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      Phenotypic connections in surprising places

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      1 , 2 ,
      Genome Biology
      BioMed Central

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          Abstract

          Connections have been revealed between very different human diseases using phenotype associations in other species

          Abstract

          Surprising correlations between human disease phenotypes are emerging. Recent work now reveals startling phenotype connections between species, which could provide new disease models.

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          Most cited references9

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          McKusick's Online Mendelian Inheritance in Man (OMIM®)

          McKusick's Online Mendelian Inheritance in Man (OMIM®; http://www.ncbi.nlm.nih.gov/omim), a knowledgebase of human genes and phenotypes, was originally published as a book, Mendelian Inheritance in Man, in 1966. The content of OMIM is derived exclusively from the published biomedical literature and is updated daily. It currently contains 18 961 full-text entries describing phenotypes and genes. To date, 2239 genes have mutations causing disease, and 3770 diseases have a molecular basis. Approximately 70 new entries are added and 700 entries are updated per month. OMIM® is expanding content and organization in response to shifting biological paradigms and advancing biotechnology.
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            The implications of human metabolic network topology for disease comorbidity.

            Most diseases are the consequence of the breakdown of cellular processes, but the relationships among genetic/epigenetic defects, the molecular interaction networks underlying them, and the disease phenotypes remain poorly understood. To gain insights into such relationships, here we constructed a bipartite human disease association network in which nodes are diseases and two diseases are linked if mutated enzymes associated with them catalyze adjacent metabolic reactions. We find that connected disease pairs display higher correlated reaction flux rate, corresponding enzyme-encoding gene coexpression, and higher comorbidity than those that have no metabolic link between them. Furthermore, the more connected a disease is to other diseases, the higher is its prevalence and associated mortality rate. The network topology-based approach also helps to uncover potential mechanisms that contribute to their shared pathophysiology. Thus, the structure and modeled function of the human metabolic network can provide insights into disease comorbidity, with potentially important consequences for disease diagnosis and prevention.
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              The modular nature of genetic diseases.

              Evidence from many sources suggests that similar phenotypes are begotten by functionally related genes. This is most obvious in the case of genetically heterogeneous diseases such as Fanconi anemia, Bardet-Biedl or Usher syndrome, where the various genes work together in a single biological module. Such modules can be a multiprotein complex, a pathway, or a single cellular or subcellular organelle. This observation suggests a number of hypotheses about the human phenome that are now beginning to be explored. First, there is now good evidence from bioinformatic analyses that human genetic diseases can be clustered on the basis of their phenotypic similarities and that such a clustering represents true biological relationships of the genes involved. Second, one may use such phenotypic similarity to predict and then test for the contribution of apparently unrelated genes to the same functional module. This concept is now being systematically tested for several diseases. Most recently, a systematic yeast two-hybrid screen of all known genes for inherited ataxias indicated that they all form part of a single extended protein-protein interaction network. Third, one can use bioinformatics to make predictions about new genes for diseases that form part of the same phenotype cluster. This is done by starting from the known disease genes and then searching for genes that share one or more functional attributes such as gene expression pattern, coevolution, or gene ontology. Ultimately, one may expect that a modular view of disease genes should help the rapid identification of additional disease genes for multifactorial diseases once the first few contributing genes (or environmental factors) have been reliably identified.
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                Author and article information

                Journal
                Genome Biol
                Genome Biology
                BioMed Central
                1465-6906
                1465-6914
                2010
                28 April 2010
                28 April 2011
                : 11
                : 4
                : 116
                Affiliations
                [1 ]Translational Sciences Department, Novartis Institutes for BioMedical Research, Cambridge, MA 02139, USA
                [2 ]Departments of Biomedical Engineering and Physics, Program in Bioinformatics and Systems Biology, Boston University, Boston, MA 02215, USA
                Article
                gb-2010-11-4-116
                10.1186/gb-2010-11-4-116
                2884535
                20423531
                62f3f2c3-cbfd-48e0-af70-d55f271002f7
                Copyright ©2010 BioMed Central Ltd
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                Genetics
                Genetics

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