TY - JOUR T1 - Mapping Gene Associations in Human Mitochondria using Clinical Disease Phenotypes A1 - Scharfe, Curt A1 - Lu, Henry Horng-Shing A1 - Neuenburg, Jutta K. A1 - Allen, Edward A. A1 - Li, Guan-Cheng A1 - Klopstock, Thomas A1 - Cowan, Tina M. A1 - Enns, Gregory M. A1 - Davis, Ronald W. Y1 - 2009/04/24 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. JF - PLOS Computational Biology JA - PLOS Computational Biology VL - 5 IS - 4 UR - https://doi.org/10.1371/journal.pcbi.1000374 SP - e1000374 EP - PB - Public Library of Science M3 - doi:10.1371/journal.pcbi.1000374 ER -