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Applying next-generation phenotyping to prioritize Cornelia de Lange Syndrome patients for additional exams and improve variant classification

Grant number: 23/12833-9
Support Opportunities:Scholarships abroad - Research Internship - Doctorate (Direct)
Effective date (Start): December 11, 2023
Effective date (End): October 31, 2024
Field of knowledge:Health Sciences - Medicine - Medical Clinics
Principal Investigator:Maria Isabel de Souza Aranha Melaragno
Grantee:Beatriz de Carvalho Nunes
Supervisor: Peter M Krawitz
Host Institution: Escola Paulista de Medicina (EPM). Universidade Federal de São Paulo (UNIFESP). Campus São Paulo. São Paulo , SP, Brazil
Research place: Universität Bonn, Germany  
Associated to the scholarship:22/03428-0 - Search for genetic variants with pathogenic potential in patients with a characteristic phenotype of coesinopathies focusing on the Cornelia de Lange Syndrome, BP.DD


Cornelia de Lange Syndrome (CdLS) is a rare disorder (RD) that affects 1 in 45,000 to 1 in 62,000 live births and has several physical, cognitive, and behavioral phenotypes. Although CdLS patients frequently have a typical facial phenotype that can help during the search for a diagnostic, some of them remain molecularly undiagnosed for many years due to factors such as mosaic mutations or variants that cannot be confirmed as pathogenic due to the lack of functional studies and in silico tools. The diagnostic odyssey for patients with rare disorders can be related to health and financial consequences for both patients and parents. Many tools have been developed in the last years to accelerate this process and help both clinicians and laboratories while searching for variants that can explain these patients' phenotypes. Next-generation phenotyping (NGP) is a growing methodology that can help during the phenotypic interpretation of RD patients. Therefore, this study aims to prioritize CdLS patients who have already undergone several exams for further exams, through performing a neural network and clustering analysis with photos of patients already molecularly diagnosed for CdLS and providing a software that can perform this analysis for other RD. Moreover, we also aim to use NGP to produce a facial phenotype score to aid during variant classification. Results obtained by this project can accelerate the diagnostic odyssey of RD patients.

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