Interpretable Clinical Genomics with a Likelihood Ratio Paradigm release_3d37dhsfffhk3gtbd2xto4art4

Abstract

Human Phenotype Ontology (HPO)-based analysis has become standard for genomic diagnostics of rare diseases. Current algorithms use a variety of semantic and statistical approaches to prioritize the typically long lists of genes with candidate pathogenic variants. These algorithms do not provide robust estimates of the strength of the predictions beyond the placement in a ranked list, nor do they provide measures of how much any individual phenotypic observation has contributed to the prioritization result. However, given that the overall success rate of genomic diagnostics is only around 25-50% or less in many cohorts, a good ranking cannot be taken to imply that the gene or disease at rank one is necessarily a good candidate. Likelihood ratios (LR) are statistics for summarizing diagnostic accuracy, providing a measure of how much more (or less) a patient with a disease has a particular test result compared to patients without the disease. Here, we present an approach to genomic diagnostics that exploits the LR framework to provide an estimate of (1) the posttest probability of candidate diagnoses; (2) the LR for each observed HPO phenotype, and (3) the predicted pathogenicity of observed genotypes. LIkelihood Ratio Interpretation of Clinical AbnormaLities (LIRICAL) placed the correct diagnosis within the first three ranks in 92.9% of 384 cases reports comprising 262 Mendelian diseases, with the correct diagnosis having a mean posttest probability of 67.3%. Simulations show that LIRICAL is robust to many typically encountered forms of genomic and phenomic noise. In summary, LIRICAL provides accurate, clinically interpretable results for phenotype-driven genomic diagnostics.
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