RT Journal Article T1 Evaluating, Filtering and Clustering Genetic Disease Cohorts Based on Human Phenotype Ontology Data with Cohort Analyzer. A1 Rojano, Elena A1 Córdoba-Caballero, José A1 Jabato, Fernando M A1 Gallego, Diana A1 Serrano, Mercedes A1 Pérez, Belén A1 Parés-Aguilar, Álvaro A1 Perkins, James R A1 Ranea, Juan A G A1 Seoane-Zonjic, Pedro K1 cluster analysis K1 cohort analyzer K1 genetic diseases K1 human phenotype ontology K1 phenotype quality assessment AB 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 measures 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. SN 2075-4426 YR 2021 FD 2021-07-27 LK https://hdl.handle.net/10668/25234 UL https://hdl.handle.net/10668/25234 LA en DS RISalud RD Apr 4, 2025