Effects of environmental risks and polygenic loading for schizophrenia on cortical thickness

Unveiling Promising Neuroimaging Biomarkers for Schizophrenia Through Clinical and Genetic Perspectives

Schizophrenia is a complex and serious brain disorder. Neuroscientists have become increasingly interested in using magnetic resonance-based brain imaging-derived phenotypes (IDPs) to investigate the etiology of psychiatric disorders. IDPs capture valuable clinical advantages and hold biological significance in identifying brain abnormalities. In this review, we aim to discuss current and prospective approaches to identify potential biomarkers for schizophrenia using clinical multimodal neuroimaging and imaging genetics. We first described IDPs through their phenotypic classification and neuroimaging genomics. Secondly, we discussed the applications of multimodal neuroimaging by clinical evidence in observational studies and randomized controlled trials. Thirdly, considering the genetic evidence of IDPs, we discussed how can utilize neuroimaging data as an intermediate phenotype to make association inferences by polygenic risk scores and Mendelian randomization. Finally, we discussed machine learning as an optimum approach for validating biomarkers. Together, future research efforts focused on neuroimaging biomarkers aim to enhance our understanding of 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.

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 and the language network: Putative compensatory reorganization in unaffected siblings

Language-related symptoms, such as disorganized, impoverished speech and communicative behaviors, are one of the core features of schizophrenia. These features most strongly correlate with cognitive deficits and polygenic risk among various symptom dimensions of schizophrenia. Nevertheless, unaffected siblings with genetic high-risk fail to show consistent deficits in language network (LN), indicating that either (1) polygenic risk has no notable effect on LN and/or (2) siblings show compensatory changes in opposing direction to patients. To answer this question, we related polygenic risk scores (PRS) to the region-level, tract-level, and systems-level structure (cortical thickness and fiber connectivity) of LN in 182 patients, 48 unaffected siblings and 135 healthy controls. We also studied the relationships between symptoms, language-related cognition, social functioning and LN structure. We observed a significantly lower thickness in LN (especially the Broca's, Wernicke's area and their right homologues) in patients. Siblings had a distinctly higher thickness in parts of the LN and a more pronounced small-world-like structural integration within the LN. Patients with reduced LN thickness had higher PRS, more disorganization and impoverished speech with lower language-related cognition and social functioning. We conclude that the genetic susceptibility and putative compensatory changes for schizophrenia operate, in part, via key regions in the Language Network.

Cumulative Environmental Risk in Early Life: Associations With Schizotypy in Childhood

BACKGROUND AND HYPOTHESIS

Psychotic disorders are associated with a growing number of recognized environmental exposures. Cumulative exposure to multiple environmental risk factors in childhood may contribute to the development of different patterns of schizotypy evident in early life. Hypotheses were that distinct profiles of schizotypy would have differential associations with a cumulative score of environmental risk factors.

STUDY DESIGN

We prospectively examined the relationship between 19 environmental exposures (which had demonstrated replicated associations with psychosis) measured from the prenatal period through to age 11 years, and 3 profiles of schizotypy in children (mean age = 11.9 years, n = 20 599) that have been established in population data from the New South Wales-Child Development Study. Multinomial logistic regression was used to examine associations between membership in each of 3 schizotypy profiles (true schizotypy, introverted schizotypy, and affective schizotypy) and exposure to a range of 19 environmental risk factors for psychosis (both individually and summed as a cumulative environmental risk score [ERS]), relative to children showing no risk.

RESULTS

Almost all environmental factors were associated with at least 1 schizotypy profile. The cumulative ERS was most strongly associated with the true schizotypy profile (OR = 1.61, 95% CI = 1.52-1.70), followed by the affective (OR = 1.33, 95% CI = 1.28-1.38), and introverted (OR = 1.32, 95% CI = 1.28-1.37) schizotypy profiles.

CONCLUSIONS

Consistent with the cumulative risk hypothesis, results indicate that an increased number of risk exposures is associated with an increased likelihood of membership in the 3 schizotypy profiles identified in middle childhood, relative to children with no schizotypy profile.

Consensus paper of the WFSBP task force on cannabis, cannabinoids and psychosis

OBJECTIVES

The liberalisation of cannabis laws, the increasing availability and potency of cannabis has renewed concern about the risk of psychosis with cannabis.

METHODS

The objective of the WFSBP task force was to review the literature about this relationship.

RESULTS

Converging lines of evidence suggest that exposure to cannabis increases the risk for psychoses ranging from transient psychotic states to chronic recurrent psychosis. The greater the dose, and the earlier the age of exposure, the greater the risk. For some psychosis outcomes, the evidence supports some of the criteria of causality. However, alternate explanations including reverse causality and confounders cannot be conclusively excluded. Furthermore, cannabis is neither necessary nor sufficient to cause psychosis. More likely it is one of the multiple causal components. In those with established psychosis, cannabis has a negative impact on the course and expression of the illness. Emerging evidence also suggests alterations in the endocannabinoid system in psychotic disorders.

CONCLUSIONS

Given that exposure to cannabis and cannabinoids is modifiable, delaying or eliminating exposure to cannabis or cannabinoids, could potentially impact the rates of psychosis related to cannabis, especially in those who are at high risk for developing the disorder.

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.

Cortical changes in patients with schizophrenia across two ethnic backgrounds

While it is known that cultural background influences the healthy brain, less is known about how it affects cortical changes in schizophrenia. Here, we tested whether schizophrenia differentially affected the brain in Japanese and German patients. In a sample of 155 patients with a diagnosis of schizophrenia and 191 healthy controls from Japan and Germany, we acquired 3 T-MRI of the brain. We subsequently compared cortical thickness and cortical surface area to identify whether differences between healthy controls and patients might be influenced by ethnicity. Additional analyses were performed to account for effects of duration of illness and medication. We found pronounced interactions between schizophrenia and cultural background in the cortical thickness of several areas, including the left inferior and middle temporal gyrus, as well as the right lateral occipital cortex. Regarding cortical surface area, interaction effects appeared in the insula and the occipital cortex, among others. Some of these brain areas are related to the expression of psychotic symptoms, which are known to differ across cultures. Our results indicate that cultural background impacts cortical structures in different ways, probably resulting in varying clinical manifestations, and call for the inclusion of more diverse samples in schizophrenia research.

Life Event Stress and Reduced Cortical Thickness in Youth at Clinical High Risk for Psychosis and Healthy Control Subjects

BACKGROUND

A decline in cortical thickness during early life appears to be a normal neuromaturational process. Accelerated cortical thinning has been linked with conversion to psychosis among individuals at clinical high risk for psychosis (CHR-P). Previous research indicates that exposure to life event stress (LES) is associated with exaggerated cortical thinning in both healthy and clinical populations, and LES is also linked with conversion to psychosis in CHR-P. To date, there are no reports on the relationship of LES with cortical thickness in CHR-P. This study examines this relationship and whether LES is linked with cortical thinning to a greater degree in individuals at CHR-P who convert to psychosis compared with individuals at CHR-P who do not convert and healthy control subjects.

METHODS

Controlling for age and gender (364 male, 262 female), this study examined associations between LES and baseline cortical thickness in 436 individuals at CHR-P (375 nonconverters and 61 converters) and 190 comparison subjects in the North American Prodrome Longitudinal Study.

RESULTS

Findings indicate that prebaseline cumulative LES is associated with reduced baseline cortical thickness in several regions among the CHR-P and control groups. Evidence suggests that LES is a risk factor for thinner cortex to the same extent across diagnostic groups, while CHR-P status is linked with thinner cortex in select regions after accounting for LES.

CONCLUSIONS

This research provides additional evidence to support the role of LES in cortical thinning in both healthy youth and those at CHR-P. Potential underlying mechanisms of the findings and implications for future research are discussed.

Morphometric Analysis of Structural MRI Using Schizophrenia Meta-analytic Priors Distinguish Patients from Controls in Two Independent Samples and in a Sample of Individuals With High Polygenic Risk

Schizophrenia (SCZ) is associated with structural brain changes, with considerable variation in the extent to which these cortical regions are influenced. We present a novel metric that summarises individual structural variation across the brain, while considering prior effect sizes, established via meta-analysis. We determine individual participant deviation from a within-sample-norm across structural MRI regions of interest (ROIs). For each participant, we weight the normalised deviation of each ROI by the effect size (Cohen's d) of the difference between SCZ/control for the corresponding ROI from the SCZ Enhancing Neuroimaging Genomics through Meta-Analysis working group. We generate a morphometric risk score (MRS) representing the average of these weighted deviations. We investigate if SCZ-MRS is elevated in a SCZ case/control sample (NCASE = 50; NCONTROL = 125), a replication sample (NCASE = 23; NCONTROL = 20) and a sample of asymptomatic young adults with extreme SCZ polygenic risk (NHIGH-SCZ-PRS = 95; NLOW-SCZ-PRS = 94). SCZ cases had higher SCZ-MRS than healthy controls in both samples (Study 1: β = 0.62, P < 0.001; Study 2: β = 0.81, P = 0.018). The high liability SCZ-PRS group also had a higher SCZ-MRS (Study 3: β = 0.29, P = 0.044). Furthermore, the SCZ-MRS was uniquely associated with SCZ status, but not attention-deficit hyperactivity disorder (ADHD), whereas an ADHD-MRS was linked to ADHD status, but not SCZ. This approach provides a promising solution when considering individual heterogeneity in SCZ-related brain alterations by identifying individual's patterns of structural brain-wide alterations.

Individual deviations from normative models of brain structure in a large cross-sectional schizophrenia cohort

The heterogeneity of schizophrenia has defied efforts to derive reproducible and definitive anatomical maps of structural brain changes associated with the disorder. We aimed to map deviations from normative ranges of brain structure for individual patients and evaluate whether the loci of individual deviations recapitulated group-average brain maps of schizophrenia pathology. For each of 48 white matter tracts and 68 cortical regions, normative percentiles of variation in fractional anisotropy (FA) and cortical thickness (CT) were established using diffusion-weighted and structural MRI from healthy adults (n = 195). Individuals with schizophrenia (n = 322) were classified as either within the normative range for healthy individuals of the same age and sex (5-95% percentiles), infra-normal (<5% percentile) or supra-normal (>95% percentile). Repeating this classification for each tract and region yielded a deviation map for each individual. Compared to the healthy comparison group, the schizophrenia group showed widespread reductions in FA and CT, involving virtually all white matter tracts and cortical regions. Paradoxically, however, no more than 15-20% of patients deviated from the normative range for any single tract or region. Furthermore, 79% of patients showed infra-normal deviations for at least one locus (healthy individuals: 59 ± 2%, p < 0.001). Thus, while infra-normal deviations were common among patients, their anatomical loci were highly inconsistent between individuals. Higher polygenic risk for schizophrenia associated with a greater number of regions with infra-normal deviations in CT (r = -0.17, p = 0.006). We conclude that anatomical loci of schizophrenia-related changes are highly heterogeneous across individuals to the extent that group-consensus pathological maps are not representative of most individual patients. Normative modeling can aid in parsing schizophrenia heterogeneity and guiding personalized interventions.

Estimating Aggregate Environmental Risk Score in Psychiatry: The Exposome Score for Schizophrenia

To understand the role of environment in the pathoetiology of psychosis spectrum disorders, research has thus far mainly investigated the effects of single exposures in isolation, such as the association between cannabis use and schizophrenia. However, this approach fails to acknowledge the complexity of the exposome, which represents the totality of the environment involving many exposures over an individual's lifetime. Therefore, contemporary research adopting the exposome paradigm has aimed at capturing the combined effect of different environmental exposures by utilizing an aggregate environmental vulnerability score for schizophrenia: the exposome score for schizophrenia. Here, we attempt to provide a comprehensive overview of studies applying the exposome score for schizophrenia. First, we describe several approaches estimating exposomic vulnerability for schizophrenia, which falls into three categories: simple environmental sum scores (sum of dichotomized exposures), meta-analysis-based environmental risk score (sum scores weighted by estimates from meta-analyses), and the exposome score (sum score weighted by estimates from an analysis in an independent training dataset). Studies show that the exposome score for schizophrenia that assumes interdependency of exposures performs better than scores that assume independence of exposures, such as the environmental sum score and the meta-analysis-based environmental risk score. Second, we discuss findings on the pluripotency of the exposome score for schizophrenia and summarize findings from gene-environment studies using the exposome score for schizophrenia. Finally, we discuss possible scientific, clinical, and population-based applications of exposome score for schizophrenia, as well as limitations and future directions for exposome research to understand the etiology of psychosis spectrum disorders.

The Maudsley environmental risk score for psychosis

BACKGROUND

Risk prediction algorithms have long been used in health research and practice (e.g. prediction of cardiovascular disease and diabetes). However, similar tools have not been developed for mental health. For example, for psychotic disorders, attempts to sum environmental risk are rare, unsystematic and dictated by available data. In light of this, we sought to develop a valid, easy to use measure of the aggregate environmental risk score (ERS) for psychotic disorders.

METHODS

We reviewed the literature to identify well-replicated and validated environmental risk factors for psychosis that combine a significant effect and large-enough prevalence. Pooled estimates of relative risks were taken from the largest available meta-analyses. We devised a method of scoring the level of exposure to each risk factor to estimate ERS. Relative risks were rounded as, due to the heterogeneity of the original studies, risk effects are imprecisely measured.

RESULTS

Six risk factors (ethnic minority status, urbanicity, high paternal age, obstetric complications, cannabis use and childhood adversity) were used to generate the ERS. A distribution for different levels of risk based on simulated data showed that most of the population would be at low/moderate risk with a small minority at increased environmental risk for psychosis.

CONCLUSIONS

This is the first systematic approach to develop an aggregate measure of environmental risk for psychoses in asymptomatic individuals. This can be used as a continuous measure of liability to disease; mostly relevant to areas where the original studies took place. Its predictive ability will improve with the collection of additional, population-specific data.

Obstetric Complications and Brain Imaging in Schizophrenia: A Systematic Review

Schizophrenia is a complex disorder in which clinical symptomatology typically reflects underlying brain abnormalities that coalign with multiple physical health comorbidities. The pathogenesis of schizophrenia involves the interplay between genetic and environmental factors, with obstetric complications widely described as key players in elevating the risk of psychosis. In this regard, understanding the anatomical and functional alterations associated with obstetric complications may help to elucidate potential mechanisms through which birth complications could contribute to schizophrenia pathogenesis. We conducted a systematic review of the extant literature describing brain abnormalities and obstetric complications in patients with schizophrenia and related disorders in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A total of 471 studies were retrieved and screened, and 33 studies met inclusion criteria for our review. Studies varied considerably in their methods, with 11 studies employing computed tomography, 1 using magnetic resonance spectroscopy, and 21 using magnetic resonance imaging. The scientific quality of the included studies was assessed and documented. Obstetric complications increase the risk of provoking brain abnormalities. These abnormalities range from decreased gray matter volume and abnormal brain-ventricle ratios to a reduction of volume in limbic regions-which relate to what is commonly observed in schizophrenia. However, current evidence from neuroimaging studies remains scant in relation to establishing obstetric complications as an independent risk factor for schizophrenia.

Examining Gene-Environment Interactions Using Aggregate Scores in a First-Episode Psychosis Cohort

Gene-environment (GxE) interactions have been related to psychosis spectrum disorders, involving multiple common genetic variants in multiple genes with very small effect sizes, and several environmental factors that constitute a dense network of exposures named the exposome. Here, we aimed to analyze GxE in a cohort of 310 first-episode psychotic (FEP) and 236 healthy controls, by using aggregate scores estimated in large populations such as the polygenic risk score for schizophrenia and (PRS-SCZ) and the Maudsley environmental risk score (ERS). In contrast to previous findings, in our study, the PRS-SCZ did not discriminate cases from controls, but the ERS score explained a similar percentage of the variance as in other studies using similar approaches. Our study supports a positive additive interaction, indicating synergy between genetic susceptibility to schizophrenia (PRS-SCZ dichotomized according to the highest quartile distribution of the control population) and the exposome (ERS > 75% of the controls). This additive interaction showed genetic and environmental dose dependence. Our study shows that the use of aggregate scores derived from large and powered studies instead of statistics derived from specific sample characteristics is a powerful tool for the study of the effects of GxE on the risk of psychotic spectrum disorders. In conclusion, by using a genetic risk score and an ERS we have provided further evidence for the role of GxE in psychosis.

Are working memory and glutamate concentrations involved in early-life stress and severity of psychosis?

OBJECTIVE

Occurrences of early-life stress (ELS) are associated with the severity of psychotic symptoms and working memory (WM) deficits in patients with psychosis (PSY). This study investigated potential mediation roles of WM behavioral performance and glutamate concentrations in prefrontal brain regions on the association between ELS and psychotic symptom severity in PSY.

METHOD

Forty-seven patients with PSY (established schizophrenia, n = 30; bipolar disorder, n = 17) completed measures of psychotic symptom severity. In addition, data on ELS and WM performance were collected in both patients with PSY and healthy controls (HC; n = 41). Resting-state glutamate concentrations in the bilateral dorsolateral prefrontal cortex (DLPFC) and anterior cingulate cortex (ACC) were also assessed with proton magnetic resonance spectroscopy for both PSY and HC groups. t tests, analyses of variance, and regression analyses were utilized.

RESULTS

Participants with PSY reported significantly more ELS occurrences and showed poorer WM performance than HC. Furthermore, individuals with PSY displayed lower glutamate concentrations in the left DLPFC than HC. Neither ELS nor WM performance were predictive of severity of psychotic symptoms in participants with PSY. However, we found a significant negative correlation between glutamate concentrations in the left DLPFC and ELS occurrence in HC only.

CONCLUSION

In individuals with PSY, the current study found no evidence that the association between ELS and psychotic symptoms is mediated by WM performance or prefrontal glutamate concentrations. In HC, the association between ELS experience and glutamate concentrations may indicate a neurometabolite effect of ELS that is independent of an illness effect in psychosis.

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.

Polygenic Risk Scores for Psychiatric Disorders Reveal Novel Clues About the Genetics of Disordered Gambling

Disordered gambling (DG) is a rare but serious condition that results in considerable financial and interpersonal harms. Twin studies indicate that DG is heritable but are silent with respect to specific genes or pathways involved. Existing genomewide association studies (GWAS) of DG have been substantially underpowered. Larger GWAS of other psychiatric disorders now permit calculation of polygenic risk scores (PRSs) that reflect the aggregated effects of common genetic variants contributing risk for the target condition. The current study investigated whether gambling and DG are associated with PRSs for four psychiatric conditions found to be comorbid with DG in epidemiologic surveys: major depressive disorder (MDD), attention-deficit hyperactivity disorder (ADHD), bipolar disorder (BD) and schizophrenia (SCZ). Genotype data and survey responses were analyzed from the Wave IV assessment (conducted in 2008) of the National Longitudinal Study of Adolescent to Adult Health, a representative sample of adolescents recruited in 1994-1995 and followed into adulthood. Among participants classified as having European ancestry based on genetic analysis (N = 5215), 78.4% reported ever having gambled, and 1.3% reported lifetime DG. Polygenic risk for BD was associated with decreased odds of lifetime gambling, OR = 0.93 [0.87, 0.99], p = .045, pseudo-R2(%) = .12. The SCZ PRS was associated with increased odds of DG, OR = 1.54 [1.07, 2.21], p = .02, pseudo-R2(%) = .85. Polygenic risk scores for MDD and ADHD were not related to either gambling outcome. Investigating features common to both SCZ and DG might generate valuable clues about the genetically influenced liabilities to DG.

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.

Brain Heterogeneity in Schizophrenia and Its Association With Polygenic Risk

Importance

Between-individual variability in brain structure is determined by gene-environment interactions, possibly reflecting differential sensitivity to environmental and genetic perturbations. Magnetic resonance imaging (MRI) studies have revealed thinner cortices and smaller subcortical volumes in patients with schizophrenia. However, group-level comparisons may mask considerable within-group heterogeneity, which has largely remained unnoticed in the literature.

Objectives

To compare brain structural variability between individuals with schizophrenia and healthy controls and to test whether respective variability reflects the polygenic risk score (PRS) for schizophrenia in an independent sample of healthy controls.

Design, Setting, and Participants

This case-control and polygenic risk analysis compared MRI-derived cortical thickness and subcortical volumes between healthy controls and patients with schizophrenia across 16 cohorts and tested for associations between PRS and MRI features in a control cohort from the UK Biobank. Data were collected from October 27, 2004, through April 12, 2018, and analyzed from December 3, 2017, through August 1, 2018.

Main Outcomes and Measures

Mean and dispersion parameters were estimated using double generalized linear models. Vertex-wise analysis was used to assess cortical thickness, and regions-of-interest analyses were used to assess total cortical volume, total surface area, and white matter, subcortical, and hippocampal subfield volumes. Follow-up analyses included within-sample analysis, test of robustness of the PRS threshold, population covariates, outlier removal, and control for image quality.

Results

A comparison of 1151 patients with schizophrenia (mean [SD] age, 33.8 [10.6] years; 68.6% male [n = 790] and 31.4% female [n = 361]) with 2010 healthy controls (mean [SD] age, 32.6 [10.4] years; 56.0% male [n = 1126] and 44.0% female [n = 884]) revealed higher heterogeneity in schizophrenia for cortical thickness and area (t = 3.34), cortical (t = 3.24) and ventricle (t range, 3.15-5.78) volumes, and hippocampal subfields (t range, 2.32-3.55). In the UK Biobank sample of 12 490 participants (mean [SD] age, 55.9 [7.5] years; 48.2% male [n = 6025] and 51.8% female [n = 6465]), higher PRS was associated with thinner frontal and temporal cortices and smaller left CA2/3 (t = -3.00) but was not significantly associated with dispersion.

Conclusions and Relevance

This study suggests that schizophrenia is associated with substantial brain structural heterogeneity beyond the mean differences. These findings may reflect higher sensitivity to environmental and genetic perturbations in patients, supporting the heterogeneous nature of schizophrenia. A higher PRS was associated with thinner frontotemporal cortices and smaller hippocampal subfield volume, but not heterogeneity. This finding suggests that brain variability in schizophrenia results from interactions between environmental and genetic factors that are not captured by the PRS. Factors contributing to heterogeneity in frontotemporal cortices and hippocampus are key to furthering our understanding of how genetic and environmental factors shape brain biology in schizophrenia.

Epigenetic Regulations in Neuropsychiatric Disorders

Precise genetic and epigenetic spatiotemporal regulation of gene expression is critical for proper brain development, function and circuitry formation in the mammalian central nervous system. Neuronal differentiation processes are tightly regulated by epigenetic mechanisms including DNA methylation, histone modifications, chromatin remodelers and non-coding RNAs. Dysregulation of any of these pathways is detrimental to normal neuronal development and functions, which can result in devastating neuropsychiatric disorders, such as depression, schizophrenia and autism spectrum disorders. In this review, we focus on the current understanding of epigenetic regulations in brain development and functions, as well as their implications in neuropsychiatric disorders.

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.

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.

Recent Advances in the Genetics of Schizophrenia

The last decade brought tremendous progress in the field of schizophrenia genetics. As a result of extensive collaborations and multiple technological advances, we now recognize many types of genetic variants that increase the risk. These include large copy number variants, rare coding inherited and de novο variants, and over 100 loci harboring common risk variants. While the type and contribution to the risk vary among genetic variants, there is concordance in the functions of genes they implicate, such as those whose RNA binds the fragile X-related protein FMRP and members of the activity-regulated cytoskeletal complex involved in learning and memory. Gene expression studies add important information on the biology of the disease and recapitulate the same functional gene groups. Studies of alternative phenotypes help us widen our understanding of the genetic architecture of mental function and dysfunction, how diseases overlap not only with each other but also with non-disease phenotypes. The challenge is to apply this new knowledge to prevention and treatment and help patients. The data generated so far and emerging technologies, including new methods in cell engineering, offer significant promise that in the next decade we will unlock the translational potential of these significant discoveries.

Polygenic Risk Scores in Clinical Psychology: Bridging Genomic Risk to Individual Differences

Genomewide association studies (GWASs) across psychiatric phenotypes have shown that common genetic variants generally confer risk with small effect sizes (odds ratio < 1.1) that additively contribute to polygenic risk. Summary statistics derived from large discovery GWASs can be used to generate polygenic risk scores (PRS) in independent, target data sets to examine correlates of polygenic disorder liability (e.g., does genetic liability to schizophrenia predict cognition?). The intuitive appeal and generalizability of PRS have led to their widespread use and new insights into mechanisms of polygenic liability. However, when currently applied across traits they account for small amounts of variance (<3%), are relatively uninformative for clinical treatment, and, in isolation, provide no insight into molecular mechanisms. Larger GWASs are needed to increase the precision of PRS, and novel approaches integrating various data sources (e.g., multitrait analysis of GWASs) may improve the utility of current PRS.

Interaction of childhood urbanicity and variation in dopamine genes alters adult prefrontal function as measured by functional magnetic resonance imaging (fMRI)

Brain phenotypes showing environmental influence may help clarify unexplained associations between urban exposure and psychiatric risk. Heritable prefrontal fMRI activation during working memory (WM) is such a phenotype. We hypothesized that urban upbringing (childhood urbanicity) would alter this phenotype and interact with dopamine genes that regulate prefrontal function during WM. Further, dopamine has been hypothesized to mediate urban-associated factors like social stress. WM-related prefrontal function was tested for main effects of urbanicity, main effects of three dopamine genes-catechol-O-methyltransferase (COMT), dopamine receptor D1 (DRD1), and dopamine receptor D2 (DRD2)-and, importantly, dopamine gene-by-urbanicity interactions. For COMT, three independent human samples were recruited (total n = 487). We also studied 253 subjects genotyped for DRD1 and DRD2. 3T fMRI activation during the N-back WM task was the dependent variable, while childhood urbanicity, dopamine genotype, and urbanicity-dopamine interactions were independent variables. Main effects of dopamine genes and of urbanicity were found. Individuals raised in an urban environment showed altered prefrontal activation relative to those raised in rural or town settings. For each gene, dopamine genotype-by-urbanicity interactions were shown in prefrontal cortex-COMT replicated twice in two independent samples. An urban childhood upbringing altered prefrontal function and interacted with each gene to alter genotype-phenotype relationships. Gene-environment interactions between multiple dopamine genes and urban upbringing suggest that neural effects of developmental environmental exposure could mediate, at least partially, increased risk for psychiatric illness in urban environments via dopamine genes expressed into adulthood.

Effects of MiR-137 genetic risk score on brain volume and cortical measures in patients with schizophrenia and controls

Multiple genome-wide association studies of schizophrenia have implicated genetic variants within the gene encoding microRNA-137. As risk variants within or regulated by MIR137 have been implicated in memory performance, we investigated the additive effects of schizophrenia-associated risk variants in genes empirically regulated by MIR137 on brain regions associated with memory function. A polygenic risk score (PRS) was calculated (at a p = 0.05 threshold), using this empirically regulated MIR137 gene set, to investigate associations between this PRS and structural brain measures. These measures included total brain volume, cortical thickness, cortical surface area, and hippocampal volume, in a sample of 216 individuals consisting of healthy participants (n = 171) and patients with psychosis (n = 45). We did not observe a significant association between MIR137 PRS and these cortical thickness, surface area or hippocampal volume measures linked to memory function; a significant association between increasing PRS and decreasing total brain volume, independent of diagnosis status (R²  = 0.008, Beta = -0.09, p = 0.029), was observed. This did not survive correction for multiple testing. In conclusion, our study yielded only suggestive evidence that risk variants interacting with MIR137 impacts on cortical structure.

Genomic and Imaging Biomarkers in Schizophrenia

Recent large-scale genomic studies have confirmed that schizophrenia is a polygenic syndrome and have implicated a number of biological pathways in its aetiology. Both common variants individually of small effect and rarer but more penetrant genetic variants have been shown to play a role in the pathogenesis of the disorder. No simple Mendelian forms of the condition have been identified, but progress has been made in stratifying risk on the basis of the polygenic burden of common variants individually of small effect, and the contribution of rarer variants of larger effect such as Copy Number Variants (CNVs). Pathway analysis of risk-associated variants has begun to identify specific biological processes implicated in risk for the disorder, including elements of the glutamatergic NMDA receptor complex and post synaptic density, voltage-gated calcium channels, targets of the Fragile X Mental Retardation Protein (FMRP targets) and immune pathways. Genetic studies have also been used to drive genomic imaging approaches to the investigation of brain markers associated with risk for the disorder. Genomic imaging approaches have been applied both to investigate the effect of polygenic risk and to study the impact of individual higher-penetrance variants such as CNVs. Both genomic and genomic imaging approaches offer potential for the stratification of patients and at-risk groups and the development of better biomarkers of risk and treatment response; however, further research is needed to integrate this work and realise the full potential of these approaches.