Long-Read Sequencing Innovations in Neurodegenerative Disease Research

Tonight, I watched the London Calling 2024 session on “breaking boundaries in neurodegenerative disease research.” Cora Vacher from Oxford Nanopore Technologies (ONT) introduced the speakers and facilitated the question and answer session. Joanne Trinh from the Institute of Neurogenetics at the University of Lubeck in Germany was the first presenter. Trinh works with a research group that studies genetic and environmental modifiers in hereditary parkinsonism. Their work includes mitochondrial genetics and leverages long-read sequencing technologies. The team has identified mosaic variants and worked with the Global Parkinson’s Genetics Program (GP2) to sequence individuals. The team is planning on doing whole-genome sequencing of individuals with Parkinson’s. Kimberley Billingsley from the National Institutes of Health in the USA was the next presenter and spoke about sequencing at CARD NIH. The goal is to develop scalable methods for ONT long-read sequencing and apply these to large disease cohorts. The team has developed high-quality protocols to obtain samples and nucleic acids. Protocols are available on protocols.io! The team has also created workflows to analyze datasets to discover variants. Two large cohorts have been undergoing sequencing, including RNA-Seq of brain samples! With GP2, the NIH team has sequenced 750 whole blood samples. During the question and answer session, Trinh and Billingsley spoke about searching for variants and obtaining longer N50s to identify variants that have remained elusive. Trinh explained that a variant they identified several years ago seems to segregate with disease, and new connections have been found through GP2. Billingsley explained that they are also investigating methylation patterns and QTL analyses. Vacher asked about the differences in the cohorts the two researchers are studying (late-onset, for example). Trinh and Billingsley mentioned that the datasets would be released.

How can neurodegenerative research be scaled to study large cohorts? AI-generated image.