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Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes

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      Abstract

      Genome-wide experimental methods to identify disease genes, such as linkage analysis and association studies, generate increasingly large candidate gene sets for which comprehensive empirical analysis is impractical. Computational methods employ data from a variety of sources to identify the most likely candidate disease genes from these gene sets. Here, we review seven independent computational disease gene prioritization methods, and then apply them in concert to the analysis of 9556 positional candidate genes for type 2 diabetes (T2D) and the related trait obesity. We generate and analyse a list of nine primary candidate genes for T2D genes and five for obesity. Two genes, LPL and BCKDHA, are common to these two sets. We also present a set of secondary candidates for T2D (94 genes) and for obesity (116 genes) with 58 genes in common to both diseases.

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      Most cited references 103

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      Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.

      The BLAST programs are widely used tools for searching protein and DNA databases for sequence similarities. For protein comparisons, a variety of definitional, algorithmic and statistical refinements described here permits the execution time of the BLAST programs to be decreased substantially while enhancing their sensitivity to weak similarities. A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approximately three times the speed of the original. In addition, a method is introduced for automatically combining statistically significant alignments produced by BLAST into a position-specific score matrix, and searching the database using this matrix. The resulting Position-Specific Iterated BLAST (PSI-BLAST) program runs at approximately the same speed per iteration as gapped BLAST, but in many cases is much more sensitive to weak but biologically relevant sequence similarities. PSI-BLAST is used to uncover several new and interesting members of the BRCT superfamily.
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        PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes.

        DNA microarrays can be used to identify gene expression changes characteristic of human disease. This is challenging, however, when relevant differences are subtle at the level of individual genes. We introduce an analytical strategy, Gene Set Enrichment Analysis, designed to detect modest but coordinate changes in the expression of groups of functionally related genes. Using this approach, we identify a set of genes involved in oxidative phosphorylation whose expression is coordinately decreased in human diabetic muscle. Expression of these genes is high at sites of insulin-mediated glucose disposal, activated by PGC-1alpha and correlated with total-body aerobic capacity. Our results associate this gene set with clinically important variation in human metabolism and illustrate the value of pathway relationships in the analysis of genomic profiling experiments.
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          Inflammation, stress, and diabetes.

          Over the last decade, an abundance of evidence has emerged demonstrating a close link between metabolism and immunity. It is now clear that obesity is associated with a state of chronic low-level inflammation. In this article, we discuss the molecular and cellular underpinnings of obesity-induced inflammation and the signaling pathways at the intersection of metabolism and inflammation that contribute to diabetes. We also consider mechanisms through which the inflammatory response may be initiated and discuss the reasons for the inflammatory response in obesity. We put forth for consideration some hypotheses regarding important unanswered questions in the field and suggest a model for the integration of inflammatory and metabolic pathways in metabolic disease.
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            Author and article information

            Affiliations
            simpleSouth African National Bioinformatics Institute, University of the Western Cape Bellville, 7535, South Africa
            1simpleMedical Genetics Section, Department of Medical Sciences, The University of Edinburgh Edinburgh, UK
            2simpleMRC Human Genetics Unit Crewe RoadsimpleWestern General Hospital Edinburgh, EH42XU, UK
            3simpleDepartment of Human Genetics, University Medical Centre Nijmegen PO Box 9101, 6500HB Nijmegen, The Netherlands
            4simpleDepartment of Molecular Biology, Nijmegen Center for Molecular Life Sciences, Radboud University 6500 HB Nijmegen, The Netherlands
            5simpleCentre for Molecular and Biomolecular Informatics, Radboud University Nijmegen PO Box 9010, 6500GL Nijmegen, The Netherlands
            6simpleResearch Unit on Biomedical Informatics (GRIB), Universitat Pompeu Fabra Passeig Martim de la Barceloneta 37–49, 08003, Barcelona, Spain
            7simpleComputational Genomics Group, The European Bioinformatics Institute EMBL Cambridge Outstation, Cambridge CB10 1SD, UK
            8simpleOntario Genomics Innovation Centre, Ottawa Health Research Institute 501 Smyth, Ottawa, ON, Canada K1H 8L6
            9simpleDepartment of Cellular and Molecular Medicine, Faculty of Medicine, University of Ottawa Ottawa, ON, Canada
            10simpleNational Human Genome Center, Howard University, Genetic Epidemiology Unit, College of Medicine 2216 6th Street, NW, Washington, DC 20059, USA
            11simpleUniversity of Ibadan, College of Medicine Ibadan, Nigeria
            12simpleHarvard Medical School, Joslin Diabetes Center 1 Joslin Place, Boston, MA 02215, USA
            Author notes
            *To whom correspondence should be addressed. Tel: +27 21 9592611; Fax: 27 21 9592512; Email: nicki@ 123456sanbi.ac.za

            Present address: Christos Ouzounis, Computational Genomics Unit & Institute of Agrobiotechnology, Centre for Research and Technology Hellas, Thessalonica, GR-57001, Greece

            Journal
            Nucleic Acids Res
            Nucleic Acids Research
            Nucleic Acids Research
            Oxford University Press
            0305-1048
            1362-4962
            2006
            2006
            6 June 2006
            : 34
            : 10
            : 3067-3081
            1475747
            10.1093/nar/gkl381
            16757574
            © 2006 The Author(s)

            This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commerical use, distribution, and reproduction in any medium, provided the original work is properly cited.

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            Survey and Summary

            Genetics

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