Pathway-based Bayesian integrative analysis for genetic association studies
Date Issued
2016
Date
2016
Author(s)
She, Chang-Xian
Abstract
The rapid advancement in biotechnology has made the genetic data from multiple platforms accessible for scientists to perform integrative analysis. Challenges arise, however, in dealing with the relationship between data from different sources, as well as the correlation between markers from the same platform. For statistical analysis, current set-based genetic analysis has been shown to exert more statistical power than single marker tests in association studies. Therefore, the incorporation of gene-sets into the integrative analysis has become a critical issue. In this thesis we propose a Pathway-based Bayesian integrative analysis (PaBIA) model to integrate RNA expression and DNA methylation data, simultaneously incorporating the concept of pathway topology to model the relationship between marker values. Based on the posterior inference, influential genes in given pathways can be identified and ranked. Simulation studies confirmed that the proposed model performed better than other traditional approaches, in terms of false discovery proportion and true negative rate. The (true positive rate +true negative rate)/2 of PaBIA is smaller than that of other methods by less than 2%. Finally, we illustrate this approach with a high-grade ductal carcinoma in situ study, and an ovarian cancer study, with KEGG pathways. The top ranking genes have been reported in previous literature to associate with breast cancer or ovarian cancer, and some have even been applied in target therapy.
Subjects
Bayesian model
DNA methylation
gene expression
gene ranking
integrative analysis
next generation sequencing
pathways
SDGs
Type
thesis
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