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. 2022 May 14;16(1):15.
doi: 10.1186/s40246-022-00388-x.

Integrative analysis of multi-omics data to detect the underlying molecular mechanisms for obesity in vivo in humans

Qiang Zhang  1   2 , Xiang-He Meng  2   3 , Chuan Qiu  2 , Hui Shen  2 , Qi Zhao  4 , Lan-Juan Zhao  2 , Qing Tian  2 , Chang-Qing Sun  1   5 , Hong-Wen Deng  6
Affiliations

Integrative analysis of multi-omics data to detect the underlying molecular mechanisms for obesity in vivo in humans

Qiang Zhang et al. Hum Genomics. .

Abstract

Background: Obesity is a complex, multifactorial condition in which genetic play an important role. Most of the systematic studies currently focuses on individual omics aspect and provide insightful yet limited knowledge about the comprehensive and complex crosstalk between various omics levels.

Subjects and methods: Therefore, we performed a most comprehensive trans-omics study with various omics data from 104 subjects, to identify interactions/networks and particularly causal regulatory relationships within and especially those between omic molecules with the purpose to discover molecular genetic mechanisms underlying obesity etiology in vivo in humans.

Results: By applying differentially analysis, we identified 8 differentially expressed hub genes (DEHGs), 14 differentially methylated regions (DMRs) and 12 differentially accumulated metabolites (DAMs) for obesity individually. By integrating those multi-omics biomarkers using Mendelian Randomization (MR) and network MR analyses, we identified 18 causal pathways with mediation effect. For the 20 biomarkers involved in those 18 pairs, 17 biomarkers were implicated in the pathophysiology of obesity or related diseases.

Conclusions: The integration of trans-omics and MR analyses may provide us a holistic understanding of the underlying functional mechanisms, molecular regulatory information flow and the interactive molecular systems among different omic molecules for obesity risk and other complex diseases/traits.

Keywords: Epigenomic; Genomic; Metabolomic; Multi-omics integration; Transcriptomic.

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Conflict of interest statement

The authors declare they have no conflict of interest.

Figures

Fig. 1
Fig. 1
Workflow of the multi-omics analysis
Fig. 2
Fig. 2
Modules hierarchy plot. Notes: Each node is a cluster identified by multiscale clustering in PFN. ‘c1_’ means the root node. Node_size: the size of the node. node.scaleFactor: scale number to adjust node sizes. Node size and label size are proportional to node degree. For detailed module clusters and complete list of genes in each module, please refer to Additional file 2: Table S2 and Additional file 1: Figs. S1–S4
Fig. 3
Fig. 3
Correlation pattern between A DEHGs and DAMs, B DEHGs and DMRs, and C DMRs and DAMs
See this image and copyright information in PMC

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References

    1. Kopelman P. Health risks associated with overweight and obesity. Obes Rev. 2007;8(Suppl 1):13–17. - PubMed
    1. Visscher PM, Brown MA, McCarthy MI, Yang J. Five years of GWAS discovery. Am J Hum Genet. 2012;90(1):7–24. - PMC - PubMed
    1. Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biol. 2017;18(1):83. - PMC - PubMed
    1. Ge S, Wang Y, Song M, Li X, Yu X, Wang H, Wang J, Zeng Q, Wang W. Type 2 diabetes mellitus: integrative analysis of multiomics data for biomarker discovery. OMICS. 2018;22(7):514–523. - PubMed
    1. Xu C, Zhang JG, Lin D, Zhang L, Shen H, Deng HW. A systemic analysis of transcriptomic and epigenomic data to reveal regulation patterns for complex disease. G3 Bethesda. 2017;7(7):2271–2279. - PMC - PubMed

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