Gene set-based Signal-Detection Analyses with Goodness-of-Fit Statistics and Their Application in Complex Diseases

Rare diseases are difficult to diagnose and uncertain to treat. The identification of specific genes associated with particular rare diseases and phenotypes can provide insight into the mechanism of certain rare disease subtypes and suggest therapeutic targets to improve patient outcomes. However, single gene-based methods for detecting rare disease-associated variants are often underpowered and can be hard to interpret. Therefore, this dissertation explores alternative approaches based on gene set-based methods. These analyses can be solved with a goodness-of-fit test that assesses whether the distribution of observed statistics of a given set of genes/variants significantly differs from the expected distribution.

This dissertation explores a flexible gene set-based signal-detection framework based on the goodness-of-fit tests. A user-friendly and efficient R program was developed for this research. In addition, this dissertation proposes a new gene-set analyses method that can leverage prior information to inform the detection of whether any of the genes within a biologically informed gene-set is associated with disease phenotypes on a special goodness-of-fit a test called higher criticism. Further, this dissertation investigates the asymptotic distribution of our higher criticism statistic based on the theoretically weighted p-values. Collectively, these methods are innovative because they based on gene set and incorporate the prior information, which enhances the power of associations between rare variants and complex diseases. These results improve the ability to identify and optimally treat genetic disease subtypes.