Association of polygenic risk for major psychiatric illness with subcortical volumes and white matter integrity in UK Biobank

Gray matter volumetric correlates of the polygenic risk of depression: A study of the Human Connectome Project data

Genetic factors confer risks for depression. Understanding the neural endophenotypes, including brain morphometrics, of genetic predisposition to depression would help in unraveling the pathophysiology of depression. We employed voxel-based morphometry (VBM) to examine how gray matter volumes (GMVs) were correlated with the polygenic risk score (PRS) for depression in 993 young adults of the Human Connectome Project. The phenotype of depression was quantified with a DSM-oriented scale of the Achenbach Adult Self-Report. The PRS for depression was computed for each subject using the Psychiatric Genomics Association Study as the base sample. In multiple regression with age, sex, race, drinking severity, and total intracranial volume as covariates, regional GMVs in positive correlation with the PRS were observed in bilateral hippocampi and right gyrus rectus. Regional GMVs in negative correlation with the PRS were observed in a wide swath of brain regions, including bilateral frontal and temporal lobes, anterior cingulate cortex, thalamus, lingual gyri, cerebellum, and the left postcentral gyrus, cuneus, and parahippocampal gyrus. We also found sex difference in anterior cingulate volumes in manifesting the genetic risk of depression. In addition, the GMV of the right cerebellum crus I partially mediated the link from PRS to depression severity. These findings add to the literature by highlighting 1) a more diverse pattern of the volumetric markers of depression, with most regions showing lower but others higher GMVs in association with the genetic risks of depression, and 2) the cerebellar GMV as a genetically informed neural phenotype of depression, in neurotypical individuals.

White Matter Tract Integrity Is Reduced in Depression and in Individuals With Genetic Liability to Depression

BACKGROUND

While major depression has been linked to changes in white matter architecture, it remains unclear whether risk factors for depression are directly associated with these alterations. We reexamined white matter fiber tracts in individuals with depressive symptoms and investigated the connection between genetic and environmental risk for depression and structural changes in the brain.

METHODS

We included 19,183 participants from the UK Biobank imaging cohort, with depression status and adverse life experience based on questionnaire data and genetic liability for depression quantified by polygenic scores. The integrity of 27 white matter tracts was assessed using mean fractional anisotropy derived from diffusion magnetic resonance imaging.

RESULTS

White matter integrity was reduced, particularly in thalamic and intracortical fiber tracts, in individuals with depressive symptoms, independent of current symptom status. In a group of healthy individuals without depression, increasing genetic risk and increasing environmental risk were associated with reduced integrity in relevant fiber tracts, particularly in thalamic radiations. This association was stronger than expected based on statistical dependencies between samples, as confirmed by subsequent in silico simulations and permutation tests.

CONCLUSIONS

White matter alterations in thalamic and association tracts are associated with depressive symptoms and genetic risk for depression in unaffected individuals, suggesting an intermediate phenotype at the brain level.

Common risk alleles for schizophrenia within the major histocompatibility complex predict white matter microstructure

Recent research has highlighted the role of complement genes in shaping the microstructure of the brain during early development, and in contributing to common allele risk for Schizophrenia. We hypothesised that common risk variants for schizophrenia within complement genes will associate with structural changes in white matter microstructure within tracts innervating the frontal lobe. Results showed that risk alleles within the complement gene set, but also intergenic alleles, significantly predict axonal density in white matter tracts connecting frontal cortex with parietal, temporal and occipital cortices. Specifically, risk alleles within the Major Histocompatibility Complex region in chromosome 6 appeared to drive these associations. No significant associations were found for the orientation dispersion index. These results suggest that changes in axonal packing - but not in axonal coherence - determined by common risk alleles within the MHC genomic region - including variants related to the Complement system - appear as a potential neurobiological mechanism for schizophrenia.

Linking Polygenic Risk of Schizophrenia to Variation in Magnetic Resonance Imaging Brain Measures: A Comprehensive Systematic Review

BACKGROUND AND HYPOTHESIS

Schizophrenia is highly heritable, with a polygenic effect of many genes conferring risk. Evidence on whether cumulative risk also predicts alterations in brain morphology and function is inconsistent. This systematic review examined evidence for schizophrenia polygenic risk score (sczPRS) associations with commonly used magnetic resonance imaging (MRI) measures. We expected consistent evidence to emerge for significant sczPRS associations with variation in structure and function, specifically in frontal, temporal, and insula cortices that are commonly implicated in schizophrenia pathophysiology.

STUDY DESIGN

In accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we searched MEDLINE, Embase, and PsycINFO for peer-reviewed studies published between January 2013 and March 2022. Studies were screened against predetermined criteria and National Institutes of Health (NIH) quality assessment tools.

STUDY RESULTS

In total, 57 studies of T1-weighted structural, diffusion, and functional MRI were included (age range = 9-80 years, Nrange = 64-76 644). We observed moderate, albeit preliminary, evidence for higher sczPRS predicting global reductions in cortical thickness and widespread variation in functional connectivity, and to a lesser extent, region-specific reductions in frontal and temporal volume and thickness. Conversely, sczPRS does not predict whole-brain surface area or gray/white matter volume. Limited evidence emerged for sczPRS associations with diffusion tensor measures of white matter microstructure in a large community sample and smaller cohorts of children and young adults. These findings were broadly consistent across community and clinical populations.

CONCLUSIONS

Our review supports the hypothesis that schizophrenia is a disorder of disrupted within and between-region brain connectivity, and points to specific whole-brain and regional MRI metrics that may provide useful intermediate phenotypes.

Cerebellar morphological differences and associations with extrinsic factors in bipolar disorder type I

BACKGROUND

The neural underpinnings of bipolar disorder (BD) remain poorly understood. The cerebellum is ideally positioned to modulate emotional regulation circuitry yet has been understudied in BD. Literature suggests differences in cerebellar activity and metabolism in BD, however findings on structural differences remain contradictory. Potential reasons include combining BD subtypes, small sample sizes, and potential moderators such as genetics, adverse childhood experiences (ACEs), and pharmacotherapy.

METHODS

We collected 3 T MRI scans from participants with (N = 131) and without (N = 81) BD type I, as well as blood and questionnaires. We assessed differences in cerebellar volumes and explored potentially influential factors.

RESULTS

The cerebellar cortex was smaller bilaterally in participants with BD. Polygenic propensity score did not predict any cerebellar volumes, suggesting that non-genetic factors may have greater influence on the cerebellar volume difference we observed in BD. Proportionate cerebellar white matter volumes appeared larger with more ACEs, but this may result from reduced ICV. Time from onset and symptom burden were not associated with cerebellar volumes. Finally, taking sedatives was associated with larger cerebellar white matter and non-significantly larger cortical volume.

LIMITATIONS

This study was cross-sectional, limiting interpretation of possible mechanisms. Most of our participants were White, which could limit the generalizability. Additionally, we did not account for potential polypharmacy interactions.

CONCLUSIONS

These findings suggest that external factors, such as sedatives and childhood experiences, may influence cerebellum structure in BD and may mask underlying differences. Accounting for such variables may be critical for consistent findings in future studies.

Association of polygenic risk for bipolar disorder with grey matter structure and white matter integrity in youth

There is a gap in knowledge regarding the polygenic underpinnings of brain anomalies observed in youth bipolar disorder (BD). This study examined the association of a polygenic risk score for BD (BD-PRS) with grey matter structure and white matter integrity in youth with and without BD. 113 participants were included in the analyses, including 78 participants with both T1-weighted and diffusion-weighted MRI images, 32 participants with T1-weighted images only, and 3 participants with diffusion-weighted images only. BD-PRS was calculated using PRS-CS-auto and was based on independent adult genome-wide summary statistics. Vertex- and voxel-wise analyses examined the associations of BD-PRS with grey matter metrics (cortical volume [CV], cortical surface area [CSA], cortical thickness [CTh]) and fractional anisotropy [FA] in the combined sample, and separately in BD and HC. In the combined sample of participants with T1-weighted images (n = 110, 66 BD, 44 HC), higher BD-PRS was associated with smaller grey matter metrics in frontal and temporal regions. In within-group analyses, higher BD-PRS was associated with lower CTh of frontal, temporal, and fusiform gyrus in BD, and with lower CV and CSA of superior frontal gyrus in HC. In the combined sample of participants with diffusion-weighted images (n = 81, 49 BD, 32 HC), higher BD-PRS was associated with lower FA in widespread white matter regions. In summary, BD-PRS calculated based on adult genetic data was negatively associated with grey matter structure and FA in youth in regions implicated in BD, which may suggest neuroimaging markers of vulnerability to BD. Future longitudinal studies are needed to examine whether BD-PRS predicts neurodevelopmental changes in BD vs. HC and its interaction with course of illness and long-term medication use.

Schizophrenia Polygenic Risk During Typical Development Reflects Multiscale Cortical Organization

Background

Schizophrenia is widely recognized as a neurodevelopmental disorder. Abnormal cortical development in otherwise typically developing children and adolescents may be revealed using polygenic risk scores for schizophrenia (PRS-SCZ).

Methods

We assessed PRS-SCZ and cortical morphometry in typically developing children and adolescents (3-21 years, 46.8% female) using whole-genome genotyping and T1-weighted magnetic resonance imaging (n = 390) from the PING (Pediatric Imaging, Neurocognition, and Genetics) cohort. We contextualized the findings using 1) age-matched transcriptomics, 2) histologically defined cytoarchitectural types and functionally defined networks, and 3) case-control differences of schizophrenia and other major psychiatric disorders derived from meta-analytic data of 6 ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) working groups, including a total of 12,876 patients and 15,670 control participants.

Results

Higher PRS-SCZ was associated with greater cortical thickness, which was most prominent in areas with heightened gene expression of dendrites and synapses. PRS-SCZ-related increases in vertexwise cortical thickness were mainly distributed in association cortical areas, particularly the ventral attention network, while relatively sparing koniocortical type cortex (i.e., primary sensory areas). The large-scale pattern of cortical thickness increases related to PRS-SCZ mirrored the pattern of cortical thinning in schizophrenia and mood-related psychiatric disorders derived from the ENIGMA consortium. Age group models illustrate a possible trajectory from PRS-SCZ-associated cortical thickness increases in early childhood toward thinning in late adolescence, with the latter resembling the adult brain phenotype of schizophrenia.

Conclusions

Collectively, combining imaging genetics with multiscale mapping, our work provides novel insight into how genetic risk for schizophrenia affects the cortex early in life.

Polygenic Risk for Schizophrenia, Major Depression, and Post-traumatic Stress Disorder and Hippocampal Subregion Volumes in Middle Childhood

Studies demonstrate that individuals with diagnoses for Major Depressive Disorder (MDD), Post-traumatic Stress Disorder (PTSD), and Schizophrenia (SCZ) may exhibit smaller hippocampal gray matter relative to otherwise healthy controls, although the effect sizes vary in each disorder. Existing work suggests that hippocampal abnormalities in each disorder may be attributable to genetic liability and/or environmental variables. The following study uses baseline data from the Adolescent Brain and Cognitive Development[Formula: see text] Study (ABCD Study[Formula: see text]) to address three open questions regarding the relationship between genetic risk for each disorder and hippocampal volume reductions: (a) whether polygenic risk scores (PGRS) for MDD, PTSD, and SCZ are related to hippocampal volume; (b) whether PGRS for MDD, PTSD, and SCZ are differentially related to specific hippocampal subregions along the longitudinal axis; and (c) whether the association between PGRS for MDD, PTSD, and SCZ and hippocampal volume is moderated by sex and/or environmental adversity. In short, we did not find associations between PGRS for MDD, PTSD, and SCZ to be significantly related to any hippocampal subregion volumes. Furthermore, neither sex nor enviornmental adversity significantly moderated these associations. Our study provides an important null finding on the relationship genetic risk for MDD, PTSD, and SCZ to measures of hippocampal volume.

Effect of schizophrenia common variants on infant brain volumes: cross-sectional study in 207 term neonates in developing Human Connectome Project

Increasing lines of evidence suggest deviations from the normal early developmental trajectory could give rise to the onset of schizophrenia during adolescence and young adulthood, but few studies have investigated brain imaging changes associated with schizophrenia common variants in neonates. This study compared the brain volumes of both grey and white matter regions with schizophrenia polygenic risk scores (PRS) for 207 healthy term-born infants of European ancestry. Linear regression was used to estimate the relationship between PRS and brain volumes, with gestational age at birth, postmenstrual age at scan, ancestral principal components, sex and intracranial volumes as covariates. The schizophrenia PRS were negatively associated with the grey (β = -0.08, p = 4.2 × 10^-3) and white (β = -0.13, p = 9.4 × 10^-3) matter superior temporal gyrus volumes, white frontal lobe volume (β = -0.09, p = 1.5 × 10^-3) and the total white matter volume (β = -0.062, p = 1.66 × 10^-2). This result also remained robust when incorporating individuals of Asian ancestry. Explorative functional analysis of the schizophrenia risk variants associated with the right frontal lobe white matter volume found enrichment in neurodevelopmental pathways. This preliminary result suggests possible involvement of schizophrenia risk genes in early brain growth, and potential early life structural alterations long before the average age of onset of the disease.

The neural signatures of psychoses in Alzheimer's disease: a neuroimaging genetics approach

Psychoses in Alzheimer's disease (AD) are associated with worse prognosis. Genetic vulnerability for schizophrenia (SCZ) may drive AD-related psychoses, yet its impact on brain constituents is still unknown. This study aimed to investigate the association between polygenic risk scores (PRSs) for SCZ and psychotic experiences (PE) and grey matter (GM) volume in patients with AD with (AD-PS) and without (AD-NP) psychosis. Clinical, genetic and T1-weighted MRI data for 800 participants were extracted from the ADNI database: 203 healthy controls, 121 AD-PS and 476 AD-NP. PRSs were calculated using a Bayesian approach and analysed at ten p-value thresholds. Standard voxel-based morphometry was used to process MRI data. Logistic regression models including both PRSs for SCZ and PE, and an AD-PRS were used to predict psychosis in AD. Associations between PRSs and GM volume were investigated in the whole sample and the three groups independently. Only the AD-PRS predicted psychosis in AD. Inconsistent associations between the SCZ-PRS and PE-PRS and GM volumes were found across groups. The SCZ-PRS was negatively associated with medio-temporal/subcortical volumes and positively with medial/orbitofrontal volumes in the AD-PS group. Only medio-temporal areas were more atrophic in the AD-PS group, while there was no significant correlation between psychosis severity and GM volume. Although not associated with psychoses, the SCZ-PRS was correlated with smaller medio-temporal and larger orbitofrontal volumes in AD-PS. Similar alterations have also been observed in SCZ patients. This finding suggest a possible disconnection between these regions associated with psychoses in more advanced AD.

Brain structure and connectivity in psoriasis and associations with depression and inflammation; findings from the UK biobank

Background

Psoriasis is a chronic systemic inflammatory skin disease, coexisting with depression in up to 25% of patients. Little is known about the drivers of comorbidity, including shared neurobiology and depression brain imaging patterns in patients. An immune-mediated crosstalk between the brain and skin has been hypothesized in psoriasis. With the aim of investigating brain structure and connectivity in psoriasis in relation to depression comorbidity, we conducted a brain imaging study including the largest psoriasis patient sample to date (to our knowledge) and the first to investigate the role of depression and systemic inflammation in brain measures. Effects of coexisting psoriatic arthritis (PsA), which represents joint involvement in psoriasis and a higher putative inflammatory state, were further explored.

Methods

Brain magnetic resonance imaging (MRI) data of 1,048 UK Biobank participants were used (131 comorbid patients with psoriasis and depression, age-and sex-matched to: 131 non-depressed psoriasis patients; 393 depressed controls; and 393 non-depressed controls). Interaction effects of psoriasis and depression on volume, thickness and surface of a-priori defined regions of interest (ROIs), white matter tracts and 55x55 partial correlation resting-state connectivity matrices were investigated using general linear models. Linear regression was employed to test associations of brain measures with C-reactive protein (CRP) and neutrophil counts.

Results

No differences in regional or global brain volumes or white matter integrity were found in patients with psoriasis compared to controls without psoriasis or PsA. Thickness in right precuneus was increased in psoriasis patients compared to controls, only when depression was present (β = 0.26, 95% CI [Confidence Intervals] 0.08, 0.44; p = 0.02). In further analysis, psoriasis patients who had PsA exhibited fronto-occipital decoupling in resting-state connectivity compared to patients without joint involvement (β = 0.39, 95% CI 0.13, 0.64; p = 0.005) and controls (β = 0.49, 95% CI 0.25, 0.74; p < 0.001), which was unrelated to depression comorbidity. Precuneus thickness and fronto-occipital connectivity were not predicted by CRP or neutrophil counts. Precuneus thickening among depressed psoriasis patients showed a marginal correlation with recurrent lifetime suicidality.

Conclusions

Our findings provide evidence for a combined effect of psoriasis and depression on the precuneus, which is not directly linked to systemic inflammation, and may relate to suicidality or altered somatosensory processing. The use of the UK Biobank may limit generalizability of results in populations with severe disease.

The effect of polygenic risk scores for major depressive disorder, bipolar disorder and schizophrenia on morphological brain measures: A systematic review of the evidence

BACKGROUND

Major depressive disorder (MDD), bipolar disorder (BD) and schizophrenia (SCZ) share clinical features and genetic bases. Magnetic Resonance Imaging (MRI) studies assessing the effect of polygenic risk score (PRS) for psychiatric disorders on brain structure show heterogeneous results. Therefore, we provided an overview of the existing evidence on the association between PRS for MDD, BD and SCZ and MRI abnormalities in clinical and healthy populations.

METHODS

A search on PubMed, Web of Science and Scopus was performed to identify the studies exploring the effect of PRS for MDD, BD and SCZ on MRI measures. A total of 25 studies were included (N = 13 on healthy individuals and N = 12 on clinical populations).

RESULTS

Both in affected and unaffected individuals, PRS for BD and SCZ showed either positive or negative correlations with cortical thickness (CT), mostly involving fronto-temporal areas, whereas PRS for MDD was associated with cortical alterations in prefrontal regions in healthy subjects.

LIMITATIONS

The heterogeneity in the methods limits the conclusions of this review.

CONCLUSIONS

Overall the evidence on the effect of PRS for MDD, BD and SCZ on brain is considerably heterogeneous and far to be conclusive. However, from the results emerged that PRS for MDD, BD and SCZ were associated with widespread cortical abnormalities in all the populations explored, suggesting that genetic risk for MDD, BD and SCZ might affect neurodevelopmental processes, resulting in cortical alterations that transcend diagnostic boundaries and seem to be independent from the clinical status.

Specificity of Psychiatric Polygenic Risk Scores and their Effects on Associated Risk Phenotypes

Background

Polygenic risk scores (PRSs) are indices of genetic liability for illness, but their clinical utility for predicting risk for a specific psychiatric disorder is limited. Genetic overlap among disorders and their effects on allied phenotypes may be a possible explanation, but this has been difficult to quantify given focus on singular disorders and/or allied phenotypes.

Methods

We constructed PRSs for 5 psychiatric disorders (schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorder, attention-deficit/hyperactivity disorder) and 3 nonpsychiatric control traits (height, type II diabetes, irritable bowel disease) in the UK Biobank (N = 31,616) and quantified associations between PRSs and phenotypes allied with mental illness: behavioral (symptoms, cognition, trauma) and brain measures from magnetic resonance imaging. We then evaluated the extent of specificity among PRSs and their effects on these allied phenotypes.

Results

Correlations among psychiatric PRSs replicated previous work, with overlap between schizophrenia and bipolar disorder, which was distinct from overlap between autism spectrum disorder and attention-deficit/hyperactivity disorder; overlap between psychiatric and control PRSs was minimal. There was, however, substantial overlap of PRS effects on allied phenotypes among psychiatric disorders and among psychiatric disorders and control traits, where the extent and pattern of overlap was phenotype specific.

Conclusions

Results show that genetic distinctions between psychiatric disorders and between psychiatric disorders and control traits exist, but this does not extend to their effects on allied phenotypes. Although overlap can be informative, work is needed to construct PRSs that will function at the level of specificity needed for clinical application.

Associations between polygenic risk scores and amplitude of low-frequency fluctuation of inferior frontal gyrus in schizophrenia

Schizophrenia (SCZ) is a serious and complex mental disorder with high heritability. Polygenic risk score (PRS) is a useful tool calculating the accumulating effects of multiple common genetic variants of schizophrenia. The amplitude of low-frequency fluctuation (ALFF) is an efficient index to reflect spontaneous, intrinsic neuronal activity. Aberrant ALFF of brain regions were reported in schizophrenia frequently, but the relationship between PRS and ALFF has not been studied. In the present study, we compared PRS and ALFF in 101 schizophrenia patients and 106 age-matched healthy controls to test their associations with schizophrenia. Then, the correlation of PRS with ALFF was measured to reveal the effect of polygenic risk on brain activity in schizophrenia. We found that schizophrenia patients showed significant differences in PRS and ALFF compared with controls. Twenty-six brain regions showed significant difference of ALFF between schizophrenia cases and controls, of which left inferior frontal gyrus, triangular part (IFGtriang.L) showed increased activity in schizophrenia. PRS-SCZ was positively correlated with ALFF in IFGtriang.L in 57 non-chronic patients. Genes involved in synaptic organization and transmission, especially in glutamatergic synapse, were highly enriched in PRS-SCZ genes, suggesting the dysfunction of synapses in schizophrenia. These results help to understand the molecular mechanism underlying schizophrenia and related brain dysfunction.

Combining Polygenic Risk Score and Voice Features to Detect Major Depressive Disorders

Background: The application of polygenic risk scores (PRSs) in major depressive disorder (MDD) detection is constrained by its simplicity and uncertainty. One promising way to further extend its usability is fusion with other biomarkers. This study constructed an MDD biomarker by combining the PRS and voice features and evaluated their ability based on large clinical samples.

Methods: We collected genome-wide sequences and utterances edited from clinical interview speech records from 3,580 women with recurrent MDD and 4,016 healthy people. Then, we constructed PRS as a gene biomarker by p value-based clumping and thresholding and extracted voice features using the i-vector method. Using logistic regression, we compared the ability of gene or voice biomarkers with the ability of both in combination for MDD detection. We also tested more machine learning models to further improve the detection capability.

Results: With a p-value threshold of 0.005, the combined biomarker improved the area under the receiver operating characteristic curve (AUC) by 9.09% compared to that of genes only and 6.73% compared to that of voice only. Multilayer perceptron can further heighten the AUC by 3.6% compared to logistic regression, while support vector machine and random forests showed no better performance.

Conclusion: The addition of voice biomarkers to genes can effectively improve the ability to detect MDD. The combination of PRS and voice biomarkers in MDD detection is feasible. This study provides a foundation for exploring the clinical application of genetic and voice biomarkers in the diagnosis of MDD.

Polygenic risk score for bipolar disorder associates with divergent thinking and brain structures in the prefrontal cortex

It has been hypothesized that a higher genetic risk of bipolar disorder (BD) is associated with greater creativity. Given the clinical importance of bipolar disorder and the importance of creativity to human society and cultural development, it is essential to reveal their associations and the neural basis of the genetic risk of bipolar disorder to gain insight into its etiology. However, despite the previous demonstration of the associations of polygenic risk score (PRS) of BD and creative jobs, the associations of BD‐PRS and creativity measured by the divergent thinking (CMDT) and regional gray matter volume (rGMV) as well as regional white matter volume (rWMV) have not been investigated. Using psychological analyses and whole‐brain voxel‐by‐voxel analyses, we examined these potential associations in 1558 young, typically developing adult students. After adjusting for confounding variables and multiple comparisons, a greater BD‐PRS was associated with a greater total CMDT fluency score, and a significant relationship was found in fluency subscores. A greater BD‐PRS was also associated with lower total mood disturbance. Neuroimaging analyses revealed that the BD‐PRS was associated with greater rGMV in the right inferior frontal gyrus, which is a consistently affected area in BD, as well as a greater rWMV in the left middle frontal gyrus, which has been suggested to play a central role in the increased creativity associated with the risk of BD with creativity. These findings suggest a relationship between the genetic risk of BD and CMDT and prefrontal cortical structures among young educated individuals.

Grey and white matter microstructure is associated with polygenic risk for schizophrenia

Recent discovery of approximately 270 common genetic variants associated with schizophrenia has enabled polygenic risk scores (PRS) to be measured in the population. We hypothesized that normal variation in PRS would be associated with magnetic resonance imaging (MRI) phenotypes of brain morphometry and tissue composition. We used the largest extant genome-wide association dataset (N = 69,369 cases and N = 236,642 healthy controls) to measure PRS for schizophrenia in a large sample of adults from the UK Biobank (N_max = 29,878) who had multiple micro- and macrostructural MRI metrics measured at each of 180 cortical areas, seven subcortical structures, and 15 major white matter tracts. Linear mixed-effect models were used to investigate associations between PRS and brain structure at global and regional scales, controlled for multiple comparisons. Polygenic risk was significantly associated with reduced neurite density index (NDI) at global brain scale, at 149 cortical regions, five subcortical structures, and 14 white matter tracts. Other microstructural parameters, e.g., fractional anisotropy, that were correlated with NDI were also significantly associated with PRS. Genetic effects on multiple MRI phenotypes were co-located in temporal, cingulate, and prefrontal cortical areas, insula, and hippocampus. Post-hoc bidirectional Mendelian randomization analyses provided preliminary evidence in support of a causal relationship between (reduced) thalamic NDI and (increased) risk of schizophrenia. Risk-related reduction in NDI is plausibly indicative of reduced density of myelinated axons and dendritic arborization in large-scale cortico-subcortical networks. Cortical, subcortical, and white matter microstructure may be linked to the genetic mechanisms of schizophrenia.

Association between body mass index and subcortical brain volumes in bipolar disorders-ENIGMA study in 2735 individuals

Individuals with bipolar disorders (BD) frequently suffer from obesity, which is often associated with neurostructural alterations. Yet, the effects of obesity on brain structure in BD are under-researched. We obtained MRI-derived brain subcortical volumes and body mass index (BMI) from 1134 BD and 1601 control individuals from 17 independent research sites within the ENIGMA-BD Working Group. We jointly modeled the effects of BD and BMI on subcortical volumes using mixed-effects modeling and tested for mediation of group differences by obesity using nonparametric bootstrapping. All models controlled for age, sex, hemisphere, total intracranial volume, and data collection site. Relative to controls, individuals with BD had significantly higher BMI, larger lateral ventricular volume, and smaller volumes of amygdala, hippocampus, pallidum, caudate, and thalamus. BMI was positively associated with ventricular and amygdala and negatively with pallidal volumes. When analyzed jointly, both BD and BMI remained associated with volumes of lateral ventricles  and amygdala. Adjusting for BMI decreased the BD vs control differences in ventricular volume. Specifically, 18.41% of the association between BD and ventricular volume was mediated by BMI (Z = 2.73, p = 0.006). BMI was associated with similar regional brain volumes as BD, including lateral ventricles, amygdala, and pallidum. Higher BMI may in part account for larger ventricles, one of the most replicated findings in BD. Comorbidity with obesity could explain why neurostructural alterations are more pronounced in some individuals with BD. Future prospective brain imaging studies should investigate whether obesity could be a modifiable risk factor for neuroprogression.

Dissecting autism and schizophrenia through neuroimaging genomics

Neuroimaging genomic studies of autism spectrum disorder and schizophrenia have mainly adopted a 'top-down' approach, beginning with the behavioural diagnosis, and moving down to intermediate brain phenotypes and underlying genetic factors. Advances in imaging and genomics have been successfully applied to increasingly large case-control studies. As opposed to diagnostic-first approaches, the bottom-up strategy begins at the level of molecular factors enabling the study of mechanisms related to biological risk, irrespective of diagnoses or clinical manifestations. The latter strategy has emerged from questions raised by top-down studies: why are mutations and brain phenotypes over-represented in individuals with a psychiatric diagnosis? Are they related to core symptoms of the disease or to comorbidities? Why are mutations and brain phenotypes associated with several psychiatric diagnoses? Do they impact a single dimension contributing to all diagnoses? In this review, we aimed at summarizing imaging genomic findings in autism and schizophrenia as well as neuropsychiatric variants associated with these conditions. Top-down studies of autism and schizophrenia identified patterns of neuroimaging alterations with small effect-sizes and an extreme polygenic architecture. Genomic variants and neuroimaging patterns are shared across diagnostic categories suggesting pleiotropic mechanisms at the molecular and brain network levels. Although the field is gaining traction; characterizing increasingly reproducible results, it is unlikely that top-down approaches alone will be able to disentangle mechanisms involved in autism or schizophrenia. In stark contrast with top-down approaches, bottom-up studies showed that the effect-sizes of high-risk neuropsychiatric mutations are equally large for neuroimaging and behavioural traits. Low specificity has been perplexing with studies showing that broad classes of genomic variants affect a similar range of behavioural and cognitive dimensions, which may be consistent with the highly polygenic architecture of psychiatric conditions. The surprisingly discordant effect sizes observed between genetic and diagnostic first approaches underscore the necessity to decompose the heterogeneity hindering case-control studies in idiopathic conditions. We propose a systematic investigation across a broad spectrum of neuropsychiatric variants to identify putative latent dimensions underlying idiopathic conditions. Gene expression data on temporal, spatial and cell type organization in the brain have also considerable potential for parsing the mechanisms contributing to these dimensions' phenotypes. While large neuroimaging genomic datasets are now available in unselected populations, there is an urgent need for data on individuals with a range of psychiatric symptoms and high-risk genomic variants. Such efforts together with more standardized methods will improve mechanistically informed predictive modelling for diagnosis and clinical outcomes.

Genetic risk for bipolar disorder and schizophrenia predicts structure and function of the ventromedial prefrontal cortex

BACKGROUND

Bipolar disorder is highly heritable and polygenic. The polygenic risk for bipolar disorder overlaps with that of schizophrenia, and polygenic scores are normally distributed in the population. Bipolar disorder has been associated with structural brain abnormalities, but it is unknown how these are linked to genetic risk factors for psychotic disorders.

METHODS

We tested whether polygenic risk scores for bipolar disorder and schizophrenia predict structural brain alterations in 98 patients with bipolar disorder and 81 healthy controls. We derived brain cortical thickness, surface area and volume from structural MRI scans. In post-hoc analyses, we correlated polygenic risk with functional hub strength, derived from resting-state functional MRI and brain connectomics.

RESULTS

Higher polygenic risk scores for both bipolar disorder and schizophrenia were associated with a thinner ventromedial prefrontal cortex (vmPFC). We found these associations in the combined group, and separately in patients and drug-naive controls. Polygenic risk for bipolar disorder was correlated with the functional hub strength of the vmPFC within the default mode network.

LIMITATIONS

Polygenic risk is a cumulative measure of genomic burden. Detailed genetic mechanisms underlying brain alterations and their cognitive consequences still need to be determined.

CONCLUSION

Our multimodal neuroimaging study linked genomic burden and brain endophenotype by demonstrating an association between polygenic risk scores for bipolar disorder and schizophrenia and the structure and function of the vmPFC. Our findings suggest that genetic factors might confer risk for psychotic disorders by influencing the integrity of the vmPFC, a brain region involved in self-referential processes and emotional regulation. Our study may also provide an imaging-genetics vulnerability marker that can be used to help identify individuals at risk for developing bipolar disorder.

Phenotypic and genetic associations between anhedonia and brain structure in UK Biobank

Anhedonia is a core symptom of multiple psychiatric disorders and has been associated with alterations in brain structure. Genome-wide association studies suggest that anhedonia is heritable, with a polygenic architecture, but few studies have explored the association between genetic loading for anhedonia-indexed by polygenic risk scores for anhedonia (PRS-anhedonia)-and structural brain imaging phenotypes. Here, we investigated how anhedonia and PRS-anhedonia were associated with brain structure within the UK Biobank cohort. Brain measures (including total grey/white matter volumes, subcortical volumes, cortical thickness (CT) and white matter integrity) were analysed using linear mixed models in relation to anhedonia and PRS-anhedonia in 19,592 participants (9225 males; mean age = 62.6 years, SD = 7.44). We found that state anhedonia was significantly associated with reduced total grey matter volume (GMV); increased total white matter volume (WMV); smaller volumes in thalamus and nucleus accumbens; reduced CT within the paracentral cortex, the opercular part of inferior frontal gyrus, precentral cortex, insula and rostral anterior cingulate cortex; and poorer integrity of many white matter tracts. PRS-anhedonia was associated with reduced total GMV; increased total WMV; reduced white matter integrity; and reduced CT within the parahippocampal cortex, superior temporal gyrus and insula. Overall, both state anhedonia and PRS-anhedonia were associated with individual differences in multiple brain structures, including within reward-related circuits. These associations may represent vulnerability markers for psychopathology relevant to a range of psychiatric disorders.

A structural brain network of genetic vulnerability to psychiatric illness

Psychiatry is undergoing a paradigm shift from the acceptance of distinct diagnoses to a representation of psychiatric illness that crosses diagnostic boundaries. How this transition is supported by a shared neurobiology remains largely unknown. In this study, we first identify single nucleotide polymorphisms (SNPs) associated with psychiatric disorders based on 136 genome-wide association studies. We then conduct a joint analysis of these SNPs and brain structural connectomes in 678 healthy children in the PING study. We discovered a strong, robust, and transdiagnostic mode of genome-connectome covariation which is positively and specifically correlated with genetic risk for psychiatric illness at the level of individual SNPs. Similarly, this mode is also significantly positively correlated with polygenic risk scores for schizophrenia, alcohol use disorder, major depressive disorder, a combined bipolar disorder-schizophrenia phenotype, and a broader cross-disorder phenotype, and significantly negatively correlated with a polygenic risk score for educational attainment. The resulting "vulnerability network" is shown to mediate the influence of genetic risks onto behaviors related to psychiatric vulnerability (e.g., marijuana, alcohol, and caffeine misuse, perceived stress, and impulsive behavior). Its anatomy overlaps with the default-mode network, with a network of cognitive control, and with the occipital cortex. These findings suggest that the brain vulnerability network represents an endophenotype funneling genetic risks for various psychiatric illnesses through a common neurobiological root. It may form part of the neural underpinning of the well-recognized but poorly explained overlap and comorbidity between psychiatric disorders.

Genetic factors influencing a neurobiological substrate for psychiatric disorders

A retrospective meta-analysis of magnetic resonance imaging voxel-based morphometry studies proposed that reduced gray matter volumes in the dorsal anterior cingulate and the left and right anterior insular cortex-areas that constitute hub nodes of the salience network-represent a common substrate for major psychiatric disorders. Here, we investigated the hypothesis that the common substrate serves as an intermediate phenotype to detect genetic risk variants relevant for psychiatric disease. To this end, after a data reduction step, we conducted genome-wide association studies of a combined common substrate measure in four population-based cohorts (n = 2271), followed by meta-analysis and replication in a fifth cohort (n = 865). After correction for covariates, the heritability of the common substrate was estimated at 0.50 (standard error 0.18). The top single-nucleotide polymorphism (SNP) rs17076061 was associated with the common substrate at genome-wide significance and replicated, explaining 1.2% of the common substrate variance. This SNP mapped to a locus on chromosome 5q35.2 harboring genes involved in neuronal development and regeneration. In follow-up analyses, rs17076061 was not robustly associated with psychiatric disease, and no overlap was found between the broader genetic architecture of the common substrate and genetic risk for major depressive disorder, bipolar disorder, or schizophrenia. In conclusion, our study identified that common genetic variation indeed influences the common substrate, but that these variants do not directly translate to increased disease risk. Future studies should investigate gene-by-environment interactions and employ functional imaging to understand how salience network structure translates to psychiatric disorder risk.

Schizophrenia Polygenic Risk and Brain Structural Changes in Methamphetamine-Associated Psychosis in a South African Population

Background

The genetic architecture of psychotic disorders is complex, with hundreds of genetic risk loci contributing to a polygenic model of disease. Overlap in the genetics of psychotic disorders and brain measures has been found in European populations, but has not been explored in populations of African ancestry. The aim of this study was to determine whether a relationship exists between a schizophrenia-derived PRS and (i) methamphetamine associated psychosis (MAP), and (ii) brain structural measures, in a South African population.

Methods

The study sample consisted of three participant groups: 31 individuals with MAP, 48 with apsychotic methamphetamine dependence, and 49 healthy controls. Using PRSice, PRS was generated for each of the participants with GWAS summary statistics from the Psychiatric Genomics Consortium Schizophrenia working group (PGC-SCZ2) as the discovery dataset. Regression analyses were performed to determine associations of PRS, with diagnosis, whole brain, and regional gray and white matter measures.

Results

Schizophrenia-derived PRS did not significantly predict MAP diagnosis. After correction for multiple testing, no significant associations were found between PRS and brain measures across all groups.

Discussion

The lack of significant associations here may indicate that the study is underpowered, that brain volumes in MAP are due to factors other than polygenic risk for schizophrenia, or that PRS derived from a largely European discovery set has limited utility in individuals of African ancestry. Larger studies, that include diverse populations, and more nuanced brain measures, may help elucidate the relationship between schizophrenia-PRS, brain structural changes, and psychosis.

Conclusion

This research presents the first PRS study to investigate shared genetic effects across psychotic disorders and brain structural measures in an African population. Ancestrally comparable discovery datasets may be useful for future African genetic research.

Multimodal MRI of Conduction Tracts and Anatomy of the Cerebral Gray Matter in Familial Risk of Affective Disorders and Schizophrenia

The present study analyzed diffusion characteristics of white matter tracts and grey matter anatomy in 48 mentally healthy participants, including first-degree relatives of patients with schizophrenia (N=13) and affective spectrum disorders (N=13). The subgroup with familial risk of schizophrenia displayed abnormalities in the structural connectivity and increased cortical thickness in the superior frontal gyrus. No differences in the analyzed characteristics were revealed in the subgroup with familial risk for affective disorders. The results are discussed within the framework of the concepts of endophenotypes and processes reflecting compensatory and protective mechanisms.

Polygenic risk for schizophrenia and subcortical brain anatomy in the UK Biobank cohort

Research has shown differences in subcortical brain volumes between participants with schizophrenia and healthy controls. However, none of these differences have been found to associate with schizophrenia polygenic risk. Here, in a large sample (n = 14,701) of unaffected participants from the UK Biobank, we test whether schizophrenia polygenic risk scores (PRS) limited to specific gene-sets predict subcortical brain volumes. We compare associations with schizophrenia PRS at the whole genome level ('genomic', including all SNPs associated with the disorder at a p-value threshold < 0.05) with 'genic' PRS (based on SNPs in the vicinity of known genes), 'intergenic' PRS (based on the remaining SNPs), and genic PRS limited to SNPs within 7 gene-sets previously found to be enriched for genetic association with schizophrenia ('abnormal behaviour,' 'abnormal long-term potentiation,' 'abnormal nervous system electrophysiology,' 'FMRP targets,' '5HT2C channels,' 'CaV2 channels' and 'loss-of-function intolerant genes'). We observe a negative association between the 'abnormal behaviour' gene-set PRS and volume of the right thalamus that survived correction for multiple testing (ß = -0.031, pFDR = 0.005) and was robust to different schizophrenia PRS p-value thresholds. In contrast, the only association with genomic PRS surviving correction for multiple testing was for right pallidum, which was observed using a schizophrenia PRS p-value threshold < 0.01 (ß = -0.032, p = 0.0003, pFDR = 0.02), but not when using other PRS P-value thresholds. We conclude that schizophrenia PRS limited to functional gene sets may provide a better means of capturing differences in subcortical brain volume than whole genome PRS approaches.

Polygenic scores for psychiatric disease: from research tool to clinical application

Propensity to psychiatric disease involves the contribution of multiple genetic variants with small individual effects (i. e., polygenicity). This contribution can be summarized using polygenic scores (PGSs). The present article discusses the methodological foundations of PGS calculation, together with the limitations and caveats of their use. Furthermore, we show that in terms of using genetic information to address the complexities of mental disorders, PGSs have become a standard tool in psychiatric research. PGS also have the potential for translation into clinical practice. Although PGSs alone do not allow reliable disease prediction, they have major potential value in terms of risk stratification, the identification of disorder subtypes, functional investigations, and case selection for experimental models. However, given the stigma associated with mental illness and the limited availability of effective interventions, risk prediction for common psychiatric disorders must be approached with particular caution, particularly in the non-regulated consumer context.

The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions

UK Biobank is a population-based cohort of half a million participants aged 40-69 years recruited between 2006 and 2010. In 2014, UK Biobank started the world's largest multi-modal imaging study, with the aim of re-inviting 100,000 participants to undergo brain, cardiac and abdominal magnetic resonance imaging, dual-energy X-ray absorptiometry and carotid ultrasound. The combination of large-scale multi-modal imaging with extensive phenotypic and genetic data offers an unprecedented resource for scientists to conduct health-related research. This article provides an in-depth overview of the imaging enhancement, including the data collected, how it is managed and processed, and future directions.

A phenome-wide association and Mendelian Randomisation study of polygenic risk for depression in UK Biobank

Depression is a leading cause of worldwide disability but there remains considerable uncertainty regarding its neural and behavioural associations. Here, using non-overlapping Psychiatric Genomics Consortium (PGC) datasets as a reference, we estimate polygenic risk scores for depression (depression-PRS) in a discovery (N = 10,674) and replication (N = 11,214) imaging sample from UK Biobank. We report 77 traits that are significantly associated with depression-PRS, in both discovery and replication analyses. Mendelian Randomisation analysis supports a potential causal effect of liability to depression on brain white matter microstructure (β: 0.125 to 0.868, p_FDR < 0.043). Several behavioural traits are also associated with depression-PRS (β: 0.014 to 0.180, p_FDR: 0.049 to 1.28 × 10⁻¹⁴) and we find a significant and positive interaction between depression-PRS and adverse environmental exposures on mental health outcomes. This study reveals replicable associations between depression-PRS and white matter microstructure. Our results indicate that white matter microstructure differences may be a causal consequence of liability to depression.

Depression is correlated with many brain-related traits. Here, Shen et al. perform phenome-wide association studies of a depression polygenic risk score (PRS) and find associations with 51 behavioural and 26 neuroimaging traits which are further followed up on using Mendelian randomization and mediation analyses.

Toward Circuit Mechanisms of Pathophysiology in Depression

The search for more effective treatments for depression is a long-standing primary objective in both psychiatry and translational neuroscience. From initial models centered on neurochemical deficits, such as the monoamine hypothesis, research toward this goal has shifted toward a focus on network and circuit models to explain how key nodes in the limbic system and beyond interact to produce persistent shifts in affective states. To build these models, researchers have turned to two complementary approaches: neuroimaging studies in human patients (and their healthy counterparts) and neurophysiology studies in animal models, facilitated in large part by optogenetic and chemogenetic techniques. As the authors discuss, functional neuroimaging studies in humans have included largely task-oriented experiments, which have identified brain regions differentially activated during processing of affective stimuli, and resting-state functional MRI experiments, which have identified brain-wide networks altered in depressive states. Future work in this area will build on a multisite approach, assembling large data sets across diverse populations, and will also leverage the statistical power afforded by longitudinal imaging studies in patient samples. Translational studies in rodents have used optogenetic and chemogenetic tools to identify not just nodes but also connections within the networks of the limbic system that are both critical and permissive for the expression of motivated behavior and affective phenotypes. Future studies in this area will exploit mesoscale imaging and multisite electrophysiology recordings to construct network models with cell-type specificity and high statistical power, identifying candidate circuit and molecular pathways for therapeutic intervention.

Psychotic-like experiences, polygenic risk scores for schizophrenia, and structural properties of the salience, default mode, and central-executive networks in healthy participants from UK Biobank

Schizophrenia is a highly heritable disorder with considerable phenotypic heterogeneity. Hallmark psychotic symptoms can be considered as existing on a continuum from non-clinical to clinical populations. Assessing genetic risk and psychotic-like experiences (PLEs) in non-clinical populations and their associated neurobiological underpinnings can offer valuable insights into symptom-associated brain mechanisms without the potential confounds of the effects of schizophrenia and its treatment. We leveraged a large population-based cohort (UKBiobank, N = 3875) including information on PLEs (obtained from the Mental Health Questionnaire (MHQ); UKBiobank Category: 144; N auditory hallucinations = 55, N visual hallucinations = 79, N persecutory delusions = 16, N delusions of reference = 13), polygenic risk scores for schizophrenia (PRSSZ) and multi-modal brain imaging in combination with network neuroscience. Morphometric (cortical thickness, volume) and water diffusion (fractional anisotropy) properties of the regions and pathways belonging to the salience, default-mode, and central-executive networks were computed. We hypothesized that these anatomical concomitants of functional dysconnectivity would be negatively associated with PRSSZ and PLEs. PRSSZ was significantly associated with a latent measure of cortical thickness across the salience network (r = -0.069, p = 0.010) and PLEs showed a number of significant associations, both negative and positive, with properties of the salience and default mode networks (involving the insular cortex, supramarginal gyrus, and pars orbitalis, pFDR < 0.050); with the cortical thickness of the insula largely mediating the relationship between PRSSZ and auditory hallucinations. Generally, these results are consistent with the hypothesis that higher genetic liability for schizophrenia is related to subtle disruptions in brain structure and may predispose to PLEs even among healthy participants. In addition, our study suggests that networks engaged during auditory hallucinations show structural associations with PLEs in the general population.

White Matter Microstructure across the Psychosis Spectrum

Diffusion-weighted imaging (DWI) is a neuroimaging technique that has allowed us an unprecedented look at the role that white matter microstructure may play in mental illnesses, such as psychosis. Psychosis-related illnesses, including schizophrenia, are increasingly viewed as existing along a spectrum; spectrums may be defined based on factors such as stage of illness, symptom severity, or genetic liability. This review first focuses on an overview of some of the recent findings from DWI studies. Then, it examines the ways in which DWI analyses have been extended across the broader psychosis spectrum, or spectrums, and what we have learned from such approaches.

Brain Connectome Mapping of Complex Human Traits and Their Polygenic Architecture Using Machine Learning

BACKGROUND

Mental disorders and individual characteristics such as intelligence and personality are complex traits sharing a largely unknown neuronal basis. Their genetic architectures are highly polygenic and overlapping, which is supported by heterogeneous phenotypic expression and substantial clinical overlap. Brain network analysis provides a noninvasive means of dissecting biological heterogeneity, yet its sensitivity, specificity, and validity in assessing individual characteristics relevant for brain function and mental health and their genetic underpinnings in clinical applications remain a challenge.

METHODS

In a machine learning approach, we predicted individual scores for educational attainment, fluid intelligence and dimensional measures of depression, anxiety, and neuroticism using functional magnetic resonance imaging-based static and dynamic temporal synchronization between large-scale brain network nodes in 10,343 healthy individuals from the UK Biobank. In addition to using age and sex to serve as our reference point, we also predicted individual polygenic scores for related phenotypes and 13 different neuroticism traits and schizophrenia.

RESULTS

Beyond high accuracy for age and sex, supporting the biological sensitivity of the connectome-based features, permutation tests revealed above chance-level prediction accuracy for trait-level educational attainment and fluid intelligence. Educational attainment and fluid intelligence were mainly negatively associated with static brain connectivity in frontal and default mode networks, whereas age showed positive correlations with a more widespread pattern. In contrast, prediction accuracy was at chance level for depression, anxiety, neuroticism, and polygenic scores across traits.

CONCLUSIONS

These novel findings provide a benchmark for future studies linking the genetic architecture of individual and mental health traits with functional magnetic resonance imaging-based brain connectomics.

Electronic health records and polygenic risk scores for predicting disease risk

Accurate prediction of disease risk based on the genetic make-up of an individual is essential for effective prevention and personalized treatment. Nevertheless, to date, individual genetic variants from genome-wide association studies have achieved only moderate prediction of disease risk. The aggregation of genetic variants under a polygenic model shows promising improvements in prediction accuracies. Increasingly, electronic health records (EHRs) are being linked to patient genetic data in biobanks, which provides new opportunities for developing and applying polygenic risk scores in the clinic, to systematically examine and evaluate patient susceptibilities to disease. However, the heterogeneous nature of EHR data brings forth many practical challenges along every step of designing and implementing risk prediction strategies. In this Review, we present the unique considerations for using genotype and phenotype data from biobank-linked EHRs for polygenic risk prediction.

Schizophrenia polygenic risk score influence on white matter microstructure

Schizophrenia (SZ) and bipolar disorder (BD) are highly heritable, share symptomatology, and have a polygenic architecture. The impact of recent polygenic risk scores (PRS) for psychosis, which combine multiple genome-wide associated risk variations, should be assessed on heritable brain phenotypes also previously associated with the illnesses, for a better understanding of the pathways to disease. We have recently reported on the current SZ PRS's ability to predict 1st episode of psychosis case-control status and general cognition. Herein, we test its penetrance on white matter microstructure, which is known to be impaired in SZ, in BD and their relatives, using 141 participants (including SZ, BP, relatives of SZ or BP patients, and healthy volunteers), and two white matter integrity indexes: fractional anisotropy (FA) and mean diffusivity (MD). No significant correlation between the SZ PRS and FA or MD was found, thus it remains unclear whether white matter changes are primarily associated with SZ genetic risk profiles.

Schizophrenia-associated genomic copy number variants and subcortical brain volumes in the UK Biobank

Schizophrenia is a highly heritable disorder for which anatomical brain alterations have been repeatedly reported in clinical samples. Unaffected at-risk groups have also been studied in an attempt to identify brain changes that do not reflect reverse causation or treatment effects. However, no robust associations have been observed between neuroanatomical phenotypes and known genetic risk factors for schizophrenia. We tested subcortical brain volume differences between 49 unaffected participants carrying at least one of the 12 copy number variants associated with schizophrenia in UK Biobank and 9063 individuals who did not carry any of the 93 copy number variants reported to be pathogenic. Our results show that CNV carriers have reduced volume in some of the subcortical structures previously shown to be reduced in schizophrenia. Moreover, these associations partially accounted for the association between pathogenic copy number variants and cognitive impairment, which is one of the features of schizophrenia.

Impact of Polygenic Risk for Schizophrenia on Cortical Structure in UK Biobank

BACKGROUND

Schizophrenia is a neurodevelopmental disorder with many genetic variants of individually small effect contributing to phenotypic variation. Lower cortical thickness (CT), surface area, and cortical volume have been demonstrated in people with schizophrenia. Furthermore, a range of obstetric complications (e.g., lower birth weight) are consistently associated with an increased risk for schizophrenia. We investigated whether a high polygenic risk score for schizophrenia (PGRS-SCZ) is associated with CT, surface area, and cortical volume in UK Biobank, a population-based sample, and tested for interactions with birth weight.

METHODS

Data were available for 2864 participants (n_male/n_female = 1382/1482; mean age = 62.35 years, SD = 7.40). Linear mixed models were used to test for associations among PGRS-SCZ and cortical volume, surface area, and CT and between PGRS-SCZ and birth weight. Interaction effects of these variables on cortical structure were also tested.

RESULTS

We found a significant negative association between PGRS-SCZ and global CT; a higher PGRS-SCZ was associated with lower CT across the whole brain. We also report a significant negative association between PGRS-SCZ and insular lobe CT. PGRS-SCZ was not associated with birth weight and no PGRS-SCZ × birth weight interactions were found.

CONCLUSIONS

These results suggest that individual differences in CT are partly influenced by genetic variants and are most likely not due to factors downstream of disease onset. This approach may help to elucidate the genetic pathophysiology of schizophrenia. Further investigation in case-control and high-risk samples could help identify any localized effects of PGRS-SCZ, and other potential schizophrenia risk factors, on CT as symptoms develop.

Multiscale Neuroscience of Psychiatric Disorders

The human brain comprises a multiscale network with multiple levels of organization. Neurons with dendritic and axonal connections form the microscale fabric of brain circuitry, and macroscale brain regions and white matter connections form the infrastructure for system-level brain communication and information integration. In this review, we discuss the emerging trend of multiscale neuroscience, the multidisciplinary field that brings together data from these different levels of nervous system organization to form a better understanding of between-scale relationships of brain structure, function, and behavior in health and disease. We provide a broad overview of this developing field and discuss recent findings of exemplary multiscale neuroscience studies that illustrate the importance of studying cross-scale interactions among the genetic, molecular, cellular, and macroscale levels of brain circuitry and connectivity and behavior. We particularly consider a central, overarching goal of these multiscale neuroscience studies of human brain connectivity: to obtain insight into how disease-related alterations at one level of organization may underlie alterations observed at other scales of brain network organization in mental disorders. We conclude by discussing the current limitations, challenges, and future directions of the field.

Effects of a neurodevelopmental genes based polygenic risk score for schizophrenia and single gene variants on brain structure in non-clinical subjects: A preliminary report

We tested whether a polygenic risk score integrating the effects of genes affecting neurodevelopment is associated to brain structural variation in healthy subjects. We acquired magnetic resonance imaging and genetic data of 167 healthy adults and computed a neurodevelopmental polygenic risk score (nPRS). We correlated the nPRS with local gyrification, cortical thickness and grey matter density and explored effects of single nucleotide polymorphisms included in the score. We did not find significant correlations of this nPRS with either measure. Individuals with the risk allele at rs11139497 show increases in cortical thickness (p < 0.05, FWE corrected) of the left superior temporal gyrus.

rs1360780 of the FKBP5 gene modulates the association between maternal acceptance and regional gray matter volume in the thalamus in children and adolescents

Investigating the effects of gene–environment interactions (G × E) with regard to brain structure may help to elucidate the putative mechanisms associated with psychiatric risk. rs1360780 (C/T) is a functional single-nucleotide polymorphism (SNP) in the gene encoding FK506–binding protein 5 (FKBP5), which is involved in the regulation of the hypothalamic–pituitary–adrenal (HPA) axis stress responses. The minor (T) allele of FKBP5 is considered a risk allele for stress-related disorders, due to the overproduction of FKBP5, which results in impaired communication of stress signals with the HPA axis. Previous studies have reported that interactions between childhood maltreatment and the rs1360780 genotype affect structures in subcortical areas of the brain. However, it is unclear how this SNP modulates the association between non-adverse environments and brain structure. In this study, we examined the interactive effect of the rs1360780 genotype and maternal acceptance on the regional gray matter volume (rGMV) in 202 Japanese children. Maternal acceptance was assessed using a Japanese psychological questionnaire for mothers. Whole-brain multiple regression analysis using voxel-based morphometry showed a significant positive association between maternal acceptance and rGMV in the left thalamus of T-allele carriers, while a significant negative association was found in C/C homozygotes. Post-hoc analysis revealed that at or below the 70th percentiles of maternal acceptance, the T-allele carriers had a reduced thalamic rGMV compared with that of C/C homozygotes. Thus, our investigation indicated that the effect of the maternal acceptance level on brain development was different, depending on the rs1360780 genotype. Importantly, we found that the differences in brain structure between the T-allele carriers and C/C homozygotes at low to moderate levels of maternal acceptance, which is not equivalent to maltreatment. The present study contributes to the G × E research by highlighting the necessity to investigate the role of non-adverse environmental factors.

Common Polygenic Variations for Psychiatric Disorders and Cognition in Relation to Brain Morphology in the General Pediatric Population

OBJECTIVE

This study examined the relation between polygenic scores (PGSs) for 5 major psychiatric disorders and 2 cognitive traits with brain magnetic resonance imaging morphologic measurements in a large population-based sample of children. In addition, this study tested for differences in brain morphology-mediated associations between PGSs for psychiatric disorders and PGSs for related behavioral phenotypes.

METHOD

Participants included 1,139 children from the Generation R Study assessed at 10 years of age with genotype and neuroimaging data available. PGSs were calculated for schizophrenia, bipolar disorder, major depression disorder, attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder, intelligence, and educational attainment using results from the most recent genome-wide association studies. Image processing was performed using FreeSurfer to extract cortical and subcortical brain volumes.

RESULTS

Greater genetic susceptibility for ADHD was associated with smaller caudate volume (strongest prior = 0.01: β = -0.07, p = .006). In boys, mediation analysis estimates showed that 11% of the association between the PGS for ADHD and the PGS attention problems was mediated by differences in caudate volume (n = 535), whereas mediation was not significant in girls or the entire sample. PGSs for educational attainment and intelligence showed positive associations with total brain volume (strongest prior = 0.5: β = 0.14, p = 7.12 × 10^-8; and β = 0.12, p = 6.87 × 10^-7, respectively).

CONCLUSION

The present findings indicate that the neurobiological manifestation of polygenic susceptibility for ADHD, educational attainment, and intelligence involve early morphologic differences in caudate and total brain volumes in childhood. Furthermore, the genetic risk for ADHD might influence attention problems through the caudate nucleus in boys.

Structural and Functional Neuroimaging of Polygenic Risk for Schizophrenia: A Recall-by-Genotype-Based Approach

Risk profile scores (RPS) derived from genome-wide association studies (GWAS) explain a considerable amount of susceptibility for schizophrenia (SCZ). However, little is known about how common genetic risk factors for SCZ influence the structure and function of the human brain, largely due to the constraints of imaging sample sizes. In the current study, we use a novel recall-by-genotype (RbG) methodological approach, where we sample young adults from a population cohort (Avon Longitudinal Study of Parents and Children: N genotyped = 8365) based on their SCZ-RPS. We compared 197 healthy individuals at extremes of low (N = 99) or high (N = 98) SCZ-RPS with behavioral tests, and structural and functional magnetic resonance imaging (fMRI). We first provide methodological details that will inform the design of future RbG studies for common SCZ genetic risk. We further provide an between group analysis of the RbG individuals (low vs high SCZ-RPS) who underwent structural neuroimaging data (T1-weighted scans) and fMRI data during a reversal learning task. While we found little evidence for morphometric differences between the low and high SCZ-RPS groups, we observed an impact of SCZ-RPS on blood oxygen level-dependent (BOLD) signal during reward processing in the ventral striatum (PFWE-VS-CORRECTED = .037), a previously investigated broader reward-related network (PFWE-ROIS-CORRECTED = .008), and across the whole brain (PFWE-WHOLE-BRAIN-CORRECTED = .013). We also describe the study strategy and discuss specific challenges of RbG for SCZ risk (such as SCZ-RPS related homoscedasticity). This study will help to elucidate the behavioral and imaging phenotypes that are associated with SCZ genetic risk.

Polygenic risk for neuropsychiatric disease and vulnerability to abnormal deep grey matter development

Neuropsychiatric disease has polygenic determinants but is often precipitated by environmental pressures, including adverse perinatal events. However, the way in which genetic vulnerability and early-life adversity interact remains obscure. We hypothesised that the extreme environmental stress of prematurity would promote neuroanatomic abnormality in individuals genetically vulnerable to psychiatric disorders. In 194 unrelated infants (104 males, 90 females), born before 33 weeks of gestation (mean gestational age 29.7 weeks), we combined Magnetic Resonance Imaging with a polygenic risk score (PRS) for five psychiatric pathologies to test the prediction that: deep grey matter abnormalities frequently seen in preterm infants are associated with increased polygenic risk for psychiatric illness. The variance explained by the PRS in the relative volumes of four deep grey matter structures (caudate nucleus, thalamus, subthalamic nucleus and lentiform nucleus) was estimated using linear regression both for the full, mixed ancestral, cohort and a subsample of European infants. Psychiatric PRS was negatively associated with lentiform volume in the full cohort (β = −0.24, p = 8 × 10⁻⁴) and a European subsample (β = −0.24, p = 8 × 10⁻³). Genetic variants associated with neuropsychiatric disease increase vulnerability to abnormal lentiform development after perinatal stress and are associated with neuroanatomic changes in the perinatal period.

The endocytic membrane trafficking pathway plays a major role in the risk of Parkinson's disease

BACKGROUND

PD is a complex polygenic disorder. In recent years, several genes from the endocytic membrane-trafficking pathway have been suggested to contribute to disease etiology. However, a systematic analysis of pathway-specific genetic risk factors is yet to be performed.

OBJECTIVES

To comprehensively study the role of the endocytic membrane-trafficking pathway in the risk of PD.

METHODS

Linkage disequilibrium score regression was used to estimate PD heritability explained by 252 genes involved in the endocytic membrane-trafficking pathway including genome-wide association studies data from 18,869 cases and 22,452 controls. We used pathway-specific single-nucleotide polymorphisms to construct a polygenic risk score reflecting the cumulative risk of common variants. To prioritize genes for follow-up functional studies, summary-data based Mendelian randomization analyses were applied to explore possible functional genomic associations with expression or methylation quantitative trait loci.

RESULTS

The heritability estimate attributed to endocytic membrane-trafficking pathway was 3.58% (standard error = 1.17). Excluding previously nominated PD endocytic membrane-trafficking pathway genes, the missing heritability was 2.21% (standard error = 0.42). Random heritability simulations were estimated to be 1.44% (standard deviation = 0.54), indicating that the unbiased total heritability explained by the endocytic membrane-trafficking pathway was 2.14%. Polygenic risk score based on endocytic membrane-trafficking pathway showed a 1.25 times increase of PD risk per standard deviation of genetic risk. Finally, Mendelian randomization identified 11 endocytic membrane-trafficking pathway genes showing functional consequence associated to PD risk.

CONCLUSIONS

We provide compelling genetic evidence that the endocytic membrane-trafficking pathway plays a relevant role in disease etiology. Further research on this pathway is warranted given that critical effort should be made to identify potential avenues within this biological process suitable for therapeutic interventions. © 2019 International Parkinson and Movement Disorder Society.

Polygenic risk for schizophrenia and associated brain structural changes: A systematic review

BACKGROUND

Genome wide association studies (GWAS) of schizophrenia allow the generation of Polygenic Risk Scores (PRS). PRS can be used to determine the contribution to altered brain structures in this disorder, which have been well described. However, findings from studies using PRS to predict brain structural changes in schizophrenia have been inconsistent. We therefore performed a systematic review to determine the association between schizophrenia PRS and brain structure.

METHODS

Following PRISMA systematic review guidelines, databases were searched for literature using key search terms. Inclusion criteria for the discovery sample required case-control schizophrenia GWAS summary statistics from European populations. The target sample was required to be of European ancestry, and have brain structure and genotype information. Quality assessment of the publications was conducted using the Mixed Methods Appraisal Tool for quantitative non-randomised studies.

MAIN FINDINGS

A total of seven studies were found to be eligible for review. Five studies found no significant association and two studies found a significant association of schizophrenia PRS with total brain, reduced white matter volume, and globus pallidus volume. However, the latter studies were conducted using smaller discovery (n_cases = 9394 n_controls = 12,462) and target samples compared to the studies with substantially larger discovery (n_cases = 33,636 n_controls = 43,008) and target samples where no association was observed. Taken together, the results suggest that schizophrenia PRS are not significantly associated with brain structural changes in this disorder.

CONCLUSIONS

The lack of significant association between schizophrenia PRS and brain structural changes may indicate that intermediate phenotypes other than brain structure should be the focus of future work. Alternatively, however, the lack of association found here may point to limitations of the current evidence-base, and so point to the need for future better powered studies.

Development of Neuroimaging-Based Biomarkers in Psychiatry

This chapter presents an overview of accumulating neuroimaging data with emphasis on translational potential. The subject will be described in the context of three disease states, i.e., schizophrenia, bipolar disorder, and major depressive disorder, and for three clinical goals, i.e., disease risk assessment, subtyping, and treatment decision.

Associations between polygenic risk scores for four psychiatric illnesses and brain structure using multivariate pattern recognition

Psychiatric illnesses are complex and polygenic. They are associated with widespread alterations in the brain, which are partly influenced by genetic factors. There have been some attempts to relate polygenic risk scores (PRS) - a measure of the overall genetic risk an individual carries for a disorder - to brain structure using univariate methods. However, PRS are likely associated with distributed and covarying effects across the brain. We therefore used multivariate machine learning in this proof-of-principle study to investigate associations between brain structure and PRS for four psychiatric disorders; attention deficit-hyperactivity disorder (ADHD), autism, bipolar disorder and schizophrenia. The sample included 213 individuals comprising patients with depression (69), bipolar disorder (33), and healthy controls (111). The five psychiatric PRSs were calculated based on summary data from the Psychiatric Genomics Consortium. T1-weighted magnetic resonance images were obtained and voxel-based morphometry was implemented in SPM12. Multivariate relevance vector regression was implemented in the Pattern Recognition for Neuroimaging Toolbox (PRoNTo). Across the whole sample, a multivariate pattern of grey matter significantly predicted the PRS for autism (r = 0.20, pFDR = 0.03; MSE = 4.20 × 10-5, pFDR = 0.02). For the schizophrenia PRS, the MSE was significant (MSE = 1.30 × 10-5, pFDR = 0.02) although the correlation was not (r = 0.15, pFDR = 0.06). These results lend support to the hypothesis that polygenic liability for autism and schizophrenia is associated with widespread changes in grey matter concentrations. These associations were seen in individuals not affected by these disorders, indicating that this is not driven by the expression of the disease, but by the genetic risk captured by the PRSs.

Association of Whole-Genome and NETRIN1 Signaling Pathway-Derived Polygenic Risk Scores for Major Depressive Disorder and White Matter Microstructure in the UK Biobank

Background

Major depressive disorder is a clinically heterogeneous psychiatric disorder with a polygenic architecture. Genome-wide association studies have identified a number of risk-associated variants across the genome and have reported growing evidence of NETRIN1 pathway involvement. Stratifying disease risk by genetic variation within the NETRIN1 pathway may provide important routes for identification of disease mechanisms by focusing on a specific process, excluding heterogeneous risk-associated variation in other pathways. Here, we sought to investigate whether major depressive disorder polygenic risk scores derived from the NETRIN1 signaling pathway (NETRIN1-PRSs) and the whole genome, excluding NETRIN1 pathway genes (genomic-PRSs), were associated with white matter microstructure.

Methods

We used two diffusion tensor imaging measures, fractional anisotropy (FA) and mean diffusivity (MD), in the most up-to-date UK Biobank neuroimaging data release (FA: n = 6401; MD: n = 6390).

Results

We found significantly lower FA in the superior longitudinal fasciculus (β = −.035, p_corrected = .029) and significantly higher MD in a global measure of thalamic radiations (β = .029, p_corrected = .021), as well as higher MD in the superior (β = .034, p_corrected = .039) and inferior (β = .029, p_corrected = .043) longitudinal fasciculus and in the anterior (β = .025, p_corrected = .046) and superior (β = .027, p_corrected = .043) thalamic radiation associated with NETRIN1-PRS. Genomic-PRS was also associated with lower FA and higher MD in several tracts.

Conclusions

Our findings indicate that variation in the NETRIN1 signaling pathway may confer risk for major depressive disorder through effects on a number of white matter tracts.