Rare diseases are predominantly caused by genetic factors, and advancements in genetic testing, particularly exome sequencing (ES), have made identifying genetic alterations easier, leading to molecular genetic diagnoses. ES involves analyzing all segments of our DNA responsible for coding proteins. A Germany-wide multicenter study collected ES data from 1,577 patients and successfully diagnosed 499 patients, including 34 individuals with previously unidentified genetic diseases. The study significantly contributes to the understanding and identification of new diseases. The study also introduced the use of artificial intelligence (AI) software, such as the “GestaltMatcher,” to assist in evaluating facial features for congenital genetic syndromes. The findings of this study, conducted across 16 university locations, have been published in the journal Nature Genetics.
Ultra-rare diseases necessitate a collaborative approach involving clinical expertise and comprehensive genetic diagnostics for optimal patient care. The TRANSLATE NAMSE project, a three-year initiative starting in late 2017, aimed to enhance the care for individuals with rare diseases by utilizing modern diagnostic methodologies. Researchers from 16 university hospitals examined ES data from 1,577 patients, including 1,309 children, referred to rare disease centers under TRANSLATE NAMSE. The project aimed to identify the genetic basis of the diseases using innovative diagnostic techniques, successfully pinpointing the genetic cause in 499 patients, with 425 being children. A total of 370 different genes showed variations. This endeavor led to the identification of 34 novel molecular diseases, showcasing the knowledge-generating patient care at university hospitals.
Unsolved Cases Resolution:
“For patients with undiagnosed conditions, we will conduct further assessments through the Genome Sequencing project (MVGenomSeq),” says Dr. Tobias Haack from University Hospital of Tübingen. MVGenomSeq, building upon TRANSLATE NAMSE’s success, enables comprehensive genome analysis in university hospitals nationwide. Pending cases can also be investigated using advanced techniques like long-read sequencing, allowing for the analysis of longer DNA fragments to detect elusive genetic changes,” mentions Dr. Nadja Ehmke from Charité’s Institute of Medical Genetics and Human Genetics.
Under TRANSLATE NAMSE, standardized protocols for expanded genetic diagnostics in suspected rare diseases were established at rare disease centers, utilizing interdisciplinary case discussions. These protocols became part of routine care post-project completion. Dr. Magdalena Danyel emphasizes the importance of these interdisciplinary meetings in providing comprehensive clinical information crucial for genetic data evaluation.
Facial Recognition for Rare Diseases:
Researchers explored the effectiveness of using machine learning and AI tools in diagnostics. The “GestaltMatcher” software developed in Bonn leverages facial analysis to aid in diagnosing rare diseases. Testing the software on 224 individuals who consented to facial image analysis showed significant clinical benefits. The GestaltMatcher AI can identify facial anomalies linked to specific diseases, helping match the phenotype with the genotype. Prof. Peter Krawitz, the corresponding author and Director of the Institute for Genomic Statistics and Bioinformatics at University Hospital Bonn (UKB), emphasizes the importance of early diagnosis for rare diseases and suggests incorporating the software in pediatric screenings for timely intervention.
Participating Institutions:
Aside from the University Hospital Bonn (UKB) and the University of Bonn, other institutions involved in the study include Charité-Universitätsmedizin Berlin, Klinikum rechts der Isar of the Technical University of Munich (TUM), University Hospital Düsseldorf, Ruhr University Bochum, University Hospital Dresden, University Hospital Essen, University Hospital Halle, University Hospital Hamburg Eppendorf, University Hospital Heidelberg, University Hospital Schleswig Holstein, LMU Hospital Munich, University Hospital RWTH Aachen, University Hospital Leipzig, University Hospital Tübingen, and Stellenbosch University in Cape Town, South Africa.
