Grant Number:	5U01NS134349-12 Interpret this number
Primary Investigator:	Hamid, Rizwan
Organization:	Vanderbilt University Medical Center
Project Title:	Vanderbilt Center of Excellence for Undiagnosed Disease (VCEUD)
Fiscal Year:	2025

PROJECT SUMMARY Undiagnosed Diseases (UD) are constellations of clinical findings evaluated specialists see over time without determining their cause(s) and for which diagnostic procedures and tests have been exhausted. The NIH NINDS Diagnostic Centers of Excellence for the UDN provides an exciting opportunity to increase the reach of UDN to a broader patient population in an efficient manner utilizing a tiered evaluation approach combined with iterative data analysis with an overarching goal of improving the diagnosis, care, and understanding of patients with UD. To this end, we propose establishing the Vanderbilt Center of Excellence in Undiagnosed Diseases (VCEUD). We formed our VCEUD by leveraging 1) a strong clinical team of Pediatricians, Internists, Endocrinologists, Psychologists, Immunologists, Rheumatologists, Neurologists, and Geneticists; 2) institutional strengths in bioinformatics, phenomics, structural biology, and artificial intelligence (AI) approaches such as Large Language Models (LLM) and Natural Language Processing (NLM); 3) institutional resources such as the BioVU DNA databank and its experts, PheWAS, MedWAS, VICTR-Studios, and REDCap database system, 4) VUMC’s Recruitment Innovation Center (RIC) to establish mutually beneficial relationships with community collaborators that serve populations with health disparities, 5) education and training of the next generation of physicians that will help sustain our program over the long-term, and importantly 6) significant institutional funding. The goal is to improve community engagement, efficiency, diagnostic rate, and development and dissemination of novel methods. We will increase our outreach efforts to populations facing health disparities. We will consider genetic and non-genetic etiologies and evaluate cases in our tiered diagnostic pipelines. For the genetic diagnostic stream (GDS), we analyze genomic data using a multi-omics approach to generate clinical and gene hypotheses and merge them to identify concordant disorders that cause undiagnosed diseases and discover new diseases. For the non-genetic diagnostic stream (NGDS), we leverage our VICTR-Studios to generate refined clinical hypotheses that are then tested by an in- depth clinical workup. We hypothesize that novel approaches in AI, phenomics, genomics, bioinformatics, structural biology, and experimental validation can be leveraged to more efficiently identify undiagnosed disorders that have proven difficult to solve with standard approaches. To do this, we will, 1) Increase recruitment, especially for populations with health disparities, by building a network of diverse community partners and equipping those partners with tools and resources, 2) Optimize efficiency and effectiveness of clinical evaluation of individuals with UD, 3) Determine the causes of challenging UD cases by integrating novel analytic and bioinformatics strategies with current methods into an efficient tiered iterative pipeline.




A statistical framework for evaluating the repeatability and reproducibility of large language models.
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Case Series of Nizon-Isidor Syndrome by Heterozygous Variants in MED12L With Further Evidence of Mitotic Instability in One Case With Diploid-Triploid Mosaicism.
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First clinical diagnosis of FAME3 via commercial Long-Read sequencing reveals mosaic repeat expansion in MARCHF6 gene.
Authors: Perera B.L.A. , Stewart R. , Furuta Y. , Ezell K.M. , Rives L. , Nunley B. , McMinn A. , Krokosky A. , Neumann S. , Koziura M.E. , et al. .
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The Undiagnosed Diseases Network (UDN) Solves Ocular Syndromic Diagnostic Dilemmas.
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Authors: Moth C.W. , Sheehan J.H. , Mamun A.A. , Sivley R.M. , Gulsevin A. , Rinker D. , Undiagnosed Diseases Network , Capra J.A. , Meiler J. .
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Prevalence of Individuals With Multiple Diagnosed Genetic Diseases in the Undiagnosed Diseases Network.
Authors: Gimeno A.F. , Tinker R.J. , Furuta Y. , Undiagnosed Diseases Network , Phillips J.A. .
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Review and metabolomic profiling of unsolved case reveals newly reported autosomal dominant congenital disorder of glycosylation, type Iw formerly thought to only be an autosomal recessive condition.
Authors: Ezell K.M. , Furuta Y. , Oglesbee D. , Pivnick E.K. , Rinker D. , Sheehan J.H. , Tinker R.J. , Hamid R. , Cogan J.D. , Rives L. , et al. .
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A review of multiple diagnostic approaches in the undiagnosed diseases network to identify inherited metabolic diseases.
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Undiagnosed Disease Network collaborative approach in diagnosing rare disease in a patient with a mosaic CACNA1D variant.
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Clustered de novo start-loss variants in GLUL result in a developmental and epileptic encephalopathy via stabilization of glutamine synthetase.
Authors: Jones A.G. , Aquilino M. , Tinker R.J. , Duncan L. , Jenkins Z. , Carvill G.L. , DeWard S.J. , Grange D.K. , Hajianpour M.J. , Halliday B.J. , et al. .
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Probable digenic inheritance of Diamond-Blackfan anemia.
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