EP01.04: Improving early gene detection using a new bioinformatics tool specific to prenatal phenotyping: a case study on CHARGE syndrome
Year: 2024
Type: conference-abstract
Abstract: To present preliminary tests of a novel bioinformatic research tool, (under development and not yet approved), designed to identify genes involved in prenatal syndromes based on ultrasound phenotypes and to compare these results with existing software. This research tool is based on an internal database linking ultrasound (USS) fetal phenotypes to genes which result from the curation of public databases by experts of prenatal diagnosis of rare diseases. The most likely gene is given according to the number of observed clinical signs described as linked to the gene and the syndrome prevalence. A detailed analysis was conducted on 10 cases of CHARGE Syndrome with a full postmortem phenotype and with an identified mutation in the CHD7 pathogenic variant. These cases were assessed using the research tool to determine their accuracy in identifying the gene and the syndrome based on the prenatal USS phenotype. The median number of phenotypic USS signs per case was 8, ranging from 3 to 14 signs. However, 31% of these signs were not referenced in public databases (i.e. HPO and Orphanet) as being associated with CHARGE syndrome or CDH7 gene, underlining the need for a database adapted to prenatal phenotypes. The research tool accurately identified respectively the CDH7 gene and the CHARGE syndrome in 90% of the cases. For the remaining 10%, the correct gene was the research tool's second “best guess”. Comparative analyses with other available solutions yielded the following results: ClinicalSigns Orphanet App (60%), PhenoGenius (50%), Phenomizer (40%) and ChatGPT (50%) of finding the correct gene and/or syndrome depending on the app. Early gene detection of rare syndromes can be significantly improved using research tools specifically designed for prenatal phenotypes.
DOI: 10.1002/uog.28019
Language: English
Topic: Genomics and Rare Diseases
Subfield: Genetics
Domain: Life Sciences
Open Access status: bronze
OpenAlex ID: W4402360246