Interpretable Clinical Genomics with a Likelihood Ratio Paradigm
Autor: | Damian Smedley, Melissa A. Haendel, Leigh C. Carmody, Michael A. Gargano, Guy Karlebach, Julie A. McMurry, Courtney Thaxton, Peter N. Robinson, Justin T. Reese, Manuel Holtgrewe, Daniel Danis, Unc Biocuration Core, Xingmin Aaron Zhang, Julius O.B. Jacobsen, Sebastian Köhler, Vida Ravanmehr |
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Rok vydání: | 2020 |
Předmět: |
Computer science
Disease Computational biology Article 03 medical and health sciences symbols.namesake Rare Diseases Databases Genetic Human Phenotype Ontology Genetics Humans Exome Medical diagnosis Genetics (clinical) Exome sequencing 030304 developmental biology 0303 health sciences 030305 genetics & heredity Rank (computer programming) Computational Biology Genomics Phenotype Ranking Likelihood-ratio test Mendelian inheritance symbols Algorithms Software |
Zdroj: | Am J Hum Genet |
ISSN: | 0002-9297 |
Popis: | 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. Here, we present an approach to genomic diagnostics that exploits the likelihood ratio (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 case reports comprising 262 Mendelian diseases, and the correct diagnosis had 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. |
Databáze: | OpenAIRE |
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