Mapping Gene Associations in Human Mitochondria using Clinical Disease Phenotypes Export

@article{citeulike:4732159, abstract = {Author Summary An important prerequisite for successful disease gene identification is the assessment, with minimal ambiguity, of a particular clinical trait or phenotype. Even with years of experience, recognizing and diagnosing mitochondrial diseases is still a major hurdle in clinical medicine. Computational tools supporting clinicians not only help identify affected individuals, but also guide studies of the genetic and biological causes of these disorders. In this study we dissect and categorize individual clinical features, signs, and symptoms of 174 disease genes and then identify gene similarities based on their shared phenotypic features. We demonstrate that genes sharing more similar phenotypes have a stronger tendency for functional interactions, proving the usefulness of phenotype similarity values in disease gene network analysis. Our study of a large functional network of mitochondrial genes revealed distinct properties that differentiate disease and non-disease genes. Disease genes showed a lower average total connectivity but a tendency to interact with each other; a finding that we used to predict 168 high-probability disease candidates. The accompanying knowledgebase allows for easy navigation between disease and gene information. We believe the open source format will support and encourage further research that will benefit this and other human phenome projects.}, author = {Scharfe, Curt and Lu, Henry H. and Neuenburg, Jutta K. and Allen, Edward A. and Li, Guan-Cheng and Klopstock, Thomas and Cowan, Tina M. and Enns, Gregory M. and Davis, Ronald W.}, citeulike-article-id = {4732159}, citeulike-linkout-0 = {http://dx.doi.org/10.1371/journal.pcbi.1000374}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/19390613}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=19390613}, day = {24}, doi = {10.1371/journal.pcbi.1000374}, issn = {1553-7358}, journal = {PLoS Comput Biol}, keywords = {disease, interaction, mitochondria, network, phenotype, prediction}, month = {April}, number = {4}, pages = {e1000374+}, posted-at = {2009-06-03 13:13:34}, priority = {0}, publisher = {Public Library of Science}, title = {Mapping Gene Associations in Human Mitochondria using Clinical Disease Phenotypes}, url = {http://dx.doi.org/10.1371/journal.pcbi.1000374}, volume = {5}, year = {2009} }

TY - JOUR ID - citeulike:4732159 L3 - citeulike-article-id:4732159 N2 - Author Summary An important prerequisite for successful disease gene identification is the assessment, with minimal ambiguity, of a particular clinical trait or phenotype. Even with years of experience, recognizing and diagnosing mitochondrial diseases is still a major hurdle in clinical medicine. Computational tools supporting clinicians not only help identify affected individuals, but also guide studies of the genetic and biological causes of these disorders. In this study we dissect and categorize individual clinical features, signs, and symptoms of 174 disease genes and then identify gene similarities based on their shared phenotypic features. We demonstrate that genes sharing more similar phenotypes have a stronger tendency for functional interactions, proving the usefulness of phenotype similarity values in disease gene network analysis. Our study of a large functional network of mitochondrial genes revealed distinct properties that differentiate disease and non-disease genes. Disease genes showed a lower average total connectivity but a tendency to interact with each other; a finding that we used to predict 168 high-probability disease candidates. The accompanying knowledgebase allows for easy navigation between disease and gene information. We believe the open source format will support and encourage further research that will benefit this and other human phenome projects. IS - 4 JF - PLoS Comput Biol SN - 1553-7358 TI - Mapping Gene Associations in Human Mitochondria using Clinical Disease Phenotypes PB - Public Library of Science VL - 5 SP - e1000374 KW - disease KW - interaction KW - mitochondria KW - network KW - phenotype KW - prediction AU - Scharfe, Curt AU - Lu, Henry AU - Neuenburg, Jutta AU - Allen, Edward AU - Li, Guan-Cheng AU - Klopstock, Thomas AU - Cowan, Tina AU - Enns, Gregory AU - Davis, Ronald PY - 2009/04/24/ UR - http://dx.doi.org/10.1371/journal.pcbi.1000374 ER -