Genomewide association for schizophrenia in the CATIE study: results of stage 1

Classification of schizophrenia, bipolar disorder and major depressive disorder with comorbid traits and deep learning algorithms

Many psychiatric disorders share genetic liabilities, but whether these shared liabilities can be utilized to classify and differentiate psychiatric disorders remains unclear. In this study, we use polygenic risk scores (PRSs) of 42 traits comorbid with schizophrenia (SCZ), bipolar disorder (BIP), and major depressive disorder (MDD) to evaluate their utilities. We found that combining target specific PRS with PRSs of comorbid traits can improve the classification of the target disorders. Importantly, without inclusion of PRSs from targeted disorders, we can still classify SCZ (accuracy 0.710 ± 0.008, AUC 0.789 ± 0.011), BIP (accuracy 0.782 ± 0.006, AUC 0.852 ± 0.004), and MDD (accuracy 0.753 ± 0.019, AUC 0.822 ± 0.010). Furthermore, PRSs from comorbid traits alone can effectively differentiate unaffected controls and patients with SCZ, BIP, and MDD (accuracy 0.861 ± 0.003, AUC 0.961 ± 0.041). Our results demonstrate that shared liabilities can be used effectively to improve the classification and differentiation of these disorders. The finding that PRSs from comorbid traits alone can classify and differentiate SCZ, BIP and MDD reasonably well implies that a majority of the risk variants composing target PRSs are shared with comorbid traits. Overall, our results suggest that a data-driven approach may be feasible to classify and differentiate these disorders.

Novel pathways linked to the expression of temperament in Merino sheep: a genome-wide association study

Animal temperament refers to the inherent behavioural and emotional characteristics of an animal, influencing how it interacts with its environment. The selection of sheep for temperament can change the temperament traits of the selected line and improve the welfare and production (reproduction, growth, immunity) of those animals. To understand the genetics that underly variation in temperament in sheep, and how selection on temperament can affect other production traits, a genome-wide association study was carried out. Merino sheep from lines selected for traits of calm and nervous temperament, and a commercial population, on which the temperament traits had never been assessed, were used. Blood samples from the three populations were genotyped using an Illumina GGP Ovine 50 K Genotyping BeadChip. The calm and nervous populations in the selected lines presented as distinct genetic populations, and 2 729 of the 45 761 single nucleotide polymorphisms (SNPs) had significantly different proportions between the two lines. Of those 2 729 SNPs, 2 084 were also associated with temperament traits in the commercial population. A genomic annotation identified 81 candidate genes for temperament, nearly half of which are associated with disorders of social behaviour in humans. Five of those 81 candidate genes are related to production traits in sheep. Two genes were associated with personality disorders in humans and with production traits in sheep. We identified significant enrichment in genes involved in nervous system processes such as the regulation of systemic arterial blood pressure, ventricular myocyte action, multicellular organismal signalling, ion transmembrane transport, and calcium ion binding, suggesting that temperament is underpinned by variation in multiple biological systems. Our results contribute to understanding of the genetic basis of animal temperament which could be applied to the genetic evaluation of temperament in sheep and other farm animals.

Classification of Schizophrenia, Bipolar Disorder and Major Depressive Disorder with Comorbid Traits and Deep Learning Algorithms

Recent GWASs have demonstrated that comorbid disorders share genetic liabilities. But whether and how these shared liabilities can be used for the classification and differentiation of comorbid disorders remains unclear. In this study, we use polygenic risk scores (PRSs) estimated from 42 comorbid traits and the deep neural networks (DNN) architecture to classify and differentiate schizophrenia (SCZ), bipolar disorder (BIP) and major depressive disorder (MDD). Multiple PRSs were obtained for individuals from the schizophrenia (SCZ) (cases = 6,317, controls = 7,240), bipolar disorder (BIP) (cases = 2,634, controls 4,425) and major depressive disorder (MDD) (cases = 1,704, controls = 3,357) datasets, and classification models were constructed with and without the inclusion of PRSs of the target (SCZ, BIP or MDD). Models with the inclusion of target PRSs performed well as expected. Surprisingly, we found that SCZ could be classified with only the PRSs from 35 comorbid traits (not including the target SCZ and directly related traits) (accuracy 0.760 ± 0.007, AUC 0.843 ± 0.005). Similar results were obtained for BIP (33 traits, accuracy 0.768 ± 0.007, AUC 0.848 ± 0.009), and MDD (36 traits, accuracy 0.794 ± 0.010, AUC 0.869 ± 0.004). Furthermore, these PRSs from comorbid traits alone could effectively differentiate unaffected controls, SCZ, BIP, and MDD patients (average categorical accuracy 0.861 ± 0.003, average AUC 0.961 ± 0.041). These results suggest that the shared liabilities from comorbid traits alone may be sufficient to classify SCZ, BIP and MDD. More importantly, these results imply that a data-driven and objective diagnosis and differentiation of SCZ, BIP and MDD may be feasible.

Genome-wide association study identified six loci associated with adverse drug reactions to aripiprazole in schizophrenia patients

Aripiprazole is recommended for routine use in schizophrenia patients. However, the biological mechanism for the adverse drug reactions (ADRs) among schizophrenia patients with the antipsychotic drug aripiprazole is far from clear. To explore the potential genetic factors that may cause movement-related adverse antipsychotic effects in patients, we conducted an association analysis between movement-related ADRs and SNPs in schizophrenia patients receiving aripiprazole monotherapy. In this study, multiple ADRs of 384 patients were quantified within 6-week treatment, and the scores of movement-related ADRs at baseline and follow-up time points during treatment were obtained. The highest score record was used as the quantitative index in analysis, and genetic analysis at the genome-wide level was conducted. The SNP rs4149181 in SLC22A8 [P = 2.28 × 10-8] showed genome-wide significance, and rs2284223 in ADCYAP1R1 [P = 9.76 × 10-8], rs73258503 in KCNIP4 [P = 1.39 × 10-7], rs678428 in SMAD9 [P = 4.70 × 10-7], rs6421034 in NAP1L4 [P = 6.80 × 10-7], and rs1394796 in ERBB4 [P = 8.60 × 10-7] were found to be significantly associated with movement-related ADRs. The combined prediction model of these six loci showed acceptable performance in predicting adverse events [area under the curve (AUC): 0.84]. Combined with the function and network of the above genes and other candidate loci (KCNA1, CACNG1, etc.), we hypothesize that SLC22A8 and KCNIP4-Kv channel perform their respective functions as transporter or channel and participate in the in vivo metabolism or effects of aripiprazole. The above results imply the important function of ion transporters and channels in movement-related adverse antipsychotic effects in aripiprazole monotherapy schizophrenia patients.

Uncovering a neurological protein signature for severe COVID-19

Coronavirus disease of 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has sparked a global pandemic with severe complications and high morbidity rate. Neurological symptoms in COVID-19 patients, and neurological sequelae post COVID-19 recovery have been extensively reported. Yet, neurological molecular signature and signaling pathways that are affected in the central nervous system (CNS) of COVID-19 severe patients remain still unknown and need to be identified. Plasma samples from 49 severe COVID-19 patients, 50 mild COVID-19 patients, and 40 healthy controls were subjected to Olink proteomics analysis of 184 CNS-enriched proteins. By using a multi-approach bioinformatics analysis, we identified a 34-neurological protein signature for COVID-19 severity and unveiled dysregulated neurological pathways in severe cases. Here, we identified a new neurological protein signature for severe COVID-19 that was validated in different independent cohorts using blood and postmortem brain samples and shown to correlate with neurological diseases and pharmacological drugs. This protein signature could potentially aid the development of prognostic and diagnostic tools for neurological complications in post-COVID-19 convalescent patients with long term neurological sequelae.

Pisani L, Yoo B, Bejerano G, Peltz G. Analysis of structural variation among inbred mouse strains

BACKGROUND

'Long read' sequencing methods have been used to identify previously uncharacterized structural variants that cause human genetic diseases. Therefore, we investigated whether long read sequencing could facilitate genetic analysis of murine models for human diseases.

RESULTS

The genomes of six inbred strains (BTBR T + Itpr3tf/J, 129Sv1/J, C57BL/6/J, Balb/c/J, A/J, SJL/J) were analyzed using long read sequencing. Our results revealed that (i) Structural variants are very abundant within the genome of inbred strains (4.8 per gene) and (ii) that we cannot accurately infer whether structural variants are present using conventional short read genomic sequence data, even when nearby SNP alleles are known. The advantage of having a more complete map was demonstrated by analyzing the genomic sequence of BTBR mice. Based upon this analysis, knockin mice were generated and used to characterize a BTBR-unique 8-bp deletion within Draxin that contributes to the BTBR neuroanatomic abnormalities, which resemble human autism spectrum disorder.

CONCLUSION

A more complete map of the pattern of genetic variation among inbred strains, which is produced by long read genomic sequencing of the genomes of additional inbred strains, could facilitate genetic discovery when murine models of human diseases are analyzed.

Machine Learning and Pharmacogenomics at the Time of Precision Psychiatry

Traditional medicine and biomedical sciences are reaching a turning point because of the constantly growing impact and volume of Big Data. Machine Learning (ML) techniques and related algorithms play a central role as diagnostic, prognostic, and decision-making tools in this field. Another promising area becoming part of everyday clinical practice is personalized therapy and pharmacogenomics. Applying ML to pharmacogenomics opens new frontiers to tailored therapeutical strategies to help clinicians choose drugs with the best response and fewer side effects, operating with genetic information and combining it with the clinical profile. This systematic review aims to draw up the state-of-the-art ML applied to pharmacogenomics in psychiatry. Our research yielded fourteen papers; most were published in the last three years. The sample comprises 9,180 patients diagnosed with mood disorders, psychoses, or autism spectrum disorders. Prediction of drug response and prediction of side effects are the most frequently considered domains with the supervised ML technique, which first requires training and then testing. The random forest is the most used algorithm; it comprises several decision trees, reduces the training set's overfitting, and makes precise predictions. ML proved effective and reliable, especially when genetic and biodemographic information were integrated into the algorithm. Even though ML and pharmacogenomics are not part of everyday clinical practice yet, they will gain a unique role in the next future in improving personalized treatments in psychiatry.

Imputed expression of schizophrenia-associated genes and cognitive measures in patients with schizophrenia

Background

Cognitive dysfunction is a core manifestation of schizophrenia and one of the best predictors of long‐term disability. Genes increasing risk for schizophrenia may partly act through the modulation of cognition.

Methods

We imputed the expression of 130 genes recently prioritized for association with schizophrenia, using PsychENCODE variant weights and genotypes of patients with schizophrenia in CATIE. Processing speed, reasoning, verbal memory, working memory, vigilance, and a composite cognitive score were used as phenotypes. We performed linear regression models for each cognitive measure and gene expression score, adjusting for age, years of education, antipsychotic treatment, years since the first antipsychotic treatment and population principal components.

Results

We included 425 patients and expression scores of 91 genes (others had no heritable expression; Bonferroni corrected alpha = 5.49e‐4). No gene expression score was associated with cognitive measures, though ENOX1 expression was very close to the threshold for verbal memory (p = 6e‐4) and processing speed (p = 7e‐4). Other genes were nominally associated with multiple phenotypes (MAN2A1 and PCGF3).

Conclusion

A better understanding of the mechanisms mediating cognitive dysfunction in schizophrenia may help in the definition of disease prognosis and in the identification of new treatments, as the treatment of cognitive impairment remains an unmet therapeutic need.

Genome-Wide Causation Studies of Complex Diseases

Despite significant progress in dissecting the genetic architecture of complex diseases by genome-wide association studies (GWAS), the signals identified by association analysis may not have specific pathological relevance to diseases so that a large fraction of disease-causing genetic variants is still hidden. Association is used to measure dependence between two variables or two sets of variables. GWAS test association between a disease and single-nucleotide polymorphisms (SNPs) (or other genetic variants) across the genome. Association analysis may detect superficial patterns between disease and genetic variants. Association signals provide limited information on the causal mechanism of diseases. The use of association analysis as a major analytical platform for genetic studies of complex diseases is a key issue that may hamper discovery of disease mechanisms, calling into the questions the ability of GWAS to identify loci-underlying diseases. It is time to move beyond association analysis toward techniques, which enables the discovery of the underlying causal genetic structures of complex diseases. To achieve this, we propose the concept of genome-wide causation studies (GWCS) as an alternative to GWAS and develop additive noise models (ANMs) for genetic causation analysis. Type 1 error rates and power of the ANMs in testing causation are presented. We conducted GWCS of schizophrenia. Both simulation and real data analysis show that the proportion of the overlapped association and causation signals is small. Thus, we anticipate that our analysis will stimulate serious discussion of the applicability of GWAS and GWCS.

Artificial image objects for classification of schizophrenia with GWAS-selected SNVs and convolutional neural network

In this article, we propose a new approach to analyze large genomics data. We considered individual genetic variants as pixels in an image and transformed a collection of variants into an artificial image object (AIO), which could be classified as a regular image by CNN algorithms. Using schizophrenia as a case study, we demonstrate the principles and their applications with 3 datasets. With 4,096 SNVs, the CNN models achieved an accuracy of 0.678 ± 0.007 and an AUC of 0.738 ± 0.008 for the diagnosis phenotype. With 44,100 SNVs, the models achieved class-specific accuracies of 0.806 ± 0.032 and 0.820 ± 0.049, and AUCs of 0.930 ± 0.017 and 0.867 ± 0.040 for the bottom and top classes stratified by the patient's polygenic risk scores. These results suggest that, once transformed to images, large genomics data can be analyzed effectively with image classification algorithms.

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Introduce a technique to transform genomics data into AIOs

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Apply CNN algorithms to classify genomics derived AIOs

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Showcase the technique with GWAS-selected SNVs to classify schizophrenia diagnosis

Genome-wide association studies have discovered many genetic variants that contribute to human diseases. However, it remains a challenge to effectively utilize these variants to facilitate early and accurate diagnosis and treatment. In this report, we propose a new approach that transforms genetic data into AIOs so that they can be classified by advanced artificial intelligence and machine learning algorithms. Using schizophrenia as a case study, we demonstrate that genetic variants can be transformed into AIOs and that the AIOs can be classified by CNN algorithms consistently. Our approach can be applied to other omics data and combine them to jointly model disease risks and treatment responses.

Genome wide study of tardive dyskinesia in schizophrenia

Tardive dyskinesia (TD) is a severe condition characterized by repetitive involuntary movement of orofacial regions and extremities. Patients treated with antipsychotics typically present with TD symptomatology. Here, we conducted the largest GWAS of TD to date, by meta-analyzing samples of East-Asian, European, and African American ancestry, followed by analyses of biological pathways and polygenic risk with related phenotypes. We identified a novel locus and three suggestive loci, implicating immune-related pathways. Through integrating trans-ethnic fine mapping, we identified putative credible causal variants for three of the loci. Post-hoc analysis revealed that SNPs harbored in TNFRSF1B and CALCOCO1 independently conferred three-fold increase in TD risk, beyond clinical risk factors like Age of onset and Duration of illness to schizophrenia. Further work is necessary to replicate loci that are reported in the study and evaluate the polygenic architecture underlying TD.

Integration of Imaging (epi)Genomics Data for the Study of Schizophrenia Using Group Sparse Joint Nonnegative Matrix Factorization

Schizophrenia (SZ) is a complex disease. Single nucleotide polymorphism (SNP), brain activity measured by functional magnetic resonance imaging (fMRI) and DNA methylation are all important biomarkers that can be used for the study of SZ. To our knowledge, there has been little effort to combine these three datasets together. In this study, we propose a group sparse joint nonnegative matrix factorization (GSJNMF) model to integrate SNP, fMRI, and DNA methylation for the identification of multi-dimensional modules associated with SZ, which can be used to study regulatory mechanisms underlying SZ at multiple levels. The proposed GSJNMF model projects multiple types of data onto a common feature space, in which heterogeneous variables with large coefficients on the same projected bases are used to identify multi-dimensional modules. We also incorporate group structure information available from each dataset. The genomic factors in such modules have significant correlations or functional associations with several brain activities. At the end, we have applied the method to the analysis of real data collected from the Mind Clinical Imaging Consortium (MCIC) for the study of SZ and identified significant biomarkers. These biomarkers were further used to discover genes and corresponding brain regions, which were confirmed to be significantly associated with SZ.

Approaches to studying the genomic architecture of complex birth defects

Every year nearly 6 percent of children worldwide are born with a serious congenital malformation, resulting in death or lifelong disability. In the United States, birth defects remain one of the leading causes of infant mortality. Among the common structural congenital defects are conditions known as neural tube defects (NTDs). These are a class of malformation of the brain and spinal cord where the neural tube fails to close during the neurulation. Although NTDs remain among the most pervasive and debilitating of all human developmental anomalies, there is insufficient understanding of their etiology. Previous studies have proposed that complex birth defects like NTDs are likely omnigenic, involving interconnected gene regulatory networks with associated signals throughout the genome. Advances in technologies have allowed researchers to more critically investigate regulatory gene networks in ever increasing detail, informing our understanding of the genetic basis of NTDs. Employing a systematic analysis of these complex birth defects using massively parallel DNA sequencing with stringent bioinformatic algorithms, it is possible to approach a greater level of understanding of the genomic architecture underlying NTDs. Herein, we present a brief overview of different approaches undertaken in our laboratory to dissect out the genetics of susceptibility to NTDs. This involves the use of mouse models to identify candidate genes, as well as large scale whole genome/whole exome (WGS/WES) studies to interrogate the genomic landscape of NTDs. The goal of this research is to elucidate the gene-environment interactions contributing to NTDs, thus encouraging global research efforts in their prevention.

Association between a TCF4 Polymorphism and Susceptibility to Schizophrenia

The basic helix-loop-helix (bHLH) transcription factor 4 (TCF4) had been identified as a susceptibility gene associated with schizophrenia (SCZ) by GWAS, but inconsistent results have been found in other studies. To validate these findings and to reveal the effects of different inheritance models, rs2958182, rs1261085, rs8766, and rs12966547 of the TCF4 gene were genotyped in the Northwest Han Chinese population (448 cases and 628 controls) via a multiplex polymerase chain reaction SNPscan assay. Single SNP, genotype, and association analyses with three different models were performed. We observed genotype and allele distributions of four SNPs that showed nonsignificant associations in the Northwest Han Chinese population. However, published datasets (51,892 cases and 68,498 controls) were collected and combined with our experimental results to ascertain the association of the TCF4 gene SNPs and SCZ, which demonstrated that rs2958182 (P=0.003) was a significant signal based on a systematic meta-analysis. To clarify the biological role of rs2958182, it is important to improve the understanding of the pathophysiology of SCZ.

Variants of GRM7 as risk factor and response to antipsychotic therapy in schizophrenia

Genome-wide association study (GWAS) has determined the metabotropic glutamate receptor 7 (GRM7) gene as potential locus for schizophrenia risk variants; However, the relationship between the GRM7 variants and the risk of schizophrenia is still uncertain, and there are significant individual variations in response to the antipsychotic drugs. In order to identify susceptible gene and drug-response-related markers, 2413 subjects in our research were chosen for determining drug-response-related markers in schizophrenia. The rs1516569 variant (OR = 0.95, P < 3.47 × 10^-4) was a significant risk factor, and a single-nucleotide polymorphism of GRM7 gene- rs9883258 (OR = 0.84, P = 2.18 × 10^-3) has been determined as potential biomarkers for therapeutic responses of seven commonly used antipsychotic drugs (aripiprazole, haloperidol, olanzapine, perphenazine, quetiapine, risperidone and ziprasidone) in Chinese Han population; Significant associations with treatment response for several single-nucleotide polymorphisms in every antipsychotic drugs, such as rs779746 (OR = 1.39, P = 0.03), rs480409 (OR = 0.73, P = 0.04), rs78137319 (OR = 3.09, P = 0.04), rs1154370 (OR = 1.51, P = 0.006) have been identified in our study. Hence our research elucidates that GRM7 variants play the critical role of predicting the risk of schizophrenia and antipsychotic effect of seven common drugs.

Genome-wide association studies in schizophrenia: Recent advances, challenges and future perspective

Genome-wide association studies (GWAS) have proved to be a powerful approach for gene discovery in schizophrenia; their findings have important implications not just for our understanding of the genetic architecture of the disorder, but for the potential applications of personalised medicine through improved classification and targeted interventions. In this article we review the current status of the GWAS literature in schizophrenia including functional annotation methods and polygenic risk scoring, as well as the directions and challenges of future research. We consider recent findings in East Asian populations and the advancements from trans-ancestry analysis, as well as the insights gained from research looking across psychiatric disorders.

Robust kernel association testing (RobKAT)

Testing the association between single-nucleotide polymorphism (SNP) effects and a response is often carried out through kernel machine methods based on least squares, such as the sequence kernel association test (SKAT). However, these least-squares procedures are designed for a normally distributed conditional response, which may not apply. Other robust procedures such as the quantile regression kernel machine (QRKM) restrict the choice of the loss function and only allow inference on conditional quantiles. We propose a general and robust kernel association test with a flexible choice of the loss function, no distributional assumptions, and has SKAT and QRKM as special cases. We evaluate our proposed robust association test (RobKAT) across various data distributions through a simulation study. When errors are normally distributed, RobKAT controls type I error and shows comparable power with SKAT. In all other distributional settings investigated, our robust test has similar or greater power than SKAT. Finally, we apply our robust testing method to data from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) clinical trial to detect associations between selected genes including the major histocompatibility complex (MHC) region on chromosome six and neurotropic herpesvirus antibody levels in schizophrenia patients. RobKAT detected significant association with four SNP sets (HST1H2BJ, MHC, POM12L2, and SLC17A1), three of which were undetected by SKAT.

Polygenic Risk Scores for Subtyping of Schizophrenia

Schizophrenia is a complex disorder with many comorbid conditions. In this study, we used polygenic risk scores (PRSs) from schizophrenia and comorbid traits to explore consistent cluster structure in schizophrenia patients. With 10 comorbid traits, we found a stable 4-cluster structure in two datasets (MGS and SSCCS). When the same traits and parameters were applied for the patients in a clinical trial of antipsychotics, the CATIE study, a 5-cluster structure was observed. One of the 4 clusters found in the MGS and SSCCS was further split into two clusters in CATIE, while the other 3 clusters remained unchanged. For the 5 CATIE clusters, we evaluated their association with the changes of clinical symptoms, neurocognitive functions, and laboratory tests between the enrollment baseline and the end of Phase I trial. Class I was found responsive to treatment, with significant reduction for the total, positive, and negative symptoms (p = 0.0001, 0.0099, and 0.0028, respectively), and improvement for cognitive functions (VIGILANCE, p = 0.0099; PROCESSING SPEED, p = 0.0006; WORKING MEMORY, p = 0.0023; and REASONING, p = 0.0015). Class II had modest reduction of positive symptoms (p = 0.0492) and better PROCESSING SPEED (p = 0.0071). Class IV had a specific reduction of negative symptoms (p = 0.0111) and modest cognitive improvement for all tested domains. Interestingly, Class IV was also associated with decreased lymphocyte counts and increased neutrophil counts, an indication of ongoing inflammation or immune dysfunction. In contrast, Classes III and V showed no symptom reduction but a higher level of phosphorus. Overall, our results suggest that PRSs from schizophrenia and comorbid traits can be utilized to classify patients into subtypes with distinctive clinical features. This genetic susceptibility based subtyping may be useful to facilitate more effective treatment and outcome prediction.

Genome-wide association analysis of autism identified multiple loci that have been reported as strong signals for neuropsychiatric disorders

Autism is a common neurodevelopmental disorder with a moderate to a high degree of heritability, but only a few common genetic variants that explain the heritability have been associated. We performed a genome-wide transmission disequilibrium test analysis of a newly genotyped autism case-parent triad samples (127 trios) in Han Chinese, identified top association signals at multiple single nucleotide polymorphisms (SNPs), including rs9839376 (OR = 2.59, P = 1.27 × 10^-05 ) at KCNMB2, rs6044680 (OR = 0.319, P = 4.82 × 10^-05 ) and rs7274133 (OR = 0.313, P = 3.22 × 10^-05 ) at PCSK2, and rs310619 (OR = 2.40, P = 7.44 × 10^-05 ) at EEF1A2. Furthermore, a genome-wide combined P-value of individual SNPs in two independent case-parent triad samples (total 402 triads, n = 1,206) identified SNPs at EGFLAM, ZDHHC2, AGBL1, and SNX29 as additional association signals for autism. While none of these signals achieved a genome-wide significance in the two samples of our study, they have been reported in a previous genome-wide association study of neuropsychiatric disorders, and the majority of these SNP have a significant cis-regulatory association with mRNA in human tissues (false discovery rate (FDR) < 0.05). Our study warrants further study or replication with additional sample for association with autism and other neuropsychiatric disorders. Autism Res 2020, 13: 382-396. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Autism is a common neurodevelopmental disorder, heritable, but only a few common genetic variants that explain the heritability have been associated. We conducted a genome-wide association study with two cohorts of autism case-parent triad samples in Han Chinese and identified multiple single nucleotide polymorphisms that were reported as strong association signals in a previous genome-wide association study of other neuropsychiatric disorders or related traits. Our study provides evidence for shared genetic variants among autism and other neuropsychiatric disorders.

Global landscape and genetic regulation of RNA editing in cortical samples from individuals with schizophrenia

RNA editing critically regulates neurodevelopment and normal neuronal function. The global landscape of RNA editing was surveyed across 364 schizophrenia cases and 383 control postmortem brain samples from the CommonMind Consortium, comprising two regions: dorsolateral prefrontal cortex and anterior cingulate cortex. In schizophrenia, RNA editing sites in genes encoding AMPA-type glutamate receptors and postsynaptic density proteins were less edited, whereas those encoding translation initiation machinery were edited more. These sites replicate between brain regions, map to 3'-untranslated regions and intronic regions, share common sequence motifs and overlap with binding sites for RNA-binding proteins crucial for neurodevelopment. These findings cross-validate in hundreds of non-overlapping dorsolateral prefrontal cortex samples. Furthermore, ~30% of RNA editing sites associate with cis-regulatory variants (editing quantitative trait loci or edQTLs). Fine-mapping edQTLs with schizophrenia risk loci revealed co-localization of eleven edQTLs with six loci. The findings demonstrate widespread altered RNA editing in schizophrenia and its genetic regulation, and suggest a causal and mechanistic role of RNA editing in schizophrenia neuropathology.

Unravelling the genetic basis of schizophrenia and bipolar disorder with GWAS: A systematic review

OBJECTIVES

To systematically review findings of GWAS in schizophrenia (SZ) and in bipolar disorder (BD); and to interpret findings, with a focus on identifying independent replications.

METHOD

PubMed search, selection and review of all independent GWAS in SZ or BD, published since March 2011, i.e. studies using non-overlapping samples within each article, between articles, and with those of the previous review (Li et al., 2012).

RESULTS

From the 22 GWAS included in this review, the genetic associations surviving standard GWAS-significance were for genetic markers in the regions of ACSL3/KCNE4, ADCY2, AMBRA1, ANK3, BRP44, DTL, FBLN1, HHAT, INTS7, LOC392301, LOC645434/NMBR, LOC729457, LRRFIP1, LSM1, MDM1, MHC, MIR2113/POU3F2, NDST3, NKAPL, ODZ4, PGBD1, RENBP, TRANK1, TSPAN18, TWIST2, UGT1A1/HJURP, WHSC1L1/FGFR1 and ZKSCAN4. All genes implicated across both reviews are discussed in terms of their function and implication in neuropsychiatry.

CONCLUSION

Taking all GWAS to date into account, AMBRA1, ANK3, ARNTL, CDH13, EFHD1 (albeit with different alleles), MHC, PLXNA2 and UGT1A1 have been implicated in either disorder in at least two reportedly non-overlapping samples. Additionally, evidence for a SZ/BD common genetic basis is most strongly supported by the implication of ANK3, NDST3, and PLXNA2.

Translational bioinformatics in mental health: open access data sources and computational biomarker discovery

Mental illness is increasingly recognized as both a significant cost to society and a significant area of opportunity for biological breakthrough. As -omics and imaging technologies enable researchers to probe molecular and physiological underpinnings of multiple diseases, opportunities arise to explore the biological basis for behavioral health and disease. From individual investigators to large international consortia, researchers have generated rich data sets in the area of mental health, including genomic, transcriptomic, metabolomic, proteomic, clinical and imaging resources. General data repositories such as the Gene Expression Omnibus (GEO) and Database of Genotypes and Phenotypes (dbGaP) and mental health (MH)-specific initiatives, such as the Psychiatric Genomics Consortium, MH Research Network and PsychENCODE represent a wealth of information yet to be gleaned. At the same time, novel approaches to integrate and analyze data sets are enabling important discoveries in the area of mental and behavioral health. This review will discuss and catalog into an organizing framework the increasingly diverse set of MH data resources available, using schizophrenia as a focus area, and will describe novel and integrative approaches to molecular biomarker discovery that make use of mental health data.

Common genetic variants shared among five major psychiatric disorders: a large-scale genome-wide combined analysis

Background. Genetic correlation and pleiotropic effects among psychiatric disorders have been reported. This study aimed to identify specific common genetic variants shared between five adult psychiatric disorders: schizophrenia, bipolar, major depressive disorder, attention deficit-hyperactivity disorder, and autism spectrum disorder. Methods. A combined p-value of about 8 million single nucleotide polymorphisms (SNPs) was calculated in an equivalent sample of 151,672 cases and 284,444 controls of European ancestry from published data based on the latest genome-wide association studies of five major psychiatric disorder. SNPs that achieved genome-wide significance (P<5x10-08) were mapped to loci and genomic regions for further investigation; gene annotation and clustering were performed to understand the biological process and molecular function of the loci identified. We also examined CNVs and performed expression quantitative trait loci analysis for SNPs by genomic region. Results. We find that 6,293 SNPs mapped to 336 loci shared by the three adult psychiatric disorders, 1,108 variants at 73 loci shared by the childhood disorders, and 713 variants at 47 genes shared by all five disorders at genome-wide significance (P<5x10-08). Of the 2,583 SNPs at the extended major histocompatibility complex identified for three adult disorders, none of them were associated with childhood disorders; and SNPs shared by all five disorders were located in regions that have been identified as containing copy number variation associated with autism and had largely neurodevelopmental functions. Conclusion. We show a number of specific SNPs associated with psychiatric disorders of childhood or adult-onset, illustrating not only genetic heterogeneity across these disorders but also developmental genes shared by them all.  These results provide a manageable list of anchors from which to investigate epigenetic mechanism or gene-gene interaction on the development of neuropsychiatric disorders and for developing a measurement matrix for disease risk to potentially develop a novel taxonomy for precision medicine.

DNA methylation correlates of PTSD: Recent findings and technical challenges

There is increasing evidence that epigenetic factors play a critical role in posttraumatic stress disorder (PTSD), by mediating the impact of environmental exposures to trauma on the regulation of gene expression. DNA methylation is one epigenetic process that has been highly studied in PTSD. This review will begin by providing an overview of DNA methylation (DNAm) methods, and will then highlight two major biological systems that have been identified in the epigenetic regulation in PTSD: (a) the immune system and (b) the stress response system. In addition to candidate gene approaches, we will review novel strategies to study epigenome-wide PTSD-related effects, including epigenome-wide algorithms that distill information from many loci into a single summary score (e.g., measures of "epigenetic age" which have been associated with PTSD). This review will also cover recent epigenome wide association studies (EWAS) of PTSD, and biological pathway models used to identify gene sets enriched in PTSD. Finally, we address technical and methodological advances and challenges to the field, and highlight exciting directions for future research.

Association of functional polymorphisms in 3'-untranslated regions of COMT, DISC1, and DTNBP1 with schizophrenia: a meta-analysis

INTRODUCTION

In recent years, various studies have accumulated evidence of the involvement of single nucleotide polymorphisms (SNPs) in introns and exons in schizophrenia. The association of functional SNPs in the 3'-untranslated regions with schizophrenia has been explored in a number of studies, but the results are inconclusive because of limited meta-analyses. To systematically analyze the association between SNPs in 3'-untranslated regions and schizophrenia, we conducted a meta-analysis by combining all available studies on schizophrenia candidate genes.

MATERIALS AND METHODS

We searched candidate genes from the schizophrenia database and performed a comprehensive meta-analysis using all the available data up to August 2017. The association between susceptible SNPs and schizophrenia was assessed by the pooled odds ratio with 95% confidence interval using fixed-effect and random-effect models.

RESULTS

A total of 21 studies including 8291 cases and 9638 controls were used for meta-analysis. Three investigated SNPs were rs165599, rs3737597, and rs1047631 of COMT, DISC1, and DTNBP1, respectively. Our results suggested that rs3737597 showed a significant association with schizophrenia in Europeans (odds ratio: 1.584, P: 0.002, 95% confidence interval: 1.176-2.134) under a random-effect framework.

CONCLUSION

This meta-analysis indicated that rs3737597 of DISC1 was significantly associated with schizophrenia in Europeans, and it can be suggested as an ethnic-specific risk genetic factor.

GWAS of Behavioral Traits

Over the past decade, genome-wide association studies (GWAS) have evolved into a powerful tool to investigate genetic risk factors for human diseases via a hypothesis-free scan of the genome. The success of GWAS for psychiatric disorders and behavioral traits have been somewhat mixed, partly owing to the complexity and heterogeneity of these traits. Significant progress has been made in the last few years in the development and implementation of complex statistical methods and algorithms incorporating GWAS. Such advanced statistical methods applied to GWAS hits in combination with incorporation of different layers of genomics data have catapulted the search for novel genes for behavioral traits and improved our understanding of the complex polygenic architecture of these traits.This chapter will give a brief overview on GWAS and statistical methods currently used in GWAS. The chapter will focus on reviewing the current literature and highlight some of the most important GWAS on psychiatric and other behavioral traits and will conclude with a discussion on future directions.

Prediction of Schizophrenia Diagnosis by Integration of Genetically Correlated Conditions and Traits

Schizophrenia is genetically heterogeneous and comorbid with many conditions. In this study, we explored polygenic scores (PGSs) from genetically related conditions and traits to predict schizophrenia diagnosis using both logistic regression and deep neural network (DNN) models. We used the combined Molecular Genetics of Schizophrenia and Swedish Schizophrenia Case Control Study (MGS + SSCCS) data for training and testing the models, and used the Clinical Antipsychotic Trials for Intervention Effectiveness (CATIE) data as independent validation. We screened 28 conditions and traits comorbid with schizophrenia to identify traits as potential predictors and used LASSO regression to select predictors for model construction. We investigated how PGS calculation influenced model performance. We found that the inclusion of comorbid traits improved model performance and PGSs calculated from two traits were more generalizable in independent validation. With a DNN model using 19 PGS predictors, we accomplished a prediction accuracy of 0.813 and an AUC of 0.905 in the MGS + SSCCS data. When this model was validated with the CATIE data, it achieved an accuracy of 0.721 and AUC of 0.747. Our results indicate that PGSs alone may not be sufficient to predict schizophrenia accurately and the inclusion of behavioral and clinical data may be necessary for more accurate prediction model.

Longitudinal analyses of the DNA methylome in deployed military servicemen identify susceptibility loci for post-traumatic stress disorder

In order to determine the impact of the epigenetic response to traumatic stress on post-traumatic stress disorder (PTSD), this study examined longitudinal changes of genome-wide blood DNA methylation profiles in relation to the development of PTSD symptoms in two prospective military cohorts (one discovery and one replication data set). In the first cohort consisting of male Dutch military servicemen (n=93), the emergence of PTSD symptoms over a deployment period to a combat zone was significantly associated with alterations in DNA methylation levels at 17 genomic positions and 12 genomic regions. Evidence for mediation of the relation between combat trauma and PTSD symptoms by longitudinal changes in DNA methylation was observed at several positions and regions. Bioinformatic analyses of the reported associations identified significant enrichment in several pathways relevant for symptoms of PTSD. Targeted analyses of the significant findings from the discovery sample in an independent prospective cohort of male US marines (n=98) replicated the observed relation between decreases in DNA methylation levels and PTSD symptoms at genomic regions in ZFP57, RNF39 and HIST1H2APS2. Together, our study pinpoints three novel genomic regions where longitudinal decreases in DNA methylation across the period of exposure to combat trauma marks susceptibility for PTSD.

A Powerful Test for SNP Effects on Multivariate Binary Outcomes using Kernel Machine Regression

Evaluating multiple binary outcomes is common in genetic studies of complex diseases. These outcomes are often correlated because they are collected from the same individual and they may share common marker effects. In this paper, we propose a procedure to test for effect of a SNP-set on multiple, possibly correlated, binary responses. We develop a score-based test using a nonparametric modeling framework that jointly models the global effect of the marker set. We account for the nonlinear effects and potentially complicated interaction between markers using reproducing kernels. Our testing procedure only requires estimation under the null hypothesis and we use multivariate generalized estimating equations (GEEs) to estimate the model components to account for the correlation among the outcomes. We evaluate finite sample performance of our test via simulation study and demonstrated our methods using the CATIE antibody study data and the CoLaus Study data.

The role of cadherin genes in five major psychiatric disorders: A literature update

Converging evidence from candidate gene, genome-wide linkage, and association studies support a role of cadherins in the pathophysiology of five major psychiatric disorders including attention deficit hyperactivity disorder, autism spectrum disorder (ASD), schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD). These molecules are transmembrane proteins which act as cell adhesives by forming adherens junctions (AJs) to bind cells within tissues. Members of the cadherin superfamily are also involved in biological processes such as signal transduction and plasticity that have been implicated in the etiology of major psychiatric conditions. Although there are over 110 genes mapped to the cadherin superfamily, our literature survey showed that evidence of association with psychiatric disorders is strongest for CDH7, CHD11, and CDH13. Gene enrichment analysis showed that those cadherin genes implicated in psychiatric disorders were overrepresented in biological processes such as in cell-cell adhesion (GO:0007156 & GO:0098742) and adherens junction organization (GO:0034332). Further, cadherin genes were also mapped to processes that have been linked to the development of psychiatric disorders such as nervous system development (GO:0007399). To further understand the role of cadherin SNPs implicated in psychiatric disorders, we utilized an in silico computational pipeline to functionally annotate associated variants. This analysis yielded eight variants mapped to PCDH1-13, CDH7, CDH11, and CDH13 that are predicted to be biologically functional. Functional genomic evaluation is now required to understand the molecular mechanism by which these variants might confer susceptibility to psychiatric disorders.

Inference on phenotype-specific effects of genes using multivariate kernel machine regression

We consider the problem of assessing the joint effect of a set of genetic markers on multiple, possibly correlated phenotypes of interest. We develop a kernel machine based multivariate regression framework, where the joint effect of the marker set on each of the phenotypes is modeled using prespecified kernel functions with unknown variance components. Unlike most existing methods that mainly focus on the global association between the marker set and the phenotype set, we develop estimation and testing procedures to study phenotype-specific associations. Specifically, we develop an estimation method based on the penalized likelihood approach to estimate phenotype-specific effects and their corresponding standard errors while accounting for possible correlation among the phenotypes. We develop testing procedures for the association of the marker set with any subset of phenotypes using a score-based variance components testing method. We assess the performance of our proposed methodology via a simulation study and demonstrate the utility of the proposed method using the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) data.

A computational algorithm for personalized medicine in schizophrenia

Despite advances in sequencing candidate genes and whole genomes, no method has accurately predicted who will or will not benefit from a specific antipsychotic medication among patients with schizophrenia. We propose a computational algorithm that utilizes a person-centered approach that directly identifies individual patients who will respond to a specific antipsychotic medication. The algorithm was applied to the data obtained from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study. The predictors were either (1) 13 single-nucleotide polymorphisms (SNPs) and 53 baseline variables or (2) 25 SNPs and the same 53 baseline variables, depending on the existing findings and data availability. The outcome variables were either (1) improvement in the Positive and Negative Syndrome Scale (PANSS) (Yes/No) or (2) completion of phase 1/1A (Yes/No). Each of those four predictor-outcome combinations was tried for each of the five antipsychotic medications (Perphenazine, Olanzapine, Quetiapine, Risperidone, and Ziprasidone), leading to 20 prediction experiments. For 18 out of 20 experiments, all three performance measures were greater than 0.50 (sensitivity 0.51-0.79, specificity 0.52-0.79, accuracy 0.52-0.74). Notably, the model provided a promising prediction for Ziprasidone for the case involving completion of phase 1/1A (Yes/No) predicted by 13 SNPs and 53 baseline variables (sensitivity 0.75, specificity 0.74, accuracy 0.74). The proposed algorithm simultaneously used both genetic information and clinical profiles to predict individual patients' response to antipsychotic medications. As the method is not disease-specific but a general algorithm, it can be easily adopted in many other clinical practices for personalized medicine.

Developing tomorrow's antipsychotics: the need for a more personalised approach

There has been little pharmacological advance in the treatment of schizophrenia since the introduction of chlorpromazine in the 1950s. This may be set to change as recent advances in molecular biology offer the prospect of a better understanding of the pathophysiology of the disorder and allow investigation of the complex interplay of genetic and environmental risk factors. In this review I discuss future approaches to antipsychotic drug development, highlighting the need to better define symptom areas and develop drugs based on an understanding of neurobiological mechanisms. The development of biomarkers has the potential in future to improve differential diagnosis and help predict response to treatment. These developments herald the possibility of a more integrated drug discovery approach and the subsequent provision of more stratified healthcare, and hopefully significant improvements in patient care and improved long-term outcomes.

A high-coverage Neandertal genome from Vindija Cave in Croatia

To date, the only Neandertal genome that has been sequenced to high quality is from an individual found in Southern Siberia. We sequenced the genome of a female Neandertal from ~50,000 years ago from Vindija Cave, Croatia, to ~30-fold genomic coverage. She carried 1.6 differences per 10,000 base pairs between the two copies of her genome, fewer than present-day humans, suggesting that Neandertal populations were of small size. Our analyses indicate that she was more closely related to the Neandertals that mixed with the ancestors of present-day humans living outside of sub-Saharan Africa than the previously sequenced Neandertal from Siberia, allowing 10 to 20% more Neandertal DNA to be identified in present-day humans, including variants involved in low-density lipoprotein cholesterol concentrations, schizophrenia, and other diseases.

Role of 108 schizophrenia-associated loci in modulating psychopathological dimensions in schizophrenia and bipolar disorder

The Schizophrenia Working Group of the Psychiatric Genomics Consortium (PGC) identified 108 loci associated with schizophrenia, but their role in modulating specific psychopathological dimensions of the disease is unknown. This study investigated which symptom dimensions may be affected by these loci in schizophrenia, and bipolar disorder. Positive, negative and depressive symptoms, suicidal ideation, cognition, violent behaviors, quality of life, and early onset were investigated in schizophrenia and bipolar disorder using the clinical antipsychotic trials of intervention effectiveness (CATIE) and systematic treatment enhancement program for bipolar disorder (STEP-BD) studies. Individual loci were investigated, then genes within 50 Kbp from polymorphisms with p < 0.10 were included in an enrichment analysis (Cytoscape GeneMania plugin) and used to estimate polygenic risk scores (PRS). Covariates were center, age, gender, ancestry-informative population, principal components, and for cognition, also years of education were considered. Eighty-nine polymorphisms were available, 479 and 810 white subjects were included from CATIE and STEP-BD, respectively. rs75059851 (IGSF9B gene) was associated with negative symptoms in CATIE (p = 0.00048). Genes within 50 Kbp from variants contributing to negative symptoms and suicide were enriched with GO terms involved in acetylcholine neurotransmission, cognition showed enrichment with GO terms involved in vitamin B6 and fucose metabolism while early onset with GO terms related to extracellular matrix structure. PRS showed nominal associations with violent behaviors and depressive symptoms. This study provided preliminary evidence that a schizophrenia-associated variant (rs75059851) may modulate negative symptoms. Multi-locus models may provide interesting insights about the biological mechanisms that mediate psychopathological dimensions.

Genetics of schizophrenia: A consensus paper of the WFSBP Task Force on Genetics

OBJECTIVES

Schizophrenia is a severe psychiatric disease affecting about 1% of the general population. The relative contribution of genetic factors has been estimated to be up to 80%. The mode of inheritance is complex, non-Mendelian, and in most cases involving the combined action of large numbers of genes.

METHODS

This review summarises recent efforts to identify genetic variants associated with schizophrenia detected, e.g., through genome-wide association studies, studies on copy-number variants or next-generation sequencing.

RESULTS

A large, new body of evidence on genetics of schizophrenia has accumulated over recent years. Many new robustly associated genetic loci have been detected. Furthermore, there is consensus that at least a dozen microdeletions and microduplications contribute to the disease. Genetic overlap between schizophrenia, other psychiatric disorders, and neurodevelopmental syndromes raised new questions regarding the current classification of psychiatric and neurodevelopmental diseases.

CONCLUSIONS

Future studies will address especially the functional characterisation of genetic variants. This will hopefully open the doors to our understanding of the pathophysiology of schizophrenia and other related diseases. Complementary, integrated systems biology approaches to genomics, transcriptomics, proteomics and metabolomics may also play crucial roles in enabling a precision medicine approach to the treatment of individual patients.

Schizophrenia genetics in the genome-wide era: a review of Japanese studies

The introduction of the genome-wide association study transformed schizophrenia genetics research and has promoted a genome-wide mindset that has stimulated the development of genomic technology, enabling departures from the traditional candidate gene approach. As result, we have witnessed a decade of major discoveries in schizophrenia genetics and the development of genome-wide approaches to the study of copy number variants. These genomic technologies have primarily been applied in populations of European descent. However, more recently both genome-wide association study and copy number variant studies in Asian populations have begun to emerge. In this invited review, we provide concise summaries of the schizophrenia genome-wide association study and copy number variant literature with specific focus on studies conducted in the Japanese population. When applicable, we compare findings observed in the Japanese population with those found in other populations. We conclude with recommendations for future research in schizophrenia genetics, relevant to Japan and beyond.

[Genetic aspects of schizophrenia]

Schizophrenia is a disease with a complex non-Mendelian inheritance mechanism in most cases involving the combined action of a large number of genes. Identifying of genomic variations associated with schizophrenia endophenotypes has a great potential. This review describes genetic markers of the disease, current methods of their analysis, including genome-wide association study (GWAS). Certain genes with mutations that increase the risk of schizophrenia are described. Functional polymorphisms with phenotypic expression, which are significantly associated with clinical manifestation of schizophrenia, can serve as useful genetic markers. The authors highlight that currently there are no certain susceptibility genes. Further global research and search for markers in different population groups are needed.

Genetic loci associated with an earlier age at onset in multiplex schizophrenia

An earlier age at onset (AAO) has been associated with greater genetic loadings in schizophrenia. This study aimed to identify modifier loci associated with an earlier AAO of schizophrenia. A genome-wide association analysis (GWAS) was conducted in 94 schizophrenia probands with the earliest AAO and 91 with the latest AAO. Candidate single nucleotide polymorphisms (SNPs) were then genotyped in the co-affected siblings and unrelated probands. Multi-SNP genetic risk scores (GRS) composed of the candidate loci were used to distinguish patients with an early or late AAO. The 14-SNP GRS could distinguish the co-affected siblings (n = 90) of the earliest probands from those (n = 91) of the latest probands. When 132 patients with an earlier AAO and 158 patients with a later AAO were included, a significant trend in the 14-SNP GRS was detected among those unrelated probands from 4 family groups with the earliest, earlier, later, and latest AAO. The overall effect of the 14 SNPs on an AAO in schizophrenia was verified using co-affected siblings of the GWAS probands and trend effect across unrelated patients. Preliminary network analysis of these loci revealed the involvement of PARK2, a gene intensively reported in Parkinson’s disease and schizophrenia research.

BDNF rescues prefrontal dysfunction elicited by pyramidal neuron-specific DTNBP1 deletion in vivo

Dystrobrevin-binding protein 1 (Dtnbp1) is one of the earliest identified schizophrenia susceptibility genes. Reduced expression of DTNBP1 is commonly found in brain areas of schizophrenic patients. Dtnbp1-null mutant mice exhibit abnormalities in behaviors and impairments in neuronal activities. However, how diminished DTNBP1 expression contributes to clinical relevant features of schizophrenia remains to be illustrated. Here, using a conditional Dtnbp1 knockout mouse line, we identified an in vivo schizophrenia-relevant function of DTNBP1 in pyramidal neurons of the medial prefrontal cortex (mPFC). We demonstrated that DTNBP1 elimination specifically in pyramidal neurons of the mPFC impaired mouse pre-pulse inhibition (PPI) behavior and reduced perisomatic GABAergic synapses. We further revealed that loss of DTNBP1 in pyramidal neurons diminished activity-dependent secretion of brain-derived neurotrophic factor (BDNF). Finally, we showed that chronic BDNF infusion in the mPFC fully rescued both GABAergic synaptic dysfunction and PPI behavioral deficit induced by DTNBP1 elimination from pyramidal neurons. Our findings highlight brain region- and cell type-specific functions of DTNBP1 in the pathogenesis of schizophrenia, and underscore BDNF restoration as a potential therapeutic strategy for schizophrenia.

A method for integrating neuroimaging into genetic models of learning performance

Specific learning disorders (SLD) are an archetypal example of how clinical neuropsychological (NP) traits can differ from underlying genetic and neurobiological risk factors. Disparate environmental influences and pathologies impact learning performance assessed through cognitive examinations and clinical evaluations, the primary diagnostic tools for SLD. We propose a neurobiological risk for SLD with neuroimaging biomarkers, which is integrated into a genome-wide association study (GWAS) of learning performance in a cohort of 479 European individuals between 8 and 21 years of age. We first identified six regions of interest (ROIs) in temporal and anterior cingulate regions where the group diagnosed with learning disability has the least overall variation, relative to the other group, in thickness, area, and volume measurements. Although we used the three imaging measures, the thickness was the leading contributor. Hence, we calculated the Euclidean distances between any two individuals based on their thickness measures in the six ROIs. Then, we defined the relative similarity of one individual according to the averaged ranking of pairwise distances from the individuals to those in the SLD group. The inverse of this relative similarity is called the neurobiological risk for the individual. Single nucleotide polymorphisms in the AGBL1 gene on chromosome 15 had a significant association with learning performance at a genome-wide level. This finding was supported in an independent cohort of 2,327 individuals of the same demographic profile. Our statistical approach for integrating genetic and neuroimaging biomarkers can be extended into studying the biological basis of other NP traits.

Epigenetic profiling of human brain differential DNA methylation networks in schizophrenia

Background

Epigenetics of schizophrenia provides important information on how the environmental factors affect the genetic architecture of the disease. DNA methylation plays a pivotal role in etiology for schizophrenia. Previous studies have focused mostly on the discovery of schizophrenia-associated SNPs or genetic variants. As postmortem brain samples became available, more and more recent studies surveyed transcriptomics of the diseases. In this study, we constructed protein-protein interaction (PPI) network using the disease associated SNP (or genetic variants), differentially expressed disease genes and differentially methylated disease genes (or promoters). By combining the different datasets and topological analyses of the PPI network, we established a more comprehensive understanding of the development and genetics of this devastating mental illness.

Results

We analyzed the previously published DNA methylation profiles of prefrontal cortex from 335 healthy controls and 191 schizophrenic patients. These datasets revealed 2014 CpGs identified as GWAS risk loci with the differential methylation profile in schizophrenia, and 1689 schizophrenic differential methylated genes (SDMGs) identified with predominant hypomethylation. These SDMGs, combined with the PPIs of these genes, were constructed into the schizophrenic differential methylation network (SDMN). On the SDMN, there are 10 hypermethylated SDMGs, including GNA13, CAPNS1, GABPB2, GIT2, LEFTY1, NDUFA10, MIOS, MPHOSPH6, PRDM14 and RFWD2. The hypermethylation to differential expression network (HyDEN) were constructed to determine how the hypermethylated promoters regulate gene expression. The enrichment analyses of biochemical pathways in HyDEN, including TNF alpha, PDGFR-beta signaling, TGF beta Receptor, VEGFR1 and VEGFR2 signaling, regulation of telomerase, hepatocyte growth factor receptor signaling, ErbB1 downstream signaling and mTOR signaling pathway, suggested that the malfunctioning of these pathways contribute to the symptoms of schizophrenia.

Conclusions

The epigenetic profiles of DNA differential methylation from schizophrenic brain samples were investigated to understand the regulatory roles of SDMGs. The SDMGs interplays with SCZCGs in a coordinated fashion in the disease mechanism of schizophrenia. The protein complexes and pathways involved in SDMN may be responsible for the etiology and potential treatment targets. The SDMG promoters are predominantly hypomethylated. Increasing methylation on these promoters is proposed as a novel therapeutic approach for schizophrenia.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-016-0229-y) contains supplementary material, which is available to authorized users.

Two-stage additional evidence support association of common variants in the HDAC3 with the increasing risk of schizophrenia susceptibility

Schizophrenia (SCZ) is a complex neuropsychiatric disorder with high heritability. Abnormal gene methylation was found to play a key role in the development of SCZ, suggesting that histone deacetylases (HDACs) may increase the expression of several key genes in the brain. However, recent studies evaluating the association between SCZ and genetic polymorphisms in histone deacetylase 3 (encoded by HDAC3) have shown conflicting results. In this study, we designed a two-stage case-control study to investigate the association of the HDAC3 with SCZ. Fourteen tag single nucleotide polymorphisms (SNPs) entirely covering the region of HDAC3 were analyzed in the testing group of 1,421 patients and 2,823 healthy controls, and the SNP rs14251 was found to be significant (and rs2530223 to be nominally significant). The significant result of rs14251 was successfully replicated in the validation group consisting of 896 cases and 1,815 healthy controls (P = 0.009276, OR = 1.219), and also confirmed by haplotype based analyses (rs976552-rs14251, global P < 0.001). To sum up, our results provide additional evidence that HDAC3 confers the increasing risk of SCZ susceptibility in Han Chinese individuals, suggesting this gene as a potential genetic modifier for SCZ development. © 2016 Wiley Periodicals, Inc.

Unraveling the genetic architecture of copy number variants associated with schizophrenia and other neuropsychiatric disorders

Recent studies show that the complex genetic architecture of schizophrenia (SZ) is driven in part by polygenic components, or the cumulative effect of variants of small effect in many genes, as well as rare single-locus variants with large effect sizes. Here we discuss genetic aberrations known as copy number variants (CNVs), which fall in the latter category and are associated with a high risk for SZ and other neuropsychiatric disorders. We briefly review recurrent CNVs associated with SZ, and then highlight one CNV in particular, a recurrent 1.6-Mb deletion on chromosome 3q29, which is estimated to confer a 40-fold increased risk for SZ. Additionally, we describe the use of genetic mouse models, behavioral tools, and patient-derived induced pluripotent stem cells as a means to study CNVs in the hope of gaining mechanistic insight into their respective disorders. Taken together, the genomic data connecting CNVs with a multitude of human neuropsychiatric disease, our current technical ability to model such chromosomal anomalies in mouse, and the existence of precise behavioral measures of endophenotypes argue that the time is ripe for systematic dissection of the genetic mechanisms underlying such disease. © 2016 Wiley Periodicals, Inc.

Genetics of Schizophrenia: Historical Insights and Prevailing Evidence

Schizophrenia's (SZ's) heritability and familial transmission have been known for several decades; however, despite the clear evidence for a genetic component, it has been very difficult to pinpoint specific causative genes. Even so genetic studies have taught us a lot, even in the pregenomic era, about the molecular underpinnings and disease-relevant pathways. Recurring themes emerged revealing the involvement of neurodevelopmental processes, glutamate regulation, and immune system differential activation in SZ etiology. The recent emergence of epigenetic studies aimed at shedding light on the biological mechanisms underlying SZ has provided another layer of information in the investigation of gene and environment interactions. However, this epigenetic insight also brings forth another layer of complexity to the (epi)genomic landscape such as interactions between genetic variants, epigenetic marks-including cross-talk between DNA methylation and histone modification processes-, gene expression regulation, and environmental influences. In this review, we seek to synthesize perspectives, including limitations and obstacles yet to overcome, from genetic and epigenetic literature on SZ through a qualitative review of risk factors and prevailing hypotheses. Encouraged by the findings of both genetic and epigenetic studies to date, as well as the continued development of new technologies to collect and interpret large-scale studies, we are left with a positive outlook for the future of elucidating the molecular genetic mechanisms underlying SZ and other complex neuropsychiatric disorders.

Consensus paper of the WFSBP Task Force on Biological Markers: Criteria for biomarkers and endophenotypes of schizophrenia part II: Cognition, neuroimaging and genetics

OBJECTIVES

Schizophrenia is a group of severe psychiatric disorders with high heritability but only low odds ratios of risk genes. Despite progress in the identification of pathophysiological processes, valid biomarkers of the disease are still lacking.

METHODS

This comprehensive review summarises recent efforts to identify genetic underpinnings, clinical and cognitive endophenotypes and symptom dimensions of schizophrenia and presents findings from neuroimaging studies with structural, functional and spectroscopy magnetic resonance imaging and positron emission tomography. The potential of findings to be biomarkers of schizophrenia is discussed.

RESULTS

Recent findings have not resulted in clear biomarkers for schizophrenia. However, we identified several biomarkers that are potential candidates for future research. Among them, copy number variations and links between genetic polymorphisms derived from genome-wide analysis studies, clinical or cognitive phenotypes, multimodal neuroimaging findings including positron emission tomography and magnetic resonance imaging, and the application of multivariate pattern analyses are promising.

CONCLUSIONS

Future studies should address the effects of treatment and stage of the disease more precisely and apply combinations of biomarker candidates. Although biomarkers for schizophrenia await validation, knowledge on candidate genomic and neuroimaging biomarkers is growing rapidly and research on this topic has the potential to identify psychiatric endophenotypes and in the future increase insight on individual treatment response in schizophrenia.

LINE1 insertions as a genomic risk factor for schizophrenia: Preliminary evidence from an affected family

Recent studies show that human-specific LINE1s (L1HS) play a key role in the development of the central nervous system (CNS) and its disorders, and that their transpositions within the human genome are more common than previously thought. Many polymorphic L1HS, that is, present or absent across individuals, are not annotated in the current release of the genome and are customarily termed "non-reference L1s." We developed an analytical workflow to identify L1 polymorphic insertions with next-generation sequencing (NGS) using data from a family in which SZ segregates. Our workflow exploits two independent algorithms to detect non-reference L1 insertions, performs local de novo alignment of the regions harboring predicted L1 insertions and resolves the L1 subfamily designation from the de novo assembled sequence. We found 110 non-reference L1 polymorphic loci exhibiting Mendelian inheritance, the vast majority of which are already reported in dbRIP and/or euL1db, thus, confirming their status as non-reference L1 polymorphic insertions. Four previously undetected L1 polymorphic loci were confirmed by PCR amplification and direct sequencing of the insert. A large fraction of our non-reference L1s is located within the open reading frame of protein-coding genes that belong to pathways already implicated in the pathogenesis of schizophrenia. The finding of these polymorphic variants among SZ offsprings is intriguing and suggestive of putative pathogenic role. Our data show the utility of NGS to uncover L1 polymorphic insertions, a neglected type of genetic variants with the potential to influence the risk to develop schizophrenia like SNVs and CNVs. © 2016 Wiley Periodicals, Inc.