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

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 (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.

Adenotonsillar microbiome: an update

“…Complex and dynamic networks of molecules are involved in mucosal inflammatory diseases, and these are influenced by many environmental factors 24. Recently, high-throughput measurement of the human genome, epigenome, metabolome, transcriptome and proteome at the population level has become a reality 24.…”

“…Recently, high-throughput measurement of the human genome, epigenome, metabolome, transcriptome and proteome at the population level has become a reality 24. It is hoped that the integrative approach of multiomic data may enhance understanding of the molecular dynamics underlying the pathophysiology of diseases such as adenotonsillar disease.…”

Strategies to avoid drowning in the deep sequencing data flood

“…With this rapid development, particularly for NGS, a big challenge came up with respect to data analysis, interpretation, and integration (Rajasundaram and Selbig, 2016;Sun and Hu, 2016;Suravajhala et al, 2016). More and more data sets are generated for the analysis of gene expression at the level of RNA and proteins as well as for the genome-wide identification of sequence variants correlating with the trait fertility (Bauersachs, 2014;Bauersachs and Wolf, 2015).…”

Multi-Omics Approach: New Potential Key Mechanisms Implicated in Cardiorenal Syndromes

“…Recent technological advances have provided high-throughput measurement of human genome, epigenome, metabolome, transcriptome and proteome at the population level [19]. The integrative approach of multi-omic data may help in the understanding of the molecular dynamics implicated in the pathophysiology of human disease, thus leading to novel strategies for their early detection, treatment and prevention.…”

“…Given the huge quantity of records generated in these studies, bioinformatics and statistic are fundamental for their understanding. Precise validation is important to confirm that findings are not just random results [19-22]. …”