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Integrating Multi–Omics Data for Gene-Environment Interactions

Department of Statistics, Kansas State University, Manhattan, KS 66506, USA
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Author to whom correspondence should be addressed.
Received: 24 December 2020 / Revised: 22 January 2021 / Accepted: 22 January 2021 / Published: 29 January 2021
(This article belongs to the Special Issue Feature Papers 2020)
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. View Full-Text
Keywords: Gene-environment (G×E) interactions; integrated analysis; multidimensional data; high-dimensional variable selection Gene-environment (G×E) interactions; integrated analysis; multidimensional data; high-dimensional variable selection
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MDPI and ACS Style

Du, Y.; Fan, K.; Lu, X.; Wu, C. Integrating Multi–Omics Data for Gene-Environment Interactions. BioTech 2021, 10, 3. https://0-doi-org.brum.beds.ac.uk/10.3390/biotech10010003

AMA Style

Du Y, Fan K, Lu X, Wu C. Integrating Multi–Omics Data for Gene-Environment Interactions. BioTech. 2021; 10(1):3. https://0-doi-org.brum.beds.ac.uk/10.3390/biotech10010003

Chicago/Turabian Style

Du, Yinhao, Kun Fan, Xi Lu, and Cen Wu. 2021. "Integrating Multi–Omics Data for Gene-Environment Interactions" BioTech 10, no. 1: 3. https://0-doi-org.brum.beds.ac.uk/10.3390/biotech10010003

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