Emma Sprooten

Identification of common variants associated with human hippocampal and intracranial volumes

Identifying genetic variants influencing human brain structures may reveal new biological mechanisms underlying cognition and neuropsychiatric illness. The volume of the hippocampus is a biomarker of incipient Alzheimers disease and is reduced in schizophrenia, major depression and mesial temporal lobe epilepsy. Whereas many brain imaging phenotypes are highly heritable, identifying and replicating genetic influences has been difficult, as small effects and the high costs of magnetic resonance imaging (MRI) have led to underpowered studies. Here we report genome-wide association meta-analyses and replication for mean bilateral hippocampal, total brain and intracranial volumes from a large multinational consortium. The intergenic variant rs7294919 was associated with hippocampal volume (12q24.22; N = 21,151; P = 6.70 × 10−16) and the expression levels of the positional candidate gene TESC in brain tissue. Additionally, rs10784502, located within HMGA2, was associated with intracranial volume (12q14.3; N = 15,782; P = 1.12 × 10−12). We also identified a suggestive association with total brain volume at rs10494373 within DDR2 (1q23.3; N = 6,500; P = 5.81 × 10−7).

Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: a pilot project of the ENIGMA-DTI working group.

The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Consortium was set up to analyze brain measures and genotypes from multiple sites across the world to improve the power to detect genetic variants that influence the brain. Diffusion tensor imaging (DTI) yields quantitative measures sensitive to brain development and degeneration, and some common genetic variants may be associated with white matter integrity or connectivity. DTI measures, such as the fractional anisotropy (FA) of water diffusion, may be useful for identifying genetic variants that influence brain microstructure. However, genome-wide association studies (GWAS) require large populations to obtain sufficient power to detect and replicate significant effects, motivating a multi-site consortium effort. As part of an ENIGMA-DTI working group, we analyzed high-resolution FA images from multiple imaging sites across North America, Australia, and Europe, to address the challenge of harmonizing imaging data collected at multiple sites. Four hundred images of healthy adults aged 18-85 from four sites were used to create a template and corresponding skeletonized FA image as a common reference space. Using twin and pedigree samples of different ethnicities, we used our common template to evaluate the heritability of tract-derived FA measures. We show that our template is reliable for integrating multiple datasets by combining results through meta-analysis and unifying the data through exploratory mega-analyses. Our results may help prioritize regions of the FA map that are consistently influenced by additive genetic factors for future genetic discovery studies. Protocols and templates are publicly available at (http://enigma.loni.ucla.edu/ongoing/dti-working-group/).

White Matter Integrity in Individuals at High Genetic Risk of Bipolar Disorder

BACKGROUND Bipolar disorder is a familial psychiatric disorder associated with reduced white matter integrity, but it is not clear whether such abnormalities are present in young unaffected relatives and, if so, whether they have behavioral correlates. We investigated with whole brain diffusion tensor imaging whether increased genetic risk for bipolar disorder is associated with reductions in white matter integrity and whether these reductions are associated with cyclothymic temperament. METHODS Diffusion tensor imaging data of 117 healthy unaffected relatives of patients with bipolar disorder and 79 control subjects were acquired. Cyclothymic temperament was measured with the cyclothymia scale of the Temperament Evaluation of Memphis, Pisa and San Diego auto-questionnaire. Voxel-wise between-group comparisons of fractional anisotropy (FA) and regression of cyclothymic temperament were performed with tract-based spatial statistics. RESULTS Compared to the control group, unaffected relatives had reduced FA in one large widespread cluster. Cyclothymic temperament was inversely related to FA in the internal capsules bilaterally and in left temporal white matter, regions also found to be reduced in high-risk subjects. CONCLUSIONS These results show that widespread white matter integrity reductions are present in unaffected relatives of bipolar patients and that more localized reductions might underpin cyclothymic temperament. These findings suggest that white matter integrity is an endophenotype for bipolar disorder with important behavioral associations previously linked to the etiology of the condition.

Arguments for the sake of endophenotypes: Examining common misconceptions about the use of endophenotypes in psychiatric genetics

Endophenotypes are measurable biomarkers that are correlated with an illness, at least in part, because of shared underlying genetic influences. Endophenotypes may improve our power to detect genes influencing risk of illness by being genetically simpler, closer to the level of gene action, and with larger genetic effect sizes or by providing added statistical power through their ability to quantitatively rank people within diagnostic categories. Furthermore, they also provide insight into the mechanisms underlying illness and will be valuable in developing biologically‐based nosologies, through efforts such as RDoC, that seek to explain both the heterogeneity within current diagnostic categories and the overlapping clinical features between them. While neuroimaging, electrophysiological, and cognitive measures are currently most used in psychiatric genetic studies, researchers currently are attempting to identify candidate endophenotypes that are less genetically complex and potentially closer to the level of gene action, such as transcriptomic and proteomic phenotypes. Sifting through tens of thousands of such measures requires automated, high‐throughput ways of assessing, and ranking potential endophenotypes, such as the Endophenotype Ranking Value. However, despite the potential utility of endophenotypes for gene characterization and discovery, there is considerable resistance to endophenotypic approaches in psychiatry. In this review, we address and clarify some of the common issues associated with the usage of endophenotypes in the psychiatric genetics community.

Multivariate analysis reveals genetic associations of the resting default mode network in psychotic bipolar disorder and schizophrenia

Significance Connectivity within the brain’s resting-state default mode network (DMN) has been shown to be compromised in multiple genetically complex/heritable neuropsychiatric disorders. Uncovering the source of such alterations will help in developing targeted treatments for these disorders. To our knowledge, this study is the first attempt to do so by using a multivariate data-driven fusion approach. We report five major DMN subnodes, all of which were found to be hypo-connected in probands with psychotic illnesses. Further, we found an overrepresentation of genes in major relevant pathways such as NMDA potentiation, PKA/immune response signalling, synaptogenesis, and axon guidance that influenced altered DMN connectivity in psychoses. The study thus identifies several putative genes and pathways related to an important biological marker known to be compromised in psychosis. The brain’s default mode network (DMN) is highly heritable and is compromised in a variety of psychiatric disorders. However, genetic control over the DMN in schizophrenia (SZ) and psychotic bipolar disorder (PBP) is largely unknown. Study subjects (n = 1,305) underwent a resting-state functional MRI scan and were analyzed by a two-stage approach. The initial analysis used independent component analysis (ICA) in 324 healthy controls, 296 SZ probands, 300 PBP probands, 179 unaffected first-degree relatives of SZ probands (SZREL), and 206 unaffected first-degree relatives of PBP probands to identify DMNs and to test their biomarker and/or endophenotype status. A subset of controls and probands (n = 549) then was subjected to a parallel ICA (para-ICA) to identify imaging–genetic relationships. ICA identified three DMNs. Hypo-connectivity was observed in both patient groups in all DMNs. Similar patterns observed in SZREL were restricted to only one network. DMN connectivity also correlated with several symptom measures. Para-ICA identified five sub-DMNs that were significantly associated with five different genetic networks. Several top-ranking SNPs across these networks belonged to previously identified, well-known psychosis/mood disorder genes. Global enrichment analyses revealed processes including NMDA-related long-term potentiation, PKA, immune response signaling, axon guidance, and synaptogenesis that significantly influenced DMN modulation in psychoses. In summary, we observed both unique and shared impairments in functional connectivity across the SZ and PBP cohorts; these impairments were selectively familial only for SZREL. Genes regulating specific neurodevelopment/transmission processes primarily mediated DMN disconnectivity. The study thus identifies biological pathways related to a widely researched quantitative trait that might suggest novel, targeted drug treatments for these diseases.

Subcortical volumetric abnormalities in bipolar disorder.

Considerable uncertainty exists about the defining brain changes associated with bipolar disorder (BD). Understanding and quantifying the sources of uncertainty can help generate novel clinical hypotheses about etiology and assist in the development of biomarkers for indexing disease progression and prognosis. Here we were interested in quantifying case–control differences in intracranial volume (ICV) and each of eight subcortical brain measures: nucleus accumbens, amygdala, caudate, hippocampus, globus pallidus, putamen, thalamus, lateral ventricles. In a large study of 1710 BD patients and 2594 healthy controls, we found consistent volumetric reductions in BD patients for mean hippocampus (Cohen’s d=−0.232; P=3.50 × 10−7) and thalamus (d=−0.148; P=4.27 × 10−3) and enlarged lateral ventricles (d=−0.260; P=3.93 × 10−5) in patients. No significant effect of age at illness onset was detected. Stratifying patients based on clinical subtype (BD type I or type II) revealed that BDI patients had significantly larger lateral ventricles and smaller hippocampus and amygdala than controls. However, when comparing BDI and BDII patients directly, we did not detect any significant differences in brain volume. This likely represents similar etiology between BD subtype classifications. Exploratory analyses revealed significantly larger thalamic volumes in patients taking lithium compared with patients not taking lithium. We detected no significant differences between BDII patients and controls in the largest such comparison to date. Findings in this study should be interpreted with caution and with careful consideration of the limitations inherent to meta-analyzed neuroimaging comparisons.

Patterns of Gray Matter Abnormalities in Schizophrenia Based on an International Mega-analysis

Analyses of gray matter concentration (GMC) deficits in patients with schizophrenia (Sz) have identified robust changes throughout the cortex. We assessed the relationships between diagnosis, overall symptom severity, and patterns of gray matter in the largest aggregated structural imaging dataset to date. We performed both source-based morphometry (SBM) and voxel-based morphometry (VBM) analyses on GMC images from 784 Sz and 936 controls (Ct) across 23 scanning sites in Europe and the United States. After correcting for age, gender, site, and diagnosis by site interactions, SBM analyses showed 9 patterns of diagnostic differences. They comprised separate cortical, subcortical, and cerebellar regions. Seven patterns showed greater GMC in Ct than Sz, while 2 (brainstem and cerebellum) showed greater GMC for Sz. The greatest GMC deficit was in a single pattern comprising regions in the superior temporal gyrus, inferior frontal gyrus, and medial frontal cortex, which replicated over analyses of data subsets. VBM analyses identified overall cortical GMC loss and one small cluster of increased GMC in Sz, which overlapped with the SBM brainstem component. We found no significant association between the component loadings and symptom severity in either analysis. This mega-analysis confirms that the commonly found GMC loss in Sz in the anterior temporal lobe, insula, and medial frontal lobe form a single, consistent spatial pattern even in such a diverse dataset. The separation of GMC loss into robust, repeatable spatial patterns across multiple datasets paves the way for the application of these methods to identify subtle genetic and clinical cohort effects.

Polygenic Risk and White Matter Integrity in Individuals at High Risk of Mood Disorder

BACKGROUND Bipolar disorder (BD) and major depressive disorder (MDD) are highly heritable and genetically overlapping conditions characterized by episodic elevation and/or depression of mood. Both demonstrate abnormalities in white matter integrity, measured with diffusion tensor magnetic resonance imaging, that are also heritable. However, it is unclear how these abnormalities relate to the underlying genetic architecture of each disorder. Genome-wide association studies have demonstrated a significant polygenic contribution to BD and MDD, where risk is attributed to the summation of many alleles of small effect. Determining the effects of an overall polygenic risk profile score on neuroimaging abnormalities might help to identify proxy measures of genetic susceptibility and thereby inform models of risk prediction. METHODS In the current study, we determined the extent to which common genetic variation underlying risk to mood disorders (BD and MDD) was related to fractional anisotropy, an index of white matter integrity. This was conducted in unaffected individuals at familial risk of mood disorder (n = 70) and comparison subjects (n = 62). Polygenic risk scores were calculated separately for BD and MDD on the basis of genome-wide association study data from the Psychiatric GWAS Consortia. RESULTS We report that a higher polygenic risk allele load for MDD was significantly associated with decreased white matter integrity across both groups in a large cluster, with a peak in the right-sided superior longitudinal fasciculus. CONCLUSIONS These findings suggest that the polygenic approach to examining brain imaging data might be a useful means of identifying traits linked to the genetic risk of mood disorders.

Impact of a microRNA MIR137 susceptibility variant on brain function in people at high genetic risk of schizophrenia or bipolar disorder

A recent ‘mega-analysis’ combining genome-wide association study data from over 40 000 individuals identified novel genetic loci associated with schizophrenia (SCZ) at genome-wide significance level. The strongest finding was a locus within an intron of a putative primary transcript for microRNA MIR137. In the current study, we examine the impact of variation at this locus (rs1625579, G/T; where T is the common and presumed risk allele) on brain activation during a sentence completion task that differentiates individuals with SCZ, bipolar disorder (BD), and their relatives from controls. We examined three groups of individuals performing a sentence completion paradigm: (i) individuals at high genetic risk of SCZ (n=44), (ii) individuals at high genetic risk of BD (n=90), and (iii) healthy controls (n=81) in order to test the hypothesis that genotype at rs1625579 would influence brain activation. Genotype groups were assigned as ‘RISK−’ for GT and GG individuals, and ‘RISK+’ for TT homozygotes. The main effect of genotype was significantly greater activation in the RISK− individuals in the posterior right medial frontal gyrus, BA 6. There was also a significant genotype*group interaction in the left amygdala and left pre/postcentral gyrus. This was due to differences between the controls (where individuals with the RISK− genotype showed greater activation than RISK+ subjects) and the SCZ high-risk group, where the opposite genotype effect was seen. These results suggest that the newly identified SCZ locus may influence brain activation in a manner that is partly dependent on the presence of existing genetic susceptibility for SCZ.

Cortical thickness in first-episode schizophrenia patients and individuals at high familial risk: A cross-sectional comparison

BACKGROUND Schizophrenia is associated with cortical thickness reductions in the brain, but it is unclear whether these are present before illness onset, and to what extent they are driven by genetic factors. METHODS In the Edinburgh High Risk Study, structural MRI scans of 150 young individuals at high familial risk for schizophrenia, 34 patients with first-episode schizophrenia and 36 matched controls were acquired, and clinical information was collected for the following 10 years for the high-risk and control group. During this time, 17 high-risk individuals developed schizophrenia, on average 2.5 years after the scan, and 57 experienced isolated or sub-clinical psychotic symptoms. We applied surface-based analysis of the cerebral cortex to this cohort, and extracted cortical thickness in automatically parcellated regions. RESULTS Analysis of variance revealed widespread thinning of the cerebral cortex in first-episode patients, most pronounced in superior frontal, medial parietal, and lateral occipital regions (corrected p<10(-4)). In contrast, cortical thickness reductions were only found in high-risk individuals in the left middle temporal gyrus (corrected p<0.05). There were no significant differences between those at high risk who later developed schizophrenia and those who remained well. CONCLUSIONS These findings confirm cortical thickness reductions in schizophrenia patients. Increased familial risk for schizophrenia is associated with thinning in the left middle temporal lobe, irrespective of subsequent disease onset. The absence of widespread cortical thinning before disease onset implies that the cortical thinning is unlikely to simply reflect genetic liability to schizophrenia but is predominantly driven by disease-associated factors.