Impaired oxysterol-liver X receptor signaling underlies aberrant cortical neurogenesis in a stem cell model of neurodevelopmental disorder

The mechanisms by which genomic risks contribute to the onset of neuropsychiatric conditions remain a key challenge and a prerequisite for successful development of effective therapies. 15q11.2 copy number variation (CNV) containing the CYFIP1 gene is associated with autism and schizophrenia. Using stem cell models, we show that 15q11.2 deletion (15q11.2del) and CYFIP1 loss of function (CYFIP1-LoF) lead to premature neuronal differentiation, while CYFIP1 gain of function (CYFIP1-GoF) favors neural progenitor maintenance. CYFIP1 dosage changes led to dysregulated cholesterol metabolism and altered levels of 24S,25-epoxycholesterol, which can mimic the 15q11.2del and CYFIP1-LoF phenotypes by promoting cortical neuronal differentiation and can restore the impaired neuronal differentiation of CYFIP1-GoF neural progenitors. Moreover, the neurogenic activity of 24S,25-epoxycholesterol is lost following genetic deletion of liver X receptor (LXRβ), while compound deletion of LXRβ in CYFIP1-/- background rescued their premature neurogenesis. This work delineates LXR-mediated oxysterol regulation of neurogenesis as a pathological mechanism in neural cells carrying 15q11.2 CNV and provides a potential target for therapeutic strategies for associated disorders.

Dissipative Pairing Interactions: Quantum Instabilities, Topological Light, and Volume-Law Entanglement

We analyze an unusual class of bosonic dynamical instabilities that arise from dissipative (or non-Hermitian) pairing interactions. We show that, surprisingly, a completely stable dissipative pairing interaction can be combined with simple hopping or beam-splitter interactions (also stable) to generate instabilities. Further, we find that the dissipative steady state in such a situation remains completely pure up until the instability threshold (in clear distinction from standard parametric instabilities). These pairing-induced instabilities also exhibit an extremely pronounced sensitivity to wave function localization. This provides a simple yet powerful method for selectively populating and entangling edge modes of photonic (or more general bosonic) lattices having a topological band structure. The underlying dissipative pairing interaction is experimentally resource friendly, requiring the addition of a single additional localized interaction to an existing lattice, and is compatible with a number of existing platforms, including superconducting circuits.

Genetic risk for schizophrenia is associated with increased proportion of indirect connections in brain networks revealed by a semi-metric analysis: evidence from population sample stratified for polygenic risk

Research studies based on tractography have revealed a prominent reduction of asymmetry in some key white-matter tracts in schizophrenia (SCZ). However, we know little about the influence of common genetic risk factors for SCZ on the efficiency of routing on structural brain networks (SBNs). Here, we use a novel recall-by-genotype approach, where we sample young adults from a population-based cohort (ALSPAC:N genotyped = 8,365) based on their burden of common SCZ risk alleles as defined by polygenic risk score (PRS). We compared 181 individuals at extremes of low (N = 91) or high (N = 90) SCZ-PRS under a robust diffusion MRI-based graph theoretical SBN framework. We applied a semi-metric analysis revealing higher SMR values for the high SCZ-PRS group compared with the low SCZ-PRS group in the left hemisphere. Furthermore, a hemispheric asymmetry index showed a higher leftward preponderance of indirect connections for the high SCZ-PRS group compared with the low SCZ-PRS group (PFDR < 0.05). These findings might indicate less efficient structural connectivity in the higher genetic risk group. This is the first study in a population-based sample that reveals differences in the efficiency of SBNs associated with common genetic risk variants for SCZ.

A transcriptome-wide association study implicates specific pre- and post-synaptic abnormalities in schizophrenia

Schizophrenia is a complex highly heritable disorder. Genome-wide association studies (GWAS) have identified multiple loci that influence the risk of developing schizophrenia, although the causal variants driving these associations and their impacts on specific genes are largely unknown. We identify a significant correlation between schizophrenia risk and expression at 89 genes in the dorsolateral prefrontal cortex (P ≤ 9.43 × 10-6), including 20 novel genes. Genes whose expression correlate with schizophrenia were enriched for those involved in abnormal CNS synaptic transmission (PFDR = 0.02) and antigen processing and presentation of peptide antigen via MHC class I (PFDR = 0.02). Within the CNS synaptic transmission set, we identify individual significant candidate genes to which we assign direction of expression changes in schizophrenia. The findings provide strong candidates for experimentally probing the molecular basis of synaptic pathology in schizophrenia.

Cortical patterning of abnormal morphometric similarity in psychosis is associated with brain expression of schizophrenia-related genes

Schizophrenia has been conceived as a disorder of brain connectivity, but it is unclear how this network phenotype is related to the underlying genetics. We used morphometric similarity analysis of MRI data as a marker of interareal cortical connectivity in three prior case-control studies of psychosis: in total, n = 185 cases and n = 227 controls. Psychosis was associated with globally reduced morphometric similarity in all three studies. There was also a replicable pattern of case-control differences in regional morphometric similarity, which was significantly reduced in patients in frontal and temporal cortical areas but increased in parietal cortex. Using prior brain-wide gene expression data, we found that the cortical map of case-control differences in morphometric similarity was spatially correlated with cortical expression of a weighted combination of genes enriched for neurobiologically relevant ontology terms and pathways. In addition, genes that were normally overexpressed in cortical areas with reduced morphometric similarity were significantly up-regulated in three prior post mortem studies of schizophrenia. We propose that this combined analysis of neuroimaging and transcriptional data provides insight into how previously implicated genes and proteins as well as a number of unreported genes in their topological vicinity on the protein interaction network may drive structural brain network changes mediating the genetic risk of schizophrenia.

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.

Predictive modeling of schizophrenia from genomic data: Comparison of polygenic risk score with kernel support vector machines approach

A major controversy in psychiatric genetics is whether nonadditive genetic interaction effects contribute to the risk of highly polygenic disorders. We applied a support vector machines (SVMs) approach, which is capable of building linear and nonlinear models using kernel methods, to classify cases from controls in a large schizophrenia case-control sample of 11,853 subjects (5,554 cases and 6,299 controls) and compared its prediction accuracy with the polygenic risk score (PRS) approach. We also investigated whether SVMs are a suitable approach to detecting nonlinear genetic effects, that is, interactions. We found that PRS provided more accurate case/control classification than either linear or nonlinear SVMs, and give a tentative explanation why PRS outperforms both multivariate regression and linear kernel SVMs. In addition, we observe that nonlinear kernel SVMs showed higher classification accuracy than linear SVMs when a large number of SNPs are entered into the model. We conclude that SVMs are a potential tool for assessing the presence of interactions, prior to searching for them explicitly.

Mutation intolerant genes and targets of FMRP are enriched for nonsynonymous alleles in schizophrenia

Risk of schizophrenia is conferred by alleles occurring across the full spectrum of frequencies from common SNPs of weak effect through to ultra rare alleles, some of which may be moderately to highly penetrant. Previous studies have suggested that some of the risk of schizophrenia is attributable to uncommon alleles represented on Illumina exome arrays. Here, we present the largest study of exomic variation in schizophrenia to date, using samples from the United Kingdom and Sweden (10,011 schizophrenia cases and 13,791 controls). Single variants, genes, and gene sets were analyzed for association with schizophrenia. No single variant or gene reached genome-wide significance. Among candidate gene sets, we found significant enrichment for rare alleles (minor allele frequency [MAF] < 0.001) in genes intolerant of loss-of-function (LoF) variation and in genes whose messenger RNAs bind to fragile X mental retardation protein (FMRP). We further delineate the genetic architecture of schizophrenia by excluding a role for uncommon exomic variants (0.01 ≤ MAF ≥ 0.001) that confer a relatively large effect (odds ratio [OR] > 4). We also show risk alleles within this frequency range exist, but confer smaller effects and should be identified by larger studies.

Analysis of Intellectual Disability Copy Number Variants for Association With Schizophrenia

IMPORTANCE

At least 11 rare copy number variants (CNVs) have been shown to be major risk factors for schizophrenia (SZ). These CNVs also increase the risk for other neurodevelopmental disorders, such as intellectual disability. It is possible that additional intellectual disability-associated CNVs increase the risk for SZ but have not yet been implicated in SZ because of previous studies being underpowered.

OBJECTIVE

To examine whether additional CNVs implicated in intellectual disability represent novel SZ risk loci.

DESIGN, SETTING, AND PARTICIPANTS

We used single-nucleotide polymorphism (SNP) array data to evaluate a set of 51 CNVs implicated in intellectual disability (excluding the known SZ loci) in a large data set of patients with SZ and healthy persons serving as controls recruited in a variety of settings. We analyzed a new sample of 6934 individuals with SZ and 8751 controls and combined those data with previously published large data sets for a total of 20 403 cases of SZ and 26 628 controls.

MAIN OUTCOMES AND MEASURES

Burden analysis of CNVs implicated in intellectual disability (excluding known SZ CNVs) for association with SZ. Association of individual intellectual disability CNV loci with SZ.

RESULTS

Of data on the 20 403 cases (6151 [30.15%] female) and 26 628 controls (14 252 [53.52%] female), 51 intellectual disability CNVs were analyzed. Collectively, intellectual disability CNVs were significantly enriched for SZ (P = 1.0 × 10-6; odds ratio [OR], 1.9 [95% CI, 1.46-2.49]). Of the 51 CNVs tested, 19 (37%) were more common in SZ cases; only 4 (8%) were more common in controls (no observations were made for the remaining 28 [55%] loci). One novel locus, deletion at 16p12.1, was significantly associated with SZ after correction for multiple testing (rate in SZ, 33 [0.16%]; rate in controls, 12 [0.05%]; corrected P = .017; OR, 3.3; 95% CI, 1.61-7.05), and 2 loci reached nominal levels of significance (deletions at 2q11.2: 6 [0.03%] vs 1 [0.004%]; OR, 9.3; 95% CI, 1.03-447.76; corrected P > .99; and duplications at 10q11.21q11.23: 5 [0.2%] vs 0 [0.03%]; OR, infinity; 95% CI, 1.26-infinity; corrected P = .71). Our new data set also provided independent support for the 11 SZ risk loci previously reported to be associated with the disorder and for the protective effect of 22q11.2 duplication.

CONCLUSIONS AND RELEVANCE

A large proportion of CNV loci implicated in intellectual disability are risk factors for SZ, but the available sample size precludes statistical confirmation for additional individual loci.

Biological overlap of attention-deficit/hyperactivity disorder and autism spectrum disorder: evidence from copy number variants

OBJECTIVE

Attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) often co-occur and share genetic risks. The aim of this analysis was to determine more broadly whether ADHD and ASD share biological underpinnings.

METHOD

We compared copy number variant (CNV) data from 727 children with ADHD and 5,081 population controls to data from 996 individuals with ASD and an independent set of 1,287 controls. Using pathway analyses, we investigated whether CNVs observed in individuals with ADHD have an impact on genes in the same biological pathways as on those observed in individuals with ASD.

RESULTS

The results suggest that the biological pathways affected by CNVs in ADHD overlap with those affected by CNVs in ASD more than would be expected by chance. Moreover, this was true even when specific CNV regions common to both disorders were excluded from the analysis. After correction for multiple testing, genes involved in 3 biological processes (nicotinic acetylcholine receptor signalling pathway, cell division, and response to drug) showed significant enrichment for case CNV hits in the combined ADHD and ASD sample.

CONCLUSION

The results of this study indicate the presence of significant overlap of shared biological processes disrupted by large rare CNVs in children with these 2 neurodevelopmental conditions.

Genetic evidence implicates the immune system and cholesterol metabolism in the aetiology of Alzheimer's disease

Background

Late Onset Alzheimer's disease (LOAD) is the leading cause of dementia. Recent large genome-wide association studies (GWAS) identified the first strongly supported LOAD susceptibility genes since the discovery of the involvement of APOE in the early 1990s. We have now exploited these GWAS datasets to uncover key LOAD pathophysiological processes.

Methodology

We applied a recently developed tool for mining GWAS data for biologically meaningful information to a LOAD GWAS dataset. The principal findings were then tested in an independent GWAS dataset.

Principal Findings

We found a significant overrepresentation of association signals in pathways related to cholesterol metabolism and the immune response in both of the two largest genome-wide association studies for LOAD.

Significance

Processes related to cholesterol metabolism and the innate immune response have previously been implicated by pathological and epidemiological studies of Alzheimer's disease, but it has been unclear whether those findings reflected primary aetiological events or consequences of the disease process. Our independent evidence from two large studies now demonstrates that these processes are aetiologically relevant, and suggests that they may be suitable targets for novel and existing therapeutic approaches.