Electronic Thesis and Dissertation Repository


Master of Science




Dr. Peter K. Rogan


A significant proportion of hereditary breast and ovarian cancer (HBOC) patients receive uninformative genetic testing results, an issue exacerbated by the overwhelming quantity of variants of uncertain significance identified. This thesis describes a framework where, aside from protein coding changes, information theory (IT)-based sequence analysis identifies and prioritizes pathogenic variants occurring within sequence elements predicted to be recognized by proteins involved in mRNA splicing, transcription, and untranslated region binding and structure. To support the utilization of IT analysis, we established IT-based variant interpretation accuracy by performing a comprehensive review of mutations altering mRNA splicing in rare and common diseases.

Custom probes targeting 20 complete HBOC genes for sequencing in 379 BRCA-uninformative patients identified 47,501 unique variants and we prioritized 429 variants in both BRCA and non-BRCA genes. Our approach focuses attention on a limited set of variants from a spectrum of functional mutation types for downstream functional and co-segregation analysis.