Abstract

Studies in adolescents and adults have demonstrated that polymorphisms in putative psychiatric risk genes are associated with differences in brain structure, but cannot address when in development these relationships arise. To determine if common genetic variants in disrupted-in-schizophrenia-1 (DISC1; rs821616 and rs6675281), catechol-O-methyltransferase (COMT; rs4680), neuregulin 1 (NRG1; rs35753505 and rs6994992), apolipoprotein E (APOE; ɛ3ɛ4 vs. ɛ3ɛ3), estrogen receptor alpha (ESR1; rs9340799 and rs2234693), brain-derived neurotrophic factor (BDNF; rs6265), and glutamate decarboxylase 1 (GAD1; rs2270335) are associated with individual differences in brain tissue volumes in neonates, we applied both automated region-of-interest volumetry and tensor-based morphometry to a sample of 272 neonates who had received high-resolution magnetic resonance imaging scans. ESR1 (rs9340799) predicted intracranial volume. Local variation in gray matter (GM) volume was significantly associated with polymorphisms in DISC1 (rs821616), COMT, NRG1, APOE, ESR1 (rs9340799), and BDNF. No associations were identified for DISC1 (rs6675281), ESR1 (rs2234693), or GAD1. Of note, neonates homozygous for the DISC1 (rs821616) serine allele exhibited numerous large clusters of reduced GM in the frontal lobes, and neonates homozygous for the COMT valine allele exhibited reduced GM in the temporal cortex and hippocampus, mirroring findings in adults. The results highlight the importance of prenatal brain development in mediating psychiatric risk.

Introduction

Decades of research have generated many putative psychiatric risk genes. Intriguingly, many of these genes are not specific to a particular diagnosis. For example, polymorphisms in DISC1 are associated with schizophrenia (Hennah et al. 2003; Ekelund et al. 2004; Qu et al. 2007), bipolar disorder (Hodgkinson et al. 2004; Hennah et al. 2009), major depression (Hashimoto et al. 2006), and autism spectrum conditions (Kilpinen et al. 2008). In addition, meta-analyses reveal extensive overlap in neuroanatomical changes associated with schizophrenia, bipolar disorder, and autism (Cheung et al. 2010; Yu et al. 2010). This strongly suggests that there are common, genetically determined neurodevelopmental pathways to risk for psychiatric illness in general, but the mechanisms by which genetic variants alter neurodevelopmental trajectories resulting in the eventual emergence of illness remain unclear.

Imaging genetics represents a powerful strategy for characterizing neural systems affected by risk variants (Meyer-Lindenberg et al. 2006; Tan et al. 2008; Bigos and Weinberger 2010). Studies in adolescents and adults demonstrate that polymorphisms in putative psychiatric risk genes predict individual differences in brain structure in healthy controls and in individuals with mental illness (Scharinger et al. 2010; Johnstone et al. 2011). However, studies, to date, are limited to adults and older children in whom gene–brain relationships may be confounded by medication, years of environmental and gene–environment interaction effects, and progression of disease. Such studies are also unable to address the reality that mental illnesses are unfolding developmental processes, which can have different consequences at different stages. Studies in infants would minimize these issues and allow a better understanding of age-specific effects, including prenatal effects. The perinatal period is an extremely dynamic stage of brain development, characterized by rapid synaptogenesis (Huttenlocher and Dabholkar 1997), exuberant dendritic (Petanjek et al. 2008) and axonal growth (Kasprian et al. 2008), and extensive myelination (Brody et al. 1987). These changes are reflected in dramatic increases in gray and white matter (WM) volumes as indexed by magnetic resonance imaging (MRI; Gilmore et al. 2007; Knickmeyer et al. 2008). This is a critical period for childhood-onset illnesses such as autism (Hazlett et al. 2005, 2011; Wolff et al. 2012). There is also extensive evidence that adult-onset diseases, such as schizophrenia, originate in early brain development (Rapoport et al. 2005; Fatemi and Folsom 2009). Early aberrations in neurodevelopment relevant to adult-onset disorders can be captured via MRI as evidenced by a recent study, showing that male neonates at high genetic risk for schizophrenia have larger intracranial, cerebrospinal fluid (CSF), total gray matter (GM), and lateral ventricle volumes than controls (Gilmore et al. 2010).

We tested whether variants in disrupted-in-schizophrenia-1 (DISC1), catechol-O-methyltransferase (COMT), neuregulin 1 (NRG1), apolipoprotein E (APOE), estrogen receptor alpha (ESR1), brain-derived neurotrophic factor (BDNF), and glutamate decarboxylase 1 (GAD1) are associated with neonatal brain structure using both automated region-of-interest (ROI) volumetry and tensor-based morphometry (TBM). In TBM, brain images are warped to a canonical template. The applied deformation is used as an index of volume differences between each subject and the template and, subsequently, between groups. The advantage of TBM over traditional voxel-based morphometry (VBM) is that false-positive findings due to systematic group differences in registration errors are less likely (Hua et al. 2008; Lepore et al. 2008). ROI volumetry and TBM approaches are complementary. Due to its summarizing nature, ROI volumetry captures overall size differences reliably, but cannot localize differences. TBM has the advantage of being able to identify group differences that are confined to small areas or that do not follow the anatomical boundaries segmented by the atlas. TBM analyses focused on localized GM changes. Localized WM changes are also of interest, but we felt that these would be best addressed through diffusion tensor imaging (DTI).

Selected genes have received significant research attention as putative psychiatric risk genes (Table 1) and have well-documented roles in brain development. DISC1 participates in neural migration, neurite outgrowth, and dendritic arborization (Porteous et al. 2011). COMT is a critical enzyme in the degradation of catecholamines, including dopamine (Yavich et al. 2007). NRG1 encodes protein isoforms that participate in neuronal migration and specification, oligodendrocyte differentiation and myelination, regulation of acetylcholine, and expression of glutamate and γ-aminobutyric acid (GABA) receptors (Mei and Xiong 2008). APOE is a very-low-density lipoprotein thought to have a key role in neuronal development, brain plasticity, and repair (Vance and Hayashi 2010). ESR1 mediates estrogen effects on synaptogenesis, growth factor production, and responses to oxidative stress (Sundermann et al. 2010). BDNF regulates cell survival, axonal outgrowth, dendritic growth, and synaptic plasticity (Reichardt 2006). GAD1 is one of the 2 major isoforms of the enzyme that converts glutamate to GABA, which plays diverse roles in corticogenesis (Wang and Kriegstein 2009). Selected variants are related to neuroimaging phenotypes in older samples and have minor allele frequencies (MAF) high enough to allow study. In light of the genotyping method employed, only single-nucleotide polymorphisms (SNPs) were included.

Table 1

Selection criteria for genes and variants

Gene Variant Gene implicated in psychiatric illness Variant implicated in psychiatric illness Variant related to structural neuroimaging phenotype Missense Molecular functionality MAFa 
DISC1 rs821616    Ser-Cys Leliveld et al. (20090.308 
rs6675281    Leu-Phe Eastwood et al. (20090.158 
COMT rs4680 Schizophrenia Lewandowski (2007)
Ayalew et al. (2012
Schizophrenia Williams et al. (2007 Val-Met Chen et al. (20040.478 
NRG1 rs6994992  Schizophrenia Li et al. (2006Ventricle volume Mata et al. (2009Law et al. (2006)
Tan et al. (2007)
Shibuya et al. (2010
0.417 
rs35753505   Frontal WM integrity Winterer et al. (2008)
Wang et al. (2009
Hoffmann et al. (20110.342 
APOE ɛ3 versus ɛ4   Temporal cortex Plassman et al. (1997)
Lemaitre et al. (2005)
Shaw et al. (2007)
Burggren et al. (2008)
Hua et al. (2008)
Biffi et al. (2010
Cys/Arg
Arg/Arg 
Dong and Weisgraber (1996)
Mahley and Huang (2006
NAb 
ESR1 rs9340799    Maruyama et al. (20000.306 
rs2234693   Amygdala den Heijer et al. (2004Unclear 0.407 
BDNF rs6265    Val-Met Reichardt (20060.195 
GAD1 rs2270335  Schizophrenia Addington et al. (2005Frontal cortex Addington et al. (2005Unclear 0.142 
Gene Variant Gene implicated in psychiatric illness Variant implicated in psychiatric illness Variant related to structural neuroimaging phenotype Missense Molecular functionality MAFa 
DISC1 rs821616    Ser-Cys Leliveld et al. (20090.308 
rs6675281    Leu-Phe Eastwood et al. (20090.158 
COMT rs4680 Schizophrenia Lewandowski (2007)
Ayalew et al. (2012
Schizophrenia Williams et al. (2007 Val-Met Chen et al. (20040.478 
NRG1 rs6994992  Schizophrenia Li et al. (2006Ventricle volume Mata et al. (2009Law et al. (2006)
Tan et al. (2007)
Shibuya et al. (2010
0.417 
rs35753505   Frontal WM integrity Winterer et al. (2008)
Wang et al. (2009
Hoffmann et al. (20110.342 
APOE ɛ3 versus ɛ4   Temporal cortex Plassman et al. (1997)
Lemaitre et al. (2005)
Shaw et al. (2007)
Burggren et al. (2008)
Hua et al. (2008)
Biffi et al. (2010
Cys/Arg
Arg/Arg 
Dong and Weisgraber (1996)
Mahley and Huang (2006
NAb 
ESR1 rs9340799    Maruyama et al. (20000.306 
rs2234693   Amygdala den Heijer et al. (2004Unclear 0.407 
BDNF rs6265    Val-Met Reichardt (20060.195 
GAD1 rs2270335  Schizophrenia Addington et al. (2005Frontal cortex Addington et al. (2005Unclear 0.142 

ADHD, attention deficit hyperactivity disorder.

aMAF from the NIH single nucleotide polymorphism database (dbSNP) (European CEU samples).

bɛ4 frequency ranges from 0.052 to 0.310 in European populations, ɛ3 ranges from 0.640 to 0.898, and ɛ2 (excluded from this analysis) ranges from 0.050 to 0.119 (Corbo and Scacchi 1999).

Materials and Methods

Subjects

This study included two hundred and seventy-two neonates (152 males and 120 females, 144 singletons, 128 twins). Mothers were recruited during the second trimester of pregnancy from outpatient obstetrics and gynecology clinics at University of North Carolina (UNC) hospitals. Exclusion criteria at enrollment were the presence of abnormalities on ultrasound or major medical illness in the mother. Demographic data are found in Table 2. The sample is enriched for individuals with parental psychiatric history (Table 3). Imaging genetics studies often focus on healthy individuals to minimize potential contamination of signal from nongenetic illness-related factors. However, studying “healthy volunteers” exclusively runs the risk of identifying genetic effects with little or no relationship to illness (Rasetti and Weinberger 2011). This dilemma is more complex in the neonate as many “high-risk” neonates will never develop a psychiatric illness, and some proportion of “low-risk” neonates will. As the distribution of genotypes did not significantly differ between groups (Supplementary Table S1), we ran our main analyses without “risk” as a covariate. We also ran sensitivity analyses with risk as a covariate. Results were highly similar.

Table 2

Demographic data for participants

Variable
 
Maternal age at start of study (years) 30.19 (5.68) 17–47 
Mean (SD) range, n= 272 
Maternal education level (years) 15.55 (3.12) 8–25 
Mean (SD) range, n= 272 
Total household income $77 943 ($50 103) 0–288 000 
Mean (SD) range, n= 261 
Gestational age at birth (days) 261.4 (19.85) 205–295 
Mean (SD) range, n= 272 
Birth weight (g) 2916 (706.2) 1289–4701 
Mean (SD) range, n= 272 
Gestational age at MRI (days) 294.2 (17.93) 261–433 
Mean (SD) range, n= 272 
Gender 
 Female, n (%) 120 (44%) 
 Male, n (%) 152 (56%) 
Singleton versus twins 
 Singleton, n (%) 144 (53%) 
 Twins, n (%) 128 (47%) 
Within twins 
 Dizygotic, n (%) 70 (58%) 
 Monozygotic, n (%) 50 (42%) 
Variable
 
Maternal age at start of study (years) 30.19 (5.68) 17–47 
Mean (SD) range, n= 272 
Maternal education level (years) 15.55 (3.12) 8–25 
Mean (SD) range, n= 272 
Total household income $77 943 ($50 103) 0–288 000 
Mean (SD) range, n= 261 
Gestational age at birth (days) 261.4 (19.85) 205–295 
Mean (SD) range, n= 272 
Birth weight (g) 2916 (706.2) 1289–4701 
Mean (SD) range, n= 272 
Gestational age at MRI (days) 294.2 (17.93) 261–433 
Mean (SD) range, n= 272 
Gender 
 Female, n (%) 120 (44%) 
 Male, n (%) 152 (56%) 
Singleton versus twins 
 Singleton, n (%) 144 (53%) 
 Twins, n (%) 128 (47%) 
Within twins 
 Dizygotic, n (%) 70 (58%) 
 Monozygotic, n (%) 50 (42%) 
Table 3

Parental psychiatric history for participants

Diagnosis Singletons Twins 
Bipolar disorder, n (%) 30 (21%) 4 (3%) 
Schizophrenia, n (%) 2 (1%) 4 (3%) 
Schizoaffective disorder, n (%) 3 (2%) 0 (0%) 
Depression, n (%) 30 (21%) 24 (19%) 
Alcohol dependence/abuse, n (%) 21 (14%) 4 (3%) 
Drug dependence/abuse, n (%) 21 (14%) 6 (5%) 
Anxiety/OCD, n (%) 13 (9%) 26 (20%) 
ADD/ADHD, n (%) 3 (2%) 8 (6%) 
Other, n (%) 13 (9%) 1 (1%) 
Total in sample with parental psychiatric history, n (%) 65 (45%) 45 (35%) 
Total in sample with no parental psychiatric history, n (%) 79 (54%) 83 (65%) 
Psychotropic medications 
 Antidepressants, n (%) 25 (17%) 16 (13%) 
 Antipsychotics, n (%) 14 (10%) 4 (3%) 
 Mood stabilizers, n (%) 12 (8%) 0 (0%) 
 Antianxiety, n (%) 3 (2%) 2 (2%) 
 ADHD, n (%) 1 (1%) 0 (0%) 
Total in sample with psychotropic medication use, n (%) 38 (26%) 16 (13%) 
Diagnosis Singletons Twins 
Bipolar disorder, n (%) 30 (21%) 4 (3%) 
Schizophrenia, n (%) 2 (1%) 4 (3%) 
Schizoaffective disorder, n (%) 3 (2%) 0 (0%) 
Depression, n (%) 30 (21%) 24 (19%) 
Alcohol dependence/abuse, n (%) 21 (14%) 4 (3%) 
Drug dependence/abuse, n (%) 21 (14%) 6 (5%) 
Anxiety/OCD, n (%) 13 (9%) 26 (20%) 
ADD/ADHD, n (%) 3 (2%) 8 (6%) 
Other, n (%) 13 (9%) 1 (1%) 
Total in sample with parental psychiatric history, n (%) 65 (45%) 45 (35%) 
Total in sample with no parental psychiatric history, n (%) 79 (54%) 83 (65%) 
Psychotropic medications 
 Antidepressants, n (%) 25 (17%) 16 (13%) 
 Antipsychotics, n (%) 14 (10%) 4 (3%) 
 Mood stabilizers, n (%) 12 (8%) 0 (0%) 
 Antianxiety, n (%) 3 (2%) 2 (2%) 
 ADHD, n (%) 1 (1%) 0 (0%) 
Total in sample with psychotropic medication use, n (%) 38 (26%) 16 (13%) 

ADD, attention deficit disorder; ADHD, attention deficit hyperactivity disorder; OCD, obsessive compulsive disorder.

Note: parental history was based on maternal report. Active drug or alcohol abuse/dependence was an exclusion criteria at the time of enrollment. However, 4 women did report drug use later in the pregnancy.

To minimize false positives due to population stratification, only individuals whose reported maternal ethnicity was white are included in this analysis. Ethnicity classification was by maternal report. We used maternal ethnicity as we expected it to be more reliable than paternal ethnicity (see Supplementary Table S2 for paternal ethnicity). Approximately 70% of mothers enrolled in the parent study report their ethnicity as white. This study was approved by the Institutional Review Board of the UNC School of Medicine. Written informed consent was obtained from the participants' mothers prior to the study.

Genotyping

DNA was extracted from buccal samples using standard methods described in the Puregene® DNA Purification Kit (Gentra Systems) using supplies from Qiagen. Genotyping was performed by Genome Quebec using Sequenom® iPLEX® Gold Genotyping Technology, which is based on a multiplex polymerase chain reaction followed by a template-directed single base extension using the probes of various sizes. The products are separated and detected by Matrix-assisted laser desorption-ionization time-of-flight mass spectrometry. Positive and negative controls are included on each 96-well plate. The genotyping call rate was >98%. The visual examination of intensity cluster plots supported high genotyping accuracy. The rate of concordant results between duplicate control samples was 100%. Genotype frequencies did not deviate from Hardy–Weinberg equilibrium (Table 4). The 2 SNPs in APOE were treated as a haplotype to permit direct comparisons to the existing literature. In the 3 other cases, where we examined 2 SNPs within the same gene (DISC1, NRG1, and ESR1), we treated the variants separately. Again this permitted a direct comparison with the existing literature. Linkage disequilibrium between the 2 variants as measured by r2 was <0.80 in all 3 cases (Gaunt et al. 2007).

Table 4
Variant Homozygotes major allele Heterozygotes Homozygotes minor allele Missing Model 
Genotype frequencies for participants, n (%)      
 DISC1 rs821616 TT 141 (52%) AT 102 (38%) AA 28 (10%) 1 (>1%)  AA versus Other 
 DISC1 rs6675281 CC 191 (70%) TC 75 (28%) TT 5 (2%) 1 (>1%)  CC versus Other 
 COMT rs4680 GG 69 (26%) GA 137 (50%) AA 61 (22%)  5 (2%)  GG versus Other 
 NRG1 rs6994992 CC 93 (34%) CT 126 (46%) TT 52 (19%)  1 (>1%)  TT versus Other 
 NRG1rs35753505 TT 112 (41%) TC 115 (42%) CC 44 (16%)  1 (>1%)  TT versus Other 
 ESR1 rs9340799 AA 110 (40%) GA 129 (47%) GG 32 (12%)  1 (>1%)  AA versus Other 
 ESR1 rs2234693 TT 76 (28%) TC 143 (53%) CC 52 (19%)  1 (>1%)  Additiveb 
 BDNF rs6265 GG 182 (67%) GA 77 (28%) AA 12 (4%)  1 (>1%)  GG versus Other 
 GAD1 rs2270335 GG 133 (49%) GA 116 (43%) AA 22 (8%) 1 (>1%) Additive 
Haplotype Frequencies for participants, n (%) 
 APOE ɛ3ɛ3 ɛ3ɛ4 Othera   
 156 (57%) 66 (24%) 47 (17%) 3 (1%) ɛ3ɛ3 versus ɛ3ɛ4 
Variant Homozygotes major allele Heterozygotes Homozygotes minor allele Missing Model 
Genotype frequencies for participants, n (%)      
 DISC1 rs821616 TT 141 (52%) AT 102 (38%) AA 28 (10%) 1 (>1%)  AA versus Other 
 DISC1 rs6675281 CC 191 (70%) TC 75 (28%) TT 5 (2%) 1 (>1%)  CC versus Other 
 COMT rs4680 GG 69 (26%) GA 137 (50%) AA 61 (22%)  5 (2%)  GG versus Other 
 NRG1 rs6994992 CC 93 (34%) CT 126 (46%) TT 52 (19%)  1 (>1%)  TT versus Other 
 NRG1rs35753505 TT 112 (41%) TC 115 (42%) CC 44 (16%)  1 (>1%)  TT versus Other 
 ESR1 rs9340799 AA 110 (40%) GA 129 (47%) GG 32 (12%)  1 (>1%)  AA versus Other 
 ESR1 rs2234693 TT 76 (28%) TC 143 (53%) CC 52 (19%)  1 (>1%)  Additiveb 
 BDNF rs6265 GG 182 (67%) GA 77 (28%) AA 12 (4%)  1 (>1%)  GG versus Other 
 GAD1 rs2270335 GG 133 (49%) GA 116 (43%) AA 22 (8%) 1 (>1%) Additive 
Haplotype Frequencies for participants, n (%) 
 APOE ɛ3ɛ3 ɛ3ɛ4 Othera   
 156 (57%) 66 (24%) 47 (17%) 3 (1%) ɛ3ɛ3 versus ɛ3ɛ4 

aIncludes ɛ2ɛ2, ɛ2ɛ3, ɛ2ɛ4, and ɛ4ɛ4.

bFor genotypes with 3 levels an ordinal model was imposed.

Image Acquisition

MRI was performed at the UNC MRI Research Center on a Siemens head-only 3-T scanner (Allegra, Siemens Medical System, Inc., Erlangen, Germany), as previously described (Gilmore et al. 2007). All subjects were studied without sedation. Once a child was asleep, he/she was fitted with earplugs and placed in the MRI scanner with head in a vac-fix immobilization device, and additional foam padding to diminish the sounds of the scanner. Scans were performed with a neonatal nurse present, and a pulse oximeter was used to monitor the heart rate and oxygen saturation. T1-weighted images were obtained using a 3-dimensional (3-D) spoiled gradient [fast low angle shot (FLASH) repetition time (TR)/echo time (TE)/flip angle 15/7 ms/25°]. Proton density and T2-weighted images were obtained with a turbo spin-echo sequence [fast low angle shot (FLASH) repetition time (TR)/echo time (TE)/flip angle 6200/20/119 ms/150°). Spatial resolution was 1 × 1 × 1 mm3 voxel size for T1-weighted images and 1.25 × 1.25 × 1.5 mm3 voxel size with a 0.5-mm interslice gap for proton density/T2-weighted images.

Automated ROI Volumetry

Brain tissue was classified as GM, unmyelinated WM, myelinated WM (mWM), and CSF using an automatic, atlas-moderated expectation maximization segmentation tool as previously described (Prastawa et al. 2005; Gilmore et al. 2007). Parcellation of each subject's brain into regions was achieved by nonlinear warping of a parcellation atlas template as previously described (Hazlett et al. 2005; Gilmore et al. 2007; Knickmeyer et al. 2008). For the neonatal brain parcellation, left and right hemispheres were subdivided into 4 regions along the anterior–posterior axis (roughly corresponding to prefrontal, frontal, parietal, and occipital regions). The cerebellum, brainstem, and subcortical structures are represented separately (Supplementary Figure S1). Note that subcortical structures are combined into a single “exclusion” area as individual subcortical structures cannot be reliably defined at this age. After deformation, the parcellation template is combined with the tissue classification maps and results in estimates of GM, WM, mWM, and CSF for each region. The volume of mWM in the cortex was very small and likely represented partial-volume effects; therefore, we did not perform statistical tests on cortical mWM. The neonatal lateral ventricles are segmented using InsightSnap (SNAP), a semiautomated 3-D segmentation tool, which uses a level-set evolution method (Yushkevich et al. 2006). SNAP is controlled by a user-defined initialization and data-specific segmentation protocols with region-growing parameters that operate in conjunction with the probabilistic CSF map generated during tissue segmentation. Segmentation results were reviewed by an expert rater, and poor quality segmentations were excluded from the analysis (final n = 236).

TBM: Image Preprocessing

Brain tissue was extracted from the original T2-weighted images and corrected for intensity inhomogeneity using an expectation maximization segmentation algorithm (Prastawa et al. 2005). T2-weighted images were used as these had better signal-to-noise ratio in our neonatal sample. The skull stripped images were then rigidly aligned to match the center and orientation, and the average image was calculated afterwards, serving as the template of the following affine alignment. The rigid and affine registrations were employed with AFNI software (Cox 1996). Intensity histogram matching was then applied on the affine aligned images to prepare for nonrigid registration. The unbiased large deformation nonrigid group-wise registration method (Joshi et al. 2004) was used to construct the atlas and to estimate deformation fields mapping each input images to the atlas. To get the final transformation from the original image space to the atlas space, we composed both the affine transformation matrix and the nonrigid deformation field. The deformation fields were examined by an image processing expert and ones with unusually large distortions were excluded (final n = 248).

Data Analysis

For demographic and descriptive data, as well as the results of automated ROI volumetry, statistical analyses were performed using the SAS statistical software, version 9.2. Mixed models were used to study the relationship between brain volumes and genetic markers. Rather than exhaustively testing all possible genetic models, comparisons were selected based on published literature (Table 4). Because many of these variants are somewhat rare, in most cases, a presence/absence approach was used rather than a genotypic approach; this increases statistical power. Mixed models methodology was used to treat twins as repeated measures, whereas singletons had no repeated measures. In other words, twin pairs were treated as a single subject. This means that the effective sample size for ROI volumetry was 186 (119 singletons and 67 unique twin mothers). The compound symmetry covariance structure was used to capture the correlation between twins in a pair (with only 2 twins in a pair there is only 1 off-diagonal correlation) (Munoz et al. 1986). Models were adjusted for gender, intracranial volume (ICV), and gestational age at MRI. All statistical hypothesis tests were 2-tailed. Tests were conducted at a significance level of 0.005 (0.05/10; Bonferroni corrected for the number of genetic tests conducted). We did not perform additional corrections for the number of phenotypes examined. However, analysis was restricted to the following variables: ICV, total GM, total WM, total CSF, lateral ventricle volume, cerebellar volume, and lobar GM and WM (14 volumes total). For significant results, we also ran 3 sensitivity analyses; one with “psychiatric risk” as a covariate, one including only 1 individual from each monozygotic (MZ) pair, and one with birthweight as a covariate.

For TBM, associations between brain structure and genetic markers were examined by fitting a multiscale adaptive generalized estimation equation (MAGEE) model to the Jacobian determinant of the deformation matrix at each voxel of the template. The MAGEE method extends the multiscale adaptive regression model (MARM; Zhu et al. 2009; Li et al. 2011) method to the correlated sample setting. The MAGEE methodology treated the twins as repeated measures. The effective sample size for TBM analyses was 154 (94 singletons and 60 unique twin mothers). The MAGEE integrates a commonly used approach for analyzing the correlated data called generalized estimation equation (GEE; Liang and Zeger 1986) with adaptive smoothing methods. GEE models account for within-twin correlation among repeated measures via the specification of a “working” correlation structure in parameter estimation. Specifically, the compound symmetry covariance structure was used to capture the correlation between twins in a pair. An attractive feature of GEE models is that the estimated regression coefficients and their associated standard deviations are valid even if the correlation structure assumed for modeling the within-twin correlation is not precisely correct (e.g., correlations may differ between MZ and dizygotic twins). As a sensitivity analysis, we also ran these tests including only 1 individual from each MZ pair.

Our primary scientific interest was to localize GM regions that differ significantly between 2 groups (for the presence/absence models) and 3 groups (for additive models) for each variant, thus when testing for significant regions, WM was masked and only GM was examined. The final form of the estimating equations in MAGEE included genotype, while adjusting for gender, ICV, and gestational age at MRI. Sensitivity analyses controlling for birthweight were also run. To clarify the details, we interpret comparisons in statistical context. For example, for rs4680 and the comparison GG versus Other, we set up a linear model: 

$$y_{ij} (d) = \beta _0 \cdot 1 + \beta _1 \cdot {\rm gender} + \beta _2 \cdot {\rm age} + \beta _3 \cdot {\bf 1}({\rm genotype} = {\rm GG}) + \beta _4 \cdot {\rm ICV} + e_i (d) + \varepsilon _{ij} (d)$$
where $$e_i (d) - N(0,\sigma _{e^{\rm 2} } )$$ and $$\varepsilon _{ij} (d) - N(0,\sigma _{a^{\rm 2} } )$$. y(d) is the imaging measure at the dth voxel, 1(·) is an indication function, and ei(d) is the shared correlation between subjects within the ith twin pair. We are interested in the estimation of the coefficient β3 and testing whether β3 = 0 is statistically significant. The value of β3 can be negative or positive at a specific voxel, if it is positive, it indicates that the imaging measure in the GG group is statistically higher than that in other genotype groups, denoted as GG > Other; otherwise, GG is statistically lower than Other, denoted as GG < Other.

For all hypothesis tests, cluster-based inference was used to identify significant genetic effects on localized GM volumes (Cao 1999; Worsley et al. 1999; Hayasaka et al. 2004). Cluster-based inference is based on random field theory, a widely used multiple testing method for determining corrected significances while accounting for the high level of spatial dependencies between adjacent voxels (Worsley et al. 1996). First, we detected clusters of contiguous suprathreshold voxels using a cluster-forming threshold of P < 0.005. We used a threshold of 0.005 to adjust for the number of genetic tests conducted (0.005 = 0.05/10; Bonferroni corrected for the number of genetic tests conducted). Secondly, we calculated a P-value for each contiguous cluster on the basis of its size/mass to test whether a ROI size was significant or not using a conservative level of P < 0.001. To further minimize false positives, a conservative cluster size threshold of 100 was applied both before and after the clustering test. The manual and code of the nonstationarity correction toolbox for cluster size P-values are available at http://www.fmri.wfubmc.edu/. Anatomical locations of clusters were established using a 90 region parcellation template for the infant brain (Gilmore et al. 2011), and regions correspond to those in the Anatomical Automatic Labeling template (Tzourio-Mazoyer et al. 2002).

Results

In the automated lobar volumetry analyses, ESR1 (rs9340799) predicted ICV. AA homozygotes had 3.7% larger ICV than G allele carriers (P = 0.0028). This comparison remained significant when including only 1 individual from each MZ twin pair (P = 0.0011), and when including birthweight as a covariate (P = 0.0037). It approached significance when controlling for parental psychiatric history (P = 0.0057). No other variants were associated with global or regional brain tissue volumes (Supplementary Tables S3 and S4). However, TBM revealed significant local GM variation associations with DISC1 (rs821616), COMT, NRG1, APOE, ESR1 (rs9340799), and BDNF (Table 5).

Table 5

Results of TBM analyses

Variant and model Cluster no. Cluster size Maximum t-value Maximum P-value Cluster P-value Anatomical regions 
DISC1 (rs821616) 
 AA(Ser/Ser) < Other 1699 6.52 <0.0001 <0.0001 Supp_motor_area_R (627)
Supp_motor_area_L (543)
Paracentral_lobule_L (238)
Paracentral_lobule_R (198)
Cingulum_mid_L (36)
Cingulum_mid_R (35)
Postcentral_L (22) 
881 5.27 <0.0001 <0.0001 Occipital_mid_L (432)
Temporal_mid_L (376)
Occipital_inf_L (47)
Temporal_inf_L (13)
Angular_L (9)
Temporal_sup_L (3)
Supramarginal_L (1) 
244 5.69 <0.0001 <0.0001 Temporal_inf_L (163)
Temporal_pole_mid_L (49)
Fusiform_L (32) 
202 4.41 <0.0001 <0.0001 Frontal_sup_medial_R (119)
Frontal_sup_R (80)
Frontal_mid_R (3) 
150 6.13 <0.0001 <0.0001 Precentral_R (141)
Frontal_sup_R (9) 
115 4.22 <0.0001 0.0001 Frontal_inf_orb_R (114)
Temporal_pole_sup_R (1) 
115 4.24 <0.0001 0.0001 Occipital_mid_R (87)
Angular_R (28) 
112 4.89 <0.0001 <0.0001 Temporal_Pole_Sup_L (112) 
112 3.78 0.0001 0.0004 Precentral_R (94)
Frontal_sup_R (18) 
10 111 4.69 <0.0001 <0.0001 Temporal_mid_R (57)
Temporal_sup_R (54) 
11 102 4.77 <0.0001 <0.0001 Temporal_sup_R (71)
Supramarginal_R (31) 
DISC1 (rs6675281) None 
COMT (rs4680) 
 GG(Val/Val) > Other 1611 4.12 <0.0001 0.0001 Supp_motor_area_L (63)
Supp_motor_area_R (59)
Frontal_sup_medial_L (18)
Frontal_sup_medial_R (14)
Cingulum_mid_R (7) 
462 4.10 <0.0001 0.0001 Cuneus_R (42)
Occipital_sup_R (263)
Occipital_mid_R (65)
Parietal_sup_R (91)
Angular_R (1) 
165 3.84 0.0001 0.0002 Cuneus_L (21)
Occipital_sup_L (120)
Occipital_mid_L (19)
Parietal_sup_L (1)
Parietal_inf_L (4) 
159 3.63 0.0002 0.0004 Angular_R (65)
Parietal_inf_R (48)
Parietal_sup_R (46) 
 GG(Val/Val) < Other 536 4.60 <0.0001 <0.0001 Temporal_sup_R (463)
Temporal_mid_R (36)
Insula_R (33)
Heschl_R (4) 
220 3.89 0.0001 0.0001 Calcarine_R (130)
Lingual_R (83)
Fusiform_R (6)
Cuneus_R (1) 
206 4.29 <0.0001 <0.0001 Precuneus_R (158)
Cingulum_post_R (48) 
204 4.30 <0.0001 <0.0001 Hippocampus_R (144)
Parahippocampal_R (38)
Fusiform_R (22) 
101 3.38 0.0004 0.0008 Temporal_sup_L (98)
Temporal_mid_L (3) 
NRG1 (rs35753505) 
 TT < Other 232 3.75 0.0001 0.0003 Temporal_mid_L (110)
Temporal_sup_L (83)
Supramarginal_L (39) 
203 4.11 <0.0001 0.0001 Supramarginal_L (166)
Temporal_sup_L (37) 
 141 4.12 <0.0001 0.0001 Precuneus_R (131)
Cuneus_R (9)
Occipital_sup_R (1) 
 108 4.15 <0.0001 0.0001 Precuneus_R (97)
Cuneus_R (6)
Calcarine_R (5) 
 100 3.71 0.0001 0.0004 Precuneus_L (96)
Cuneus_L (2)
Calcarine_L (2) 
 NRG1 (rs6994992) 
  TT > Other 746 ∞ <0.0001 <0.0001 Temporal_sup_R (430)
Temporal_mid_R (301)
Temporal_pole_sup_R (12)
Heschl_R (2)
Temporal_pole_mid_R (1) 
 598 8.91 <0.0001 <0.0001 Temporal_mid_L (367)
Temporal_sup_L (184)
Supramarginal_L (33)
Temporal_inf_L (14) 
 550 ∞ <0.0001 <0.0001 Frontal_mid_L (406)
Frontal_sup_L (144) 
 251 7.69 <0.0001 <0.0001 Rolandic_oper_R (197)
Heschl_R (51)
Insula_R (3) 
 131 6.22 <0.0001 <0.0001 Paracentral_lobule_R (109)
Supp_motor_area_R (17)
Precuneus_R (5) 
 126 5.72 <0.0001 <0.0001 Temporal_sup_L (57)
Temporal_mid_L (56)
Insula_L (12)
Temporal_pole_sup_L (1) 
 124 5.13 <0.0001 <0.0001 Temporal_mid_L (112) 
  TT < Other 4192 ∞ <0.0001 <0.0001 Temporal_inf_L (1577)
Temporal_mid_L (1479)
Temporal_pole_sup_L (312)
Temporal_sup_L (294)
Temporal_pole_mid_L (262)
Frontal_inf_orb_L (192)
Fusiform_L (74)
Rolandic_oper_L (2) 
2564 ∞ <0.0001 <0.0001 Temporal_inf_R (1004)
Temporal_mid_R (568)
Temporal_sup_R (290)
Rolandic_oper_R (149)
Temporal_pole_mid_R (136)
Precentral_R (99)
Frontal_inf_oper_R (88)
Fusiform_R (83)
Temporal_pole_sup_R (69)
Postcentral_R (64)
Frontal_inf_tri_R (11)
Heschl_R (3) 
10 2163 ∞ <0.0001 <0.0001 Parahippocampal_L (656)
Hippocampus_L (394)
Lingual_L (329)
Caudate_L (215)
Olfactory_L (118)
Fusiform_L (77)
Putamen_L (71)
Precuneus_L (69)
Insula_L (57)
Amygdala_L (50)
Pallidum_L (44)
Cingulum_post_L (35)
Frontal_inf_orb_L (30)
Thalamus_L (6)
Frontal_sup_orb_L (6)
Rectus_L (6) 
11 576 ∞ <0.0001 <0.0001 Cuneus_R (180)
Calcarine_L (166)
Calcarine_R (103)
Lingual_R (57)
Cuneus_L (40)
Occipital_sup_R (30) 
12 394 6.65 <0.0001 <0.0001 Cuneus_L (140)
Cuneus_R (129)
Occipital_sup_R (51)
Occipital_sup_L (46)
Precuneus_L (28) 
 13 385 7.59 <0.0001 <0.0001 Insula_L (219)
Frontal_inf_tri_L (126)
Frontal_inf_oper_L (36)
Frontal_inf_orb_L (4) 
14 346 5.84 <0.0001 <0.0001 Supp_motor_area_R (127)
Supp_motor_area_L (104)
Frontal_sup_R (51)
Frontal_sup_medial_L (31)
Frontal_sup_medial_R (26)
Cingulum_mid_R (7) 
15 310 6.84 <0.0001 <0.0001 Frontal_inf_orb_R (99)
Putamen_R (86)
Insula_R (81)
Temporal_pole_sup_R (23)
Caudate_R (21) 
16 265 ∞ <0.0001 <0.0001 Insula_R (199)
Frontal_inf_oper_R (65)
Rolandic_oper_R (1) 
17 259 6.96 <0.0001 <0.0001 Parietal_inf_L (105)
Parietal_sup_L (104)
Occipital_mid_L (29)
Angular_L (21) 
18 183 ∞ <0.0001 <0.0001 Parahippocampal_R (133)
Amygdala_R (30)
Hippocampus_R (20) 
19 181 ∞ <0.0001 <0.0001 Precentral_R (158)
Frontal_mid_R (20)
Postcentral_R (3) 
20 179 7.33 <0.0001 <0.0001 Occipital_mid_R (93)
Occipital_sup_R (77)
Angular_R (9) 
21 155 5.16 <0.0001 <0.0001 Cingulum_ant_L (82)
Cingulum_ant_R (52)
Frontal_sup_medial_L (18)
Cingulum_mid_R (3) 
22 148 ∞ <0.0001 <0.0001 Frontal_inf_orb_R (106)
Temporal_pole_sup_R (33)
Frontal_inf_tri_R (6)
Frontal_inf_oper_R (3) 
23 148 7.25 <0.0001 <0.0001 Occipital_mid_R (133)
Occipital_inf_R (12)
Temporal_mid_R (3) 
24 141 7.62 <0.0001 <0.0001 Precentral_R (120)
Postcentral_R (21) 
25 122 5.62 <0.0001 <0.0001 Paracentral_lobule_L (65)
Postcentral_L (49)
Precuneus_L (8) 
26 115 7.09 <0.0001 <0.0001 Parietal_inf_L (108)
Angular_L (7) 
27 110 6.91 <0.0001 <0.0001 Supramarginal_R (110) 
APOE 
 ɛ3ɛ3 < ɛ3ɛ4 2795 ∞ <0.0001 <0.0001 Parietal_sup_L (1088)
Parietal_inf_L (646)
Precuneus_L (347)
Occipital_sup_L (205)
Occipital_mid_L (191)
Postcentral_L (182)
Angular_L (80)
Cingulum_post_L (47)
Cuneus_L (9) 
960 ∞ <0.0001 <0.0001 Postcentral_R (731)
Parietal_inf_R (143)
Paracentral lobule_R (27)
Parietal_sup_R (22)
Supramarginal_R (20)
Precentral_R (16)
Precuneus_R (1) 
 449 7.52 <0.0001 <0.0001 Angular-R (199)
Occipital_sup_R (106)
Parietal_sup_R (120)
Parietal_inf_R (9)
Precuneus_R (3)
Occipital_mid_R (9)
Cuneus_R (3) 
 376 ∞ <0.0001 <0.0001 Precentral_R (298)
Frontal_mid_R (76)
Postcentral_R (2) 
240 ∞ <0.0001 <0.0001 Postcentral_L (220)
Precentral_L (20) 
237 ∞ <0.0001 <0.0001 Frontal_inf_oper_R (207)
Frontal_mid_R (15)
Frontal_inf_tri_R (12)
Precentral_R (3) 
155 8.75 <0.0001 <0.0001 Frontal_sup_orb_L (89)
Rectus_L (57)
Frontal_mid_orb_L (8)
Frontal_med_orb_L (1) 
135 6.85 <0.0001 <0.0001 Paracentral_lobule_R (34)
Paracentral_lobule_L (28)
Supp_motor_area_R (22)
Precuneus_L (22)
Precuneus_R (12)
Cingulum_mid_R (12)
Cingulum_mid_L (5) 
134 7.41 <0.0001 <0.0001 Rolandic_oper_R (48)
Supramarginal_R (42)
Postcentral_R (37)
Temporal_sup_R (6)
Heschl_R (1) 
10 114 5.46 <0.0001 <0.0001 Precuneus_R (63)
Parietal_sup_R (48)
Precuneus_L (3) 
11 102 8.13 <0.0001 <0.0001 Frontal_sup_R (101)
Frontal_mid_R (1) 
12 100 7.27 <0.0001 <0.0001 Temporal_inf_R (55)
Occipital_inf_R (45) 
 ɛ3ɛ3 > ɛ3ɛ4 13 514 ∞ <0.0001 <0.0001 Fusiform_L (125)
Hippocampus_L (117)
Temporal_inf_L (90)
Temporal_mid_L (71)
Temporal_sup_L (68)
Parahippocampal_L (43) 
14 188 ∞ <0.0001 <0.0001 Frontal_mid_L (143)
Frontal_sup_L (39)
Precentral_L (6) 
15 133 ∞ <0.0001 <0.0001 Frontal_inf_oper_R (118)
Frontal_inf_tri_R (10)
Frontal_mid_R (5) 
16 123 ∞ <0.0001 0.0002 Fusiform_R (67)
Parahippocampal_R (48)
Hippocampus_R (8) 
17 109 ∞ <0.0001 <0.0001 Parietal_sup_R (107)
Precuneus_R (2) 
ESR1 (rs9340799) 
 AA > Other 195 6.52 <0.0001 <0.0001 Parietal_sup_L (119)
Parietal_inf_L (47)
Occipital_mid_L (19)
Angular_L (10) 
184 5.16 <0.0001 <0.0001 Postcentral_R (178)
Parietal_inf_R (6) 
162 6.48 <0.0001 <0.0001 Frontal_mid_R (16)
Frontal_inf_oper_R (138)
Frontal_inf_tri_R (8) 
111 4.56 <0.0001 <0.0001 Precentral_R (106)
Frontal_Mid_R (5) 
102 ∞ <0.0001 <0.0001 Postcentral_R (35)
Temporal_sup_R (30)
Rolandic_oper_R (27)
Precentral_R (8)
Heschl_R (2) 
 AA < Other 3549 ∞ <0.0001 <0.0001 Frontal_sup_R (1260)
Frontal_mid_R (1154)
Supp_motor_area_R (558)
Precentral_R (294)
Frontal_inf_oper_R (136)
Frontal_sup_medial_R (128)
Frontal_inf_tri_R (14)
Postcentral_R (5) 
 2400 ∞ <0.0001 <0.0001 Frontal_mid_L (876)
Frontal_sup_L (738)
Supp_motor_area_L (542)
Frontal_sup_medial_L (194)
Precentral_L (50) 
499 8.62 <0.0001 <0.0001 Supramarginal_R (317)
Parietal_inf_R (77)
Postcentral_R (61)
Angular_R (36)
Rolandic_oper_R (8) 
385 8.03 <0.0001 <0.0001 Paracentral_lobule_R (235)
Precentral_R (83)
Postcentral_R (60)
Precuneus_R (7) 
10 287 6.34 <0.0001 <0.0001 Postcentral_L (188)
Supramarginal_L (51)
Parietal_inf_L (46)
Precentral_L (2) 
11 229 8.75 <0.0001 <0.0001 Precentral_L (148)
Postcentral_L (81) 
12 198 ∞ <0.0001 <0.0001 Postcentral_L (133)
Precentral L (41)
Paracentral_lobule_L (21)
Precuneus_L (3) 
13 153 6.97 <0.0001 <0.0001 Supramarginal_R (99)
Postcentral_R (54) 
14 139 ∞ <0.0001 <0.0001 Postcentral_L (120)
Parietal_inf_L (15)
Precentral_L (4) 
15 120 ∞ <0.0001 <0.0001 Frontal_sup_L (74)
Precentral_L (46) 
16 104 6.44 <0.0001 <0.0001 Precentral_R (53)
Postcentral_R (51) 
ESR1 (rs2234693) None 
BDNF (rs6265) 
 GG(Val/Val) > Other 262 7.73 <0.0001 <0.0001 Occipital_inf_R (156)
Occipital_mid_R (102)
Calcarine_R (4) 
172 ∞ <0.0001 <0.0001 Parahippocampal_L (66)
Fusiform_L (62)
Hippocampus_L (37)
Temporal_inf_L (7) 
 GG(Val/Val) < Other 682 8.91 <0.0001 <0.0001 Postcentral_R (450)
Parietal_inf_R (183)
Supramarginal_R (26)
Parietal_sup_R (21)
Angular_R (2) 
298 7.60 <0.0001 <0.0001 Precentral_R (243)
Frontal_mid_R (46)
Postcentral_R (9) 
200 6.91 <0.0001 <0.0001 Parietal_sup_L (126)
Parietal_inf_L (40)
Angular_L (20)
Occipital_mid_L (14) 
169 6.83 <0.0001 <0.0001 Frontal_inf_oper_R (141)
Frontal_inf_tri_R (14)
Frontal_mid_R (14) 
147 5.61 <0.0001 <0.0001 Postcentral_L (147) 
146 6.45 <0.0001 <0.0001 Parietal_inf_L (89)
Parietal_sup_L (34)
Postcentral_L (23) 
113 6.02 <0.0001 <0.0001 Frontal_inf_tri_R (62)
Frontal_inf_oper_R (49)
Frontal_mid_R (2) 
GAD1 (rs2270335) None 
Variant and model Cluster no. Cluster size Maximum t-value Maximum P-value Cluster P-value Anatomical regions 
DISC1 (rs821616) 
 AA(Ser/Ser) < Other 1699 6.52 <0.0001 <0.0001 Supp_motor_area_R (627)
Supp_motor_area_L (543)
Paracentral_lobule_L (238)
Paracentral_lobule_R (198)
Cingulum_mid_L (36)
Cingulum_mid_R (35)
Postcentral_L (22) 
881 5.27 <0.0001 <0.0001 Occipital_mid_L (432)
Temporal_mid_L (376)
Occipital_inf_L (47)
Temporal_inf_L (13)
Angular_L (9)
Temporal_sup_L (3)
Supramarginal_L (1) 
244 5.69 <0.0001 <0.0001 Temporal_inf_L (163)
Temporal_pole_mid_L (49)
Fusiform_L (32) 
202 4.41 <0.0001 <0.0001 Frontal_sup_medial_R (119)
Frontal_sup_R (80)
Frontal_mid_R (3) 
150 6.13 <0.0001 <0.0001 Precentral_R (141)
Frontal_sup_R (9) 
115 4.22 <0.0001 0.0001 Frontal_inf_orb_R (114)
Temporal_pole_sup_R (1) 
115 4.24 <0.0001 0.0001 Occipital_mid_R (87)
Angular_R (28) 
112 4.89 <0.0001 <0.0001 Temporal_Pole_Sup_L (112) 
112 3.78 0.0001 0.0004 Precentral_R (94)
Frontal_sup_R (18) 
10 111 4.69 <0.0001 <0.0001 Temporal_mid_R (57)
Temporal_sup_R (54) 
11 102 4.77 <0.0001 <0.0001 Temporal_sup_R (71)
Supramarginal_R (31) 
DISC1 (rs6675281) None 
COMT (rs4680) 
 GG(Val/Val) > Other 1611 4.12 <0.0001 0.0001 Supp_motor_area_L (63)
Supp_motor_area_R (59)
Frontal_sup_medial_L (18)
Frontal_sup_medial_R (14)
Cingulum_mid_R (7) 
462 4.10 <0.0001 0.0001 Cuneus_R (42)
Occipital_sup_R (263)
Occipital_mid_R (65)
Parietal_sup_R (91)
Angular_R (1) 
165 3.84 0.0001 0.0002 Cuneus_L (21)
Occipital_sup_L (120)
Occipital_mid_L (19)
Parietal_sup_L (1)
Parietal_inf_L (4) 
159 3.63 0.0002 0.0004 Angular_R (65)
Parietal_inf_R (48)
Parietal_sup_R (46) 
 GG(Val/Val) < Other 536 4.60 <0.0001 <0.0001 Temporal_sup_R (463)
Temporal_mid_R (36)
Insula_R (33)
Heschl_R (4) 
220 3.89 0.0001 0.0001 Calcarine_R (130)
Lingual_R (83)
Fusiform_R (6)
Cuneus_R (1) 
206 4.29 <0.0001 <0.0001 Precuneus_R (158)
Cingulum_post_R (48) 
204 4.30 <0.0001 <0.0001 Hippocampus_R (144)
Parahippocampal_R (38)
Fusiform_R (22) 
101 3.38 0.0004 0.0008 Temporal_sup_L (98)
Temporal_mid_L (3) 
NRG1 (rs35753505) 
 TT < Other 232 3.75 0.0001 0.0003 Temporal_mid_L (110)
Temporal_sup_L (83)
Supramarginal_L (39) 
203 4.11 <0.0001 0.0001 Supramarginal_L (166)
Temporal_sup_L (37) 
 141 4.12 <0.0001 0.0001 Precuneus_R (131)
Cuneus_R (9)
Occipital_sup_R (1) 
 108 4.15 <0.0001 0.0001 Precuneus_R (97)
Cuneus_R (6)
Calcarine_R (5) 
 100 3.71 0.0001 0.0004 Precuneus_L (96)
Cuneus_L (2)
Calcarine_L (2) 
 NRG1 (rs6994992) 
  TT > Other 746 ∞ <0.0001 <0.0001 Temporal_sup_R (430)
Temporal_mid_R (301)
Temporal_pole_sup_R (12)
Heschl_R (2)
Temporal_pole_mid_R (1) 
 598 8.91 <0.0001 <0.0001 Temporal_mid_L (367)
Temporal_sup_L (184)
Supramarginal_L (33)
Temporal_inf_L (14) 
 550 ∞ <0.0001 <0.0001 Frontal_mid_L (406)
Frontal_sup_L (144) 
 251 7.69 <0.0001 <0.0001 Rolandic_oper_R (197)
Heschl_R (51)
Insula_R (3) 
 131 6.22 <0.0001 <0.0001 Paracentral_lobule_R (109)
Supp_motor_area_R (17)
Precuneus_R (5) 
 126 5.72 <0.0001 <0.0001 Temporal_sup_L (57)
Temporal_mid_L (56)
Insula_L (12)
Temporal_pole_sup_L (1) 
 124 5.13 <0.0001 <0.0001 Temporal_mid_L (112) 
  TT < Other 4192 ∞ <0.0001 <0.0001 Temporal_inf_L (1577)
Temporal_mid_L (1479)
Temporal_pole_sup_L (312)
Temporal_sup_L (294)
Temporal_pole_mid_L (262)
Frontal_inf_orb_L (192)
Fusiform_L (74)
Rolandic_oper_L (2) 
2564 ∞ <0.0001 <0.0001 Temporal_inf_R (1004)
Temporal_mid_R (568)
Temporal_sup_R (290)
Rolandic_oper_R (149)
Temporal_pole_mid_R (136)
Precentral_R (99)
Frontal_inf_oper_R (88)
Fusiform_R (83)
Temporal_pole_sup_R (69)
Postcentral_R (64)
Frontal_inf_tri_R (11)
Heschl_R (3) 
10 2163 ∞ <0.0001 <0.0001 Parahippocampal_L (656)
Hippocampus_L (394)
Lingual_L (329)
Caudate_L (215)
Olfactory_L (118)
Fusiform_L (77)
Putamen_L (71)
Precuneus_L (69)
Insula_L (57)
Amygdala_L (50)
Pallidum_L (44)
Cingulum_post_L (35)
Frontal_inf_orb_L (30)
Thalamus_L (6)
Frontal_sup_orb_L (6)
Rectus_L (6) 
11 576 ∞ <0.0001 <0.0001 Cuneus_R (180)
Calcarine_L (166)
Calcarine_R (103)
Lingual_R (57)
Cuneus_L (40)
Occipital_sup_R (30) 
12 394 6.65 <0.0001 <0.0001 Cuneus_L (140)
Cuneus_R (129)
Occipital_sup_R (51)
Occipital_sup_L (46)
Precuneus_L (28) 
 13 385 7.59 <0.0001 <0.0001 Insula_L (219)
Frontal_inf_tri_L (126)
Frontal_inf_oper_L (36)
Frontal_inf_orb_L (4) 
14 346 5.84 <0.0001 <0.0001 Supp_motor_area_R (127)
Supp_motor_area_L (104)
Frontal_sup_R (51)
Frontal_sup_medial_L (31)
Frontal_sup_medial_R (26)
Cingulum_mid_R (7) 
15 310 6.84 <0.0001 <0.0001 Frontal_inf_orb_R (99)
Putamen_R (86)
Insula_R (81)
Temporal_pole_sup_R (23)
Caudate_R (21) 
16 265 ∞ <0.0001 <0.0001 Insula_R (199)
Frontal_inf_oper_R (65)
Rolandic_oper_R (1) 
17 259 6.96 <0.0001 <0.0001 Parietal_inf_L (105)
Parietal_sup_L (104)
Occipital_mid_L (29)
Angular_L (21) 
18 183 ∞ <0.0001 <0.0001 Parahippocampal_R (133)
Amygdala_R (30)
Hippocampus_R (20) 
19 181 ∞ <0.0001 <0.0001 Precentral_R (158)
Frontal_mid_R (20)
Postcentral_R (3) 
20 179 7.33 <0.0001 <0.0001 Occipital_mid_R (93)
Occipital_sup_R (77)
Angular_R (9) 
21 155 5.16 <0.0001 <0.0001 Cingulum_ant_L (82)
Cingulum_ant_R (52)
Frontal_sup_medial_L (18)
Cingulum_mid_R (3) 
22 148 ∞ <0.0001 <0.0001 Frontal_inf_orb_R (106)
Temporal_pole_sup_R (33)
Frontal_inf_tri_R (6)
Frontal_inf_oper_R (3) 
23 148 7.25 <0.0001 <0.0001 Occipital_mid_R (133)
Occipital_inf_R (12)
Temporal_mid_R (3) 
24 141 7.62 <0.0001 <0.0001 Precentral_R (120)
Postcentral_R (21) 
25 122 5.62 <0.0001 <0.0001 Paracentral_lobule_L (65)
Postcentral_L (49)
Precuneus_L (8) 
26 115 7.09 <0.0001 <0.0001 Parietal_inf_L (108)
Angular_L (7) 
27 110 6.91 <0.0001 <0.0001 Supramarginal_R (110) 
APOE 
 ɛ3ɛ3 < ɛ3ɛ4 2795 ∞ <0.0001 <0.0001 Parietal_sup_L (1088)
Parietal_inf_L (646)
Precuneus_L (347)
Occipital_sup_L (205)
Occipital_mid_L (191)
Postcentral_L (182)
Angular_L (80)
Cingulum_post_L (47)
Cuneus_L (9) 
960 ∞ <0.0001 <0.0001 Postcentral_R (731)
Parietal_inf_R (143)
Paracentral lobule_R (27)
Parietal_sup_R (22)
Supramarginal_R (20)
Precentral_R (16)
Precuneus_R (1) 
 449 7.52 <0.0001 <0.0001 Angular-R (199)
Occipital_sup_R (106)
Parietal_sup_R (120)
Parietal_inf_R (9)
Precuneus_R (3)
Occipital_mid_R (9)
Cuneus_R (3) 
 376 ∞ <0.0001 <0.0001 Precentral_R (298)
Frontal_mid_R (76)
Postcentral_R (2) 
240 ∞ <0.0001 <0.0001 Postcentral_L (220)
Precentral_L (20) 
237 ∞ <0.0001 <0.0001 Frontal_inf_oper_R (207)
Frontal_mid_R (15)
Frontal_inf_tri_R (12)
Precentral_R (3) 
155 8.75 <0.0001 <0.0001 Frontal_sup_orb_L (89)
Rectus_L (57)
Frontal_mid_orb_L (8)
Frontal_med_orb_L (1) 
135 6.85 <0.0001 <0.0001 Paracentral_lobule_R (34)
Paracentral_lobule_L (28)
Supp_motor_area_R (22)
Precuneus_L (22)
Precuneus_R (12)
Cingulum_mid_R (12)
Cingulum_mid_L (5) 
134 7.41 <0.0001 <0.0001 Rolandic_oper_R (48)
Supramarginal_R (42)
Postcentral_R (37)
Temporal_sup_R (6)
Heschl_R (1) 
10 114 5.46 <0.0001 <0.0001 Precuneus_R (63)
Parietal_sup_R (48)
Precuneus_L (3) 
11 102 8.13 <0.0001 <0.0001 Frontal_sup_R (101)
Frontal_mid_R (1) 
12 100 7.27 <0.0001 <0.0001 Temporal_inf_R (55)
Occipital_inf_R (45) 
 ɛ3ɛ3 > ɛ3ɛ4 13 514 ∞ <0.0001 <0.0001 Fusiform_L (125)
Hippocampus_L (117)
Temporal_inf_L (90)
Temporal_mid_L (71)
Temporal_sup_L (68)
Parahippocampal_L (43) 
14 188 ∞ <0.0001 <0.0001 Frontal_mid_L (143)
Frontal_sup_L (39)
Precentral_L (6) 
15 133 ∞ <0.0001 <0.0001 Frontal_inf_oper_R (118)
Frontal_inf_tri_R (10)
Frontal_mid_R (5) 
16 123 ∞ <0.0001 0.0002 Fusiform_R (67)
Parahippocampal_R (48)
Hippocampus_R (8) 
17 109 ∞ <0.0001 <0.0001 Parietal_sup_R (107)
Precuneus_R (2) 
ESR1 (rs9340799) 
 AA > Other 195 6.52 <0.0001 <0.0001 Parietal_sup_L (119)
Parietal_inf_L (47)
Occipital_mid_L (19)
Angular_L (10) 
184 5.16 <0.0001 <0.0001 Postcentral_R (178)
Parietal_inf_R (6) 
162 6.48 <0.0001 <0.0001 Frontal_mid_R (16)
Frontal_inf_oper_R (138)
Frontal_inf_tri_R (8) 
111 4.56 <0.0001 <0.0001 Precentral_R (106)
Frontal_Mid_R (5) 
102 ∞ <0.0001 <0.0001 Postcentral_R (35)
Temporal_sup_R (30)
Rolandic_oper_R (27)
Precentral_R (8)
Heschl_R (2) 
 AA < Other 3549 ∞ <0.0001 <0.0001 Frontal_sup_R (1260)
Frontal_mid_R (1154)
Supp_motor_area_R (558)
Precentral_R (294)
Frontal_inf_oper_R (136)
Frontal_sup_medial_R (128)
Frontal_inf_tri_R (14)
Postcentral_R (5) 
 2400 ∞ <0.0001 <0.0001 Frontal_mid_L (876)
Frontal_sup_L (738)
Supp_motor_area_L (542)
Frontal_sup_medial_L (194)
Precentral_L (50) 
499 8.62 <0.0001 <0.0001 Supramarginal_R (317)
Parietal_inf_R (77)
Postcentral_R (61)
Angular_R (36)
Rolandic_oper_R (8) 
385 8.03 <0.0001 <0.0001 Paracentral_lobule_R (235)
Precentral_R (83)
Postcentral_R (60)
Precuneus_R (7) 
10 287 6.34 <0.0001 <0.0001 Postcentral_L (188)
Supramarginal_L (51)
Parietal_inf_L (46)
Precentral_L (2) 
11 229 8.75 <0.0001 <0.0001 Precentral_L (148)
Postcentral_L (81) 
12 198 ∞ <0.0001 <0.0001 Postcentral_L (133)
Precentral L (41)
Paracentral_lobule_L (21)
Precuneus_L (3) 
13 153 6.97 <0.0001 <0.0001 Supramarginal_R (99)
Postcentral_R (54) 
14 139 ∞ <0.0001 <0.0001 Postcentral_L (120)
Parietal_inf_L (15)
Precentral_L (4) 
15 120 ∞ <0.0001 <0.0001 Frontal_sup_L (74)
Precentral_L (46) 
16 104 6.44 <0.0001 <0.0001 Precentral_R (53)
Postcentral_R (51) 
ESR1 (rs2234693) None 
BDNF (rs6265) 
 GG(Val/Val) > Other 262 7.73 <0.0001 <0.0001 Occipital_inf_R (156)
Occipital_mid_R (102)
Calcarine_R (4) 
172 ∞ <0.0001 <0.0001 Parahippocampal_L (66)
Fusiform_L (62)
Hippocampus_L (37)
Temporal_inf_L (7) 
 GG(Val/Val) < Other 682 8.91 <0.0001 <0.0001 Postcentral_R (450)
Parietal_inf_R (183)
Supramarginal_R (26)
Parietal_sup_R (21)
Angular_R (2) 
298 7.60 <0.0001 <0.0001 Precentral_R (243)
Frontal_mid_R (46)
Postcentral_R (9) 
200 6.91 <0.0001 <0.0001 Parietal_sup_L (126)
Parietal_inf_L (40)
Angular_L (20)
Occipital_mid_L (14) 
169 6.83 <0.0001 <0.0001 Frontal_inf_oper_R (141)
Frontal_inf_tri_R (14)
Frontal_mid_R (14) 
147 5.61 <0.0001 <0.0001 Postcentral_L (147) 
146 6.45 <0.0001 <0.0001 Parietal_inf_L (89)
Parietal_sup_L (34)
Postcentral_L (23) 
113 6.02 <0.0001 <0.0001 Frontal_inf_tri_R (62)
Frontal_inf_oper_R (49)
Frontal_mid_R (2) 
GAD1 (rs2270335) None 

Note: numbers in parentheses represent the number of voxels in that particular region.

For DISC1 (rs821616), TBM identified multiple areas of reduced volume in Ser/Ser homozygotes in the frontal lobes, particularly in supplementary motor areas, paracentral lobules, and the right medial superior frontal gyrus, and to a lesser extent, in the occipital lobes and lateral temporal cortex (Table 5 and Fig. 1).

Figure 1.

The effect of DISC1 (rs821616) genotype on brain structure. Upper images show the locations of significant GM decreases (blue) in Ser/Ser homozygotes when compared with Cys carriers projected onto surface-rendered views of the left and right hemispheres; lateral view (top) and medial view (middle). Bottom images are selected 2-dimensional (2-D) slices (axial and sagittal) with significant clusters displayed on the T2 atlas of the neonate brain. Color bar gives the t-value at each voxel. Blue/green indicates Ser/Ser <Cys carrier.

Figure 1.

The effect of DISC1 (rs821616) genotype on brain structure. Upper images show the locations of significant GM decreases (blue) in Ser/Ser homozygotes when compared with Cys carriers projected onto surface-rendered views of the left and right hemispheres; lateral view (top) and medial view (middle). Bottom images are selected 2-dimensional (2-D) slices (axial and sagittal) with significant clusters displayed on the T2 atlas of the neonate brain. Color bar gives the t-value at each voxel. Blue/green indicates Ser/Ser <Cys carrier.

For COMT, TBM revealed multiple clusters of reduced volume in Val/Val homozygotes in the temporal cortex that included portions of the right hippocampus, right parahippocampus, right fusiform, and bilateral middle and superior temporal gyri. One cluster of reduced volume was identified in the right occipital lobe and consisted primarily of calcarine and lingual areas, and another cluster of reduced volume was identified in the right parietal lobe (precuneus) and overlapped with the posterior cingulate. TBM also revealed multiple clusters of increased volumes in Val/Val homozygotes, including the supplementary motor areas and parietal and occipital cortex (Table 5 and Fig. 2).

Figure 2.

The effect of COMT (rs4680) genotype on brain structure. Upper images show the locations of GM increases (red) and decreases (blue) in Val/Val homozygotes when compared with Met carriers projected onto surface-rendered views of the left and right hemispheres; lateral view (top) and medial view (middle). Bottom images are selected 2D slices (1 coronal and 2 sagittal) with significant clusters displayed on the atlas of the neonate brain. Color bar gives the t-value at each voxel. Red/yellow indicates Val/Val > other. Blue/green indicates Val/Val < other.

Figure 2.

The effect of COMT (rs4680) genotype on brain structure. Upper images show the locations of GM increases (red) and decreases (blue) in Val/Val homozygotes when compared with Met carriers projected onto surface-rendered views of the left and right hemispheres; lateral view (top) and medial view (middle). Bottom images are selected 2D slices (1 coronal and 2 sagittal) with significant clusters displayed on the atlas of the neonate brain. Color bar gives the t-value at each voxel. Red/yellow indicates Val/Val > other. Blue/green indicates Val/Val < other.

For NRG1 (rs35753505), TBM identified 5 clusters where TT homozygotes had reduced volumes, which encompassed temporal, parietal, and occipital regions. For NRG1 (rs6994992), TBM identified 7 clusters where TT homozygotes had greater volume than other genotypes, primarily in the frontal and temporal lobes. TBM also identified 20 clusters of reduced volumes in TT homozygotes, which were widely distributed across the brain with 2 large clusters in the left temporal cortex (Table 5 and Fig. 3).

Figure 3.

The effect of NRG1 (rs35753505 and rs6994992) genotype on brain structure. (a) rs35753505. Upper images show the locations of GM decreases (blue) in TT homozygotes when compared with C carriers projected onto surface-rendered views of the left lateral and right medial hemispheres, and a selected 2D axial slice with significant clusters displayed on the atlas of the neonate brain. Color bar gives the t-value at each voxel. Blue/green indicates TT < Other. (b) rs6994992. Upper images show the locations of GM increases (red) and decreases (blue) in TT homozygotes when compared with C carriers projected onto surface-rendered views of the left and right hemispheres; lateral view (top) and medial view (middle). Bottom images are selected 2D slices (axial and coronal) with significant clusters displayed on the atlas of the neonate brain. Color bar gives the t-value at each voxel. Red/yellow indicates TT > other. Blue/green indicates TT < Other.

Figure 3.

The effect of NRG1 (rs35753505 and rs6994992) genotype on brain structure. (a) rs35753505. Upper images show the locations of GM decreases (blue) in TT homozygotes when compared with C carriers projected onto surface-rendered views of the left lateral and right medial hemispheres, and a selected 2D axial slice with significant clusters displayed on the atlas of the neonate brain. Color bar gives the t-value at each voxel. Blue/green indicates TT < Other. (b) rs6994992. Upper images show the locations of GM increases (red) and decreases (blue) in TT homozygotes when compared with C carriers projected onto surface-rendered views of the left and right hemispheres; lateral view (top) and medial view (middle). Bottom images are selected 2D slices (axial and coronal) with significant clusters displayed on the atlas of the neonate brain. Color bar gives the t-value at each voxel. Red/yellow indicates TT > other. Blue/green indicates TT < Other.

For APOE, TBM revealed that ɛ3ɛ4 heterozygotes had significant reductions in volumes in temporal regions, including bilateral hippocampus, parahippocampus, and fusiform, and middle and inferior temporal gyri when compared with ɛ3 homozygotes and reductions in frontal and parietal lobes. ɛ3ɛ4 heterozygotes had significantly greater volumes in extensive areas of the parietal lobe and, to a lesser extent, in frontal and occipital cortex (Table 5 and Fig. 4).

Figure 4.

The effect of APOE genotype on brain structure. Upper images show the locations of GM increases (red) and decreases (blue) in ɛ3ɛ4 heterozygotes when compared with ɛ3ɛ3 homozygotes projected onto surface-rendered views of the left and right hemispheres; lateral view (top) and medial view (middle). Bottom images are selected 2D slices (coronal and sagittal) with significant clusters displayed on the atlas of the neonate brain. Color bar gives the t-value at each voxel. Red/yellow indicates ɛ3ɛ4 > ɛ3ɛ3. Blue/green indicates ɛ3ɛ4 < ɛ3ɛ3.

Figure 4.

The effect of APOE genotype on brain structure. Upper images show the locations of GM increases (red) and decreases (blue) in ɛ3ɛ4 heterozygotes when compared with ɛ3ɛ3 homozygotes projected onto surface-rendered views of the left and right hemispheres; lateral view (top) and medial view (middle). Bottom images are selected 2D slices (coronal and sagittal) with significant clusters displayed on the atlas of the neonate brain. Color bar gives the t-value at each voxel. Red/yellow indicates ɛ3ɛ4 > ɛ3ɛ3. Blue/green indicates ɛ3ɛ4 < ɛ3ɛ3.

For ESR1 (rs9340799), TBM identified multiple regions of decreased volume in AA homozygotes in the frontal lobes (bilaterally), particularly in the frontal superior and middle gyri. Another large cluster was located in the right parietal cortex and consisted primarily of supramarginal gyrus. There were also a number of smaller clusters, broadly distributed across the brain, where AA homozygotes showed increased volumes (Table 5 and Fig. 5).

Figure 5.

The effect of ESR1 (rs9340799) genotype on brain structure. Upper images show the locations of GM increases (red) and decreases (blue) in AA homozygotes when compared with G carriers projected onto surface-rendered views of the left and right hemispheres; lateral view (top) and medial view (middle). Bottom image is a selected 2D axial slice with significant clusters displayed on the atlas of the neonate brain. Color bar gives the t-value at each voxel. Red/yellow indicates AA homozygotes > G carriers. Blue/green indicates AA homozygotes < G carriers.

Figure 5.

The effect of ESR1 (rs9340799) genotype on brain structure. Upper images show the locations of GM increases (red) and decreases (blue) in AA homozygotes when compared with G carriers projected onto surface-rendered views of the left and right hemispheres; lateral view (top) and medial view (middle). Bottom image is a selected 2D axial slice with significant clusters displayed on the atlas of the neonate brain. Color bar gives the t-value at each voxel. Red/yellow indicates AA homozygotes > G carriers. Blue/green indicates AA homozygotes < G carriers.

For BDNF, TBM identified 2 clusters of increased volume in Val/Val homozygotes, 1 primarily consisting of the right occipital cortex and 1 consisting of the portions of left hippocampus, parahippocampus, fusiform gyrus, and inferior temporal gyrus. TBM also revealed multiple clusters of decreased volume in Val/Val homozygotes, particularly in the primary motor and somatosensory cortex (Table 5 and Fig. 6).

Figure 6.

The effect of BDNF genotype on brain structure. Upper images show the locations of GM increases (red) and decreases (blue) in Val/Val homozygotes when compared with Met carriers projected onto surface-rendered views of the left and right hemispheres, lateral view. Bottom images are selected 2D sagittal slices with significant clusters displayed on the atlas of the neonate brain. Color bar gives the t-value at each voxel. Red/yellow indicates Val/Val > other. Blue/green indicates Val/Val < other.

Figure 6.

The effect of BDNF genotype on brain structure. Upper images show the locations of GM increases (red) and decreases (blue) in Val/Val homozygotes when compared with Met carriers projected onto surface-rendered views of the left and right hemispheres, lateral view. Bottom images are selected 2D sagittal slices with significant clusters displayed on the atlas of the neonate brain. Color bar gives the t-value at each voxel. Red/yellow indicates Val/Val > other. Blue/green indicates Val/Val < other.

No associations were identified for DISC1 (rs6675281), ESR1 (rs2234693), or GAD1.

Results are highly similar when controlling for parental psychiatric history, when including only 1 individual from each MZ pair [with the exception of ESR1 (rs9340799, AA > Other) and BDNF], and when controlling for birthweight (Supplementary Tables S5–S7).

Discussion

This study demonstrates that common genetic polymorphisms in putative psychiatric risk genes predict individual differences in brain structure shortly after birth. Some effects are highly similar to those reported in adults. As genetic variation interacts with experiential and environmental factors dynamically, varying across developmental stages, this represents a surprising and important finding. These variants and the associated neuroimaging phenotypes likely represent stable markers of risk and highlight the critical role of the perinatal period in the etiology of mental illness. Other findings are unique to this developmental period and may represent transient biomarkers of risk. Finally, by comparing our results with the existing literature, we can identify relationships that have been reported in adolescents/adults, which were not present in neonates. We hypothesize that these variants influence brain development and psychiatric risk in later childhood or adolescence and as such may be particularly promising targets for intervention.

As regards DISC1, Ser/Ser homozygotes (rs821616) exhibited reduced volumes in the frontal lobes, particularly in the medial superior frontal gyrus and supplementary motor areas, as well as in the lateral temporal cortex including the superior and middle temporal gyri. Reduced volumes and cortical thickness in the superior medial frontal gyrus (Takahashi et al. 2009; Brauns et al. 2011) have been reported in young adults, and reduced cortical thickness in supplementary motor and lateral temporal cortices has been reported in adolescents (Raznahan, Lee, et al. 2011) in Ser/Ser homozygotes. It is notable that multiple meta-analyses have shown GM reductions in the medial frontal cortex and superior temporal gyri in schizophrenia (Fornito et al. 2009; Ellison-Wright and Bullmore 2010; Bora et al. 2011). Abnormalities in the medial frontal cortex may be related to negative symptoms, while that in superior temporal gyri may be related to positive symptoms (Bora et al. 2011). Meta-analyses also suggest that middle temporal gyri are reduced in bipolar disorder (Yu et al. 2010; Selvaraj et al. 2012) with possible consequences for the experience of emotion (Selvaraj et al. 2012). We also observed local reductions in the occipital lobes in this group, which have not been previously reported. We did not observe increased volumes in the cingulate cortex of Ser/Ser homozygotes, which have been reported in middle-aged adults (Hashimoto et al. 2006). We did not observe any effects of rs6675281 despite reports in adolescents and adults that Phe carriers demonstrate multiple areas of reduced volume and/or cortical thickness (Szeszko et al. 2008; Brauns et al. 2011; Raznahan, Lee, et al. 2011). Intriguingly, studies in adolescents suggest very little overlap in cortical regions whose maturation is associated with rs821616 and rs6675281 (Raznahan, Lee, et al. 2011). As DISC1 interacts with numerous proteins implicated in neurodevelopment (Porteous et al. 2011), the divergent impact of these polymorphisms in terms of their regional and temporal effects may reflect regional and temporal differences in key signaling pathways. There is evidence that rs821616 exerts its effects through altered binding to centrosomal-associated nuclear distribution element-like 1 (NDEL1) and its homolog, NDE1 (Burdick et al. 2008), while rs6675281 disrupts nuclear targeting and regulation of activating transcription factor 4 (ATF4)-mediated transcription (Malavasi et al. 2012).

For COMT, our findings of reduced volumes in the temporal lobes, including right hippocampus, are highly similar to previous reports in adolescents and adults (Ohnishi et al. 2006; Taylor et al. 2007; Cerasa et al. 2008; Mechelli et al. 2009; Shaw et al. 2009). Our finding of increased parietal volumes in Val/Val homozygotes is also consistent with a previous report (Dumontheil et al. 2011). As noted previously, alterations in the temporal lobes are frequently observed in schizophrenia. Parietal alterations have also been reported, albeit less consistently (Fornito et al. 2009). In addition, functional neuroimaging studies suggest that key cognitive impairments in schizophrenia, including working memory deficits, might reflect fronto-parietal and frontal-temporal disconnections (Peled et al. 2001; Karlsgodt et al. 2008; Henseler et al. 2010). Our results suggest that variation in COMT effects neural systems implicated in the pathophysiology of schizophrenia before birth. We did not observe effects on dorsolateral prefrontal cortex, which is implicated in functional MRI studies of COMT and which has been implicated in several structural studies (Cerasa et al. 2008; Shaw et al. 2009). In addition, TBM revealed significant effects in the occipital cortex that have not been reported in older samples. Age-specific effects may reflect developmental changes in the distribution of catecholamines and their receptors in the brain and/or emerge as a result of genotype modulating the tempo of cortical maturation. In regard to the former, postmortem studies suggest that dopamine concentration and dopamine receptor 2 levels are higher in neonates than in older individuals (Weickert et al. 2007). In regard to the latter, COMT genotype is associated with cortical thickness changes in prefrontal, temporal, and superior parietal regions across adolescence (Raznahan, Greenstein, et al. 2011).

Four studies have examined whether SNPs in NRG1 are associated with brain tissue volumes. Winterer et al. (2008) found no effect of rs35753505 on brain tissue volumes using VBM, although they reported a relationship with subcortical medial frontal WM microstructure as indexed by DTI. Mata et al. (2009) reported an association of rs6994992 with ventricle volume in adult schizophrenics, but did not observe effects on frontal or parietal volumes in schizophrenics or a small sample of controls (n = 16). Addington et al. (2007) reported that a microsatellite in NRG1 was associated with total GM volume in children with childhood-onset schizophrenia, but not in controls. Tosato et al. (2012) reported an association between rs4623364 and volume of the left superior temporal gyrus, but not the right superior temporal gyrus, in chronic schizophrenia (other regions were not examined and no controls were included). In contrast, we observed several clusters where TT homozygotes (rs35753505) had reduced volumes. We also observed extensive effects of rs6994992. Additional comparative studies in adolescents and adults are needed to clarify whether these are developmentally limited effects or stable ones. However, these variants may be of particular relevance to the perinatal period. rs35753505 has recently been associated with NRG1 mRNA levels in human umbilical venous endothelial cells, and the C allele has been implicated as a protective factor against cerebral palsy and developmental delay in children born preterm (Hoffmann et al. 2011). rs6994992 may impact on type IV NRG1 expression, a transcript which is abundantly expressed in fetal brain (Tan et al. 2007).

For APOE, our findings of reduced volumes in the temporal cortex of ɛ4 carriers are highly similar to those reported in the elderly (Plassman et al. 1997; Lemaitre et al. 2005; Hua et al. 2008; Biffi et al. 2010). Reduced cortical thickness in the medial temporal cortex has also been reported in adults (Burggren et al. 2008), children, and adolescents (Shaw et al. 2007) carrying the ɛ4 variant, with the most consistent results reported for the thickness of entorhinal cortex (here included in the parahippocampal region). Our results indicate that the ɛ4 variant's contribution to brain phenotypes associated with Alzheimer's risk is likely present before birth and represents a stable risk factor. The mechanisms underlying the particular sensitivity of medial temporal cortex to the ɛ4 variant are not fully understood. Presynaptic derangements in APOE-deficient mice are a function of the distance between nerve terminals and their cell bodies and of neuronal class (Chapman and Michaelson 1998), and experiments in transgenic mice have demonstrated that the ɛ4 variant inhibits synaptogenesis following environmental enrichment in hippocampus and entorhinal cortex, but not in motor or auditory cortex (Levi et al. 2005). It should be noted that, despite its relation to cognitive decline in the elderly (Schiepers et al. 2012), APOE genotype does not affect intelligence quotient or school attainment test scores in late childhood (Taylor et al. 2011), and some studies have documented early cognitive benefits of the ɛ4 allele (Wright et al. 2003). While the relationship between regional cortical volume and cognitive performance at early ages remains to be clarified, it is possible that the increased volumes we observed in ɛ3ɛ4 infants represent compensatory changes or beneficial effects that result in balancing selection.

Regarding ESR1, a study of rs9340799 in postmenopausal women reported increased volume in AA homozygotes in the middle temporal cortex, occipital cortex, and cerebellum (Boccardi et al. 2008), which do not appear to overlap with the regions in which we observed increased volumes, although the A allele of rs9340799 was associated with increased ICV. The same study reported no regions of decreased volume in AA homozygotes, whereas we observed extensive regions of decreased volume in AA homozygotes, suggesting that this polymorphism exerts distinct effects dependent on age and/or hormonal status. rs2234693 has been reported to predict amygdala volume in elderly women, but not in elderly men (den Heijer et al. 2004). We observed no effects of this variant in neonates. We did not stratify our analyses by sex as the fetal ovary is generally considered inactive until late in development and, both male and female fetuses are exposed to high levels of estrogen produced by the placenta (Grumbach et al. 2003). Sex-specific effects are certainly of research interest, but would best be tested in a larger sample.

Regarding BDNF, adult studies of rs6265 have shown reduced volumes in Met carriers in diverse cortical regions (Ho et al. 2006; Takahashi et al. 2008; Montag et al. 2009), though results are inconsistent across studies with the exception of the hippocampus (Pezawas et al. 2004; Bueller et al. 2006; Frodl et al. 2007; Montag et al. 2009). We observed 2 clusters of reduced volume in Met carriers, one in the right occipital cortex and another in the temporal cortex, which did overlap with the hippocampus. Hippocampal dysfunction is thought to play a key role in a broad range of psychiatric conditions including depression, schizophrenia, and Alzheimer's (Small et al. 2011). We also observed multiple clusters of decreased volume in Val/Val homozygotes. The lack of consistency between studies could arise from age differences in the effect of BDNF genotype on brain structure. Participating children are returning for the follow-up neuroimaging scans and detailed developmental assessments at 1, 2, 4, and 6 years of age, which will ultimately allow us to test for age by genotype interactions and to determine the functional impact of variants on positive and problematic behaviors.

The G allele of GAD1 (rs2270335) has been associated with frontal GM volume loss in childhood-onset schizophrenia (Addington et al. 2005). We did not see any association of this variant with neonatal brain structure.

In conclusion, we have shown that variation in putative psychiatric risk genes affects neural systems implicated in the pathophysiology of psychiatric disorders prior to birth. As this is the first study to test this possibility, replication in independent samples and in other ethnic groups is critical. Larger studies would also provide more power for the detection of additive effects and gene–gene and gene–environment interactions. In addition, a better understanding of the mechanisms through which these variants impact cortical development in a localized and temporally specific manner would provide important context for the current results and could be achieved through innovative animal models. We also note that the targeted imaging genetics strategy used in this study, which focuses on a discrete set of common functional variants, is limited in that the variants we investigated likely represent a very small fraction of all variants involved in the development of human brain. Testing additional candidate variants would clearly be of interest, but it could also be extremely fruitful to directly relate genetic variants to early brain development on a genome-wide scale without being constrained by current biological knowledge. Neuroimaging of infants with rare and highly penetrant genetic variants associated with psychiatric disorders would also be of interest, given recent studies demonstrating that such variants make a larger contribution to psychiatric risk than previously recognized (Hosak et al. 2012). Nevertheless, the targeted imaging genetics strategy continues to be a powerful method for elucidating how genetic variants impact brain structure, function, and vulnerability to disease (Meyer-Lindenberg and Weinberger 2006; Tan et al. 2008; Bigos and Weinberger 2010). Our findings contribute to our understanding of how risk genes alter neurodevelopmental trajectories and provide new biological evidence for how early on in life the roots of adolescent/adult emergent disorders might lie.

Supplementary Material

Supplementary material can be found at: http://www.cercor.oxfordjournals.org/

Funding

This work was supported by the National Institutes of Health (MH064065 and MH070890 to J.H.G., MH083045 and MH092335 to R.C.K., HD03110 and MH091645 to M.S., and RR025747, P01CA142538, MH086633, EB005149, and AG033387 to H.Z.).

Notes

We thank the participating families who made this project possible as well as the staff of the UNC MRI Research Center, the UNC Neuro Image Research and Analysis Laboratories, and the UNC Early Brain Development Program. Conflict of Interest: None declared

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