Graduate Thesis Or Dissertation
Computer Aided Genetic Variant Validation for Rare Diseases Public Deposited
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25 to 30-million people in the United States are affected by rare genetic diseases. Key challenges in studying rare diseases include assessing the technical validity of called variants and determining the clinical validity of gene-disease connections.
Here, I present SeeNV, a tool designed for visualizing and assessing the technical validity of copy number variations (CNVs) in whole exome sequencing data. SeeNV provides comprehensive information essential for making curation decisions about CNVs. Developed in close collaboration with the Children’s Hospital of Colorado, SeeNV has been integrated into their clinical pipelines since 2021.
In the context of rare disease cases, clinicians and researchers often encounter variants of uncertain significance (VUS) concerning their association with specific diseases or phenotypes. Experimentally validating all VUSs can be excessively time-consuming and resource-intensive. To address this challenge, I investigated the use of biological knowledge graphs for hypothesis generation and variant prioritization of VUS. As part of this effort, I developed the biological ontology cluster classifier (BOCC) to facilitate the exploration of potential gene-to-phenotype connections. Furthermore, I explored the limitations of knowledge graph embedding (KGE) link prediction models in variant prioritization, identifying critical methodological adjustments necessary for their effectiveness in the rare disease domain.
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- 2024-07-11
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- 2025-01-07
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Bradshaw_colorado_0051E_18987.pdf | 2024-12-13 | Public | Download | |
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