Genome-wide association analysis of lifetime cannabis use (N=184,765) identifies new risk loci, genetic overlap with mental health, and a causal influence of schizophrenia on cannabis use, bioRxiv, 2018-01-09
Cannabis use is a heritable trait [1] that has been associated with adverse mental health outcomes. To identify risk variants and improve our knowledge of the genetic etiology of cannabis use, we performed the largest genome-wide association study (GWAS) meta-analysis for lifetime cannabis use (N=184,765) to date. We identified 4 independent loci containing genome-wide significant SNP associations. Gene-based tests revealed 29 genome-wide significant genes located in these 4 loci and 8 additional regions. All SNPs combined explained 10% of the variance in lifetime cannabis use. The most significantly associated gene, CADM2, has previously been associated with substance use and risk-taking phenotypes [2–4]. We used S-PrediXcan to explore gene expression levels and found 11 unique eGenes. LD-score regression uncovered genetic correlations with smoking, alcohol use and mental health outcomes, including schizophrenia and bipolar disorder. Mendelian randomisation analysis provided evidence for a causal positive influence of schizophrenia risk on lifetime cannabis use.
biorxiv genetics 200-500-users 2018Improved Aedes aegypti mosquito reference genome assembly enables biological discovery and vector control, bioRxiv, 2017-12-30
Female Aedes aegypti mosquitoes infect hundreds of millions of people each year with dangerous viral pathogens including dengue, yellow fever, Zika, and chikungunya. Progress in understanding the biology of this insect, and developing tools to fight it, has been slowed by the lack of a high-quality genome assembly. Here we combine diverse genome technologies to produce AaegL5, a dramatically improved and annotated assembly, and demonstrate how it accelerates mosquito science and control. We anchored the physical and cytogenetic maps, resolved the size and composition of the elusive sex-determining “M locus”, significantly increased the known members of the glutathione-S-transferase genes important for insecticide resistance, and doubled the number of chemosensory ionotropic receptors that guide mosquitoes to human hosts and egg-laying sites. Using high-resolution QTL and population genomic analyses, we mapped new candidates for dengue vector competence and insecticide resistance. We predict that AaegL5 will catalyse new biological insights and intervention strategies to fight this deadly arboviral vector.
biorxiv genomics 200-500-users 2017Firefly genomes illuminate parallel origins of bioluminescence in beetles, bioRxiv, 2017-12-22
AbstractFireflies and their fascinating luminous courtships have inspired centuries of scientific study. Today firefly luciferase is widely used in biotechnology, but the evolutionary origin of their bioluminescence remains unclear. To shed light on this long-standing question, we sequenced the genomes of two firefly species that diverged over 100 million-years-ago the North American Photinus pyralis and Japanese Aquatica lateralis. We also sequenced the genome of a related click-beetle, the Caribbean Ignelater luminosus, with bioluminescent biochemistry near-identical to fireflies, but anatomically unique light organs, suggesting the intriguing but contentious hypothesis of parallel gains of bioluminescence. Our analyses support two independent gains of bioluminescence between fireflies and click-beetles, and provide new insights into the genes, chemical defenses, and symbionts that evolved alongside their luminous lifestyle.One Sentence SummaryComparative analyses of the first linkage-group-resolution genomes of fireflies and related bioluminescent beetles address long-standing questions of the origin and evolution of bioluminescence and its associated traits.
biorxiv genomics 200-500-users 2017Single-cell transcriptomic characterization of 20 organs and tissues from individual mice creates a Tabula Muris, bioRxiv, 2017-12-21
The Tabula Muris ConsortiumWe have created a compendium of single cell transcriptome data from the model organism Mus musculus comprising more than 100,000 cells from 20 organs and tissues. These data represent a new resource for cell biology, revealing gene expression in poorly characterized cell populations and allowing for direct and controlled comparison of gene expression in cell types shared between tissues, such as T-lymphocytes and endothelial cells from distinct anatomical locations. Two distinct technical approaches were used for most tissues one approach, microfluidic droplet-based 3’-end counting, enabled the survey of thousands of cells at relatively low coverage, while the other, FACS-based full length transcript analysis, enabled characterization of cell types with high sensitivity and coverage. The cumulative data provide the foundation for an atlas of transcriptomic cell biology.
biorxiv cell-biology 200-500-users 2017Sequence variation aware genome references and read mapping with the variation graph toolkit, bioRxiv, 2017-12-16
AbstractReference genomes guide our interpretation of DNA sequence data. However, conventional linear references are fundamentally limited in that they represent only one version of each locus, whereas the population may contain multiple variants. When the reference represents an individual’s genome poorly, it can impact read mapping and introduce bias. Variation graphs are bidirected DNA sequence graphs that compactly represent genetic variation, including large scale structural variation such as inversions and duplications.1 Equivalent structures are produced by de novo genome assemblers.2,3 Here we present vg, a toolkit of computational methods for creating, manipulating, and utilizing these structures as references at the scale of the human genome. vg provides an efficient approach to mapping reads onto arbitrary variation graphs using generalized compressed suffix arrays,4 with improved accuracy over alignment to a linear reference, creating data structures to support downstream variant calling and genotyping. These capabilities make using variation graphs as reference structures for DNA sequencing practical at the scale of vertebrate genomes, or at the topological complexity of new species assemblies.
biorxiv genomics 200-500-users 2017Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks, bioRxiv, 2017-12-15
AbstractSkeletal bone age assessment is a common clinical practice to diagnose endocrine and metabolic disorders in child development. In this paper, we describe a fully automated deep learning approach to the problem of bone age assessment using data from the 2017 Pediatric Bone Age Challenge organized by the Radiological Society of North America. The dataset for this competition consists of 12,600 radiological images. Each radiograph in this dataset is an image of a left hand labeled with bone age and sex of a patient. Our approach utilizes several deep neural network architectures trained end-to-end. We use images of whole hands as well as specific parts of a hand for both training and prediction. This approach allows us to measure the importance of specific hand bones for automated bone age analysis. We further evaluate the performance of the suggested method in the context of skeletal development stages. Our approach outperforms other common methods for bone age assessment.
biorxiv pathology 200-500-users 2017