Title: Integrative gene-gene interaction analysis for high dimensional data
Authors: Yang Li - Renmin University of China (China) [presenting]
Abstract: For many complex diseases, extensive omics profiling has been extensively conducted. It has been shown that gene-gene interactions may have important implications beyond the main genetic effects. The number of unknown parameters in a gene-gene interaction analysis is usually much larger than the sample size. As such, results generated from analyzing a single dataset are often unsatisfactory. Integrative analysis, which jointly analyzes the raw data from multiple independent studies, has been conducted in a series of recent studies and shown to outperform single-dataset analysis, meta-analysis, and other multi-datasets analyses. The goal is to conduct integrative analysis in the identification of gene-gene interactions. For regularized estimation and selection of important interactions (and main effects), we apply a Threshold Gradient Directed Regularization (TGDR) approach. Advancing from the exiting studies, the TGDR approach is modified to respect the ``main effects, interactions'' hierarchy. The proposed approach has an intuitive formulation and is computationally simple and broadly applicable. Simulations and the analysis of cancer prognosis data with gene expression measurements demonstrate its satisfactory practical performance.