Association and Mutation Analyses of 16p11.2 Autism Candidate Genes

Autism is a complex childhood neurodevelopmental disorder with a strong genetic basis. Microdeletion or duplication of a approximately 500-700-kb genomic rearrangement on 16p11.2 that contains 24 genes represents the second most frequent chromosomal disorder associated with autism. The role of common and rare 16p11.2 sequence variants in autism etiology is unknown.To identify common 16p11.2 variants with a potential role in autism, we performed association studies using existing data generated from three microarray platforms: Affymetrix 5.0 (777 families), Illumina 550 K (943 families), and Affymetrix 500 K (60 families). No common variants were identified that were significantly associated with autism. To look for rare variants, we performed resequencing of coding and promoter regions for eight candidate genes selected based on their known expression patterns and functions. In total, we identified 26 novel variants in autism: 13 exonic (nine non-synonymous, three synonymous, and one untranslated region) and 13 promoter variants. We found a significant association between autism and a coding variant in the seizure-related gene SEZ6L2 (12/1106 autism vs. 3/1161 controls; p = 0.018). Sez6l2 expression in mouse embryos was restricted to the spinal cord and brain. SEZ6L2 expression in human fetal brain was highest in post-mitotic cortical layers, hippocampus, amygdala, and thalamus. Association analysis of SEZ6L2 in an independent sample set failed to replicate our initial findings.We have identified sequence variation in at least one candidate gene in 16p11.2 that may represent a novel genetic risk factor for autism. However, further studies are required to substantiate these preliminary findings

Analysis of shared heritability in common disorders of the brain

ience, this issue p. eaap8757 Structured Abstract INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological comorbidity, inciting debate about their etiologic overlap. However, detailed study of phenotypes with different ages of onset, severity, and presentation poses a considerable challenge. Recently developed heritability methods allow us to accurately measure correlation of genome-wide common variant risk between two phenotypes from pools of different individuals and assess how connected they, or at least their genetic risks, are on the genomic level. We used genome-wide association data for 265,218 patients and 784,643 control participants, as well as 17 phenotypes from a total of 1,191,588 individuals, to quantify the degree of overlap for genetic risk factors of 25 common brain disorders. RATIONALE Over the past century, the classification of brain disorders has evolved to reflect the medical and scientific communities' assessments of the presumed root causes of clinical phenomena such as behavioral change, loss of motor function, or alterations of consciousness. Directly observable phenomena (such as the presence of emboli, protein tangles, or unusual electrical activity patterns) generally define and separate neurological disorders from psychiatric disorders. Understanding the genetic underpinnings and categorical distinctions for brain disorders and related phenotypes may inform the search for their biological mechanisms. RESULTS Common variant risk for psychiatric disorders was shown to correlate significantly, especially among attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder (MDD), and schizophrenia. By contrast, neurological disorders appear more distinct from one another and from the psychiatric disorders, except for migraine, which was significantly correlated to ADHD, MDD, and Tourette syndrome. We demonstrate that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine. We also identify significant genetic sharing between disorders and early life cognitive measures (e.g., years of education and college attainment) in the general population, demonstrating positive correlation with several psychiatric disorders (e.g., anorexia nervosa and bipolar disorder) and negative correlation with several neurological phenotypes (e.g., Alzheimer's disease and ischemic stroke), even though the latter are considered to result from specific processes that occur later in life. Extensive simulations were also performed to inform how statistical power, diagnostic misclassification, and phenotypic heterogeneity influence genetic correlations. CONCLUSION The high degree of genetic correlation among many of the psychiatric disorders adds further evidence that their current clinical boundaries do not reflect distinct underlying pathogenic processes, at least on the genetic level. This suggests a deeply interconnected nature for psychiatric disorders, in contrast to neurological disorders, and underscores the need to refine psychiatric diagnostics. Genetically informed analyses may provide important "scaffolding" to support such restructuring of psychiatric nosology, which likely requires incorporating many levels of information. By contrast, we find limited evidence for widespread common genetic risk sharing among neurological disorders or across neurological and psychiatric disorders. We show that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures. Further study is needed to evaluate whether overlapping genetic contributions to psychiatric pathology may influence treatment choices. Ultimately, such developments may pave the way toward reduced heterogeneity and improved diagnosis and treatment of psychiatric disorders

Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders

Genetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci. However, the nature and mechanisms of these pleiotropic effects remain unclear. We performed analyses of 232,964 cases and 494,162 controls from genome-wide studies of anorexia nervosa, attention-deficit/hyper-activity disorder, autism spectrum disorder, bipolar disorder, major depression, obsessive-compulsive disorder, schizophrenia, and Tourette syndrome. Genetic correlation analyses revealed a meaningful structure within the eight disorders, identifying three groups of inter-related disorders. Meta-analysis across these eight disorders detected 109 loci associated with at least two psychiatric disorders, including 23 loci with pleiotropic effects on four or more disorders and 11 loci with antagonistic effects on multiple disorders. The pleiotropic loci are located within genes that show heightened expression in the brain throughout the lifespan, beginning prenatally in the second trimester, and play prominent roles in neurodevelopmental processes. These findings have important implications for psychiatric nosology, drug development, and risk prediction.Peer reviewe

Personal Knowledge Mapping with semantic web technologies

Semantic Web technologies promise great benefits for Personal Knowledge Management (PKM) and Knowledge Management (KM) in general when data needs to be exchanged or integrated. However, the Semantic Web also introduces new issues rooted in its distributed nature as multiple ontologies exist to encode data in the Personal Information Management (PIM) domain. This poses problems for applications processing this data as they would need to support all current and future PIM ontologies. In this paper, we introduce an approach that decouples applications from the data representation by providing a mapping service which translates Semantic Web data between different vocabularies. Our approach consists of the RDF Data Transformation Language (RDTL) to define mappings between different but related ontologies and the prototype implementation RDFTransformer to apply mappings. This allows the definition of mappings that are more complex than simple one-to-one matches

WEESA - web engineering for semantic web applications

Die wachsende Popularität des World Wide Webs hat zu einer exponentiellen Steigerung der Zahl der Webseiten geführt. Die große Anzahl der verfügbaren Webseiten macht es Benutzern immer schwerer benötigte Informationen zu finden. Sucht man im Web nach einer spezifischen Information, läuft man Gefahr, die relevanten Daten in der großen Anzahl von irrelevanten Suchergebnissen zu übersehen.Web-Applikationen stellen derzeit Webseiten in HTML Format zur Verfügung, in denen der Inhalt in natürlicher Sprache ausgedrückt ist.Daher ist die Semantik des Inhalts für Computer nicht zugängig. Um es Computern zu ermöglichen dem Benutzer bei Informationsproblemen zu unterstützen, schlägt das Semantik Web eine Erweiterung des existierende Webs vor, welche die Semantik der Webseiten für Computer verarbeitbar macht. Die Semantik des Inhalts einer Webseite wird dabei mit RDF Meta-Daten beschrieben. Diese Meta-Daten beschreiben den Inhalt der Webseite in einer maschinen-verarbeitbaren Form. Die Existenz von semantisch annotierten Webseiten ist daher die Voraussetzung für das Semantik Web.Semantische Annotation beschäftigt sich mit diesem Problem und zielt darauf ab, semantische Meta-Daten zu natürlichsprachigen Dokumenten hinzuzufügen, um den Inhalt maschinen-verarbeitbar zu machen. Viele Werkzeuge wurden entwickelt, um den Benutzer beim Annotierungsprozess zu unterstützen. Der Annotierungsprozess ist jedoch immer noch ein eigenständigen Prozess der nicht in den Entwicklungsprozess der Web-Applikation integriert ist. Auf der anderen Seite hat die Forschung im Bereich von Web Engineering zu Methoden geführt, um Web-Applikationen zu entwickeln und zu warten. Die vorgeschlagen Methoden unterstützen jedoch nicht das hinzufügen von semantischen Meta-Daten.Diese Dissertation stellt eine Technik vor, um existierende XML-basierte Web Entwicklungsmethoden zu erweitern, um semantisch annotierte Webseiten zu erzeugen. Die Innovation des vorgestellten Ansatzes, genannt WEESA, ist die Verknüpfung von Elementen aus einem XML Schema mit den Konzepten die in einer Ontologie definiert sind.Diese Verknüpfung wird dann verwendet um aus XML-Dokumenten RDF Meta-Daten zu generieren. Weiters stellen wir die Integration des WEESA Meta-Daten Generators in die Apache Cocoon Webentwicklungsumgebung vor, das die Entwicklung von semantisch annotierten Webapplikationen erleichtert.Betrachtet man nur die Meta-Daten einer einzelnen Webseite, hat man nur eine eingeschränkte Sicht auf die Meta-Daten die die Web-Applikation zur Verfügung stellt. Für Anfragen und logische Schlüsse ist es besser man hat die vollständigen Meta-Daten der ganzen Web-Applikation zur Verfügung. In dieser Dissertation stellen wir die WEESA Wissensbasis vor. Diese Wissensbasis wir serverseitig durch das Akkumulieren der Meta-Daten der individuellen Webseiten erzeugt und steht dann für Anfragen und zum Herunterladen zur Verfügung.Die Wiener Festwochen Fallstudie zeigt den praktischen Einsatz von WEESA in einer Apache Cocoon Web-Applikation. Wir diskutieren die Erfahrungen aus der Entwicklung der Fallstudie und präsentieren Richtlinien zum Entwickeln von semantisch annotierten Web-Applikationen mit WEESA.In the last decade the increasing popularity of the World Wide Web has lead to an exponential growth in the number of pages available on the Web. This huge number of Web pages makes it increasingly difficult for users to find required information. In searching the Web for specific information, one gets lost in the vast number of irrelevant search results and may miss relevant material. Current Web applications provide Web pages in HTML format representing the content in natural language only and the semantics of the content is therefore not accessible by machines. To enable machines to support the user in solving information problems, the Semantic Web proposes an extension to the existing Web that makes the semantics of the Web pages machine-processable. The semantics of the information of a Web page is formalized using RDF meta-data describing the meaning of the content.The existence of semantically annotated Web pages is therefore crucial in bringing the Semantic Web into existence.Semantic annotation addresses this problem and aims to turn human-understandable content into a machine-processable form by adding semantic markup. Many tools have been developed that support the user during the annotation process. The annotation process, however, is a separate task and is not integrated in the Web engineering process.Web engineering proposes methodologies to design, implement and maintain Web applications but these methodologies lack the generation of meta-data.In this thesis we introduce a technique to extend existing XML-based Web engineering methodologies to develop semantically annotated Web pages. The novelty of this approach is the definition of a mapping from XML Schema to ontologies, called WEESA, that can be used to automatically generate RDF meta-data from XML content documents. We further demonstrate the integration of the WEESA meta-data generator into the Apache Cocoon Web development framework to easily extend XML-based Web applications to semantically annotated Web application.Looking at the meta-data of a single Web page gives only a limited view of the of the information available in a Web application. For querying and reasoning purposes it is better to have the full meta-data model of the whole Web application as a knowledge base at hand. In this thesis we introduce the WEESA knowledge base, which is generated at server side by accumulating the meta-data from individual Web pages. The WEESA knowledge base is then offered for download and querying by software agents.Finally, the Vienna International Festival industry case study illustrates the use of WEESA within an Apache Cocoon Web application in real life. We discuss the lessons learned while implementing the case study and give guidelines for developing Semantic Web applications using WEESA.12

A Comparison of RDB-to-RDF Mapping Languages

Mapping Relational Databases (RDB) to RDF is an active field of research. The majority of data on the current Web is stored in RDBs. Therefore, bridging the conceptual gap between the relational model and RDF is needed to make the data available on the Semantic Web. In addition, recent research has shown that Semantic Web technologies are useful beyond the Web, especially if data from different sources has to be exchanged or integrated. Many mapping languages and approaches were explored leading to the ongoing standardization effort of the World Wide Web Consortium (W3C) carried out in the RDB2RDF Working Group (WG). The goal and contribution of this paper is to provide a feature-based comparison of the state-of-the-art RDB-to-RDF mapping languages. It should act as a guide in selecting a RDB-to-RDF mapping language for a given application scenario and its requirements w.r.t. mapping features. Our comparison framework is based on use cases and requirements for mapping RDBs to RDF as identified by the RDB2RDF WG. We apply this comparison framework to the state-of-the-art RDB-to-RDF mapping languages and report the findings in this paper. As a result, our classification proposes four categories of mapping languages: direct mapping, read-only general-purpose mapping, read-write general-purpose mapping, and special-purpose mapping. We further provide recommendations for selecting a mapping language

UpLink - A Linked Data Editor for RDB-to-RDF Data

Linked Data builds a machine-processable Web of Data based on a large and growing number of RDF datasets and typed links among them. For the human user, Web-based interfaces were developed to enable browsing and editing Linked Data that is stored as native RDF. However, the majority of data on the current Web is stored in Relational Databases (RDB). This is a challenge for Linked Data browsers and especially for Linked Data editors. In this paper, we present UpLink which is to the best of our knowledge the first Linked Data editor for RDB-to-RDF data, i.e., RDF data that is mapped on demand from a RDB. We further present usage scenarios to demonstrate that UpLink supports the basic CRUD operations for editing Linked Data

WEESA - Web Engineering for Semantic Web Applications

The success of the Semantic Web crucially depends on the existence of Web pages that provide machine-understandable meta-data. This meta-data is typically added in the semantic annotation process which is currently not part of the Web engineering process. Web engineering, however, proposes methodologies to design, implement and maintain Web applications but lack the generation of meta-data. In this paper we introduce a technique to extend existing Web engineering methodologies to develop semantically annotated Web pages. The novelty of this approach is the definition of a mapping from XML Schema to ontologies, called WEESA, that can be used to automatically generate RDF meta-data from XML content documents. We further show how we integrated the WEESA mapping into an Apache Cocoon transformer to easily extend XML based Web applications to semantically annotated Web application

348 Data Integration and the Semantic Web An Architecture for a Semantic Portal

Abstract. Current Web applications provide their information and functionalities to human users only. To make Web applications also accessible for machines, the Semantic Web proposes an extension of the current Web, that describes the semantics of the content and the services explicitly with machine-processable meta-data. In this paper we introduce an architecture of a Semantic Portal that provides a unique front-end to the information and functionalities of individual Semantic Web applications. To realize the portal we use WEESA to semantically annotate Web applications and provide the annotations in a knowledge base (KB) for download and querying. Based on that, the Semantic Harvester collects the KBs from individual Semantic Web applications to build the global KB of the Semantic Portal. Finally, we use Semantic Web services to make the portal a unique interface to the services of the Web applications.