Deep phenotyping for patients by patients: a lay-friendly version of the Human Phenotype Ontology

The Human Phenotype Ontology (HPO) has become the de facto standard representation of clinical “deep phenotype” data for computational comparison of abnormalities and for use in genetic disease diagnostics. The HPO enables non-exact matching of sets of phenotypic features (phenotype profile) against known diseases, other patients, and model organisms. The algorithms have been implemented into many variant prioritization tools and are used by the 100,000 Genomes project, the NIH Undiagnosed Diseases Program/Network, and thousands of other clinics, labs, tools, and databases. <br>Patients themselves are an eager and untapped source of accurate information about symptoms and phenotypes - some of which may go unnoticed by the clinician. However, medical terminology is often perplexing to patients, making it difficult to use resources like the HPO. Here, we systematically added layperson synonyms with approximately 60% being translatable into layperson terminology. Analyses suggest that the lay-HPO has the features required to be useful in a diagnostic setting, in that lay terms are: a) sufficiently specific and, b) well-represented in our disease-to-phenotype database that is utilized by the aforementioned tools for differential diagnostics. A new patient-centered tool aims to help patients assist clinicians in creating robust computational phenotype profiles to improve molecular diagnostic rates and be active participants in their diagnostic odysseys.人类表型本体（Human Phenotype Ontology, HPO）现已成为临床“深度表型”数据的事实上标准表征，可用于异常状态的计算比较，并应用于遗传病诊断领域。HPO支持将表型特征集合（表型谱）与已知疾病、其他患者及模式生物开展非精确匹配。相关算法已被集成至众多变异优先级排序工具中，并为十万基因组计划、美国国立卫生研究院（National Institutes of Health, NIH）未确诊疾病项目/网络，以及数千家其他诊所、实验室、工具与数据库所采用。 患者自身是症状与表型精准信息的热切且尚未被充分利用的来源——其中部分信息可能未被临床医师察觉。然而，医学术语往往令患者难以理解，致使他们无法便捷使用HPO这类资源。本研究中，我们系统性地为其添加了通俗同义词，其中约60%可转化为通俗术语。分析结果显示，通俗版HPO具备在诊断场景中发挥效用所需的两项核心特征：其一，通俗术语具备足够的特异性；其二，在我们前述工具用于鉴别诊断的疾病-表型数据库中，这些通俗术语拥有良好的覆盖度。一款全新的以患者为中心的工具旨在帮助患者协助临床医师构建可靠的计算表型谱，以提升分子诊断率，并让患者能够主动参与到自身的诊断历程中。