Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions, bioRxiv, 2018-10-09
AbstractMajor depression is a debilitating psychiatric illness that is typically associated with low mood, anhedonia and a range of comorbidities. Depression has a heritable component that has remained difficult to elucidate with current sample sizes due to the polygenic nature of the disorder. To maximise sample size, we meta-analysed data on 807,553 individuals (246,363 cases and 561,190 controls) from the three largest genome-wide association studies of depression. We identified 102 independent variants, 269 genes, and 15 gene-sets associated with depression, including both genes and gene-pathways associated with synaptic structure and neurotransmission. Further evidence of the importance of prefrontal brain regions in depression was provided by an enrichment analysis. In an independent replication sample of 1,306,354 individuals (414,055 cases and 892,299 controls), 87 of the 102 associated variants were significant following multiple testing correction. Based on the putative genes associated with depression this work also highlights several potential drug repositioning opportunities. These findings advance our understanding of the complex genetic architecture of depression and provide several future avenues for understanding aetiology and developing new treatment approaches.
biorxiv genetics 200-500-users 2018Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Supplementary Information, bioRxiv, 2018-10-09
Major depression is a debilitating psychiatric illness that is typically associated with low mood, anhedonia and a range of comorbidities. Depression has a heritable component that has remained difficult to elucidate with current sample sizes due to the polygenic nature of the disorder. To maximise sample size, we meta-analysed data on 807,553 individuals (246,363 cases and 561,190 controls) from the three largest genome-wide association studies of depression. We identified 102 independent variants, 269 genes, and 15 gene-sets associated with depression, including both genes and gene-pathways associated with synaptic structure and neurotransmission. Further evidence of the importance of prefrontal brain regions in depression was provided by an enrichment analysis. In an independent replication sample of 1,306,354 individuals (414,055 cases and 892,299 controls), 87 of the 102 associated variants were significant following multiple testing correction. Based on the putative genes associated with depression this work also highlights several potential drug repositioning opportunities. These findings advance our understanding of the complex genetic architecture of depression and provide several future avenues for understanding aetiology and developing new treatment approaches.
biorxiv genetics 200-500-users 2018MetaCell analysis of single cell RNA-seq data using k-NN graph partitions, bioRxiv, 2018-10-09
ABSTRACTSingle cell RNA-seq (scRNA-seq) has become the method of choice for analyzing mRNA distributions in heterogeneous cell populations. scRNA-seq only partially samples the cells in a tissue and the RNA in each cell, resulting in sparse data that challenge analysis. We develop a methodology that addresses scRNA-seq’s sparsity through partitioning the data into metacells disjoint, homogenous and highly compact groups of cells, each exhibiting only sampling variance. Metacells constitute local building blocks for clustering and quantitative analysis of gene expression, while not enforcing any global structure on the data, thereby maintaining statistical control and minimizing biases. We illustrate the MetaCell framework by re-analyzing cell type and transcriptional gradients in peripheral blood and whole organism scRNA-seq maps. Our algorithms are implemented in the new MetaCell RC++ software package.
biorxiv bioinformatics 0-100-users 2018Cytoskeletal tension actively sustains the migratory T cell synaptic contact, bioRxiv, 2018-10-08
SummaryWhen migratory T cells encounter antigen presenting cells (APCs), they arrest and form radially symmetric, stable intercellular junctions termed immunological synapses which facilitate exchange of crucial biochemical information and are critical for T cell immunity. While the cellular processes underlying synapse formation have been well-characterized, those that maintain the symmetry, and thereby the stability of the synapse remain unknown. Here we identify an antigen-triggered mechanism that actively promotes T cell synapse symmetry by generating cytoskeletal tension in the plane of the synapse through focal nucleation of actin via Wiskott -Aldrich syndrome Protein (WASP), and contraction of the resultant actin filaments by myosin II. Following T cell activation, WASP is degraded, leading to cytoskeletal rearrangement and tension decay, which result in synapse breaking. Thus, our study identifies and characterizes a mechanical program within otherwise highly motile T cells that sustains the symmetry and stability of the T cell-APC synaptic contact.
biorxiv cell-biology 0-100-users 2018Combining Gene Ontology with Deep Neural Networks to Enhance the Clustering of Single Cell RNA-Seq Data, bioRxiv, 2018-10-07
AbstractBackgroundSingle cell RNA sequencing (scRNA-seq) is applied to assay the individual transcriptomes of large numbers of cells. The gene expression at single-cell level provides an opportunity for better understanding of cell function and new discoveries in biomedical areas. To ensure that the single-cell based gene expression data are interpreted appropriately, it is crucial to develop new computational methods.ResultsIn this article, we try to construct the structure of neural networks based on the prior knowledge of Gene Ontology (GO). By integrating GO with both unsupervised and supervised models, two novel methods are proposed, named GOAE (Gene Ontology AutoEncoder) and GONN (Gene Ontology Neural Network) respectively, for clustering of scRNA-seq data.ConclusionsThe evaluation results show that the proposed models outperform some state-of-the-art approaches. Furthermore, incorporating with GO, we provide an opportunity to interpret the underlying biological mechanism behind the neural network-based model.
biorxiv bioinformatics 0-100-users 2018Single-cell virus sequencing of influenza infections that trigger innate immunity, bioRxiv, 2018-10-07
SUMMARYThe outcome of viral infection is extremely heterogeneous, with infected cells only sometimes activating innate immunity. Here we develop a new approach to assess how the genetic variation inherent in viral populations contributes to this heterogeneity. We do this by determining both the transcriptome and full-length sequences of all viral genes in single influenza-infected cells. Most cells are infected by virions with defects such as amino-acid mutations, internal deletions, or failure to express a gene. We identify instances of each type of defect that increase the likelihood that a cell activates an innate-immune response. However, immune activation remains stochastic in cells infected by virions with these defects, and sometimes occurs even when a cell is infected by a virion that expresses unmutated copies of all genes. Our work shows that viral genetic variation substantially contributes to but does not fully explain the heterogeneity in single influenza-infected cells.
biorxiv microbiology 100-200-users 2018