With CMTA support of $423,000, researchers at the GENESIS Project Foundation (TGP), led by CMTA Strategy To Accelerate Research (CMTA-STAR) Advisory Board member Stephan Züchner, MD, PhD, are expanding a large-scale genomic resource to accelerate gene discovery and close the diagnostic gap in CMT.
Despite major advances in CMT genetics, up to half of individuals living with CMT still lack a confirmed genetic diagnosis. This project builds on the GENESIS platform to incorporate thousands of new datasets, including long-read and short-read genomes, exomes, and RNA sequencing data. These datasets are being processed and shared across the research community to support the discovery of new CMT disease genes and potential therapeutic targets.
The team is applying artificial intelligence and advanced computational tools to help interpret variants of uncertain significance (VUS) and improve the accuracy of pathogenicity predictions. With previous CMTA funding support, TGP developed several of these tools to assist researchers in diagnosing unsolved cases and identifying new genetic causes of CMT.
January 2026 Update
Dr. Züchner’s team has now added 80 CMT long-read genomes into the GENESIS database, making this the largest known collection of long-read genomic data for CMT. Long-read sequencing improves the ability to detect complex genetic variants that are challenging to capture using traditional genetic testing approaches, which strengthens efforts to resolve previously unsolved cases.
The team has also begun sharing additional genomic data through collaboration with other CMT researchers, allowing the database to continue growing. To make participation easier, an automated data upload tool has been developed that simplifies how researchers outside the Inherited Neuropathy Consortium (INC) can contribute CMT data, including long-read genome data.
The GENESIS database now supports combined analysis of genome sequencing and gene expression data (RNA sequencing). This allows researchers to examine both genetic changes and how genes are functioning in cells within a single system, providing additional insight into potential genetic causes of CMT.
Work is ongoing to add new artificial intelligence–based analysis tools, including AlphaMissense and DeepVariant, along with an updated reference genome (hg38, T2T) planned for early 2026. The Solve-CMT resource continues to expand its shared data and analytical tools, furthering ongoing research into the genetics of CMT.
This CMTA-STAR project continues a longstanding partnership between CMTA and TGP, which has already contributed to the discovery of more than 30 CMT genes, including SORD (CMT-SORD), ITPR3 (CMT1J), COQ7 (CMT-COQ7), and CADM3 (CMT2FF). By increasing data access, streamlining analysis, and applying AI-powered methods, the Solve-CMT project aims to drive impactful progress in CMT gene discovery and precision diagnostics.
