A robust phenotype-driven likelihood ratio analysis approach assisting interpretable clinical diagnosis of rare diseases

• Published in: Journal of biomedical informatics 2023-06, Vol.142, p.104372-104372, Article 104372
• Main Authors: Yang, Jian, Shu, Liqi, Duan, Huilong, Li, Haomin
• Format: Article
• Language: English
• Subjects: Differential diagnosis; Likelihood ratio; Phenotype; Rare diseases; Visualization
• ISSN: 1532-0464; 1532-0480
• DOI: 10.1016/j.jbi.2023.104372

[Display omitted] •Estimating the point prevalence value of rare diseases.•Propagation protocol to overcome imprecise phenotype.•Likelihood ratio was used to drive phenotype based rare disease diagnose.•PheLR shows significant advantages over current approaches. Phenotype-based prioritization of candidate genes and diseases has become a well-established approach for multi-omics diagnostics of rare diseases. Most current algorithms exploit semantic analysis and probabilistic statistics based on Human Phenotype Ontology and are commonly superior to naive search methods. However, these algorithms are mostly less interpretable and do not perform well in real clinical scenarios due to noise and imprecision of query terms, and the fact that individuals may not display all phenotypes of the disease they belong to. We present a Phenotype-driven Likelihood Ratio analysis approach (PheLR) assisting interpretable clinical diagnosis of rare diseases. With a likelihood ratio paradigm, PheLR estimates the posterior probability of candidate diseases and how much a phenotypic feature contributes to the prioritization result. Benchmarked using simulated and realistic patients, PheLR shows significant advantages over current approaches and is robust to noise and inaccuracy. To facilitate clinical practice and visualized differential diagnosis, PheLR is implemented as an online web tool (https://phelr.nbscn.org).