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A genome-wide association study identifies novel risk loci for type 2 diabetes
Uppsala University, Disciplinary Domain of Science and Technology, Chemistry, Department of Physical and Analytical Chemistry.
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2007 (English)In: Nature, ISSN 0028-0836, Vol. 445, no 7130, 881-885 p.Article in journal (Refereed) Published
Abstract [en]

Type 2 diabetes mellitus results from the interaction of environmental factors with a combination of genetic variants, most of which were hitherto unknown. A systematic search for these variants was recently made possible by the development of high-density arrays that permit the genotyping of hundreds of thousands of polymorphisms. We tested 392,935 single-nucleotide polymorphisms in a French case-control cohort. Markers with the most significant difference in genotype frequencies between cases of type 2 diabetes and controls were fast-tracked for testing in a second cohort. This identified four loci containing variants that confer type 2 diabetes risk, in addition to confirming the known association with the TCF7L2 gene. These loci include a non-synonymous polymorphism in the zinc transporter SLC30A8, which is expressed exclusively in insulin-producing beta-cells, and two linkage disequilibrium blocks that contain genes potentially involved in beta-cell development or function (IDE-KIF11-HHEX and EXT2-ALX4). These associations explain a substantial portion of disease risk and constitute proof of principle for the genome-wide approach to the elucidation of complex genetic traits.

Place, publisher, year, edition, pages
2007. Vol. 445, no 7130, 881-885 p.
National Category
Chemical Sciences
URN: urn:nbn:se:uu:diva-95870DOI: 10.1038/nature05616OAI: oai:DiVA.org:uu-95870DiVA: diva2:170233
Available from: 2007-04-25 Created: 2007-04-25 Last updated: 2010-02-24Bibliographically approved
In thesis
1. Signals and Noise in Complex Biological Systems
Open this publication in new window or tab >>Signals and Noise in Complex Biological Systems
2007 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In every living cell, millions of different types of molecules constantly interact and react chemically in a complex system that can adapt to fluctuating environments and extreme conditions, living to survive and reproduce itself. The information required to produce these components is stored in the genome, which is copied in each cell division and transferred and mixed with another genome from parent to child. The regulatory mechanisms that control biological systems, for instance the regulation of expression levels for each gene, has evolved so that global robustness and ability to survive under harsh conditions is a strength, at the same time as biological tasks on a detailed molecular level must be carried out with good precision and without failures. This has resulted in systems that can be described as a hierarchy of levels of complexity: from the lowest level, where molecular mechanisms control other components at the same level, to pathways of coordinated interactions between components, formed to carry out particular biological tasks, and up to large-scale systems consisting of all components, connected in a network with a topology that makes the system robust and flexible. This thesis reports on work that model and analyze complex biological systems, and the signals and noise that regulate them, at all different levels of complexity. Also, it shows how signals are transduced vertically from one level to another, as when a single mutation can cause errors in low level mechanisms, disrupting pathways and create systemwide imbalances, such as in type 2 diabetes. The advancement of our knowledge of biological systems requires both that we go deeper and towards more detail, of single molecules in single cells, as well as taking a step back to understand the organisation and dynamics in the large networks of all components, and unite the different levels of complexity.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2007. 102 p.
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 305
Engineering physics, complex systems, computational biology, gene networks, genome-wide, stochastic resonance, microarray, diabetes, association study, transcription regulation, gene expression, Teknisk fysik
urn:nbn:se:uu:diva-7862 (URN)978-91-554-6888-0 (ISBN)
Public defence
2007-05-16, Häggsalen, Ångströmlaboratoriet, Lägerhyddsvägen 1, Uppsala, 09:30
Available from: 2007-04-25 Created: 2007-04-25Bibliographically approved

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