Detection of Gene-Gene Interactions by Multistage Sparse and Low-Rank Regression

TL;DR

This paper introduces a low-rank interaction model combined with an Extended Screen-and-Clean approach to efficiently detect gene-gene interactions in high-dimensional biological data, improving power and stability.

Contribution

The paper proposes a novel low-rank interaction model and an extended screening method for gene-gene interaction detection, enhancing efficiency and accuracy over traditional approaches.

Findings

Effective detection of gene-gene interactions in simulations.

Application to warfarin dosage data revealed significant effects.

Improved power and selection consistency in high-dimensional settings.

Abstract

A daunting challenge faced by modern biological sciences is finding an efficient and computationally feasible approach to deal with the curse of high dimensionality. The problem becomes even more severe when the research focus is on interactions. To improve the performance, we propose a low-rank interaction model, where the interaction effects are modeled using a low-rank matrix. With parsimonious parameterization of interactions, the proposed model increases the stability and efficiency of statistical analysis. Built upon the low-rank model, we further propose an Extended Screen-and-Clean approach, based on the Screen and Clean (SC) method (Wasserman and Roeder, 2009; Wu et al., 2010), to detect gene-gene interactions. In particular, the screening stage utilizes a combination of a low-rank structure and a sparsity constraint in order to achieve higher power and higher selection-consistency probability. We demonstrate the effectiveness of the method using simulations and apply the proposed procedure on the warfarin dosage study. The data analysis identified main and interaction effects that would have been neglected using conventional methods.