These data show preferential fetal CHRFAM7A expression in the human prefrontal cortex and suggest abnormalities in the CHRFAM7A/CHRNA7 ratios in schizophrenia and bipolar disorder, due mainly to overexpression of CHRFAM7A. Given that these transcripts are coexpressed in a subset of human cortical neurons and can interact to alter function of nAChRs, these results support the concept of aberrant function of nAChRs in mental illness.

Background Perfusing fixatives through the cerebrovascular system is the gold standard approach in animals to prepare brain tissue for spatial biomolecular profiling, circuit tracing, and ultrastructural studies such as connectomics. Translating these discoveries to humans requires examination of postmortem autopsy brain tissue. Yet banked brain tissue is routinely prepared using immersion fixation, which is a significant barrier to optimal preservation of tissue architecture. The challenges involved in adopting perfusion fixation in brain banks and the extent to which it improves histology quality are not well defined. Methodology We searched four databases to identify studies that have performed perfusion fixation in human brain tissue and screened the references of the eligible studies to identify further studies. From the included studies, we extracted data about the methods that they used, as well as any data comparing perfusion fixation to immersion fixation. The protocol was preregistered at the Open Science Framework: https://osf.io/cv3ys/ . Results We screened 4489 abstracts, 214 full-text publications, and identified 35 studies that met our inclusion criteria, which collectively reported on the perfusion fixation of 558 human brains. We identified a wide variety of approaches to perfusion fixation, including perfusion fixation of the brain in situ and ex situ, perfusion fixation through different sets of blood vessels, and perfusion fixation with different washout solutions, fixatives, perfusion pressures, and postfixation tissue processing methods. Through a qualitative synthesis of data comparing the outcomes of perfusion and immersion fixation, we found moderate confidence evidence showing that perfusion fixation results in equal or greater subjective histology quality compared to immersion fixation of relatively large volumes of brain tissue, in an equal or shorter amount of time. Conclusions This manuscript serves as a resource for investigators interested in building upon the methods and results of previous research in designing their own perfusion fixation studies in human brains or other large animal brains. We also suggest several future research directions, such as comparing the in situ and ex situ approaches to perfusion fixation, studying the efficacy of different washout solutions, and elucidating the types of brain donors in which perfusion fixation is likely to result in higher fixation quality than immersion fixation. Electronic supplementary material The online version of this article (10.1186/s40478-019-0799-y) contains supplementary material, which is available to authorized users.

60Bipolar disorder is a complex neuropsychiatric disorder presenting with episodic mood 61 disturbances. In this study we use a transcriptomic imputation approach to identify novel genes 62 and pathways associated with bipolar disorder, as well as three diagnostically and genetically 63 distinct subtypes. Transcriptomic imputation approaches leverage well-curated and publicly 64 available eQTL reference panels to create gene-expression prediction models, which may then 65 be applied to "impute" genetically regulated gene expression (GREX) in large GWAS datasets. 66By testing for association between phenotype and GREX, rather than genotype, we hope to 67 identify more biologically interpretable associations, and thus elucidate more of the genetic 68 architecture of bipolar disorder. 69 70We applied GREX prediction models for 13 brain regions (derived from CommonMind 71Consortium and GTEx eQTL reference panels) to 21,488 bipolar cases and 54,303 matched 72 controls, constituting the largest transcriptomic imputation study of bipolar disorder (BPD) to 73 date. Additionally, we analyzed three specific BPD subtypes, including 14,938 individuals with 74 subtype 1 (BD-I), 3,543 individuals with subtype 2 (BD-II), and 1,500 individuals with 75 schizoaffective subtype (SAB). 76 77We identified 125 gene-tissue associations with BPD, of which 53 represent independent 78 associations after FINEMAP analysis. 29/53 associations were novel; i.e., did not lie within 1Mb 79 of a locus identified in the recent PGC-BD GWAS. We identified 37 independent BD-I gene-80 tissue associations (10 novel), 2 BD-II associations, and 2 SAB associations. Our BPD, BD-I and 81 BD-II associations were significantly more likely to be differentially expressed in post-mortem 82 brain tissue of BPD, BD-I and BD-II cases than we might expect by chance. Together with our 83 pathway analysis, our results support long-standing hypotheses about bipolar disorder risk, 84 including a role for oxidative stress and mitochondrial dysfunction, the post-synaptic density, 85 and an enrichment of circadian rhythm and clock genes within our results. 86 87 88Bipolar disorder (BPD) is a serious episodic neuropsychiatric disorder presenting with extreme 89 elation, or mania, and severe depressive states 1 . In tandem, individuals with bipolar often 90 experience disturbances in thinking and behavior, as well as psychotic features such as 91 delusions and hallucinations 1 . Estimates of the prevalence of BPD within the general population 92 range from 0.5-1.5% 1,2 . Bipolar disorder is highly heritable, with siblings of probands at an 8-93 fold increased risk of the disorder 1,2 , and twin studies producing strikingly high estimates of 94 heritability, around 89-93% 1,3,4 . More recently, genetic studies of BPD have indicated SNP 95 heritability estimates of 17-23% 5 . 96 97

No abstract

Trio family and case-control studies of next-generation sequencing data have proven integral to understanding the contribution of rare inherited and de novo single-nucleotide variants to the genetic architecture of complex disease. Ideally, such studies should identify individual risk genes of moderate to large effect size to generate novel treatment hypotheses for further follow-up. However, due to insufficient power, gene set enrichment analyses have come to be relied upon for detecting differences between cases and controls, implicating sets of hundreds of genes rather than specific targets for further investigation. Here, we present a Bayesian statistical framework, termed gTADA, that integrates gene-set membership information with gene-level de novo and rare inherited case-control counts, to prioritize risk genes with excess rare variant burden within enriched gene sets. Applying gTADA to available whole-exome sequencing datasets for several neuropsychiatric conditions, we replicated previously reported gene set enrichments and identified novel risk genes. For epilepsy, gTADA prioritized 40 risk genes (posterior probabilities > 0.95), 6 of which replicate in an independent whole-genome sequencing study. In addition, 30/40 genes are novel genes. We found that epilepsy genes had high protein-protein interaction (PPI) network connectivity, and show specific expression during human brain development. Some of the top prioritized EPI genes were connected to a PPI subnetwork of immune genes and show specific expression in prenatal microglia. We also identified multiple enriched drug-target gene sets for EPI which included immunostimulants as well as known antiepileptics. Immune biology was supported specifically by case-control variants from familial epilepsies rather than do novo mutations in generalized encephalitic epilepsy. meta-analyzing DNMs and rare case-control (CC) variants, an approach that has been particularly successful for autism spectrum disorders (ASD) 9,10 . For epilepsy (EPI), multiple associated genes have been identified through DN based studies 4,5,11 , and in recent years, a number of EPI significant genes have also been identified through CC studies 12,13 . We hypothesized that, as for ASD, additional significant EPI genes could be discovered through the integration of DN and CC data. EPI is a serious brain disorder which includes multiple subtypes. Studies of cases/controls and twins have shown that genetic components have played important roles in EPI [14][15][16] . Some of EPI's subtypes can be explained by single genes, but multiple subtypes might be caused by multiple genes 15 . It is still challenging to develop specific drugs for this disorder. There have been multiple antiepileptic drugs used for EPI treatments; however, 20-30% of EPI patients have not been successful in controlling their seizures by using current medications 17 . Identifying additional genes or gene sets might help better understand its etiology as well as better design drug targets for the disorder.Due to the high p...

Anorexia nervosa (AN) is a complex and serious eating disorder, occurring in ~1% of individuals. Despite having the highest mortality rate of any psychiatric disorder, little is known about the aetiology of AN, and few effective treatments exist.Global efforts to collect large sample sizes of individuals with AN have been highly successful, and a recent study consequently identified the first genome-wide significant locus involved in AN. This result, coupled with other recent studies and epidemiological evidence, suggest that previous characterizations of AN as a purely psychiatric disorder are over-simplified. Rather, both neurological and metabolic pathways may also be involved.In order to elucidate more of the system-specific aetiology of AN, we applied transcriptomic imputation methods to 3,495 cases and 10,982 controls, collected by the Eating Disorders Working Group of the Psychiatric Genomics Consortium (PGC-ED). Transcriptomic Imputation (TI) methods approaches use machine-learning methods to impute tissue-specific gene expression from large genotype data using curated eQTL reference panels. These offer an exciting opportunity to compare gene associations across neurological and metabolic tissues. Here, we applied CommonMind Consortium (CMC) and GTEx-derived gene expression prediction models for 13 brain tissues and 12 tissues with potential metabolic involvement (adipose, adrenal gland, 2 colon, 3 esophagus, liver, pancreas, small intestine, spleen, stomach).We identified 35 significant gene-tissue associations within the large chromosome 12 region described in the recent PGC-ED GWAS. We applied forward stepwise conditional analyses and FINEMAP to associations within this locus to identify putatively causal signals. We identified four independently associated genes; RPS26, C12orf49, SUOX, and RDH16. We also identified two further genome-wide significant gene-tissue associations, both in brain tissues; REEP5, in the dorso-lateral pre-frontal cortex (DLPFC; p=8.52×10−07), and CUL3, in the caudate basal ganglia (p=1.8×10−06). These genes are significantly enriched for associations with anthropometric phenotypes in the UK BioBank, as well as multiple psychiatric, addiction, and appetite/satiety pathways. Our results support a model of AN risk influenced by both metabolic and psychiatric factors.