Evidence of statistical epistasis between DISC1, CIT and NDEL1 impacting risk for schizophrenia: biological validation with functional neuroimaging

Unraveling schizophrenia's genetic complexity through advanced causal inference and chromatin 3D conformation

Schizophrenia is a polygenic complex disease with a heritability as high as 80 %, yet the mechanism of polygenic interaction in its pathogenesis remains unclear. Studying the interaction and regulation of schizophrenia susceptibility genes is crucial for unraveling the pathogenesis of schizophrenia and developing antipsychotic drugs. Therefore, we developed a bioinformatics method named GRACI (Gene Regulation Analysis based on Causal Inference) based on the principles of information theory, a causal inference model, and high order chromatin 3D conformation. GRACI captures the interaction and regulatory relationships between schizophrenia susceptibility genes by analyzing genotyping data. Two datasets, comprising 1459 and 2065 samples respectively, were analyzed, and the gene networks from both datasets were constructed. GRACI showcased superior accuracy when compared to widely adopted methods for detecting gene-gene interactions and intergenic regulation. This alignment was further substantiated by its correlation with chromatin high-order conformation patterns. Using GRACI, we identified three potential genes-KCNN3, KCNH1, and KCND3-that are directly associated with schizophrenia pathogenesis. Furthermore, the results of GRACI on the standalone dataset illustrated the method's applicability to other complex diseases. GRACI download: https://github.com/liuliangjie19/GRACI.

A primer on the use of machine learning to distil knowledge from data in biological psychiatry

Applications of machine learning in the biomedical sciences are growing rapidly. This growth has been spurred by diverse cross-institutional and interdisciplinary collaborations, public availability of large datasets, an increase in the accessibility of analytic routines, and the availability of powerful computing resources. With this increased access and exposure to machine learning comes a responsibility for education and a deeper understanding of its bases and bounds, borne equally by data scientists seeking to ply their analytic wares in medical research and by biomedical scientists seeking to harness such methods to glean knowledge from data. This article provides an accessible and critical review of machine learning for a biomedically informed audience, as well as its applications in psychiatry. The review covers definitions and expositions of commonly used machine learning methods, and historical trends of their use in psychiatry. We also provide a set of standards, namely Guidelines for REporting Machine Learning Investigations in Neuropsychiatry (GREMLIN), for designing and reporting studies that use machine learning as a primary data-analysis approach. Lastly, we propose the establishment of the Machine Learning in Psychiatry (MLPsych) Consortium, enumerate its objectives, and identify areas of opportunity for future applications of machine learning in biological psychiatry. This review serves as a cautiously optimistic primer on machine learning for those on the precipice as they prepare to dive into the field, either as methodological practitioners or well-informed consumers.

Cortical structural changes of morphometric similarity network in early-onset schizophrenia correlate with specific transcriptional expression patterns

BACKGROUND

This study aimed to investigate the neuroanatomical subtypes among early-onset schizophrenia (EOS) patients by exploring the association between structural alterations and molecular mechanisms using a combined analysis of morphometric similarity network (MSN) changes and specific transcriptional expression patterns.

METHODS

We recruited 206 subjects aged 7 to 17 years, including 100 EOS patients and 106 healthy controls (HC). Heterogeneity through discriminant analysis (HYDRA) was used to identify the EOS subtypes within the MSN strength. The differences in morphometric similarity between each EOS subtype and HC were compared. Furthermore, we examined the link between morphometric changes and brain-wide gene expression in different EOS subtypes using partial least squares regression (PLS) weight mapping, evaluated genetic commonalities with psychiatric disorders, identified functional enrichments of PLS-weighted genes, and assessed cellular transcriptional signatures.

RESULTS

Two distinct MSN-based EOS subtypes were identified, each exhibiting different abnormal MSN strength and cognitive functions compared to HC. The PLS1 score mapping demonstrated anterior-posterior gradients of gene expression in EOS1, whereas inverse distributions were observed in EOS2 cohorts. Genetic commonalities were identified in autistic disorder and adult schizophrenia with EOS1 and inflammatory bowel diseases with EOS2 cohorts. The EOS1 PLS1- genes (Z < -5) were significantly enriched in synaptic signaling-related functions, whereas EOS2 demonstrated enrichments in virtual infection-related pathways. Furthermore, the majority of observed associations with EOS1-specific MSN strength differences contributed to specific transcriptional changes in astrocytes and neurons.

CONCLUSIONS

The findings of this study provide a comprehensive analysis of neuroanatomical subtypes in EOS, shedding light on the intricate relationships between macrostructural and molecular aspects of the EOS disease.

Expression of the schizophrenia associated gene FEZ1 in the early developing fetal human forebrain

Introduction

The protein fasciculation and elongation zeta-1 (FEZ1) is involved in axon outgrowth but potentially interacts with various proteins with roles ranging from intracellular transport to transcription regulation. Gene association and other studies have identified FEZ1 as being directly, or indirectly, implicated in schizophrenia susceptibility. To explore potential roles in normal early human forebrain neurodevelopment, we mapped FEZ1 expression by region and cell type.

Methods

All tissues were provided with maternal consent and ethical approval by the Human Developmental Biology Resource. RNAseq data were obtained from previously published sources. Thin paraffin sections from 8 to 21 post-conceptional weeks (PCW) samples were used for RNAScope in situ hybridization and immunohistochemistry against FEZ1 mRNA and protein, and other marker proteins.

Results

Tissue RNAseq revealed that FEZ1 is highly expressed in the human cerebral cortex between 7.5-17 PCW and single cell RNAseq at 17-18 PCW confirmed its expression in all neuroectoderm derived cells. The highest levels were found in more mature glutamatergic neurons, the lowest in GABAergic neurons and dividing progenitors. In the thalamus, single cell RNAseq similarly confirmed expression in multiple cell types. In cerebral cortex sections at 8-10 PCW, strong expression of mRNA and protein appeared confined to post-mitotic neurons, with low expression seen in progenitor zones. Protein expression was observed in some axon tracts by 16-19 PCW. However, in sub-cortical regions, FEZ1 was highly expressed in progenitor zones at early developmental stages, showing lower expression in post-mitotic cells.

Discussion

FEZ1 has different expression patterns and potentially diverse functions in discrete forebrain regions during prenatal human development.

Advancing preclinical models of psychiatric disorders with human brain organoid cultures

Psychiatric disorders are often distinguished from neurological disorders in that the former do not have characteristic lesions or findings from cerebrospinal fluid, electroencephalograms (EEGs), or brain imaging, and furthermore do not have commonly recognized convergent mechanisms. Psychiatric disorders commonly involve clinical diagnosis of phenotypic behavioral disturbances of mood and psychosis, often with a poorly understood contribution of environmental factors. As such, psychiatric disease has been challenging to model preclinically for mechanistic understanding and pharmaceutical development. This review compares commonly used animal paradigms of preclinical testing with evolving techniques of induced pluripotent cell culture with a focus on emerging three-dimensional models. Advances in complexity of 3D cultures, recapitulating electrical activity in utero, and disease modeling of psychosis, mood, and environmentally induced disorders are reviewed. Insights from these rapidly expanding technologies are discussed as they pertain to the utility of human organoid and other models in finding novel research directions, validating pharmaceutical action, and recapitulating human disease.

Knowledge structure and emerging trends in the application of deep learning in genetics research: A bibliometric analysis [2000–2021]

Introduction: Deep learning technology has been widely used in genetic research because of its characteristics of computability, statistical analysis, and predictability. Herein, we aimed to summarize standardized knowledge and potentially innovative approaches for deep learning applications of genetics by evaluating publications to encourage more research.

Methods: The Science Citation Index Expanded TM (SCIE) database was searched for deep learning applications for genomics-related publications. Original articles and reviews were considered. In this study, we derived a clustered network from 69,806 references that were cited by the 1,754 related manuscripts identified. We used CiteSpace and VOSviewer to identify countries, institutions, journals, co-cited references, keywords, subject evolution, path, current characteristics, and emerging topics.

Results: We assessed the rapidly increasing publications concerned about deep learning applications of genomics approaches and identified 1,754 articles that published reports focusing on this subject. Among these, a total of 101 countries and 2,487 institutes contributed publications, The United States of America had the most publications (728/1754) and the highest h-index, and the US has been in close collaborations with China and Germany. The reference clusters of SCI articles were clustered into seven categories: deep learning, logic regression, variant prioritization, random forests, scRNA-seq (single-cell RNA-seq), genomic regulation, and recombination. The keywords representing the research frontiers by year were prediction (2016–2021), sequence (2017–2021), mutation (2017–2021), and cancer (2019–2021).

Conclusion: Here, we summarized the current literature related to the status of deep learning for genetics applications and analyzed the current research characteristics and future trajectories in this field. This work aims to provide resources for possible further intensive exploration and encourages more researchers to overcome the research of deep learning applications in genetics.

Combining fMRI and DISC1 gene haplotypes to understand working memory-related brain activity in schizophrenia

The DISC1 gene is one of the most relevant susceptibility genes for psychosis. However, the complex genetic landscape of this locus, which includes protective and risk variants in interaction, may have hindered consistent conclusions on how DISC1 contributes to schizophrenia (SZ) liability. Analysis from haplotype approaches and brain-based phenotypes can contribute to understanding DISC1 role in the neurobiology of this disorder. We assessed the brain correlates of DISC1 haplotypes associated with SZ through a functional neuroimaging genetics approach. First, we tested the association of two DISC1 haplotypes, the HEP1 (rs6675281-1000731-rs999710) and the HEP3 (rs151229-rs3738401), with the risk for SZ in a sample of 138 healthy subjects (HS) and 238 patients. This approach allowed the identification of three haplotypes associated with SZ (HEP1-CTG, HEP3-GA and HEP3-AA). Second, we explored whether these haplotypes exerted differential effects on n-back associated brain activity in a subsample of 70 HS compared to 70 patients (diagnosis × haplotype interaction effect). These analyses evidenced that HEP3-GA and HEP3-AA modulated working memory functional response conditional to the health/disease status in the cuneus, precuneus, middle cingulate cortex and the ventrolateral and dorsolateral prefrontal cortices. Our results are the first to show a diagnosis-based effect of DISC1 haplotypes on working memory-related brain activity, emphasising its role in SZ.

The Association between Depression and Perceived Stress among Parents of Autistic and Non-Autistic Children-The Role of Loneliness

Having an autistic child significantly impairs the functioning of the family, including the wellbeing of the parents. The aim of this study was to assess whether loneliness mediates the relationship between perceived stress and the severity of depressive symptoms in the studied sample of parents. This cross-sectional study involved 39 parents of autistic children and 45 parents of non-autistic children. They completed a set of tests: a survey on sociodemographic and clinical data and psychometric questionnaires, i.e., Beck Depression Inventory II (BDI), De Jong Gierveld Loneliness Scale (DJGLS), and Perceived Stress Questionnaire (KPS). A rise in external and intrapsychic stress, independently, was linked to a rise in the severity of depressive symptoms. The severity of depression, loneliness and stress was higher among parents of autistic children compared with parents of non-autistic children. Intrapsychic stress exhibited an indirect effect through loneliness on the worsening of depressive symptoms.

Mutations in DISC1 alter IP3R and voltage-gated Ca2+ channel functioning, implications for major mental illness

Disrupted in Schizophrenia 1 (DISC1) participates in a wide variety of developmental processes of central neurons. It also serves critical roles that underlie cognitive functioning in adult central neurons. Here we summarize DISC1’s general properties and discuss its use as a model system for understanding major mental illnesses (MMIs). We then discuss the cellular actions of DISC1 that involve or regulate Ca²⁺ signaling in adult central neurons. In particular, we focus on the tethering role DISC1 plays in transporting RNA particles containing Ca²⁺ channel subunit RNAs, including IP3R1, CACNA1C and CACNA2D1, and in transporting mitochondria into dendritic and axonal processes. We also review DISC1’s role in modulating IP₃R1 activity within mitochondria-associated ER membrane (MAM). Finally, we discuss DISC1-glycogen synthase kinase 3β (GSK3β) signaling that regulates functional expression of voltage-gated Ca²⁺ channels (VGCCs) at central synapses. In each case, DISC1 regulates the movement of molecules that impact Ca²⁺ signaling in neurons.

Behavioral Deficits in Mice with Postnatal Disruption of Ndel1 in Forebrain Excitatory Neurons: Implications for Epilepsy and Neuropsychiatric Disorders

Dysfunction of nuclear distribution element-like 1 (Ndel1) is associated with schizophrenia, a neuropsychiatric disorder characterized by cognitive impairment and with seizures as comorbidity. The levels of Ndel1 are also altered in human and models with epilepsy, a chronic condition whose hallmark feature is the occurrence of spontaneous recurrent seizures and is typically associated with comorbid conditions including learning and memory deficits, anxiety, and depression. In this study, we analyzed the behaviors of mice postnatally deficient for Ndel1 in forebrain excitatory neurons (Ndel1 CKO) that exhibit spatial learning and memory deficits, seizures, and shortened lifespan. Ndel1 CKO mice underperformed in species-specific tasks, that is, the nest building, open field, Y maze, forced swim, and dry cylinder tasks. We surveyed the expression and/or activity of a dozen molecules related to Ndel1 functions and found changes that may contribute to the abnormal behaviors. Finally, we tested the impact of Reelin glycoprotein that shows protective effects in the hippocampus of Ndel1 CKO, on the performance of the mutant animals in the nest building task. Our study highlights the importance of Ndel1 in the manifestation of species-specific animal behaviors that may be relevant to our understanding of the clinical conditions shared between neuropsychiatric disorders and epilepsy.

Insular functional alterations in emotional processing of schizophrenia patients revealed by Multivariate Pattern Analysis fMRI

Emotion perception is impaired in schizophrenia patients (SP) and related to reduced social skills performance. There is a remarkable variability across subjects for functional neuroimaging alterations related to this phenomenon. In contrast to the univariate approaches of fMRI, Multivariate Pattern Analysis (MVPA) maintains the within-subject voxel-level variability. The purpose of this study was to assess emotion processing in SP, in previously identified ROIs -i.e. amygdala, hippocampus, insula, and thalamus-, while retaining the functional heterogeneity that may exist between subjects. We evaluated 23 SP and 23 healthy controls (HC). Happy, sad, and neutral faces were presented. A single trial fMRI model was applied. Patterns of activation within each ROI were classified at the subject level. Within each group, stimuli classification scores were tested against random label classification scores. In ROIs with significant results, a whole ROI classification was performed, to test whether en bloc stimuli discrimination was present. A between-group analysis was conducted also. For the classification of stimuli above chance, in the HC results were significant in the left insula in all of the stimuli dichotomies, but were non-significant in SP for happy vs. sad. In whole ROI classification, SP had significant results in bilateral insular cortex for happy vs. neutral. The left amygdala showed diminished stimuli classification scores in SP for sad vs. neutral. In conclusion, MVPA seems useful to study emotional processing in schizophrenia. In SP, either en bloc or no stimuli discrimination was seen in the insula, and reduced stimuli discrimination was seen in the left amygdala.

Two Thalamic Regions Screened Using Laser Capture Microdissection with Whole Human Genome Microarray in Schizophrenia Postmortem Samples

We used whole human genome microarray screening of highly enriched neuronal populations from two thalamic regions in postmortem samples from subjects with schizophrenia and controls to identify brain region-specific gene expression changes and possible transcriptional targets. The thalamic anterior nucleus is reciprocally connected to anterior cingulate, a schizophrenia-affected cortical region, and is also thought to be schizophrenia affected; the other thalamic region is not. Using two regions in the same subject to identify disease-relevant gene expression differences was novel and reduced intersubject heterogeneity of findings. We found gene expression differences related to miRNA-137 and other SZ-associated microRNAs, ELAVL1, BDNF, DISC-1, MECP2 and YWHAG associated findings, synapses, and receptors. Manual curation of our data may support transcription repression.

Sequential phosphorylation of NDEL1 by the DYRK2-GSK3β complex is critical for neuronal morphogenesis

Neuronal morphogenesis requires multiple regulatory pathways to appropriately determine axonal and dendritic structures, thereby to enable the functional neural connectivity. Yet, however, the precise mechanisms and components that regulate neuronal morphogenesis are still largely unknown. Here, we newly identified the sequential phosphorylation of NDEL1 critical for neuronal morphogenesis through the human kinome screening and phospho-proteomics analysis of NDEL1 from mouse brain lysate. DYRK2 phosphorylates NDEL1 S336 to prime the phosphorylation of NDEL1 S332 by GSK3β. TARA, an interaction partner of NDEL1, scaffolds DYRK2 and GSK3β to form a tripartite complex and enhances NDEL1 S336/S332 phosphorylation. This dual phosphorylation increases the filamentous actin dynamics. Ultimately, the phosphorylation enhances both axonal and dendritic outgrowth and promotes their arborization. Together, our findings suggest the NDEL1 phosphorylation at S336/S332 by the TARA-DYRK2-GSK3β complex as a novel regulatory mechanism underlying neuronal morphogenesis.

Gene-gene interaction among cell adhesion genes and risk of nonsyndromic cleft lip with or without cleft palate in Chinese case-parent trios

Background

Nonsyndromic cleft lip with or without cleft palate (NSCL/P) is a common birth defect with complex etiology. One strategy for studying the genetic risk factors of NSCL/P is to consider gene–gene interaction (G × G) among gene pathways having a role in craniofacial development. The present study aimed to investigate the G × G among cell adhesion gene pathway.

Methods

We carried out an interaction analysis of eight genes involved in cell adherens junctions among 806 NSCL/P Chinese case‐parent trios originally recruited for a genome‐wide association study (GWAS). Regression‐based approach was used to test for two‐way G × G interaction, while machine learning algorithm was run for exploring both two‐way and multi‐way interaction that may affect the risk of NSCL/P.

Results

A two‐way ACTN1 × CTNNB1 interaction reached the adjusted significance level. The single nucleotide polymorphisms pair composed of rs17252114 (CTNNB1) and rs1274944 (ACTN1) yielded a p value of .0002, and this interaction was also supported by the logic regression algorithm. Higher order interactions involving ACTN1, CTNNB1, and CDH1 were picked out by logic regression, suggesting a potential role in NSCL/P risk.

Conclusion

This study suggests for the first time evidence of both two‐way and multi‐way G × G interactions among cell adhesion genes contributing to the NSCL/P risk.

Machine learning in mental health: a scoping review of methods and applications

BACKGROUND

This paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice.

METHODS

We employed a scoping review methodology to rapidly map the field of ML in mental health. Eight health and information technology research databases were searched for papers covering this domain. Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type, and study results. Articles were then synthesised via narrative review.

RESULTS

Three hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health, and; (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's disease. ML techniques used included support vector machines, decision trees, neural networks, latent Dirichlet allocation, and clustering.

CONCLUSIONS

Overall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration. With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to other areas of psychology and mental health. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field.

Identification of interactions of binary variables associated with survival time using survivalFS

Many medical studies aim to identify factors associated with a time to an event such as survival time or time to relapse. Often, in particular, when binary variables are considered in such studies, interactions of these variables might be the actual relevant factors for predicting, e.g., the time to recurrence of a disease. Testing all possible interactions is often not possible, so that procedures such as logic regression are required that avoid such an exhaustive search. In this article, we present an ensemble method based on logic regression that can cope with the instability of the regression models generated by logic regression. This procedure called survivalFS also provides measures for quantifying the importance of the interactions forming the logic regression models on the time to an event and for the assessment of the individual variables that take the multivariate data structure into account. In this context, we introduce a new performance measure, which is an adaptation of Harrel's concordance index. The performance of survivalFS and the proposed importance measures is evaluated in a simulation study as well as in an application to genotype data from a urinary bladder cancer study. Furthermore, we compare the performance of survivalFS and its importance measures for the individual variables with the variable importance measure used in random survival forests, a popular procedure for the analysis of survival data. These applications show that survivalFS is able to identify interactions associated with time to an event and to outperform random survival forests.

Unravelling the GSK3β-related genotypic interaction network influencing hippocampal volume in recurrent major depressive disorder

OBJECTIVE

Glycogen synthase kinase 3β (GSK3β) has been implicated in mood disorders. We previously reported associations between a GSK3β polymorphism and hippocampal volume in major depressive disorder (MDD). We then reported similar associations for a subset of GSK3β-regulated genes. We now investigate an algorithm-derived comprehensive list of genes encoding proteins that directly interact with GSK3β to identify a genotypic network influencing hippocampal volume in MDD.

PARTICIPANTS AND METHODS

We used discovery (N=141) and replication (N=77) recurrent MDD samples. Our gene list was generated from the NetworKIN database. Hippocampal measures were derived using an optimized Freesurfer protocol. We identified interacting single nucleotide polymorphisms using the machine learning algorithm Random Forest and verified interactions using likelihood ratio tests between nested linear regression models.

RESULTS

The discovery sample showed multiple two-single nucleotide polymorphism interactions with hippocampal volume. The replication sample showed a replicable interaction (likelihood ratio test: P=0.0088, replication sample; P=0.017, discovery sample; Stouffer's combined P=0.0007) between genes associated previously with endoplasmic reticulum stress, calcium regulation and histone modifications.

CONCLUSION

Our results provide genetic evidence supporting associations between hippocampal volume and MDD, which may reflect underlying cellular stress responses. Our study provides evidence of biological mechanisms that should be further explored in the search for disease-modifying therapeutic targets for depression.

On the overestimation of random forest's out-of-bag error

The ensemble method random forests has become a popular classification tool in bioinformatics and related fields. The out-of-bag error is an error estimation technique often used to evaluate the accuracy of a random forest and to select appropriate values for tuning parameters, such as the number of candidate predictors that are randomly drawn for a split, referred to as mtry. However, for binary classification problems with metric predictors it has been shown that the out-of-bag error can overestimate the true prediction error depending on the choices of random forests parameters. Based on simulated and real data this paper aims to identify settings for which this overestimation is likely. It is, moreover, questionable whether the out-of-bag error can be used in classification tasks for selecting tuning parameters like mtry, because the overestimation is seen to depend on the parameter mtry. The simulation-based and real-data based studies with metric predictor variables performed in this paper show that the overestimation is largest in balanced settings and in settings with few observations, a large number of predictor variables, small correlations between predictors and weak effects. There was hardly any impact of the overestimation on tuning parameter selection. However, although the prediction performance of random forests was not substantially affected when using the out-of-bag error for tuning parameter selection in the present studies, one cannot be sure that this applies to all future data. For settings with metric predictor variables it is therefore strongly recommended to use stratified subsampling with sampling fractions that are proportional to the class sizes for both tuning parameter selection and error estimation in random forests. This yielded less biased estimates of the true prediction error. In unbalanced settings, in which there is a strong interest in predicting observations from the smaller classes well, sampling the same number of observations from each class is a promising alternative.

Genomic Prediction of Breeding Values Using a Subset of SNPs Identified by Three Machine Learning Methods

The analysis of large genomic data is hampered by issues such as a small number of observations and a large number of predictive variables (commonly known as “large P small N”), high dimensionality or highly correlated data structures. Machine learning methods are renowned for dealing with these problems. To date machine learning methods have been applied in Genome-Wide Association Studies for identification of candidate genes, epistasis detection, gene network pathway analyses and genomic prediction of phenotypic values. However, the utility of two machine learning methods, Gradient Boosting Machine (GBM) and Extreme Gradient Boosting Method (XgBoost), in identifying a subset of SNP makers for genomic prediction of breeding values has never been explored before. In this study, using 38,082 SNP markers and body weight phenotypes from 2,093 Brahman cattle (1,097 bulls as a discovery population and 996 cows as a validation population), we examined the efficiency of three machine learning methods, namely Random Forests (RF), GBM and XgBoost, in (a) the identification of top 400, 1,000, and 3,000 ranked SNPs; (b) using the subsets of SNPs to construct genomic relationship matrices (GRMs) for the estimation of genomic breeding values (GEBVs). For comparison purposes, we also calculated the GEBVs from (1) 400, 1,000, and 3,000 SNPs that were randomly selected and evenly spaced across the genome, and (2) from all the SNPs. We found that RF and especially GBM are efficient methods in identifying a subset of SNPs with direct links to candidate genes affecting the growth trait. In comparison to the estimate of prediction accuracy of GEBVs from using all SNPs (0.43), the 3,000 top SNPs identified by RF (0.42) and GBM (0.46) had similar values to those of the whole SNP panel. The performance of the subsets of SNPs from RF and GBM was substantially better than that of evenly spaced subsets across the genome (0.18–0.29). Of the three methods, RF and GBM consistently outperformed the XgBoost in genomic prediction accuracy.

Of rings and spines: The multiple facets of Citron proteins in neural development

The Citron protein was originally identified for its capability to specifically bind the active form of RhoA small GTPase, leading to the simplistic hypothesis that it may work as a RhoA downstream effector in actin remodeling. More than two decades later, a much more complex picture has emerged. In particular, it has become clear that in animals, and especially in mammals, the functions of the Citron gene (CIT) are intimately linked to many aspects of central nervous system (CNS) development and function, although the gene is broadly expressed. More specifically, CIT encodes two main isoforms, Citron-kinase (CIT-K) and Citron-N (CIT-N), characterized by complementary expression pattern and different functions. Moreover, in many of their activities, CIT proteins act more as upstream regulators than as downstream effectors of RhoA. Finally it has been found that, besides working through actin, CIT proteins have many crucial functional interactions with the microtubule cytoskeleton and may directly affect genome stability. In this review, we will summarize these advances and illustrate their actual or potential relevance for CNS diseases, including microcephaly and psychiatric disorders.

Neuroimaging genomics in psychiatry-a translational approach

Neuroimaging genomics is a relatively new field focused on integrating genomic and imaging data in order to investigate the mechanisms underlying brain phenotypes and neuropsychiatric disorders. While early work in neuroimaging genomics focused on mapping the associations of candidate gene variants with neuroimaging measures in small cohorts, the lack of reproducible results inspired better-powered and unbiased large-scale approaches. Notably, genome-wide association studies (GWAS) of brain imaging in thousands of individuals around the world have led to a range of promising findings. Extensions of such approaches are now addressing epigenetics, gene–gene epistasis, and gene–environment interactions, not only in brain structure, but also in brain function. Complementary developments in systems biology might facilitate the translation of findings from basic neuroscience and neuroimaging genomics to clinical practice. Here, we review recent approaches in neuroimaging genomics—we highlight the latest discoveries, discuss advantages and limitations of current approaches, and consider directions by which the field can move forward to shed light on brain disorders.

DISC1 Regulates Neurogenesis via Modulating Kinetochore Attachment of Ndel1/Nde1 during Mitosis

Mutations of DISC1 (disrupted-in-schizophrenia 1) have been associated with major psychiatric disorders. Despite the hundreds of DISC1-binding proteins reported, almost nothing is known about how DISC1 interacts with other proteins structurally to impact human brain development. Here we solved the high-resolution structure of DISC1 C-terminal tail in complex with its binding domain of Ndel1. Mechanistically, DISC1 regulates Ndel1's kinetochore attachment, but not its centrosome localization, during mitosis. Functionally, disrupting DISC1/Ndel1 complex formation prolongs mitotic length and interferes with cell-cycle progression in human cells, and it causes cell-cycle deficits of radial glial cells in the embryonic mouse cortex and human forebrain organoids. We also observed similar deficits in organoids derived from schizophrenia patient induced pluripotent stem cells (iPSCs) with a DISC1 mutation that disrupts its interaction with Ndel1. Our study uncovers a new mechanism of action for DISC1 based on its structure, and it has implications for how genetic insults may contribute to psychiatric disorders.

Ndel1 and Reelin Maintain Postnatal CA1 Hippocampus Integrity

How the integrity of laminar structures in the postnatal brain is maintained impacts neuronal functions. Ndel1, the mammalian homolog of NuDE from the filamentous fungus Aspergillus nidulans, is an atypical microtubule (MT)-associated protein that was initially investigated in the contexts of neurogenesis and neuronal migration. Constitutive knock-out mice for Ndel1 are embryonic lethal, thereby necessitating the creation a conditional knock-out to probe the roles of Ndel1 in postnatal brains. Here we report that CA1 pyramidal neurons from mice postnatally lacking Ndel1 (Ndel1 conditional knock-out) exhibit fragmented MTs, dendritic/synaptic pathologies, are intrinsically hyperexcitable and undergo dispersion independently of neuronal migration defect. Secondary to the pyramidal cell changes is the decreased inhibitory drive onto pyramidal cells from interneurons. Levels of the glycoprotein Reelin that regulates MTs, neuronal plasticity, and cell compaction are significantly reduced in hippocampus of mutant mice. Strikingly, a single injection of Reelin into the hippocampus of Ndel1 conditional knock-out mice ameliorates ultrastructural, cellular, morphological, and anatomical CA1 defects. Thus, Ndel1 and Reelin contribute to maintain postnatal CA1 integrity.

SIGNIFICANCE STATEMENT

The significance of this study rests in the elucidation of a role for Nde1l and Reelin in postnatal CA1 integrity using a new conditional knock-out mouse model for the cytoskeletal protein Ndel1, one that circumvents the defects associated with neuronal migration and embryonic lethality. Our study serves as a basis for understanding the mechanisms underlying postnatal hippocampal maintenance and function, and the significance of decreased levels of Ndel1 and Reelin observed in patients with neurological disorders.

Genome-wide association study of working memory brain activation

In a population-based genome-wide association (GWA) study of n-back working memory task-related brain activation, we extracted the average percent BOLD signal change (2-back minus 0-back) from 46 regions-of-interest (ROIs) in functional MRI scans from 863 healthy twins and siblings. ROIs were obtained by creating spheres around group random effects analysis local maxima, and by thresholding a voxel-based heritability map of working memory brain activation at 50%. Quality control for test-retest reliability and heritability of ROI measures yielded 20 reliable (r>0.7) and heritable (h2>20%) ROIs. For GWA analysis, the cohort was divided into a discovery (n=679) and replication (n=97) sample. No variants survived the stringent multiple-testing-corrected genome-wide significance threshold (p<4.5×10-9), or were replicated (p<0.0016), but several genes were identified that are worthy of further investigation. A search of 529,379 genomic markers resulted in discovery of 31 independent single nucleotide polymorphisms (SNPs) associated with BOLD signal change at a discovery level of p<1×10-5. Two SNPs (rs7917410 and rs7672408) were associated at a significance level of p<1×10-7. Only one, most strongly affecting BOLD signal change in the left supramarginal gyrus (R2=5.5%), had multiple SNPs associated at p<1×10-5 in linkage disequilibrium with it, all located in and around the BANK1 gene. BANK1 encodes a B-cell-specific scaffold protein and has been shown to negatively regulate CD40-mediated AKT activation. AKT is part of the dopamine-signaling pathway, suggesting a mechanism for the involvement of BANK1 in the BOLD response to working memory. Variants identified here may be relevant to (the susceptibility to) common disorders affecting brain function.

NDE1 and NDEL1 from genes to (mal)functions: parallel but distinct roles impacting on neurodevelopmental disorders and psychiatric illness

NDE1 (Nuclear Distribution Element 1, also known as NudE) and NDEL1 (NDE-Like 1, also known as NudEL) are the mammalian homologues of the fungus nudE gene, with important and at least partially overlapping roles for brain development. While a large number of studies describe the various properties and functions of these proteins, many do not directly compare the similarities and differences between NDE1 and NDEL1. Although sharing a high degree structural similarity and multiple common cellular roles, each protein presents several distinct features that justify their parallel but also unique functions. Notably both proteins have key binding partners in dynein, LIS1 and DISC1, which impact on neurodevelopmental and psychiatric illnesses. Both are implicated in schizophrenia through genetic and functional evidence, with NDE1 also strongly implicated in microcephaly, as well as other neurodevelopmental and psychiatric conditions through copy number variation, while NDEL1 possesses an oligopeptidase activity with a unique potential as a biomarker in schizophrenia. In this review, we aim to give a comprehensive overview of the various cellular roles of these proteins in a "bottom-up" manner, from their biochemistry and protein-protein interactions on the molecular level, up to the consequences for neuronal differentiation, and ultimately to their importance for correct cortical development, with direct consequences for the pathophysiology of neurodevelopmental and mental illness.

Epistasis in Neuropsychiatric Disorders

The contribution of epistasis to human disease remains unclear. However, several studies have now identified epistatic interactions between common variants that increase the risk of a neuropsychiatric disorder, while there is growing evidence that genetic interactions contribute to the pathogenicity of rare, multigenic copy-number variants (CNVs) that have been observed in patients. This review discusses the current evidence for epistatic events and genetic interactions in neuropsychiatric disorders, how paradigm shifts in the phenotypic classification of patients would empower the search for epistatic effects, and how network and cellular models might be employed to further elucidate relevant epistatic interactions.

Phenotyping: Using Machine Learning for Improved Pairwise Genotype Classification Based on Root Traits

Phenotyping local crop cultivars is becoming more and more important, as they are an important genetic source for breeding - especially in regard to inherent root system architectures. Machine learning algorithms are promising tools to assist in the analysis of complex data sets; novel approaches are need to apply them on root phenotyping data of mature plants. A greenhouse experiment was conducted in large, sand-filled columns to differentiate 16 European Pisum sativum cultivars based on 36 manually derived root traits. Through combining random forest and support vector machine models, machine learning algorithms were successfully used for unbiased identification of most distinguishing root traits and subsequent pairwise cultivar differentiation. Up to 86% of pea cultivar pairs could be distinguished based on top five important root traits (Timp5) - Timp5 differed widely between cultivar pairs. Selecting top important root traits (Timp) provided a significant improved classification compared to using all available traits or randomly selected trait sets. The most frequent Timp of mature pea cultivars was total surface area of lateral roots originating from tap root segments at 0-5 cm depth. The high classification rate implies that culturing did not lead to a major loss of variability in root system architecture in the studied pea cultivars. Our results illustrate the potential of machine learning approaches for unbiased (root) trait selection and cultivar classification based on rather small, complex phenotypic data sets derived from pot experiments. Powerful statistical approaches are essential to make use of the increasing amount of (root) phenotyping information, integrating the complex trait sets describing crop cultivars.

Evidence of an epistatic effect between Dysbindin-1 and Neuritin-1 genes on the risk for schizophrenia spectrum disorders

BACKGROUND

The interest in studying gene-gene interactions is increasing for psychiatric diseases such as schizophrenia-spectrum disorders (SSD), where multiple genes are involved. Dysbindin-1 (DTNBP1) and Neuritin-1 (NRN1) genes have been previously associated with SSD and both are involved in synaptic plasticity. We aimed to study whether these genes show an epistatic effect on the risk for SSD.

METHODS

The sample comprised 388 SSD patients and 397 healthy subjects. Interaction was tested between: (i) three DTNBP1 SNPs (rs2619537, rs2743864, rs1047631) related to changes in gene expression; and (ii) an haplotype in NRN1 previously associated with the risk for SSD (rs645649-rs582262: HAP-risk C-C).

RESULTS

An interaction between DTNBP1 rs2743864 and NRN1 HAP-risk was detected by using the model based multifactor dimensionality reduction (MB-MDR) approach (P=0.0049, after permutation procedure), meaning that the risk for SSD is significantly higher in those subjects carrying both the A allele of rs2743864 and the HAP-risk C-C. This interaction was confirmed by using a logistic regression model (P=0.033, OR (95%CI)=2.699 (1.08-6.71), R²=0.162).

DISCUSSION

Our results suggest that DTNBP1 and NRN1 genes show a joint effect on the risk for SSD. Although the precise mechanism underlying this effect is unclear, the fact that these genes have been involved in synaptic maturation, connectivity and glutamate signalling suggests that our findings could be of value as a link to the schizophrenia aetiology.

An examination of the language construct in NIMH's research domain criteria: Time for reconceptualization!

The National Institute of Mental Health's Research Domain Criteria (RDoC) Initiative "calls for the development of new ways of classifying psychopathology based on dimensions of observable behavior." As a result of this ambitious initiative, language has been identified as an independent construct in the RDoC matrix. In this article, we frame language within an evolutionary and neuropsychological context and discuss some of the limitations to the current measurements of language. Findings from genomics and the neuroimaging of performance during language tasks are discussed in relation to serious mental illness and within the context of caveats regarding measuring language. Indeed, the data collection and analysis methods employed to assay language have been both aided and constrained by the available technologies, methodologies, and conceptual definitions. Consequently, different fields of language research show inconsistent definitions of language that have become increasingly broad over time. Individually, they have also shown significant improvements in conceptual resolution, as well as in experimental and analytic techniques. More recently, language research has embraced collaborations across disciplines, notably neuroscience, cognitive science, and computational linguistics and has ultimately re-defined classical ideas of language. As we move forward, the new models of language with their remarkably multifaceted constructs force a re-examination of the NIMH RDoC conceptualization of language and thus the neuroscience and genetics underlying this concept. © 2016 The Authors. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics Published by Wiley Periodicals, Inc.

Evaluation of voltage-dependent calcium channel γ gene families identified several novel potential susceptible genes to schizophrenia

Voltage-gated L-type calcium channels (VLCC) are distributed widely throughout the brain. Among the genes involved in schizophrenia (SCZ), genes encoding VLCC subunits have attracted widespread attention. Among the four subunits comprising the VLCC (α − 1, α −2/δ, β, and γ), the γ subunit that comprises an eight-member protein family is the least well understood. In our study, to further investigate the risk susceptibility by the γ subunit gene family to SCZ, we conducted a large-scale association study in Han Chinese individuals. The SNP rs17645023 located in the intergenic region of CACNG4 and CACNG5 was identified to be significantly associated with SCZ (OR = 0.856, P = 5.43 × 10⁻⁵). Similar results were obtained in the meta-analysis with the current SCZ PGC data (OR = 0.8853). We also identified a two-SNP haplotype (rs10420331-rs11084307, P = 1.4 × 10⁻⁶) covering the intronic region of CACNG8 to be significantly associated with SCZ. Epistasis analyses were conducted, and significant statistical interaction (OR = 0.622, P = 2.93 × 10⁻⁶, P_perm < 0.001) was observed between rs192808 (CACNG6) and rs2048137 (CACNG5). Our results indicate that CACNG4, CACNG5, CACNG6 and CACNG8 may contribute to the risk of SCZ. The statistical epistasis identified between CACNG5 and CACNG6 suggests that there may be an underlying biological interaction between the two genes.

Genome-wide investigation of schizophrenia associated plasma Ndel1 enzyme activity

Ndel1 is a DISC1-interacting oligopeptidase that cleaves in vitro neuropeptides as neurotensin and bradykinin, and which has been associated with both neuronal migration and neurite outgrowth. We previously reported that plasma Ndel1 enzyme activity is lower in patients with schizophrenia (SCZ) compared to healthy controls (HCs). To our knowledge, no previous study has investigated the genetic factors associated with the plasma Ndel1 enzyme activity. In the current analyses, samples from 83 SCZ patients and 92 control subjects that were assayed for plasma Ndel1 enzyme activity were genotyped on Illumina Omni Express arrays. A genetic relationship matrix using genome-wide information was then used for ancestry correction, and association statistics were calculated genome-wide. Ndel1 enzyme activity was significantly lower in patients with SCZ (t=4.9; p<0.001) and was found to be associated with CAMK1D, MAGI2, CCDC25, and GABGR3, at a level of suggestive significance (p<10(-6)), independent of the clinical status. Then, we performed a model to investigate the observed differences for case/control measures. 2 SNPs at region 1p22.2 reached the p<10(-7) level. ZFPM2 and MAD1L1 were the only two genes with more than one hit at 10(-6) order of p value. Therefore, Ndel1 enzyme activity is a complex trait influenced by many different genetic variants that may contribute to SCZ physiopathology.

Increased expression of NDEL1 and MBP genes in the peripheral blood of antipsychotic-naïve patients with first-episode psychosis

Schizophrenia is a multifactorial neurodevelopmental disorder with high heritability. First-episode psychosis (FEP) is a critical period for determining the disease prognosis and is especially helpful for identifying potential biomarkers associated with the onset and progression of the disorder. We investigated the mRNA expression of 12 schizophrenia-related genes in the blood of antipsychotic-naïve FEP patients (N=73) and healthy controls (N=73). To evaluate the influences of antipsychotic treatment and progression of the disorder, we compared the gene expression within patients before and after two months of treatment with risperidone (N=64). We observed a significantly increased myelin basic protein (MBP) and nuclear distribution protein nudE-like 1 (NDEL1) mRNA levels in FEP patients compared with controls. Comparing FEP before and after risperidone treatment, no significant differences were identified; however; a trend of relatively low NDEL1 expression was observed after risperidone treatment. Animals chronically treated with saline or risperidone exhibited no significant change in Ndel1 expression levels in the blood or the prefrontal cortex (PFC), suggesting that the trend of low NDEL1 expression observed in FEP patients after treatment is likely due to factors other than risperidone treatment (i.e., disease progression). In addition to the recognized association with schizophrenia, MBP and NDEL1 gene products also play an essential role in the functions that are deregulated in schizophrenia, such as neurodevelopment. Our data strengthen the importance of these biological processes in psychotic disorders, indicating that these changes can be detected peripherally and potentially represent putative novel blood biomarkers of susceptibility and disorder progression.

Individualized relapse prediction: Personality measures and striatal and insular activity during reward-processing robustly predict relapse

BACKGROUND

Nearly half of individuals with substance use disorders relapse in the year after treatment. A diagnostic tool to help clinicians make decisions regarding treatment does not exist for psychiatric conditions. Identifying individuals with high risk for relapse to substance use following abstinence has profound clinical consequences. This study aimed to develop neuroimaging as a robust tool to predict relapse.

METHODS

68 methamphetamine-dependent adults (15 female) were recruited from 28-day inpatient treatment. During treatment, participants completed a functional MRI scan that examined brain activation during reward processing. Patients were followed 1 year later to assess abstinence. We examined brain activation during reward processing between relapsing and abstaining individuals and employed three random forest prediction models (clinical and personality measures, neuroimaging measures, a combined model) to generate predictions for each participant regarding their relapse likelihood.

RESULTS

18 individuals relapsed. There were significant group by reward-size interactions for neural activation in the left insula and right striatum for rewards. Abstaining individuals showed increased activation for large, risky relative to small, safe rewards, whereas relapsing individuals failed to show differential activation between reward types. All three random forest models yielded good test characteristics such that a positive test for relapse yielded a likelihood ratio 2.63, whereas a negative test had a likelihood ratio of 0.48.

CONCLUSIONS

These findings suggest that neuroimaging can be developed in combination with other measures as an instrument to predict relapse, advancing tools providers can use to make decisions about individualized treatment of substance use disorders.

Co-expression network of neural-differentiation genes shows specific pattern in schizophrenia

Background

Schizophrenia is a neurodevelopmental disorder with genetic and environmental factors contributing to its pathogenesis, although the mechanism is unknown due to the difficulties in accessing diseased tissue during human neurodevelopment. The aim of this study was to find neuronal differentiation genes disrupted in schizophrenia and to evaluate those genes in post-mortem brain tissues from schizophrenia cases and controls.

Methods

We analyzed differentially expressed genes (DEG), copy number variation (CNV) and differential methylation in human induced pluripotent stem cells (hiPSC) derived from fibroblasts from one control and one schizophrenia patient and further differentiated into neuron (NPC). Expression of the DEG were analyzed with microarrays of post-mortem brain tissue (frontal cortex) cohort of 29 schizophrenia cases and 30 controls. A Weighted Gene Co-expression Network Analysis (WGCNA) using the DEG was used to detect clusters of co-expressed genes that werenon-conserved between adult cases and controls brain samples.

Results

We identified methylation alterations potentially involved with neuronal differentiation in schizophrenia, which displayed an over-representation of genes related to chromatin remodeling complex (adjP = 0.04). We found 228 DEG associated with neuronal differentiation. These genes were involved with metabolic processes, signal transduction, nervous system development, regulation of neurogenesis and neuronal differentiation. Between adult brain samples from cases and controls there were 233 DEG, with only four genes overlapping with the 228 DEG, probably because we compared single cell to tissue bulks and more importantly, the cells were at different stages of development. The comparison of the co-expressed network of the 228 genes in adult brain samples between cases and controls revealed a less conserved module enriched for genes associated with oxidative stress and negative regulation of cell differentiation.

Conclusion

This study supports the relevance of using cellular approaches to dissect molecular aspects of neurogenesis with impact in the schizophrenic brain. We showed that, although generated by different approaches, both sets of DEG associated to schizophrenia were involved with neocortical development. The results add to the hypothesis that critical metabolic changes may be occurring during early neurodevelopment influencing faulty development of the brain and potentially contributing to further vulnerability to the illness.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-015-0098-9) contains supplementary material, which is available to authorized users.

What is the impact of genome-wide supported risk variants for schizophrenia and bipolar disorder on brain structure and function? A systematic review

The powerful genome-wide association studies (GWAS) revealed common mutations that increase susceptibility for schizophrenia (SZ) and bipolar disorder (BD), but the vast majority were not known to be functional or associated with these illnesses. To help fill this gap, their impact on human brain structure and function has been examined. We systematically discuss this output to facilitate its timely integration in the psychosis research field; and encourage reflection for future research. Irrespective of imaging modality, studies addressing the effect of SZ/BD GWAS risk genes (ANK3, CACNA1C, MHC, TCF4, NRGN, DGKH, PBRM1, NCAN and ZNF804A) were included. Most GWAS risk variations were reported to affect neuroimaging phenotypes implicated in SZ/BD: white-matter integrity (ANK3 and ZNF804A), volume (CACNA1C and ZNF804A) and density (ZNF804A); grey-matter (CACNA1C, NRGN, TCF4 and ZNF804A) and ventricular (TCF4) volume; cortical folding (NCAN) and thickness (ZNF804A); regional activation during executive tasks (ANK3, CACNA1C, DGKH, NRGN and ZNF804A) and functional connectivity during executive tasks (CACNA1C and ZNF804A), facial affect recognition (CACNA1C and ZNF804A) and theory-of-mind (ZNF804A); but inconsistencies and non-replications also exist. Further efforts such as standardizing reporting and exploring complementary designs, are warranted to test the reproducibility of these early findings.

Epistasis in the risk of human neuropsychiatric disease

Neuropsychiatric disease represents the ideal class of disease to assess the role of epistasis, as more genes are expressed in the brain than in any other tissue. In this chapter, two well-studied neuropsychiatric diseases are examined, Alzheimer's disease (AD) and schizophrenia, which have been shown to have multiple and, often, replicated interactions that associate with clinical endpoints or related phenotypes. In each case, a single gene is represented in a plurality of epistatic interactions, apolipoprotein E (APOE) for AD and catechol-O-methyltransferase for schizophrenia. Interestingly, of the two, only APOE has clear-cut and consistent evidence for a marginal association. Unraveling the underlying reasons is important in understanding both genetic etiology and architecture as well as how to use genetics to provide better personalized treatments.

A model to investigate SNPs' interaction in GWAS studies

Genome-wide association studies (GWAS) are able to identify the role of individual SNPs in influencing a phenotype. Nevertheless, such analysis is unable to explain the biological complexity of several diseases. We elaborated an algorithm that starting from genes in molecular pathways implicated in a phenotype is able to identify SNP-SNP interaction's role in association with the phenotype. The algorithm is based on three steps. Firstly, it identifies the biological pathways (gene ontology) in which the genes under analysis play a role (GeneMANIA). Secondly, it identifies the group of SNPs that best fits the phenotype (and covariates) under analysis, not considering individual SNP regression coefficients but fitting the regression for the group itself. Finally, it operates an analysis of SNP interactions for each possible couple of SNPs within the group. The sensitivity and specificity of our algorithm was validated in simulated datasets (HapGen and Simulate Phenotypes programs). The impact on efficiency deriving from changes in the number of SNPs/patients under analysis, linkage disequilibrium and minor allele frequency thresholds was analyzed. Our algorithm showed a strong stability throughout all analysis operated, resulting in an overall sensitivity of 81.67 % and a specificity of 98.35 %. We elaborated a stable algorithm that may detect SNPs interactions, especially those effects that pass undetected in classical GWAS. This method may contribute to face the two relevant limitations of GWAS: lack of biological informative power and amount of time needed for the analysis.

Disrupted-In-Schizophrenia-1 (DISC1) interactome and mental disorders: impact of mouse models

Disrupted-In-Schizophrenia-1 (DISC1) has captured much attention because it predisposes individuals to a wide range of mental illnesses. Notably, a number of genes encoding proteins interacting with DISC1 are also considered to be relevant risk factors of mental disorders. We reasoned that the understanding of DISC1-associated mental disorders in the context of network principles will help to address fundamental properties of DISC1 as a disease gene. Systematic integration of behavioural phenotypes of genetic mouse lines carrying perturbation in DISC1 interacting proteins would contribute to a better resolution of neurobiological mechanisms of mental disorders associated with the impaired DISC1 interactome and lead to a development of network medicine. This review also makes specific recommendations of how to assess DISC1 associated mental disorders in mouse models and discuss future directions.

Variability in working memory performance explained by epistasis vs polygenic scores in the ZNF804A pathway

IMPORTANCE

We investigated the variation in neuropsychological function explained by risk alleles at the psychosis susceptibility gene ZNF804A and its interacting partners using single nucleotide polymorphisms (SNPs), polygenic scores, and epistatic analyses. Of particular importance was the relative contribution of the polygenic score vs epistasis in variation explained.

OBJECTIVES

To (1) assess the association between SNPs in ZNF804A and the ZNF804A polygenic score with measures of cognition in cases with psychosis and (2) assess whether epistasis within the ZNF804A pathway could explain additional variation above and beyond that explained by the polygenic score.

DESIGN, SETTING, AND PARTICIPANTS

Patients with psychosis (n = 424) were assessed in areas of cognitive ability impaired in schizophrenia including IQ, memory, attention, and social cognition. We used the Psychiatric GWAS Consortium 1 schizophrenia genome-wide association study to calculate a polygenic score based on identified risk variants within this genetic pathway. Cognitive measures significantly associated with the polygenic score were tested for an epistatic component using a training set (n = 170), which was used to develop linear regression models containing the polygenic score and 2-SNP interactions. The best-fitting models were tested for replication in 2 independent test sets of cases: (1) 170 individuals with schizophrenia or schizoaffective disorder and (2) 84 patients with broad psychosis (including bipolar disorder, major depressive disorder, and other psychosis).

MAIN OUTCOMES AND MEASURES

Participants completed a neuropsychological assessment battery designed to target the cognitive deficits of schizophrenia including general cognitive function, episodic memory, working memory, attentional control, and social cognition.

RESULTS

Higher polygenic scores were associated with poorer performance among patients on IQ, memory, and social cognition, explaining 1% to 3% of variation on these scores (range, P = .01 to .03). Using a narrow psychosis training set and independent test sets of narrow phenotype psychosis (schizophrenia and schizoaffective disorder), broad psychosis, and control participants (n = 89), the addition of 2 interaction terms containing 2 SNPs each increased the R2 for spatial working memory strategy in the independent psychosis test sets from 1.2% using the polygenic score only to 4.8% (P = .11 and .001, respectively) but did not explain additional variation in control participants.

CONCLUSIONS AND RELEVANCE

These data support a role for the ZNF804A pathway in IQ, memory, and social cognition in cases. Furthermore, we showed that epistasis increases the variation explained above the contribution of the polygenic score.

Functional neuroimaging and schizophrenia: a view towards effective connectivity modeling and polygenic risk

We review critical trends in imaging genetics as applied to schizophrenia research, and then discuss some future directions of the field. A plethora of imaging genetics studies have investigated the impact of genetic variation on brain function, since the paradigm of a neuroimaging intermediate phenotype for schizophrenia first emerged. It was initially posited that the effects of schizophrenia susceptibility genes would be more penetrant at the level of biologically based neuroimaging intermediate phenotypes than at the level of a complex and phenotypically heterogeneous psychiatric syndrome. The results of many studies support this assumption, most of which show single genetic variants to be associated with changes in activity of localized brain regions, as determined by select cognitive controlled tasks. From these basic studies, functional neuroimaging analysis of intermediate phenotypes has progressed to more complex and realistic models of brain dysfunction, incorporating models of functional and effective connectivity, including the modalities of psycho-physiological interaction, dynamic causal modeling, and graph theory metrics. The genetic association approaches applied to imaging genetics have also progressed to more sophisticated multivariate effects, including incorporation of two-way and three-way epistatic interactions, and most recently polygenic risk models. Imaging genetics is a unique and powerful strategy for understanding the neural mechanisms of genetic risk for complex CNS disorders at the human brain level.

Single-subject anxiety treatment outcome prediction using functional neuroimaging

The possibility of individualized treatment prediction has profound implications for the development of personalized interventions for patients with anxiety disorders. Here we utilize random forest classification and pre-treatment functional magnetic resonance imaging (fMRI) data from individuals with generalized anxiety disorder (GAD) and panic disorder (PD) to generate individual subject treatment outcome predictions. Before cognitive behavioral therapy (CBT), 48 adults (25 GAD and 23 PD) reduced (via cognitive reappraisal) or maintained their emotional responses to negative images during fMRI scanning. CBT responder status was predicted using activations from 70 anatomically defined regions. The final random forest model included 10 predictors contributing most to classification accuracy. A similar analysis was conducted using the clinical and demographic variables. Activations in the hippocampus during maintenance and anterior insula, superior temporal, supramarginal, and superior frontal gyri during reappraisal were among the best predictors, with greater activation in responders than non-responders. The final fMRI-based model yielded 79% accuracy, with good sensitivity (0.86), specificity (0.68), and positive and negative likelihood ratios (2.73, 0.20). Clinical and demographic variables yielded poorer accuracy (69%), sensitivity (0.79), specificity (0.53), and likelihood ratios (1.67, 0.39). This is the first use of random forest models to predict treatment outcome from pre-treatment neuroimaging data in psychiatry. Together, random forest models and fMRI can provide single-subject predictions with good test characteristics. Moreover, activation patterns are consistent with the notion that greater activation in cortico-limbic circuitry predicts better CBT response in GAD and PD.

Identification of cichlid fishes from Lake Malawi using computer vision

BACKGROUND

The explosively radiating evolution of cichlid fishes of Lake Malawi has yielded an amazing number of haplochromine species estimated as many as 500 to 800 with a surprising degree of diversity not only in color and stripe pattern but also in the shape of jaw and body among them. As these morphological diversities have been a central subject of adaptive speciation and taxonomic classification, such high diversity could serve as a foundation for automation of species identification of cichlids.

METHODOLOGY/PRINCIPAL FINDING

Here we demonstrate a method for automatic classification of the Lake Malawi cichlids based on computer vision and geometric morphometrics. For this end we developed a pipeline that integrates multiple image processing tools to automatically extract informative features of color and stripe patterns from a large set of photographic images of wild cichlids. The extracted information was evaluated by statistical classifiers Support Vector Machine and Random Forests. Both classifiers performed better when body shape information was added to the feature of color and stripe. Besides the coloration and stripe pattern, body shape variables boosted the accuracy of classification by about 10%. The programs were able to classify 594 live cichlid individuals belonging to 12 different classes (species and sexes) with an average accuracy of 78%, contrasting to a mere 42% success rate by human eyes. The variables that contributed most to the accuracy were body height and the hue of the most frequent color.

CONCLUSIONS

Computer vision showed a notable performance in extracting information from the color and stripe patterns of Lake Malawi cichlids although the information was not enough for errorless species identification. Our results indicate that there appears an unavoidable difficulty in automatic species identification of cichlid fishes, which may arise from short divergence times and gene flow between closely related species.

NDE1 and NDEL1: twin neurodevelopmental proteins with similar 'nature' but different 'nurture'

Nuclear distribution element 1 (NDE1, also known as NudE) and NDE-like 1 (NDEL1, also known as Nudel) are paralogous proteins essential for mitosis and neurodevelopment that have been implicated in psychiatric and neurodevelopmental disorders. The two proteins possess high sequence similarity and have been shown to physically interact with one another. Numerous lines of experimental evidence in vivo and in cell culture have demonstrated that these proteins share common functions, although instances of differing functions between the two have recently emerged. We review the key aspects of NDE1 and NDEL1 in terms of recent advances in structure elucidation and cellular function, with an emphasis on their differing mechanisms of post-translational modification. Based on a review of the literature and bioinformatics assessment, we advance the concept that the twin proteins NDE1 and NDEL1, while sharing a similar 'nature' in terms of their structure and basic functions, appear to be different in their 'nurture', the manner in which they are regulated both in terms of expression and of post-translational modification within the cell. These differences are likely to be of significant importance in understanding the specific roles of NDE1 and NDEL1 in neurodevelopment and disease.

Role of DISC1 interacting proteins in schizophrenia risk from genome-wide analysis of missense SNPs

A balanced translocation affecting DISC1 cosegregates with several psychiatric disorders, including schizophrenia, in a Scottish family. DISC1 is a hub protein of a network of protein-protein interactions involved in multiple developmental pathways within the brain. Gene set-based analysis has been proposed as an alternative to individual analysis of single nucleotide polymorphisms (SNPs) to get information from genome-wide association studies. In this work, we tested for an overrepresentation of the DISC1 interacting proteins within the top results of our ranked list of genes based on our previous genome-wide association study of missense SNPs in schizophrenia. Our data set consisted of 5100 common missense SNPs genotyped in 476 schizophrenic patients and 447 control subjects from Galicia, NW Spain. We used a modification of the Gene Set Enrichment Analysis adapted for SNPs, as implemented in the GenGen software. The analysis detected an overrepresentation of the DISC1 interacting proteins (permuted P-value=0.0158), indicative of the role of this gene set in schizophrenia risk. We identified seven leading-edge genes, MACF1, UTRN, DST, DISC1, KIF3A, SYNE1, and AKAP9, responsible for the overrepresentation. These genes are involved in neuronal cytoskeleton organization and intracellular transport through the microtubule cytoskeleton, suggesting that these processes may be impaired in schizophrenia.