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Interrelations between alcohol use disorder and chronic pain have received increasing empirical attention, and several lines of evidence support the possibility of shared genetic liability. However, research on the genetic contributions to the component processes of these complex and potentially overlapping phenotypes remains scarce. The goal of the present study was to test polygenic risk scores (PRSs) for alcohol consumption and multisite chronic pain as predictors of ad lib drinking behavior during an experimental taste test. PRSs were calculated for 209 pain-free, moderate-to-heavy drinkers (57.9% male; 63.6% White). Among White participants, the alcohol and chronic pain PRSs showed nominally significant (

Background: Aberrant reward behavior is known to associate with various forms of psychopathology. ‘Effort’, as one component of reward, is defined as the perception of the value of the prospective outcome as a function of its magnitude and the perceived costs of the physical or cognitive labor required to obtain it. This study sought to empirically validate the ‘effort’ construct via behavioral assessments and self-reports. Methods: Participants were 1536 children and 1270 of their parents. ‘Effort’ was assessed via the Effort Expenditure for Rewards Task (EEfRT). Child psychopathology was measured using the Child Behavior Checklist; adult psychopathology was measured using the Adult Self Report. Multitrait-multimethod matrices were used to demonstrate the divergent validity of ‘effort’ measures from other measures of reward behavior. We used linear regression models to assess familial transmission of ‘effort’, and zero-inflated negative binomial regression models to analyze the relationship between ‘effort’ and psychopathology and the moderating effects of sex. Results: For adults and their children, measures of ‘effort’ did not correlate with any other measures of reward constructs, suggesting divergent validity. Measures of ‘effort’ were positively correlated between siblings, while correlations between children and adults were not significant. In children, significant interactions were observed between sex and ‘effort’ valuation, in association with anxiety and thought problems, consistent with previous work. The moderating effects of sex had the greatest effect at low ‘effort’, with females having higher severity of psychopathology than males on average. Conclusions: This study identified the ‘effort’ component of reward behavior as a significant correlate of multiple psychopathologies, moderated by sex. These findings provide further evidence supporting ‘effort’ as a relevant construct for understanding psychopathology. The results consistently demonstrate aberrant ‘effort’ is associated with greater anxiety and thought problems. The presence of sibling-sibling correlations accompanied by the absence of parent-child correlations suggests a lack of continuity between child and parent measures and suggests that further evaluation and refinement of age-appropriate measures of effort is warranted.

Applications of machine learning (ML) in biomedical science are growing rapidly, spurred by interdisciplinary collaborations, aggregation of large datasets, accessibility of analytic routines, and availability of powerful computers. With this increased usage comes a responsibility for education, borne equally by data scientists plying their wares in medical research and biomedical scientists harnessing such methods to glean knowledge from data. This article provides a critical review of ML, covering common ML methods and historical trends of their use in psychiatry, and identifying areas of opportunity for future applications of ML in biological psychiatry. We also establish the ML in Psychiatry (MLPsych) Consortium, enumerate its objectives, and provide a set of standards (Guidelines for REporting ML Investigations in Neuropsychiatry [GREMLIN]) for designing and reporting studies that use ML. This review serves as a cautiously optimistic primer on ML for those on the precipice as they prepare to dive into the field, either as dedicated methodological practitioners or, at the very least, well-informed consumers.

The global pandemic of coronavirus disease 2019 (COVID-19) has killed almost two million people worldwide and over 400 thousand in the United States (US). As the pandemic evolves, informed policy-making and strategic resource allocation relies on accurate forecasts. To predict the spread of the virus within US counties, we curated an array of county-level demographic and COVID-19-relevant health risk factors. In combination with the county-level case and death numbers curated by John Hopkins university, we developed a forecasting model using deep learning (DL). We implemented an autoencoder-based Seq2Seq model with gated recurrent units (GRUs) in the deep recurrent layers. We trained the model to predict future incident cases, deaths and the reproductive number, R. For most counties, it makes accurate predictions of new incident cases, deaths and R values, up to 30 days in the future. Our framework can also be used to predict other targets that are useful indices for policymaking, for example hospitalization or the occupancy of intensive care units. Our DL framework is publicly available on GitHub and can be adapted for other indices of the COVID-19 spread. We hope that our forecasts and model can help local governments in the continued fight against COVID-19.

Objective: To describe consequences of the nonmedical use (NMU) of prescription amphetamines (AMPs). Method: Data from the U.S. National Poison Data System yielded four groups: intravenous NMU ( IV NMU) intentionally injected AMP, Nasal NMU intentionally inhaled AMP but did not inject, Oral NMU intentionally ingested AMP, and controls reported unintentional oral exposure to AMP. Results: The Nasal NMU group was at greater risk of admission to a health care facility. All NMU groups were at greater risk for adverse clinical effects. IV NMU had the greatest number of adverse effects, followed by Nasal and Oral NMU. Nasal NMU had a greater risk for major medical outcomes versus Oral NMU. The IV NMU group was 21.9 times more likely to die from AMP NMU than controls. Oral NMU conferred a significantly greater risk of suicide attempts. Conclusion: Oral and nonoral NMU of AMP are associated with significant risks of morbidity and mortality.

OBJECTIVE: Developmental theory posits interacting individual and contextual factors that contribute to alcohol use across adolescence. Despite the well-documented salience of peer environmental influences on adolescent drinking, it is not known whether peer environments moderate polygenic risks for trajectories of alcohol use. The current theoretically based investigation aimed to test developmental gene–environment interaction (G×E) effects across adolescence. METHOD: Latent growth curve models tested interactive associations of polygenic risk scores and adolescents’ perceived friend drinking and disruptive behavior with adolescents’ initial level of alcohol use frequency at age 16 years old and change in alcohol frequency from ages 16 to 20. The sample comprised 8,941 White adolescents (49% female) from Great Britain within the Avon Longitudinal Study of Parents and Children (ALSPAC). RESULTS: Greater polygenic risk was associated with more frequent initial drinking as well as escalations in drinking frequency over the subsequent 5 years in latent growth curve models. Contrary to study hypotheses, no significant G×E effects were identified after controlling for confounding main and interaction effects. CONCLUSIONS: Adolescents at heightened genetic risk may accelerate their alcohol use across adolescence, although not significantly more so in the presence of these alcohol-promoting peer environments. Future well-powered, theoretically driven replication efforts are needed to examine generalizability of these findings across diverse samples.

In vivo experimental analysis of human brain tissue poses substantial challenges and ethical concerns. We developed a novel method called the Brain Gene Expression and Network Imputation Engine (BrainGENIE) that uses peripheral-blood transcriptomes to predict brain-tissue-specific gene-expression levels. BrainGENIE reliably predicted brain-tissue-specific expression levels for 1,733 – 11,569 genes (false-discovery rate-adjusted pPrediXcan. We tested the generalizability of BrainGENIE in external within-individual data from ex vivo peripheral blood and postmortem brain samples from the Religious Orders Study and Memory and Aging Project, wherein we validated 39% of predicted gene expression levels as concordant with observed expression levels in dorsolateral prefrontal cortex and 23% in caudate. BrainGENIE recapitulated diagnosis-related gene expression changes in brain better than direct correlations from blood and predictions from PrediXcan. BrainGENIE complements and, in some ways, outperforms existing transcriptome-imputation tools, providing biologically meaningful predictions and opening new research avenues.

The U.S. National Institute of Mental Health (NIMH) introduced the research domain criteria (RDoC) initiative to promote the integration of information across multiple units of analysis (i.e., brain circuits, physiology, behavior, self-reports) to better understand the basic dimensions of behavior and cognitive functioning underlying normal and abnormal mental conditions. Along those lines, this study examined the association between peripheral blood gene expression levels and emotional and behavioral problems in school-age children. Children were chosen from two age- and sex-matched groups: those with or without parental reports of any prior or current psychiatric diagnosis. RNA-sequencing was performed on whole blood from 96 probands aged 6–12 years who were medication-free at the time of assessment. Module eigengenes were derived using weighted gene co-expression network analysis (WGCNA). Associations were tested between module eigengene expression levels and eight syndrome scales from parent ratings on the Child Behavior Checklist (CBCL). Nine out of the 36 modules were significantly associated with at least one syndrome scale measured by the CBCL (i.e., aggression, social problems, attention problems, and/or thought problems) after accounting for covariates and correcting for multiple testing. Our study demonstrates that variation in peripheral blood gene expression relates to emotional and behavioral profiles in children. If replicated and validated, our results may help in identifying problem or at-risk behavior in pediatric populations, and in elucidating the biological pathways that modulate complex human behavior.

Large-scale brain imaging studies by the ENIGMA Consortium identified structural changes associated with attention-deficit/hyperactivity disorder (ADHD). It is not clear why some brain regions are impaired and others spared by the etiological risks for ADHD. We hypothesized that spatial variation in brain cell organization and/or pathway expression levels contribute to selective brain region vulnerability (SBRV) in ADHD. In this study, we used the largest available collection of magnetic resonance imaging (MRI) results from the ADHD ENIGMA Consortium (subcortical MRI n = 3242; cortical MRI n = 4180) along with high-resolution postmortem brain microarray data from Allen Brain Atlas (donors n = 6) from 22 brain regions to investigate our SBRV hypothesis. We performed deconvolution of the bulk transcriptomic data to determine abundances of neuronal and nonneuronal cells in the brain. We assessed the relationships between gene-set expression levels, cell abundance, and standardized effect sizes representing regional changes in brain sizes in cases of ADHD. Our analysis yielded significant correlations between apoptosis, autophagy, and neurodevelopment genes with smaller brain sizes in ADHD, along with associations to regional abundances of astrocytes and oligodendrocytes. The lack of enrichment of common genetic risk variants for ADHD within implicated gene sets suggests an environmental etiology to these differences. This work provides novel mechanistic clues about SBRV in ADHD.

Blood-based microarray studies comparing individuals affected with autism spectrum disorder (ASD) and typically developing individuals help characterize differences in circulating immune cell functions and offer potential biomarker signal. We sought to combine the subject-level data from previously published studies by mega-analysis to increase the statistical power. We identified studies that compared ex vivo blood or lymphocytes from ASD-affected individuals and unrelated comparison subjects using Affymetrix or Illumina array platforms. Raw microarray data and clinical meta-data were obtained from seven studies, totaling 626 affected and 447 comparison subjects. Microarray data were processed using uniform methods. Covariate-controlled mixed-effect linear models were used to identify gene transcripts and co-expression network modules that were significantly associated with diagnostic status. Permutation-based gene-set analysis was used to identify functionally related sets of genes that were over- and under-expressed among ASD samples. Our results were consistent with diminished interferon-, EGF-, PDGF-, PI3K-AKT-mTOR-, and RAS-MAPK-signaling cascades, and increased ribosomal translation and NK-cell related activity in ASD. We explored evidence for sex-differences in the ASD-related transcriptomic signature. We also demonstrated that machine-learning classifiers using blood transcriptome data perform with moderate accuracy when data are combined across studies. Comparing our results with those from blood-based studies of protein biomarkers (e.g., cytokines and trophic factors), we propose that ASD may feature decoupling between certain circulating signaling proteins (higher in ASD samples) and the transcriptional cascades which they typically elicit within circulating immune cells (lower in ASD samples). These findings provide insight into ASD-related transcriptional differences in circulating immune cells. © 2016 Wiley Periodicals, Inc.

The application of microarray technology in schizophrenia research was heralded as paradigm-shifting, as it allowed for high-throughput assessment of cell and tissue function. This technology was widely adopted, initially in studies of postmortem brain tissue, and later in studies of peripheral blood. The collective body of schizophrenia microarray literature contains apparent inconsistencies between studies, with failures to replicate top hits, in part due to small sample sizes, cohort-specific effects, differences in array types, and other confounders. In an attempt to summarize existing studies of schizophrenia cases and non-related comparison subjects, we performed two mega-analyses of a combined set of microarray data from postmortem prefrontal cortices (n = 315) and from ex-vivo blood tissues (n = 578). We adjusted regression models per gene to remove non-significant covariates, providing best-estimates of transcripts dysregulated in schizophrenia. We also examined dysregulation of functionally related gene sets and gene co-expression modules, and assessed enrichment of cell types and genetic risk factors. The identities of the most significantly dysregulated genes were largely distinct for each tissue, but the findings indicated common emergent biological functions (e.g. immunity) and regulatory factors (e.g., predicted targets of transcription factors and miRNA species across tissues). Our network-based analyses converged upon similar patterns of heightened innate immune gene expression in both brain and blood in schizophrenia. We also constructed generalizable machine-learning classifiers using the blood-based microarray data. Our study provides an informative atlas for future pathophysiologic and biomarker studies of schizophrenia.

Genetically influenced changes in brain organization occur over the course of development. Large-scale brain imaging studies by the ENIGMA Consortium identified structural changes associated with attention-deficit/hyperactivity disorder (ADHD). It is not clear why some brain regions are impaired and others spared by the etiological risks for ADHD. We hypothesized that spatial variation in brain cell organization and/or pathway expression levels contribute to selective brain region vulnerability (SBRV) in ADHD. In this study, we used the largest available collection of imaging results from the ADHD ENIGMA Consortium along with high-resolution postmortem brain microarray data from Allen Brain Atlas from 22 sub-cortical and cortical brain regions to investigate our selective brain region vulnerability (SBRV) hypothesis. We performed deconvolution of the bulk transcriptomic data to determine abundances of neuronal and non-neuronal cells in the brain. We then assessed the relationships between gene set expression levels, cell abundance, and standardized effect sizes representing regional changes in brain sizes in cases of ADHD. Our analysis yielded significant correlations between apoptosis, autophagy, and neurodevelopment genes with reductions in regional brain sizes in ADHD, along with associations to regional abundances of dopaminergic neurons, astrocytes, oligodendrocytes, and neural progenitor cells. This works provides novel mechanistic insights into SBRV in ADHD.

We performed a transcriptome-wide meta-analysis and gene co-expression network analysis to identify genes and gene networks dysregulated in the peripheral blood of bipolar disorder (BD) cases relative to unaffected comparison subjects, and determined the specificity of the transcriptomic signatures of BD and schizophrenia (SZ). Nineteen genes and 4 gene modules were significantly differentially expressed in BD cases. Thirteen gene modules were shown to be differentially expressed in a combined case-group of BD and SZ subjects called “major psychosis”, including genes biologically linked to apoptosis, reactive oxygen, chromatin remodeling, and immune signaling. No modules were differentially expressed between BD and SZ cases. Machine-learning classifiers trained to separate diagnostic classes based solely on gene expression profiles could distinguish BD cases from unaffected comparison subjects with an area under the curve (AUC) of 0.724, as well as BD cases from SZ cases with AUC = 0.677 in withheld test samples. We introduced a novel and straightforward method called “polytranscript risk scoring” that could distinguish BD cases from unaffected subjects (AUC = 0.672) and SZ cases (AUC = 0.607) significantly better than expected by chance. Taken together, our results highlighted gene expression alterations common to BD and SZ that involve biological processes of inflammation, oxidative stress, apoptosis, and chromatin regulation, and highlight disorder-specific changes in gene expression that discriminate the major psychoses.

Individuals with psychiatric disorders have elevated rates of autoimmune comorbidity and altered immune signaling. It is unclear whether these altered immunological states have a shared genetic basis with those psychiatric disorders. The present study sought to use existing summary-level data from previous genome-wide association studies (GWASs) to determine if commonly varying single nucleotide polymorphisms (SNPs) are shared between psychiatric and immune-related phenotypes. We estimated heritability and examined pair-wise genetic correlations using the linkage disequilibrium score regression (LDSC) and heritability estimation from summary statistics (HESS) methods. Using LDSC, we observed significant genetic correlations between immune-related disorders and several psychiatric disorders, including anorexia nervosa, attention deficit-hyperactivity disorder, bipolar disorder, major depression, obsessive compulsive disorder, schizophrenia, smoking behavior, and Tourette syndrome. Loci significantly mediating genetic correlations were identified for schizophrenia when analytically paired with Crohn’s disease, primary biliary cirrhosis, systemic lupus erythematosus, and ulcerative colitis. We report significantly correlated loci and highlight those containing genome-wide associations and candidate genes for respective disorders. We also used the LDSC method to characterize genetic correlations amongst the immune-related phenotypes. We discuss our findings in the context of relevant genetic and epidemiological literature, as well as the limitations and caveats of the study.

Genome-wide expression studies of samples derived from individuals with autism spectrum disorder (ASD) and their unaffected siblings have been widely used to shed light on transcriptomic differences associated with this condition. Females have historically been under-represented in ASD genomic studies. Emerging evidence from studies of structural genetic variants and peripheral biomarkers suggest that sex-differences may exist in the biological correlates of ASD. Relatively few studies have explicitly examined whether sex-differences exist in the transcriptomic signature of ASD. The present study quantified genome-wide expression values by performing RNA sequencing on transformed lymphoblastoid cell lines and identified transcripts differentially expressed between same-sex, proximal-aged sibling pairs. We found that performing separate analyses for each sex improved our ability to detect ASD-related transcriptomic differences; we observed a larger number of dysregulated genes within our smaller set of female samples (n = 12 sibling pairs), as compared with the set of male samples (n = 24 sibling pairs), with small, but statistically significant overlap between the sexes. Permutation-based gene-set analyses and weighted gene co-expression network analyses also supported the idea that the transcriptomic signature of ASD may differ between males and females. We discuss our findings in the context of the relevant literature, underscoring the need for future ASD studies to explicitly account for differences between the sexes. Autism Res 2017, 10: 439-455. © 2016 International Society for Autism Research, Wiley Periodicals, Inc.

Transcriptome-wide screens of peripheral blood during the onset and development of posttraumatic stress disorder (PTSD) indicate widespread immune dysregulation. However, little is known as to whether biological sex and the type of traumatic event influence shared or distinct biological pathways in PTSD. We performed a combined analysis of five independent PTSD blood transcriptome studies covering seven types of trauma in 229 PTSD and 311 comparison individuals to synthesize the extant data. Analyses by trauma type revealed a clear pattern of PTSD gene expression signatures distinguishing interpersonal (IP)-related traumas from combat-related traumas. Co-expression network analyses integrated all data and identified distinct gene expression perturbations across sex and modes of trauma in PTSD, including one wound-healing module down-regulated in men exposed to combat traumas, one IL12-mediated signaling module up-regulated in men exposed to IP-related traumas, and two modules associated with lipid metabolism and MAPK-activity up-regulated in women exposed to IP-related traumas. Remarkably, a high degree of sharing of transcriptional dysregulation across sex and modes of trauma in PTSD was also observed converging on common signaling cascades, including cytokine, innate immune and type I interferon pathways. Collectively, these findings provide a broad view of immune dysregulation in PTSD and demonstrate inflammatory pathways of molecular convergence and specificity, which may inform mechanisms and diagnostic biomarkers for the disorder.

Human genome-wide association studies (GWAS), transcriptome analyses of animal models, and candidate gene studies have advanced our understanding of the genetic architecture of aggressive behaviors. However, each of these methods presents unique limitations. To generate a more confident and comprehensive view of the complex genetics underlying aggression, we undertook an integrated, cross-species approach. We focused on human and rodent models to derive eight gene lists from three main categories of genetic evidence: two sets of genes identified in GWAS studies, four sets implicated by transcriptome-wide studies of rodent models, and two sets of genes with causal evidence from online Mendelian inheritance in man (OMIM) and knockout (KO) mice reports. These gene sets were evaluated for overlap and pathway enrichment to extract their similarities and differences. We identified enriched common pathways such as the G-protein coupled receptor (GPCR) signaling pathway, axon guidance, reelin signaling in neurons, and ERK/MAPK signaling. Also, individual genes were ranked based on their cumulative weights to quantify their importance as risk factors for aggressive behavior, which resulted in 40 top-ranked and highly interconnected genes. The results of our cross-species and integrated approach provide insights into the genetic etiology of aggression.

Background Na+/H+ exchanger 9 (SLC9A9) is an autism-risk gene localized in the endosomal system, which is a novel biological pathway that over-represents autism-risk genes. SLC9A9 regulates the endosome-recycling compartment through alkalinizing the endosomal lumen; although, it's broader biological functions have not been elucidated. Objectives Achieve a broader biological understanding of SLC9A9 through co-expression analysis and provide in vitro data confirming the endocytic regulation of autophagy and cell survival. Methods We use bioinformatics techniques to determine gene co-regulations, protein-protein interactions and pathway enrichments and confirm those results in an in vitro system. We perform WGCNA on human RNA-sequencing data from Allen Brain Atlas focusing on SLC9A9 co-expression modules in the pre- and postnatal hippocampus, cortex and amygdala. We use N2a cells as a neuronal model system to provide evidence that SLC9A9 gene-dosage affects autophagy induction and cell survival signals through lipofectamine 2000 transfections of SLC9A9 cDNA constructs. We assess immunoblots of N2a protein lysate for critical autophagy protein LC3-I/II, and cell survival protein BCL2. In addition, we challenge N2a cells with chloroquine to assess their viability at increasing levels of dosage. Results Our bioinformatics study shows SLC9A9 co-expression modules are enriched with mTOR and autophagic intermediates, as well as Bcl-2 pathway constituents. Our in vitro work demonstrates that SLC9A9 overexpression in N2a cells significantly decreases LC3-I to LC3-II conversion and significantly increases Bcl-2 levels. In addition, it increases phospho-AKT and phospho-mTOR levels, decreases LC3-II puncta formation and autophagic rate and provides resistance to drug treatments that typically result in cell death. Discussion Our results confirm the co-regulation of SLC9A9 and autophagic molecules. The in vitro data better defines the role of SLC9A9 in autophagic regulation, in that it inhibits the conversion of LC3-I to LC3-II and reduces the rate of autophagy. Although in need of replication in a true neuronal model, our data suggests that SLC9A9 expression could be a signaling and ion-transport brake, effectively inhibiting autophagic processes. When dysregulated, this has the potential to impact pruning and thus neuronal morphology and function. Conclusions: these results provide further insight into how SLC9A9 is impacting neuronal function that eventually leads to autistic phenotypes. We provide preliminary evidence as to how SLC9A9 could potentially be associated with autism spectrum disorders via autophagy and cell survival pathways.

Background Genome-Wide Association Studies (GWASs) provide a wealth of information that can serve as a backbone for making biological inferences. There are over 160 loci reported with genome-wide significant association with Schizophrenia (SZ) and Bipolar Disorder (BD), and thousands more nominally associated loci that contribute to risk (Purcell et al., 2009; Welter et al., 2014). SNPs with the strongest association to SZ and BD are predominately in non-coding DNA, thus it is often challenging to decipher the function of risk SNPs. Functional and regulatory maps from the ENCODE and Roadmap projects offer detailed annotations of the genome, which can help bridge the gap between GWAS signals and biological mechanisms. We present a new software tool called Functional LD-clump EnrichmEnt Test (FLEET) to decode the underlying biology of GWAS signals via overlaying information from the ENCODE and Roadmap Projects. Possible utilities of this software include: prioritizing risk genes for fine-mapping, illuminating potential regulatory mechanisms that underlie susceptibility, and identifying molecular substrates that are common to psychiatric disorders. Methods FLEET is available for download on Github (https://github.com/hessJ/FLEET). FLEET is currently adapted as a modular script written in R and is under active development. Users must supply a file of genome-wide association summary statistics and modify a few flags in FLEET to run the analysis properly. FLEET performs data reformatting, GWAS pruning, SNP-to-annotation mapping, statistical analyses (i.e., weighted linear regression and permutations), and plotting of results. Results GWAS results of SZ (Ripke et al., 2014) and BD (Hou et al., 2016) revealed widespread associations with histone markers and DNA accessibility in brain, immune and other peripheral tissues. In addition, significant enrichment was detected for gene sets that participate in neurotransmission and neurodevelopment, histone modification enzymes, and target sites of transcription factors, microRNAs, and RNA-binding proteins. Discussion Our software provides unique insight into the underlying biology of SZ and BD risk signals and a framework for hypothesis generation. FLEET is a flexible and efficient pipeline for analyzing biological relationships among GWASs of complex polygenic disorders. FLEET is capable of modeling genome-wide enrichment statistics and performing permutation tests on top GWAS markers; both tests include adjustments for confounding variation (i.e., LD and minor allele frequency). This software is included with a pre-formatted database of >5,800 annotations, and users can provide custom annotations for analysis.

For most species, aggressive behavior is an evolutionally conserved and genetically specified trait that is essential for survival and fitness. Like many other heritable traits, aggressive behavior has complex genetic bases. Human GWAS studies, genetic mapping and transcriptome analysis of animal models and candidate gene studies in the past decades have provided many useful information for our understanding of genetic architecture underlying aggressive behaviors. However, no genome-wide association has been achieved due to underpowered samples. We present a cross-species and network-based approach to integrate the genetic findings from different types of studies in human and rodent models. We investigated various types of networks formed by these candidate genes, including gene co-expression, protein-protein interaction, canonical pathways and evolutionary profile similarity networks. This analysis helped us to identify top-ranked candidate genes and additional candidates based on their topological importance and the relationship with known candidate genes.

Background Bipolar Disorder (BD) is a complex and highly heritable psychiatric disorder with a world-wide prevalence near 1%. There is converging evidence from genetic, molecular, and clinical studies of BD that implicate numerous neurotransmitter and immune signaling abnormalities. These abnormalities have been described previously in Schizophrenia (SZ), which has close genetic and molecular ties to BD. Methods The purpose of this investigation was to uncover generalizable biomarkers for BD using a cross-study analysis of six microarray data sets comprising peripheral blood transcriptomes for patients with a DSM-diagnosis of BD (n=77) and unaffected comparison subjects (n=81); furthermore, we sought to compare transcriptomic signatures between BD and SZ (obtained from a prior study). We performed a combined-subject differential expression mega-analysis with regression models applied per gene, each adjusted for clinical factors and study heterogeneity. In addition, we surveyed gene sets and gene networks for consistent differential expression in BD. Results Three genes were associated with BD at a level of significance that withstood rigorous statistical correction for multiple testing. In addition, differential expression of gene sets in BD was found; functions of these gene sets included innate immunity, energy production in mitochondrial complexes, and metabolism of RNA and proteins, among other biological pathways previously related to BD or psychosis. Lastly, we deployed a machine-learning pipeline, which discriminated BD patients from unaffected comparison subjects and SZ patients with high accuracy based on 77 genes (mean AUCROC=0.849, 95% CI=0.837 – 0.859). Discussion Our study provides strong evidence in support of candidate biomarkers for BD and introduces potentially generalizable blood-based classifiers for BD. Our findings have relevance for cross-disorder genomic analyses. This work warrants expansion to identifying gene signatures that discriminate other adult and childhood neuropsychiatric disorders.

Background Schizophrenia is a highly heritable disorder with complex etiology. Genetic risk scores provide an index of an individual's liability for schizophrenia, and may shed light on its biological underpinnings. Methods We investigated the association of genetic risk scores for schizophrenia using transcriptomic and genome-wide genotyping data from Lymphoblastoid Cells (LCLs) and postmortem brain. We removed the major histocompatibility region from our data and calculated genetic risk scores using linkage-independent risk markers for schizophrenia. Linear models were used to test the association of diagnosis or genetic risk scores with expression levels of gene networks in LCLs and brain, in addition to exon networks derived from brain. We specified covariates in our regression models to control for population stratification along with biological and technical variation. Results We identified gene networks whose expression was significantly associated with schizophrenia genetic risk scores in LCLs and brain. Pathways involving cell growth, apoptosis, and immunity were enriched in modules of interest, in addition to astrocyte-specific markers. Discussion Converging evidence shows that schizophrenia polygenes represent multiple biological pathways and processes. We identified relationships between schizophrenia polygenes and the expression of genes in pathways related to cell development and immunity, which recapitulates evidence from genetic, transcriptomic, epigenetic, and epidemiologic studies. Despite practical limitations, our study provides key insights into the role of schizophrenia polygenes in gene expression and splicing regulation.

The gene that encodes zinc finger protein 804A (ZNF804A) became a candidate risk gene for schizophrenia (SZ) after surpassing genome-wide significance thresholds in replicated genome-wide association scans and meta-analyses. Much remains unknown about this reported gene expression regulator; however, preliminary work has yielded insights into functional and biological effects of ZNF804A by targeting its regulatory activities in vitro and by characterizing allele-specific interactions with its risk-conferring single nucleotide polymorphisms (SNPs). There is now strong epidemiologic evidence for a role of ZNF804A polymorphisms in both SZ and bipolar disorder (BD); however, functional links between implicated variants and susceptible biological states have not been solidified. Here we briefly review the genetic evidence implicating ZNF804A polymorphisms as genetic risk factors for both SZ and BD, and discuss the potential functional consequences of these variants on the regulation of ZNF804A and its downstream targets. Empirical work and predictive bioinformatic analyses of the alternate alleles of the two most strongly implicated ZNF804A polymorphisms suggest they might alter the affinity of the gene sequence for DNA- and/or RNA-binding proteins, which might in turn alter expression levels of the gene or particular ZNF804A isoforms. Future work should focus on clarifying the critical periods and cofactors regulating these genetic influences on ZNF804A expression, as well as the downstream biological consequences of an imbalance in the expression of ZNF804A and its various mRNA isoforms.

SLC9A9 is a sodium hydrogen exchanger present in the recycling endosome and highly expressed in the brain. It is implicated in neuropsychiatric disorders, including autism spectrum disorders (ASDs). Little research concerning its gene expression patterns and biological pathways has been conducted. We sought to investigate its possible biological roles in autism-associated brain regions throughout development. We conducted a weighted gene co-expression network analysis on RNA-seq data downloaded from Brainspan. We compared prenatal and postnatal gene expression networks for three ASD-associated brain regions known to have high SLC9A9 gene expression. We also performed an ASD-associated single nucleotide polymorphism enrichment analysis and a cell signature enrichment analysis. The modules showed differences in gene constituents (membership), gene number, and connectivity throughout time. SLC9A9 was highly associated with immune system functions, metabolism, apoptosis, endocytosis, and signaling cascades. Gene list comparison with co-immunoprecipitation data was significant for multiple modules. We found a disproportionately high autism risk signal among genes constituting the prenatal hippocampal module. The modules were enriched with astrocyte and oligodendrocyte markers. SLC9A9 is potentially involved in the pathophysiology of ASDs. Our investigation confirmed proposed functions for SLC9A9, such as endocytosis and immune regulation, while also revealing potential roles in mTOR signaling and cell survival.. By providing a concise molecular map and interactions, evidence of cell type and implicated brain regions we hope this will guide future research on SLC9A9. Autism Res 2017, 10: 414-429. © 2016 International Society for Autism Research, Wiley Periodicals, Inc.

Advances in molecular genetics, fueled by the results of large-scale genome-wide association studies, meta-analyses, and mega-analyses, have provided the means of identifying genetic risk factors for human disease, thereby enriching our understanding of the functionality of the genome in the post-genomic era. In the past half-decade, research on neuropsychiatric disorders has reached an important milestone: the identification of susceptibility genes reliably associated with complex psychiatric disorders at genome-wide levels of significance. This age of discovery provides the groundwork for follow-up studies designed to elucidate the mechanism(s) by which genetic variants confer susceptibility to these disorders. The gene encoding zinc-finger protein 804 A (ZNF804A) is among these candidate genes, recently being found to be strongly associated with schizophrenia and bipolar disorder via one of its non-coding mutations, rs1344706. Neurobiological, molecular, and bioinformatic analyses have improved our understanding of ZNF804A in general and this variant in particular; however, more work is needed to establish the mechanism(s) by which ZNF804A variants impinge on the biological substrates of the two disorders. Here, we review literature recently published on ZNF804A, and analyze critical concepts related to the biology of ZNF804A and the role of rs1344706 in schizophrenia and bipolar disorder. We synthesize the results of new bioinformatic analyses of ZNF804A with key elements of the existing literature and knowledge base. Furthermore, we suggest some potentially fruitful short- and long-term research goals in the assessment of ZNF804A. © 2014 Wiley Periodicals, Inc.

Indices of inter-item associative strength were obtained on the basis of word association norms for 15 free recall lists consisting of 12 items each. Recall performance on these lists was found to be reliably related to the index of inter-item associative strength for 4th, 5th, and 6th grade children. Consistent with results reported by Deese (1959), it was found that, for all Ss, inter-item associative strength was positively related to the number of words recalled, negatively related to the number of extra-list intrusions, and positively related to the commonality of intrusions. In agreement with the experimental hypothesis, most of the coefficients reported by Deese for college Ss were higher than those obtained for grade school Ss.

Rats were handled on days 1–5 or 1–20 at ambient temperature (22.2°C); a third group was handled for the first 5 days at body temperature (35.5°C) while a control group group was not disturbed. At 35–37 days of age all animals were exposed to a novel stimulus and then their plasma was assayed for adrenocorticosterone. The corticosterone levels of all experimental groups was significantly less than that of the control group. In addition, the rate of return to a resting level was significantly more rapid in the handled groups than in the control.

Litters of experimental mice were fostered to lactating rat mothers when 4 days old. Control groups consisted of mice reared by mouse mothers and rats reared by rat mothers. At 21 days of age all animals were exposed to a novel stimulus and were killed 15, 30, 45 or 60 min afterward. Mice reared by mouse mothers were found to have significantly higher plasma corticosterone levels than rats reared by rat mothers. However, mice reared by rat mothers showed a significantly depressed corticosterone level when compared to the other 2 groups. In addition, mice reared by rat mothers were less active in an open-field test than were mice reared by mouse mothers. These results provide evidence of a marked maternal mediation of subsequent adi-enocortical activity in the offspring. (Endocrinology 83: 900, 1968)

After collecting word-association norms for 12 high frequency responses to 36 stimulus words drawn from the Minnesota norms (3), Deese (2) found that the relative frequency of occurrence of a stimulus word as an associate to these responses was significantly correlated with the extent to which the stimulus word occurred as an extra-list intrusion in tests of immediate free recall. Using college students as Ss, the product-moment correlation between the frequency of occurrence of the stimulus word as an associate and the frequency of occurrence of the stimulus word as an intrusion was ,873. The purpose of the present study was to examine this same relationship in grade school children. The procedure employed by Deese (2) was followed as closely as possible with three exceptions: fewer recall lists and words per list were used. the recall tests were administered in groups rather than individually (1) and the word associations were collected by having E read the stimulus words aloud instead of having the stimulus words printed on sheets of paper. Ss were school children drawn from the 4th, 5th, and 6th grades. In Part I of the experiment, 19 free recall tests consisting of 10 high frequency responses each were administered to 29 Ss each from these grades. The stimulus and response words were culled from word-association norms collected by Simon, Lotsof, and Pease (4) from 399 4th. 5th. and 6th grade children. Jn Part I1 of the experiment, word-association norms were collected from 90 different Ss drawn from the same population as that employed in Part I of the experiment. For all grades combined, the product-moment correlation between the number of times the stimulus word occurred as a free association in Part I1 and the number of times the stimulus word occurred as an intrusion in Pan I was ,725 (p< .01). The correlations for the 4th, 5th, and 6th grades, respectively, were ,675, ,719, and ,604 (p < .O1 in all three instances). None of the latter ss were significantly different from one another. These results agree well with those reported by Deese (1) and suggest that his findings may reliably be extended to include children in the 4th, 5th, and 6th grades.