Date of Award
2022
Degree Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Graduate Group
Genomics & Computational Biology
First Advisor
Yoseph Barash
Second Advisor
Elizabeth J. Bhoj
Abstract
Exome sequencing is the most advanced standard-of-care genetic test for people with suspected Mendelian disorders. Yet, the diagnostic rate of exome sequencing is only 31%. RNA sequencing (RNA-seq) is a promising molecular test for detecting potentially pathogenic changes in RNA splicing as part of obtaining a molecular diagnosis. In this dissertation, I develop new computational tools and perform analyses towards improving how we detect these potentially pathogenic changes in RNA splicing with the goal of improving the molecular diagnostic rate. First, in Chapter 1, I review background on how we diagnose patients and how RNA splicing and RNA-seq could be used to improve this process. Then, in Chapter 2, I describe my contributions to MAJIQ v2 as methodology to study RNA splicing from large and heterogeneous RNA-seq datasets. Afterwards, I use MAJIQ v2 in Chapter 3 to evaluate how tissue-specific expression and splicing affects what clinically-relevant splicing changes we can identify from clinically-accessible tissues. Then, in Chapter 4, I describe the limitations of MAJIQ v2 for our approach to detect splicing aberrations and the development and evaluation of MAJIQ v3 to address these challenges. With MAJIQ v3, I develop MAJIQ-CLIN in Chapter 5 to identify and prioritize splicing aberrations in patient RNA-seq data and compare our method to previous approaches. Finally, in Chapter 6, I discuss overall conclusions for the work and exciting areas for future work. Together, the work in this dissertation pushes forward how we can study and use RNA-seq to improve the diagnostic rate of patients with suspected Mendelian disorders.
Recommended Citation
Aicher, Joseph Krittameth, "Improving Molecular Diagnosis Of Suspected Mendelian Disorders With Rna Splicing Analysis" (2022). Publicly Accessible Penn Dissertations. 4691.
https://repository.upenn.edu/edissertations/4691