ClinPrior : an algorithm for diagnosis and novel gene discovery by network-based prioritization

Agatha Schlüter, Valentina Vélez-Santamaría, Edgard Verdura, Agustí Rodríguez-Palmero, Montserrat Ruiz, Stéphane Fourcade, Laura Planas-Serra, Nathalie Launay, Cristina Guilera, Juan José Martínez, Christian Homedes-Pedret, M. Antonia Albertí-Aguiló, Miren Zulaika, Itxaso Martí, Mónica Troncoso, Miguel Tomás-Vila, Gemma Bullich Vilanova, M. Asunción García-Pérez, María-Jesús Sobrido-Gómez, Eduardo López-LasoCarme Fons, Mireia Del Toro, Alfons Macaya Ruiz, Sergi Beltran i Agulló, Luis G. Gutiérrez-Solana, Luis A. Pérez-Jurado, Sergio Aguilera-Albesa, Adolfo López de Munain, Carlos Casasnovas, Aurora Pujol

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Whole-exome sequencing (WES) and whole-genome sequencing (WGS) have become indispensable tools to solve rare Mendelian genetic conditions. Nevertheless, there is still an urgent need for sensitive, fast algorithms to maximise WES/WGS diagnostic yield in rare disease patients. Most tools devoted to this aim take advantage of patient phenotype information for prioritization of genomic data, although are often limited by incomplete gene-phenotype knowledge stored in biomedical databases and a lack of proper benchmarking on real-world patient cohorts. We developed ClinPrior, a novel method for the analysis of WES/WGS data that ranks candidate causal variants based on the patient's standardized phenotypic features (in Human Phenotype Ontology (HPO) terms). The algorithm propagates the data through an interactome network-based prioritization approach. This algorithm was thoroughly benchmarked using a synthetic patient cohort and was subsequently tested on a heterogeneous prospective, real-world series of 135 families affected by hereditary spastic paraplegia (HSP) and/or cerebellar ataxia (CA). ClinPrior successfully identified causative variants achieving a final positive diagnostic yield of 70% in our real-world cohort. This includes 10 novel candidate genes not previously associated with disease, 7 of which were functionally validated within this project. We used the knowledge generated by ClinPrior to create a specific interactome for HSP/CA disorders thus enabling future diagnoses as well as the discovery of novel disease genes. ClinPrior is an algorithm that uses standardized phenotype information and interactome data to improve clinical genomic diagnosis. It helps in identifying atypical cases and efficiently predicts novel disease-causing genes. This leads to increasing diagnostic yield, shortening of the diagnostic Odysseys and advancing our understanding of human illnesses. The online version contains supplementary material available at 10.1186/s13073-023-01214-2.
Original languageEnglish
JournalGenome Medicine
Volume15
DOIs
Publication statusPublished - 2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Algorithm
  • WES/WGS
  • HPOs
  • Variant prioritization
  • Interactome
  • Hereditary spastic paraplegia
  • Cerebellar ataxia
  • Candidate gene

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