Evaluating, Filtering and Clustering Genetic Disease Cohorts Based on Human Phenotype Ontology Data with Cohort Analyzer

Elena Rojano, José Córdoba-Caballero, Fernando M. Jabato, Diana Gallego, Mercedes Serrano, Belén Pérez, Álvaro Parés-Aguilar, James R. Perkins, Juan A. G. Ranea, Pedro Seoane-Zonjic
2021 Journal of Personalized Medicine  
Exhaustive and comprehensive analysis of pathological traits is essential to understanding genetic diseases, performing precise diagnosis and prescribing personalized treatments. It is particularly important for disease cohorts, as thoroughly detailed phenotypic profiles allow patients to be compared and contrasted. However, many disease cohorts contain patients that have been ascribed low numbers of very general and relatively uninformative phenotypes. We present Cohort Analyzer, a tool that
more » ... asures the phenotyping quality of patient cohorts. It calculates multiple statistics to give a general overview of the cohort status in terms of the depth and breadth of phenotyping, allowing us to detect less well-phenotyped patients for re-examining or excluding from further analyses. In addition, it performs clustering analysis to find subgroups of patients that share similar phenotypic profiles. We used it to analyse three cohorts of genetic diseases patients with very different properties. We found that cohorts with the most specific and complete phenotypic characterization give more potential insights into the disease than those that were less deeply characterised by forming more informative clusters. For two of the cohorts, we also analysed genomic data related to the patients, and linked the genomic data to the patient-subgroups by mapping shared variants to genes and functions. The work highlights the need for improved phenotyping in this era of personalized medicine. The tool itself is freely available alongside a workflow to allow the analyses shown in this work to be applied to other datasets.
doi:10.3390/jpm11080730 pmid:34442375 pmcid:PMC8398478 fatcat:a4psvjd3d5dvnaoaegpycinuoe