Designs and Analytical Methods In Multi-Omics Studies of Complex Diseases

Abstract

Complex human diseases involve multiple molecular mechanisms and pathways with dynamic relationships. 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. Genomic studies discovered many loci and genes associated with disease traits but provided limited insights about their functional roles and 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. Multi-omics approaches integrate data obtained from different omic levels to understand their interrelation and combined influence on the disease processes. Thus, properly designed multi-omics studies may improve the understanding the molecular function and disease etiology. 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.