How to Cite this Article: Whalley HC, Sussmann JE, Johnstone M, Romaniuk L, Redpath H, Chakirova G, Mukherjee P, Hall J, Johnstone EC, Lawrie SM, McIntosh AM. 2012. Effects of a Mis-Sense DISC1 Variant on Brain Activation in Two Cohorts at High Risk of Bipolar Disorder or Schizophrenia. Am J Med Genet Part B 159B:343–353.
Effects of a mis-sense DISC1 variant on brain activation in two cohorts at high risk of bipolar disorder or schizophrenia†
Article first published online: 15 FEB 2012
Copyright © 2012 Wiley Periodicals, Inc.
American Journal of Medical Genetics Part B: Neuropsychiatric Genetics
Volume 159B, Issue 3, pages 343–353, April 2012
How to Cite
Whalley, H. C., Sussmann, J. E., Johnstone, M., Romaniuk, L., Redpath, H., Chakirova, G., Mukherjee, P., Hall, J., Johnstone, E. C., Lawrie, S. M. and McIntosh, A. M. (2012), Effects of a mis-sense DISC1 variant on brain activation in two cohorts at high risk of bipolar disorder or schizophrenia. Am. J. Med. Genet., 159B: 343–353. doi: 10.1002/ajmg.b.32035
- Issue published online: 7 MAR 2012
- Article first published online: 15 FEB 2012
- Manuscript Accepted: 24 JAN 2012
- Manuscript Received: 8 JUL 2011
- Dorothy Hodgkin Fellowship from the Royal Society. Grant Number: DH080018
- Health Foundation through a Clinician Scientist Fellowship. Grant Number: 2268/4295
- Clinical Research Training Fellowship from the Wellcome Trust
- Senior Clinical Fellowship from the Chief Scientists Office in Scotland
- Academy of Medical Sciences/Wellcome Trust. Grant Number: R41455
- The Dr Mortimer and Theresa Sackler Foundation
- bipolar disorder;
- Top of page
- MATERIALS AND METHODS
- Supporting Information
Bipolar disorder and schizophrenia share a number of clinical features and genetic risk variants of small effect, suggesting overlapping pathogenic mechanisms. The effect of single genetic risk variants on brain function is likely to differ in people at high familial risk versus controls as these individuals have a higher overall genetic loading and are therefore closer to crossing a threshold of disease liability. Therefore, whilst the effects of genetic risk variants on brain function may be similar across individuals at risk of both disorders, they are hypothesized to differ compared to that seen in control subjects. We sought to examine the effects of the DISC1 Leu607Phe polymorphism on brain activation in young healthy individuals at familial risk of bipolar disorder (n = 84), in a group of controls (n = 78), and in a group at familial risk of schizophrenia (n = 47), performing a language task. We assessed whether genotype effects on brain activation differed according to risk status. There was a significant genotype × group interaction in a cluster centered on the left pre/postcentral gyrus, extending to the inferior frontal gyrus. The origin of this genotype × group effect originated from a significant effect of the presumed risk variant (Phe) on brain activation in the control group, which was absent in both high-risk groups. Differential effects of this polymorphism in controls compared to the two familial groups suggests a commonality of effect across individuals at high-risk of the disorders, which is likely to be dependant upon existing genetic background. © 2012 Wiley Periodicals, Inc.
- Top of page
- MATERIALS AND METHODS
- Supporting Information
Bipolar disorder (BD) and schizophrenia (SCZ) are common, disabling psychiatric disorders each of which affect approximately 1 in 100 people respectively during their lifetime. Although each disorder has characteristic clinical features, there is considerable overlap in symptoms found in individuals with each diagnosis [Siris, 2000; Keck et al., 2003; Murray et al., 2004; Kempf et al., 2005; Bora et al., 2008]. Common neurocognitive deficits have also been found in several domains including executive function, although these are typically of smaller magnitude in BD than in SCZ [Krabbendam et al., 2005; Bora et al., 2008]. Similarly, these disorders share many abnormalities in brain structure and function, particularly in prefrontal regions [Lawrie and Abukmeil, 1998; Shenton et al., 2001; Phillips et al., 2003; Strakowski et al., 2005; Glahn et al., 2008; Arnone et al., 2009].
The importance of genetic factors in both SCZ and BD is also now well established [Kendler et al., 1994; Cannon et al., 1998; Cardno et al., 1999; McGuffin et al., 2003]. Indeed, unaffected relatives, who share a proportion of genetic material with the affected patient, also display several trait-related neurobiological markers of illness. These provide insight into the underlying disease mechanisms conferred by genetic risk factors, in the absence of medication and other treatment and illness related effects. Nevertheless, it should be noted that studies on unaffected relatives also invariably involve aspects of both shared genetic and environmental risk factors. There is evidence from structural and functional imaging studies that relatives of affected individuals share similar, although less extreme, abnormalities than those of patients [Whalley et al., 2005; Fusar-Poli et al., 2007]. They have also been shown to demonstrate similar but less severe deficits in cognitive functioning, including verbal fluency, an executive task known to involve prefrontal brain regions [Szoke et al., 2005; Snitz et al., 2006; Arts et al., 2008].
Several specific genetic risk factors have now been shown to contribute to both disorders [Park et al., 2004; Craddock et al., 2005, 2009; Lichtenstein et al., 2009; Moskvina et al., 2009; Purcell et al., 2009; Huang et al., 2010]. Common genetic risk factors, together with evidence of shared perturbations of brain function and structure further suggests the presence of at least partially convergent pathophysiological mechanisms [Craddock and Owen, 2010]. One such candidate is the gene Disrupted in Schizophrenia 1 (DISC1) [Blackwood et al., 2001, 2007; Hennah et al., 2003, 2009; Hodgkinson et al., 2004; Chubb et al., 2008; Schosser et al., 2010].
DISC1 functions as a molecular scaffold protein interacting with other proteins contributing multiple neural processes involved in early corticogenesis [Millar et al., 2005; Porteous et al., 2006; Mao et al., 2009; Ming and Song, 2009]. DISC1 was first identified at the breakpoint of a balanced t(1;11) chromosomal translocation that co-segregated with schizophrenia and other affective disorders [St Clair et al., 1990; Millar et al., 2000]. Further studies demonstrated that DISC1 was disrupted at the chromosomal breakpoint [Millar et al., 2000] and that family members who carried the translocation showed deficits in core brain processing as measured by event-related potentials (ERP) [Blackwood et al., 2001]. Subsequently, independent evidence for the involvement of the DISC1 locus in SCZ and BD has emerged from linkage, case–control, and family-based association studies in different populations [Hamshere et al., 2005; Hashimoto et al., 2006; Chubb et al., 2008]. One association study identified that a mis-sense mutation at the single nucleotide polymorphism (SNP) that resulted in a phenylalanine for leucine substitution at position 607 (Leu607Phe, rs6675281) which was overrepresented in patients (including SCZ and BD) compared to healthy individuals [Hodgkinson et al., 2004]. Variations in DISC1 have also been shown to affect brain morphology, cognitive function, and psychosis-related behavioral traits, particularly involving the prefrontal cortex [Callicott et al., 2005; Cannon et al., 2005; Hashimoto et al., 2006; Di Giorgio et al., 2008; Tomppo et al., 2009b; Prata et al., 2010]. They have also been associated with clinical features in schizophrenia [Hennah et al., 2003; DeRosse et al., 2007; Szeszko et al., 2008; Takahashi et al., 2009]. One recent study reported an absence of effect of DISC1 on prefrontal brain function in patients with BD and SCZ to that seen in healthy controls, suggesting differential effect of DISC1 mutation in controls versus patients [Prata et al., 2010].
A popular hypothesis regarding the genetic architecture underlying complex diseases such as BD and SCZ proposes that much of the genetic risk is due to the effects of many common genetic variants (termed the “common disease/common variant” hypothesis). Each common variant individually has a small additive effect until a threshold of genetic liability is reached and the individual expresses the full clinical syndrome. The penetrance of these individual common risk variants at the level of brain function may also depend on existing genetic loading, as individuals approach a threshold for liability the neural systems become unable to compensate for the accumulation of hits. Indeed the importance of genetic background is becoming increasingly realized, particularly in animal models of illness where mutations imposed on different strains can result in a different phenotypic expression [Gerlai, 2001; Linder, 2001; Wolfer et al., 2002].
In the current study we investigate such effects by examining the influence of a common mis-sense DISC1 variant (Leu607Phe) in two cohorts with increased genetic loading; one at familial risk of bipolar disorder and the other at familial risk of schizophrenia, against a group of healthy controls with no family history of either disorder. We used functional magnetic resonance imaging (fMRI) to measure activation during an executive function/language task—the Hayling sentence completion paradigm. This paradigm is considered an extension of verbal fluency tasks and has been shown to activate prefrontal brain regions, to distinguish patient populations, and is sensitive to activation differences relating to increased genetic liability to BD and SCZ [Whalley et al., 2004, 2011; McIntosh et al., 2008]. We hypothesized that DISC1 Leu607Phe may contribute to increased risk of bipolar disorder and schizophrenia by modifying prefrontal brain physiology. Specifically we hypothesized that the effects of the common Leu607Phe variant would depend upon genetic context. We anticipated that the presumed risk allele (Phe) would have a differential effect in the presence of a background of genetic risk than would be the case if there was an absence of other risk factors. We tested this by examining genotype-related brain activation in the three groups of subjects, testing specifically for significant genotype × group interactions. Further, if there are common mechanisms underlying genetic influences on both disorders, we hypothesized that these effects would not differ between the familial high-risk groups.
MATERIALS AND METHODS
- Top of page
- MATERIALS AND METHODS
- Supporting Information
The current study examined control subjects, a group at familial risk of bipolar disorder and a group at familial risk of schizophrenia. All subjects were recruited from the same geographical location, that is, Scotland-wide. The controls and bipolar high-risk group were recruited as part of the Scottish Bipolar Family Study, and the schizophrenia high-risk group was collected as part of the Edinburgh High Risk Study. Sensitive to the issue of different sample collections we proceeded cautiously by conducting two separate analyses which are described in detail below. (i) Initially we compared the controls and bipolar high-risk sample (recruited as part of the same study) to determine any differential effect of DISC1 between these groups. (ii) This was followed by an analysis including the third group, the schizophrenia high-risk sample. Potential confounding variables were included in the model.
The Bipolar Family study examines individuals at high familial risk for developing bipolar disorder. Individuals with a diagnosis of bipolar I disorder were identified from the caseloads of psychiatrists across Scotland. The presence of psychotic features in the proband was not an exclusion criteria, given the high prevalence of such features in the disorder. Diagnosis of affected subjects was confirmed with the OPCRIT (OPCRIT [McGuffin et al., 1991]) symptom checklist using data from clinical case notes and the structured clinical interview for DSM IV (SCID). Each affected subject was asked to identify members of close family aged 16–25 years. Following informed consent, unaffected individuals with at least one first degree, or two second degree relatives with bipolar I disorder were invited to participate. Unaffected, unrelated control subjects with no personal or family history of a psychiatric disorder were identified from the social networks of the bipolar high-risk subjects and group-matched on age, sex, parental education (classified as manual, nonmanual, or unclassified) and IQ to the high-risk group. Comparison subjects were also screened using the SCID.
The schizophrenia high-risk group was recruited as part of the Edinburgh High Risk Study [Hodges et al., 1999; Johnstone et al., 2000] a study of individuals at high familial risk of developing schizophrenia. Individuals with a diagnosis of schizophrenia were identified from the case loads of psychiatrists across Scotland. Individuals with schizoaffective disorder were excluded. Each affected individual was asked to identify members of close family aged 16–25 years. Diagnosis of affected subjects was confirmed using Operational Criteria Checklist [McGuffin et al., 1991]. Following informed consent, unaffected individuals with at least one first degree, or two-second degree relatives with schizophrenia were invited to participate.
The rationale for both studies was primarily to recruit individuals with an inherited predisposition for the disorders, above that seen in the general population, based on a positive proximal family history. In terms of schizophrenia the presence of a single first degree relative confers a lifetime disease risk of approximately 10%, for a second degree relative it is approximately 2–6%, which increases with additional relatives [Gottesman, 1991]. Measures for bipolar disorder are also in a similar range, for a first degree relative it is around 10%, and for a second degree relative it is also around 5%, increasing with additional relatives [Mortensen et al., 2003]. Therefore we consider it to be the case that risk is raised to both of the disorders in their respective cohorts is in the order of approximately 5–10%. Only unrelated individuals were included in the current analysis. Exclusion criteria for all groups included a personal history of major depression, mania or hypomania, psychosis, or any major neurological disorder, a history of substance dependence, learning disability, or head injury with loss of consciousness and any contraindications to MRI. A total of 84 unrelated bipolar high-risk, 78 controls, and 47 schizophrenia unrelated high-risk participants provided suitable fMRI data along with genetic information, see Table I. Written informed consent was provided by all participants and studies were approved by the relevant multicenter ethics committee.
|Controls (n = 78)||Bipolar high-risk (n = 84)||Schizophrenia high-risk (n = 47)||Between group comparison (F/Z/χ2, p)|
|Leu/Leu homozygotes (n = 59)||Phe carrier (n = 19)||Within group comparison (T/Z, p)||Leu/Leu homozygotes (n = 54)||Phe carrier (n = 30)||Within group comparison (T/Z, p)||Leu/Leu homozygotes (n = 37)||Phe carrier (n = 10)||Within group comparison (T/Z, p)|
|Mean age (yrs) (std dev)||20.89 (2.32)||21.37 (2.49)||0.60, 0.44||21.17 (2.90)||21.46 (2.61)||0.22, 0.64||26.78 (3.00)||25.61 (4.01)||1.04, 0.31||70.21, <0.01|
|Gender (M:F), %||41:59||58:42||1.72, 0.19||46:54||50:50||0.11, 0.75||49:51||20:80||2.64, 0.10||0.33, 0.85|
|Handedness (R:Other), %||95:5||90:10||0.71, 0.40||91:9||93:7||0.17, 0.68||84:16||70:30||0.97, 0.33||5.72, 0.06|
|Mean NART IQ||111.19 (8.79)||110.10 (4.75)||0.26, 0.61||107.74 (8.78)||112.77 (7.40)||7.04, 0.10||102.14 (10.16)||97.80 (8.18)||1.62, 0.21||19.67, <0.01|
|Mean Reaction Time||2485 (620)||2480 (532)||0.01, 0.98||2564 (588)||2403 (712)||1.24, 0.27||2510 (645)||2038 (829)||2.97, 0.09||0.31 0.73|
|Mean Word Appropriateness||3.10 (0.55)||2.81 (0.45)||2.95, 0.09||2.85 (0.61)||2.80 (0.43)||0.18, 0.68||3.21 (0.38)||3.01 (0.56)||1.97, 0.17||6.10, <0.01|
|Clinical measures# (median, interquartile range)|
|PANSS positive total||7 (0)||7 (0)||1.50, 0.13||7 (1)||7 (0)||0.91, 0.36||7 (1.75)||8.5 (2.25)||0.93, 0.35||23.85, <0.01|
|PANSS negative total||7 (0)||7 (0)||1.81, 0.07||7 (0)||7 (0)||0.96, 0.34||7 (2)||7 (3)||0.41, 0.68||19.49, <0.01|
|PANSS general total||16 (1)||16 (1)||0.48, 0.63||16 (3)||16.5 (1.5)||0.31, 0.76||19 (4.5)||19.5 (8)||0.62, 0.54||42.65, <0.01|
Genomic DNA was extracted from venous blood samples. The genotyping was conducted by the Wellcome Trust Clinical Research Facility, Edinburgh, UK (www.wtcrf.ed.ac.uk) and used standard TaqMan assays, by the TaqMan polymerase chain reaction (PCR) based method (TaqMan, AssayByDesign, Applied Biosystems, Foster City, CA). Subjects were typed for the C/T alleles at SNP rs6675281.
Subjects performed the verbal initiation section of the Hayling sentence completion test [Burgess and Shallice, 1997] in the scanner [Whalley et al., 2004]. This task is considered an extension of the verbal fluency task, where constraint is based on sentence context rather than letter or semantic category. Briefly, subjects were shown sentences with the last word missing and asked to silently think of an appropriate word to complete the sentence and press a button when they had done so. The task had four levels of difficulty, according to the range of suitable completion words suggested by the sentence context. Sentences were assigned difficulty levels based on the frequency of the most typically presented word in the list of norms [Bloom and Fischler, 1980b]. For low, medium low, medium high and high frequencies of the most typically produced words were (0.09–0.26, 0.53–0.59, 0.74–0.80, 0.95–1). This design allowed both a standard subtraction analysis (sentence completion versus baseline) and the more constrained parametric analysis (examining areas of increasing activation with increasing task difficulty). Sentences were presented in blocks of fixed difficulty. Each block lasted 40 sec and included eight sentences. Sentences were presented for a period of 3 sec followed by a fixation cross for 2 sec. The baseline condition consisted of viewing a screen of white circles on a black background for 40 sec. The order of the blocks was pseudo-random, and each block was repeated four times using different sentences. Standardized verbal instructions were given prior to scanning.
Immediately after scanning, subjects were given the same sequence of sentences on paper and requested to complete each sentence with the word they first thought of in the scanner. ‘Word appropriateness’ scores were determined from the word frequency list of sentence completion norms [Bloom and Fischler, 1980a]. A score of one was given to the most frequently produced word in the word frequency list, a score of two for the next most frequently produced word, etc. Hence, a low score implies greater consistency with the most frequently produced word supplied in the list of completion norms.
Imaging was carried out at the Brain Imaging Research Centre (BIRC) for Scotland on a GE 1.5 T Signa scanner (GE Medical, Milwaukee, WI). For the bipolar family study the functional imaging protocol consisted of axial gradient-echo planar images (EPI) (TR/TE = 2,000/40 msec; matrix = 64 × 64; field of view (fov) = 24 cm) acquired continually during the experimental paradigm. Twenty-seven contiguous 5 mm slices were acquired within each TR. Each EPI acquisition was run for 404 volumes. The first 4 were discarded. Data for the schizophrenia family study was collected nonconcurrently and hence there were unavoidable technical differences to the above (TR/TE = 4,000/40 msec; matrix 64 × 128; FOV 220 mm × 440 mm). Thirty-eight contiguous 5 mm slices was acquired within each TR and each EPI acquisition was run for 204 volumes, of which the first 4 were discarded. The T1 sequence yielded 180 contiguous 1.2 mm coronal slices (matrix = 192 × 192; fov = 24 cm; flip angle 8°). Visual stimuli were presented using a screen (IFIS, MRI Devices, Waukesha, WI) placed in the bore of the magnet.
Image Processing and Analysis
EPI and T1 images were reconstructed into ANALYZE format (Mayo Foundation, Rochester, MN) using DICOM convert functions available in SPM5 (Statistical Parametric Mapping: The Wellcome Department of Cognitive Neurology and collaborators, Institute of Neurology, London) running in Matlab (The MathWorks, Natick, MA).
Images were pre-processed using standard protocols available in SPM5. EPI images were realigned to the mean volume in the series. The functional images were then normalized using standard co-registration procedures using the individuals structural scan. Finally all realigned and normalized images were smoothed with a 8 mm × 8 mm × 8 mm full width half maximum (FWHM) Gaussian filter. One subject, from the schizophrenia high-risk group, presented large correlations (>0.5) between movement parameters and task regressors and was excluded from the analysis.
First level statistical analysis was performed using the general linear model approach. At the individual subject level the data was modelled with four conditions corresponding to the four difficulty levels each modelled by a boxcar convolved with a synthetic hemodynamic response function. Estimates of the subject's movement during the scan were entered as “covariates of no interest.” Before fitting the model, the participants data was filtered in the time domain using high pass filter (128 s cut-off) and serial correlations were accounted for using the autoregressive (AR(1)) model. Contrasts were constructed to examine all four-sentence completion conditions versus baseline, and areas of increasing activation with increasing task difficulty (the parametric contrast). In this task the “high” constraint represents the easy condition; for example, “He posted the letter without a ….” and “low” constraint represents the hard condition, for example, “She left the house without her ….”), so for the four conditions high, medium high, medium low and low, the contrast entered to examine parametric effects was [−3 −1 1 3].
Second Level Analysis
All second level statistical analyses were conducted in SPM5. For each contrast of interest (sentence completion versus baseline and parametric effects), one contrast image per subject was entered into a second-level random effects analysis. Main effect of genotype was initially examined followed by genotype × group interactions using a full factorial ANOVA model comparing the bipolar high-risk group against the healthy controls. Genotype and group were entered as two factors in the design matrix with three levels of genotype (LeuLeu, LeuPhe, PhePhe; where LeuPhe and PhePhe were subsequently combined at the contrast stage) and two diagnostic groups (healthy controls, individuals at high-risk of bipolar disorder). A second analysis was then conducted including the schizophrenia high-risk cohort in the model to examine whether differences between the bipolar high-risk cohort and controls were also present in the schizophrenia high-risk sample. Here genotype and group were entered as two factors in the design matrix with three levels of genotype (LeuLeu, LeuPhe, PhePhe; where LeuPhe and PhePhe were subsequently combined) and three diagnostic groups (healthy controls, individuals at high-risk of bipolar disorder and individuals at high-risk of schizophrenia). This analysis was conducted controlling for age, handedness and NART IQ, as analysis of demographic data indicated group differences in these measures between the three groups (see Table I). Where significant interactions were found, pair-wise group comparisons were explored and the effect of genotype within each diagnostic group was examined. In order to provide a graphical summary of the result, data were extracted from the cluster of interest using the volume of interest (VOI) eigenvariate extraction tool in SPM5.
Statistical maps were thresholded at a level of P < 0.001 (uncorrected) and regions were considered significant at a cluster level of P < 0.05, corrected for multiple comparisons. All coordinates are quoted in Montreal Neurological Institute (MNI) convention (http://www.mni.mcgill.ca). All images are overlaid onto standard brain in MNI space using Mango software package (http://ric.uthscsa.edu/mango).
- Top of page
- MATERIALS AND METHODS
- Supporting Information
Demographics and Behavioral Measures
For the controls, 59 individuals were Leu/Leu homozygous and 19 were Phe carriers. For the bipolar high-risk group 54 individuals were Leu/Leu homozygous and 30 were Phe carriers. Finally for the schizophrenia high-risk group 37 individuals were Leu/Leu homozygous and 10 were Phe carriers. There were only two Phe/Phe homozygotes in each of the three groups, these were combined with the heterozygotes to form the Phe carrier group. These frequencies did not differ between the groups (χ2 = 4.02, P > 0.05). None of the allele frequencies differed from Hardy–Weinberg equilibrium either within the groups (controls χ2 = 0.33, P > 0.05, bipolar high-risk χ2 = 0.55, P > 0.05, schizophrenia high-risk χ2 = 2.61, P > 0.05) or with the groups combined (χ2 = 0.25, P > 0.05).
Demographic details are presented in Table I. There were significant differences between the groups in terms of age, and NART IQ, and there was a trend for a difference in handedness. Pair-wise comparisons indicated these stemmed from differences between the schizophrenia high-risk cohort and the other groups (see Table I). These measures were therefore entered as covariates into the relevant analysis. There were no significant within group differences based on genotype for any of these measures and there was no significant difference between the controls and bipolar high-risk subjects on any of the demographic variables.
With regard to the behavioral measures there was a significant difference between the groups in terms of their mean word appropriateness scores. Pair-wise comparisons again indicated differences between schizophrenia high-risk sample and the other groups, but not between the controls and bipolar high-risk group. These measures are explored below against the functional data by performing correlations between the extracted data for the cluster of difference and these measures.
Task-Related Brain Activation Patterns
All subjects demonstrated the expected patterns of brain activation and behavioral responses indicating subjects were performing the tasks appropriately in the scanner (see Supplementary Figure and Supplementary Table). Regions activated across the groups for the sentence completion versus baseline contrast included the left medial and lateral prefrontal regions, left lateral temporal cortex, sub-cortical structures, left lateral parietal cortex, and occipital lobes bilaterally, see [Whalley et al., 2004, 2011]. For the parametric contrast all groups demonstrated activation in similar areas including left lateral and medial prefrontal cortex, left lateral temporal cortex, and right cerebellum [Whalley et al., 2004, 2011].
(i) Analysis of controls versus bipolar high-risk
Main effect of genotype
For sentence completion versus baseline there was a main effect of genotype for a cluster centered on the left precentral gyrus BA 6 extending to the postcentral gyrus and inferior frontal gyrus (P < 0.001, cluster-corrected across whole brain, KE = 535, Z = 4.53, x = −46, y = −10, z = 24). There was no significant effect for the parametric contrast.
Genotype × group interaction
For sentence completion versus baseline there was a significant genotype × group interaction also in this cluster centered on the left precentral gyrus (BA 6), which extended to the left inferior frontal gyrus and encompassed part of the postcentral gyrus (BA9 + 44), P = 0.001 cluster-corrected across whole brain (KE = 417, Z = 4.08, x = −54, y = 0, z = 14). There was no significant interaction for the parametric contrast.
(ii) Analysis of controls versus bipolar high-risk, including schizophrenia high-risk
The above cluster was also significant on testing the main effect of genotype (P = 0.03, KE = 297, Z = 4.08, x = −62, y = −6, z = 18) and genotype × group interactions with the schizophrenia high-risk group included in the analysis, controlling for age, NART IQ and handedness, P = 0.004 cluster-corrected across whole brain (KE = 241, Z = 3.82, x = −48, y = −10, z = 24) see Figure 1. There were no significant interactions for the parametric contrast.
Pair-wise comparisons indicated that this effect was significant between controls and bipolar high-risk (P < 0.001, KE = 953, Z = 4.45, x = −56, y = 2, z = 16), and between controls and schizophrenia high-risk (P < 0.001, KE = 669, Z = 4.06, x = −56, y = −6, z = 12) for the interaction of Phe carrier > LeuLeu homozygotes in controls versus the reverse in the high-risk groups (Phe carrier < LeuLeu homozygotes), see Table II. There were no significant differential effect of the DISC1 genotype between the two high-risk groups, or for the inverse contrast (controls: Phe carrier > LeuLeu homozygotes versus the reverse contrast in the high-risk groups).
|P value||Extent||Z||Peak height||Region|
|Genotype × group interaction||0.004||241||3.82||−48 −10 24||Left pre/postcentral gyrus extending to inferior frontal gyrus|
|C (Phe carrier > LeuLeu) vs. BHR (Phe carrier < LeuLeu)||<0.001||953||4.45||−56 2 16||Left pre/postcentral gyrus extending to inferior frontal gyrus|
|0.022||305||4.16||−6 14 22||Left cingulate gyrus|
|0.016||327||3.92||−36 26 −10||Left inferior frontal gyrus|
|C (Phe carrier > LeuLeu) vs. SHR (Phe carrier < LeuLeu)||<0.001||669||4.06||−56 −6 12||Left pre/postcentral gyrus extending to inferior frontal gyrus|
|BHR (Phe carrier > LeuLeu) vs. SHR (Phe carrier < LeuLeu)||n/s||—||—||—||—|
|SHR (Phe carrier > LeuLeu) vs. BHR (Phe carrier < LeuLeu)||n/s||—||—||—||—|
Within group DISC1 effects
Within group comparisons indicated that the above group difference stemmed from a significant effect in the controls (Phe carriers > LeuLeu homozygotes, P < 0.001, KE = 1712, Z = 5.28, x = −60, y = −6, z = 16), an effect not seen in the high-risk groups (Phe carriers < or > LeuLeu homozygotes: n/s). This can be seen in the graph of the extracted data for this cluster depicted in Figure 2. Further statistical comparison of the groups revealed a significant difference between the Phe carrier controls versus bipolar high-risk Phe carriers (P < 0.001) and versus the schizophrenia high-risk Phe carriers (P < 0.001).
We examined correlations between data extracted from the cluster of interest and behavioral measures. There were no significant correlations for reaction time and word appropriateness across the groups combined, within the groups, or within genotype groups, indicating that these activation differences were unlikely to be attributable to performance differences between the groups.
- Top of page
- MATERIALS AND METHODS
- Supporting Information
In the current study we have demonstrated differential effects of the DISC1 Leu607Phe variant on prefrontal cortex function in controls versus individuals at increased risk of BD or SCZ. From the graph of the extracted data (Fig. 2) the origin of the genotype × group effect is likely to originate from a significant effect of the presumed risk variant (Phe) on brain activation in the control group, which was absent in both high-risk groups. Both the bipolar high-risk and schizophrenia high-risk subjects, irrespective of genotype, appeared to have a similar response in this region to that seen in the control Leu homozygotes. This lack of effect in the high-risk groups is consistent with the hypothesis that Leu607Phe effects are dependent upon the presence of family history for the disorders and the potential effects of other genetic loci. In other words, the effect of the Leu607Phe variant is only observable in controls in the absence of other risk genes. The high-risk groups may differ from the controls based on an already present genetic loading for the disorders and potential interactions with other, as yet unknown, genetic factors. In addition, although there were significant group-wise differences in brain activation between both high-risk groups and controls, the DISC1 607Phe effects did not differ between the two high-risk samples inferring some commonality of effect in the familial cohorts. This fits with other evidence of commonalities between the two disorders and with the possibility that there are at least partially convergent genetically related pathological mechanisms.
This lack of effect of DISC1 in the familial groups, which is seen in controls, is consistent with a recent report examining the effects of DISC1 in patient populations using a similar task type [Prata et al., 2010]. The authors also interpreted their findings as reflecting interactions of the effects of the DISC1 genotype with the effects of other genes associated with the disorders [Prata et al., 2010]. It is notable that other neuroimaging studies have also indicated a disease-specific nature of the influence of various potential susceptibility genes on brain structure and function. In these studies differential effects of candidate SNPs are seen in controls to that seen in patient groups, consistent with the findings reported here, which may similarly relate to differences in genetic context between the groups [Addington et al., 2007; Mechelli et al., 2008; Prata et al., 2008; Narr et al., 2009; Fallgatter et al., 2010; Prata et al., 2010].
This main finding involved a large cluster which included pre and postcentral regions and inferior frontal gyrus. The former areas are primarily considered to be involved in somatosensory processing and motor functions, but their role has more recently been shown to include higher cognitive tasks including executive functioning and linguistic processing [Whalley et al., 2004; Minzenberg et al., 2009]. Other imaging studies of risk genes associated with schizophrenia have also reported abnormalities in similar brain regions, for example, DISC1 [Carless et al., 2011; Chakirova et al., 2011], DAOA [Prata et al., 2011], and COMT [Pomarol-Clotet et al., 2011]. Although the exact role of this brain region has yet to be fully realized, mounting evidence therefore indicates its involvement in the etiology of these disorders. In the current task this region formed part of a larger cluster of activation for the contrast of sentence completion versus baseline. Activation in this region did not however, correlate with task performance. We conclude that for all levels of task difficulty this region was engaged. However, there was no direct association with difficulty levels and therefore group differences in brain activation were unlikely to be driven by differences in task performance. Since the greater activity of the Phe carrier controls was not associated with reduced task performance, this suggests that, in this sample, carrying the presumed risk variant did not have compromised language function. It may be the case therefore that this increased activation in the Phe carrier controls reflects compensatory over-activation. There were also no differences between the genotype groups within the controls or within either of the familial groups in terms of psychosis scores. This likely reflects that these were all well individuals who did not meet criteria for a psychiatric disorder and therefore all scored low for these measures.
Two previous structural imaging studies examining the effects of the Leu607Phe SNP reported gray matter volume reductions in Phe carrier adults versus LeuLeu individuals, also in prefrontal regions [Cannon et al., 2005; Szeszko et al., 2005]. Furthermore, a more recent detailed MRI study of cortical maturation, comparing child and adolescent carriers of Leu607Phe and Ser704Cys, showed that the rate of cortical thinning was dependent on the DISC1 genotype [Raznahan et al., 2010]. Consistent with the results shown by Szeszko et al. , Phe carriers showed attenuated rates of cortical thinning, compared to Leu/Leu homozygotes in bilateral superior frontal and left angular gyri. These studies were however conducted on combined patient and control groups, making it difficult to compare to the current study where we report genotype by group interaction effects between controls and the two cohorts at risk of psychiatric illness.
In support of our proposed explanation, DISC1 is known to interact with numerous neuronal proteins, reflecting the diversity of potential roles it plays and how critical it is to brain function. These interactors can be broadly classified into those involving the cytoskeleton, cell cycle, signal transduction, intracellular transport/exocytosis, golgi and neurodevelopment processes [Chubb et al., 2008; Tomppo et al., 2009a]. One possible interactor, for example, is pericentriolar material-1 (PCM1) [Kamiya et al., 2008]. PCM1, is a putative schizophrenia susceptibility gene [Tabares-Seisdedos and Rubenstein, 2009], which has also been shown to be associated with prefrontal gray matter deficits in schizophrenia [Gurling et al., 2006]. Further elucidating the interaction of the DISC1 ‘hub’ with its associated partners could throw further light on underlying etiological mechanisms contributing to these disorders and effects on brain function.
The main limitation of the current study was that the two high-risk samples were not collected as part of the same study, with consequent unavoidable minor differences in the samples. We therefore took the cautious approach to conduct a two-step analysis procedure initially comparing the bipolar high-risk and controls and then examined effects in the schizophrenia high-risk group. The main demographic differences were controlled for using standard statistical approaches. The main methodological difference was that a TR of four was used for the schizophrenia high-risk study and a TR of two was used for the bipolar family study. It should be stressed, however, that both studies were conducted on the same scanner using the same paradigm design. Differences in TR are reported to primarily effect the number of time points collected and therefore efficiency of the experiment, and have minimal effect on the noise and smoothness of the data [Smith et al., 2007]. For the current study with relatively long blocks optimized for the longer TR we considered the impact of the different TRs to be minimal, especially given quantitative analysis of activation maps suggesting the location and extent of activation was consistent across the studies (see Supplementary Figure 1). We do however accept that we cannot completely exclude the possibility that these methodological differences may have some impact on the current findings. Any such confounding methodological effects are nonetheless most likely to be evident at the between diagnostic group level. DISC1 genotype effects and interactions between genotype and diagnostic group are less susceptible to the effects of differences in imaging methods since individuals of each genotype groups were scanned using both prescriptions and the interaction effects were estimated using a model which took account of these differences. We have also previously reported acceptable reproducibility both between and within scanning sites at the group and subject level, suggesting the viability of combining functional datasets [Gountouna et al., 2011]. Further, the pattern of results suggests that the differences stemmed from the control group, and that inclusion of the schizophrenia risk subjects reinforces this result rather than adding further differences which would have been expected if the methodological differences were substantially affecting the findings.
One other important point relates to the task itself. The design of the Hayling task was to permit both the subtraction analysis (sentence completion versus rest) and the parametric contrast (increasing activation with increasing task difficulty). Since the parametric contrast more tightly controls for the task-based cognitive processes, the baseline condition was primarily considered to be a “rest-like” control condition. For this reason we implemented the visual baseline rather than, for example, a simple sentence reading condition. Interpretation of the sentence completion versus baseline contrast should therefore be considered in this light. This contrast will not be restricted to cognitive processes purely involved in verbal fluency, since other lower-level processes are likely to be involved, for example, sentence reading.
Finally, it is also possible that lifestyle and environmental factors could also be influencing these findings, since studies on unaffected relatives invariably involve shared genetic and environmental effects, although we did not find any direct empirical evidence for this within the current study.
In summary, we report a differential effect of DISC1 Leu607Phe on brain activation dependant on the presence of a family history of schizophrenia or bipolar disorder. Allele effects on brain activation were significant in controls but in neither bipolar high-risk subjects, nor a schizophrenia high-risk subject group. We interpret our results to suggest that the relationship between genotype and functional architecture in the high-risk groups may differ from controls based on an already present genetic loading for the disorders and potential interactions with other, as yet unknown, genetic factors. These findings suggest that Leu607Phe may have differential influence on brain activation depending on background vulnerability, reflecting complex underlying genetic and etiological mechanisms.
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We would like to thank all of the participants who took part in the study and the radiographers who acquired the MRI scans. This study was conducted at the Scottish Brain Imaging Research Centre which is supported by SINAPSE (Scottish Imaging Network, a Platform for Scientific Excellence, www.sinapse.ac.uk). Support was also provided by the Scottish Mental Health Research network (www.smhrn.org.uk) who provided assistance with subject recruitment and cognitive assessments. HCW is supported by a Dorothy Hodgkin Fellowship from the Royal Society (DH080018). AMM is currently supported by the Health Foundation through a Clinician Scientist Fellowship (Ref: 2268/4295) and by NARSAD. JES is supported by a Clinical Research Training Fellowship from the Wellcome Trust. JH is supported by a Senior Clinical Fellowship from the Chief Scientists Office in Scotland. MJ is supported by a starter grant from The Academy of Medical Sciences/Wellcome Trust (R41455). All imaging aspects received financial support from the Dr Mortimer and Theresa Sackler Foundation AMM, JH, HCW and SML have previously received financial support from Pfizer (formerly Wyeth) in relation to other imaging studies of people with schizophrenia and bipolar disorder. JES, LR, HR, MJ, GC, PM, ECJ report no financial interests or conflicts of interest.
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