Computational approaches for characterization and prioritization of human genetic variantsLiang, Qianqian (2024) Computational approaches for characterization and prioritization of human genetic variants. Doctoral Dissertation, University of Pittsburgh. (Unpublished)
AbstractProtein-coding and non-protein-coding genetic variants both play essential roles in contributing to human diseases. Therefore, better approaches for characterizing and prioritizing genetic variants can advance our understanding of the genetic causes of disease and contribute to the design of diagnostic and therapeutic strategies. In this dissertation, I explore both coding and non-coding genetic variants and report on new computational methods for their annotation and prioritization. First, I developed a disease-specific approach for prioritizing non-coding variants. Integrating tissue-specific functional genomics data with non-coding disease-associated variants from the NHGRI-EBI GWAS catalog allowed me to design a model for disease-specific variant prioritization. This approach outperformed other variant-prioritization approaches, yielded interpretable and sensible associations between tissues and diseases, and enabled the calculation of disease similarities and the identification of biologically meaningful disease groups. Next, I further improved this disease-specific approach by combining disease-associated variants across different disease terms, in order to enable information sharing. Through a systematic evaluation of all pairs of disease terms in the GWAS catalog, I discovered that combining variants from related diseases improved the performance of variant prioritization. Finally, I focused on a specific type of protein-coding variant that introduces a premature termination codon (PTC) and can lead to mRNA non-sense mediated decay (NMD). Since not all PTC-causing variants trigger NMD, I contributed to the development of a software tool called "aenmd" that annotates whether such a variant is predicted to trigger NMD, or not (NMD escape). Applying aenmd to coding variants from the GWAS Catalog identified disease terms that were enriched with NMD-escaping and NMD-triggering variants, respectively. Altogether, my thesis presents novel approaches for effectively characterizing and prioritizing protein-coding and non-protein-coding genetic variants in the context of human diseases. The tools I developed will contribute to improved annotation and understanding of genetic variants; they also can assist geneticists in the discovery of genetic factors contributing to human diseases, thereby ultimately facilitating the development of more efficacious diagnostic strategies and therapeutic interventions. Share
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