresults of Ohnishi and coworkers, who observed no genotype effects in controls, but did findvolume effects in the cingulate and left medial temporal gyrus in patients, at very lenientstatistical thresholds, who carried val, with a gene by diagnosis interaction in leftparahippocampus-amygdala (Ohnishi et al., 2006). These authors used yet anothermethodological approach, tensor-based morphometry, which does not account for local graymatter intensity.

While the discrepant findings could be accounted for by methodological, study population andstatistical threshold differences, it also appears likely that the val158met variant, in isolation,may not identify a sufficiently homogeneous population of subjects. This parallels theinconsistent association of COMT with schizophrenia (Fan et al., 2005; Munafo et al., 2005),and suggests that additional loci within COMT have an effect on gene function, ultimatelyaffecting enzyme activity and adding complexity to the neural and clinical implications ofCOMT variation. Regarding risk for schizophrenia, this idea receives strong support fromseveral studies. A large Ashkenazi sample showed highly significant association for two otherpolymorphisms (in intron 1, rs737865, and 3′ UTR (rs165599), as well as for a core haplotypeof three markers including val (P=9.5 × 10 -8) (Reenila and Mannisto, 2001). Handoko et al,in a recent analysis of these SNPs and this three-marker haplotype (rs737865-rs4680-rs165599), found that while none of the individual SNPs showed significant association (whencorrected for multiple comparisons), the haplotype showed significant evidence of association(Handoko et al., 2005). Nicodemus et al (2007) (Nicodemus et al., 2007) recently reported thatdiplotypes comprised of haplotypes shown to be most deleterious in terms of prefrontalfunction in our earlier fMRI analysis (Meyer-Lindenberg et al., 2006) were significantlyenriched in a case control sample of patients with schizophrenia that was negative for any ofthe individual SNPs in the haplotype.

Our data indeed demonstrate that interacting functional variants impact on regional brainvolume. We found that while overall, val-allele carrying subjects had less hippocampal volumethan those homozygote for the met allele, two subpopulations with relatively higher or lowervolume could be distinguished for both val- and met-allele chromosomes based on the P2promoter genotype. Specifically, we found a u-shaped relationship between hippocampalvolume and presumed COMT activity, such that very low activity (COMT-met combined withthe low activity G-allele of the P2 promoter) and very highly active COMT variants (val158on the ancestral P2-A allele) had larger volumes than haplotypes with predicted intermediateCOMT activity (P2-G allele and val158, and P2-A allele and 158met) (Figure 3). Thisnonlinearity in genotype relating to structure mirrors our previous findings on the effects ofthese variants on brain function (Meyer-Lindenberg et al., 2006). Preclinical data suggest thatthese functional effects are related to a lawful effect of D1-mediated extracellular dopamine

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stimulation on the firing rate and tuning of prefrontal neurons, which exhibit a middle rangeof dopamine at which ‘tuning,’ --or ratio of task-related to task-unrelated neural firing--(Vijayraghavan et al., 2007;Williams and Goldman-Rakic, 1995), is optimal. This may relateto risk for schizophrenia through reduced prefrontal signal to noise in the context of reducedprefrontal D1 stimulation (Meyer-Lindenberg and Weinberger, 2006;Vijayraghavan et al.,2007). Neuroimaging data further indicate that the position on this u-shaped curve is a trait-like enduring characteristic (Meyer-Lindenberg et al., 2005).

Our data suggest a similar impact of trait-like variation in dopaminergic neurotransmission (inparticular: extracellular dopamine concentration) on neural growth and survival in humans.Several lines of preclinical evidence support this hypothesis. Establishment of dopaminergicconnections coincides with neuronal growth and differentiation in striatum (Fishell and vander Kooy, 1987) and dopamine induces the neurotrophic factor, BDNF, in neuronal culture(Kuppers and Beyer, 2001). These effects have been linked to D1 receptor stimulation, but alsoto antioxidative activity (Ma and Zhou, 2006). Reductions in dopamine signaling in knockoutmodels of the dopamine D1 receptor (Xu et al., 1994) and tyrosine hydroxylase (Zhou andPalmiter, 1995) severely impair neuronal differentiation. While these data show that dopaminestimulation is relevant for growth and differentiation of dopaminoceptive neurons, clinical andpreclinical work also indicates that excess dopamine can impair neuronal integrity and survival.Excess extracellular dopamine is neurotoxic (Santiago et al., 2000). In mouse knockouts forthe dopamine transporter, which exhibit chronically elevated extracellular dopamine levels,BDNF gene expression is reduced in frontal cortex (Fumagalli et al., 2003). In the brain ofhuman methamphetamine abusers, pronounced reductions in cortical volume were found inmedial prefrontal and limbic cortex, as well as in hippocampus (Thompson et al., 2004). Takentogether, these data indicate that both over- and under-stimulation with dopamine may resultin impaired neuronal survival and growth, implying that an optimum range for extracellulardopamine in cortex and hippocampus may exist for structural integrity.

To our knowledge, this is the first report of combined genetic variants in COMT impacting oncortical structure. This complements previous findings in function (Meyer-Lindenberg et al.,2006), enzymatic activity (Chen et al., 2004a), and mRNA expression (Matsumoto et al.,2003b), and may provide further understanding for the putative involvement of COMT variantsin risk for schizophrenia (Nicodemus et al., 2007).

Although predominantly studied within the context of prefrontal function and molecularsubtleties; we clearly identified structural effects in the hippocampus and parahippocampalgyrus, as well as a trend in the prefrontal cortex. This is in agreement with the abundance ofCOMT in these structures (Hong et al., 1998) and, again, may be involved in the risk forschizophrenia since both these regions have been prominently involved in the pathophysiologyof the illness (Weinberger, 1999a). In addition VBM studies of subjects at high-risk forschizophrenia have shown gray matter volume reductions in the hippocampus and prefrontalcortex (Job et al., 2005; Job et al., 2003; McIntosh et al., 2004; McIntosh et al., 2006b). It isinteresting to note that structural effects of variation of rs4680 were opposite in direction inthese two structures (i.e. met-alleles predicted decreased volume in the hippocampus, butrelative (non-significant) increases in PFC), although the mechanism of this observation, whichcould include differential effects of extracellular dopamine on allocortex and neocortex orcompensatory PFC-hippocampal interactions, is unclear. However, we previously observedopposite effects of COMT variation on hippocampal activation and connectivity (Drabant etal., 2006) from those found in PFC (Egan et al., 2001; Mattay et al., 2003; Meyer-Lindenberget al., 2006), indicating that a similar phenomenon is present in functional activation as well.Therefore, whatever the neurobiological proximate mechanisms, there is a multimodalconvergence showing qualitative differences in the impact of COMT genetic variation indifferent brain regions.

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We were careful to control for potential confounding factors such as occult stratification andgenetic admixture using this genomic control analysis. However, replication of these resultsin independent samples is necessary. It should be noted that the genetics even of intermediatephenotypes is still likely to be complex, and other genetic variants, for example in BDNF, arealso expected to have effects (Ahtila et al., 1995) that may interact with those studied here. Afocused study of such epistatic interactions in adequately powered samples will be a fruitfulfurther research avenue.

Finally, the scope of VBM, while it has proved very useful for the analysis of genetic variationnot constrained by traditional anatomical landmarks, is limited to regional volume change ingray matter and should be supplemented by analyses looking at cortical thickness, shape, ordensity. These analyses would be particularly useful considering the location of the genotypeeffect in our sample, as more ventral regions surrounded by CSF are particularly susceptibleto segmentation error and partial volume affects. In addition, MRI gray matter signal shouldnot be naively assumed to reflect neuronal volume, since it is affected by hydration, medication,scanner sequence, and environmental exposures (Davatzikos, 2004; McClure et al., 2006).Spatial normalization in VBM can not perfectly register individuals (Nieto-Castanon et al.,2003), and significant differences, especially those not corrected for multiple comparisons,may be a function of misregistration (Bookstein, 2001). To date, data on hippocampal volumefrom MRI and post-mortem imaging literature do not always correspond (Weinberger,1999a), and VBM may be reflecting other more complex changes going on at the neuronallevel other than gross volume loss. However, this study implemented the same scannersequence on a large group of healthy controls, used a custom template, and used an optimizedVBM protocol, therefore reducing possible partial volume effects and reducing noise in thisdataset (Good et al., 2001).

Overall this study provides evidence that interacting functional variants in COMT affect graymatter regional volume in human brain, providing further in vivo validation of the biologicalimpact of complex genetic variation in COMT on neural systems relevant for thepathophysiology of schizophrenia, especially hippocampus and DLPFC, and extendingobservations of nonlinear dependence of prefrontal neurons on extracellular dopamine to thedomain of brain structure.

AcknowledgmentsThis study was supported by the NIMH/IRP and by a bench-to-bedside award by NIMH, NIAAA and ORD to A.M.L.We thank Katherine Hobbs and Aaron Goldman for their assistance in data preprocessing and Joshua Buckholtz forhis advice in the process of manuscript preparation.

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Figure 1.A. Whole brain VBM results overlaid on MRICRON 3-D mean 154 brain showing significantdecreases in volume in a cluster of voxels including the left hippocampus and parahippocampalgyrus in val-allele carriers and val homozygotes relative to met homozygotes. B. Overlaydemonstrating a trend for volume reductions in met-carriers in (val/val > val/met > met/met)an ROI in the DLPFC (including BA 9, 10, 45, and 46).

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Figure 2.Results overlaid on MNI T1 axial slice from the analysis of the 2-SNP haplotype, composedof the P2 promoter SNP and val158met SNP. Displayed is the significant effect of val158metvariation on gray matter volume when contrasted on the P2 promoter-A background (P2-Aallele & val/val (A-val) > P2-A allele & met-carriers (A-met)), (p < 0.06, whole-brain corrected,p < 0.01, corrected in hippocampal ROI, (-34, -14, -13)).

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Figure 3.Figure 3 (B) demonstrates the trend for an effect of variation in the hippocampal ROI in thecontrast of the P2-G allele & met-carriers (G-met) > P2-G allele & val-carriers (G-val). (A)When ordered by putative genetic variation of enzymatic activity, such that the highest activitywas for A-val, then G-val (effect of reduced expression at promoter SNP in val-carriers), thenA-met and finally G-met (corresponding to the met-carriers on each of the two P2 promoterbackgrounds), a nonlinear U-shape relationship of haplotype effects was found in absolutehippocampal volumes, where both A-met and G-val were associated with lower volumes thanA-val and G-met haplotypes

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Honea et al. Page 16Ta

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Honea et al. Page 17

Table 2Genotype (coding strand)/estimated haplotype frequencies (%)

Genotype/haplotype Genotype/estimated haplotype frequency

rs2097603(A → G) 42 (AA) 81 (AG) 23 (GG)

rs4680 (G-val → A-met) 38 (val/val) 78 (val/met) 35 (met/met)

rs165599 (A → G) 65 (AA) 64 (AG) 13 (GG)

2-SNP haplotype (rs2097603–rs4680)

A-val 39 ± .3

A-met 17 ± .8

G-val 11 ± .1

G-met 31 ± .7

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