Polygenic determinants of white matter volume derived from GWAS lack reproducibility in a replicate sample

MRI-based cortical gray/white matter contrast in young adults who endorse psychotic experiences or are at genetic risk for psychosis

Research has reported group-level differences in cortical grey/white matter contrast (GWC) in individuals with psychotic disorders. However, no studies to date have explored GWC in individuals at elevated risk for psychosis. In this study, we examined brain microstructure differences between young adults with psychotic-like experiences or a high genetic risk for psychosis and unaffected individuals. Moreover, we investigated the association between GWC and the number of and experiences of psychosis-like symptoms. The sample was obtained from the Avon Longitudinal Study of Parents and Children (ALSPAC): the psychotic experiences study, consisting of young adults with psychotic-like symptoms (n = 119) and unaffected individuals (n = 117), and the schizophrenia recall-by-genotype study, consisting of individuals with a high genetic risk for psychosis (n = 95) and those with low genetic risk for psychosis (n = 95). Statistical analyses were performed using FSL's Permutation Analysis of Linear Models (PALM), controlling for age and sex. The results showed no statistically significant differences in GWC between any of the groups and no significant associations between GWC and the number and experiences of psychosis-like symptoms. In conclusion, the results indicate there are no differences in GWC in individuals with high, low or no risk for psychosis.

Inferring the genetic relationship between brain imaging-derived phenotypes and risk of complex diseases by Mendelian randomization and genome-wide colocalization

Observational studies consistently disclose brain imaging-derived phenotypes (IDPs) as critical markers for early diagnosis of both brain disorders and cardiovascular diseases. However, it remains unclear about the shared genetic landscape between brain IDPs and the risk of brain disorders and cardiovascular diseases, restricting the applications of potential diagnostic techniques through brain IDPs. Here, we reported genetic correlations and putative causal relationships between 921 brain IDPs, 20 brain disorders and six cardiovascular diseases by leveraging their large-scale genome-wide association study (GWAS) summary statistics. Applications of Mendelian randomization (MR) identified significant putative causal effects of multiple region-specific brain IDPs in relation to the increased risks for amyotrophic lateral sclerosis (ALS), major depressive disorder (MDD), autism spectrum disorder (ASD) and schizophrenia (SCZ). We also found brain IDPs specifically from temporal lobe as a putatively causal consequence of hypertension. The genome-wide colocalization analysis identified three genomic regions in which MDD, ASD and SCZ colocalized with the brain IDPs, and two novel SNPs to be associated with ASD, SCZ, and multiple brain IDPs. Furthermore, we identified a list of candidate genes involved in the shared genetics underlying pairs of brain IDPs and MDD, ASD, SCZ, ALS and hypertension. Our results provide novel insights into the genetic relationships between brain disorders and cardiovascular diseases and brain IDP, which may server as clues for using brain IDPs to predict risks of diseases.

Structural neuroimaging phenotypes and associated molecular and genomic underpinnings in autism: a review

Autism has been associated with differences in the developmental trajectories of multiple neuroanatomical features, including cortical thickness, surface area, cortical volume, measures of gyrification, and the gray-white matter tissue contrast. These neuroimaging features have been proposed as intermediate phenotypes on the gradient from genomic variation to behavioral symptoms. Hence, examining what these proxy markers represent, i.e., disentangling their associated molecular and genomic underpinnings, could provide crucial insights into the etiology and pathophysiology of autism. In line with this, an increasing number of studies are exploring the association between neuroanatomical, cellular/molecular, and (epi)genetic variation in autism, both indirectly and directly in vivo and across age. In this review, we aim to summarize the existing literature in autism (and neurotypicals) to chart a putative pathway from (i) imaging-derived neuroanatomical cortical phenotypes to (ii) underlying (neuropathological) biological processes, and (iii) associated genomic variation.

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 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.

Proportionality of single nucleotide causation

Assessing the proportionality of causal relationships help us pick out the most relevant causes of an effect. In this paper, I nuance and apply the concept of proportionality from the philosophy of causation debate to the mapping of associations between variations in single nucleotides in the DNA and complex phenotypic traits, such as cancer, bipolar disorder, and happiness. I discuss under what circumstances single nucleotides, as far as these can be understood as causes, may satisfy the criterion of proportionality in an interventionist understanding of causality. I suggest that the overall relatively low stability and explanatory power of such variants may indicate that, in the causation of complex phenotypic traits, there are alternative causal levels that are more proportional than the level of nucleotides. I suggest network modules as candidates for more proportional organism-internal causes of complex phenotypes. Additionally, I address the broadness of many phenotypic traits investigated in GWAS, as well as the selection between several different proportional causes of an effect.

Association and epistatic analysis of white matter related genes across the continuum schizophrenia and autism spectrum disorders: The joint effect of NRG1-ErbB genes

BACKGROUND

Schizophrenia-spectrum disorders (SSD) and Autism spectrum disorders (ASD) are neurodevelopmental disorders that share clinical, cognitive, and genetic characteristics, as well as particular white matter (WM) abnormalities. In this study, we aimed to investigate the role of a set of oligodendrocyte/myelin-related (OMR) genes and their epistatic effect on the risk for SSD and ASD.

METHODS

We examined 108 SNPs in a set of 22 OMR genes in 1749 subjects divided into three independent samples (187 SSD trios, 915 SSD cases/control, and 91 ASD trios). Genetic association and gene-gene interaction analyses were conducted with PLINK and MB-MDR, and permutation procedures were implemented in both.

RESULTS

Some OMR genes showed an association trend with SSD, while after correction, the ones that remained significantly associated were MBP, ERBB3, and AKT1. Significant gene-gene interactions were found between (i) NRG1*MBP (perm p-value = 0.002) in the SSD trios sample, (ii) ERBB3*AKT1 (perm p-value = 0.001) in the SSD case-control sample, and (iii) ERBB3*QKI (perm p-value = 0.0006) in the ASD trios sample.

DISCUSSION

Our results suggest the implication of OMR genes in the risk for both SSD and ASD and highlight the role of NRG1 and ERBB genes. These findings are in line with the previous evidence and may suggest pathophysiological mechanisms related to NRG1/ERBBs signalling in these disorders.

Do we measure or compute polygenic risk scores? Why language matters

Here, we argue that polygenic risk scores (PRSs) are different epistemic objects as compared to other biomarkers such as blood pressure or sodium level. While the latter two may be subject to variation, measured inaccurately or interpreted in various ways, blood flow has pressure and sodium is available in a concentration that can be quantified and visualised. In stark contrast, PRSs are calculated, compiled or constructed through the statistical assemblage of genetic variants. How researchers frame and name PRSs has consequences for how we interpret and value their results. We distinguish between the tangible and inferential understanding of PRS and the corresponding languages of measurement and computation, respectively. The conflation of these frames obscures important questions we need to ask: what PRS seeks to represent, whether current ways of 'doing PRS' are optimal and responsible, and upon what we base the credibility of PRS-based knowledge claims.

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.

Neonatal amygdalae and hippocampi are influenced by genotype and prenatal environment, and reflected in the neonatal DNA methylome

The amygdala and hippocampus undergo rapid development in early life. The relative contribution of genetic and environmental factors to the establishment of their developmental trajectories has yet to be examined. We performed imaging on neonates and examined how the observed variation in volume and microstructure of the amygdala and hippocampus varied by genotype, and compared with prenatal maternal mental health and socioeconomic status. Gene × Environment models outcompeted models containing genotype or environment only to best explain the majority of measures but some, especially of the amygdaloid microstructure, were best explained by genotype only. Models including DNA methylation measured in the neonate umbilical cords outcompeted the Gene and Gene × Environment models for the majority of amygdaloid measures and minority of hippocampal measures. This study identified brain region-specific gene networks associated with individual differences in fetal brain development. In particular, genetic and epigenetic variation within CUX1 was highlighted.

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.

Interaction of schizophrenia polygenic risk and cortisol level on pre-adolescent brain structure

The etiology of schizophrenia is multi-factorial with early neurodevelopmental antecedents, likely to result from a complex interaction of genetic and environmental risk. However, few studies have examined how schizophrenia polygenic risk scores (PRS) are moderated by environmental factors in shaping neurodevelopmental brain structure, prior to the onset of psychotic symptoms. Here, we examined whether hair cortisol, a quantitative metric of chronic stress, moderated the association between genetic risk for schizophrenia and pre-adolescent brain structure. This study was embedded within the Generation R Study, involving pre-adolescents of European ancestry assessed regarding schizophrenia PRS, hair cortisol, and brain imaging (n = 498 structural; n = 526 diffusion tensor imaging). Linear regression was performed to determine the association between schizophrenia PRS, hair cortisol level, and brain imaging outcomes. Although no single measure exceeded the multiple testing threshold, nominally significant interactions were observed for total ventricle volume (P_interaction = 0.02) and global white matter microstructure (P_interaction = 0.01) - two of the most well replicated brain structural findings in schizophrenia. These findings provide suggestive evidence for the joint effects of schizophrenia liability and cortisol levels on brain correlates in the pediatric general population. Given the widely replicated finding of ventricular enlargement and lower white matter integrity among schizophrenia patients, our findings generate novel hypotheses for future research on gene-environment interactions affecting the neurodevelopmental pathophysiology of schizophrenia.

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.

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.

Relationships between substance abuse/dependence and psychiatric disorders based on polygenic scores

Genetic susceptibility to substance use disorders (SUDs) is partially shared between substances. Heritability of any substance dependence, estimated as 54%, is partly explained by additive effects of common variants. Comorbidity between SUDs and other psychiatric disorders is frequent. The present study aims to analyze the additive role of common variants in this comorbidity using polygenic scores (PGSs) based on genome-wide association study discovery samples of schizophrenia (SCZ), bipolar disorder, attention-deficit/hyperactivity disorder, autism spectrum disorder, major depressive disorder and anxiety disorders, available from large consortia. PGSs were calculated for 534 patients meeting DSM-IV criteria for dependence of a substance and abuse/dependence of another substance between alcohol, tobacco, cannabis, cocaine, opiates, hypnotics, stimulants, hallucinogens and solvents; and 587 blood donors from the same population, Iberians from Galicia, as controls. Significance of the PGS and percentage of variance explained were calculated by logistic regression. Using discovery samples of similar size, significant associations with SUDs were detected for SCZ PGS. SCZ PGS explained more variance in SUDs than in most psychiatric disorders. Cross-disorder PGS based on five psychiatric disorders was significant after adjustment for the effect of SCZ PGS. SCZ PGS was significantly higher in women than in men abusing alcohol. Our findings indicate that SUDs share genetic susceptibility with SCZ to a greater extent than with other psychiatric disorders, including externalizing disorders such as attention-deficit/hyperactivity disorder. Women have lower probability to develop substance abuse/dependence than men at similar PGS probably because of a higher social pressure against excessive drug use in women.

OTTO: a new strategy to extract mental disease-relevant combinations of GWAS hits from individuals

Despite high heritability of schizophrenia, genome-wide association studies (GWAS) have not yet revealed distinct combinations of single-nucleotide polymorphisms (SNPs), relevant for mental disease-related, quantifiable behavioral phenotypes. Here we propose an individual-based model to use genome-wide significant markers for extracting first genetic signatures of such behavioral continua. 'OTTO' (old Germanic=heritage) marks an individual characterized by a prominent phenotype and a particular load of phenotype-associated risk SNPs derived from GWAS that likely contributed to the development of his personal mental illness. This load of risk SNPs is shared by a small squad of 'similars' scattered under the genetically and phenotypically extremely heterogeneous umbrella of a schizophrenia end point diagnosis and to a variable degree also by healthy subjects. In a discovery sample of >1000 deeply phenotyped schizophrenia patients and several independent replication samples, including the general population, a gradual increase in the severity of 'OTTO's phenotype' expression is observed with an increasing share of 'OTTO's risk SNPs', as exemplified here by autistic and affective phenotypes. These data suggest a model in which the genetic contribution to dimensional behavioral traits can be extracted from combinations of GWAS SNPs derived from individuals with prominent phenotypes. Even though still in the 'model phase' owing to a world-wide lack of sufficiently powered, deeply phenotyped replication samples, the OTTO approach constitutes a conceptually novel strategy to delineate biological subcategories of mental diseases starting from GWAS findings and individual subjects.

Imaging genetics of schizophrenia in the post-GWAS era

Imaging genetics is a research methodology studying the effect of genetic variation on brain structure, function, behavior, and risk for psychopathology. Since the early 2000s, imaging genetics has been increasingly used in the research of schizophrenia (SZ). SZ is a severe mental disorder with no precise knowledge of its underlying neurobiology, however, new genetic and neurobiological data generate a climate for new avenues. The accumulating data of genome wide association studies (GWAS) continuously decode SZ risk genes. Global neuroimaging consortia produce collections of brain phenotypes from tens of thousands of people. In this context, imaging genetics will be strategically important both for the validation and discovery of SZ related findings. Thus, the study of GWAS supported risk variants as candidate genes to validate by neuroimaging is one trend. The study of epigenetic differences in relation to variations of brain phenotypes and the study of large scale multivariate analysis of genome wide and brain wide associations are other trends. While these studies hold a big potential for understanding the neurobiology of SZ, the problem of reproducibility appears as a major challenge, which requires standardizations in study designs and compensations of methodological limitations such as sensitivity and specificity. On the other hand, advancements of neuroimaging, optical and electron microscopy along with the use of genetically encoded fluorescent probes and robust statistical approaches will not only catalyze integrative methodologies but also will help better design the imaging genetics studies. In this invited paper, I will discuss the current perspective of imaging genetics and emerging opportunities of SZ research.

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.

A polygenic risk score analysis of psychosis endophenotypes across brain functional, structural, and cognitive domains

This large multi-center study investigates the relationships between genetic risk for schizophrenia and bipolar disorder, and multi-modal endophenotypes for psychosis. The sample included 4,242 individuals; 1,087 patients with psychosis, 822 unaffected first-degree relatives of patients, and 2,333 controls. Endophenotypes included the P300 event-related potential (N = 515), lateral ventricular volume (N = 798), and the cognitive measures block design (N = 3,089), digit span (N = 1,437), and the Ray Auditory Verbal Learning Task (N = 2,406). Data were collected across 11 sites in Europe and Australia; all genotyping and genetic analyses were done at the same laboratory in the United Kingdom. We calculated polygenic risk scores for schizophrenia and bipolar disorder separately, and used linear regression to test whether polygenic scores influenced the endophenotypes. Results showed that higher polygenic scores for schizophrenia were associated with poorer performance on the block design task and explained 0.2% (p = 0.009) of the variance. Associations in the same direction were found for bipolar disorder scores, but this was not statistically significant at the 1% level (p = 0.02). The schizophrenia score explained 0.4% of variance in lateral ventricular volumes, the largest across all phenotypes examined, although this was not significant (p = 0.063). None of the remaining associations reached significance after correction for multiple testing (with alpha at 1%). These results indicate that common genetic variants associated with schizophrenia predict performance in spatial visualization, providing additional evidence that this measure is an endophenotype for the disorder with shared genetic risk variants. The use of endophenotypes such as this will help to characterize the effects of common genetic variation in psychosis.

Polygenic risk for schizophrenia and neurocognitive performance in patients with schizophrenia

Both neurocognitive deficits and schizophrenia are highly heritable. Genetic overlap between neurocognitive deficits and schizophrenia has been observed in both the general population and in the clinical samples. This study aimed to examine if the polygenic architecture of susceptibility to schizophrenia modified neurocognitive performance in schizophrenia patients. Schizophrenia polygenic risk scores (PRSs) were first derived from the Psychiatric Genomics Consortium (PGC) on schizophrenia, and then the scores were calculated in our independent sample of 1130 schizophrenia trios, who had PsychChip data and were part of the Schizophrenia Families from Taiwan project. Pseudocontrols generated from the nontransmitted parental alleles of the parents in these trios were compared with alleles in schizophrenia patients in assessing the replicability of PGC-derived susceptibility variants. Schizophrenia PRS at the P-value threshold (PT) of 0.1 explained 0.2% in the variance of disease status in this Han-Taiwanese samples, and the score itself had a P-value 0.05 for the association test with the disorder. Each patient underwent neurocognitive evaluation on sustained attention using the continuous performance test and executive function using the Wisconsin Card Sorting Test. We applied a structural equation model to construct the neurocognitive latent variable estimated from multiple measured indices in these 2 tests, and then tested the association between the PRS and the neurocognitive latent variable. Higher schizophrenia PRS generated at the PT of 0.1 was significantly associated with poorer neurocognitive performance with explained variance 0.5%. Our findings indicated that schizophrenia susceptibility variants modify the neurocognitive performance in schizophrenia patients.

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

There are established differences in cortical thickness (CT) in schizophrenia (SCZ) and bipolar (BD) patients when compared to healthy controls (HC). However, it is unknown to what extent environmental or genetic risk factors impact on CT in these populations. We have investigated the effect of Environmental Risk Scores (ERS) and Polygenic Risk Scores for SCZ (PGRS-SCZ) on CT. Structural MRI scans were acquired at 3T for patients with SCZ or BD (n=57) and controls (n=41). Cortical reconstructions were generated in FreeSurfer (v5.3). The ERS was created by determining exposure to cannabis use, childhood adverse events, migration, urbanicity and obstetric complications. The PGRS-SCZ were generated, for a subset of the sample (Patients=43, HC=32), based on the latest PGC GWAS findings. ANCOVAs were used to test the hypotheses that ERS and PGRS-SCZ relate to CT globally, and in frontal and temporal lobes. An increase in ERS was negatively associated with CT within temporal lobe for patients. A higher PGRS-SCZ was also related to global cortical thinning for patients. ERS effects remained significant when including PGRS-SCZ as a fixed effect. No relationship which survived FDR correction was found for ERS and PGRS-SCZ in controls. Environmental risk for SCZ was related to localised cortical thinning in patients with SCZ and BD, while increased PGRS-SCZ was associated with global cortical thinning. Genetic and environmental risk factors for SCZ appear therefore to have differential effects. This provides a mechanistic means by which different risk factors may contribute to the development of SCZ and BD.

Polygenic Risk for Schizophrenia Influences Cortical Gyrification in 2 Independent General Populations

Schizophrenia is highly heritable, whereas the effect of each genetic variant is very weak. Since clinical heterogeneity and complexity of schizophrenia is high, considerable effort has been made to relate genetic variants to underlying neurobiological aspects of schizophrenia (endophenotypes). Given the polygenic nature of schizophrenia, our goal was to form a measure of additive genetic risk and explore its relationship to cortical morphology. Utilizing the data from a recent genome-wide association study that included nearly 37 000 cases of schizophrenia, we computed a polygenic risk score (PGRS) for each subject in 2 independent and healthy general populations. We then investigated the effect of polygenic risk for schizophrenia on cortical gyrification calculated from 3.0T structural imaging data in the discovery dataset (N = 315) and replication dataset (N = 357). We found a consistent effect of the polygenic risk for schizophrenia on cortical gyrification in the inferior parietal lobules in 2 independent general-population samples. A higher PGRS was significantly associated with a lower local gyrification index in the bilateral inferior parietal lobles, where case-control differences have been reported in previous studies on schizophrenia. Our findings strongly support the effectiveness of both PGRSs and endophenotypes in establishing the genetic architecture of psychiatry. Our findings may provide some implications regarding individual differences in the genetic risk for schizophrenia to cortical morphology and brain development.

Predicting brain structure in population-based samples with biologically informed genetic scores for schizophrenia

Schizophrenia is associated with brain structural abnormalities including gray and white matter volume reductions. Whether these alterations are caused by genetic risk variants for schizophrenia is unclear. Previous attempts to detect associations between polygenic factors for schizophrenia and structural brain phenotypes in healthy subjects have been negative or remain non-replicated. In this study, we used genetic risk scores that were based on the accumulated effect of selected risk variants for schizophrenia belonging to specific biological systems like synaptic function, neurodevelopment, calcium signaling, and glutamatergic neurotransmission. We hypothesized that this "biologically informed" approach would provide the missing link between genetic risk for schizophrenia and brain structural phenotypes. We applied whole-brain voxel-based morphometry (VBM) analyses in two population-based target samples and subsequent regions of interest (ROIs) analyses in an independent replication sample (total N = 2725). No consistent association between the genetic scores and brain volumes were observed in the investigated samples. These results suggest that in healthy subjects with a higher genetic risk for schizophrenia additional factors apart from common genetic variants (e.g., infection, trauma, rare genetic variants, or gene-gene interactions) are required to induce structural abnormalities of the brain. Further studies are recommended to test for possible gene-gene or gene-environment effects. © 2017 Wiley Periodicals, Inc.

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

Major depressive disorder (MDD), schizophrenia (SCZ) and bipolar disorder (BP) are common, disabling and heritable psychiatric diseases with a complex overlapping polygenic architecture. Individuals with these disorders, as well as their unaffected relatives, show widespread structural differences in corticostriatal and limbic networks. Structural variation in many of these brain regions is also heritable and polygenic but whether their genetic architecture overlaps with that of major psychiatric disorders is unknown. We sought to address this issue by examining the impact of polygenic risk of MDD, SCZ, and BP on subcortical brain volumes and white matter (WM) microstructure in a large single sample of neuroimaging data; the UK Biobank Imaging study. The first release of UK Biobank imaging data comprised participants with overlapping genetic data and subcortical volumes (N = 978) and WM measures (N = 816). The calculation of polygenic risk scores was based on genome-wide association study results generated by the Psychiatric Genomics Consortium. Our findings indicated no statistically significant associations between either subcortical volumes or WM microstructure, and polygenic risk for MDD, SCZ or BP. These findings suggest that subcortical brain volumes and WM microstructure may not be closely linked to the genetic mechanisms of major psychiatric disorders.

Impact of polygenic schizophrenia-related risk and hippocampal volumes on the onset of psychosis

Alterations in hippocampal volume are a known marker for first-episode psychosis (FEP) as well as for the clinical high-risk state. The Polygenic Schizophrenia-related Risk Score (PSRS), derived from a large case-control study, indicates the polygenic predisposition for schizophrenia in our clinical sample. A total of 65 at-risk mental state (ARMS) and FEP patients underwent structural magnetic resonance imaging. We used automatic segmentation of hippocampal volumes using the FSL-FIRST software and an odds-ratio-weighted PSRS based on the publicly available top single-nucleotide polymorphisms from the Psychiatric Genomics Consortium genome-wide association study (GWAS). We observed a negative association between the PSRS and hippocampal volumes (β=-0.42, P=0.01, 95% confidence interval (CI)=(-0.72 to -0.12)) across FEP and ARMS patients. Moreover, a higher PSRS was significantly associated with a higher probability of an individual being assigned to the FEP group relative to the ARMS group (β=0.64, P=0.03, 95% CI=(0.08-1.29)). These findings provide evidence that a subset of schizophrenia risk variants is negatively associated with hippocampal volumes, and higher values of this PSRS are significantly associated with FEP compared with the ARMS. This implies that FEP patients have a higher genetic risk for schizophrenia than the total cohort of ARMS patients. The identification of associations between genetic risk variants and structural brain alterations will increase our understanding of the neurobiology underlying the transition to psychosis.

Limited Evidence for Association of Genome-Wide Schizophrenia Risk Variants on Cortical Neuroimaging Phenotypes

BACKGROUND

There are now over 100 established genetic risk variants for schizophrenia; however, their influence on brain structure and circuitry across the human lifespan are not known.

METHODS

We examined healthy individuals 8-86 years of age, from the Centre for Addiction and Mental Health, the Zucker Hillside Hospital, and the Philadelphia Neurodevelopmental Cohort. Following thorough quality control procedures, we investigated associations of established genetic risk variants with heritable neuroimaging phenotypes relevant to schizophrenia, namely thickness of frontal and temporal cortical regions (n = 565) and frontotemporal and interhemispheric white matter tract fractional anisotropy (FA) (n = 530).

RESULTS

There was little evidence for association of risk variants with imaging phenotypes. No association with cortical thickness of any region was present. Only rs12148337, near a long noncoding RNA region, was associated with white matter FA (splenium of corpus callosum) following multiple comparison correction (corrected p = .012); this single nucleotide polymorphism was also associated with genu FA and superior longitudinal fasciculus FA at p <.005 (uncorrected). There was no association of polygenic risk score with white matter FA or cortical thickness.

CONCLUSIONS

In sum, our findings provide limited evidence for association of schizophrenia risk variants with cortical thickness or diffusion imaging white matter phenotypes. When taken with recent lack of association of these variants with subcortical brain volumes, our results either suggest that structural neuroimaging approaches at current resolution are not sufficiently sensitive to detect effects of these risk variants or that multiple comparison correction in correlated phenotypes is too stringent, potentially "eliminating" biologically important signals.

Associations between polygenic risk for schizophrenia and brain function during probabilistic learning in healthy individuals

A substantial proportion of schizophrenia liability can be explained by additive genetic factors. Risk profile scores (RPS) directly index risk using a summated total of common risk variants weighted by their effect. Previous studies suggest that schizophrenia RPS predict alterations to neural networks that support working memory and verbal fluency. In this study, we apply schizophrenia RPS to fMRI data to elucidate the effects of polygenic risk on functional brain networks during a probabilistic‐learning neuroimaging paradigm. The neural networks recruited during this paradigm have previously been shown to be altered to unmedicated schizophrenia patients and relatives of schizophrenia patients, which may reflect genetic susceptibility. We created schizophrenia RPS using summary data from the Psychiatric Genetic Consortium (Schizophrenia Working Group) for 83 healthy individuals and explore associations between schizophrenia RPS and blood oxygen level dependency (BOLD) during periods of choice behavior (switch–stay) and reflection upon choice outcome (reward–punishment). We show that schizophrenia RPS is associated with alterations in the frontal pole (P_{WHOLE‐BRAIN‐CORRECTED} = 0.048) and the ventral striatum (P_{ROI‐CORRECTED} = 0.036), during choice behavior, but not choice outcome. We suggest that the common risk variants that increase susceptibility to schizophrenia can be associated with alterations in the neural circuitry that support the processing of changing reward contingencies. Hum Brain Mapp 37:491–500, 2016. © 2015 Wiley Periodicals, Inc.

Schizophrenia risk variants modulate white matter volume across the psychosis spectrum: evidence from two independent cohorts

Polygenic risk scores, based on risk variants identified in genome-wide-association-studies (GWAS), explain a considerable portion of the heritability for schizophrenia (SZ) and bipolar disorder (BD). However, little is known about the combined effects of these variants, although polygenic neuroimaging has developed into a powerful tool of translational neuroscience. In this study, we used genome wide significant SZ risk variants to test the predictive capacity of the polygenic model and explored potential associations with white matter volume, a key candidate in imaging phenotype for psychotic disorders. By calculating the combined additive schizophrenia risk of seven SNPs (significant hits from a recent schizophrenia GWAS study), we show that increased additive genetic risk for SZ was associated with reduced white matter volume in a group of participants (n = 94) consisting of healthy individuals, SZ first-degree relatives, SZ patients and BD patients. This effect was also seen in a second independent sample of healthy individuals (n = 89). We suggest that a moderate portion of variance (~4%) of white matter volume can be explained by the seven hits from the recent schizophrenia GWAS. These results provide evidence for associations between cumulative genetic risk for schizophrenia and intermediate neuroimaging phenotypes in models of psychosis. Our work contributes to a growing body of literature suggesting that polygenic risk may help to explain white matter alterations associated with familial risk for psychosis.

Cognitive and structural neuroimaging characteristics of schizophrenia patients with large, rare copy number deletions

Large (>500 Kb), rare (frequency <1%) deletions are associated with risk for schizophrenia. The aim of the study was to characterise patients with these deletions using measures of cognition, grey-matter volume and white-matter integrity. Patients with schizophrenia and large, rare deletions (SZ-del) (n=17) were assessed on a test of intelligence, the Wechsler Abbreviated Scale of Intelligence (WASI), and compared with age- and sex-matched schizophrenia patients without large, rare deletions (SZ-nodel) (n=65), and healthy controls (HCs) (n=50). Regional grey-matter differences were investigated using voxel-based morphometry (SZ-del=9; SZ-nodel=26; HC=19). White-matter integrity was assessed using fractional anisotropy (SZ-del=9; SZ-nodel=24; HC=15). Compared with schizophrenia patients without large, rare deletions, those with large, rare deletions had lower IQ; greater grey-matter volume in clusters with peaks in the left and right cerebellum, left hippocampus, and right rectal gyrus; and increased white-matter anisotropy in the body and genu of the corpus callosum. Compared with healthy controls, patients with large, rare deletions had reduced grey matter volume in the right calcarine gyrus. In sum, patients with large, rare deletions had structural profiles intermediate to those observed in healthy controls and schizophrenia patients without large, rare deletions, but had greater impairment in intelligence.

Accumulated environmental risk determining age at schizophrenia onset: a deep phenotyping-based study

BACKGROUND

Schizophrenia is caused by a combination of genetic and environmental factors, as first evidenced by twin studies. Extensive efforts have been made to identify the genetic roots of schizophrenia, including large genome-wide association studies, but these yielded very small effect sizes for individual markers. In this study, we aimed to assess the relative contribution of genome-wide association study-derived genetic versus environmental risk factors to crucial determinants of schizophrenia severity: disease onset, disease severity, and socioeconomic measures.

METHODS

In this parallel analysis, we studied 750 male patients from the Göttingen Research Association for Schizophrenia (GRAS) dataset (Germany) with schizophrenia for whom both genome-wide coverage of single-nucleotide polymorphisms and deep clinical phenotyping data were available. Specifically, we investigated the potential effect of schizophrenia risk alleles as identified in the most recent large genome-wide association study versus the effects of environmental hazards (ie, perinatal brain insults, cannabis use, neurotrauma, psychotrauma, urbanicity, and migration), alone and upon accumulation, on age at disease onset, age at prodrome, symptom expression, and socioeconomic parameters.

FINDINGS

In this study, we could show that frequent environmental factors become a major risk for early schizophrenia onset when accumulated (prodrome begins up to 9 years earlier; p=2·9×10(-10)). In particular, cannabis use-an avoidable environmental risk factor-is highly significantly associated with earlier age at prodrome (p=3·8×10(-20)). By contrast, polygenic genome-wide association study risk scores did not have any detectable effects on schizophrenia phenotypes.

INTERPRETATION

These findings should be translated to preventive measures to reduce environmental risk factors, since age at onset of schizophrenia is a crucial determinant of an affected individual's fate and the total socioeconomic cost of the illness.

FUNDING

German Research Foundation (Research Center for Nanoscale Microscopy and Molecular Physiology of the Brain), Max Planck Society, Max Planck Förderstiftung, EXTRABRAIN EU-FP7, ERA-NET NEURON.

Phenotype-Based Genetic Association Studies (PGAS)-Towards Understanding the Contribution of Common Genetic Variants to Schizophrenia Subphenotypes

Neuropsychiatric diseases ranging from schizophrenia to affective disorders and autism are heritable, highly complex and heterogeneous conditions, diagnosed purely clinically, with no supporting biomarkers or neuroimaging criteria. Relying on these "umbrella diagnoses", genetic analyses, including genome-wide association studies (GWAS), were undertaken but failed to provide insight into the biological basis of these disorders. "Risk genotypes" of unknown significance with low odds ratios of mostly <1.2 were extracted and confirmed by including ever increasing numbers of individuals in large multicenter efforts. Facing these results, we have to hypothesize that thousands of genetic constellations in highly variable combinations with environmental co-factors can cause the individual disorder in the sense of a final common pathway. This would explain why the prevalence of mental diseases is so high and why mutations, including copy number variations, with a higher effect size than SNPs, constitute only a small part of variance. Elucidating the contribution of normal genetic variation to (disease) phenotypes, and so re-defining disease entities, will be extremely labor-intense but crucial. We have termed this approach PGAS ("phenotype-based genetic association studies"). Ultimate goal is the definition of biological subgroups of mental diseases. For that purpose, the GRAS (Göttingen Research Association for Schizophrenia) data collection was initiated in 2005. With >3000 phenotypical data points per patient, it comprises the world-wide largest currently available schizophrenia database (N > 1200), combining genome-wide SNP coverage and deep phenotyping under highly standardized conditions. First PGAS results on normal genetic variants, relevant for e.g., cognition or catatonia, demonstrated proof-of-concept. Presently, an autistic subphenotype of schizophrenia is being defined where an unfortunate accumulation of normal genotypes, so-called pro-autistic variants of synaptic genes, explains part of the phenotypical variance. Deep phenotyping and comprehensive clinical data sets, however, are expensive and it may take years before PGAS will complement conventional GWAS approaches in psychiatric genetics.