Ending the reign of short-acting β2-agonists in Australia?

Acknowledgements We would like to acknowledge and thank Steph James, Kiran Dhillon, Sophie Jones, Rob Campbell, Ying Liu, Marion Magee, Ondrej Rejda, Lisa Sugg, and Nicole O'Sullivan for their valuable contributions.Peer reviewe

Pleiotropic meta-analysis of cognition, education, and schizophrenia differentiates roles of early neurodevelopmental and adult synaptic pathways

Susceptibility to schizophrenia is inversely correlated with general cognitive ability at both the phenotypic and the genetic level. Paradoxically, a modest but consistent positive genetic correlation has been reported between schizophrenia and educational attainment, despite the strong positive genetic correlation between cognitive ability and educational attainment. Here we leverage published genome-wide association studies (GWASs) in cognitive ability, education, and schizophrenia to parse biological mechanisms underlying these results. Association analysis based on subsets (ASSET), a pleiotropic meta-analytic technique, allowed jointly associated loci to be identified and characterized. Specifically, we identified subsets of variants associated in the expected (“concordant”) direction across all three phenotypes (i.e., greater risk for schizophrenia, lower cognitive ability, and lower educational attainment); these were contrasted with variants that demonstrated the counterintuitive (“discordant”) relationship between education and schizophrenia (i.e., greater risk for schizophrenia and higher educational attainment). ASSET analysis revealed 235 independent loci associated with cognitive ability, education, and/or schizophrenia at p < 5 × 10−8. Pleiotropic analysis successfully identified more than 100 loci that were not significant in the input GWASs. Many of these have been validated by larger, more recent single-phenotype GWASs. Leveraging the joint genetic correlations of cognitive ability, education, and schizophrenia, we were able to dissociate two distinct biological mechanisms—early neurodevelopmental pathways that characterize concordant allelic variation and adulthood synaptic pruning pathways—that were linked to the paradoxical positive genetic association between education and schizophrenia. Furthermore, genetic correlation analyses revealed that these mechanisms contribute not only to the etiopathogenesis of schizophrenia but also to the broader biological dimensions implicated in both general health outcomes and psychiatric illness

Identifying nootropic drug targets via large-scale cognitive GWAS and transcriptomics

Broad-based cognitive deficits are an enduring and disabling symptom for many patients with severe mental illness, and these impairments are inadequately addressed by current medications. While novel drug targets for schizophrenia and depression have emerged from recent large-scale genome-wide association studies (GWAS) of these psychiatric disorders, GWAS of general cognitive ability can suggest potential targets for nootropic drug repurposing. Here, we (1) meta-analyze results from two recent cognitive GWAS to further enhance power for locus discovery; (2) employ several complementary transcriptomic methods to identify genes in these loci that are credibly associated with cognition; and (3) further annotate the resulting genes using multiple chemoinformatic databases to identify "druggable" targets. Using our meta-analytic data set (N = 373,617), we identified 241 independent cognition-associated loci (29 novel), and 76 genes were identified by 2 or more methods of gene identification. Actin and chromatin binding gene sets were identified as novel pathways that could be targeted via drug repurposing. Leveraging our transcriptomic and chemoinformatic databases, we identified 16 putative genes targeted by existing drugs potentially available for cognitive repurposing.Peer reviewe

Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence

Intelligence is highly heritable(1) and a major determinant of human health and well-being(2). Recent genome-wide meta-analyses have identified 24 genomic loci linked to variation in intelligence3-7, but much about its genetic underpinnings remains to be discovered. Here, we present a large-scale genetic association study of intelligence (n = 269,867), identifying 205 associated genomic loci (190 new) and 1,016 genes (939 new) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and associations with 146 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain, specifically in striatal medium spiny neurons and hippocampal pyramidal neurons. Gene set analyses implicate pathways related to nervous system development and synaptic structure. We confirm previous strong genetic correlations with multiple health-related outcomes, and Mendelian randomization analysis results suggest protective effects of intelligence for Alzheimer's disease and ADHD and bidirectional causation with pleiotropic effects for schizophrenia. These results are a major step forward in understanding the neurobiology of cognitive function as well as genetically related neurological and psychiatric disorders.Peer reviewe

Age at first birth in women is genetically associated with increased risk of schizophrenia

Prof. Paunio on PGC:n jäsenPrevious studies have shown an increased risk for mental health problems in children born to both younger and older parents compared to children of average-aged parents. We previously used a novel design to reveal a latent mechanism of genetic association between schizophrenia and age at first birth in women (AFB). Here, we use independent data from the UK Biobank (N = 38,892) to replicate the finding of an association between predicted genetic risk of schizophrenia and AFB in women, and to estimate the genetic correlation between schizophrenia and AFB in women stratified into younger and older groups. We find evidence for an association between predicted genetic risk of schizophrenia and AFB in women (P-value = 1.12E-05), and we show genetic heterogeneity between younger and older AFB groups (P-value = 3.45E-03). The genetic correlation between schizophrenia and AFB in the younger AFB group is -0.16 (SE = 0.04) while that between schizophrenia and AFB in the older AFB group is 0.14 (SE = 0.08). Our results suggest that early, and perhaps also late, age at first birth in women is associated with increased genetic risk for schizophrenia in the UK Biobank sample. These findings contribute new insights into factors contributing to the complex bio-social risk architecture underpinning the association between parental age and offspring mental health.Peer reviewe

Author Correction:Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function

Christina M. Lill, who contributed to analysis of data, was inadvertently omitted from the author list in the originally published version of this article. This has now been corrected in both the PDF and HTML versions of the article

Active rules for sensor databases

Recent years have witnessed a rapidly growing interest in query processing in sensor and actuator networks. This is mainly due to the increased awareness of query processing as the most appropriate computational paradigm for a wide range of sensor network applications, such as environmental monitoring. In this paper we propose a second database technology, namely active rules, that provides a natural computational paradigm for sensor network applications which require reactive behavior, such as security management and rapid forest fire response. Like query processing, efficient and effective active rule execution mechanisms have to address several technical challenges including language design, data aggregation, data verification, robustness under topology changes, routing, power management and many more. Nonetheless, active rules change the context and the requirements of these issues and hence a new set of solutions is appropriate. To this end, we outline the implications of active rules for sensor networks and contrast these against query processing. We then proceed to discuss work in progress carried out in project Asene that aims to effectively address these issues. Finally, we introduce our architecture for a decentralized event broker based on the publish/subscribe paradigm and our early design of an ECA language for sensor networks

Active rules for wireless networks of sensors & actuators

The last few years have witnessed a flurry of research in the field of query processing for networks of sensors and actuators. It is widely accepted that query processing is the method of choice for acquiring data from a sensor field. Although query processing offers a very good computational model for a variety of applications such as environmental monitoring, it is a poor match for application scenarios where a timely response to an event is required by the system. With this in mind, we propose a mature database technology, namely active rules, that provides a natural computational paradigm for sensor network applications that require reactive behavior, such as rapid forest fire response and security management. For the remainder of this paper we will outline the implications of active rules for sensor networks and contrast these against query processing. We will then proceed to discuss work in progress carried out by project Asene (Active SEnsor NEtworks) that aims to address these implications. We conclude by introducing our architecture for a decentralised event broker based on the publish/subscribe paradigm and our early design of an Event-Condition-Action (ECA) language for sensor networks

Poster Abstract: Active Rules for Wireless Networks of Sensors &amp; Actuators ABSTRACT

The last few years have witnessed a flurry of research in the field of query processing for networks of sensors and actuators. It is widely accepted that query processing is the method of choice for acquiring data from a sensor field. Although query processing offers a very good computational model for a variety of applications such as environmental monitoring, it is a poor match for application scenarios where a timely response to an event is required by the system. With this in mind, we propose a mature database technology, namely active rules, that provides a natural computational paradigm for sensor network applications that require reactive behavior, such as rapid forest fire response and security management. For the remainder of this paper we will outline the implications of active rules for sensor networks and contrast these against query processing. We will then proceed to discuss work in progress carried out by project Asene (Active SEnsor NEtworks) that aims to address these implications. We conclude by introducing our architecture for a decentralised event broker based on the publish/subscribe paradigm and our early design of an Event-Condition-Action (ECA) language for sensor networks. Categories and Subject Descriptors H.2.4 [Database Management]: Systems—Distributed databases