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Integrating Multi–Omics Data for Gene-Environment Interactions
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. D...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9245467/ https://www.ncbi.nlm.nih.gov/pubmed/35822775 http://dx.doi.org/10.3390/biotech10010003 |
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author | Du, Yinhao Fan, Kun Lu, Xi Wu, Cen |
author_facet | Du, Yinhao Fan, Kun Lu, Xi Wu, Cen |
author_sort | Du, Yinhao |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9245467 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-92454672022-07-06 Integrating Multi–Omics Data for Gene-Environment Interactions Du, Yinhao Fan, Kun Lu, Xi Wu, Cen BioTech (Basel) Article 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. MDPI 2021-01-29 /pmc/articles/PMC9245467/ /pubmed/35822775 http://dx.doi.org/10.3390/biotech10010003 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ). |
spellingShingle | Article Du, Yinhao Fan, Kun Lu, Xi Wu, Cen Integrating Multi–Omics Data for Gene-Environment Interactions |
title | Integrating Multi–Omics Data for Gene-Environment Interactions |
title_full | Integrating Multi–Omics Data for Gene-Environment Interactions |
title_fullStr | Integrating Multi–Omics Data for Gene-Environment Interactions |
title_full_unstemmed | Integrating Multi–Omics Data for Gene-Environment Interactions |
title_short | Integrating Multi–Omics Data for Gene-Environment Interactions |
title_sort | integrating multi–omics data for gene-environment interactions |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9245467/ https://www.ncbi.nlm.nih.gov/pubmed/35822775 http://dx.doi.org/10.3390/biotech10010003 |
work_keys_str_mv | AT duyinhao integratingmultiomicsdataforgeneenvironmentinteractions AT fankun integratingmultiomicsdataforgeneenvironmentinteractions AT luxi integratingmultiomicsdataforgeneenvironmentinteractions AT wucen integratingmultiomicsdataforgeneenvironmentinteractions |