Integrative approach for inference of gene regulatory networks using lasso-based random featuring and application to psychiatric disorders

Background

Inferring gene regulatory networks is one of the most interesting research areas in the systems biology. Many inference methods have been developed by using a variety of computational models and approaches. However, there are two issues to solve. First, depending on the structural or computational model of inference method, the results tend to be inconsistent due to innately different advantages and limitations of the methods. Therefore the combination of dissimilar approaches is demanded as an alternative way in order to overcome the limitations of standalone methods through complementary integration. Second, sparse linear regression that is penalized by the regularization parameter (lasso) and bootstrapping-based sparse linear regression methods were suggested in state of the art methods for network inference but they are not effective for a small sample size data and also a true regulator could be missed if the target gene is strongly affected by an indirect regulator with high correlation or another true regulator.

Results

We present two novel network inference methods based on the integration of three different criteria, (i) z-score to measure the variation of gene expression from knockout data, (ii) mutual information for the dependency between two genes, and (iii) linear regression-based feature selection.

Based on these criterion, we propose a lasso-based random feature selection algorithm (LARF) to achieve better performance overcoming the limitations of bootstrapping as mentioned above.

Conclusions

In this work, there are three main contributions. First, our z score-based method to measure gene expression variations from knockout data is more effective than similar criteria of related works. Second, we confirmed that the true regulator selection can be effectively improved by LARF. Lastly, we verified that an integrative approach can clearly outperform a single method when two different methods are effectively jointed. In the experiments, our methods were validated by outperforming the state of the art methods on DREAM challenge data, and then LARF was applied to inferences of gene regulatory network associated with psychiatric disorders.

Genome-wide association study of 40,000 individuals identifies two novel loci associated with bipolar disorder

Bipolar disorder (BD) is a genetically complex mental illness characterized by severe oscillations of mood and behaviour. Genome-wide association studies (GWAS) have identified several risk loci that together account for a small portion of the heritability. To identify additional risk loci, we performed a two-stage meta-analysis of >9 million genetic variants in 9,784 bipolar disorder patients and 30,471 controls, the largest GWAS of BD to date. In this study, to increase power we used ∼2,000 lithium-treated cases with a long-term diagnosis of BD from the Consortium on Lithium Genetics, excess controls, and analytic methods optimized for markers on the X-chromosome. In addition to four known loci, results revealed genome-wide significant associations at two novel loci: an intergenic region on 9p21.3 (rs12553324, P =  5.87 × 10 -  9; odds ratio (OR) = 1.12) and markers within ERBB2 (rs2517959, P =  4.53 × 10 -  9; OR = 1.13). No significant X-chromosome associations were detected and X-linked markers explained very little BD heritability. The results add to a growing list of common autosomal variants involved in BD and illustrate the power of comparing well-characterized cases to an excess of controls in GWAS.

Impact of a cis-associated gene expression SNP on chromosome 20q11.22 on bipolar disorder susceptibility, hippocampal structure and cognitive performance

BACKGROUND

Bipolar disorder is a highly heritable polygenic disorder. Recent enrichment analyses suggest that there may be true risk variants for bipolar disorder in the expression quantitative trait loci (eQTL) in the brain.

AIMS

We sought to assess the impact of eQTL variants on bipolar disorder risk by combining data from both bipolar disorder genome-wide association studies (GWAS) and brain eQTL.

METHOD

To detect single nucleotide polymorphisms (SNPs) that influence expression levels of genes associated with bipolar disorder, we jointly analysed data from a bipolar disorder GWAS (7481 cases and 9250 controls) and a genome-wide brain (cortical) eQTL (193 healthy controls) using a Bayesian statistical method, with independent follow-up replications. The identified risk SNP was then further tested for association with hippocampal volume (n = 5775) and cognitive performance (n = 342) among healthy individuals.

RESULTS

Integrative analysis revealed a significant association between a brain eQTL rs6088662 on chromosome 20q11.22 and bipolar disorder (log Bayes factor = 5.48; bipolar disorder P = 5.85 × 10(-5)). Follow-up studies across multiple independent samples confirmed the association of the risk SNP (rs6088662) with gene expression and bipolar disorder susceptibility (P = 3.54 × 10(-8)). Further exploratory analysis revealed that rs6088662 is also associated with hippocampal volume and cognitive performance in healthy individuals.

CONCLUSIONS

Our findings suggest that 20q11.22 is likely a risk region for bipolar disorder; they also highlight the informative value of integrating functional annotation of genetic variants for gene expression in advancing our understanding of the biological basis underlying complex disorders, such as bipolar disorder.

Genes with de novo mutations are shared by four neuropsychiatric disorders discovered from NPdenovo database

Currently, many studies on neuropsychiatric disorders have utilized massive trio-based whole-exome sequencing (WES) and whole-genome sequencing (WGS) to identify numerous de novo mutations (DNMs). Here, we retrieved 17,104 DNMs from 3555 trios across four neuropsychiatric disorders: autism spectrum disorder, epileptic encephalopathy, intellectual disability and schizophrenia, in addition to unaffected siblings (control), from 36 studies by WES/WGS. After eliminating non-exonic variants, we focused on 3334 exonic DNMs for evaluation of their association with these diseases. Our results revealed a higher prevalence of DNMs in the probands of all four disorders compared with the one in the controls (P<1.3 × 10(-7)). The elevated DNM frequency is dominated by loss-of-function/deleterious single-nucleotide variants and frameshift indels (that is, extreme mutations, P<4.5 × 10(-5)). With extensive annotation of these 'extreme' mutations, we prioritized 764 candidate genes in these four disorders. A combined analysis of Gene Ontology, microRNA targets and transcription factor targets revealed shared biological process and non-coding regulatory elements of candidate genes in the pathology of neuropsychiatric disorders. In addition, weighted gene co-expression network analysis of human laminar-specific neocortical expression data showed that candidate genes are convergent on eight shared modules with specific layer enrichment and biological process features. Furthermore, we identified that 53 candidate genes are associated with more than one disorder (P<0.000001), suggesting a possibly shared genetic etiology underlying these disorders. Particularly, DNMs of the SCN2A gene are frequently occurred across all four disorders. Finally, we constructed a freely available NPdenovo database, which provides a comprehensive catalog of the DNMs identified in neuropsychiatric disorders.

Mathematical modelling of transcriptional heterogeneity identifies novel markers and subpopulations in complex tissues

Tissue heterogeneity is both a major confounding factor and an underexploited information source. While a handful of reports have demonstrated the potential of supervised computational methods to deconvolute tissue heterogeneity, these approaches require a priori information on the marker genes or composition of known subpopulations. To address the critical problem of the absence of validated marker genes for many (including novel) subpopulations, we describe convex analysis of mixtures (CAM), a fully unsupervised in silico method, for identifying subpopulation marker genes directly from the original mixed gene expressions in scatter space that can improve molecular analyses in many biological contexts. Validated with predesigned mixtures, CAM on the gene expression data from peripheral leukocytes, brain tissue, and yeast cell cycle, revealed novel marker genes that were otherwise undetectable using existing methods. Importantly, CAM requires no a priori information on the number, identity, or composition of the subpopulations present in mixed samples, and does not require the presence of pure subpopulations in sample space. This advantage is significant in that CAM can achieve all of its goals using only a small number of heterogeneous samples, and is more powerful to distinguish between phenotypically similar subpopulations.

A genome-wide association study of bipolar disorder with comorbid eating disorder replicates the SOX2-OT region

BACKGROUND

Bipolar disorder is a heterogeneous mood disorder associated with several important clinical comorbidities, such as eating disorders. This clinical heterogeneity complicates the identification of genetic variants contributing to bipolar susceptibility. Here we investigate comorbidity of eating disorders as a subphenotype of bipolar disorder to identify genetic variation that is common and unique to both disorders.

METHODS

We performed a genome-wide association analysis contrasting 184 bipolar subjects with eating disorder comorbidity against both 1370 controls and 2006 subjects with bipolar disorder only from the Bipolar Genome Study (BiGS).

RESULTS

The most significant genome-wide finding was observed bipolar with comorbid eating disorder vs. controls within SOX2-OT (p=8.9×10(-8) for rs4854912) with a secondary peak in the adjacent FXR1 gene (p=1.2×10(-6) for rs1805576) on chromosome 3q26.33. This region was also the most prominent finding in the case-only analysis (p=3.5×10(-7) and 4.3×10(-6), respectively). Several regions of interest containing genes involved in neurodevelopment and neuroprotection processes were also identified.

LIMITATIONS

While our primary finding did not quite reach genome-wide significance, likely due to the relatively limited sample size, these results can be viewed as a replication of a recent study of eating disorders in a large cohort.

CONCLUSIONS

These findings replicate the prior association of SOX2-OT with eating disorders and broadly support the involvement of neurodevelopmental/neuroprotective mechanisms in the pathophysiology of both disorders. They further suggest that different clinical manifestations of bipolar disorder may reflect differential genetic contributions and argue for the utility of clinical subphenotypes in identifying additional molecular pathways leading to illness.

Psychiatric genetics in China: achievements and challenges

To coordinate research efforts in psychiatric genetics in China, a group of Chinese and foreign investigators have established an annual “Summit on Chinese Psychiatric Genetics” to present their latest research and discuss the current state and future directions of this field. To date, two Summits have been held, the first in Changsha in April, 2014, and the second in Kunming in April, 2015. The consensus of roundtable discussions held at these meetings is that psychiatric genetics in China is in need of new policies to promote collaborations aimed at creating a framework for genetic research appropriate for the Chinese population: relying solely on Caucasian population-based studies may result in missed opportunities to diagnose and treat psychiatric disorders. In addition, participants agree on the importance of promoting collaborations and data sharing in areas where China has especially strong resources, such as advanced facilities for non-human primate studies and traditional Chinese medicine: areas that may also provide overseas investigators with unique research opportunities. In this paper, we present an overview of the current state of psychiatric genetics research in China, with emphasis on genome-level studies, and describe challenges and opportunities for future advances, particularly at the dawn of “precision medicine.” Together, we call on administrative bodies, funding agencies, the research community, and the public at large for increased support for research on the genetic basis of psychiatric disorders in the Chinese population. In our opinion, increased public awareness and effective collaborative research hold the keys to the future of psychiatric genetics in China.

The PsychENCODE project

Recent research on disparate psychiatric disorders has implicated rare variants in genes involved in global gene regulation and chromatin modification, as well as many common variants located primarily in regulatory regions of the genome. Understanding precisely how these variants contribute to disease will require a deeper appreciation for the mechanisms of gene regulation in the developing and adult human brain. The PsychENCODE project aims to produce a public resource of multidimensional genomic data using tissue- and cell type–specific samples from approximately 1,000 phenotypically well-characterized, high-quality healthy and disease-affected human post-mortem brains, as well as functionally characterize disease-associated regulatory elements and variants in model systems. We are beginning with a focus on autism spectrum disorder, bipolar disorder and schizophrenia, and expect that this knowledge will apply to a wide variety of psychiatric disorders. This paper outlines the motivation and design of PsychENCODE.

Multiblock discriminant analysis for integrative genomic study

Human diseases are abnormal medical conditions in which multiple biological components are complicatedly involved. Nevertheless, most contributions of research have been made with a single type of genetic data such as Single Nucleotide Polymorphism (SNP) or Copy Number Variation (CNV). Furthermore, epigenetic modifications and transcriptional regulations have to be considered to fully exploit the knowledge of the complex human diseases as well as the genomic variants. We call the collection of the multiple heterogeneous data "multiblock data." In this paper, we propose a novel Multiblock Discriminant Analysis (MultiDA) method that provides a new integrative genomic model for the multiblock analysis and an efficient algorithm for discriminant analysis. The integrative genomic model is built by exploiting the representative genomic data including SNP, CNV, DNA methylation, and gene expression. The efficient algorithm for the discriminant analysis identifies discriminative factors of the multiblock data. The discriminant analysis is essential to discover biomarkers in computational biology. The performance of the proposed MultiDA was assessed by intensive simulation experiments, where the outstanding performance comparing the related methods was reported. As a target application, we applied MultiDA to human brain data of psychiatric disorders. The findings and gene regulatory network derived from the experiment are discussed.

Transcriptome sequencing and genome-wide association analyses reveal lysosomal function and actin cytoskeleton remodeling in schizophrenia and bipolar disorder

Schizophrenia (SCZ) and bipolar disorder (BPD) are severe mental disorders with high heritability. Clinicians have long noticed the similarities of clinic symptoms between these disorders. In recent years, accumulating evidence indicates some shared genetic liabilities. However, what is shared remains elusive. In this study, we conducted whole transcriptome analysis of post-mortem brain tissues (cingulate cortex) from SCZ, BPD and control subjects, and identified differentially expressed genes in these disorders. We found 105 and 153 genes differentially expressed in SCZ and BPD, respectively. By comparing the t-test scores, we found that many of the genes differentially expressed in SCZ and BPD are concordant in their expression level (q⩽0.01, 53 genes; q⩽0.05, 213 genes; q⩽0.1, 885 genes). Using genome-wide association data from the Psychiatric Genomics Consortium, we found that these differentially and concordantly expressed genes were enriched in association signals for both SCZ (P<10(-7)) and BPD (P=0.029). To our knowledge, this is the first time that a substantially large number of genes show concordant expression and association for both SCZ and BPD. Pathway analyses of these genes indicated that they are involved in the lysosome, Fc gamma receptor-mediated phagocytosis, regulation of actin cytoskeleton pathways, along with several cancer pathways. Functional analyses of these genes revealed an interconnected pathway network centered on lysosomal function and the regulation of actin cytoskeleton. These pathways and their interacting network were principally confirmed by an independent transcriptome sequencing data set of the hippocampus. Dysregulation of lysosomal function and cytoskeleton remodeling has direct impacts on endocytosis, phagocytosis, exocytosis, vesicle trafficking, neuronal maturation and migration, neurite outgrowth and synaptic density and plasticity, and different aspects of these processes have been implicated in SCZ and BPD.

Three dysconnectivity patterns in treatment-resistant schizophrenia patients and their unaffected siblings

Among individuals diagnosed with schizophrenia, approximately 20%–33% are recognized as treatment-resistant schizophrenia (TRS) patients. These TRS patients suffer more severely from the disease but struggle to benefit from existing antipsychotic treatments. A few recent studies suggested that schizophrenia may be caused by impaired synaptic plasticity that manifests as functional dysconnectivity in the brain, however, few of those studies focused on the functional connectivity changes in the brains of TRS groups. In this study, we compared the whole brain connectivity variations in TRS patients, their unaffected siblings, and healthy controls. Connectivity network features between and within the 116 automated anatomical labeling (AAL) brain regions were calculated and compared using maps created with three contrasts: patient vs. control, patient vs. sibling, and sibling vs. control. To evaluate the predictive power of the selected features, we performed a multivariate classification approach. We also evaluated the influence of six important clinical measures (e.g. age, education level) on the connectivity features. This study identified abnormal significant connectivity changes of three patterns in TRS patients and their unaffected siblings: 1) 69 patient-specific connectivity (PCN); 2) 102 shared connectivity (SCN); and 3) 457 unshared connectivity (UCN). While the first two patterns were widely reported by previous non-TRS specific studies, we were among the first to report widespread significant connectivity differences between TRS patient groups and their healthy sibling groups. Observations of this study may provide new insights for the understanding of the neurophysiological mechanisms of TRS.

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We first compared global functional connectivity in treatment-resistant schizophrenia and their unaffected siblings.

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Widespread unshared significant functional connectivity in unaffected siblings of treatment-resistant schizophrenia

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We studied the association of brain connectivity to clinical measures.

Genetics and epigenetics of circadian rhythms and their potential roles in neuropsychiatric disorders

Circadian rhythm alterations have been implicated in multiple neuropsychiatric disorders, particularly those of sleep, addiction, anxiety, and mood. Circadian rhythms are known to be maintained by a set of classic clock genes that form complex mutual and self-regulatory loops. While many other genes showing rhythmic expression have been identified by genome-wide studies, their roles in circadian regulation remain largely unknown. In attempts to directly connect circadian rhythms with neuropsychiatric disorders, genetic studies have identified gene mutations associated with several rare sleep disorders or sleep-related traits. Other than that, genetic studies of circadian genes in psychiatric disorders have had limited success. As an important mediator of environmental factors and regulators of circadian rhythms, the epigenetic system may hold the key to the etiology or pathology of psychiatric disorders, their subtypes or endophenotypes. Epigenomic regulation of the circadian system and the related changes have not been thoroughly explored in the context of neuropsychiatric disorders. We argue for systematic investigation of the circadian system, particularly epigenetic regulation, and its involvement in neuropsychiatric disorders to improve our understanding of human behavior and disease etiology.

Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder

Genetic risk prediction has several potential applications in medical research and clinical practice and could be used, for example, to stratify a heterogeneous population of patients by their predicted genetic risk. However, for polygenic traits, such as psychiatric disorders, the accuracy of risk prediction is low. Here we use a multivariate linear mixed model and apply multi-trait genomic best linear unbiased prediction for genetic risk prediction. This method exploits correlations between disorders and simultaneously evaluates individual risk for each disorder. We show that the multivariate approach significantly increases the prediction accuracy for schizophrenia, bipolar disorder, and major depressive disorder in the discovery as well as in independent validation datasets. By grouping SNPs based on genome annotation and fitting multiple random effects, we show that the prediction accuracy could be further improved. The gain in prediction accuracy of the multivariate approach is equivalent to an increase in sample size of 34% for schizophrenia, 68% for bipolar disorder, and 76% for major depressive disorders using single trait models. Because our approach can be readily applied to any number of GWAS datasets of correlated traits, it is a flexible and powerful tool to maximize prediction accuracy. With current sample size, risk predictors are not useful in a clinical setting but already are a valuable research tool, for example in experimental designs comparing cases with high and low polygenic risk.

Psychiatric genome-wide association study analyses implicate neuronal, immune and histone pathways

Genome-wide association studies (GWAS) of psychiatric disorders have identified multiple genetic associations with such disorders, but better methods are needed to derive the underlying biological mechanisms that these signals indicate. We sought to identify biological pathways in GWAS data from over 60,000 participants from the Psychiatric Genomics Consortium. We developed an analysis framework to rank pathways that requires only summary statistics. We combined this score across disorders to find common pathways across three adult psychiatric disorders: schizophrenia, major depression and bipolar disorder. Histone methylation processes showed the strongest association, and we also found statistically significant evidence for associations with multiple immune and neuronal signaling pathways and with the postsynaptic density. Our study indicates that risk variants for psychiatric disorders aggregate in particular biological pathways and that these pathways are frequently shared between disorders. Our results confirm known mechanisms and suggest several novel insights into the etiology of psychiatric disorders.

Cross-disorder genome-wide analyses suggest a complex genetic relationship between Tourette's syndrome and OCD

OBJECTIVE

Obsessive-compulsive disorder (OCD) and Tourette's syndrome are highly heritable neurodevelopmental disorders that are thought to share genetic risk factors. However, the identification of definitive susceptibility genes for these etiologically complex disorders remains elusive. The authors report a combined genome-wide association study (GWAS) of Tourette's syndrome and OCD.

METHOD

The authors conducted a GWAS in 2,723 cases (1,310 with OCD, 834 with Tourette's syndrome, 579 with OCD plus Tourette's syndrome/chronic tics), 5,667 ancestry-matched controls, and 290 OCD parent-child trios. GWAS summary statistics were examined for enrichment of functional variants associated with gene expression levels in brain regions. Polygenic score analyses were conducted to investigate the genetic architecture within and across the two disorders.

RESULTS

Although no individual single-nucleotide polymorphisms (SNPs) achieved genome-wide significance, the GWAS signals were enriched for SNPs strongly associated with variations in brain gene expression levels (expression quantitative loci, or eQTLs), suggesting the presence of true functional variants that contribute to risk of these disorders. Polygenic score analyses identified a significant polygenic component for OCD (p=2×10(-4)), predicting 3.2% of the phenotypic variance in an independent data set. In contrast, Tourette's syndrome had a smaller, nonsignificant polygenic component, predicting only 0.6% of the phenotypic variance (p=0.06). No significant polygenic signal was detected across the two disorders, although the sample is likely underpowered to detect a modest shared signal. Furthermore, the OCD polygenic signal was significantly attenuated when cases with both OCD and co-occurring Tourette's syndrome/chronic tics were included in the analysis (p=0.01).

CONCLUSIONS

Previous work has shown that Tourette's syndrome and OCD have some degree of shared genetic variation. However, the data from this study suggest that there are also distinct components to the genetic architectures of these two disorders. Furthermore, OCD with co-occurring Tourette's syndrome/chronic tics may have different underlying genetic susceptibility compared with OCD alone.

Correlation between DNA methylation and gene expression in the brains of patients with bipolar disorder and schizophrenia

OBJECTIVES

Aberrant DNA methylation and gene expression have been reported in postmortem brain tissues of psychotic patients, but until now there has been no systematic evaluation of synergistic changes in methylation and expression on a genome-wide scale in brain tissue.

METHODS

In this study, genome-wide methylation and expression analyses were performed on cerebellum samples from 39 patients with schizophrenia, 36 patients with bipolar disorder, and 43 unaffected controls, to screen for a correlation between gene expression and CpG methylation.

RESULTS

Out of 71,753 CpG gene pairs (CGPs) tested across the genome, 204 were found to significantly correlate with gene expression after correction for multiple testing [p < 0.05, false discovery rate (FDR) q < 0.05]. The correlated CGPs were tested for disease-associated expression and methylation by comparing psychotic patients with bipolar disorder and schizophrenia to healthy controls. Four of the identified CGPs were found to significantly correlate with the differential expression and methylation of genes encoding phosphoinositide-3-kinase, regulatory subunit 1 (PIK3R1), butyrophilin, subfamily 3, member A3 (BTN3A3), nescient helix-loop-helix 1 (NHLH1), and solute carrier family 16, member 7 (SLC16A7) in psychotic patients (p < 0.05, FDR q < 0.2). Additional expression and methylation datasets were used to validate the relationship between DNA methylation, gene expression, and neuropsychiatric diseases.

CONCLUSIONS

These results suggest that the identified differentially expressed genes with an aberrant methylation pattern may represent novel candidate factors in the etiology and pathology of neuropsychiatric disorders.

eQTL epistasis: detecting epistatic effects and inferring hierarchical relationships of genes in biological pathways

MOTIVATION

Epistasis is the interactions among multiple genetic variants. It has emerged to explain the 'missing heritability' that a marginal genetic effect does not account for by genome-wide association studies, and also to understand the hierarchical relationships between genes in the genetic pathways. The Fisher's geometric model is common in detecting the epistatic effects. However, despite the substantial successes of many studies with the model, it often fails to discover the functional dependence between genes in an epistasis study, which is an important role in inferring hierarchical relationships of genes in the biological pathway.

RESULTS

We justify the imperfectness of Fisher's model in the simulation study and its application to the biological data. Then, we propose a novel generic epistasis model that provides a flexible solution for various biological putative epistatic models in practice. The proposed method enables one to efficiently characterize the functional dependence between genes. Moreover, we suggest a statistical strategy for determining a recessive or dominant link among epistatic expression quantitative trait locus to enable the ability to infer the hierarchical relationships. The proposed method is assessed by simulation experiments of various settings and is applied to human brain data regarding schizophrenia.

AVAILABILITY AND IMPLEMENTATION

The MATLAB source codes are publicly available at: http://biomecis.uta.edu/epistasis.

A rare mutation of CACNA1C in a patient with bipolar disorder, and decreased gene expression associated with a bipolar-associated common SNP of CACNA1C in brain

Timothy Syndrome (TS) is caused by very rare exonic mutations of the CACNA1C gene that produce delayed inactivation of Cav1.2 voltage-gated calcium channels during cellular action potentials, with greatly increased influx of calcium into the activated cells. The major clinical feature of this syndrome is a long QT interval that results in cardiac arrhythmias. However, TS also includes cognitive impairment, autism and major developmental delays in many of the patients. We observed the appearance of bipolar disorder (BD) in a patient with a previously reported case of TS, who is one of the very few patients to survive childhood. This is most interesting because the common single-nucleotide polymorphism (SNP) most highly associated with BD is rs1006737, which we show here is a cis-expression quantitative trait locus for CACNA1C in human cerebellum, and the risk allele (A) is associated with decreased expression. To combine the CACNA1C perturbations in the presence of BD in this patient and in patients with the common CACNA1C SNP risk allele, we would propose that either increase or decrease in calcium influx in excitable cells can be associated with BD. In treatment of BD with calcium channel blocking drugs, we would predict better response in patients without the risk allele, because they have increased CACNA1C expression.

Genome-wide association study implicates NDST3 in schizophrenia and bipolar disorder

Schizophrenia and bipolar disorder are major psychiatric disorders with high heritability and overlapping genetic variance. Here we perform a genome-wide association study in an ethnically homogeneous cohort of 904 schizophrenia cases and 1,640 controls drawn from the Ashkenazi Jewish population. We identify a novel genome-wide significant risk locus at chromosome 4q26, demonstrating the potential advantages of this founder population for gene discovery. The top single-nucleotide polymorphism (SNP; rs11098403) demonstrates consistent effects across 11 replication and extension cohorts, totalling 23, 191 samples across multiple ethnicities, regardless of diagnosis (schizophrenia or bipolar disorder), resulting in P_meta=9.49 × 10⁻¹² (odds ratio (OR)=1.13, 95% confidence interval (CI): 1.08–1.17) across both disorders and P_meta=2.67 × 10⁻⁸ (OR=1.15, 95% CI: 1.08–1.21) for schizophrenia alone. In addition, this intergenic SNP significantly predicts postmortem cerebellar gene expression of NDST3, which encodes an enzyme critical to heparan sulphate metabolism. Heparan sulphate binding is critical to neurite outgrowth, axon formation and synaptic processes thought to be aberrant in these disorders.

Schizophrenia and bipolar disorder are important psychiatric disorders with overlapping genetic components. Here, the authors identify and replicate a genome-wide significant risk locus for the two disorders, and suggest a role for NDST3 in severe psychiatric disease.

Inference of SNP-gene regulatory networks by integrating gene expressions and genetic perturbations

In order to elucidate the overall relationships between gene expressions and genetic perturbations, we propose a network inference method to infer gene regulatory network where single nucleotide polymorphism (SNP) is involved as a regulator of genes. In the most of the network inferences named as SNP-gene regulatory network (SGRN) inference, pairs of SNP-gene are given by separately performing expression quantitative trait loci (eQTL) mappings. In this paper, we propose a SGRN inference method without predefined eQTL information assuming a gene is regulated by a single SNP at most. To evaluate the performance, the proposed method was applied to random data generated from synthetic networks and parameters. There are three main contributions. First, the proposed method provides both the gene regulatory inference and the eQTL identification. Second, the experimental results demonstrated that integration of multiple methods can produce competitive performances. Lastly, the proposed method was also applied to psychiatric disorder data in order to explore how the method works with real data.

Identification of pathways for bipolar disorder: a meta-analysis

IMPORTANCE

Genome-wide investigations provide systematic information regarding the neurobiology of psychiatric disorders.

OBJECTIVE

To identify biological pathways that contribute to risk for bipolar disorder (BP) using genes with consistent evidence for association in multiple genome-wide association studies (GWAS).

DATA SOURCES

Four independent data sets with individual genome-wide data available in July 2011 along with all data sets contributed to the Psychiatric Genomics Consortium Bipolar Group by May 2012. A prior meta-analysis was used as a source for brain gene expression data.

STUDY SELECTION

The 4 published GWAS were included in the initial sample. All independent BP data sets providing genome-wide data in the Psychiatric Genomics Consortium were included as a replication sample.

DATA EXTRACTION AND SYNTHESIS

We identified 966 genes that contained 2 or more variants associated with BP at P < .05 in 3 of 4 GWAS data sets (n = 12,127 [5253 cases, 6874 controls]). Simulations using 10,000 replicates of these data sets corrected for gene size and allowed the calculation of an empirical P value for each gene; empirically significant genes were entered into a pathway analysis. Each of these pathways was then tested in the replication sample (n = 8396 [3507 cases, 4889 controls]) using gene set enrichment analysis for single-nucleotide polymorphisms. The 226 genes were also compared with results from a meta-analysis of gene expression in the dorsolateral prefrontal cortex.

MAIN OUTCOMES AND MEASURES

Empirically significant genes and biological pathways. RESULTS Among 966 genes, 226 were empirically significant (P < .05). Seventeen pathways were overrepresented in analyses of the initial data set. Six of the 17 pathways were associated with BP in both the initial and replication samples: corticotropin-releasing hormone signaling, cardiac β-adrenergic signaling, phospholipase C signaling, glutamate receptor signaling, endothelin 1 signaling, and cardiac hypertrophy signaling. Among the 226 genes, 9 differed in expression in the dorsolateral prefrontal cortex in patients with BP: CACNA1C, DTNA, FOXP1, GNG2, ITPR2, LSAMP, NPAS3, NCOA2, and NTRK3.

CONCLUSIONS AND RELEVANCE

Pathways involved in the genetic predisposition to BP include hormonal regulation, calcium channels, second messenger systems, and glutamate signaling. Gene expression studies implicate neuronal development pathways as well. These results tend to reinforce specific hypotheses regarding BP neurobiology and may provide clues for new approaches to treatment and prevention.

Molecular network analysis enhances understanding of the biology of mental disorders

We provide an introduction to network theory, evidence to support a connection between molecular network structure and neuropsychiatric disease, and examples of how network approaches can expand our knowledge of the molecular bases of these diseases. Without systematic methods to derive their biological meanings and inter-relatedness, the many molecular changes associated with neuropsychiatric disease, including genetic variants, gene expression changes, and protein differences, present an impenetrably complex set of findings. Network approaches can potentially help integrate and reconcile these findings, as well as provide new insights into the molecular architecture of neuropsychiatric diseases. Network approaches to neuropsychiatric disease are still in their infancy, and we discuss what might be done to improve their prospects.

Maternal preconception body mass index and offspring cord blood DNA methylation: exploration of early life origins of disease

Maternal obesity is associated with a variety of common diseases in the offspring. One possible underlying mechanism could be maternal obesity induced alterations in DNA methylation. However, this hypothesis is yet to be tested. We performed epigenomic mapping of cord blood among 308 Black mother-infant pairs delivered at term at the Boston Medical Center using the Illumina HumanMethylation27 BeadChip. Linear regression and pathway analyses were conducted to evaluate the associations between DNA methylation levels and prepregnancy maternal BMI (<25, 25-30, ≥30 kg/m(2) ). The methylation levels of 20 CpG sites were associated with maternal BMI at a significance level of P-value <10(-4) in the overall sample, and boys and girls, separately. One CpG site remained statistically significant after correction for multiple comparisons (FDR corrected P-value = 0.04) and was annotated to a potential cancer gene, ZCCHC10. Some of the other CpG site annotated genes appear to be critical to the development of cancers and cardiovascular diseases (i.e., WNT16, C18orf8, ANGPTL2, SAPCD2, ADCY3, PRR16, ERBB2, DOK2, PLAC1). Significant findings from pathway analysis, such as infectious and inflammatory and lipid metabolism pathways, lends support for the potential impact of maternal BMI on the above stated disorders. This study demonstrates that prepregnancy maternal BMI might lead to alterations in offspring DNA methylation in genes relevant to the development of a range of complex chronic diseases, providing evidence of trans-generational influence on disease susceptibility via epigenetic mechanism.

Expression of the G72/G30 gene in transgenic mice induces behavioral changes

The G72/G30 gene complex is a candidate gene for schizophrenia and bipolar disorder. However, G72 and G30 mRNAs are expressed at very low levels in human brain, with only rare splicing forms observed. We report here G72/G30 expression profiles and behavioral changes in a G72/G30 transgenic mouse model. A human BAC clone containing the G72/G30 genomic region was used to establish the transgenic mouse model, on which gene expression studies, western blot and behavioral tests were performed. Relative to their minimal expression in humans, G72 and G30 mRNAs were highly expressed in the transgenic mice, and had a more complex splicing pattern. The highest G72 transcript levels were found in testis, followed by cerebral cortex, with very low or undetectable levels in other tissues. No LG72 (the long putative isoform of G72) protein was detected in the transgenic mice. Whole-genome expression profiling identified 361 genes differentially expressed in transgenic mice compared with wild-type, including genes previously implicated in neurological and psychological disorders. Relative to wild-type mice, the transgenic mice exhibited fewer stereotypic movements in the open field test, higher baseline startle responses in the course of the prepulse inhibition test, and lower hedonic responses in the sucrose preference test. The transcriptome profile changes and multiple mouse behavioral effects suggest that the G72 gene may play a role in modulating behaviors relevant to psychiatric disorders.

Genetic imaging of the association of oxytocin receptor gene (OXTR) polymorphisms with positive maternal parenting

Background: Well-validated models of maternal behavior in small-brain mammals posit a central role of oxytocin in parenting, by reducing stress and enhancing the reward value of social interactions with offspring. In contrast, human studies are only beginning to gain insights into how oxytocin modulates maternal behavior and affiliation.

Methods: To explore associations between oxytocin receptor genes and maternal parenting behavior in humans, we conducted a genetic imaging study of women selected to exhibit a wide range of observed parenting when their children were 4–6 years old.

Results: In response to child stimuli during functional magnetic resonance imaging (fMRI), hemodynamic responses in brain regions that mediate affect, reward, and social behavior were significantly correlated with observed positive parenting. Furthermore, single nucleotide polymorphisms (SNPs) (rs53576 and rs1042778) in the gene encoding the oxytocin receptor were significantly associated with both positive parenting and hemodynamic responses to child stimuli in orbitofrontal cortex (OFC), anterior cingulate cortex (ACC), and hippocampus.

Conclusions: These findings contribute to the emerging literature on the role of oxytocin in human social behavior and support the feasibility of tracing biological pathways from genes to neural regions to positive maternal parenting behaviors in humans using genetic imaging methods.