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.

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.

A Positive Role of Negative Mood on Creativity: The Opportunity in the Crisis of the COVID-19 Epidemic

The COVID-19 epidemic is associated with negative mood, which has the potential to be a powerful driver of creativity. However, the influence of negative mood on cognitive creativity and emotional creativity remains elusive. Previous research has indicated that self-focused attention is likely to be related to both negative mood and creativity. The current study introduced two self-focused attention variables (i.e., rumination, reflection) to explore how negative mood might contribute to cognitive creativity and emotional creativity. Based on a sample of 351 participants, our study found that (1) negative mood during the outbreak of COVID-19 was associated with cognitive creativity and emotional creativity. Meanwhile, there were significant serial mediation effects of rumination and reflection in the relationship between negative mood and creativity and (2) the psychological impact after exposure to the COVID-19 epidemic was positively correlated with emotional creativity but not with cognitive creativity. These results suggested that individuals, in real life and work, could achieve better creative performance through moderate self-focus. Moreover, individuals with different mood states can be induced to enhance their creativity in times of crisis through intervention training to promote reflection.

Database of genetic studies of bipolar disorder

This study describes the construction and preliminary analysis of a database of summary level genetic findings for bipolar disorder from the literature. The database is available for noncommercial use at http://bioprogramming.bsd.uchicago.edu/BDStudies/. This may be the first complete collection of published gene-specific linkage and association findings on bipolar disorder, including genome-wide association studies. Both the positive and negative findings have been incorporated so that the statistical and contextual significance of each finding may be compared semi-quantitatively and qualitatively across studies of mixed technologies. The database is appropriate for searching a literature populated by mainly underpowered studies, and if 'hits' are viewed as tentative knowledge for future hypothesis generation. It can serve as the basis for a mega-analysis of candidate genes. Herein, we discuss the most robust and best replicated gene findings to date in a contextual manner.

Premenstrual mood symptoms: study of familiality and personality correlates in mood disorder pedigrees

We sought to determine whether premenstrual mood symptoms exhibit familial aggregation in bipolar disorder or major depression pedigrees. Two thousand eight hundred seventy-six women were interviewed with the Diagnostic Interview for Genetic Studies as part of either the NIMH Genetics Initiative Bipolar Disorder Collaborative study or the Genetics of Early Onset Major Depression (GenRED) study and asked whether they had experienced severe mood symptoms premenstrually. In families with two or more female siblings with bipolar disorder (BP) or major depressive disorder (MDD), we examined the odds of having premenstrual mood symptoms given one or more siblings with these symptoms. For the GenRED MDD sample we also assessed the impact of personality as measured by the NEO-FFI. Premenstrual mood symptoms did not exhibit familial aggregation in families with BP or MDD. We unexpectedly found an association between high NEO openness scores and premenstrual mood symptoms, but neither this factor, nor NEO neuroticism influenced evidence for familial aggregation of symptoms. Limitations include the retrospective interview, the lack of data on premenstrual dysphoric disorder, and the inability to control for factors such as medication use.

Common and rare variants of DAOA in bipolar disorder

The D-amino acid oxidase activator (DAOA, previously known as G72) gene, mapped on 13q33, has been reported to be genetically associated with bipolar disorder (BP) in several populations. The consistency of associated variants is unclear and rare variants in exons of the DAOA gene have not been investigated in psychiatric diseases. We employed a conditional linkage method-STatistical Explanation for Positional Cloning (STEPC) to evaluate whether any associated single nucleotide polymorphisms (SNPs) account for the evidence of linkage in a pedigree series that previously has been linked to marker D13S779 at 13q33. We also performed an association study in a sample of 376 Caucasian BP parent-proband trios by genotyping 38 common SNPs in the gene region. Besides, we resequenced coding regions and flanking intronic sequences of DAOA in 555 Caucasian unrelated BP patients and 564 mentally healthy controls, to identify putative functional rare variants that may contribute to disease. One SNP rs1935058 could "explain" the linkage signal in the family sample set (P = 0.055) using STEPC analysis. No significant allelic association was detected in an association study by genotyping 38 common SNPs in 376 Caucasian BP trios. Resequencing identified 53 SNPs, of which 46 were novel SNPs. There was no significant excess of rare variants in cases relative to controls. Our results suggest that DAOA does not have a major effect on BP susceptibility. However, DAOA may contribute to bipolar susceptibility in some specific families as evidenced by the STEPC analysis.

PDLIM5 and susceptibility to bipolar disorder: a family-based association study and meta-analysis

OBJECTIVES

The postsynaptic density-95/discs large/zone occludens-1 (PDZ) domain and LIM (Lin-11, Isl-1, and Mec-3) domain 5 (PDLIM5) gene has been analyzed as a candidate gene for both schizophrenia and bipolar disorder (BP) in Japanese samples. We performed a family-based association study to test the hypothesis that variants in PDLIM5 increase susceptibility to BP in European-Americans and a meta-analysis to clarify whether there is a single marker consistently contributing to risk for BP.

METHODS

Five single nucleotide polymorphisms in the PDLIM5 gene were genotyped in 290 European-American BP families. Programs Sibling-Transmission/Disequilibrium Test (sib_tdt) and PDTPHASE were used for allelic and haplotypic association, respectively. We carried out a meta-analysis combing our family-based data and case-control data from two Japanese sample sets and from two genome-wide association (GWA) studies.

RESULTS

Our association analysis showed no single nucleotide polymorphism associated with BP. A rare haplotype consisted of rs10008257 and rs2433320 had nominal association (P=0.045), which failed to survive correction for multiple tests. The meta-analysis identified a significant allelic association at rs2433320 in all combined samples (excluding overlapped samples in GWA: overall odds ratio=0.897, 95% confidence interval: 0.838-0.961, adjusted P=0.012) and in all Caucasian samples (excluding overlapped samples in GWA: overall odds ratio=0.905, 95% confidence interval: 0.843-0.971, adjusted P=0.032), but not in the Japanese samples.

CONCLUSION

PDLIM5 may have a minor effect on susceptibility to BP in Caucasians. The findings in Japanese need further confirmation in larger independent samples.

Genetic analysis of deep phenotyping projects in common disorders

Several studies of complex psychotic disorders with large numbers of neurobiological phenotypes are currently under way, in living patients and controls, and on assemblies of brain specimens. Genetic analyses of such data typically present challenges, because of the choice of underlying hypotheses on genetic architecture of the studied disorders and phenotypes, large numbers of phenotypes, the appropriate multiple testing corrections, limited numbers of subjects, imputations required on missing phenotypes and genotypes, and the cross-disciplinary nature of the phenotype measures. Advances in genotype and phenotype imputation, and in genome-wide association (GWAS) methods, are useful in dealing with these challenges. As compared with the more traditional single-trait analyses, deep phenotyping with simultaneous genome-wide analyses serves as a discovery tool for previously unsuspected relationships of phenotypic traits with each other, and with specific molecular involvements.

BrainEXP: a database featuring with spatiotemporal expression variations and co-expression organizations in human brains

Summary

Gene expression changes over the lifespan and varies among different tissues or cell types. Gene co-expression also changes by sex, age, different tissues or cell types. However, gene expression under the normal state and gene co-expression in the human brain has not been fully defined and quantified. Here we present a database named Brain EXPression Database (BrainEXP) which provides spatiotemporal expression of individual genes and co-expression in normal human brains. BrainEXP consists of 4567 samples from 2863 healthy individuals gathered from existing public databases and our own data, in either microarray or RNA-Seq library types. We mainly provide two analysis results based on the large dataset: (i) basic gene expression across specific brain regions, age ranges and sexes; (ii) co-expression analysis from different platforms.

Availability and implementation

http://www.brainexp.org/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Genome-Wide DNA Methylation Changes Associated with Intermittent Explosive Disorder: A Gene-Based Functional Enrichment Analysis

BACKGROUND

Intermittent explosive disorder is defined as a recurrent, problematic, and impulsive aggression that affects 3% to 4% of the US population. While behavioral genetic studies report a substantial degree of genetic influence on aggression and impulsivity, epigenetic mechanisms underlying aggression and intermittent explosive disorder are not well known.

METHODS

The sample included 44 subjects (22 with a DSM-5 diagnosis of intermittent explosive disorder and 22 comparable subjects without intermittent explosive disorder). Peripheral blood DNA methylome was profiled using the Illumina Infinium HumanMethylation450 Beadchip. Intermittent explosive disorder-associated genome-wide DNA methylation changes were analyzed using the CpGassoc R package, with covariates age, sex, and race being adjusted. A gene-based functional enrichment analysis was performed to identify pathways that were overrepresented by genes harboring highly differentially methylated CpG sites.

RESULTS

A total of 27 CpG sites were differentially methylated in IED participants (P<5.0×10-5), but none reached genome-wide significant threshold. Functional enrichment analysis revealed that genes mapped by these CpG sites are involved in the inflammatory/immune system, the endocrine system, and neuronal differentiation.

CONCLUSIONS

Consistent with our previous studies showing an association of inflammatory response with aggressive behavior in intermittent explosive disorder subjects, our gene-based pathway analysis using differentially methylated CpG sites supports inflammatory response as an important mechanism involved in intermittent explosive disorder and reveals other novel biological processes possibly associated with intermittent explosive disorder.

Genetic association of bipolar disorder with the β(3) nicotinic receptor subunit gene

OBJECTIVE

Owing to the clinical relationship between bipolar disorder and nicotine dependence, we investigated two research questions: (i) are genetic associations with nicotine dependence different in individuals with bipolar disorder as compared with individuals without bipolar disorder, and (ii) do loci earlier associated with nicotine dependence have pleiotropic effects on these two diseases.

METHOD

Our study consisted of 916 cases with bipolar disorder and 1028 controls. On the basis of known associations with nicotine dependence, we genotyped eight single-nucleotide polymorphisms (SNPs) on chromosome 8 (three bins) in the regions of CHRNB3 and CHRNA6, and six SNPs on chromosome 15 (three bins) in the regions of CHRNA5 and CHRNA3.

RESULTS

To determine whether the genetic associations with nicotine dependence are different in bipolar disorder than in the general population, we compared allele frequencies of candidate SNPs between individuals with nicotine dependence only and individuals with both nicotine dependence and bipolar disorder. There were no statistical differences between these frequencies, indicating that genetic association with nicotine dependence is similar in individuals with bipolar disorder as in the general population. In the investigation of pleiotropic effects of these SNPs on bipolar disorder, two highly correlated synonymous SNPs in CHRNB3, rs4952 and rs4953, were significantly associated with bipolar disorder (odds ratio 1.7, 95% confidence interval: 1.2-2.4, P=0.001). This association remained significant both after adjusting for a smoking covariate and analyzing the association in nonsmokers only.

CONCLUSION

Our results suggest that (i) bipolar disorder does not modify the association between nicotine dependence and nicotinic receptor subunit genes, and (ii) variants in CHRNB3/CHRNA6 are independently associated with bipolar disorder.

Identifying nootropic drug targets via large-scale cognitive GWAS and transcriptomics

Broad-based cognitive deficits are an enduring and disabling symptom for many patients with severe mental illness, and these impairments are inadequately addressed by current medications. While novel drug targets for schizophrenia and depression have emerged from recent large-scale genome-wide association studies (GWAS) of these psychiatric disorders, GWAS of general cognitive ability can suggest potential targets for nootropic drug repurposing. Here, we (1) meta-analyze results from two recent cognitive GWAS to further enhance power for locus discovery; (2) employ several complementary transcriptomic methods to identify genes in these loci that are credibly associated with cognition; and (3) further annotate the resulting genes using multiple chemoinformatic databases to identify "druggable" targets. Using our meta-analytic data set (N = 373,617), we identified 241 independent cognition-associated loci (29 novel), and 76 genes were identified by 2 or more methods of gene identification. Actin and chromatin binding gene sets were identified as novel pathways that could be targeted via drug repurposing. Leveraging our transcriptomic and chemoinformatic databases, we identified 16 putative genes targeted by existing drugs potentially available for cognitive repurposing.

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.

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.

Detecting significant genotype-phenotype association rules in bipolar disorder: market research meets complex genetics

Background

Disentangling the etiology of common, complex diseases is a major challenge in genetic research. For bipolar disorder (BD), several genome-wide association studies (GWAS) have been performed. Similar to other complex disorders, major breakthroughs in explaining the high heritability of BD through GWAS have remained elusive. To overcome this dilemma, genetic research into BD, has embraced a variety of strategies such as the formation of large consortia to increase sample size and sequencing approaches. Here we advocate a complementary approach making use of already existing GWAS data: a novel data mining procedure to identify yet undetected genotype–phenotype relationships. We adapted association rule mining, a data mining technique traditionally used in retail market research, to identify frequent and characteristic genotype patterns showing strong associations to phenotype clusters. We applied this strategy to three independent GWAS datasets from 2835 phenotypically characterized patients with BD. In a discovery step, 20,882 candidate association rules were extracted.

Results

Two of these rules—one associated with eating disorder and the other with anxiety—remained significant in an independent dataset after robust correction for multiple testing. Both showed considerable effect sizes (odds ratio ~ 3.4 and 3.0, respectively) and support previously reported molecular biological findings.

Conclusion

Our approach detected novel specific genotype–phenotype relationships in BD that were missed by standard analyses like GWAS. While we developed and applied our method within the context of BD gene discovery, it may facilitate identifying highly specific genotype–phenotype relationships in subsets of genome-wide data sets of other complex phenotype with similar epidemiological properties and challenges to gene discovery efforts.

Electronic supplementary material

The online version of this article (10.1186/s40345-018-0132-x) contains supplementary material, which is available to authorized users.

Unraveling the epigenomic and transcriptomic interplay during alcohol-induced anxiolysis

Positive effects of alcohol drinking such as anxiolysis and euphoria appear to be a crucial factor in the initiation and maintenance of alcohol use disorder (AUD). However, the mechanisms that lead from chromatin reorganization to transcriptomic changes after acute ethanol exposure remain unknown. Here, we used Assay for Transposase-Accessible Chromatin followed by high throughput sequencing (ATAC-seq) and RNA-seq to investigate epigenomic and transcriptomic changes that underlie anxiolytic effects of acute ethanol using an animal model. Analysis of ATAC-seq data revealed an overall open or permissive chromatin state that was associated with transcriptomic changes in the amygdala after acute ethanol exposure. We identified a candidate gene, Hif3a (Hypoxia-inducible factor 3, alpha subunit), that had 'open' chromatin regions (ATAC-seq peaks), associated with significantly increased active epigenetic histone acetylation marks and decreased DNA methylation at these regions. The mRNA levels of Hif3a were increased by acute ethanol exposure, but decreased in the amygdala during withdrawal after chronic ethanol exposure. Knockdown of Hif3a expression in the central nucleus of amygdala attenuated acute ethanol-induced increases in Hif3a mRNA levels and blocked anxiolysis in rats. These data indicate that chromatin accessibility and transcriptomic signatures in the amygdala after acute ethanol exposure underlie anxiolysis and possibly prime the chromatin for the development of AUD.

swCAM: estimation of subtype-specific expressions in individual samples with unsupervised sample-wise deconvolution

MOTIVATION

Complex biological tissues are often a heterogeneous mixture of several molecularly distinct cell subtypes. Both subtype compositions and subtype-specific (STS) expressions can vary across biological conditions. Computational deconvolution aims to dissect patterns of bulk tissue data into subtype compositions and STS expressions. Existing deconvolution methods can only estimate averaged STS expressions in a population, while many downstream analyses such as inferring co-expression networks in particular subtypes require subtype expression estimates in individual samples. However, individual-level deconvolution is a mathematically underdetermined problem because there are more variables than observations.

RESULTS

We report a sample-wise Convex Analysis of Mixtures (swCAM) method that can estimate subtype proportions and STS expressions in individual samples from bulk tissue transcriptomes. We extend our previous CAM framework to include a new term accounting for between-sample variations and formulate swCAM as a nuclear-norm and ℓ2,1-norm regularized matrix factorization problem. We determine hyperparameter values using cross-validation with random entry exclusion and obtain a swCAM solution using an efficient alternating direction method of multipliers. Experimental results on realistic simulation data show that swCAM can accurately estimate STS expressions in individual samples and successfully extract co-expression networks in particular subtypes that are otherwise unobtainable using bulk data. In two real-world applications, swCAM analysis of bulk RNASeq data from brain tissue of cases and controls with bipolar disorder or Alzheimer's disease identified significant changes in cell proportion, expression pattern and co-expression module in patient neurons. Comparative evaluation of swCAM versus peer methods is also provided.

AVAILABILITY AND IMPLEMENTATION

The R Scripts of swCAM are freely available at https://github.com/Lululuella/swCAM. A user's guide and a vignette are provided.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Mutation screening of two candidate genes from 13q32 in families affected with Bipolar disorder: human peptide transporter (SLC15A1) and human glypican5 (GPC5)

BACKGROUND

Multiple candidate regions as sites for Schizophrenia and Bipolar susceptibility genes have been reported, suggesting heterogeneity of susceptibility genes or oligogenic inheritance. Linkage analysis has suggested chromosome 13q32 as one of the regions with evidence of linkage to Schizophrenia and, separately, to Bipolar disorder (BP). SLC15A1 and GPC5 are two of the candidate genes within an approximately 10-cM region of linkage on chromosome 13q32. In order to identify a possible role for these candidates as susceptibility genes, we performed mutation screening on the coding regions of these two genes in 7 families (n-20) affected with Bipolar disorder showing linkage to 13q32.

RESULTS

Genomic organization revealed 23 exons in SLC15A1 and 8 exons in GPC5 gene respectively. Sequencing of the exons did not reveal mutations in the GPC5 gene in the 7 families affected with BP. Two polymorphic variants were discovered in the SLC15A1 gene. One was T to C substitution in the third position of codon encoding alanine at 1403 position of mRNA in exon 17, and the other was A to G substitution in the untranslated region at position 2242 of mRNA in exon 23.

CONCLUSIONS

Mutation analysis of 2 candidate genes for Bipolar disorder on chromosome 13q32 did not identify any potentially causative mutations within the coding regions or splice junctions of the SLC15A1 or GPC5 genes in 7 families showing linkage to 13q32. Further studies of the regulatory regions are needed to completely exclude these genes as causative for Bipolar disorder.

Risperidone-induced changes in DNA methylation in peripheral blood from first-episode schizophrenia patients parallel changes in neuroimaging and cognitive phenotypes

BACKGROUND

Second generation antipsychotics such as risperidone are first-line pharmacotherapy treatment choices for schizophrenia. However, our ability to reliably predict and monitor treatment reaction is impeded by the lack of relevant biomarkers. As a biomarker for the susceptibility of schizophrenia and clozapine treatment response, DNA methylation (DNAm) has been studied, but the impact of antipsychotics on DNAm has not been explored in drug-naïve patients.

OBJECTIVE

The aim of the present study was to examine changes of DNAm after short-term antipsychotic therapy in first-episode drug-naïve schizophrenia (FES) to identify the beneficial and adverse effect of risperidone on DNAm and their relation to treatment outcome.

METHODS

Thirty-eight never treated schizophrenia patients and 38 demographically matched individuals (healthy controls) were assessed at baseline and at 8-week follow-up with symptom ratings, and cognitive and functional imaging procedures, at which time a blood draw for DNAm studies was performed. During the 8-week period, patients received treatment with risperidone monotherapy. An independent data set was used as replication.

RESULTS

We identified brain related pathways enriched in 4,888 FES-associated CpG sites relative to controls. Risperidone administration in patients altered DNAm in 5,979 CpG sites relative to baseline. Significant group differences in DNAm at follow-up were seen in FES patients at 6,760 CpG sites versus healthy controls. Through comparison of effect size, we found 87.54% out of the risperidone-associated changes in DNAm showed possible beneficial effect, while only 12.46% showed potential adverse effect. There were 580 DNAm sites in which changes shifted methylation levels to be indistinguishable from controls after risperidone treatment. The DNAm changes of some sites that normalized after treatment were correlated with treatment-related changes in symptom severity, spontaneous neurophysiological activity, and cognition. We replicated our results in an independent data set.

CONCLUSION

The normalizing effect of risperidone monotherapy on gene DNAm, and its correlation with clinically relevant phenotypes, indicates that risperidone therapy is associated with DNAm changes that are related to changes in brain physiology, cognition and symptom severity.

Evaluating the challenges and reproducibility of studies investigating DNA methylation signatures of psychological stress

Psychological stress can increase the risk of a wide range of negative health outcomes. Studies have been completed to determine if DNA methylation changes occur in the human brain because of stress and are associated with long-term effects and disease, but results have been inconsistent. Human candidate gene studies (150) and epigenome-wide association studies (67) were systematically evaluated to assess how DNA methylation is impacted by stress during the prenatal period, early childhood and adulthood. The association between DNA methylation of NR3C1 exon 1F and child maltreatment and early life adversity was well demonstrated, but other genes did not exhibit a clear association. The reproducibility of individual CpG sites in epigenome-wide association studies was also poor. However, biological pathways, including stress response, brain development and immunity, have been consistently identified across different stressors throughout the life span. Future studies would benefit from the increased sample size, longitudinal design, standardized methodology, optimal quality control, and improved statistical procedures.

DRAMS: A tool to detect and re-align mixed-up samples for integrative studies of multi-omics data

Studies of complex disorders benefit from integrative analyses of multiple omics data. Yet, sample mix-ups frequently occur in multi-omics studies, weakening statistical power and risking false findings. Accurately aligning sample information, genotype, and corresponding omics data is critical for integrative analyses. We developed DRAMS (https://github.com/Yi-Jiang/DRAMS) to Detect and Re-Align Mixed-up Samples to address the sample mix-up problem. It uses a logistic regression model followed by a modified topological sorting algorithm to identify the potential true IDs based on data relationships of multi-omics. According to tests using simulated data, the more types of omics data used or the smaller the proportion of mix-ups, the better that DRAMS performs. Applying DRAMS to real data from the PsychENCODE BrainGVEX project, we detected and corrected 201 (12.5% of total data generated) mix-ups. Of the 21 mix-ups involving errors of racial identity, DRAMS re-assigned all data to the correct racial group in the 1000 Genomes project. In doing so, quantitative trait loci (QTL) (FDR<0.01) increased by an average of 1.62-fold. The use of DRAMS in multi-omics studies will strengthen statistical power of the study and improve quality of the results. Even though very limited studies have multi-omics data in place, we expect such data will increase quickly with the needs of DRAMS.

Efficient region-based test strategy uncovers genetic risk factors for functional outcome in bipolar disorder

Genome-wide association studies of case-control status have advanced the understanding of the genetic basis of psychiatric disorders. Further progress may be gained by increasing sample size but also by new analysis strategies that advance the exploitation of existing data, especially for clinically important quantitative phenotypes. The functionally-informed efficient region-based test strategy (FIERS) introduced herein uses prior knowledge on biological function and dependence of genotypes within a powerful statistical framework with improved sensitivity and specificity for detecting consistent genetic effects across studies. As proof of concept, FIERS was used for the first genome-wide single nucleotide polymorphism (SNP)-based investigation on bipolar disorder (BD) that focuses on an important aspect of disease course, the functional outcome. FIERS identified a significantly associated locus on chromosome 15 (hg38: chr15:48965004 - 49464789 bp) with consistent effect strength between two independent studies (GAIN/TGen: European Americans, BOMA: Germans; n = 1592 BD patients in total). Protective and risk haplotypes were found on the most strongly associated SNPs. They contain a CTCF binding site (rs586758); CTCF sites are known to regulate sets of genes within a chromatin domain. The rs586758 - rs2086256 - rs1904317 haplotype is located in the promoter flanking region of the COPS2 gene, close to microRNA4716, and the EID1, SHC4, DTWD1 genes as plausible biological candidates. While implication with BD is novel, COPS2, EID1, and SHC4 are known to be relevant for neuronal differentiation and function and DTWD1 for psychopharmacological side effects. The test strategy FIERS that enabled this discovery is equally applicable for tag SNPs and sequence data.

An investigation of candidate regions for association with bipolar disorder

We performed a case-control study of 1,000 cases and 1,028 controls on 1,509 markers, 1,139 of which were located in a 8 Mb region on chromosome 6 (105-113 Mb). This region has shown evidence of involvement in bipolar disorder (BP) in a number of other studies. We find association between BP and two SNPs in the gene LACE1. SNP rs9486880 and rs11153113 (both have P-values of 2 × 10(-5)). Both P-values are in the top 5% of the distribution derived from null simulations (P = 0.02 and 0.01, respectively). LACE is a good candidate for BP; it is an ATPase. We genotyped 173 other markers in 17 other positional and/or functional loci but found no further evidence of association with BP.