Precision diagnosis is crucial for the proper implementation of modern Personalized Medicine and is a key aspect of the Andalusian Plan for Personalized and Precision Medicine. In particular, rare diseases pose unique challenges in healthcare, often requiring specialized diagnostic tools to identify them accurately. This pilot project focuses on overcoming the barriers of traditional genetic testing by employing NGS, the gold standard for genetic diagnosis, and by using advanced algorithms and a specialized software to deal with the complexities of genomic information and the hurdles in clinical implementation. This pilot project demonstrates the use of a scalable and easy-to-use bioinformatic application to manage genomic data in the clinic practice. Currently more than 6500 rare disease patients have been diagnosed in this project.
Who is involved?
This project started as a collaboration between the Platform of Computational Medicine (FPS) and the UGC Medicina Maternofetal, Genética y Reproducción (Hospital Universitario Virgen del Rocío) although other teams, as the Servicio de Bioquímica Clinica (Hospital Universitario Virgen del Rocío) have made use of the bioinformatic tool.
Technical details
Next Generation Sequencing
To implement routine genetic testing for all inherited disorders included in the services portfolios of all public Andalusian hospitals, different versions of custom designed panels were used by the UGC Medicina Maternofetal, Genetica y Reproduccion, which included all exons, exon-intron boundaries and selected deep-intronic regions for 1397 genes (D3), globally covering more than 1800 rare inherited conditions. The Servicio de Bioquimica Clinica use exons to agnostically cover as many rare diseases as possible.
Personalized Medicine Module (PMM)
The Personalized Medicine Module (PMM), an evolved version of a bioinformatic tool for variant prioritization, BiERapp with specific orientation to the clinic, has been used for generating genetic reports in this project, which ultimately helped in the clinical decision and the diagnosis. Briefly, it is a web tool with a backend that indexes VCF files and annotates them using a local evolved version of CellBase, a database with biological knowledge useful for automated and user driven discovery of causal variants, which is queried by a frontend for prioritizing the most likely causal variant. Variant prioritization of carried out based in different criteria such as clinical databases, distinct pathogenicity indexes (e.g. Polyphen, SIFT, CADD, etc.) population frequencies, human phenotype ontology, virtual panels, etc.