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Little is known about circular RNAs (circRNAs) in specific brain cells and human neuropsychiatric disease. Here, we systematically identified over 11,039 circRNAs expressed in vulnerable dopamine and pyramidal neurons laser-captured from 190 human brains and non-neuronal cells using ultra-deep, total RNA sequencing. 1,526 and 3,308 circRNAs were custom-tailored to the cell identity of dopamine and pyramidal neurons and enriched in synapse pathways. 88% of Parkinson’s and 80% of Alzheimer’s disease-associated genes produced circRNAs.circDNAJC6,produced from a juvenile-onset Parkinson’s gene, was already dysregulated during prodromal, onset stages of common Parkinson’s disease neuropathology. Globally, addiction-associated genes preferentially produced circRNAs in dopamine neurons, autism-associated genes in pyramidal neurons, and cancers in non-neuronal cells. This study shows that circular RNAs in the human brain are tailored to neuron identity and implicate circRNA- regulated synaptic specialization in neuropsychiatric diseases.

SummaryGenome-wide association studies (GWAS) have revealed thousands of genetic loci for common diseases. One of the main challenges in the post-GWAS era is to understand the causality of the genetic variants. Expression quantitative trait locus (eQTL) analysis has been proven to be an effective way to address this question by examining the relationship between gene expression and genetic variation in a sufficiently powered cohort. However, it is often tricky to determine the sample size at which a variant with a specific allele frequency will be detected to associate with gene expression with sufficient power. This is particularly demanding with single-cell RNAseq studies. Therefore, a user-friendly tool to perform power analysis for eQTL at both bulk tissue and single-cell level will be critical. Here, we presented an R package called powerEQTL with flexible functions to calculate power, minimal sample size, or detectable minor allele frequency in both bulk tissue and single-cell eQTL analysis. A user-friendly, program-free web application is also provided, allowing customers to calculate and visualize the parameters interactively.Availability and implementationThe powerEQTL R package source code and online tutorial are freely available at CRAN: https://cran.r-project.org/web/packages/powerEQTL/. The R shiny application is publicly hosted at https://bwhbioinfo.shinyapps.io/powerEQTL/.ContactXianjun Dong (xdong@rics.bwh.harvard.edu), Weiliang Qiu (weiliang.qiu@sanofi.com)Supplementary informationSupplementary data are available at Bioinformatics online.

BackgroundWhole-genome sequencing (WGS) data is available from several large studies across a variety of diseases and traits. However, massive storage and computation resources are required to use these data, and, to achieve the sufficient power for discoveries, harmonization of multiple cohorts is critical.ObjectivesThe Accelerating Medicines Partnership Parkinson’s Disease (AMP PD) program has developed a research platform for Parkinson’s disease (PD) which integrates the storage and analysis of WGS data, RNA expression data, and clinical data, harmonized across multiple cohort studies.MethodsThe version 1 release contains WGS data derived from 3,941 participants from 4 cohorts. Samples underwent joint genotyping by the TOPMed Freeze 9 Variant Calling Pipeline. We performed descriptive analyses of these WGS data using the AMP PD platform.ResultsThe clinical diagnosis of participants in version 1 release includes 2,005 idiopathic PD patients, 963 healthy controls, 64 prodromal subjects, 62 clinically diagnosed PD subjects without evidence of dopamine deficit (SWEDD) and 705 participants of genetically enriched cohorts carrying PD risk associated GBA variants or LRRK2 variants in which 304 were affected. We did not observe a significant enrichment of pathogenic variants in the idiopathic PD group, but the polygenic risk score (PRS) was higher in PD both in non-genetically enriched cohorts and genetically enriched cohorts. The population analysis showed a correlation between genetically enriched cohorts and Ashkenazi Jewish ancestry.ConclusionsWe describe the genetic component of the AMP PD platform, a solution to democratise data access and analysis for the PD research community.(d) Financial Disclosure/CoI

BackgroundSleep apnoea is characterised by periods of halted breathing during sleep. Despite its association with severe health conditions, the aetiology of sleep apnoea remains understudied, and previous genetic analyses have not identified many robustly associated genetic risk variants.MethodsWe performed a genome-wide association study (GWAS) meta-analysis of sleep apnoea across five cohorts (NTotal=523,366), followed by a multi-trait analysis of GWAS (MTAG) to boost power, leveraging the high genetic correlation between sleep apnoea and snoring. We then adjusted our results for the genetic effects of body mass index (BMI) using multi-trait-based conditional & joint analysis (mtCOJO) and sought replication of lead hits in a large cohort of participants from 23andMe, Inc (NTotal=1,477,352; Ncases=175,522). We also explored genetic correlations with other complex traits and performed a phenome-wide screen for causally associated phenotypes using the latent causal variable method.ResultsOur MTAG analysis uncovered 49 significant independent loci associated with sleep apnoea risk. Twenty-nine variants were replicated in the 23andMe cohort. We observed genetic correlations with several complex traits, including multisite chronic pain, diabetes, eye disorders, high blood pressure, osteoarthritis, chronic obstructive pulmonary disease, and BMI-associated conditions.ConclusionsOur study uncovered multiple genetic loci associated with sleep apnoea risk, thus increasing our understanding of the aetiology of this condition and its relationship with other complex traits.

BackgroundObservational studies have identified correlations between environmental and lifestyle factors and Parkinson’s disease (PD). However, the causal direction of many of these relationships remains unclear.ObjectiveTo infer causal relationships between smoking and alcohol intake and PD.MethodsWe use a two-sample Mendelian randomization (MR) experimental design to infer causal relationships between smoking (initiation, age of initiation, heaviness, and cessation) and alcohol (drinks per week) consumption as exposure variables and PD as the health outcome. We also conduct sensitivity analyses, including testing for pleiotropic effects MR-Egger and MR-PRESSO, and multivariable MR to jointly model the effects of drinking and smoking behavior on PD risk.ResultsBoth alcohol intake (OR = 0.69; 95% CI 0.56-0.86; p=0.001). and smoking cessation (comparing current vs. former smokers) (IVW OR = 0.39; 95% CI 0.22 to 0.69; p=0.001) were causally associated with a reduced risk of PD. In addition, our multivariable MR results provide additional assurance that the causal association between drinks per week and PD is unlikely due to confounding by smoking behavior.ConclusionOur findings support the role of smoking as a protective factor against PD, but only when comparing current vs. former smokers. Increased alcohol intake also had a protective effect over PD risk, and the alcohol dehydrogenase 1B (ADH1B) locus is a candidate for further investigating the mechanisms underlying this association.

Chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-seq) has become the dominant technique for mapping transcription factor (TF) binding regions genome-wide. We performed an integrative analysis centered around 457 ChIP-seq data sets on 119 human TFs generated by the ENCODE Consortium. We identified highly enriched sequence motifs in most data sets, revealing new motifs and validating known ones. The motif sites (TF binding sites) are highly conserved evolutionarily and show distinct footprints upon DNase I digestion. We frequently detected secondary motifs in addition to the canonical motifs of the TFs, indicating tethered binding and cobinding between multiple TFs. We observed significant position and orientation preferences between many cobinding TFs. Genes specifically expressed in a cell line are often associated with a greater occurrence of nearby TF binding in that cell line. We observed cell-line–specific secondary motifs that mediate the binding of the histone deacetylase HDAC2 and the enhancer-binding protein EP300. TF binding sites are located in GC-rich, nucleosome-depleted, and DNase I sensitive regions, flanked by well-positioned nucleosomes, and many of these features show cell type specificity. The GC-richness may be beneficial for regulating TF binding because, when unoccupied by a TF, these regions are occupied by nucleosomes in vivo. We present the results of our analysis in a TF-centric web repository Factorbook (http://factorbook.org) and will continually update this repository as more ENCODE data are generated.