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

• Published in: Human genomics Vol. 16; no. 1; pp. 15 - 13
• Main Authors: Zhang, Qiang, Meng, Xiang-He, Qiu, Chuan, Shen, Hui, Zhao, Qi, Zhao, Lan-Juan, Tian, Qing, Sun, Chang-Qing, Deng, Hong-Wen
• Format: Journal Article
• Language: English
• Published: London BioMed Central 14.05.2022; Springer Nature B.V; BMC
• Subjects: Bioinformatics; Biomarkers; Biomedical and Life Sciences; Biomedicine; Body mass index; Datasets; Diabetes; Discriminant analysis; DNA methylation; Epigenomic; Etiology; Gene expression; Genomic; Genomics; Human Genetics; Humans; Laboratories; Metabolism; Metabolites; Metabolomic; Molecular modelling; Multi-omics integration; Obesity; Obesity - genetics; Overweight; Primary Research; Proteomics; Public Health Genomics; Transcriptomic; United States > US; Metabolomic; Multi-omics integration; Transcriptomic; Genomic; Epigenomic
• ISSN: 1479-7364; 1473-9542; 1479-7364
• DOI: 10.1186/s40246-022-00388-x

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.