Linking diseases and phenotypes for clinical usage

Background: In recent years a large volume of clinical genomics data has become available due to rapid advances in sequencing<br> technologies. Efficient exploitation of genomics data requires linkage to patient phenotype profiles. Current resources,<br> providing disease-phenotype associations, are not comprehensive, and they often do not cover all of the disease terminologies, particularly ICD-10, which is still the primary terminology used in clinical settings. Methods:<br> We used a text-mining method that utilizes semantic relations in phenotype ontologies, and applied statistical methods to extract associations between diseases in ICD-10 and phenotype ontology classes from the literature. In addition, we present a semi-automatic way to curate ICD-10:phenotype associations from existing resources containing known associations. Results:<br> We created two datasets linking diseases to their phenotypes based on each strategy. We extensively validated these datasets based on comparison to gold standard manually created disease--phenotype associations, based on their similarity to disease--phenotype associations found in databases, and based on how well they can be used to recover gene--disease<br> associations using phenotype similarity. We find that our text mining method can produce phenotypes that are useful but often too<br> specific or too general. However, the dataset generated from integrating multiple knowledgebases is more granular