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

Yan V Sun, Yi-Juan Hu, Yan V Sun, Yi-Juan Hu

Abstract

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 (eg, 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 genome-wide association studies, although thousands of single nucleotide polymorphisms 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.

Keywords: DNA methylation; Epigenome; GWAS; Gene expression; Genomic epidemiology; Integrative genomics; Metabolome; Proteome; Quantitative trait loci; Transcriptome.

Copyright © 2016 Elsevier Inc. All rights reserved.

Figures

Figure 1. Conceptual model of multi-omics and…
Figure 1. Conceptual model of multi-omics and human disease
Figure 2. Causal diagram of SNP (G),…
Figure 2. Causal diagram of SNP (G), gene expression (E), and disease outcome (Y)
Gene expression is a potential mediator of genetic effects on the disease outcome.
Figure 3. Causal diagram of SNP (G),…
Figure 3. Causal diagram of SNP (G), DNA methylation (Me), gene expression (E), and disease outcome (Y)
Three path-specific effects are 1) Direct effect of SNPs on outcome (dashed red line), 2). Indirect effect of SNP mediated through gene expression but not through methylation (dotted blue lines), and 3). Indirect effect of SNP mediated through methylation (solid black lines).
Figure 4. Matching the genetic signatures of…
Figure 4. Matching the genetic signatures of gene expression traits (eQTLs) to that of the disease trait to identify gene expression-disease associations
Ui: binary indicator variables to represent the true SNP-gene expression causal relationship;, Vi: binary indicator variables for the true SNP-disease relationship. Z is a binary variable indicating whether the gene expression trait influences the disease.
Figure 5. Aggregation model of multi-omics evidence
Figure 5. Aggregation model of multi-omics evidence
An omnibus test of pathways enriched for trait-associated SNPs, gene expressions, CpG sites, proteins and metabolomic features. This multi-layer approach allows aggregation of single association signals from individual markers to genes to pathways. The original aggregation model limited to SNPs and gene expression levels within the dashed box.

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