Initiative for Clinical Long-read Sequencing

Genetic Predisposition

Long-read sequencing, Rare diseases, Cancer preposition, Translate-NAMSE, German initiative for genomic medicine (genomDE)

The study aims to comprehensively introduce Long-read Genome sequencing (LR-GS) based genetic testing into clinical routine. In order to demonstrate the superiority of untargeted LR-GS over Short-read Genome sequencing (SR-GS) to establish firm genetic diagnoses, the investigators will rely on a multi-center Translate Nationale Aktionsbündnis für Menschen mit Seltenen Erkrankungen (Translate National Action Alliance for People with Rare Diseases Germany, TNAMSE) cohort of unsolved patients with neurological, neurodevelopmental, and imprinting disorders that is expectedly enriched for complex genomic variation. Within the framework of genomDE, the investigators will then implement, for the first time, LR-GS in the diagnostic work-up of a prospective cohort of patients with a broad range of clinical indications including rare diseases and cancer predisposition.

The proposed study aims to develop a blueprint for the implementation of LR-GS in clinical diagnostics. Hence Standard Operating Procedures (SOPs) and guidelines for library preparation, bioinformatic analysis, and clinical interpretation will be compiled. Furthermore, the investigators intend to develop an open source 'gold standard' bioinformatics pipeline, addressing all relevant types of genomic alterations, thus providing the bioinformatic basis for a streamlined implementation of LR-GS at other sites. In addition to in-depth phenotype information the availability of SR-GS will be instrumental to benchmark the ability to detect different types of genomic variation. Additional relevant issues for genetic testing such as variant calling in difficult-to-map genomic regions, detection of genomic methylation patterns, characterization of repeat expansion and duplicated genes, and haplotype-phased genome de novo assembly will be addressed. Moreover, based on the strong background in Artificial Intelligence (AI) driven variant prioritization in the consortium, the investigators aim to implement and/or develop tools that enable an efficient prioritization of disease-causing variants. Beyond the usage within the context of the proposed study, generated datasets will be made available according to the Findable, Accessible, Interoperable and Reusable (FAIR) principles for national (German Human Genome-Phenome Archive, GHGA) and international (European Genome-Phenome Archive, EGA, Genome-Phenome Analysis Platform, GPaP) data repositories. the investigators aim to establish a population scale reference dataset for Structural variants (SV), which is absolutely mandatory in the context of rare disease diagnostics.

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