Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases

By Yan V. Sun, Yijuan Hu. Advances in Genetics, 2016

342 citations recorded by the source corpus.

Complex and dynamic networks of molecules are involved in human diseases. High-throughput technologies enable omics studies interrogating thousands to millions of makers with similar biochemical properties (e.g. transcriptomics for RNA transcripts). However, a single layer of ‘omics’ can only provide limited insights into the biological mechanisms of a disease. In the case of GWAS, although thousands of SNPs have been identified for complex diseases and traits, the functional implications and mechanisms of the associated loci are largely unknown. Additionally, the genomic variants alone are not able to explain the changing disease risk across the life span. DNA, RNA, protein, and metabolite often have complementary roles to jointly perform a certain biological function. Such complementary effects and synergistic interactions between omic layers in the life-course can only be captured by integrative study of multiple molecular layers. Building upon the success in single-omics discovery research, population studies started adopting the multi-omics approach to better understanding the molecular function and disease etiology. Multi-omics approaches integrate data obtained from different omic levels to understand their interrelation and combined influence on the disease processes. Here, we summarize major omics approaches available in population research, and review integrative approaches and methodologies interrogating multiple omic layers, which enhance the gene discovery and functional analysis of human diseases. We seek to provide analytical recommendations for different types of multi-omics data and study designs to guide the emerging multi-omic research, and to suggest improvement of the existing analytical methods.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b8604c1c00ec3457f66a188faa1232fed33f1e22

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Citations in the corpus

Integration strategies of multi-omics data for machine learning analysis (2021, methodology): “Clinical information and omics data can be directly retrieved from databases or collected with screening technologies for disease [6], class prediction [7], biomarkers discovery [8], disease subtyping [6], improved system biology knowledge [9], drug repurposing and so on.”
The Need for Multi-Omics Biomarker Signatures in Precision Medicine (2019, background): “Indeed, this suggestion is not specific to the field of cancer research, and other human diseases would likely benefit from similar approaches [80].”