Proceeding talk – Theme: Genes.
Abstract
Identifying gene-gene interaction is one of the most popular approaches for finding the missing heritability of complex traits in GWAS. The multifactor dimensionality reduction method has been widely studied for detecting interactions. However, existing MDR-based approaches have several disadvantages, such as the lack of an efficient way to evaluate the significance of models, a high burden of computation because intensive permutation is needed, and the inability to distinguish marginal effects from pure interaction effects. We propose a two-step unified model-based MDR approach (UM-MDR), in which the significance of a multi-locus model can be easily obtained through a regression framework. Compared to the conventional permutation approach, the proposed semiparametric correction avoids heavy computational costs for achieving the significance of a multi-locus model. The proposed UM-MDR approach would be quite useful in detecting gene-gene interaction and quite flexible in the sense of its ability to incorporate different types of traits.
Authors
Wenbao Yu, University of Pensylvania, United States
Seungyeoun Lee, Sejong University, Republic of Korea
Taesung Park, Seoul National University, Republic of Korea