Integrating Multi–Omics Data for Gene-Environment Interactions

Abstract

Gene-environment (G×E) interaction is critical for understanding the genetic basis of complex disease beyond genetic and environment main effects. In addition to existing tools for interaction studies, penalized variable selection emerges as a promising alternative for dissecting G×E interactions. Despite the success, variable selection is limited in terms of accounting for multidimensional measurements. Published variable selection methods cannot accommodate structured sparsity in the framework of integrating multiomics data for disease outcomes. In this paper, we have developed a novel variable selection method in order to integrate multi-omics measurements in G×E interaction studies. Extensive studies have already revealed that analyzing omics data across multi-platforms is not only sensible biologically, but also resulting in improved identification and prediction performance. Our integrative model can efficiently pinpoint important regulators of gene expressions through sparse dimensionality reduction, and link the disease outcomes to multiple effects in the integrative G×E studies through accommodating a sparse bi-level structure. The simulation studies show the integrative model leads to better identification of G×E interactions and regulators than alternative methods. In two G×E lung cancer studies with high dimensional multi-omics data, the integrative model leads to an improved prediction and findings with important biological implications.

Pathological Imaging-Assisted Cancer Gene–Environment Interaction Analysis

“…Despite many successes, G–E interaction analysis is still often unsatisfactory, and the need for “more information” has been well recognized (Du et al., 2021; McAllister et al., 2017). Multiple strategies have been proposed to bring in “additional information.” The “simplest” solution is to increase sample size, which is often not feasible.…”

Environmental measurements and genetic effects for cancer survival integration data

“…The importance of genetic factors and shared environmental influences on outcomes has recently received much attention in the literature. Specifically, the combination of environmental factors and one or more types of genetic effects (e.g., gene expression data and methylation) have highlighted the necessity to account for multi-omics measurements [3,4]. For example, the use of DNA methylation or copy number variations (CNV) can help to identify new relationships between gene expression factors and their regulators to clarify the genetic basis of complex diseases.…”